A dynamic programming approach to adaptive fractionation
NASA Astrophysics Data System (ADS)
Ramakrishnan, Jagdish; Craft, David; Bortfeld, Thomas; Tsitsiklis, John N.
2012-03-01
We conduct a theoretical study of various solution methods for the adaptive fractionation problem. The two messages of this paper are as follows: (i) dynamic programming (DP) is a useful framework for adaptive radiation therapy, particularly adaptive fractionation, because it allows us to assess how close to optimal different methods are, and (ii) heuristic methods proposed in this paper are near-optimal, and therefore, can be used to evaluate the best possible benefit of using an adaptive fraction size. The essence of adaptive fractionation is to increase the fraction size when the tumor and organ-at-risk (OAR) are far apart (a ‘favorable’ anatomy) and to decrease the fraction size when they are close together. Given that a fixed prescribed dose must be delivered to the tumor over the course of the treatment, such an approach results in a lower cumulative dose to the OAR when compared to that resulting from standard fractionation. We first establish a benchmark by using the DP algorithm to solve the problem exactly. In this case, we characterize the structure of an optimal policy, which provides guidance for our choice of heuristics. We develop two intuitive, numerically near-optimal heuristic policies, which could be used for more complex, high-dimensional problems. Furthermore, one of the heuristics requires only a statistic of the motion probability distribution, making it a reasonable method for use in a realistic setting. Numerically, we find that the amount of decrease in dose to the OAR can vary significantly (5-85%) depending on the amount of motion in the anatomy, the number of fractions and the range of fraction sizes allowed. In general, the decrease in dose to the OAR is more pronounced when: (i) we have a high probability of large tumor-OAR distances, (ii) we use many fractions (as in a hyper-fractionated setting) and (iii) we allow large daily fraction size deviations.
Differentially Private Histogram Publication For Dynamic Datasets: An Adaptive Sampling Approach.
Li, Haoran; Jiang, Xiaoqian; Xiong, Li; Liu, Jinfei
2015-10-01
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.
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
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.
NASA Technical Reports Server (NTRS)
Johnson, C. R., Jr.
1979-01-01
The widespread modal analysis of flexible spacecraft and recognition of the poor a priori parameterization possible of the modal descriptions of individual structures have prompted the consideration of adaptive modal control strategies for distributed parameter systems. The current major approaches to computationally efficient adaptive digital control useful in these endeavors are explained in an original, lucid manner using modal second order structure dynamics for algorithm explication. Difficulties in extending these lumped-parameter techniques to distributed-parameter system expansion control are cited.
NASA Astrophysics Data System (ADS)
Hunt, Jason Daniel
An adaptive three-dimensional Cartesian approach for the parallel computation of compressible flow about static and dynamic configurations has been developed and validated. This is a further step towards a goal that remains elusive for CFD codes: the ability to model complex dynamic-geometry problems in a quick and automated manner. The underlying flow-solution method solves the three-dimensional Euler equations using a MUSCL-type finite-volume approach to achieve higher-order spatial accuracy. The flow solution, either steady or unsteady, is advanced in time via a two-stage time-stepping scheme. This basic solution method has been incorporated into a parallel block-adaptive Cartesian framework, using a block-octtree data structure to represent varying spatial resolution, and to compute flow solutions in parallel. The ability to represent static geometric configurations has been introduced by cutting a geometric configuration out of a background block-adaptive Cartesian grid, then solving for the flow on the resulting volume grid. This approach has been extended for dynamic geometric configurations: components of a given configuration were permitted to independently move, according to prescribed rigid-body motion. Two flow-solver difficulties arise as a result of introducing static and dynamic configurations: small time steps; and the disappearance/appearance of cell volume during a time integration step. Both of these problems have been remedied through cell merging. The concept of cell merging and its implementation within the parallel block-adaptive method is described. While the parallelization of certain grid-generation and cell-cutting routines resulted from this work, the most significant contribution was developing the novel cell-merging paradigm that was incorporated into the parallel block-adaptive framework. Lastly, example simulations both to validate the developed method and to demonstrate its full capabilities have been carried out. A simple, steady
Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs
ERIC Educational Resources Information Center
Adamson, David; Dyke, Gregory; Jang, Hyeju; Rosé, Carolyn Penstein
2014-01-01
This paper investigates the use of conversational agents to scaffold on-line collaborative learning discussions through an approach called Academically Productive Talk (APT). In contrast to past work on dynamic support for collaborative learning, where agents were used to elevate conceptual depth by leading students through directed lines of…
NASA Astrophysics Data System (ADS)
Wei, Qing-Lai; Liu, De-Rong; Xu, Yan-Cai
2015-03-01
A policy iteration algorithm of adaptive dynamic programming (ADP) is developed to solve the optimal tracking control for a class of discrete-time chaotic systems. By system transformations, the optimal tracking problem is transformed into an optimal regulation one. The policy iteration algorithm for discrete-time chaotic systems is first described. Then, the convergence and admissibility properties of the developed policy iteration algorithm are presented, which show that the transformed chaotic system can be stabilized under an arbitrary iterative control law and the iterative performance index function simultaneously converges to the optimum. By implementing the policy iteration algorithm via neural networks, the developed optimal tracking control scheme for chaotic systems is verified by a simulation. Project supported by the National Natural Science Foundation of China (Grant Nos. 61034002, 61233001, 61273140, 61304086, and 61374105) and the Beijing Natural Science Foundation, China (Grant No. 4132078).
A knowledge-based approach to identification and adaptation in dynamical systems control
NASA Technical Reports Server (NTRS)
Glass, B. J.; Wong, C. M.
1988-01-01
Artificial intelligence techniques are applied to the problems of model form and parameter identification of large-scale dynamic systems. The object-oriented knowledge representation is discussed in the context of causal modeling and qualitative reasoning. Structured sets of rules are used for implementing qualitative component simulations, for catching qualitative discrepancies and quantitative bound violations, and for making reconfiguration and control decisions that affect the physical system. These decisions are executed by backward-chaining through a knowledge base of control action tasks. This approach was implemented for two examples: a triple quadrupole mass spectrometer and a two-phase thermal testbed. Results of tests with both of these systems demonstrate that the software replicates some or most of the functionality of a human operator, thereby reducing the need for a human-in-the-loop in the lower levels of control of these complex systems.
Lee, William; van Baalen, Minus; Jansen, Vincent A A
2016-01-07
Like many other bacteria, Pseudomonas aeruginosa sequesters iron from the environment through the secretion, and subsequent uptake, of iron-binding molecules. As these molecules can be taken up by other bacteria in the population than those who secreted them, this is a form of cooperation through a public good. Traditionally, this problem has been studied by comparing the relative fitnesses of siderophore-producing and non-producing strains, but this gives no information about the fate of strains that do produce intermediate amounts of siderophores. Here, we investigate theoretically how the amount invested in this form of cooperation evolves. We use a mechanistic description of the laboratory protocols used in experimental evolution studies to describe the competition and cooperation of the bacteria. From this dynamical model we derive the fitness following the adaptive dynamics method. The results show how selection is driven by local siderophore production and local competition. Because siderophore production reduces the growth rate, local competition decreases with the degree of relatedness (which is a dynamical variable in our model). Our model is not restricted to the analysis of small phenotypic differences and allows for theoretical exploration of the effects of large phenotypic differences between cooperators and cheats. We predict that an intermediate ESS level of cooperation (molecule production) should exist. The adaptive dynamics approach allows us to assess evolutionary stability, which is often not possible in other kin-selection models. We found that selection can lead to an intermediate strategy which in our model is always evolutionarily stable, yet can allow invasion of strategies that are much more cooperative. Our model describes the evolution of a public good in the context of the ecology of the microorganism, which allows us to relate the extent of production of the public good to the details of the interactions.
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.
NASA Astrophysics Data System (ADS)
Zhao, Hui; Li, Lixiang; Peng, Haipeng; Kurths, Jürgen; Xiao, Jinghua; Yang, Yixian
2015-05-01
In this paper, exponential anti-synchronization in mean square of an uncertain memristor-based neural network is studied. The uncertain terms include non-modeled dynamics with boundary and stochastic perturbations. Based on the differential inclusions theory, linear matrix inequalities, Gronwall's inequality and adaptive control technique, an adaptive controller with update laws is developed to realize the exponential anti-synchronization. Adaptive controller can adjust itself behavior to get the best performance, according to the environment is changing or the environment has changed, which has the ability to adapt to environmental change. Furthermore, a numerical example is provided to validate the effectiveness of the proposed method.
The fluid dynamic approach to equidistribution methods for grid generation and adaptation
Delzanno, Gian Luca; Finn, John M
2009-01-01
The equidistribution methods based on L{sub p} Monge-Kantorovich optimization [Finn and Delzanno, submitted to SISC, 2009] and on the deformation [Moser, 1965; Dacorogna and Moser, 1990, Liao and Anderson, 1992] method are analyzed primarily in the context of grid generation. It is shown that the first class of methods can be obtained from a fluid dynamic formulation based on time-dependent equations for the mass density and the momentum density, arising from a variational principle. In this context, deformation methods arise from a fluid formulation by making a specific assumption on the time evolution of the density (but with some degree of freedom for the momentum density). In general, deformation methods do not arise from a variational principle. However, it is possible to prescribe an optimal deformation method, related to L{sub 1} Monge-Kantorovich optimization, by making a further assumption on the momentum density. Some applications of the L{sub p} fluid dynamic formulation to imaging are also explored.
Dynamic scenario of metabolic pathway adaptation in tumors and therapeutic approach
Peppicelli, Silvia; Bianchini, Francesca; Calorini, Lido
2015-01-01
Cancer cells need to regulate their metabolic program to fuel several activities, including unlimited proliferation, resistance to cell death, invasion and metastasis. The aim of this work is to revise this complex scenario. Starting from proliferating cancer cells located in well-oxygenated regions, they may express the so-called “Warburg effect” or aerobic glycolysis, meaning that although a plenty of oxygen is available, cancer cells choose glycolysis, the sole pathway that allows a biomass formation and DNA duplication, needed for cell division. Although oxygen does not represent the primary font of energy, diffusion rate reduces oxygen tension and the emerging hypoxia promotes “anaerobic glycolysis” through the hypoxia inducible factor-1α-dependent up-regulation. The acquired hypoxic phenotype is endowed with high resistance to cell death and high migration capacities, although these cells are less proliferating. Cells using aerobic or anaerobic glycolysis survive only in case they extrude acidic metabolites acidifying the extracellular space. Acidosis drives cancer cells from glycolysis to OxPhos, and OxPhos transforms the available alternative substrates into energy used to fuel migration and distant organ colonization. Thus, metabolic adaptations sustain different energy-requiring ability of cancer cells, but render them responsive to perturbations by anti-metabolic agents, such as inhibitors of glycolysis and/or OxPhos. PMID:25897425
Dynamic Adaption of Vascular Morphology
Okkels, Fridolin; Jacobsen, Jens Christian Brings
2012-01-01
The structure of vascular networks adapts continuously to meet changes in demand of the surrounding tissue. Most of the known vascular adaptation mechanisms are based on local reactions to local stimuli such as pressure and flow, which in turn reflects influence from the surrounding tissue. Here we present a simple two-dimensional model in which, as an alternative approach, the tissue is modeled as a porous medium with intervening sharply defined flow channels. Based on simple, physiologically realistic assumptions, flow-channel structure adapts so as to reach a configuration in which all parts of the tissue are supplied. A set of model parameters uniquely determine the model dynamics, and we have identified the region of the best-performing model parameters (a global optimum). This region is surrounded in parameter space by less optimal model parameter values, and this separation is characterized by steep gradients in the related fitness landscape. Hence it appears that the optimal set of parameters tends to localize close to critical transition zones. Consequently, while the optimal solution is stable for modest parameter perturbations, larger perturbations may cause a profound and permanent shift in systems characteristics. We suggest that the system is driven toward a critical state as a consequence of the ongoing parameter optimization, mimicking an evolutionary pressure on the system. PMID:23060814
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.
NASA Technical Reports Server (NTRS)
Griffin, Brian Joseph; Burken, John J.; Xargay, Enric
2010-01-01
This paper presents an L(sub 1) adaptive control augmentation system design for multi-input multi-output nonlinear systems in the presence of unmatched uncertainties which may exhibit significant cross-coupling effects. A piecewise continuous adaptive law is adopted and extended for applicability to multi-input multi-output systems that explicitly compensates for dynamic cross-coupling. In addition, explicit use of high-fidelity actuator models are added to the L1 architecture to reduce uncertainties in the system. The L(sub 1) multi-input multi-output adaptive control architecture is applied to the X-29 lateral/directional dynamics and results are evaluated against a similar single-input single-output design approach.
Chaotic satellite attitude control by adaptive approach
NASA Astrophysics Data System (ADS)
Wei, Wei; Wang, Jing; Zuo, Min; Liu, Zaiwen; Du, Junping
2014-06-01
In this article, chaos control of satellite attitude motion is considered. Adaptive control based on dynamic compensation is utilised to suppress the chaotic behaviour. Control approaches with three control inputs and with only one control input are proposed. Since the adaptive control employed is based on dynamic compensation, faithful model of the system is of no necessity. Sinusoidal disturbance and parameter uncertainties are considered to evaluate the robustness of the closed-loop system. Both of the approaches are confirmed by theoretical and numerical results.
Automated adaptive inference of phenomenological dynamical models
NASA Astrophysics Data System (ADS)
Daniels, Bryan C.; Nemenman, Ilya
2015-08-01
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.
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
Adaptive approaches to biosecurity governance.
Cook, David C; Liu, Shuang; Murphy, Brendan; Lonsdale, W Mark
2010-09-01
This article discusses institutional changes that may facilitate an adaptive approach to biosecurity risk management where governance is viewed as a multidisciplinary, interactive experiment acknowledging uncertainty. Using the principles of adaptive governance, evolved from institutional theory, we explore how the concepts of lateral information flows, incentive alignment, and policy experimentation might shape Australia's invasive species defense mechanisms. We suggest design principles for biosecurity policies emphasizing overlapping complementary response capabilities and the sharing of invasive species risks via a polycentric system of governance.
Yu, Rong; Zhong, Weifeng; Xie, Shengli; Zhang, Yan; Zhang, Yun
2016-02-01
As the next-generation power grid, smart grid will be integrated with a variety of novel communication technologies to support the explosive data traffic and the diverse requirements of quality of service (QoS). Cognitive radio (CR), which has the favorable ability to improve the spectrum utilization, provides an efficient and reliable solution for smart grid communications networks. In this paper, we study the QoS differential scheduling problem in the CR-based smart grid communications networks. The scheduler is responsible for managing the spectrum resources and arranging the data transmissions of smart grid users (SGUs). To guarantee the differential QoS, the SGUs are assigned to have different priorities according to their roles and their current situations in the smart grid. Based on the QoS-aware priority policy, the scheduler adjusts the channels allocation to minimize the transmission delay of SGUs. The entire transmission scheduling problem is formulated as a semi-Markov decision process and solved by the methodology of adaptive dynamic programming. A heuristic dynamic programming (HDP) architecture is established for the scheduling problem. By the online network training, the HDP can learn from the activities of primary users and SGUs, and adjust the scheduling decision to achieve the purpose of transmission delay minimization. Simulation results illustrate that the proposed priority policy ensures the low transmission delay of high priority SGUs. In addition, the emergency data transmission delay is also reduced to a significantly low level, guaranteeing the differential QoS in smart grid.
Dynamic patterns of adaptive radiation.
Gavrilets, Sergey; Vose, Aaron
2005-12-13
Adaptive radiation is defined as the evolution of ecological and phenotypic diversity within a rapidly multiplying lineage. When it occurs, adaptive radiation typically follows the colonization of a new environment or the establishment of a "key innovation," which opens new ecological niches and/or new paths for evolution. Here, we take advantage of recent developments in speciation theory and modern computing power to build and explore a large-scale, stochastic, spatially explicit, individual-based model of adaptive radiation driven by adaptation to multidimensional ecological niches. We are able to model evolutionary dynamics of populations with hundreds of thousands of sexual diploid individuals over a time span of 100,000 generations assuming realistic mutation rates and allowing for genetic variation in a large number of both selected and neutral loci. Our results provide theoretical support and explanation for a number of empirical patterns including "area effect," "overshooting effect," and "least action effect," as well as for the idea of a "porous genome." Our findings suggest that the genetic architecture of traits involved in the most spectacular radiations might be rather simple. We show that a great majority of speciation events are concentrated early in the phylogeny. Our results emphasize the importance of ecological opportunity and genetic constraints in controlling the dynamics of adaptive radiation.
Dynamics of Adaptation in Spatially Heterogeneous Metapopulations
Papaïx, Julien; David, Olivier; Lannou, Christian; Monod, Hervé
2013-01-01
The selection pressure experienced by organisms often varies across the species range. It is hence crucial to characterise the link between environmental spatial heterogeneity and the adaptive dynamics of species or populations. We address this issue by studying the phenotypic evolution of a spatial metapopulation using an adaptive dynamics approach. The singular strategy is found to be the mean of the optimal phenotypes in each habitat with larger weights for habitats present in large and well connected patches. The presence of spatial clusters of habitats in the metapopulation is found to facilitate specialisation and to increase both the level of adaptation and the evolutionary speed of the population when dispersal is limited. By showing that spatial structures are crucial in determining the specialisation level and the evolutionary speed of a population, our results give insight into the influence of spatial heterogeneity on the niche breadth of species. PMID:23424618
The Limits to Adaptation; A Systems Approach
The Limits to Adaptation: A Systems Approach. The ability to adapt to climate change is delineated by capacity thresholds, after which climate damages begin to overwhelm the adaptation response. Such thresholds depend upon physical properties (natural processes and engineering...
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
Middleware for dynamic adaptation of component applications.
Norris, B.; Bhowmick, S.; Kaushik, D.; McInnes, L. C.
2007-01-01
Component- and service-based software engineering approaches have been gaining popularity in high-performance scientific computing, facilitating the creation and management of large multidisciplinary, multideveloper applications, and providing opportunities for improved performance and numerical accuracy. These software engineering approaches enable the development of middleware infrastructure for computational quality of service (CQoS), which provides performance optimizations through dynamic algorithm selection and configuration in a mostly automated fashion. The factors that affect performance are closely tied to a component's parallel implementation, its management of parallel communication and memory, the algorithms executed, the algorithmic parameters employed, and other operational characteristics. We present the design of a component middleware CQoS architecture for automated composition and adaptation of high-performance component- or service-based applications. We describe its initial implementation and corresponding experimental results for parallel simulations involving time-dependent nonlinear partial differential equations.
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.
Acquiring case adaptation knowledge: A hybrid approach
Leake, D.B.; Kinley, A.; Wilson, D.
1996-12-31
The ability of case-based reasoning (CBR) systems to apply cases to novel situations depends on their case adaptation knowledge. However, endowing CBR systems with adequate adaptation knowledge has proven to be a very difficult task. This paper describes a hybrid method for performing case adaptation, using a combination of rule-based and case-based reasoning. It shows how this approach provides a framework for acquiring flexible adaptation knowledge from experiences with autonomous adaptation and suggests its potential as a basis for acquisition of adaptation knowledge from interactive user guidance. It also presents initial experimental results examining the benefits of the approach and comparing the relative contributions of case learning and adaptation learning to reasoning performance.
Flight Test Approach to Adaptive Control Research
NASA Technical Reports Server (NTRS)
Pavlock, Kate Maureen; Less, James L.; Larson, David Nils
2011-01-01
The National Aeronautics and Space Administration s Dryden Flight Research Center completed flight testing of adaptive controls research on a full-scale F-18 testbed. The validation of adaptive controls has the potential to enhance safety in the presence of adverse conditions such as structural damage or control surface failures. This paper describes the research interface architecture, risk mitigations, flight test approach and lessons learned of adaptive controls research.
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
Adaptive Networks Foundations: Modeling, Dynamics, and Applications
2013-02-13
22-Mar. 2, 2012. • Shaw, L.B., Long, Y., and Gross, T. Simultaneous spread of infection and information in adaptive networks. Casablanca ...International Workshop on Mathematical Biology, Casablanca , Morocco, Jun. 20-24, 2011. • Tunc, I. and Shaw, L.B. Dynamics of infection spreading in adaptive...Defense The number of undergraduates funded by your agreement who graduated during this period and will receive scholarships or fellowships for further
Flight Approach to Adaptive Control Research
NASA Technical Reports Server (NTRS)
Pavlock, Kate Maureen; Less, James L.; Larson, David Nils
2011-01-01
The National Aeronautics and Space Administration's Dryden Flight Research Center completed flight testing of adaptive controls research on a full-scale F-18 testbed. The testbed served as a full-scale vehicle to test and validate adaptive flight control research addressing technical challenges involved with reducing risk to enable safe flight in the presence of adverse conditions such as structural damage or control surface failures. This paper describes the research interface architecture, risk mitigations, flight test approach and lessons learned of adaptive controls research.
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.
Multiprocessor Adaptive Control Of A Dynamic System
NASA Technical Reports Server (NTRS)
Juang, Jer-Nan; Hyland, David C.
1995-01-01
Architecture for fully autonomous digital electronic control system developed for use in identification and adaptive control of dynamic system. Architecture modular and hierarchical. Combines relatively simple, standardized processing units into complex parallel-processing subsystems. Although architecture based on neural-network concept, processing units themselves not neural networks; processing units implemented by programming of currently available microprocessors.
Adaptive dynamics of extortion and compliance.
Hilbe, Christian; Nowak, Martin A; Traulsen, Arne
2013-01-01
Direct reciprocity is a mechanism for the evolution of cooperation. For the iterated prisoner's dilemma, a new class of strategies has recently been described, the so-called zero-determinant strategies. Using such a strategy, a player can unilaterally enforce a linear relationship between his own payoff and the co-player's payoff. In particular the player may act in such a way that it becomes optimal for the co-player to cooperate unconditionally. In this way, a player can manipulate and extort his co-player, thereby ensuring that the own payoff never falls below the co-player's payoff. However, using a compliant strategy instead, a player can also ensure that his own payoff never exceeds the co-player's payoff. Here, we use adaptive dynamics to study when evolution leads to extortion and when it leads to compliance. We find a remarkable cyclic dynamics: in sufficiently large populations, extortioners play a transient role, helping the population to move from selfish strategies to compliance. Compliant strategies, however, can be subverted by altruists, which in turn give rise to selfish strategies. Whether cooperative strategies are favored in the long run critically depends on the size of the population; we show that cooperation is most abundant in large populations, in which case average payoffs approach the social optimum. Our results are not restricted to the case of the prisoners dilemma, but can be extended to other social dilemmas, such as the snowdrift game. Iterated social dilemmas in large populations do not lead to the evolution of strategies that aim to dominate their co-player. Instead, generosity succeeds.
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.
A modular approach to adaptive structures.
Pagitz, Markus; Pagitz, Manuel; Hühne, Christian
2014-10-07
A remarkable property of nastic, shape changing plants is their complete fusion between actuators and structure. This is achieved by combining a large number of cells whose geometry, internal pressures and material properties are optimized for a given set of target shapes and stiffness requirements. An advantage of such a fusion is that cell walls are prestressed by cell pressures which increases, decreases the overall structural stiffness, weight. Inspired by the nastic movement of plants, Pagitz et al (2012 Bioinspir. Biomim. 7) published a novel concept for pressure actuated cellular structures. This article extends previous work by introducing a modular approach to adaptive structures. An algorithm that breaks down any continuous target shapes into a small number of standardized modules is presented. Furthermore it is shown how cytoskeletons within each cell enhance the properties of adaptive modules. An adaptive passenger seat and an aircrafts leading, trailing edge is used to demonstrate the potential of a modular approach.
Cross-Cultural Adaptation: Current Approaches.
ERIC Educational Resources Information Center
Kim, Young Yun, Ed.; Gudykunst, William B., Ed.
1988-01-01
Reflecting multidisciplinary and multisocietal approaches, this collection presents 14 theoretical or research-based essays dealing with cross-cultural adaptation of individuals who are born and raised in one culture and find themselves in need of modifying their customary life patterns in a foreign culture. Papers in the collection are:…
Matched filter based iterative adaptive approach
NASA Astrophysics Data System (ADS)
Nepal, Ramesh; Zhang, Yan Rockee; Li, Zhengzheng; Blake, William
2016-05-01
Matched Filter sidelobes from diversified LPI waveform design and sensor resolution are two important considerations in radars and active sensors in general. Matched Filter sidelobes can potentially mask weaker targets, and low sensor resolution not only causes a high margin of error but also limits sensing in target-rich environment/ sector. The improvement in those factors, in part, concern with the transmitted waveform and consequently pulse compression techniques. An adaptive pulse compression algorithm is hence desired that can mitigate the aforementioned limitations. A new Matched Filter based Iterative Adaptive Approach, MF-IAA, as an extension to traditional Iterative Adaptive Approach, IAA, has been developed. MF-IAA takes its input as the Matched Filter output. The motivation here is to facilitate implementation of Iterative Adaptive Approach without disrupting the processing chain of traditional Matched Filter. Similar to IAA, MF-IAA is a user parameter free, iterative, weighted least square based spectral identification algorithm. This work focuses on the implementation of MF-IAA. The feasibility of MF-IAA is studied using a realistic airborne radar simulator as well as actual measured airborne radar data. The performance of MF-IAA is measured with different test waveforms, and different Signal-to-Noise (SNR) levels. In addition, Range-Doppler super-resolution using MF-IAA is investigated. Sidelobe reduction as well as super-resolution enhancement is validated. The robustness of MF-IAA with respect to different LPI waveforms and SNR levels is also demonstrated.
Adaptation dynamics of the quasispecies model
NASA Astrophysics Data System (ADS)
Jain, Kavita
2009-02-01
We study the adaptation dynamics of an initially maladapted population evolving via the elementary processes of mutation and selection. The evolution occurs on rugged fitness landscapes which are defined on the multi-dimensional genotypic space and have many local peaks separated by low fitness valleys. We mainly focus on the Eigen's model that describes the deterministic dynamics of an infinite number of self-replicating molecules. In the stationary state, for small mutation rates such a population forms a {\\it quasispecies} which consists of the fittest genotype and its closely related mutants. The quasispecies dynamics on rugged fitness landscape follow a punctuated (or step-like) pattern in which a population jumps from a low fitness peak to a higher one, stays there for a considerable time before shifting the peak again and eventually reaches the global maximum of the fitness landscape. We calculate exactly several properties of this dynamical process within a simplified version of the quasispecies model.
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 synchronization and anticipatory dynamical systems
NASA Astrophysics Data System (ADS)
Yang, Ying-Jen; Chen, Chun-Chung; Lai, Pik-Yin; Chan, C. K.
2015-09-01
Many biological systems can sense periodical variations in a stimulus input and produce well-timed, anticipatory responses after the input is removed. Such systems show memory effects for retaining timing information in the stimulus and cannot be understood from traditional synchronization consideration of passive oscillatory systems. To understand this anticipatory phenomena, we consider oscillators built from excitable systems with the addition of an adaptive dynamics. With such systems, well-timed post-stimulus responses similar to those from experiments can be obtained. Furthermore, a well-known model of working memory is shown to possess similar anticipatory dynamics when the adaptive mechanism is identified with synaptic facilitation. The last finding suggests that this type of oscillator can be common in neuronal systems with plasticity.
An adaptive multi-swarm optimizer for dynamic optimization problems.
Li, Changhe; Yang, Shengxiang; Yang, Ming
2014-01-01
The multipopulation method has been widely used to solve dynamic optimization problems (DOPs) with the aim of maintaining multiple populations on different peaks to locate and track multiple changing optima simultaneously. However, to make this approach effective for solving DOPs, two challenging issues need to be addressed. They are how to adapt the number of populations to changes and how to adaptively maintain the population diversity in a situation where changes are complicated or hard to detect or predict. Tracking the changing global optimum in dynamic environments is difficult because we cannot know when and where changes occur and what the characteristics of changes would be. Therefore, it is necessary to take these challenging issues into account in designing such adaptive algorithms. To address the issues when multipopulation methods are applied for solving DOPs, this paper proposes an adaptive multi-swarm algorithm, where the populations are enabled to be adaptive in dynamic environments without change detection. An experimental study is conducted based on the moving peaks problem to investigate the behavior of the proposed method. The performance of the proposed algorithm is also compared with a set of algorithms that are based on multipopulation methods from different research areas in the literature of evolutionary computation.
A Novel Approach for Adaptive Signal Processing
NASA Technical Reports Server (NTRS)
Chen, Ya-Chin; Juang, Jer-Nan
1998-01-01
Adaptive linear predictors have been used extensively in practice in a wide variety of forms. In the main, their theoretical development is based upon the assumption of stationarity of the signals involved, particularly with respect to the second order statistics. On this basis, the well-known normal equations can be formulated. If high- order statistical stationarity is assumed, then the equivalent normal equations involve high-order signal moments. In either case, the cross moments (second or higher) are needed. This renders the adaptive prediction procedure non-blind. A novel procedure for blind adaptive prediction has been proposed and considerable implementation has been made in our contributions in the past year. The approach is based upon a suitable interpretation of blind equalization methods that satisfy the constant modulus property and offers significant deviations from the standard prediction methods. These blind adaptive algorithms are derived by formulating Lagrange equivalents from mechanisms of constrained optimization. In this report, other new update algorithms are derived from the fundamental concepts of advanced system identification to carry out the proposed blind adaptive prediction. The results of the work can be extended to a number of control-related problems, such as disturbance identification. The basic principles are outlined in this report and differences from other existing methods are discussed. The applications implemented are speech processing, such as coding and synthesis. Simulations are included to verify the novel modelling method.
Nonhydrostatic adaptive mesh dynamics for multiscale climate models (Invited)
NASA Astrophysics Data System (ADS)
Collins, W.; Johansen, H.; McCorquodale, P.; Colella, P.; Ullrich, P. A.
2013-12-01
Many of the atmospheric phenomena with the greatest potential impact in future warmer climates are inherently multiscale. Such meteorological systems include hurricanes and tropical cyclones, atmospheric rivers, and other types of hydrometeorological extremes. These phenomena are challenging to simulate in conventional climate models due to the relatively coarse uniform model resolutions relative to the native nonhydrostatic scales of the phenomonological dynamics. To enable studies of these systems with sufficient local resolution for the multiscale dynamics yet with sufficient speed for climate-change studies, we have adapted existing adaptive mesh dynamics for the DOE-NSF Community Atmosphere Model (CAM). In this talk, we present an adaptive, conservative finite volume approach for moist non-hydrostatic atmospheric dynamics. The approach is based on the compressible Euler equations on 3D thin spherical shells, where the radial direction is treated implicitly (using a fourth-order Runga-Kutta IMEX scheme) to eliminate time step constraints from vertical acoustic waves. Refinement is performed only in the horizontal directions. The spatial discretization is the equiangular cubed-sphere mapping, with a fourth-order accurate discretization to compute flux averages on faces. By using both space-and time-adaptive mesh refinement, the solver allocates computational effort only where greater accuracy is needed. The resulting method is demonstrated to be fourth-order accurate for model problems, and robust at solution discontinuities and stable for large aspect ratios. We present comparisons using a simplified physics package for dycore comparisons of moist physics. Hadley cell lifting an advected tracer into upper atmosphere, with horizontal adaptivity
Adaptive approach for nonlinear sensitivity analysis of reaction kinetics.
Horenko, Illia; Lorenz, Sönke; Schütte, Christof; Huisinga, Wilhelm
2005-07-15
We present a unified approach for linear and nonlinear sensitivity analysis for models of reaction kinetics that are stated in terms of systems of ordinary differential equations (ODEs). The approach is based on the reformulation of the ODE problem as a density transport problem described by a Fokker-Planck equation. The resulting multidimensional partial differential equation is herein solved by extending the TRAIL algorithm originally introduced by Horenko and Weiser in the context of molecular dynamics (J. Comp. Chem. 2003, 24, 1921) and discussed it in comparison with Monte Carlo techniques. The extended TRAIL approach is fully adaptive and easily allows to study the influence of nonlinear dynamical effects. We illustrate the scheme in application to an enzyme-substrate model problem for sensitivity analysis w.r.t. to initial concentrations and parameter values.
Variable Neural Adaptive Robust Control: A Switched System Approach
Lian, Jianming; Hu, Jianghai; Zak, Stanislaw H.
2015-05-01
Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multi-input multi-output uncertain systems. The controllers incorporate a variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. The variable-structure RBF network solves the problem of structure determination associated with fixed-structure RBF networks. It can determine the network structure on-line dynamically by adding or removing radial basis functions according to the tracking performance. The structure variation is taken into account in the stability analysis of the closed-loop system using a switched system approach with the aid of the piecewise quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.
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.
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.
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.
Optimal spectral tracking--adapting to dynamic regime change.
Brittain, John-Stuart; Halliday, David M
2011-01-30
Real world data do not always obey the statistical restraints imposed upon them by sophisticated analysis techniques. In spectral analysis for instance, an ergodic process--the interchangeability of temporal for spatial averaging--is assumed for a repeat-trial design. Many evolutionary scenarios, such as learning and motor consolidation, do not conform to such linear behaviour and should be approached from a more flexible perspective. To this end we previously introduced the method of optimal spectral tracking (OST) in the study of trial-varying parameters. In this extension to our work we modify the OST routines to provide an adaptive implementation capable of reacting to dynamic transitions in the underlying system state. In so doing, we generalise our approach to characterise both slow-varying and rapid fluctuations in time-series, simultaneously providing a metric of system stability. The approach is first applied to a surrogate dataset and compared to both our original non-adaptive solution and spectrogram approaches. The adaptive OST is seen to display fast convergence and desirable statistical properties. All three approaches are then applied to a neurophysiological recording obtained during a study on anaesthetic monitoring. Local field potentials acquired from the posterior hypothalamic region of a deep brain stimulation patient undergoing anaesthesia were analysed. The characterisation of features such as response delay, time-to-peak and modulation brevity are considered.
Adaptive typography for dynamic mapping environments
NASA Astrophysics Data System (ADS)
Bardon, Didier
1991-08-01
When typography moves across a map, it passes over areas of different colors, densities, and textures. In such a dynamic environment, the aspect of typography must be constantly adapted to provide disernibility for every new background. Adaptive typography undergoes two adaptive operations: background control and contrast control. The background control prevents the features of the map (edges, lines, abrupt changes of densities) from destroying the integrity of the letterform. This is achieved by smoothing the features of the map in the area where a text label is displayed. The modified area is limited to the space covered by the characters of the label. Dispositions are taken to insure that the smoothing operation does not introduce any new visual noise. The contrast control assures that there are sufficient lightness differences between the typography and its ever-changing background. For every new situation, background color and foreground color are compared and the foreground color lightness is adjusted according to a chosen contrast value. Criteria and methods of choosing the appropriate contrast value are presented as well as the experiments that led to them.
Adaptive molecular resolution approach in Hamiltonian form: An asymptotic analysis.
Zhu, Jinglong; Klein, Rupert; Delle Site, Luigi
2016-10-01
Adaptive molecular resolution approaches in molecular dynamics are becoming relevant tools for the analysis of molecular liquids characterized by the interplay of different physical scales. The essential difference among these methods is in the way the change of molecular resolution is made in a buffer (transition) region. In particular a central question concerns the possibility of the existence of a global Hamiltonian which, by describing the change of resolution, is at the same time physically consistent, mathematically well defined, and numerically accurate. In this paper we present an asymptotic analysis of the adaptive process complemented by numerical results and show that under certain mathematical conditions a Hamiltonian, which is physically consistent and numerically accurate, may exist. Such conditions show that molecular simulations in the current computational implementation require systems of large size, and thus a Hamiltonian approach such as the one proposed, at this stage, would not be practical from the numerical point of view. However, the Hamiltonian proposed provides the basis for a simplification and generalization of the numerical implementation of adaptive resolution algorithms to other molecular dynamics codes.
Hydrodynamics in adaptive resolution particle simulations: Multiparticle collision dynamics
Alekseeva, Uliana; Winkler, Roland G.; Sutmann, Godehard
2016-06-01
A new adaptive resolution technique for particle-based multi-level simulations of fluids is presented. In the approach, the representation of fluid and solvent particles is changed on the fly between an atomistic and a coarse-grained description. The present approach is based on a hybrid coupling of the multiparticle collision dynamics (MPC) method and molecular dynamics (MD), thereby coupling stochastic and deterministic particle-based methods. Hydrodynamics is examined by calculating velocity and current correlation functions for various mixed and coupled systems. We demonstrate that hydrodynamic properties of the mixed fluid are conserved by a suitable coupling of the two particle methods, and that the simulation results agree well with theoretical expectations.
Adaptive control of force microscope cantilever dynamics
NASA Astrophysics Data System (ADS)
Jensen, S. E.; Dougherty, W. M.; Garbini, J. L.; Sidles, J. A.
2007-09-01
Magnetic resonance force microscopy (MRFM) and other emerging scanning probe microscopies entail the detection of attonewton-scale forces. Requisite force sensitivities are achieved through the use of soft force microscope cantilevers as high resonant-Q micromechanical oscillators. In practice, the dynamics of these oscillators are greatly improved by the application of force feedback control computed in real time by a digital signal processor (DSP). Improvements include increased sensitive bandwidth, reduced oscillator ring up/down time, and reduced cantilever thermal vibration amplitude. However, when the cantilever tip and the sample are in close proximity, electrostatic and Casimir tip-sample force gradients can significantly alter the cantilever resonance frequency, foiling fixed-gain narrow-band control schemes. We report an improved, adaptive control algorithm that uses a Hilbert transform technique to continuously measure the vibration frequency of the thermally-excited cantilever and seamlessly adjust the DSP program coefficients. The closed-loop vibration amplitude is typically 0.05 nm. This adaptive algorithm enables narrow-band formally-optimal control over a wide range of resonance frequencies, and preserves the thermally-limited signal to noise ratio (SNR).
Airport Characterization for the Adaptation of Surface Congestion Management Approaches
2013-02-01
1 of 2 Airport Characterization for the Adaptation of Surface Congestion Management Approaches Melanie Sandberg, Tom Reynolds...TYPE 3. DATES COVERED 00-00-2013 to 00-00-2013 4. TITLE AND SUBTITLE Airport Characterization for the Adaptation of Surface Congestion Management...1 Airport Characterization for the Adaptation of Surface Congestion Management Approaches* Melanie
Analyzing Hedges in Verbal Communication: An Adaptation-Based Approach
ERIC Educational Resources Information Center
Wang, Yuling
2010-01-01
Based on Adaptation Theory, the article analyzes the production process of hedges. The procedure consists of the continuous making of choices in linguistic forms and communicative strategies. These choices are made just for adaptation to the contextual correlates. Besides, the adaptation process is dynamic, intentional and bidirectional.
Approaching neuropsychological tasks through adaptive neurorobots
NASA Astrophysics Data System (ADS)
Gigliotta, Onofrio; Bartolomeo, Paolo; Miglino, Orazio
2015-04-01
Neuropsychological phenomena have been modelized mainly, by the mainstream approach, by attempting to reproduce their neural substrate whereas sensory-motor contingencies have attracted less attention. In this work, we introduce a simulator based on the evolutionary robotics platform Evorobot* in order to setting up in silico neuropsychological tasks. Moreover, in this study we trained artificial embodied neurorobotic agents equipped with a pan/tilt camera, provided with different neural and motor capabilities, to solve a well-known neuropsychological test: the cancellation task in which an individual is asked to cancel target stimuli surrounded by distractors. Results showed that embodied agents provided with additional motor capabilities (a zooming/attentional actuator) outperformed simple pan/tilt agents, even those equipped with more complex neural controllers and that the zooming ability is exploited to correctly categorising presented stimuli. We conclude that since the sole neural computational power cannot explain the (artificial) cognition which emerged throughout the adaptive process, such kind of modelling approach can be fruitful in neuropsychological modelling where the importance of having a body is often neglected.
Mohamad Ali, Mohd. Shukuri; Mohd Fuzi, Siti Farhanie; Ganasen, Menega; Abdul Rahman, Raja Noor Zaliha Raja; Basri, Mahiran; Salleh, Abu Bakar
2013-01-01
The psychrophilic enzyme is an interesting subject to study due to its special ability to adapt to extreme temperatures, unlike typical enzymes. Utilizing computer-aided software, the predicted structure and function of the enzyme lipase AMS8 (LipAMS8) (isolated from the psychrophilic Pseudomonas sp., obtained from the Antarctic soil) are studied. The enzyme shows significant sequence similarities with lipases from Pseudomonas sp. MIS38 and Serratia marcescens. These similarities aid in the prediction of the 3D molecular structure of the enzyme. In this study, 12 ns MD simulation is performed at different temperatures for structural flexibility and stability analysis. The results show that the enzyme is most stable at 0°C and 5°C. In terms of stability and flexibility, the catalytic domain (N-terminus) maintained its stability more than the noncatalytic domain (C-terminus), but the non-catalytic domain showed higher flexibility than the catalytic domain. The analysis of the structure and function of LipAMS8 provides new insights into the structural adaptation of this protein at low temperatures. The information obtained could be a useful tool for low temperature industrial applications and molecular engineering purposes, in the near future. PMID:23738333
Mohamad Ali, Mohd Shukuri; Mohd Fuzi, Siti Farhanie; Ganasen, Menega; Abdul Rahman, Raja Noor Zaliha Raja; Basri, Mahiran; Salleh, Abu Bakar
2013-01-01
The psychrophilic enzyme is an interesting subject to study due to its special ability to adapt to extreme temperatures, unlike typical enzymes. Utilizing computer-aided software, the predicted structure and function of the enzyme lipase AMS8 (LipAMS8) (isolated from the psychrophilic Pseudomonas sp., obtained from the Antarctic soil) are studied. The enzyme shows significant sequence similarities with lipases from Pseudomonas sp. MIS38 and Serratia marcescens. These similarities aid in the prediction of the 3D molecular structure of the enzyme. In this study, 12 ns MD simulation is performed at different temperatures for structural flexibility and stability analysis. The results show that the enzyme is most stable at 0°C and 5°C. In terms of stability and flexibility, the catalytic domain (N-terminus) maintained its stability more than the noncatalytic domain (C-terminus), but the non-catalytic domain showed higher flexibility than the catalytic domain. The analysis of the structure and function of LipAMS8 provides new insights into the structural adaptation of this protein at low temperatures. The information obtained could be a useful tool for low temperature industrial applications and molecular engineering purposes, in the near future.
The Limits to Adaptation: A Systems Approach
The ability to adapt to climate change is delineated by capacity thresholds, after which climate damages begin to overwhelm the adaptation response. Such thresholds depend upon physical properties (natural processes and engineering parameters), resource constraints (expressed th...
An Adaptive Critic Approach to Reference Model Adaptation
NASA Technical Reports Server (NTRS)
Krishnakumar, K.; Limes, G.; Gundy-Burlet, K.; Bryant, D.
2003-01-01
Neural networks have been successfully used for implementing control architectures for different applications. In this work, we examine a neural network augmented adaptive critic as a Level 2 intelligent controller for a C- 17 aircraft. This intelligent control architecture utilizes an adaptive critic to tune the parameters of a reference model, which is then used to define the angular rate command for a Level 1 intelligent controller. The present architecture is implemented on a high-fidelity non-linear model of a C-17 aircraft. The goal of this research is to improve the performance of the C-17 under degraded conditions such as control failures and battle damage. Pilot ratings using a motion based simulation facility are included in this paper. The benefits of using an adaptive critic are documented using time response comparisons for severe damage situations.
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.
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.
Anomalous human behavior detection: an adaptive approach
NASA Astrophysics Data System (ADS)
van Leeuwen, Coen; Halma, Arvid; Schutte, Klamer
2013-05-01
Detection of anomalies (outliers or abnormal instances) is an important element in a range of applications such as fault, fraud, suspicious behavior detection and knowledge discovery. In this article we propose a new method for anomaly detection and performed tested its ability to detect anomalous behavior in videos from DARPA's Mind's Eye program, containing a variety of human activities. In this semi-unsupervised task a set of normal instances is provided for training, after which unknown abnormal behavior has to be detected in a test set. The features extracted from the video data have high dimensionality, are sparse and inhomogeneously distributed in the feature space making it a challenging task. Given these characteristics a distance-based method is preferred, but choosing a threshold to classify instances as (ab)normal is non-trivial. Our novel aproach, the Adaptive Outlier Distance (AOD) is able to detect outliers in these conditions based on local distance ratios. The underlying assumption is that the local maximum distance between labeled examples is a good indicator of the variation in that neighborhood, and therefore a local threshold will result in more robust outlier detection. We compare our method to existing state-of-art methods such as the Local Outlier Factor (LOF) and the Local Distance-based Outlier Factor (LDOF). The results of the experiments show that our novel approach improves the quality of the anomaly detection.
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
The Limits to Adaptation: A Systems Approach
NASA Astrophysics Data System (ADS)
Felgenhauer, T. N.
2013-12-01
The ability to adapt to climate change is delineated by capacity thresholds, after which climate damages begin to overwhelm the adaptation response. Such thresholds depend upon physical properties (natural processes and engineering parameters), resource constraints (expressed through market prices), and societal preferences (from prices as well as cultural norms). Exceedance of adaptation capacity will require substitution either with other pre-existing policy responses or with new adaptation responses that have yet to be developed and tested. Previous modeling research shows that capacity limited adaptation will play a policy-significant role in future climate change decision-making. The aim of this study is to describe different types of adaptation response and climate damage systems and postulate how these systems might behave when the limits to adaptation are reached. The hypothesis is that this behavior will be governed by the characteristics and level of the adaptation limit, the shape of the damage curve in that specific damage area, and the availability of alternative adaptation responses once the threshold is passed, whether it is more of the old technology, a new response type, or a transformation of the climate damage and response system itself.
Russian Loanword Adaptation in Persian; Optimal Approach
ERIC Educational Resources Information Center
Kambuziya, Aliye Kord Zafaranlu; Hashemi, Eftekhar Sadat
2011-01-01
In this paper we analyzed some of the phonological rules of Russian loanword adaptation in Persian, on the view of Optimal Theory (OT) (Prince & Smolensky, 1993/2004). It is the first study of phonological process on Russian loanwords adaptation in Persian. By gathering about 50 current Russian loanwords, we selected some of them to analyze. We…
Adaptive Role Playing Games: An Immersive Approach for Problem Based Learning
ERIC Educational Resources Information Center
Sancho, Pilar; Moreno-Ger, Pablo; Fuentes-Fernandez, Ruben; Fernandez-Manjon, Baltasar
2009-01-01
In this paper we present a general framework, called NUCLEO, for the application of socio-constructive educational approaches in higher education. The underlying pedagogical approach relies on an adaptation model in order to improve group dynamics, as this has been identified as one of the key features in the success of collaborative learning…
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.
An Adaptive Multipopulation Differential Evolution With Dynamic Population Reduction.
Ali, Mostafa Z; Awad, Noor H; Suganthan, Ponnuthurai Nagaratnam; Reynolds, Robert G
2016-10-25
Developing efficient evolutionary algorithms attracts many researchers due to the existence of optimization problems in numerous real-world applications. A new differential evolution algorithm, sTDE-dR, is proposed to improve the search quality, avoid premature convergence, and stagnation. The population is clustered in multiple tribes and utilizes an ensemble of different mutation and crossover strategies. In this algorithm, a competitive success-based scheme is introduced to determine the life cycle of each tribe and its participation ratio for the next generation. In each tribe, a different adaptive scheme is used to control the scaling factor and crossover rate. The mean success of each subgroup is used to calculate the ratio of its participation for the next generation. This guarantees that successful tribes with the best adaptive schemes are only the ones that guide the search toward the optimal solution. The population size is dynamically reduced using a dynamic reduction method. Comprehensive comparison of the proposed heuristic over a challenging set of benchmarks from the CEC2014 real parameter single objective competition against several state-of-the-art algorithms is performed. The results affirm robustness of the proposed approach compared to other state-of-the-art algorithms.
Recruitment dynamics in adaptive social networks
NASA Astrophysics Data System (ADS)
Shkarayev, Maxim S.; Schwartz, Ira B.; Shaw, Leah B.
2013-06-01
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).
The Feldenkrais Method: A Dynamic Approach to Changing Motor Behavior.
ERIC Educational Resources Information Center
Buchanan, Patricia A.; Ulrich, Beverly D.
2001-01-01
Describes the Feldenkrais Method of somatic education, noting parallels with a dynamic systems theory (DST) approach to motor behavior. Feldenkrais uses movement and perception to foster individualized improvement in function. DST explains that a human-environment system continually adapts to changing conditions and assembles behaviors…
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.
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
Passive and active adaptive management: approaches and an example.
Williams, Byron K
2011-05-01
Adaptive management is a framework for resource conservation that promotes iterative learning-based decision making. Yet there remains considerable confusion about what adaptive management entails, and how to actually make resource decisions adaptively. A key but somewhat ambiguous distinction in adaptive management is between active and passive forms of adaptive decision making. The objective of this paper is to illustrate some approaches to active and passive adaptive management with a simple example involving the drawdown of water impoundments on a wildlife refuge. The approaches are illustrated for the drawdown example, and contrasted in terms of objectives, costs, and potential learning rates. Some key challenges to the actual practice of AM are discussed, and tradeoffs between implementation costs and long-term benefits are highlighted.
Passive and active adaptive management: Approaches and an example
Williams, B.K.
2011-01-01
Adaptive management is a framework for resource conservation that promotes iterative learning-based decision making. Yet there remains considerable confusion about what adaptive management entails, and how to actually make resource decisions adaptively. A key but somewhat ambiguous distinction in adaptive management is between active and passive forms of adaptive decision making. The objective of this paper is to illustrate some approaches to active and passive adaptive management with a simple example involving the drawdown of water impoundments on a wildlife refuge. The approaches are illustrated for the drawdown example, and contrasted in terms of objectives, costs, and potential learning rates. Some key challenges to the actual practice of AM are discussed, and tradeoffs between implementation costs and long-term benefits are highlighted. ?? 2010 Elsevier Ltd.
On adaptive robustness approach to Anti-Jam signal processing
NASA Astrophysics Data System (ADS)
Poberezhskiy, Y. S.; Poberezhskiy, G. Y.
An effective approach to exploiting statistical differences between desired and jamming signals named adaptive robustness is proposed and analyzed in this paper. It combines conventional Bayesian, adaptive, and robust approaches that are complementary to each other. This combining strengthens the advantages and mitigates the drawbacks of the conventional approaches. Adaptive robustness is equally applicable to both jammers and their victim systems. The capabilities required for realization of adaptive robustness in jammers and victim systems are determined. The employment of a specific nonlinear robust algorithm for anti-jam (AJ) processing is described and analyzed. Its effectiveness in practical situations has been proven analytically and confirmed by simulation. Since adaptive robustness can be used by both sides in electronic warfare, it is more advantageous for the fastest and most intelligent side. Many results obtained and discussed in this paper are also applicable to commercial applications such as communications in unregulated or poorly regulated frequency ranges and systems with cognitive capabilities.
Responsiveness-to-Intervention: A "Systems" Approach to Instructional Adaptation
ERIC Educational Resources Information Center
Fuchs, Douglas; Fuchs, Lynn S.
2016-01-01
Classroom research on adaptive teaching indicates few teachers modify instruction for at-risk students in a manner that benefits them. Responsiveness-To-Intervention, with its tiers of increasingly intensive instruction, represents an alternative approach to adaptive instruction that may prove more workable in today's schools.
Concept Based Approach for Adaptive Personalized Course Learning System
ERIC Educational Resources Information Center
Salahli, Mehmet Ali; Özdemir, Muzaffer; Yasar, Cumali
2013-01-01
One of the most important factors for improving the personalization aspects of learning systems is to enable adaptive properties to them. The aim of the adaptive personalized learning system is to offer the most appropriate learning path and learning materials to learners by taking into account their profiles. In this paper, a new approach to…
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 agents with asymmetric information
NASA Astrophysics Data System (ADS)
DeMartino, Andrea; Galla, Tobias
2005-08-01
We apply path integral techniques to study the dynamics of agent-based models with asymmetric information structures. In particular, we devise a batch version of a model proposed originally by Berg et al (2001 Quantitative Finance 1 203), and convert the coupled multi-agent processes into an effective-agent problem from which the dynamical order parameters in ergodic regimes can be derived self-consistently together with the corresponding phase structure. Our dynamical study complements and extends the available static theory. Results are confirmed by numerical simulations.
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.
ERIC Educational Resources Information Center
Storey, Brian; Butler, Joy
2013-01-01
Background: This article draws on the literature relating to game-centred approaches (GCAs), such as Teaching Games for Understanding, and dynamical systems views of motor learning to demonstrate a convergence of ideas around games as complex adaptive learning systems. This convergence is organized under the title "complexity thinking"…
Multicriteria adaptation of robotic groups to dynamically changing conditions
NASA Astrophysics Data System (ADS)
Misyurin, S. Yu; Nelyubin, A. P.; Ivlev, V. I.
2017-01-01
A new approach is proposed to design complex robotic systems composed of many robots that can operate under different conditions and perform various tasks. Bio-inspired ideas of adaptation of robotic groups are discussed.
Silicon-Neuron Design: A Dynamical Systems Approach
Arthur, John V.; Boahen, Kwabena
2010-01-01
We present an approach to design spiking silicon neurons based on dynamical systems theory. Dynamical systems theory aids in choosing the appropriate level of abstraction, prescribing a neuron model with the desired dynamics while maintaining simplicity. Further, we provide a procedure to transform the prescribed equations into subthreshold current-mode circuits. We present a circuit design example, a positive-feedback integrate-and-fire neuron, fabricated in 0.25 μm CMOS. We analyze and characterize the circuit, and demonstrate that it can be configured to exhibit desired behaviors, including spike-frequency adaptation and two forms of bursting. PMID:21617741
NASA Astrophysics Data System (ADS)
Yucelen, Tansel; De La Torre, Gerardo; Johnson, Eric N.
2014-11-01
Although adaptive control theory offers mathematical tools to achieve system performance without excessive reliance on dynamical system models, its applications to safety-critical systems can be limited due to poor transient performance and robustness. In this paper, we develop an adaptive control architecture to achieve stabilisation and command following of uncertain dynamical systems with improved transient performance. Our framework consists of a new reference system and an adaptive controller. The proposed reference system captures a desired closed-loop dynamical system behaviour modified by a mismatch term representing the high-frequency content between the uncertain dynamical system and this reference system, i.e., the system error. In particular, this mismatch term allows the frequency content of the system error dynamics to be limited, which is used to drive the adaptive controller. It is shown that this key feature of our framework yields fast adaptation without incurring high-frequency oscillations in the transient performance. We further show the effects of design parameters on the system performance, analyse closeness of the uncertain dynamical system to the unmodified (ideal) reference system, discuss robustness of the proposed approach with respect to time-varying uncertainties and disturbances, and make connections to gradient minimisation and classical control theory. A numerical example is provided to demonstrate the efficacy of the proposed architecture.
Leadership: Enhancing Team Adaptability in Dynamic Settings
2008-04-01
regulatory processes (Karoly, 1993), team development ( Tuckman , 1965), and multilevel theory (Rousseau, 1985) to develop a normative theory of dynamic...De Meuse, K. P., & Futrell, D. (1990). Work teams: Applications and effectiveness. American Psychologist, 45, 120-133. Tuckman , B.W. (1965
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
2012-05-09
A fundamental question in neuroscience is how entire neural circuits generate behaviour and adapt it to changes in sensory feedback. Here we use two-photon calcium imaging to record the activity of large populations of neurons at the cellular level, throughout the brain of larval zebrafish expressing a genetically encoded calcium sensor, while the paralysed animals interact fictively with a virtual environment and rapidly adapt their motor output to changes in visual feedback. We decompose the network dynamics involved in adaptive locomotion into four types of neuronal response properties, and provide anatomical maps of the corresponding sites. A subset of these signals occurred during behavioural adjustments and are candidates for the functional elements that drive motor learning. Lesions to the inferior olive indicate a specific functional role for olivocerebellar circuitry in adaptive locomotion. This study enables the analysis of brain-wide dynamics at single-cell resolution during behaviour.
Brain-wide neuronal dynamics during motor adaptation in zebrafish
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
Concurrency and Complexity in Verifying Dynamic Adaptation: A Case Study
2005-01-01
Concurrency and Complexity in Verifying Dynamic Adaptation: A Case Study ? Karun N. Biyani?? Sandeep S. Kulkarni? ? ? Department of Computer Science...lattice. References 1. Sandeep S. Kulkarni, Karun N. Biyani, and Umamaheswaran Arumugam. Compos- ing distributed fault-tolerance components. In...and Autonomic Computing. PhD thesis, Michigan State University, 2004. 7. Sandeep Kulkarni and Karun Biyani. Correctness of component-based adaptation
Adapting to the Digital Age: A Narrative Approach
ERIC Educational Resources Information Center
Cousins, Sarah; Bissar, Dounia
2012-01-01
The article adopts a narrative inquiry approach to foreground informal learning and exposes a collection of stories from tutors about how they adapted comfortably to the digital age.We were concerned that despite substantial evidence that bringing about changes in pedagogic practices can be difficult, there is a gap in convincing approaches to…
Ravera, Federica; Martín-López, Berta; Pascual, Unai; Drucker, Adam
2016-12-01
This paper examines climate change adaptation and gender issues through an application of a feminist intersectional approach. This approach permits the identification of diverse adaptation responses arising from the existence of multiple and fragmented dimensions of identity (including gender) that intersect with power relations to shape situation-specific interactions between farmers and ecosystems. Based on results from contrasting research cases in Bihar and Uttarakhand, India, this paper demonstrates, inter alia, that there are geographically determined gendered preferences and adoption strategies regarding adaptation options and that these are influenced by the socio-ecological context and institutional dynamics. Intersecting identities, such as caste, wealth, age and gender, influence decisions and reveal power dynamics and negotiation within the household and the community, as well as barriers to adaptation among groups. Overall, the findings suggest that a feminist intersectional approach does appear to be useful and worth further exploration in the context of climate change adaptation. In particular, future research could benefit from more emphasis on a nuanced analysis of the intra-gender differences that shape adaptive capacity to climate change.
Dynamical weights and enhanced synchronization in adaptive complex networks.
Zhou, Changsong; Kurths, Jürgen
2006-04-28
Dynamical organization of connection weights is studied in scale-free networks of chaotic oscillators, where the coupling strength of a node from its neighbors develops adaptively according to the local synchronization property between the node and its neighbors. We find that when complete synchronization is achieved, the coupling strength becomes weighted and correlated with the topology due to a hierarchical transition to synchronization in heterogeneous networks. Importantly, such an adaptive process enhances significantly the synchronizability of the networks, which could have meaningful implications in the manipulation of dynamical networks.
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.
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.
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.
Abakumov, A I
2000-01-01
The general approach for modelling of abundance dynamic of biological populations and communities is offered. The mechanisms of individual adaptation in changing environment are considered. The approach is detailed for population models without structure and with age structure. The property of solutions are investigated. As examples the author studies the concrete definitions of general models by analogy with models of Ricker and May. Theoretical analysis and calculations shows that survival of model population in extreme situation increases if adaptive behaviour is taking into account.
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.
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
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.
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.
A Decentralized Adaptive Approach to Fault Tolerant Flight Control
NASA Technical Reports Server (NTRS)
Wu, N. Eva; Nikulin, Vladimir; Heimes, Felix; Shormin, Victor
2000-01-01
This paper briefly reports some results of our study on the application of a decentralized adaptive control approach to a 6 DOF nonlinear aircraft model. The simulation results showed the potential of using this approach to achieve fault tolerant control. Based on this observation and some analysis, the paper proposes a multiple channel adaptive control scheme that makes use of the functionally redundant actuating and sensing capabilities in the model, and explains how to implement the scheme to tolerate actuator and sensor failures. The conditions, under which the scheme is applicable, are stated in the paper.
Function-valued adaptive dynamics and optimal control theory.
Parvinen, Kalle; Heino, Mikko; Dieckmann, Ulf
2013-09-01
In this article we further develop the theory of adaptive dynamics of function-valued traits. Previous work has concentrated on models for which invasion fitness can be written as an integral in which the integrand for each argument value is a function of the strategy value at that argument value only. For this type of models of direct effect, singular strategies can be found using the calculus of variations, with singular strategies needing to satisfy Euler's equation with environmental feedback. In a broader, more mechanistically oriented class of models, the function-valued strategy affects a process described by differential equations, and fitness can be expressed as an integral in which the integrand for each argument value depends both on the strategy and on process variables at that argument value. In general, the calculus of variations cannot help analyzing this much broader class of models. Here we explain how to find singular strategies in this class of process-mediated models using optimal control theory. In particular, we show that singular strategies need to satisfy Pontryagin's maximum principle with environmental feedback. We demonstrate the utility of this approach by studying the evolution of strategies determining seasonal flowering schedules.
A mathematical approach to HIV infection dynamics
NASA Astrophysics Data System (ADS)
Ida, A.; Oharu, S.; Oharu, Y.
2007-07-01
In order to obtain a comprehensive form of mathematical models describing nonlinear phenomena such as HIV infection process and AIDS disease progression, it is efficient to introduce a general class of time-dependent evolution equations in such a way that the associated nonlinear operator is decomposed into the sum of a differential operator and a perturbation which is nonlinear in general and also satisfies no global continuity condition. An attempt is then made to combine the implicit approach (usually adapted for convective diffusion operators) and explicit approach (more suited to treat continuous-type operators representing various physiological interactions), resulting in a semi-implicit product formula. Decomposing the operators in this way and considering their individual properties, it is seen that approximation-solvability of the original model is verified under suitable conditions. Once appropriate terms are formulated to describe treatment by antiretroviral therapy, the time-dependence of the reaction terms appears, and such product formula is useful for generating approximate numerical solutions to the governing equations. With this knowledge, a continuous model for HIV disease progression is formulated and physiological interpretations are provided. The abstract theory is then applied to show existence of unique solutions to the continuous model describing the behavior of the HIV virus in the human body and its reaction to treatment by antiretroviral therapy. The product formula suggests appropriate discrete models describing the dynamics of host pathogen interactions with HIV1 and is applied to perform numerical simulations based on the model of the HIV infection process and disease progression. Finally, the results of our numerical simulations are visualized and it is observed that our results agree with medical and physiological aspects.
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.
Dynamic reconstruction and multivariable control for force-actuated, thin facesheet adaptive optics
NASA Astrophysics Data System (ADS)
Grocott, Simon C. O.
1997-10-01
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. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253- 1690).
Iyengar, Srinivasan S; Jakowski, Jacek
2005-03-15
A methodology to efficiently conduct simultaneous dynamics of electrons and nuclei is presented. The approach involves quantum wave packet dynamics using an accurate banded, sparse and Toeplitz representation for the discrete free propagator, in conjunction with ab initio molecular dynamics treatment of the electronic and classical nuclear degree of freedom. The latter may be achieved either by using atom-centered density-matrix propagation or by using Born-Oppenheimer dynamics. The two components of the methodology, namely, quantum dynamics and ab initio molecular dynamics, are harnessed together using a time-dependent self-consistent field-like coupling procedure. The quantum wave packet dynamics is made computationally robust by using adaptive grids to achieve optimized sampling. One notable feature of the approach is that important quantum dynamical effects including zero-point effects, tunneling, as well as over-barrier reflections are treated accurately. The electronic degrees of freedom are simultaneously handled at accurate levels of density functional theory, including hybrid or gradient corrected approximations. Benchmark calculations are provided for proton transfer systems and the dynamics results are compared with exact calculations to determine the accuracy of the approach.
Physical science: A dynamic approach
Dixon, R.T.
1986-01-01
A partial table of contents is: Early concepts of nature. The rebirth of science. Energy, work, and power. Relativity. The atom. The periodic nature of elements. Chemical energy. The dynamic Earth. The solar system. Stars and nebulae. Extraterrestrial life. The author presents an introduction to physical science and the spirit of scientific inquiry through a historical survey of scientific thought. Specific forces, processes, energies and phenomena are outlined. Various tables, illustrations and questions accompany the text.
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
Creative-Dynamics Approach To Neural Intelligence
NASA Technical Reports Server (NTRS)
Zak, Michail A.
1992-01-01
Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.
Hardware friendly adaptive support-weight approach for stereo matching
NASA Astrophysics Data System (ADS)
Hou, Zuoxun; Han, Pei; Zhang, Hongwei; An, Ran
2016-10-01
In this paper, the hardware friendly adaptive support-weight approach is proposed to simplify the weight calculation process of the standard approach, which employs the support region to simplify the calculation of the similarity and uses the fixed distance dependent weight to present the proximity. In addition, the complete stereo matching algorithm and the hardware structure for FPGA implementation compatible with the approach is proposed. The experimental results show that the algorithm produces the disparity map accurately in different illumination conditions and different scenes, and its processing average bad pixel rate is only 6.65% for the standard test images of the Middlebury database, which is approximate to the performance of the standard adaptive support-weight approach. The proposed hardware structure provides a basis for design and implementation of real-time accurate stereo matching FPGA system.
Approaches for modeling magnetic nanoparticle dynamics
Reeves, Daniel B; Weaver, John B
2014-01-01
Magnetic nanoparticles are useful biological probes as well as therapeutic agents. There have been several approaches used to model nanoparticle magnetization dynamics for both Brownian as well as Néel rotation. The magnetizations are often of interest and can be compared with experimental results. Here we summarize these approaches including the Stoner-Wohlfarth approach, and stochastic approaches including thermal fluctuations. Non-equilibrium related temperature effects can be described by a distribution function approach (Fokker-Planck equation) or a stochastic differential equation (Langevin equation). Approximate models in several regimes can be derived from these general approaches to simplify implementation. PMID:25271360
Long-time atomistic dynamics through a new self-adaptive accelerated molecular dynamics method
NASA Astrophysics Data System (ADS)
Gao, N.; Yang, L.; Gao, F.; Kurtz, R. J.; West, D.; Zhang, S.
2017-04-01
A self-adaptive accelerated molecular dynamics method is developed to model infrequent atomic-scale events, especially those events that occur on a rugged free-energy surface. Key in the new development is the use of the total displacement of the system at a given temperature to construct a boost-potential, which is slowly increased to accelerate the dynamics. The temperature is slowly increased to accelerate the dynamics. By allowing the system to evolve from one steady-state configuration to another by overcoming the transition state, this self-evolving approach makes it possible to explore the coupled motion of species that migrate on vastly different time scales. The migrations of single vacancy (V) and small He-V clusters, and the growth of nano-sized He-V clusters in Fe for times in the order of seconds are studied by this new method. An interstitial-assisted mechanism is first explored for the migration of a helium-rich He-V cluster, while a new two-component Ostwald ripening mechanism is suggested for He-V cluster growth.
Long-time atomistic dynamics through a new self-adaptive accelerated molecular dynamics method.
Gao, N; Yang, L; Gao, F; Kurtz, R J; West, D; Zhang, S
2017-04-12
A self-adaptive accelerated molecular dynamics method is developed to model infrequent atomic-scale events, especially those events that occur on a rugged free-energy surface. Key in the new development is the use of the total displacement of the system at a given temperature to construct a boost-potential, which is slowly increased to accelerate the dynamics. The temperature is slowly increased to accelerate the dynamics. By allowing the system to evolve from one steady-state configuration to another by overcoming the transition state, this self-evolving approach makes it possible to explore the coupled motion of species that migrate on vastly different time scales. The migrations of single vacancy (V) and small He-V clusters, and the growth of nano-sized He-V clusters in Fe for times in the order of seconds are studied by this new method. An interstitial-assisted mechanism is first explored for the migration of a helium-rich He-V cluster, while a new two-component Ostwald ripening mechanism is suggested for He-V cluster growth.
Model-adaptive hybrid dynamic control for robotic assembly tasks
Austin, D.J.; McCarragher, B.J.
1999-10-01
A new task-level adaptive controller is presented for the hybrid dynamic control of robotic assembly tasks. Using a hybrid dynamic model of the assembly task, velocity constraints are derived from which satisfactory velocity commands are obtained. Due to modeling errors and parametric uncertainties, the velocity commands may be erroneous and may result in suboptimal performance. Task-level adaptive control schemes, based on the occurrence of discrete events, are used to change the model parameters from which the velocity commands are determined. Two adaptive schemes are presented: the first is based on intuitive reasoning about the vector spaces involved whereas the second uses a search region that is reduced with each iteration. For the first adaptation law, asymptotic convergence to the correct model parameters is proven except for one case. This weakness motivated the development of the second adaptation law, for which asymptotic convergence is proven in all cases. Automated control of a peg-in-hole assembly task is given as an example, and simulations and experiments for this task are presented. These results demonstrate the success of the method and also indicate properties for rapid convergence.
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.
Dynamic model of heat inactivation kinetics for bacterial adaptation.
Corradini, Maria G; Peleg, Micha
2009-04-01
The Weibullian-log logistic (WeLL) inactivation model was modified to account for heat adaptation by introducing a logistic adaptation factor, which rendered its "rate parameter" a function of both temperature and heating rate. The resulting model is consistent with the observation that adaptation is primarily noticeable in slow heat processes in which the cells are exposed to sublethal temperatures for a sufficiently long time. Dynamic survival patterns generated with the proposed model were in general agreement with those of Escherichia coli and Listeria monocytogenes as reported in the literature. Although the modified model's rate equation has a cumbersome appearance, especially for thermal processes having a variable heating rate, it can be solved numerically with commercial mathematical software. The dynamic model has five survival/adaptation parameters whose determination will require a large experimental database. However, with assumed or estimated parameter values, the model can simulate survival patterns of adapting pathogens in cooked foods that can be used in risk assessment and the establishment of safe preparation conditions.
Searching for adaptive traits in genetic resources - phenology based approach
NASA Astrophysics Data System (ADS)
Bari, Abdallah
2015-04-01
Searching for adaptive traits in genetic resources - phenology based approach Abdallah Bari, Kenneth Street, Eddy De Pauw, Jalal Eddin Omari, and Chandra M. Biradar International Center for Agricultural Research in the Dry Areas, Rabat Institutes, Rabat, Morocco Phenology is an important plant trait not only for assessing and forecasting food production but also for searching in genebanks for adaptive traits. Among the phenological parameters we have been considering to search for such adaptive and rare traits are the onset (sowing period) and the seasonality (growing period). Currently an application is being developed as part of the focused identification of germplasm strategy (FIGS) approach to use climatic data in order to identify crop growing seasons and characterize them in terms of onset and duration. These approximations of growing period characteristics can then be used to estimate flowering and maturity dates for dryland crops, such as wheat, barley, faba bean, lentils and chickpea, and assess, among others, phenology-related traits such as days to heading [dhe] and grain filling period [gfp]. The approach followed here is based on first calculating long term average daily temperatures by fitting a curve to the monthly data over days from beginning of the year. Prior to the identification of these phenological stages the onset is extracted first from onset integer raster GIS layers developed based on a model of the growing period that considers both moisture and temperature limitations. The paper presents some examples of real applications of the approach to search for rare and adaptive traits.
Eco-evolutionary feedbacks, adaptive dynamics and evolutionary rescue theory.
Ferriere, Regis; Legendre, Stéphane
2013-01-19
Adaptive dynamics theory has been devised to account for feedbacks between ecological and evolutionary processes. Doing so opens new dimensions to and raises new challenges about evolutionary rescue. Adaptive dynamics theory predicts that successive trait substitutions driven by eco-evolutionary feedbacks can gradually erode population size or growth rate, thus potentially raising the extinction risk. Even a single trait substitution can suffice to degrade population viability drastically at once and cause 'evolutionary suicide'. In a changing environment, a population may track a viable evolutionary attractor that leads to evolutionary suicide, a phenomenon called 'evolutionary trapping'. Evolutionary trapping and suicide are commonly observed in adaptive dynamics models in which the smooth variation of traits causes catastrophic changes in ecological state. In the face of trapping and suicide, evolutionary rescue requires that the population overcome evolutionary threats generated by the adaptive process itself. Evolutionary repellors play an important role in determining how variation in environmental conditions correlates with the occurrence of evolutionary trapping and suicide, and what evolutionary pathways rescue may follow. In contrast with standard predictions of evolutionary rescue theory, low genetic variation may attenuate the threat of evolutionary suicide and small population sizes may facilitate escape from evolutionary traps.
Adaptive primal-dual genetic algorithms in dynamic environments.
Wang, Hongfeng; Yang, Shengxiang; Ip, W H; Wang, Dingwei
2009-12-01
Recently, there has been an increasing interest in applying genetic algorithms (GAs) in dynamic environments. Inspired by the complementary and dominance mechanisms in nature, a primal-dual GA (PDGA) has been proposed for dynamic optimization problems (DOPs). In this paper, an important operator in PDGA, i.e., the primal-dual mapping (PDM) scheme, is further investigated to improve the robustness and adaptability of PDGA in dynamic environments. In the improved scheme, two different probability-based PDM operators, where the mapping probability of each allele in the chromosome string is calculated through the statistical information of the distribution of alleles in the corresponding gene locus over the population, are effectively combined according to an adaptive Lamarckian learning mechanism. In addition, an adaptive dominant replacement scheme, which can probabilistically accept inferior chromosomes, is also introduced into the proposed algorithm to enhance the diversity level of the population. Experimental results on a series of dynamic problems generated from several stationary benchmark problems show that the proposed algorithm is a good optimizer for DOPs.
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.
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.
Reduced dynamics with initial correlations: Multiconfigurational approach
NASA Astrophysics Data System (ADS)
Burghardt, I.
2001-01-01
Dynamical equations for a subsystem interacting with an environment are proposed which are adapted to a multiconfigurational form of the density operator. Initial correlations are accounted for in a non-Markovian master equation. Two variants of the latter are derived by projection operator techniques and cumulant expansion techniques, respectively. The present scheme is developed in view of describing the ultrafast dynamics in solute-solvent complexes where the details of system-environment correlations are of importance. The master equation is readily integrated into the equations of motion derived by the multiconfiguration time-dependent Hartree method, which provides an efficient scheme for the numerical propagation of the density operator.
A New Multi-Agent Approach to Adaptive E-Education
NASA Astrophysics Data System (ADS)
Chen, Jing; Cheng, Peng
Improving customer satisfaction degree is important in e-Education. This paper describes a new approach to adaptive e-Education taking into account the full spectrum of Web service techniques and activities. It presents a multi-agents architecture based on artificial psychology techniques, which makes the e-Education process both adaptable and dynamic, and hence up-to-date. Knowledge base techniques are used to support the e-Education process, and artificial psychology techniques to deal with user psychology, which makes the e-Education system more effective and satisfying.
A new approach for designing self-organizing systems and application to adaptive control
NASA Technical Reports Server (NTRS)
Ramamoorthy, P. A.; Zhang, Shi; Lin, Yueqing; Huang, Song
1993-01-01
There is tremendous interest in the design of intelligent machines capable of autonomous learning and skillful performance under complex environments. A major task in designing such systems is to make the system plastic and adaptive when presented with new and useful information and stable in response to irrelevant events. A great body of knowledge, based on neuro-physiological concepts, has evolved as a possible solution to this problem. Adaptive resonance theory (ART) is a classical example under this category. The system dynamics of an ART network is described by a set of differential equations with nonlinear functions. An approach for designing self-organizing networks characterized by nonlinear differential equations is proposed.
NASA Astrophysics Data System (ADS)
Jin, Xiao-Zheng; Yang, Guang-Hong
2011-03-01
In this article, a robust tracking control problem of a class of dynamical complex networks is presented through a distributed adaptive approach. Uncertain network topology with unknown coupling strength, delayed and perturbed communications and external disturbances are considered, while the bounds of channel noises and coupling delays and disturbances are assumed to be unknown. Adaptation laws are proposed to estimate the network coupling strength and the upper and lower bounds of communication state errors and disturbances on-line. Based on the information from adaptive schemes, a class of distributed robust adaptive controllers is constructed to automatically compensate for the imperfect network and disturbance effects. Then, according to the Lyapunov stability theory, it is shown that the achievement of tracking for complex networks is effective on imperfect communications and disturbances. The effectiveness of the proposed design is illustrated via a decoupled longitudinal model of an F-18 aircraft.
An Investigation of Adaptive Signal Processing Approaches to Active Combustion Control
2001-06-01
stabilizing control using an adaptive feedback architecture. As discussed by Annaswamy et al. (1998), previous researchers have not been able to...accurately represents the dynamics of the limit cycling system and can ultimately be used for stabilizing control . System Identification The approach to...achieve stabilizing control . The first is easily identifiable as a feedback loop instability (see Equation 4), whereas the second is less well-defined as a
Adaptive Modeling: An Approach for Incorporating Nonlinearity in Regression Analyses.
Knafl, George J; Barakat, Lamia P; Hanlon, Alexandra L; Hardie, Thomas; Knafl, Kathleen A; Li, Yimei; Deatrick, Janet A
2017-02-01
Although regression relationships commonly are treated as linear, this often is not the case. An adaptive approach is described for identifying nonlinear relationships based on power transforms of predictor (or independent) variables and for assessing whether or not relationships are distinctly nonlinear. It is also possible to model adaptively both means and variances of continuous outcome (or dependent) variables and to adaptively power transform positive-valued continuous outcomes, along with their predictors. Example analyses are provided of data from parents in a nursing study on emotional-health-related quality of life for childhood brain tumor survivors as a function of the effort to manage the survivors' condition. These analyses demonstrate that relationships, including moderation relationships, can be distinctly nonlinear, that conclusions about means can be affected by accounting for non-constant variances, and that outcome transformation along with predictor transformation can provide distinct improvements and can resolve skewness problems.© 2017 Wiley Periodicals, Inc.
NASA Astrophysics Data System (ADS)
Klein, R.; Gordon, E.
2010-12-01
Scholars and policy analysts often contend that an effective climate adaptation strategy must entail "mainstreaming," or incorporating responses to possible climate impacts into existing planning and management decision frameworks. Such an approach, however, makes it difficult to assess the degree to which decisionmaking entities are engaging in adaptive activities that may or may not be explicitly framed around a changing climate. For example, a drought management plan may not explicitly address climate change, but the activities and strategies outlined in it may reduce vulnerabilities posed by a variable and changing climate. Consequently, to generate a strategic climate adaptation plan requires identifying the entire suite of activities that are implicitly linked to climate and may affect adaptive capacity within the system. Here we outline a novel, two-pronged approach, leveraging social science methods, to understanding adaptation throughout state government in Colorado. First, we conducted a series of interviews with key actors in state and federal government agencies, non-governmental organizations, universities, and other entities engaged in state issues. The purpose of these interviews was to elicit information about current activities that may affect the state’s adaptive capacity and to identify future climate-related needs across the state. Second, we have developed an interactive database cataloging organizations, products, projects, and people actively engaged in adaptive planning and policymaking that are relevant to the state of Colorado. The database includes a wiki interface, helping create a dynamic component that will enable frequent updating as climate-relevant information emerges. The results of this project are intended to paint a clear picture of sectors and agencies with higher and lower levels of adaptation awareness and to provide a roadmap for the next gubernatorial administration to pursue a more sophisticated climate adaptation agenda
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.
A Gradient Optimization Approach to Adaptive Multi-Robot Control
2009-09-01
optimization through the evolution of a dynamical system. Some existing approaches do not fit under the framework we propose in this chap- ter. A...parameters are coupled among robots, we must consider the evolution of all the robots’ parameters together. Let = [ a]. (4.39) be a concatenated...dynamics * Synchronous evolution of equa- tions * Exact Voronoi cells computed from exact positions of all Voronoi neighbors * Exact integrals over
An Approach to V&V of Embedded Adaptive Systems
NASA Technical Reports Server (NTRS)
Liu, Yan; Yerramalla, Sampath; Fuller, Edgar; Cukic, Bojan; Gururajan, Srikaruth
2004-01-01
Rigorous Verification and Validation (V&V) techniques are essential for high assurance systems. Lately, the performance of some of these systems is enhanced by embedded adaptive components in order to cope with environmental changes. Although the ability of adapting is appealing, it actually poses a problem in terms of V&V. Since uncertainties induced by environmental changes have a significant impact on system behavior, the applicability of conventional V&V techniques is limited. In safety-critical applications such as flight control system, the mechanisms of change must be observed, diagnosed, accommodated and well understood prior to deployment. In this paper, we propose a non-conventional V&V approach suitable for online adaptive systems. We apply our approach to an intelligent flight control system that employs a particular type of Neural Networks (NN) as the adaptive learning paradigm. Presented methodology consists of a novelty detection technique and online stability monitoring tools. The novelty detection technique is based on Support Vector Data Description that detects novel (abnormal) data patterns. The Online Stability Monitoring tools based on Lyapunov's Stability Theory detect unstable learning behavior in neural networks. Cases studies based on a high fidelity simulator of NASA's Intelligent Flight Control System demonstrate a successful application of the presented V&V methodology. ,
NASA Astrophysics Data System (ADS)
Yang, Yueneng; Wu, Jie; Zheng, Wei
2013-04-01
This paper presents a novel approach for station-keeping control of a stratospheric airship platform in the presence of parametric uncertainty and external disturbance. First, conceptual design of the stratospheric airship platform is introduced, including the target mission, configuration, energy sources, propeller and payload. Second, the dynamics model of the airship platform is presented, and the mathematical model of its horizontal motion is derived. Third, a fuzzy adaptive backstepping control approach is proposed to develop the station-keeping control system for the simplified horizontal motion. The backstepping controller is designed assuming that the airship model is accurately known, and a fuzzy adaptive algorithm is used to approximate the uncertainty of the airship model. The stability of the closed-loop control system is proven via the Lyapunov theorem. Finally, simulation results illustrate the effectiveness and robustness of the proposed control approach.
A System Approach to Adaptive Multi-Modal Sensor Designs
2010-02-01
Email: rhody@cis.rit.edu Program Managers: Dr. Douglas Cochran <douglas.cochran@afosr.af.mil> Dr. Kitt C. Reinhardt <kitt.reinhardt...DEPARTMENT OF COMPUTER SCIENCE CONVENT AVE & 138TH ST SCHOOL OF ENGINEERING NEW YORK, NY 10031 Approved for public release...FA9550-08-1-0199 A System Approach to Adaptive Multi-Modal Sensor Designs 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d
Dynamical Systems in Psychology: Linguistic Approaches
NASA Astrophysics Data System (ADS)
Sulis, William
Major goals for psychoanalysis and psychology are the description, analysis, prediction, and control of behaviour. Natural language has long provided the medium for the formulation of our theoretical understanding of behavior. But with the advent of nonlinear dynamics, a new language has appeared which offers promise to provide a quantitative theory of behaviour. In this paper, some of the limitations of natural and formal languages are discussed. Several approaches to understanding the links between natural and formal languages, as applied to the study of behavior, are discussed. These include symbolic dynamics, Moore's generalized shifts, Crutchfield's ɛ machines, and dynamical automata.
The adaptive, cut-cell Cartesian approach (warts and all)
NASA Astrophysics Data System (ADS)
Powell, Kenneth G.
1995-10-01
Solution-adaptive methods based on cutting bodies out of Cartesian grids are gaining popularity now that the ways of circumventing the accuracy problems associated with small cut cells have been developed. Researchers are applying Cartesian-based schemes to a broad class of problems now, and, although there is still development work to be done, it is becoming clearer which problems are best suited to the approach (and which are not). The purpose of this paper is to give a candid assessment, based on applying Cartesian schemes to a variety of problems, of the strengths and weaknesses of the approach as it is currently implemented.
The adaptive, cut-cell Cartesian approach (warts and all)
NASA Technical Reports Server (NTRS)
Powell, Kenneth G.
1995-01-01
Solution-adaptive methods based on cutting bodies out of Cartesian grids are gaining popularity now that the ways of circumventing the accuracy problems associated with small cut cells have been developed. Researchers are applying Cartesian-based schemes to a broad class of problems now, and, although there is still development work to be done, it is becoming clearer which problems are best suited to the approach (and which are not). The purpose of this paper is to give a candid assessment, based on applying Cartesian schemes to a variety of problems, of the strengths and weaknesses of the approach as it is currently implemented.
Distributed Adaptive Particle Swarm Optimizer in Dynamic Environment
Cui, Xiaohui; Potok, Thomas E
2007-01-01
In the real world, we have to frequently deal with searching and tracking an optimal solution in a dynamical and noisy environment. This demands that the algorithm not only find the optimal solution but also track the trajectory of the changing solution. Particle Swarm Optimization (PSO) is a population-based stochastic optimization technique, which can find an optimal, or near optimal, solution to a numerical and qualitative problem. In PSO algorithm, the problem solution emerges from the interactions between many simple individual agents called particles, which make PSO an inherently distributed algorithm. However, the traditional PSO algorithm lacks the ability to track the optimal solution in a dynamic and noisy environment. In this paper, we present a distributed adaptive PSO (DAPSO) algorithm that can be used for tracking a non-stationary optimal solution in a dynamically changing and noisy environment.
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.
An Evolutionary Dynamics Model Adapted to Eusocial Insects
van Oudenhove, Louise; Cerdá, Xim; Bernstein, Carlos
2013-01-01
This study aims to better understand the evolutionary processes allowing species coexistence in eusocial insect communities. We develop a mathematical model that applies adaptive dynamics theory to the evolutionary dynamics of eusocial insects, focusing on the colony as the unit of selection. The model links long-term evolutionary processes to ecological interactions among colonies and seasonal worker production within the colony. Colony population dynamics is defined by both worker production and colony reproduction. Random mutations occur in strategies, and mutant colonies enter the community. The interactions of colonies at the ecological timescale drive the evolution of strategies at the evolutionary timescale by natural selection. This model is used to study two specific traits in ants: worker body size and the degree of collective foraging. For both traits, trade-offs in competitive ability and other fitness components allows to determine conditions in which selection becomes disruptive. Our results illustrate that asymmetric competition underpins diversity in ant communities. PMID:23469162
An evolutionary dynamics model adapted to eusocial insects.
van Oudenhove, Louise; Cerdá, Xim; Bernstein, Carlos
2013-01-01
This study aims to better understand the evolutionary processes allowing species coexistence in eusocial insect communities. We develop a mathematical model that applies adaptive dynamics theory to the evolutionary dynamics of eusocial insects, focusing on the colony as the unit of selection. The model links long-term evolutionary processes to ecological interactions among colonies and seasonal worker production within the colony. Colony population dynamics is defined by both worker production and colony reproduction. Random mutations occur in strategies, and mutant colonies enter the community. The interactions of colonies at the ecological timescale drive the evolution of strategies at the evolutionary timescale by natural selection. This model is used to study two specific traits in ants: worker body size and the degree of collective foraging. For both traits, trade-offs in competitive ability and other fitness components allows to determine conditions in which selection becomes disruptive. Our results illustrate that asymmetric competition underpins diversity in ant communities.
Adaptive dynamic programming for auto-resilient video streaming
NASA Astrophysics Data System (ADS)
Zhao, Juan; Li, Xingmei; Wang, Wei; Wu, Guoping
2007-11-01
Wireless video transmission encounters higher error rate than in wired network, which introduces distortion into the error-sensitive compressed data, reducing the quality of the playback video. Therefore, to ensure the end-to-end quality, wireless video needs a transmission system including both efficient source coding scheme and transmission technology against the influence of the channel error. This paper tackles a dynamic programming algorithm for robust video streaming over error-prone channels. An auto-resilient multiple-description coding with optimized transmission strategy has been proposed. Further study is done on the computational complexity of rate-distortion optimized video streaming and a dynamic programming algorithm is considered. Experiment results show that video streaming with adaptive dynamic programming gains better playback video quality at the receiver when transmitted through error-prone mobile channel.
Adaptive MANET Multipath Routing Algorithm Based on the Simulated Annealing Approach
Kim, Sungwook
2014-01-01
Mobile ad hoc network represents a system of wireless mobile nodes that can freely and dynamically self-organize network topologies without any preexisting communication infrastructure. Due to characteristics like temporary topology and absence of centralized authority, routing is one of the major issues in ad hoc networks. In this paper, a new multipath routing scheme is proposed by employing simulated annealing approach. The proposed metaheuristic approach can achieve greater and reciprocal advantages in a hostile dynamic real world network situation. Therefore, the proposed routing scheme is a powerful method for finding an effective solution into the conflict mobile ad hoc network routing problem. Simulation results indicate that the proposed paradigm adapts best to the variation of dynamic network situations. The average remaining energy, network throughput, packet loss probability, and traffic load distribution are improved by about 10%, 10%, 5%, and 10%, respectively, more than the existing schemes. PMID:25032241
Air-Breathing Hypersonic Vehicle Tracking Control Based on Adaptive Dynamic Programming.
Mu, Chaoxu; Ni, Zhen; Sun, Changyin; He, Haibo
2017-03-01
In this paper, we propose a data-driven supplementary control approach with adaptive learning capability for air-breathing hypersonic vehicle tracking control based on action-dependent heuristic dynamic programming (ADHDP). The control action is generated by the combination of sliding mode control (SMC) and the ADHDP controller to track the desired velocity and the desired altitude. In particular, the ADHDP controller observes the differences between the actual velocity/altitude and the desired velocity/altitude, and then provides a supplementary control action accordingly. The ADHDP controller does not rely on the accurate mathematical model function and is data driven. Meanwhile, it is capable to adjust its parameters online over time under various working conditions, which is very suitable for hypersonic vehicle system with parameter uncertainties and disturbances. We verify the adaptive supplementary control approach versus the traditional SMC in the cruising flight, and provide three simulation studies to illustrate the improved performance with the proposed approach.
An algorithmic approach to adaptive state filtering using recurrent neural networks.
Parlos, A G; Menon, S K; Atiya, A
2001-01-01
Practical algorithms are presented for adaptive state filtering in nonlinear dynamic systems when the state equations are unknown. The state equations are constructively approximated using neural networks. The algorithms presented are based on the two-step prediction-update approach of the Kalman filter. The proposed algorithms make minimal assumptions regarding the underlying nonlinear dynamics and their noise statistics. Non-adaptive and adaptive state filtering algorithms are presented with both off-line and online learning stages. The algorithms are implemented using feedforward and recurrent neural network and comparisons are presented. Furthermore, extended Kalman filters (EKFs) are developed and compared to the filter algorithms proposed. For one of the case studies, the EKF converges but results in higher state estimation errors that the equivalent neural filters. For another, more complex case study with unknown system dynamics and noise statistics, the developed EKFs do not converge. The off-line trained neural state filters converge quite rapidly and exhibit acceptable performance. Online training further enhances the estimation accuracy of the developed adaptive filters, effectively decoupling the eventual filter accuracy from the accuracy of the process model.
Reducing False Negative Reads in RFID Data Streams Using an Adaptive Sliding-Window Approach
Massawe, Libe Valentine; Kinyua, Johnson D. M.; Vermaak, Herman
2012-01-01
Unreliability of the data streams generated by RFID readers is among the primary factors which limit the widespread adoption of the RFID technology. RFID data cleaning is, therefore, an essential task in the RFID middleware systems in order to reduce reading errors, and to allow these data streams to be used to make a correct interpretation and analysis of the physical world they are representing. In this paper we propose an adaptive sliding-window based approach called WSTD which is capable of efficiently coping with both environmental variation and tag dynamics. Our experimental results demonstrate the efficacy of the proposed approach. PMID:22666027
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
Overstress and flowstress approaches to dynamic viscoplasticity
NASA Astrophysics Data System (ADS)
Partom, Yehuda
2015-09-01
Viscoplasticity is mostly modelled by the
Zhang, Hao; Sheng, Yin; Zeng, Zhigang
2017-03-15
This paper investigates the synchronization issue of coupled reaction-diffusion neural networks with directed topology via an adaptive approach. Due to the complexity of the network structure and the presence of space variables, it is difficult to design proper adaptive strategies on coupling weights to accomplish the synchronous goal. Under the assumptions of two kinds of special network structures, that is, directed spanning path and directed spanning tree, some novel edge-based adaptive laws, which utilized the local information of node dynamics fully are designed on the coupling weights for reaching synchronization. By constructing appropriate energy function, and utilizing some analytical techniques, several sufficient conditions are given. Finally, some simulation examples are given to verify the effectiveness of the obtained theoretical results.
New multigrid approach for three-dimensional unstructured, adaptive grids
NASA Technical Reports Server (NTRS)
Parthasarathy, Vijayan; Kallinderis, Y.
1994-01-01
A new multigrid method with adaptive unstructured grids is presented. The three-dimensional Euler equations are solved on tetrahedral grids that are adaptively refined or coarsened locally. The multigrid method is employed to propagate the fine grid corrections more rapidly by redistributing the changes-in-time of the solution from the fine grid to the coarser grids to accelerate convergence. A new approach is employed that uses the parent cells of the fine grid cells in an adapted mesh to generate successively coaser levels of multigrid. This obviates the need for the generation of a sequence of independent, nonoverlapping grids as well as the relatively complicated operations that need to be performed to interpolate the solution and the residuals between the independent grids. The solver is an explicit, vertex-based, finite volume scheme that employs edge-based data structures and operations. Spatial discretization is of central-differencing type combined with a special upwind-like smoothing operators. Application cases include adaptive solutions obtained with multigrid acceleration for supersonic and subsonic flow over a bump in a channel, as well as transonic flow around the ONERA M6 wing. Two levels of multigrid resulted in reduction in the number of iterations by a factor of 5.
New multigrid approach for three-dimensional unstructured, adaptive grids
NASA Astrophysics Data System (ADS)
Parthasarathy, Vijayan; Kallinderis, Y.
1994-05-01
A new multigrid method with adaptive unstructured grids is presented. The three-dimensional Euler equations are solved on tetrahedral grids that are adaptively refined or coarsened locally. The multigrid method is employed to propagate the fine grid corrections more rapidly by redistributing the changes-in-time of the solution from the fine grid to the coarser grids to accelerate convergence. A new approach is employed that uses the parent cells of the fine grid cells in an adapted mesh to generate successively coarser levels of multigrid. This obviates the need for the generation of a sequence of independent, nonoverlapping grids as well as the relatively complicated operations that need to be performed to interpolate the solution and the residuals between the independent grids. The solver is an explicit, vertex-based, finite volume scheme that employs edge-based data structures and operations. Spatial discretization is of central-differencing type combined with special upwind-like smoothing operators. Application cases include adaptive solutions obtained with multigrid acceleration for supersonic and subsonic flow over a bump in a channel, as well as transonic flow around the ONERA M6 wing. Two levels of multigrid resulted in reduction in the number of iterations by a factor of 5.
New multigrid approach for three-dimensional unstructured, adaptive grids
NASA Astrophysics Data System (ADS)
Parthasarathy, Vijayan; Kallinderis, Y.
1994-05-01
A new multigrid method with adaptive unstructured grids is presented. The three-dimensional Euler equations are solved on tetrahedral grids that are adaptively refined or coarsened locally. The multigrid method is employed to propagate the fine grid corrections more rapidly by redistributing the changes-in-time of the solution from the fine grid to the coarser grids to accelerate convergence. A new approach is employed that uses the parent cells of the fine grid cells in an adapted mesh to generate successively coaser levels of multigrid. This obviates the need for the generation of a sequence of independent, nonoverlapping grids as well as the relatively complicated operations that need to be performed to interpolate the solution and the residuals between the independent grids. The solver is an explicit, vertex-based, finite volume scheme that employs edge-based data structures and operations. Spatial discretization is of central-differencing type combined with a special upwind-like smoothing operators. Application cases include adaptive solutions obtained with multigrid acceleration for supersonic and subsonic flow over a bump in a channel, as well as transonic flow around the ONERA M6 wing. Two levels of multigrid resulted in reduction in the number of iterations by a factor of 5.
Adaptive learning by extremal dynamics and negative feedback
Bak, Per; Chialvo, Dante R.
2001-03-01
We describe a mechanism for biological learning and adaptation based on two simple principles: (i) Neuronal activity propagates only through the network's strongest synaptic connections (extremal dynamics), and (ii) the strengths of active synapses are reduced if mistakes are made, otherwise no changes occur (negative feedback). The balancing of those two tendencies typically shapes a synaptic landscape with configurations which are barely stable, and therefore highly flexible. This allows for swift adaptation to new situations. Recollection of past successes is achieved by punishing synapses which have once participated in activity associated with successful outputs much less than neurons that have never been successful. Despite its simplicity, the model can readily learn to solve complicated nonlinear tasks, even in the presence of noise. In particular, the learning time for the benchmark parity problem scales algebraically with the problem size N, with an exponent k{approx}1.4.
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
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.
Adaptive Kalman filtering methods for tracking GPS signals in high noise/high dynamic environments
NASA Astrophysics Data System (ADS)
Zuo, Qiyao; Yuan, Hong; Lin, Baojun
2007-11-01
GPS C/A signal tracking algorithms have been developed based on adaptive Kalman filtering theory. In the research, an adaptive Kalman filter is used to substitute for standard tracking loop filters. The goal is to improve estimation accuracy and tracking stabilization in high noise and high dynamic environments. The linear dynamics model and the measurements model are designed to estimate code phase, carrier phase, Doppler shift, and rate of change of Doppler shift. Two adaptive algorithms are applied to improve robustness and adaptive faculty of the tracking, one is Sage adaptive filtering approach and the other is strong tracking method. Both the new algorithms and the conventional tracking loop have been tested by using simulation data. In the simulation experiment, the highest jerk of the receiver is set to 10G m/s 3 with the lowest C/No 30dBHz. The results indicate that the Kalman filtering algorithms are more robust than the standard tracking loop, and performance of tracking loop using the algorithms is satisfactory in such extremely adverse circumstances.
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.
An adaptive online learning approach for Support Vector Regression: Online-SVR-FID
NASA Astrophysics Data System (ADS)
Liu, Jie; Zio, Enrico
2016-08-01
Support Vector Regression (SVR) is a popular supervised data-driven approach for building empirical models from available data. Like all data-driven methods, under non-stationary environmental and operational conditions it needs to be provided with adaptive learning capabilities, which might become computationally burdensome with large datasets cumulating dynamically. In this paper, a cost-efficient online adaptive learning approach is proposed for SVR by combining Feature Vector Selection (FVS) and Incremental and Decremental Learning. The proposed approach adaptively modifies the model only when different pattern drifts are detected according to proposed criteria. Two tolerance parameters are introduced in the approach to control the computational complexity, reduce the influence of the intrinsic noise in the data and avoid the overfitting problem of SVR. Comparisons of the prediction results is made with other online learning approaches e.g. NORMA, SOGA, KRLS, Incremental Learning, on several artificial datasets and a real case study concerning time series prediction based on data recorded on a component of a nuclear power generation system. The performance indicators MSE and MARE computed on the test dataset demonstrate the efficiency of the proposed online learning method.
Elucidating Microbial Adaptation Dynamics via Autonomous Exposure and Sampling
NASA Technical Reports Server (NTRS)
Grace, Joseph M.; Verseux, Cyprien; Gentry, Diana; Moffet, Amy; Thayabaran, Ramanen; Wong, Nathan; Rothschild, Lynn
2013-01-01
The adaptation of micro-organisms to their environments is a complex process of interaction between the pressures of the environment and of competition. Reducing this multifactorial process to environmental exposure in the laboratory is a common tool for elucidating individual mechanisms of evolution, such as mutation rates. Although such studies inform fundamental questions about the way adaptation and even speciation occur, they are often limited by labor-intensive manual techniques. Current methods for controlled study of microbial adaptation limit the length of time, the depth of collected data, and the breadth of applied environmental conditions. Small idiosyncrasies in manual techniques can have large effects on outcomes; for example, there are significant variations in induced radiation resistances following similar repeated exposure protocols. We describe here a project under development to allow rapid cycling of multiple types of microbial environmental exposure. The system allows continuous autonomous monitoring and data collection of both single species and sampled communities, independently and concurrently providing multiple types of controlled environmental pressure (temperature, radiation, chemical presence or absence, and so on) to a microbial community in dynamic response to the ecosystem's current status. When combined with DNA sequencing and extraction, such a controlled environment can cast light on microbial functional development, population dynamics, inter- and intra-species competition, and microbe-environment interaction. The project's goal is to allow rapid, repeatable iteration of studies of both natural and artificial microbial adaptation. As an example, the same system can be used both to increase the pH of a wet soil aliquot over time while periodically sampling it for genetic activity analysis, or to repeatedly expose a culture of bacteria to the presence of a toxic metal, automatically adjusting the level of toxicity based on the
NASA Astrophysics Data System (ADS)
Schwing, Alan Michael
For computational fluid dynamics, the governing equations are solved on a discretized domain of nodes, faces, and cells. The quality of the grid or mesh can be a driving source for error in the results. While refinement studies can help guide the creation of a mesh, grid quality is largely determined by user expertise and understanding of the flow physics. Adaptive mesh refinement is a technique for enriching the mesh during a simulation based on metrics for error, impact on important parameters, or location of important flow features. This can offload from the user some of the difficult and ambiguous decisions necessary when discretizing the domain. This work explores the implementation of adaptive mesh refinement in an implicit, unstructured, finite-volume solver. Consideration is made for applying modern computational techniques in the presence of hanging nodes and refined cells. The approach is developed to be independent of the flow solver in order to provide a path for augmenting existing codes. It is designed to be applicable for unsteady simulations and refinement and coarsening of the grid does not impact the conservatism of the underlying numerics. The effect on high-order numerical fluxes of fourth- and sixth-order are explored. Provided the criteria for refinement is appropriately selected, solutions obtained using adapted meshes have no additional error when compared to results obtained on traditional, unadapted meshes. In order to leverage large-scale computational resources common today, the methods are parallelized using MPI. Parallel performance is considered for several test problems in order to assess scalability of both adapted and unadapted grids. Dynamic repartitioning of the mesh during refinement is crucial for load balancing an evolving grid. Development of the methods outlined here depend on a dual-memory approach that is described in detail. Validation of the solver developed here against a number of motivating problems shows favorable
Bayesian approaches for adaptive spatial sampling : an example application.
Johnson, R. L.; LePoire, D.; Huttenga, A.; Quinn, J.
2005-05-25
BAASS (Bayesian Approaches for Adaptive Spatial Sampling) is a set of computational routines developed to support the design and deployment of spatial sampling programs for delineating contamination footprints, such as those that might result from the accidental or intentional environmental release of radionuclides. BAASS presumes the existence of real-time measurement technologies that provide information quickly enough to affect the progress of data collection. This technical memorandum describes the application of BAASS to a simple example, compares the performance of a BAASS-based program with that of a traditional gridded program, and explores the significance of several of the underlying assumptions required by BAASS. These assumptions include the range of spatial autocorrelation present, the value of prior information, the confidence level required for decision making, and ''inside-out'' versus ''outside-in'' sampling strategies. In the context of the example, adaptive sampling combined with prior information significantly reduced the number of samples required to delineate the contamination footprint.
Collective Fluctuations in the Dynamics of Adaptation and Other Traveling Waves
Hallatschek, Oskar; Geyrhofer, Lukas
2016-01-01
The dynamics of adaptation are difficult to predict because it is highly stochastic even in large populations. The uncertainty emerges from random genetic drift arising in a vanguard of particularly fit individuals of the population. Several approaches have been developed to analyze the crucial role of genetic drift on the expected dynamics of adaptation, including the mean fitness of the entire population, or the fate of newly arising beneficial deleterious mutations. However, little is known about how genetic drift causes fluctuations to emerge on the population level, where it becomes palpable as variations in the adaptation speed and the fitness distribution. Yet these phenomena control the decay of genetic diversity and variability in evolution experiments and are key to a truly predictive understanding of evolutionary processes. Here, we show that correlations induced by these emergent fluctuations can be computed at any arbitrary order by a suitable choice of a dynamical constraint. The resulting linear equations exhibit fluctuation-induced terms that amplify short-distance correlations and suppress long-distance ones. These terms, which are in general not small, control the decay of genetic diversity and, for wave-tip dominated (“pulled”) waves, lead to anticorrelations between the tip of the wave and the lagging bulk of the population. While it is natural to consider the process of adaptation as a branching random walk in fitness space subject to a constraint (due to finite resources), we show that other traveling wave phenomena in ecology and evolution likewise fall into this class of constrained branching random walks. Our methods, therefore, provide a systematic approach toward analyzing fluctuations in a wide range of population biological processes, such as adaptation, genetic meltdown, species invasions, or epidemics. PMID:26819246
Ultra-Low Power Dynamic Knob in Adaptive Compressed Sensing Towards Biosignal Dynamics.
Wang, Aosen; Lin, Feng; Jin, Zhanpeng; Xu, Wenyao
2016-06-01
Compressed sensing (CS) is an emerging sampling paradigm in data acquisition. Its integrated analog-to-information structure can perform simultaneous data sensing and compression with low-complexity hardware. To date, most of the existing CS implementations have a fixed architectural setup, which lacks flexibility and adaptivity for efficient dynamic data sensing. In this paper, we propose a dynamic knob (DK) design to effectively reconfigure the CS architecture by recognizing the biosignals. Specifically, the dynamic knob design is a template-based structure that comprises a supervised learning module and a look-up table module. We model the DK performance in a closed analytic form and optimize the design via a dynamic programming formulation. We present the design on a 130 nm process, with a 0.058 mm (2) fingerprint and a 187.88 nJ/event energy-consumption. Furthermore, we benchmark the design performance using a publicly available dataset. Given the energy constraint in wireless sensing, the adaptive CS architecture can consistently improve the signal reconstruction quality by more than 70%, compared with the traditional CS. The experimental results indicate that the ultra-low power dynamic knob can provide an effective adaptivity and improve the signal quality in compressed sensing towards biosignal dynamics.
Effect of Adaptive Delivery Capacity on Networked Traffic Dynamics
NASA Astrophysics Data System (ADS)
Cao, Xian-Bin; Du, Wen-Bo; Chen, Cai-Long; Zhang, Jun
2011-05-01
We introduce an adaptive delivering capacity mechanism into the traffic dynamic model on scale-free networks under shortest path routing strategy and focus on its effect on the network capacity measured by the critical point (Rc) of phase transition from free now to congestion. Under this mechanism, the total node's delivering capacity is fixed and the allocation of delivering capacity on node i is proportional to nivarphi, where ni is the queue length of node i and varphi is the adjustable parameter. It is found that the network capacity monotonously increases with the increment of varphi, but there exists an optimal value of parameter varphi leading to the highest transportation efficiency measured by average travelling time (
Dynamics of epidemic diseases on a growing adaptive network
NASA Astrophysics Data System (ADS)
Demirel, Güven; Barter, Edmund; Gross, Thilo
2017-02-01
The study of epidemics on static networks has revealed important effects on disease prevalence of network topological features such as the variance of the degree distribution, i.e. the distribution of the number of neighbors of nodes, and the maximum degree. Here, we analyze an adaptive network where the degree distribution is not independent of epidemics but is shaped through disease-induced dynamics and mortality in a complex interplay. We study the dynamics of a network that grows according to a preferential attachment rule, while nodes are simultaneously removed from the network due to disease-induced mortality. We investigate the prevalence of the disease using individual-based simulations and a heterogeneous node approximation. Our results suggest that in this system in the thermodynamic limit no epidemic thresholds exist, while the interplay between network growth and epidemic spreading leads to exponential networks for any finite rate of infectiousness when the disease persists.
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.
Dynamics of epidemic diseases on a growing adaptive network
Demirel, Güven; Barter, Edmund; Gross, Thilo
2017-01-01
The study of epidemics on static networks has revealed important effects on disease prevalence of network topological features such as the variance of the degree distribution, i.e. the distribution of the number of neighbors of nodes, and the maximum degree. Here, we analyze an adaptive network where the degree distribution is not independent of epidemics but is shaped through disease-induced dynamics and mortality in a complex interplay. We study the dynamics of a network that grows according to a preferential attachment rule, while nodes are simultaneously removed from the network due to disease-induced mortality. We investigate the prevalence of the disease using individual-based simulations and a heterogeneous node approximation. Our results suggest that in this system in the thermodynamic limit no epidemic thresholds exist, while the interplay between network growth and epidemic spreading leads to exponential networks for any finite rate of infectiousness when the disease persists. PMID:28186146
Dynamical maximum entropy approach to flocking
NASA Astrophysics Data System (ADS)
Cavagna, Andrea; Giardina, Irene; Ginelli, Francesco; Mora, Thierry; Piovani, Duccio; Tavarone, Raffaele; Walczak, Aleksandra M.
2014-04-01
We derive a new method to infer from data the out-of-equilibrium alignment dynamics of collectively moving animal groups, by considering the maximum entropy model distribution consistent with temporal and spatial correlations of flight direction. When bird neighborhoods evolve rapidly, this dynamical inference correctly learns the parameters of the model, while a static one relying only on the spatial correlations fails. When neighbors change slowly and the detailed balance is satisfied, we recover the static procedure. We demonstrate the validity of the method on simulated data. The approach is applicable to other systems of active matter.
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 Wing Camber Optimization: A Periodic Perturbation Approach
NASA Technical Reports Server (NTRS)
Espana, Martin; Gilyard, Glenn
1994-01-01
Available redundancy among aircraft control surfaces allows for effective wing camber modifications. As shown in the past, this fact can be used to improve aircraft performance. To date, however, algorithm developments for in-flight camber optimization have been limited. This paper presents a perturbational approach for cruise optimization through in-flight camber adaptation. The method uses, as a performance index, an indirect measurement of the instantaneous net thrust. As such, the actual performance improvement comes from the integrated effects of airframe and engine. The algorithm, whose design and robustness properties are discussed, is demonstrated on the NASA Dryden B-720 flight simulator.
A Dynamical System that Describes Vein Graft Adaptation and Failure
Garbey, Marc; Berceli, Scott A.
2013-01-01
Adaptation of vein bypass grafts to the mechanical stresses imposed by the arterial circulation is thought to be the primary determinant for lesion development, yet an understanding of how the various forces dictate local wall remodeling is lacking. We develop a dynamical system that summarizes the complex interplay between the mechanical environment and cell/matrix kinetics, ultimately dictating changes in the vein graft architecture. Based on a systematic mapping of the parameter space, three general remodeling response patterns are observed: 1) shear stabilized intimal thickening, 2) tension induced wall thinning and lumen expansion, and 3) tension stabilized wall thickening. Notable is our observation that the integration of multiple feedback mechanisms leads to a variety of non-linear responses that would be unanticipated by an analysis of each system component independently. This dynamic analysis supports the clinical observation that the majority of vein grafts proceed along an adaptive trajectory, where grafts dilate and mildly thicken in response to the increased tension and shear, but a small portion of the grafts demonstrate a maladaptive phenotype, where progressive inward remodeling and accentuated wall thickening lead to graft failure. PMID:23871714
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.
Smart dynamic system design: an integrated approach
NASA Astrophysics Data System (ADS)
Carpenter, Mike J.; Skelton, Robert T.
1994-05-01
A dynamic system with satisfactory performance generally consists of a mechanical system (the plant) and a controller that drives the mechanical system to meet certain performance requirements. Traditionally the control engineer designs the controller only after the plant design is completed. This two-step approach to plant and controller design does not provide the best system design because the dynamics of the plant and the dynamics of the controller often oppose each other. This paper presents an application of the iterative system equivalent optimal mix algorithm to perform a smart design of a nine-member truss substructure and its accompanying controller. The objective of the design algorithm is to reduce the amount of energy used by the controller to maintain control performance, subject to the structure design constraints. Two unique features of the algorithm are that each iteration of the design problem is stated as a convex quadratic programming problem, and the control effort monotonically converges to its final value.
An analytic approach to cyber adversarial dynamics
NASA Astrophysics Data System (ADS)
Sweeney, Patrick; Cybenko, George
2012-06-01
To date, cyber security investment by both the government and commercial sectors has been largely driven by the myopic best response of players to the actions of their adversaries and their perception of the adversarial environment. However, current work in applying traditional game theory to cyber operations typically assumes that games exist with prescribed moves, strategies, and payos. This paper presents an analytic approach to characterizing the more realistic cyber adversarial metagame that we believe is being played. Examples show that understanding the dynamic metagame provides opportunities to exploit an adversary's anticipated attack strategy. A dynamic version of a graph-based attack-defend game is introduced, and a simulation shows how an optimal strategy can be selected for success in the dynamic environment.
The modern temperature-accelerated dynamics approach
Zamora, Richard J.; Uberuaga, Blas P.; Perez, Danny; ...
2016-06-01
Accelerated molecular dynamics (AMD) is a class of MD-based methods used to simulate atomistic systems in which the metastable state-to-state evolution is slow compared with thermal vibrations. Temperature-accelerated dynamics (TAD) is a particularly efficient AMD procedure in which the predicted evolution is hastened by elevating the temperature of the system and then recovering the correct state-to-state dynamics at the temperature of interest. TAD has been used to study various materials applications, often revealing surprising behavior beyond the reach of direct MD. This success has inspired several algorithmic performance enhancements, as well as the analysis of its mathematical framework. Recently, thesemore » enhancements have leveraged parallel programming techniques to enhance both the spatial and temporal scaling of the traditional approach. Here, we review the ongoing evolution of the modern TAD method and introduce the latest development: speculatively parallel TAD.« less
The Modern Temperature-Accelerated Dynamics Approach.
Zamora, Richard J; Uberuaga, Blas P; Perez, Danny; Voter, Arthur F
2016-06-07
Accelerated molecular dynamics (AMD) is a class of MD-based methods used to simulate atomistic systems in which the metastable state-to-state evolution is slow compared with thermal vibrations. Temperature-accelerated dynamics (TAD) is a particularly efficient AMD procedure in which the predicted evolution is hastened by elevating the temperature of the system and then recovering the correct state-to-state dynamics at the temperature of interest. TAD has been used to study various materials applications, often revealing surprising behavior beyond the reach of direct MD. This success has inspired several algorithmic performance enhancements, as well as the analysis of its mathematical framework. Recently, these enhancements have leveraged parallel programming techniques to enhance both the spatial and temporal scaling of the traditional approach. We review the ongoing evolution of the modern TAD method and introduce the latest development: speculatively parallel TAD.
The modern temperature-accelerated dynamics approach
Zamora, Richard J.; Uberuaga, Blas P.; Perez, Danny; Voter, Arthur F.
2016-06-01
Accelerated molecular dynamics (AMD) is a class of MD-based methods used to simulate atomistic systems in which the metastable state-to-state evolution is slow compared with thermal vibrations. Temperature-accelerated dynamics (TAD) is a particularly efficient AMD procedure in which the predicted evolution is hastened by elevating the temperature of the system and then recovering the correct state-to-state dynamics at the temperature of interest. TAD has been used to study various materials applications, often revealing surprising behavior beyond the reach of direct MD. This success has inspired several algorithmic performance enhancements, as well as the analysis of its mathematical framework. Recently, these enhancements have leveraged parallel programming techniques to enhance both the spatial and temporal scaling of the traditional approach. Here, we review the ongoing evolution of the modern TAD method and introduce the latest development: speculatively parallel TAD.
NASA Astrophysics Data System (ADS)
He, Xuefei; Nguyen, Chuong Vinh; Pratap, Mrinalini; Zheng, Yujie; Wang, Yi; Nisbet, David R.; Rug, Melanie; Maier, Alexander G.; Lee, Woei Ming
2016-12-01
Here we propose a region-recognition approach with iterative thresholding, which is adaptively tailored to extract the appropriate region or shape of spatial frequency. In order to justify the method, we tested it with different samples and imaging conditions (different objectives). We demonstrate that our method provides a useful method for rapid imaging of cellular dynamics in microfluidic and cell cultures.
Optimal control based on adaptive model reduction approach to control transfer phenomena
NASA Astrophysics Data System (ADS)
Oulghelou, Mourad; Allery, Cyrille
2017-01-01
The purpose of optimal control is to act on a set of parameters characterizing a dynamical system to achieve a target dynamics. In order to reduce CPU time and memory storage needed to perform control on evolution systems, it is possible to use reduced order models (ROMs). The mostly used one is the Proper Orthogonal Decomposition (POD). However the bases constructed in this way are sensitive to the configuration of the dynamical system. Consequently, the need of full simulations to build a basis for each configuration is time consuming and makes that approach still relatively expensive. In this paper, to overcome this difficulty we suggest to use an adequate bases interpolation method. It consists in computing the associated bases to a distribution of control parameters. These bases are afterwards called in the control algorithm to build a reduced basis adapted to a given control parameter. This interpolation method involves results of the calculus of Geodesics on Grassmann manifold.
Adaptive Optics Correction in Real-Time for Dynamic Wavefront Errors
1990-03-15
This paper reports on the principles for the use of, and the experimental results obtained from, an adaptive optics system for correcting dynamic...control system. Keywords: Adaptive optics ; Wavefront sensing; Deformable mirror; Chinese translations.
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.
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.
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.
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.
Mansouri, Mohammad; Teshnehlab, Mohammad; Aliyari Shoorehdeli, Mahdi
2015-05-01
In this paper, a novel adaptive hierarchical fuzzy control system based on the variable structure control is developed for a class of SISO canonical nonlinear systems in the presence of bounded disturbances. It is assumed that nonlinear functions of the systems be completely unknown. Switching surfaces are incorporated into the hierarchical fuzzy control scheme to ensure the system stability. A fuzzy soft switching system decides the operation area of the hierarchical fuzzy control and variable structure control systems. All the nonlinearly appeared parameters of conclusion parts of fuzzy blocks located in different layers of the hierarchical fuzzy control system are adjusted through adaptation laws deduced from the defined Lyapunov function. The proposed hierarchical fuzzy control system reduces the number of rules and consequently the number of tunable parameters with respect to the ordinary fuzzy control system. Global boundedness of the overall adaptive system and the desired precision are achieved using the proposed adaptive control system. In this study, an adaptive hierarchical fuzzy system is used for two objectives; it can be as a function approximator or a control system based on an intelligent-classic approach. Three theorems are proven to investigate the stability of the nonlinear dynamic systems. The important point about the proposed theorems is that they can be applied not only to hierarchical fuzzy controllers with different structures of hierarchical fuzzy controller, but also to ordinary fuzzy controllers. Therefore, the proposed algorithm is more general. To show the effectiveness of the proposed method four systems (two mechanical, one mathematical and one chaotic) are considered in simulations. Simulation results demonstrate the validity, efficiency and feasibility of the proposed approach to control of nonlinear dynamic systems.
Bosson, Maël; Grudinin, Sergei; Redon, Stephane
2013-03-05
We present a novel Block-Adaptive Quantum Mechanics (BAQM) approach to interactive quantum chemistry. Although quantum chemistry models are known to be computationally demanding, we achieve interactive rates by focusing computational resources on the most active parts of the system. BAQM is based on a divide-and-conquer technique and constrains some nucleus positions and some electronic degrees of freedom on the fly to simplify the simulation. As a result, each time step may be performed significantly faster, which in turn may accelerate attraction to the neighboring local minima. By applying our approach to the nonself-consistent Atom Superposition and Electron Delocalization Molecular Orbital theory, we demonstrate interactive rates and efficient virtual prototyping for systems containing more than a thousand of atoms on a standard desktop computer.
Long timestep dynamics of peptides by the dynamics driver approach.
Derreumaux, P; Schlick, T
1995-04-01
Previous experience with the Langevin/implicit-Euler scheme for dynamics ("LI") on model systems (butane, water) has shown that LI is numerically stable for timesteps in the 5-20 fs range but quenches high-frequency modes. To explore applications to polypeptides, we apply LI to model systems (several dipeptides, a tetrapeptide, and a 13-residue oligoalanine) and also develop a new dynamics driver approach ("DA"). The DA scheme, based on LI, addresses the important issue of proper sampling, which is unlikely to be solved by small-timestep integration methods or implicit methods with intrinsic damping at room temperature, such as LI. Equilibrium averages, time-dependent molecular properties, and sampling trends at room temperature are reported for both LI and DA dynamics simulations, which are then compared to those generated by a standard explicit discretization of the Langevin equation with a 1 fs timestep. We find that LI's quenching effects are severe on both the fast and slow (due to vibrational coupling) frequency modes of all-atom polypeptides and lead to more restricted dynamics at moderate timesteps (40 fs). The DA approach empirically counteracts these damping effects by adding random atomic perturbations to the coordinates at each step (before the minimization of a dynamics function). By restricting the energetic fluctuations and controlling the kinetic energy, we are able with a 60 fs timestep to generate continuous trajectories that sample more of the relevant conformational space and also reproduce reasonably Boltzmann statistics. Although the timescale for transition may be accelerated by the DA approach, the transitional information obtained for the alanine dipeptide and the tetrapeptide is consistent with that obtained by several other theoretical approaches that focus specifically on the determination of pathways. While the trajectory for oligoalanine by the explicit scheme over the nanosecond timeframe remains in the vicinity of the full alpha R
NASA Technical Reports Server (NTRS)
Balas, M. J.; Kaufman, H.; Wen, J.
1985-01-01
A command generator tracker approach to model following contol of linear distributed parameter systems (DPS) whose dynamics are described on infinite dimensional Hilbert spaces is presented. This method generates finite dimensional controllers capable of exponentially stable tracking of the reference trajectories when certain ideal trajectories are known to exist for the open loop DPS; we present conditions for the existence of these ideal trajectories. An adaptive version of this type of controller is also presented and shown to achieve (in some cases, asymptotically) stable finite dimensional control of the infinite dimensional DPS.
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.
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
Gauge-invariant approach to quark dynamics
NASA Astrophysics Data System (ADS)
Sazdjian, H.
2016-02-01
The main aspects of a gauge-invariant approach to the description of quark dynamics in the nonperturbative regime of quantum chromodynamics (QCD) are first reviewed. The role of the parallel transport operation in constructing gauge-invariant Green's functions is then presented, and the relevance of Wilson loops for the representation of the interaction is emphasized. Recent developments, based on the use of polygonal lines for the parallel transport operation, are presented. An integro-differential equation, obtained for the quark Green's function defined with a phase factor along a single, straight line segment, is solved exactly and analytically in the case of two-dimensional QCD in the large- N c limit. The solution displays the dynamical mass generation phenomenon for quarks, with an infinite number of branch-cut singularities that are stronger than simple poles.
Stability threshold approach for complex dynamical systems
NASA Astrophysics Data System (ADS)
Klinshov, Vladimir V.; Nekorkin, Vladimir I.; Kurths, Jürgen
2016-01-01
A new measure to characterize the stability of complex dynamical systems against large perturbations is suggested, the stability threshold (ST). It quantifies the magnitude of the weakest perturbation capable of disrupting the system and switch it to an undesired dynamical regime. In the phase space, the ST corresponds to the ‘thinnest site’ of the attraction basin and therefore indicates the most ‘dangerous’ direction of perturbations. We introduce a computational algorithm for quantification of the ST and demonstrate that the suggested approach is effective and provides important insights. The generality of the obtained results defines their vast potential for application in such fields as engineering, neuroscience, power grids, Earth science and many others where the robustness of complex systems is studied.
Adaptive dynamic surface control for a class of MIMO nonlinear systems with actuator failures
NASA Astrophysics Data System (ADS)
Amezquita S., Kendrick; Yan, Lin; Butt, Waseem A.
2013-03-01
In this article, an adaptive dynamic surface control scheme for a class of MIMO nonlinear systems with actuator failures and uncertainties is presented. In the proposed control scheme, the dynamic changes and disturbances induced by actuator failures are detected and isolated by means of radial basis function neural networks, which also compensate system uncertainties that arise from the mismatch between nominal model and real plant. In the presence of unknown actuation functions, the effectiveness of the control scheme is guaranteed by imposing a structural condition on the actuation matrix. Moreover, the singularity problem that arises from the approximation of unknown actuation functions is circumvented, and thus the use parameter projection is avoided. In this work, the nominal plant is transformed into a suitable form via diffeomorphism. Dynamic surface control design technique is used to develop the control laws. The closed-loop signals are proven to be uniformly ultimately bounded through Lyapunov approach, and the output tracking error is shown to be bounded within a residual set which can be made arbitrarily small by appropriately tuning the controller parameters. Finally, the proposed adaptive control scheme effectiveness is verified by simulation of the longitudinal dynamics of a twin otter aircraft undergoing actuator failures.
Adaptive intelligent systems for pHealth - an architectural approach.
González, Carolina; Blobel, Bernd; López, Diego M
2012-01-01
Health systems around the globe, especially in developing countries, are facing the challenge of delivering effective, safe, and high quality public health and individualized health services independent of time and location, and with minimum of allocated resources (pHealth). In this context, health promotion and health education services are very important, especially in primary care settings. The objective of this paper is to describe the architecture of an adaptive intelligent system mainly developed to support education and training of citizens, but also of health professionals. The proposed architecture describes a system consisting of several agents that cooperatively interact to find and process tutoring materials to disseminate them to users (multi-agent system). A prototype is being implemented which includes medical students from the Medical Faculty at University of Cauca (Colombia). In the experimental process, the student´s learning style - detected with the Bayesian Model - is compared against the learning style obtained from a questioner (manual approach).
Adaptive Neuro-fuzzy approach in friction identification
NASA Astrophysics Data System (ADS)
Zaiyad Muda @ Ismail, Muhammad
2016-05-01
Friction is known to affect the performance of motion control system, especially in terms of its accuracy. Therefore, a number of techniques or methods have been explored and implemented to alleviate the effects of friction. In this project, the Artificial Intelligent (AI) approach is used to model the friction which will be then used to compensate the friction. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is chosen among several other AI methods because of its reliability and capabilities of solving complex computation. ANFIS is a hybrid AI-paradigm that combines the best features of neural network and fuzzy logic. This AI method (ANFIS) is effective for nonlinear system identification and compensation and thus, being used in this project.
An Approach to Automated Fusion System Design and Adaptation
Fritze, Alexander; Mönks, Uwe; Holst, Christoph-Alexander; Lohweg, Volker
2017-01-01
Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in all disciplines. Consequently, diverse tasks such as information processing, extensive networking, or system monitoring using sensor and information fusion systems need to be reconsidered. The focus of this contribution is on distributed sensor and information fusion systems for system monitoring, which must reflect the increasing flexibility of fusion systems. This contribution thus proposes an approach, which relies on a network of self-descriptive intelligent sensor nodes, for the automatic design and update of sensor and information fusion systems. This article encompasses the fusion system configuration and adaptation as well as communication aspects. Manual interaction with the flexibly changing system is reduced to a minimum. PMID:28300762
Dynamic skeletal muscle stimulation and its potential in bone adaptation
Qin, Y-X.; Lam, H.; Ferreri, S.; Rubin, C.
2016-01-01
To identify mechanotransductive signals for combating musculoskeletal deterioration, it is essential to determine the components and mechanisms critical to the anabolic processes of musculoskeletal tissues. It is hypothesized that the interaction between bone and muscle may depend on fluid exchange in these tissues by mechanical loading. It has been shown that intramedullary pressure (ImP) and low-level bone strain induced by muscle stimulation (MS) has the potential to mitigate bone loss induced by disuse osteopenia. Optimized MS signals, i.e., low-intensity and high frequency, may be critical in maintaining bone mass and mitigating muscle atrophy. The objectives for this review are to discuss the potential for MS to induce ImP and strains on bone, to regulate bone adaptation, and to identify optimized stimulation frequency in the loading regimen. The potential for MS to regulate blood and fluid flow will also be discussed. The results suggest that oscillatory MS regulates fluid dynamics with minimal mechanical strain in bone. The response was shown to be dependent on loading frequency, serving as a critical mediator in mitigating bone loss. A specific regimen of dynamic MS may be optimized in vivo to attenuate disuse osteopenia and serve as a biomechanical intervention in the clinical setting. PMID:20190376
A New Approach to Interference Excision in Radio Astronomy: Real-Time Adaptive Cancellation
NASA Astrophysics Data System (ADS)
Barnbaum, Cecilia; Bradley, Richard F.
1998-11-01
Every year, an increasing amount of radio-frequency (RF) spectrum in the VHF, UHF, and microwave bands is being utilized to support new commercial and military ventures, and all have the potential to interfere with radio astronomy observations. Such services already cause problems for radio astronomy even in very remote observing sites, and the potential for this form of light pollution to grow is alarming. Preventive measures to eliminate interference through FCC legislation and ITU agreements can be effective; however, many times this approach is inadequate and interference excision at the receiver is necessary. Conventional techniques such as RF filters, RF shielding, and postprocessing of data have been only somewhat successful, but none has been sufficient. Adaptive interference cancellation is a real-time approach to interference excision that has not been used before in radio astronomy. We describe here, for the first time, adaptive interference cancellation in the context of radio astronomy instrumentation, and we present initial results for our prototype receiver. In the 1960s, analog adaptive interference cancelers were developed that obtain a high degree of cancellation in problems of radio communications and radar. However, analog systems lack the dynamic range, noised performance, and versatility required by radio astronomy. The concept of digital adaptive interference cancellation was introduced in the mid-1960s as a way to reduce unwanted noise in low-frequency (audio) systems. Examples of such systems include the canceling of maternal ECG in fetal electrocardiography and the reduction of engine noise in the passenger compartments of automobiles. These audio-frequency applications require bandwidths of only a few tens of kilohertz. Only recently has high-speed digital filter technology made high dynamic range adaptive canceling possible in a bandwidth as large as a few megahertz, finally opening the door to application in radio astronomy. We have
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
Multiscale model approach for magnetization dynamics simulations
NASA Astrophysics Data System (ADS)
De Lucia, Andrea; Krüger, Benjamin; Tretiakov, Oleg A.; Kläui, Mathias
2016-11-01
Simulations of magnetization dynamics in a multiscale environment enable the rapid evaluation of the Landau-Lifshitz-Gilbert equation in a mesoscopic sample with nanoscopic accuracy in areas where such accuracy is required. We have developed a multiscale magnetization dynamics simulation approach that can be applied to large systems with spin structures that vary locally on small length scales. To implement this, the conventional micromagnetic simulation framework has been expanded to include a multiscale solving routine. The software selectively simulates different regions of a ferromagnetic sample according to the spin structures located within in order to employ a suitable discretization and use either a micromagnetic or an atomistic model. To demonstrate the validity of the multiscale approach, we simulate the spin wave transmission across the regions simulated with the two different models and different discretizations. We find that the interface between the regions is fully transparent for spin waves with frequency lower than a certain threshold set by the coarse scale micromagnetic model with no noticeable attenuation due to the interface between the models. As a comparison to exact analytical theory, we show that in a system with a Dzyaloshinskii-Moriya interaction leading to spin spirals, the simulated multiscale result is in good quantitative agreement with the analytical calculation.
Dynamically Reconfigurable Approach to Multidisciplinary Problems
NASA Technical Reports Server (NTRS)
Alexandrov, Natalie M.; Lewis, Robert Michael
2003-01-01
The complexity and autonomy of the constituent disciplines and the diversity of the disciplinary data formats make the task of integrating simulations into a multidisciplinary design optimization problem extremely time-consuming and difficult. We propose a dynamically reconfigurable approach to MDO problem formulation wherein an appropriate implementation of the disciplinary information results in basic computational components that can be combined into different MDO problem formulations and solution algorithms, including hybrid strategies, with relative ease. The ability to re-use the computational components is due to the special structure of the MDO problem. We believe that this structure can and should be used to formulate and solve optimization problems in the multidisciplinary context. The present work identifies the basic computational components in several MDO problem formulations and examines the dynamically reconfigurable approach in the context of a popular class of optimization methods. We show that if the disciplinary sensitivity information is implemented in a modular fashion, the transfer of sensitivity information among the formulations under study is straightforward. This enables not only experimentation with a variety of problem formations in a research environment, but also the flexible use of formulations in a production design environment.
Time Discretization Approach to Dynamic Localization Conditions
NASA Astrophysics Data System (ADS)
Papp, E.
An alternative wavefunction to the description of the dynamic localization of a charged particle moving on a one-dimensional lattice under the influence of a periodic time dependent electric field is written down. For this purpose the method of characteristics such as applied by Dunlap and Kenkre [Phys. Rev. B 34, 3625 (1986)] has been modified by using a different integration variable. Handling this wavefunction one is faced with the selection of admissible time values. This results in a conditionally exactly solvable problem, now by accounting specifically for the implementation of a time discretization working in conjunction with a related dynamic localization condition. In addition, one resorts to the strong field limit, which amounts to replace, to leading order, the large order zeros of the Bessel function J0(z), used before in connection with the cosinusoidal modulation, by integral multiples of π. Here z stands for the ratio between the field amplitude and the frequency. The modulation function of the electric field vanishes on the nodal points of the time grid, which stands for an effective field-free behavior. This opens the way to propose quickly tractable dynamic localization conditions for arbitrary periodic modulations. We have also found that the present time discretization approach produces the minimization of the mean square displacement characterizing the usual exact wavefunction. Other realizations and comparisons have also been presented.
A disturbance observer-based adaptive control approach for flexure beam nano manipulators.
Zhang, Yangming; Yan, Peng; Zhang, Zhen
2016-01-01
This paper presents a systematic modeling and control methodology for a two-dimensional flexure beam-based servo stage supporting micro/nano manipulations. Compared with conventional mechatronic systems, such systems have major control challenges including cross-axis coupling, dynamical uncertainties, as well as input saturations, which may have adverse effects on system performance unless effectively eliminated. A novel disturbance observer-based adaptive backstepping-like control approach is developed for high precision servo manipulation purposes, which effectively accommodates model uncertainties and coupling dynamics. An auxiliary system is also introduced, on top of the proposed control scheme, to compensate the input saturations. The proposed control architecture is deployed on a customized-designed nano manipulating system featured with a flexure beam structure and voice coil actuators (VCA). Real time experiments on various manipulating tasks, such as trajectory/contour tracking, demonstrate precision errors of less than 1%.
Adaptive speed/position control of induction motor based on SPR approach
NASA Astrophysics Data System (ADS)
Lee, Hou-Tsan
2014-11-01
A sensorless speed/position tracking control scheme for induction motors is proposed subject to unknown load torque via adaptive strictly positive real (SPR) approach design. A special nonlinear coordinate transform is first provided to reform the dynamical model of the induction motor. The information on rotor fluxes can thus be derived from the dynamical model to decide on the proportion of input voltage in the d-q frame under the constraint of the maximum power transfer property of induction motors. Based on the SPR approach, the speed and position control objectives can be achieved. The proposed control scheme is to provide the speed/position control of induction motors while lacking the knowledge of some mechanical system parameters, such as the motor inertia, motor damping coefficient, and the unknown payload. The adaptive control technique is thus involved in the field oriented control scheme to deal with the unknown parameters. The thorough proof is derived to guarantee the stability of the speed and position of control systems of induction motors. Besides, numerical simulation and experimental results are also provided to validate the effectiveness of the proposed control scheme.
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 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'…
ERIC Educational Resources Information Center
Wu, Huey-Min; Kuo, Bor-Chen; Wang, Su-Chen
2017-01-01
In this study, a computerized dynamic assessment test with both immediately individualized feedback and adaptively property was applied to Mathematics learning in primary school. For evaluating the effectiveness of the computerized dynamic adaptive test, the performances of three types of remedial instructions were compared by a pre-test/post-test…
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)
Yao, Zhenjian; Wang, Zhongyu; Yi-Lin Forrest, Jeffrey; Wang, Qiyue; Lv, Jing
2017-04-01
In this paper, an approach combining empirical mode decomposition (EMD) with adaptive least squares (ALS) is proposed to improve the dynamic calibration accuracy of pressure sensors. With EMD, the original output of the sensor can be represented as sums of zero-mean amplitude modulation frequency modulation components. By identifying and excluding those components involved in noises, the noise-free output could be reconstructed with the useful frequency modulation ones. Then the least squares method is iteratively performed to estimate the optimal order and parameters of the mathematical model. The dynamic characteristic parameters of the sensor can be derived from the model in both time and frequency domains. A series of shock tube calibration tests are carried out to validate the performance of this method. Experimental results show that the proposed method works well in reducing the influence of noise and yields an appropriate mathematical model. Furthermore, comparative experiments also demonstrate the superiority of the proposed method over the existing ones.
Adaptive life simulator: A novel approach to modeling the cardiovascular system
Kangas, L.J.; Keller, P.E.; Hashem, S.
1995-06-01
In this paper, an adaptive life simulator (ALS) is introduced. The ALS models a subset of the dynamics of the cardiovascular behavior of an individual by using a recurrent artificial neural network. These models are developed for use in applications that require simulations of cardiovascular systems, such as medical mannequins, and in medical diagnostic systems. This approach is unique in that each cardiovascular model is developed from physiological measurements of an individual. Any differences between the modeled variables and the actual variables of an individual can subsequently be used for diagnosis. This approach also exploits sensor fusion applied to biomedical sensors. Sensor fusion optimizes the utilization of the sensors. The advantage of sensor fusion has been demonstrated in applications including control and diagnostics of mechanical and chemical processes.
Adaptive control of unknown unstable steady states of dynamical systems.
Pyragas, K; Pyragas, V; Kiss, I Z; Hudson, J L
2004-08-01
A simple adaptive controller based on a low-pass filter to stabilize unstable steady states of dynamical systems is considered. The controller is reference-free; it does not require knowledge of the location of the fixed point in the phase space. A topological limitation similar to that of the delayed feedback controller is discussed. We show that the saddle-type steady states cannot be stabilized by using the conventional low-pass filter. The limitation can be overcome by using an unstable low-pass filter. The use of the controller is demonstrated for several physical models, including the pendulum driven by a constant torque, the Lorenz system, and an electrochemical oscillator. Linear and nonlinear analyses of the models are performed and the problem of the basins of attraction of the stabilized steady states is discussed. The robustness of the controller is demonstrated in experiments and numerical simulations with an electrochemical oscillator, the dissolution of nickel in sulfuric acid; a comparison of the effect of using direct and indirect variables in the control is made. With the use of the controller, all unstable phase-space objects are successfully reconstructed experimentally.
Adaptive optics optical coherence tomography with dynamic retinal tracking
Kocaoglu, Omer P.; Ferguson, R. Daniel; Jonnal, Ravi S.; Liu, Zhuolin; Wang, Qiang; Hammer, Daniel X.; Miller, Donald T.
2014-01-01
Adaptive optics optical coherence tomography (AO-OCT) is a highly sensitive and noninvasive method for three dimensional imaging of the microscopic retina. Like all in vivo retinal imaging techniques, however, it suffers the effects of involuntary eye movements that occur even under normal fixation. In this study we investigated dynamic retinal tracking to measure and correct eye motion at KHz rates for AO-OCT imaging. A customized retina tracking module was integrated into the sample arm of the 2nd-generation Indiana AO-OCT system and images were acquired on three subjects. Analyses were developed based on temporal amplitude and spatial power spectra in conjunction with strip-wise registration to independently measure AO-OCT tracking performance. After optimization of the tracker parameters, the system was found to correct eye movements up to 100 Hz and reduce residual motion to 10 µm root mean square. Between session precision was 33 µm. Performance was limited by tracker-generated noise at high temporal frequencies. PMID:25071963
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.
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.
Adaptive optics optical coherence tomography with dynamic retinal tracking.
Kocaoglu, Omer P; Ferguson, R Daniel; Jonnal, Ravi S; Liu, Zhuolin; Wang, Qiang; Hammer, Daniel X; Miller, Donald T
2014-07-01
Adaptive optics optical coherence tomography (AO-OCT) is a highly sensitive and noninvasive method for three dimensional imaging of the microscopic retina. Like all in vivo retinal imaging techniques, however, it suffers the effects of involuntary eye movements that occur even under normal fixation. In this study we investigated dynamic retinal tracking to measure and correct eye motion at KHz rates for AO-OCT imaging. A customized retina tracking module was integrated into the sample arm of the 2nd-generation Indiana AO-OCT system and images were acquired on three subjects. Analyses were developed based on temporal amplitude and spatial power spectra in conjunction with strip-wise registration to independently measure AO-OCT tracking performance. After optimization of the tracker parameters, the system was found to correct eye movements up to 100 Hz and reduce residual motion to 10 µm root mean square. Between session precision was 33 µm. Performance was limited by tracker-generated noise at high temporal frequencies.
Fogarty, Aoife C. Potestio, Raffaello Kremer, Kurt
2015-05-21
A fully atomistic modelling of many biophysical and biochemical processes at biologically relevant length- and time scales is beyond our reach with current computational resources, and one approach to overcome this difficulty is the use of multiscale simulation techniques. In such simulations, when system properties necessitate a boundary between resolutions that falls within the solvent region, one can use an approach such as the Adaptive Resolution Scheme (AdResS), in which solvent particles change their resolution on the fly during the simulation. Here, we apply the existing AdResS methodology to biomolecular systems, simulating a fully atomistic protein with an atomistic hydration shell, solvated in a coarse-grained particle reservoir and heat bath. Using as a test case an aqueous solution of the regulatory protein ubiquitin, we first confirm the validity of the AdResS approach for such systems, via an examination of protein and solvent structural and dynamical properties. We then demonstrate how, in addition to providing a computational speedup, such a multiscale AdResS approach can yield otherwise inaccessible physical insights into biomolecular function. We use our methodology to show that protein structure and dynamics can still be correctly modelled using only a few shells of atomistic water molecules. We also discuss aspects of the AdResS methodology peculiar to biomolecular simulations.
Xu, Haojie; Lu, Yunfeng; Zhu, Shanan; He, Bin
2014-07-01
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 nonzero 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 this study, we first 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 time
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
Polymer Fluid Dynamics: Continuum and Molecular Approaches.
Bird, R B; Giacomin, A J
2016-06-07
To solve problems in polymer fluid dynamics, one needs the equations of continuity, motion, and energy. The last two equations contain the stress tensor and the heat-flux vector for the material. There are two ways to formulate the stress tensor: (a) One can write a continuum expression for the stress tensor in terms of kinematic tensors, or (b) one can select a molecular model that represents the polymer molecule and then develop an expression for the stress tensor from kinetic theory. The advantage of the kinetic theory approach is that one gets information about the relation between the molecular structure of the polymers and the rheological properties. We restrict the discussion primarily to the simplest stress tensor expressions or constitutive equations containing from two to four adjustable parameters, although we do indicate how these formulations may be extended to give more complicated expressions. We also explore how these simplest expressions are recovered as special cases of a more general framework, the Oldroyd 8-constant model. Studying the simplest models allows us to discover which types of empiricisms or molecular models seem to be worth investigating further. We also explore equivalences between continuum and molecular approaches. We restrict the discussion to several types of simple flows, such as shearing flows and extensional flows, which are of greatest importance in industrial operations. Furthermore, if these simple flows cannot be well described by continuum or molecular models, then it is not necessary to lavish time and energy to apply them to more complex flow problems.
A macroscopic approach to glacier dynamics
Harrison, W.D.; Raymond, C.F.; Echelmeyer, K.A.; Krimmel, R.M.
2003-01-01
A simple approach to glacier dynamics is explored in which there is postulated to be a relationship between area and volume with three parameters: the time for area to respond to changes in volume, a thickness scale, and an area characterizing the condition of the initial state. This approach gives a good fit to the measurements of cumulative balance and area on South Cascade Glacier from 1970-97; the area time-scale is roughly 8 years, the thickness scale about 123 m, and the 1970 area roughly 4% larger than required for adjustment with volume. Combining this relationship with a version of mass continuity expressed in terms of area and volume produces a theory of glacier area and volume response to climate in which another time constant, the volume time-scale, appears. Area and volume both respond like a damped spring and mass system. The damping of the South Cascade response is approximately critical, and the volume time-scale is roughly 48 years, six times the area time-scale. The critically damped spring and mass analogy reproduces the time dependence predicted by the more complicated traditional theory of Nye.
Indoor human thermal adaptation: dynamic processes and weighting factors.
Luo, M; Cao, B; Ouyang, Q; Zhu, Y
2017-03-01
In this study, we explore the correlations between indoor climate change and human thermal adaptation, especially with regard to the timescale and weighting factors of physiological adaptation. A comparative experiment was conducted in China where wintertime indoor climate in the southern region (devoid of space heating) is much colder than in the northern region (with pervasive district heating). Four subject groups with different indoor thermal experiences participated in this climate chamber experiment. The results indicate that previous indoor thermal exposure is an important contributor to occupants' physiological adaptation. More specifically, subjects acclimated to neutral-warm indoors tended to have stronger physiological responses and felt more uncomfortable in moderate cold exposures than those adapted to the cold. As for the driving force of thermal adaptation, physiological acclimation is an important aspect among all the supposed adaptive layers. However, the physiological adaptation speed lags behind changes in the overall subjective perception.
Hamiltonian-Driven Adaptive Dynamic Programming for Continuous Nonlinear Dynamical Systems.
Yang, Yongliang; Wunsch, Donald; Yin, Yixin
2017-02-01
This paper presents a Hamiltonian-driven framework of adaptive dynamic programming (ADP) for continuous time nonlinear systems, which consists of evaluation of an admissible control, comparison between two different admissible policies with respect to the corresponding the performance function, and the performance improvement of an admissible control. It is showed that the Hamiltonian can serve as the temporal difference for continuous-time systems. In the Hamiltonian-driven ADP, the critic network is trained to output the value gradient. Then, the inner product between the critic and the system dynamics produces the value derivative. Under some conditions, the minimization of the Hamiltonian functional is equivalent to the value function approximation. An iterative algorithm starting from an arbitrary admissible control is presented for the optimal control approximation with its convergence proof. The implementation is accomplished by a neural network approximation. Two simulation studies demonstrate the effectiveness of Hamiltonian-driven ADP.
Adaptive inverse control of linear and nonlinear systems using dynamic neural networks.
Plett, G L
2003-01-01
In this paper, we see adaptive control as a three-part adaptive-filtering problem. First, the dynamical system we wish to control is modeled using adaptive system-identification techniques. Second, the dynamic response of the system is controlled using an adaptive feedforward controller. No direct feedback is used, except that the system output is monitored and used by an adaptive algorithm to adjust the parameters of the controller. Third, disturbance canceling is performed using an additional adaptive filter. The canceler does not affect system dynamics, but feeds back plant disturbance in a way that minimizes output disturbance power. The techniques work to control minimum-phase or nonminimum-phase, linear or nonlinear, single-input-single-output (SISO) or multiple-input-multiple-ouput (MIMO), stable or stabilized systems. Constraints may additionally be placed on control effort for a practical implementation. Simulation examples are presented to demonstrate that the proposed methods work very well.
An adaptive neural swarm approach for intrusion defense in ad hoc networks
NASA Astrophysics Data System (ADS)
Cannady, James
2011-06-01
Wireless sensor networks (WSN) and mobile ad hoc networks (MANET) are being increasingly deployed in critical applications due to the flexibility and extensibility of the technology. While these networks possess numerous advantages over traditional wireless systems in dynamic environments they are still vulnerable to many of the same types of host-based and distributed attacks common to those systems. Unfortunately, the limited power and bandwidth available in WSNs and MANETs, combined with the dynamic connectivity that is a defining characteristic of the technology, makes it extremely difficult to utilize traditional intrusion detection techniques. This paper describes an approach to accurately and efficiently detect potentially damaging activity in WSNs and MANETs. It enables the network as a whole to recognize attacks, anomalies, and potential vulnerabilities in a distributive manner that reflects the autonomic processes of biological systems. Each component of the network recognizes activity in its local environment and then contributes to the overall situational awareness of the entire system. The approach utilizes agent-based swarm intelligence to adaptively identify potential data sources on each node and on adjacent nodes throughout the network. The swarm agents then self-organize into modular neural networks that utilize a reinforcement learning algorithm to identify relevant behavior patterns in the data without supervision. Once the modular neural networks have established interconnectivity both locally and with neighboring nodes the analysis of events within the network can be conducted collectively in real-time. The approach has been shown to be extremely effective in identifying distributed network attacks.
NASA Astrophysics Data System (ADS)
Yoo, Sung Jin
2016-11-01
This paper presents a theoretical design approach for output-feedback formation tracking of multiple mobile robots under wheel perturbations. It is assumed that these perturbations are unknown and the linear and angular velocities of the robots are unmeasurable. First, adaptive state observers for estimating unmeasurable velocities of the robots are developed under the robots' kinematics and dynamics including wheel perturbation effects. Then, we derive a virtual-structure-based formation tracker scheme according to the observer dynamic surface design procedure. The main difficulty of the output-feedback control design is to manage the coupling problems between unmeasurable velocities and unknown wheel perturbation effects. These problems are avoided by using the adaptive technique and the function approximation property based on fuzzy logic systems. From the Lyapunov stability analysis, it is shown that point tracking errors of each robot and synchronisation errors for the desired formation converge to an adjustable neighbourhood of the origin, while all signals in the controlled closed-loop system are semiglobally uniformly ultimately bounded.
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
Reinschmidt, Kerstin M; Teufel-Shone, Nicolette I; Bradford, Gail; Drummond, Rebecca L; Torres, Emma; Redondo, Floribella; Elenes, Jo Jean; Sanders, Alicia; Gastelum, Sylvia; Moore-Monroy, Martha; Barajas, Salvador; Fernandez, Lourdes; Alvidrez, Rosy; de Zapien, Jill Guernsey; Staten, Lisa K
2010-04-01
Diabetes health disparities among Hispanic populations have been countered with federally funded health promotion and disease prevention programs. Dissemination has focused on program adaptation to local cultural contexts for greater acceptability and sustainability. Taking a broader approach and drawing on our experience in Mexican American communities at the U.S.-Mexico Border, we demonstrate how interventions are adapted at the intersection of multiple cultural contexts: the populations targeted, the community- and university-based entities designing and implementing interventions, and the field team delivering the materials. Program adaptation involves negotiations between representatives of all contexts and is imperative in promoting local ownership and program sustainability.
Adaptive fusion of infrared and visible images in dynamic scene
NASA Astrophysics Data System (ADS)
Yang, Guang; Yin, Yafeng; Man, Hong; Desai, Sachi
2011-11-01
Multiple modalities sensor fusion has been widely employed in various surveillance and military applications. A variety of image fusion techniques including PCA, wavelet, curvelet and HSV has been proposed in recent years to improve human visual perception for object detection. One of the main challenges for visible and infrared image fusion is to automatically determine an optimal fusion strategy for different input scenes along with an acceptable computational cost. This paper, we propose a fast and adaptive feature selection based image fusion method to obtain high a contrast image from visible and infrared sensors for targets detection. At first, fuzzy c-means clustering is applied on the infrared image to highlight possible hotspot regions, which will be considered as potential targets' locations. After that, the region surrounding the target area is segmented as the background regions. Then image fusion is locally applied on the selected target and background regions by computing different linear combination of color components from registered visible and infrared images. After obtaining different fused images, histogram distributions are computed on these local fusion images as the fusion feature set. The variance ratio which is based on Linear Discriminative Analysis (LDA) measure is employed to sort the feature set and the most discriminative one is selected for the whole image fusion. As the feature selection is performed over time, the process will dynamically determine the most suitable feature for the image fusion in different scenes. Experiment is conducted on the OSU Color-Thermal database, and TNO Human Factor dataset. The fusion results indicate that our proposed method achieved a competitive performance compared with other fusion algorithms at a relatively low computational cost.
The puzzle of partial migration: Adaptive dynamics and evolutionary game theory perspectives.
De Leenheer, Patrick; Mohapatra, Anushaya; Ohms, Haley A; Lytle, David A; Cushing, J M
2017-01-07
We consider the phenomenon of partial migration which is exhibited by populations in which some individuals migrate between habitats during their lifetime, but others do not. First, using an adaptive dynamics approach, we show that partial migration can be explained on the basis of negative density dependence in the per capita fertilities alone, provided that this density dependence is attenuated for increasing abundances of the subtypes that make up the population. We present an exact formula for the optimal proportion of migrants which is expressed in terms of the vital rates of migrant and non-migrant subtypes only. We show that this allocation strategy is both an evolutionary stable strategy (ESS) as well as a convergence stable strategy (CSS). To establish the former, we generalize the classical notion of an ESS because it is based on invasion exponents obtained from linearization arguments, which fail to capture the stabilizing effects of the nonlinear density dependence. These results clarify precisely when the notion of a "weak ESS", as proposed in Lundberg (2013) for a related model, is a genuine ESS. Secondly, we use an evolutionary game theory approach, and confirm, once again, that partial migration can be attributed to negative density dependence alone. In this context, the result holds even when density dependence is not attenuated. In this case, the optimal allocation strategy towards migrants is the same as the ESS stemming from the analysis based on the adaptive dynamics. The key feature of the population models considered here is that they are monotone dynamical systems, which enables a rather comprehensive mathematical analysis.
Learning to adapt: Dynamics of readaptation to geometrical distortions.
Yehezkel, Oren; Sagi, Dov; Sterkin, Anna; Belkin, Michael; Polat, Uri
2010-07-21
The visual system can adapt to optical blur, whereby the adapted image is perceived as sharp. Here we show that adaptation reduces blur-induced biases in shape perception, with repeated adaptations (perceptual learning), leading to unbiased perception upon re-exposure to blur. Observers wore a cylindrical lens of +1.00 D on one eye, thus simulating monocular astigmatism. The other eye was either masked with a translucent blurred lens (monocular) or unmasked (dichoptic). Adaptation was tested in several repeated sessions with a proximity-grouping task, using horizontally or vertically arranged dot-arrays, without feedback, before, after, and throughout the adaptation period. A robust bias in global-orientation judgment was observed with the lens, in accordance with the blur axes. After the observer wore the lens for 2 h, there was no significant change in the bias, but after 4 h, the monocular condition, but not the dichoptic, resulted in reduced bias. The adaptation effect of the monocular 4-h adaptation was preserved, and even improved, when the lens was re-applied the next day, indicating learning. After-effects were observed under all experimental conditions except for the 4-h monocular condition, where learning took place. We suggest that, with long experience, adaptation is transferred to a long-term memory that can be instantly engaged when blur is re-applied, or disengaged when blur is removed, thus leaving no after-effects. The comparison between the monocular and dichoptic conditions indicates a binocular cortical site of plasticity.
Dynamical Approach Study of Spurious Numerics in Nonlinear Computations
NASA Technical Reports Server (NTRS)
Yee, H. C.; Mansour, Nagi (Technical Monitor)
2002-01-01
The last two decades have been an era when computation is ahead of analysis and when very large scale practical computations are increasingly used in poorly understood multiscale complex nonlinear physical problems and non-traditional fields. Ensuring a higher level of confidence in the predictability and reliability (PAR) of these numerical simulations could play a major role in furthering the design, understanding, affordability and safety of our next generation air and space transportation systems, and systems for planetary and atmospheric sciences, and in understanding the evolution and origin of life. The need to guarantee PAR becomes acute when computations offer the ONLY way of solving these types of data limited problems. Employing theory from nonlinear dynamical systems, some building blocks to ensure a higher level of confidence in PAR of numerical simulations have been revealed by the author and world expert collaborators in relevant fields. Five building blocks with supporting numerical examples were discussed. The next step is to utilize knowledge gained by including nonlinear dynamics, bifurcation and chaos theories as an integral part of the numerical process. The third step is to design integrated criteria for reliable and accurate algorithms that cater to the different multiscale nonlinear physics. This includes but is not limited to the construction of appropriate adaptive spatial and temporal discretizations that are suitable for the underlying governing equations. In addition, a multiresolution wavelets approach for adaptive numerical dissipation/filter controls for high speed turbulence, acoustics and combustion simulations will be sought. These steps are corner stones for guarding against spurious numerical solutions that are solutions of the discretized counterparts but are not solutions of the underlying governing equations.
NASA Technical Reports Server (NTRS)
Nguyen, Nhan
2013-01-01
This paper presents the optimal control modification for linear uncertain plants. The Lyapunov analysis shows that the modification parameter has a limiting value depending on the nature of the uncertainty. The optimal control modification exhibits a linear asymptotic property that enables it to be analyzed in a linear time invariant framework for linear uncertain plants. The linear asymptotic property shows that the closed-loop plants in the limit possess a scaled input-output mapping. Using this property, we can derive an analytical closed-loop transfer function in the limit as the adaptive gain tends to infinity. The paper revisits the Rohrs counterexample problem that illustrates the nature of non-robustness of model-reference adaptive control in the presence of unmodeled dynamics. An analytical approach is developed to compute exactly the modification parameter for the optimal control modification that stabilizes the plant in the Rohrs counterexample. The linear asymptotic property is also used to address output feedback adaptive control for non-minimum phase plants with a relative degree 1.
Approach for Using Learner Satisfaction to Evaluate the Learning Adaptation Policy
ERIC Educational Resources Information Center
Jeghal, Adil; Oughdir, Lahcen; Tairi, Hamid; Radouane, Abdelhay
2016-01-01
The learning adaptation is a very important phase in a learning situation in human learning environments. This paper presents the authors' approach used to evaluate the effectiveness of learning adaptive systems. This approach is based on the analysis of learner satisfaction notices collected by a questionnaire on a learning situation; to analyze…
Time-and-Spatially Adapting Simulations for Efficient Dynamic Stall Predictions
2015-09-01
SIMULATIONS FOR EFFICIENTDYNAMIC STALL PREDICTIONS The ability to accurately and efficiently predict the occurrence and severity of dynamic stall...The ability to accurately and efficiently predict the occurrence and severity of dynamic stall remains a major roadblock in the design and analysis...SPATIALLY ADAPTING SIMULATIONS FOR EFFICIENT DYNAMIC STALL PREDICTIONS Marilyn J. Smith Professor Georgia Tech Rohit Jain Aerospace Engineer US Army
O'Connor, Mike; Paci, Emanuele; McIntosh-Smith, Simon; Glowacki, David R
2016-12-22
The past decade has seen the development of a new class of rare event methods in which molecular configuration space is divided into a set of boundaries/interfaces, and then short trajectories are run between boundaries. For all these methods, an important concern is how to generate boundaries. In this paper, we outline an algorithm for adaptively generating boundaries along a free energy surface in multi-dimensional collective variable (CV) space, building on the boxed molecular dynamics (BXD) rare event algorithm. BXD is a simple technique for accelerating the simulation of rare events and free energy sampling which has proven useful for calculating kinetics and free energy profiles in reactive and non-reactive molecular dynamics (MD) simulations across a range of systems, in both NVT and NVE ensembles. Two key developments outlined in this paper make it possible to automate BXD, and to adaptively map free energy and kinetics in complex systems. First, we have generalized BXD to multidimensional CV space. Using strategies from rigid-body dynamics, we have derived a simple and general velocity-reflection procedure that conserves energy for arbitrary collective variable definitions in multiple dimensions, and show that it is straightforward to apply BXD to sampling in multidimensional CV space so long as the Cartesian gradients ∇CV are available. Second, we have modified BXD to undertake on-the-fly statistical analysis during a trajectory, harnessing the information content latent in the dynamics to automatically determine boundary locations. Such automation not only makes BXD considerably easier to use; it also guarantees optimal boundaries, speeding up convergence. We have tested the multidimensional adaptive BXD procedure by calculating the potential of mean force for a chemical reaction recently investigated using both experimental and computational approaches - i.e., F + CD3CN → DF + D2CN in both the gas phase and a strongly coupled explicit CD3CN solvent
Liu, Hu-Chen; Liu, Long; Lin, Qing-Lian; Liu, Nan
2013-06-01
The two most important issues of expert systems are the acquisition of domain experts' professional knowledge and the representation and reasoning of the knowledge rules that have been identified. First, during expert knowledge acquisition processes, the domain expert panel often demonstrates different experience and knowledge from one another and produces different types of knowledge information such as complete and incomplete, precise and imprecise, and known and unknown because of its cross-functional and multidisciplinary nature. Second, as a promising tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) still suffer a couple of deficiencies. The parameters in current FPN models could not accurately represent the increasingly complex knowledge-based systems, and the rules in most existing knowledge inference frameworks could not be dynamically adjustable according to propositions' variation as human cognition and thinking. In this paper, we present a knowledge acquisition and representation approach using the fuzzy evidential reasoning approach and dynamic adaptive FPNs to solve the problems mentioned above. As is illustrated by the numerical example, the proposed approach can well capture experts' diversity experience, enhance the knowledge representation power, and reason the rule-based knowledge more intelligently.
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.
A mathematical programming approach for sequential clustering of dynamic networks
NASA Astrophysics Data System (ADS)
Silva, Jonathan C.; Bennett, Laura; Papageorgiou, Lazaros G.; Tsoka, Sophia
2016-02-01
A common analysis performed on dynamic networks is community structure detection, a challenging problem that aims to track the temporal evolution of network modules. An emerging area in this field is evolutionary clustering, where the community structure of a network snapshot is identified by taking into account both its current state as well as previous time points. Based on this concept, we have developed a mixed integer non-linear programming (MINLP) model, SeqMod, that sequentially clusters each snapshot of a dynamic network. The modularity metric is used to determine the quality of community structure of the current snapshot and the historical cost is accounted for by optimising the number of node pairs co-clustered at the previous time point that remain so in the current snapshot partition. Our method is tested on social networks of interactions among high school students, college students and members of the Brazilian Congress. We show that, for an adequate parameter setting, our algorithm detects the classes that these students belong more accurately than partitioning each time step individually or by partitioning the aggregated snapshots. Our method also detects drastic discontinuities in interaction patterns across network snapshots. Finally, we present comparative results with similar community detection methods for time-dependent networks from the literature. Overall, we illustrate the applicability of mathematical programming as a flexible, adaptable and systematic approach for these community detection problems. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.
Temporal dynamics of tunneling: Hydrodynamic approach
NASA Astrophysics Data System (ADS)
Dekel, G.; Fleurov, V.; Soffer, A.; Stucchio, C.
2007-04-01
We use the hydrodynamic representation of the Gross-Pitaevskii and nonlinear Schrödinger equations in order to analyze the dynamics of macroscopic tunneling processes. We observe a tendency to wave breaking and shock formation during the early stages of the tunneling process. A blip in the density distribution appears on the outskirts of the barrier and under proper conditions it may transform into a bright soliton. Our approach, based on the theory of shock formation in solutions of the Burgers equation, allows us to find the parameters of the ejected blip (or soliton if formed), including the velocity of its propagation. The blip in the density is formed regardless of the value and sign of the nonlinearity parameter. However, a soliton may be formed only if this parameter is negative (attraction) and large enough. A criterion is proposed. An ejection of a soliton is also observed numerically. We demonstrate, theoretically and numerically, controlled formation of a soliton through tunneling. The mass of the ejected soliton is controlled by the initial state.
Students' Adaptation in the Social and Cultural Dynamics
ERIC Educational Resources Information Center
Sadyrin, Vladimir Vitalievich; Potapova, Marina Vladimirovna; Gnatyshina, Elena Alexandrovna; Uvarina, Nataliya Viktorovna; Danilova, Viktoriya Valerievna
2016-01-01
Modern scientific literature views issues on adaptation based on various aspects: biological, medical, pedagogical, sociological, cybernetic, interdisciplinary, etc. The given article is devoted to the analysis of the problem of adaptation as social and psychological phenomenon including peculiarities of its functioning in the conditions of social…
An adaptive finite element approach to modelling sediment laden density currents
NASA Astrophysics Data System (ADS)
Parkinson, S.; Hill, J.; Allison, P. A.; Piggott, M. D.
2012-04-01
, and with significantly shorter run times, using a dynamic adaptive mesh approach.
Discrete adaptive zone light elements (DAZLE): a new approach to adaptive imaging
NASA Astrophysics Data System (ADS)
Kellogg, Robert L.; Escuti, Michael J.
2007-09-01
New advances in Liquid Crystal Spatial Light Modulators (LCSLM) offer opportunities for large adaptive optics in the midwave infrared spectrum. A light focusing adaptive imaging system, using the zero-order diffraction state of a polarizer-free liquid crystal polarization grating modulator to create millions of high transmittance apertures, is envisioned in a system called DAZLE (Discrete Adaptive Zone Light Elements). DAZLE adaptively selects large sets of LCSLM apertures using the principles of coded masks, embodied in a hybrid Discrete Fresnel Zone Plate (DFZP) design. Issues of system architecture, including factors of LCSLM aperture pattern and adaptive control, image resolution and focal plane array (FPA) matching, and trade-offs between filter bandwidths, background photon noise, and chromatic aberration are discussed.
NASA Astrophysics Data System (ADS)
Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang
2016-02-01
Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses
Adaptive Methods within a Sequential Bayesian Approach for Structural Health Monitoring
NASA Astrophysics Data System (ADS)
Huff, Daniel W.
computational burden is decreased significantly and the number of possible observation modes can be increased. Using sensor measurements from real experiments, the overall sequential Bayesian estimation approach, with the adaptive capability of varying the state dynamics and observation modes, is demonstrated for tracking crack damage.
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
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.
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)
Haer, Toon; Aerts, Jeroen
2015-04-01
Between 1998 and 2009, Europe suffered over 213 major damaging floods, causing 1126 deaths, displacing around half a million people. In this period, floods caused at least 52 billion euro in insured economic losses making floods the most costly natural hazard faced in Europe. In many low-lying areas, the main strategy to cope with floods is to reduce the risk of the hazard through flood defence structures, like dikes and levees. However, it is suggested that part of the responsibility for flood protection needs to shift to households and businesses in areas at risk, and that governments and insurers can effectively stimulate the implementation of individual protective measures. However, adaptive behaviour towards flood risk reduction and the interaction between the government, insurers, and individuals has hardly been studied in large-scale flood risk assessments. In this study, an European Agent-Based Model is developed including agent representatives for the administrative stakeholders of European Member states, insurers and reinsurers markets, and individuals following complex behaviour models. The Agent-Based Modelling approach allows for an in-depth analysis of the interaction between heterogeneous autonomous agents and the resulting (non-)adaptive behaviour. Existing flood damage models are part of the European Agent-Based Model to allow for a dynamic response of both the agents and the environment to changing flood risk and protective efforts. By following an Agent-Based Modelling approach this study is a first contribution to overcome the limitations of traditional large-scale flood risk models in which the influence of individual adaptive behaviour towards flood risk reduction is often lacking.
An adaptive remaining energy prediction approach for lithium-ion batteries in electric vehicles
NASA Astrophysics Data System (ADS)
Wang, Yujie; Zhang, Chenbin; Chen, Zonghai
2016-02-01
With the growing number of electric vehicle (EV) applications, the function of the battery management system (BMS) becomes more sophisticated. The accuracy of remaining energy estimation is critical for energy optimization and management in EVs. Therefore the state-of-energy (SoE) is defined to indicate the remaining available energy of the batteries. Considering that there are inevitable accumulated errors caused by current and voltage integral method, an adaptive SoE estimator is first established in this paper. In order to establish a reasonable battery equivalent model, based on the experimental data of the LiFePO4 battery, a data-driven model is established to describe the relationship between the open-circuit voltage (OCV) and the SoE. What is more, the forgetting factor recursive least-square (RLS) method is used for parameter identification to get accurate model parameters. Finally, in order to analyze the robustness and the accuracy of the proposed approach, different types of dynamic current profiles are conducted on the lithium-ion batteries and the performances are calculated and compared. The results indicate that the proposed approach has robust and accurate SoE estimation results under dynamic working conditions.
Kersting, Anna R.; Bornberg-Bauer, Erich; Moore, Andrew D.; Grath, Sonja
2012-01-01
Plant genomes are generally very large, mostly paleopolyploid, and have numerous gene duplicates and complex genomic features such as repeats and transposable elements. Many of these features have been hypothesized to enable plants, which cannot easily escape environmental challenges, to rapidly adapt. Another mechanism, which has recently been well described as a major facilitator of rapid adaptation in bacteria, animals, and fungi but not yet for plants, is modular rearrangement of protein-coding genes. Due to the high precision of profile-based methods, rearrangements can be well captured at the protein level by characterizing the emergence, loss, and rearrangements of protein domains, their structural, functional, and evolutionary building blocks. Here, we study the dynamics of domain rearrangements and explore their adaptive benefit in 27 plant and 3 algal genomes. We use a phylogenomic approach by which we can explain the formation of 88% of all arrangements by single-step events, such as fusion, fission, and terminal loss of domains. We find many domains are lost along every lineage, but at least 500 domains are novel, that is, they are unique to green plants and emerged more or less recently. These novel domains duplicate and rearrange more readily within their genomes than ancient domains and are overproportionally involved in stress response and developmental innovations. Novel domains more often affect regulatory proteins and show a higher degree of structural disorder than ancient domains. Whereas a relatively large and well-conserved core set of single-domain proteins exists, long multi-domain arrangements tend to be species-specific. We find that duplicated genes are more often involved in rearrangements. Although fission events typically impact metabolic proteins, fusion events often create new signaling proteins essential for environmental sensing. Taken together, the high volatility of single domains and complex arrangements in plant genomes
NASA Technical Reports Server (NTRS)
Oakley, David R.; Knight, Norman F., Jr.
1994-01-01
A parallel adaptive dynamic relaxation (ADR) algorithm has been developed for nonlinear structural analysis. This algorithm has minimal memory requirements, is easily parallelizable and scalable to many processors, and is generally very reliable and efficient for highly nonlinear problems. Performance evaluations on single-processor computers have shown that the ADR algorithm is reliable and highly vectorizable, and that it is competitive with direct solution methods for the highly nonlinear problems considered. The present algorithm is implemented on the 512-processor Intel Touchstone DELTA system at Caltech, and it is designed to minimize the extent and frequency of interprocessor communication. The algorithm has been used to solve for the nonlinear static response of two and three dimensional hyperelastic systems involving contact. Impressive relative speedups have been achieved and demonstrate the high scalability of the ADR algorithm. For the class of problems addressed, the ADR algorithm represents a very promising approach for parallel-vector processing.
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.
Vencels, Juris; Delzanno, Gian Luca; Johnson, Alec; ...
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
Dynamics modeling and adaptive control of flexible manipulators
NASA Technical Reports Server (NTRS)
Sasiadek, J. Z.
1991-01-01
An application of Model Reference Adaptive Control (MRAC) to the position and force control of flexible manipulators and robots is presented. A single-link flexible manipulator is analyzed. The problem was to develop a mathematical model of a flexible robot that is accurate. The objective is to show that the adaptive control works better than 'conventional' systems and is suitable for flexible structure control.
ERIC Educational Resources Information Center
Reinschmidt, Kerstin M.; Teufel-Shone, Nicolette I.; Bradford, Gail; Drummond, Rebecca L.; Torres, Emma; Redondo, Floribella; Elenes, Jo Jean; Sanders, Alicia; Gastelum, Sylvia; Moore-Monroy, Martha; Barajas, Salvador; Fernandez, Lourdes; Alvidrez, Rosy; de Zapien, Jill Guernsey; Staten, Lisa K.
2010-01-01
Diabetes health disparities among Hispanic populations have been countered with federally funded health promotion and disease prevention programs. Dissemination has focused on program adaptation to local cultural contexts for greater acceptability and sustainability. Taking a broader approach and drawing on our experience in Mexican American…
Hierarchy-Direction Selective Approach for Locally Adaptive Sparse Grids
Stoyanov, Miroslav K
2013-09-01
We consider the problem of multidimensional adaptive hierarchical interpolation. We use sparse grids points and functions that are induced from a one dimensional hierarchical rule via tensor products. The classical locally adaptive sparse grid algorithm uses an isotropic refinement from the coarser to the denser levels of the hierarchy. However, the multidimensional hierarchy provides a more complex structure that allows for various anisotropic and hierarchy selective refinement techniques. We consider the more advanced refinement techniques and apply them to a number of simple test functions chosen to demonstrate the various advantages and disadvantages of each method. While there is no refinement scheme that is optimal for all functions, the fully adaptive family-direction-selective technique is usually more stable and requires fewer samples.
Highly Dynamic and Adaptive Traffic Congestion Avoidance in Real-Time Inspired by Honey Bee Behavior
NASA Astrophysics Data System (ADS)
Wedde, Horst F.; Lehnhoff, Sebastian; van Bonn, Bernhard; Bay, Z.; Becker, S.; Böttcher, S.; Brunner, C.; Büscher, A.; Fürst, T.; Lazarescu, A. M.; Rotaru, E.; Senge, S.; Steinbach, B.; Yilmaz, F.; Zimmermann, T.
Traffic congestions have become a major problem in metropolitan areas world-wide, within and between cities, to an extent where they make driving and transportation times largely unpredictable. Due to the highly dynamic character of congestion building and dissolving this phenomenon appears even to resist a formal treatment. Static approaches, and even more their global management, have proven counterproductive in practice. Given the latest progress in VANET technology and the remarkable commercially driven efforts like in the European C2C consortium, or the VSC Project in the US, allow meanwhile to tackle various aspects of traffic regulation through VANET communication. In this paper we introduce a novel, completely decentralized multi-agent routing algorithm (termed BeeJamA) which we have derived from the foraging behavior of honey bees. It is highly dynamic, adaptive, robust, and scalable, and it allows for both avoiding congestions, and minimizing traveling times to individual destinations. Vehicle guidance is provided well ahead of every intersection, depending on the individual speeds. Thus strict deadlines are imposed on, and respected by, the BeeJamA algorithm. We report on extensive simulation experiments which show the superior performance of BeeJamA over conventional approaches.
Liu, Zhi; Wang, Fang; Zhang, Yun; Chen, Xin; Chen, C L Philip
2014-10-01
This paper focuses on an input-to-state practical stability (ISpS) problem of nonlinear systems which possess unmodeled dynamics in the presence of unstructured uncertainties and dynamic disturbances. The dynamic disturbances depend on the states and the measured output of the system, and its assumption conditions are relaxed compared with the common restrictions. Based on an input-driven filter, fuzzy logic systems are directly used to approximate the unknown and desired control signals instead of the unknown nonlinear functions, and an integrated backstepping technique is used to design an adaptive output-feedback controller that ensures robustness with respect to unknown parameters and uncertain nonlinearities. This paper, by applying the ISpS theory and the generalized small-gain approach, shows that the proposed adaptive fuzzy controller guarantees the closed-loop system being semi-globally uniformly ultimately bounded. A main advantage of the proposed controller is that it contains only three adaptive parameters that need to be updated online, no matter how many states there are in the systems. Finally, the effectiveness of the proposed approach is illustrated by two simulation examples.
Zhu, Yuanheng; Zhao, Dongbin; Li, Xiangjun
2017-03-01
H∞ control is a powerful method to solve the disturbance attenuation problems that occur in some control systems. The design of such controllers relies on solving the zero-sum game (ZSG). But in practical applications, the exact dynamics is mostly unknown. Identification of dynamics also produces errors that are detrimental to the control performance. To overcome this problem, an iterative adaptive dynamic programming algorithm is proposed in this paper to solve the continuous-time, unknown nonlinear ZSG with only online data. A model-free approach to the Hamilton-Jacobi-Isaacs equation is developed based on the policy iteration method. Control and disturbance policies and value are approximated by neural networks (NNs) under the critic-actor-disturber structure. The NN weights are solved by the least-squares method. According to the theoretical analysis, our algorithm is equivalent to a Gauss-Newton method solving an optimization problem, and it converges uniformly to the optimal solution. The online data can also be used repeatedly, which is highly efficient. Simulation results demonstrate its feasibility to solve the unknown nonlinear ZSG. When compared with other algorithms, it saves a significant amount of online measurement time.
Inference for Optimal Dynamic Treatment Regimes using an Adaptive m-out-of-n Bootstrap Scheme
Chakraborty, Bibhas; Laber, Eric B.; Zhao, Yingqi
2013-01-01
Summary 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 more 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. PMID:23845276
A First Approach to Filament Dynamics
ERIC Educational Resources Information Center
Silva, P. E. S.; de Abreu, F. Vistulo; Simoes, R.; Dias, R. G.
2010-01-01
Modelling elastic filament dynamics is a topic of high interest due to the wide range of applications. However, it has reached a high level of complexity in the literature, making it unaccessible to a beginner. In this paper we explain the main steps involved in the computational modelling of the dynamics of an elastic filament. We first derive…
Approaches for Resolving Dynamic IP Addressing.
ERIC Educational Resources Information Center
Foo, Schubert; Hui, Siu Cheung; Yip, See Wai; He, Yulan
1997-01-01
A problem with dynamic Internet protocol (IP) addressing arises when the Internet connection is through an Internet provider since the IP address is allocated only at connection time. This article examines a number of online and offline methods for resolving the problem. Suggests dynamic domain name system (DNS) and directory service look-up are…
Kampfner, Roberto R
2006-07-01
The structure of a system influences its adaptability. An important result of adaptability theory is that subsystem independence increases adaptability [Conrad, M., 1983. Adaptability. Plenum Press, New York]. Adaptability is essential in systems that face an uncertain environment such as biological systems and organizations. Modern organizations are the product of human design. And so it is their structure and the effect that it has on their adaptability. In this paper we explore the potential effects of computer-based information processing on the adaptability of organizations. The integration of computer-based processes into the dynamics of the functions they support and the effect it has on subsystem independence are especially relevant to our analysis.
The Canadian approach to the settlement and adaptation of immigrants.
1986-01-01
Canada has been the host to over 400,000 refugees since World War II. The settlement and adaptation process is supported by the federal government and by the majority of provincial governments. Under the national and regional Employment and Immigration Commission CEIC) settlement organizations the major programs administered to effect the adaptation of newcomers are: 1) the Adjustment Assistance Program, 2) the Immigrant Settlement and Adaptation Program, 3) the Language/Skill Training Program, and 4) the Employment Services Program. Ontario, the recipient of more than 1/2 the newcomers that arrive in Canada each year, pursues active programs in the reception of newcomers through their Welcome House Program which offers a wide range of reception services to the newcomers. The employment and unemployment experiences of refugees is very much influenced by the prevailing labor market conditions, the refugees' proficiency in the country's official languages, the amount of sympathy evoked by the media reports on the plight of refugees, the availability of people of the same ethnic origin already well settled in the country, and the adaptability of the refugees themselves. The vast majority of refugee groups that came to Canada during the last 1/4 century seem to have adjusted well economically, despite having had difficulty in entering the occupations they intended to join. It is calculated that an average of $6607 per arrival is needed to cover the CEIC program costs of 1983-1984.
The Detroit Approach to Adapted Physical Education and Recreation.
ERIC Educational Resources Information Center
Elkins, Bruce; Czapski, Stephen
The report describes Detroit's Adaptive Physical Education Consortium Project in Michigan. Among the main objectives of the project are to coordinate all physical education and recreation services to the handicapped in the Detroit area; to facilitate the mainstreaming of capable handicapped individuals into existing "regular" physical…
Enhancing Adaptive Filtering Approaches for Land Data Assimilation Systems
Technology Transfer Automated Retrieval System (TEKTRAN)
Recent work has presented the initial application of adaptive filtering techniques to land surface data assimilation systems. Such techniques are motivated by our current lack of knowledge concerning the structure of large-scale error in either land surface modeling output or remotely-sensed estima...
Adaptive E-Learning Environments: Research Dimensions and Technological Approaches
ERIC Educational Resources Information Center
Di Bitonto, Pierpaolo; Roselli, Teresa; Rossano, Veronica; Sinatra, Maria
2013-01-01
One of the most closely investigated topics in e-learning research has always been the effectiveness of adaptive learning environments. The technological evolutions that have dramatically changed the educational world in the last six decades have allowed ever more advanced and smarter solutions to be proposed. The focus of this paper is to depict…
An Intelligent Tutoring System Approach to Adaptive Instructional Systems
2005-09-01
gr prototypes (scripts) (Schank, 1977). Many software systems have been developed that employ these representations. Many instructional theories and...specific performance or skill-acquisition. However, there are many theories about the number and type of these general abilities. Researchers...Computer Generated Forces and Behavioral Representations. 7.1.2 Role of MAMID in an Adaptive Instructional System Motivation Currently, the student
A Monte Carlo Approach for Adaptive Testing with Content Constraints
ERIC Educational Resources Information Center
Belov, Dmitry I.; Armstrong, Ronald D.; Weissman, Alexander
2008-01-01
This article presents a new algorithm for computerized adaptive testing (CAT) when content constraints are present. The algorithm is based on shadow CAT methodology to meet content constraints but applies Monte Carlo methods and provides the following advantages over shadow CAT: (a) lower maximum item exposure rates, (b) higher utilization of the…
Detection of synchronization between chaotic signals: An adaptive similarity-based approach
NASA Astrophysics Data System (ADS)
Chen, Shyan-Shiou; Chen, Li-Fen; Wu, Yu-Te; Wu, Yu-Zu; Lee, Po-Lei; Yeh, Tzu-Chen; Hsieh, Jen-Chuen
2007-12-01
We present an adaptive similarity-based approach to detect generalized synchronization (GS) with n:m phase synchronization (PS), where n and m are integers and one of them is 1. This approach is based on the similarity index (SI) and Gaussian mixture model with the minimum description length criterion. The clustering method, which is shown to be superior to the closeness and connectivity of a continuous function, is employed in this study to detect the existence of GS with n:m PS. We conducted a computer simulation and a finger-lifting experiment to illustrate the effectiveness of the proposed method. In the simulation of a Rössler-Lorenz system, our method outperformed the conventional SI, and GS with 2:1 PS within the coupled system was found. In the experiment of self-paced finger-lifting movement, cortico-muscular GS with 1:2 and 1:3 PS was found between the surface electromyogram signals on the first dorsal interossei muscle and the magnetoencephalographic data in the motor area. The GS with n:m PS ( n or m=1 ) has been simultaneously resolved from both simulation and experiment. The proposed approach thereby provides a promising means for advancing research into both nonlinear dynamics and brain science.
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.
Insider Threat Mitigation Project: A Dynamic Network Approach (Poster)
2014-10-23
OCT 2014 2. REPORT TYPE N/A 3. DATES COVERED 4. TITLE AND SUBTITLE Insider Threat Mitigation Project: A Dynamic Network Approach 5a...Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 Insider Threat Mitigation Project A Dynamic Network Approach Approach: • Semi-automated coding...to- external communication • Remove suspected distribution lists • Identify “normal behavior” using Enron • Develop pattern for “ insiders ” in
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.
Terminal Dynamics Approach to Discrete Event Systems
NASA Technical Reports Server (NTRS)
Zak, Michail; Meyers, Ronald
1995-01-01
This paper presents and discusses a mathematical formalism for simulation of discrete event dynamic (DED)-a special type of 'man-made' systems to serve specific purposes of information processing. The main objective of this work is to demonstrate that the mathematical formalism for DED can be based upon a terminal model of Newtonian dynamics which allows one to relax Lipschitz conditions at some discrete points.!.
Dissociating conflict adaptation from feature integration: a multiple regression approach.
Notebaert, Wim; Verguts, Tom
2007-10-01
Congruency effects are typically smaller after incongruent than after congruent trials. One explanation is in terms of higher levels of cognitive control after detection of conflict (conflict adaptation; e.g., M. M. Botvinick, T. S. Braver, D. M. Barch, C. S. Carter, & J. D. Cohen, 2001). An alternative explanation for these results is based on feature repetition and/or integration effects (e.g., B. Hommel, R. W. Proctor, & K.-P. Vu, 2004; U. Mayr, E. Awh, & P. Laurey, 2003). Previous attempts to dissociate feature integration from conflict adaptation focused on a particular subset of the data in which feature transitions were held constant (J. G. Kerns et al., 2004) or in which congruency transitions were held constant (C. Akcay & E. Hazeltine, in press), but this has a number of disadvantages. In this article, the authors present a multiple regression solution for this problem and discuss its possibilities and pitfalls.
A Kalman filter approach to adaptive estimation of multispectral signatures
NASA Technical Reports Server (NTRS)
Crane, R. B.
1973-01-01
The signatures of remote sensing data from agricultural crops exhibit significant non-stationarity, so that the performance of fixed parameter classifiers degenerates with time and distance from the initial training data. A class of adaptive decision-directed classifiers are being developed, based on Kalman filter theory. Limited results to date on two data sets indicate approximately a 25 to 40% reduction in rates of misclassification.
Yoo, Sung Jin; Park, Jin Bae; Choi, Yoon Ho
2006-12-01
A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system.
A regional approach to climate adaptation in the Nile Basin
NASA Astrophysics Data System (ADS)
Butts, Michael B.; Buontempo, Carlo; Lørup, Jens K.; Williams, Karina; Mathison, Camilla; Jessen, Oluf Z.; Riegels, Niels D.; Glennie, Paul; McSweeney, Carol; Wilson, Mark; Jones, Richard; Seid, Abdulkarim H.
2016-10-01
The Nile Basin is one of the most important shared basins in Africa. Managing and developing the water resources within the basin must not only address different water uses but also the trade-off between developments upstream and water use downstream, often between different countries. Furthermore, decision-makers in the region need to evaluate and implement climate adaptation measures. Previous work has shown that the Nile flows can be highly sensitive to climate change and that there is considerable uncertainty in climate projections in the region with no clear consensus as to the direction of change. Modelling current and future changes in river runoff must address a number of challenges; including the large size of the basin, the relative scarcity of data, and the corresponding dramatic variety of climatic conditions and diversity in hydrological characteristics. In this paper, we present a methodology, to support climate adaptation on a regional scale, for assessing climate change impacts and adaptation potential for floods, droughts and water scarcity within the basin.
The adaptive significance of adult neurogenesis: an integrative approach
Konefal, Sarah; Elliot, Mick; Crespi, Bernard
2013-01-01
Adult neurogenesis in mammals is predominantly restricted to two brain regions, the dentate gyrus (DG) of the hippocampus and the olfactory bulb (OB), suggesting that these two brain regions uniquely share functions that mediate its adaptive significance. Benefits of adult neurogenesis across these two regions appear to converge on increased neuronal and structural plasticity that subserves coding of novel, complex, and fine-grained information, usually with contextual components that include spatial positioning. By contrast, costs of adult neurogenesis appear to center on potential for dysregulation resulting in higher risk of brain cancer or psychological dysfunctions, but such costs have yet to be quantified directly. The three main hypotheses for the proximate functions and adaptive significance of adult neurogenesis, pattern separation, memory consolidation, and olfactory spatial, are not mutually exclusive and can be reconciled into a simple general model amenable to targeted experimental and comparative tests. Comparative analysis of brain region sizes across two major social-ecological groups of primates, gregarious (mainly diurnal haplorhines, visually-oriented, and in large social groups) and solitary (mainly noctural, territorial, and highly reliant on olfaction, as in most rodents) suggest that solitary species, but not gregarious species, show positive associations of population densities and home range sizes with sizes of both the hippocampus and OB, implicating their functions in social-territorial systems mediated by olfactory cues. Integrated analyses of the adaptive significance of adult neurogenesis will benefit from experimental studies motivated and structured by ecologically and socially relevant selective contexts. PMID:23882188
Mega-evolutionary dynamics of the adaptive radiation of birds.
Cooney, Christopher R; Bright, Jen A; Capp, Elliot J R; Chira, Angela M; Hughes, Emma C; Moody, Christopher J A; Nouri, Lara O; Varley, Zoë K; Thomas, Gavin H
2017-02-16
The origin and expansion of biological diversity is regulated by both developmental trajectories and limits on available ecological niches. As lineages diversify, an early and often rapid phase of species and trait proliferation gives way to evolutionary slow-downs as new species pack into ever more densely occupied regions of ecological niche space. Small clades such as Darwin's finches demonstrate that natural selection is the driving force of adaptive radiations, but how microevolutionary processes scale up to shape the expansion of phenotypic diversity over much longer evolutionary timescales is unclear. Here we address this problem on a global scale by analysing a crowdsourced dataset of three-dimensional scanned bill morphology from more than 2,000 species. We find that bill diversity expanded early in extant avian evolutionary history, before transitioning to a phase dominated by packing of morphological space. However, this early phenotypic diversification is decoupled from temporal variation in evolutionary rate: rates of bill evolution vary among lineages but are comparatively stable through time. We find that rare, but major, discontinuities in phenotype emerge from rapid increases in rate along single branches, sometimes leading to depauperate clades with unusual bill morphologies. Despite these jumps between groups, the major axes of within-group bill-shape evolution are remarkably consistent across birds. We reveal that macroevolutionary processes underlying global-scale adaptive radiations support Darwinian and Simpsonian ideas of microevolution within adaptive zones and accelerated evolution between distinct adaptive peaks.
New developments in adaptive methods for computational fluid dynamics
NASA Technical Reports Server (NTRS)
Oden, J. T.; Bass, Jon M.
1990-01-01
New developments in a posteriori error estimates, smart algorithms, and h- and h-p adaptive finite element methods are discussed in the context of two- and three-dimensional compressible and incompressible flow simulations. Applications to rotor-stator interaction, rotorcraft aerodynamics, shock and viscous boundary layer interaction and fluid-structure interaction problems are discussed.
Adaptive and Optimal Control of Stochastic Dynamical Systems
2015-09-14
control and stochastic differential games . Stochastic linear-quadratic, continuous time, stochastic control problems are solved for systems with noise...control problems for systems with arbitrary correlated n 15. SUBJECT TERMS Adaptive control, optimal control, stochastic differential games 16. SECURITY...explicit results have been obtained for problems of stochastic control and stochastic differential games . Stochastic linear- quadratic, continuous time
ERIC Educational Resources Information Center
Mao, Xiuzhen; Xin, Tao
2013-01-01
The Monte Carlo approach which has previously been implemented in traditional computerized adaptive testing (CAT) is applied here to cognitive diagnostic CAT to test the ability of this approach to address multiple content constraints. The performance of the Monte Carlo approach is compared with the performance of the modified maximum global…
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
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
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.
An Adaptive Approach to Managing Knowledge Development in a Project-Based Learning Environment
ERIC Educational Resources Information Center
Tilchin, Oleg; Kittany, Mohamed
2016-01-01
In this paper we propose an adaptive approach to managing the development of students' knowledge in the comprehensive project-based learning (PBL) environment. Subject study is realized by two-stage PBL. It shapes adaptive knowledge management (KM) process and promotes the correct balance between personalized and collaborative learning. The…
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.
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.
NASA Astrophysics Data System (ADS)
Qin, Chunbin; Zhang, Huaguang; Luo, Yanhong
2014-05-01
In this paper, a novel theoretic formulation based on adaptive dynamic programming (ADP) is developed to solve online the optimal tracking problem of the continuous-time linear system with unknown dynamics. First, the original system dynamics and the reference trajectory dynamics are transformed into an augmented system. Then, under the same performance index with the original system dynamics, an augmented algebraic Riccati equation is derived. Furthermore, the solutions for the optimal control problem of the augmented system are proven to be equal to the standard solutions for the optimal tracking problem of the original system dynamics. Moreover, a new online algorithm based on the ADP technique is presented to solve the optimal tracking problem of the linear system with unknown system dynamics. Finally, simulation results are given to verify the effectiveness of the theoretic results.
A residual flexibility approach to multibody dynamics
NASA Technical Reports Server (NTRS)
Blelloch, Paul A.; Antal, Gregory W.
1993-01-01
Many complex systems can be modeled as a collection of interacting bodies, where the relative motion of the bodies may be large. The dynamics of such systems are simulated using multibody dynamic formulations. Many of these treat each body as a rigid component, but recently the flexibility of the components has been incorporated. This paper presents a residual flexibility formulation of the multibody dynamics problem. The formulation is very simple and offers great computational efficiency since it treats each body as a free structure in space, interacting with other bodies only through interface forces. Each body's accelerations can be solved independently, as can each set of interface forces. We have applied the technique successfully to several special applications, and the initial implementation in a general mechanisms code has given excellent results in comparison to a direct finite element representation of flexibility.
Nonlinear dynamical system approaches towards neural prosthesis
Torikai, Hiroyuki; Hashimoto, Sho
2011-04-19
An asynchronous discrete-state spiking neurons is a wired system of shift registers that can mimic nonlinear dynamics of an ODE-based neuron model. The control parameter of the neuron is the wiring pattern among the registers and thus they are suitable for on-chip learning. In this paper an asynchronous discrete-state spiking neuron is introduced and its typical nonlinear phenomena are demonstrated. Also, a learning algorithm for a set of neurons is presented and it is demonstrated that the algorithm enables the set of neurons to reconstruct nonlinear dynamics of another set of neurons with unknown parameter values. The learning function is validated by FPGA experiments.
An object-oriented approach for parallel self adaptive mesh refinement on block structured grids
NASA Technical Reports Server (NTRS)
Lemke, Max; Witsch, Kristian; Quinlan, Daniel
1993-01-01
Self-adaptive mesh refinement dynamically matches the computational demands of a solver for partial differential equations to the activity in the application's domain. In this paper we present two C++ class libraries, P++ and AMR++, which significantly simplify the development of sophisticated adaptive mesh refinement codes on (massively) parallel distributed memory architectures. The development is based on our previous research in this area. The C++ class libraries provide abstractions to separate the issues of developing parallel adaptive mesh refinement applications into those of parallelism, abstracted by P++, and adaptive mesh refinement, abstracted by AMR++. P++ is a parallel array class library to permit efficient development of architecture independent codes for structured grid applications, and AMR++ provides support for self-adaptive mesh refinement on block-structured grids of rectangular non-overlapping blocks. Using these libraries, the application programmers' work is greatly simplified to primarily specifying the serial single grid application and obtaining the parallel and self-adaptive mesh refinement code with minimal effort. Initial results for simple singular perturbation problems solved by self-adaptive multilevel techniques (FAC, AFAC), being implemented on the basis of prototypes of the P++/AMR++ environment, are presented. Singular perturbation problems frequently arise in large applications, e.g. in the area of computational fluid dynamics. They usually have solutions with layers which require adaptive mesh refinement and fast basic solvers in order to be resolved efficiently.
Luo, Shaohua; Wu, Songli; Gao, Ruizhen
2015-07-15
This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.
NASA Astrophysics Data System (ADS)
Luo, Shaohua; Wu, Songli; Gao, Ruizhen
2015-07-01
This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.
Luo, Shaohua; Wu, Songli; Gao, Ruizhen
2015-07-01
This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.
Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales
Xiu, Dongbin
2016-06-21
The focus of the project is the development of mathematical methods and high-performance com- putational tools for stochastic simulations, with a particular emphasis on computations on extreme scales. The core of the project revolves around the design of highly e cient and scalable numer- ical algorithms that can adaptively and accurately, in high dimensional spaces, resolve stochastic problems with limited smoothness, even containing discontinuities.
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.
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.
Arévalo, Orlando; Bornschlegl, Mona A.; Eberhardt, Sven; Ernst, Udo; Pawelzik, Klaus; Fahle, Manfred
2013-01-01
In everyday life, humans interact with a dynamic environment often requiring rapid adaptation of visual perception and motor control. In particular, new visuo–motor mappings must be learned while old skills have to be kept, such that after adaptation, subjects may be able to quickly change between two different modes of generating movements (‘dual–adaptation’). A fundamental question is how the adaptation schedule determines the acquisition speed of new skills. Given a fixed number of movements in two different environments, will dual–adaptation be faster if switches (‘phase changes’) between the environments occur more frequently? We investigated the dynamics of dual–adaptation under different training schedules in a virtual pointing experiment. Surprisingly, we found that acquisition speed of dual visuo–motor mappings in a pointing task is largely independent of the number of phase changes. Next, we studied the neuronal mechanisms underlying this result and other key phenomena of dual–adaptation by relating model simulations to experimental data. We propose a simple and yet biologically plausible neural model consisting of a spatial mapping from an input layer to a pointing angle which is subjected to a global gain modulation. Adaptation is performed by reinforcement learning on the model parameters. Despite its simplicity, the model provides a unifying account for a broad range of experimental data: It quantitatively reproduced the learning rates in dual–adaptation experiments for both direct effect, i.e. adaptation to prisms, and aftereffect, i.e. behavior after removal of prisms, and their independence on the number of phase changes. Several other phenomena, e.g. initial pointing errors that are far smaller than the induced optical shift, were also captured. Moreover, the underlying mechanisms, a local adaptation of a spatial mapping and a global adaptation of a gain factor, explained asymmetric spatial transfer and generalization of
A shape dynamical approach to holographic renormalization
NASA Astrophysics Data System (ADS)
Gomes, Henrique; Gryb, Sean; Koslowski, Tim; Mercati, Flavio; Smolin, Lee
2015-01-01
We provide a bottom-up argument to derive some known results from holographic renormalization using the classical bulk-bulk equivalence of General Relativity and Shape Dynamics, a theory with spatial conformal (Weyl) invariance. The purpose of this paper is twofold: (1) to advertise the simple classical mechanism, trading off gauge symmetries, that underlies the bulk-bulk equivalence of General Relativity and Shape Dynamics to readers interested in dualities of the type of AdS/conformal field theory (CFT); and (2) to highlight that this mechanism can be used to explain certain results of holographic renormalization, providing an alternative to the AdS/CFT conjecture for these cases. To make contact with the usual semiclassical AdS/CFT correspondence, we provide, in addition, a heuristic argument that makes it plausible that the classical equivalence between General Relativity and Shape Dynamics turns into a duality between radial evolution in gravity and the renormalization group flow of a CFT. We believe that Shape Dynamics provides a new perspective on gravity by giving conformal structure a primary role within the theory. It is hoped that this work provides the first steps toward understanding what this new perspective may be able to teach us about holographic dualities.
Making CORBA objects persistent: The object database adapter approach
Reverbel, F.C.R.
1997-05-01
In spite of its remarkable successes in promoting standards for distributed object systems, the Object Management Group (OMG) has not yet settled the issue of object persistence in the Object Request Broker (ORB) environment. The Common Object Request Broker Architecture (CORBA) specification briefly mentions an Object-Oriented Database Adapter that makes objects stored in an object-oriented database accessible through the ORB. This idea is pursued in the Appendix B of the ODMG standard, which identifies a number of issues involved in using an Object Database Management System (ODBMS) in a CORBA environment, and proposes an Object Database Adapter (ODA) to realize the integration of the ORB with the ODBMS. This paper discusses the design and implementation of an ODA that integrates an ORB and an ODBMS with C++ bindings. For the author`s purposes, an ODBMS is a system with programming interfaces. It may be a pure object-oriented DBMS (an OODBMS), or a combination of a relational DBMS and an object-relational mapper.
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.
De Stefano, Lucia; Hernández-Mora, Nuria; Iglesias, Ana; Sánchez, Berta
2016-11-08
The uncertainty associated with the definition of strategies for climate change adaptation poses a challenge that cannot be faced by science alone. We present a participatory experience where, instead of having science defining solutions and eliciting stakeholders' feedback, local actors actually drove the process. While principles and methods of the approach are easily adaptable to different local contexts, this paper shows the contribution of participatory dynamics to the design of adaptation measures in the biodiversity-rich socio-ecological region surrounding the Doñana wetlands (Southern Spain). During the process, stakeholders and scientists collaboratively designed a common scenario for the future in which to define and assess a portfolio of potential adaptation measures, and found a safe, informal space for open dialogue and information exchange. Through this dialogue, points of connection among local actors emerged around the need for more integrated, transparent design of adaptation measures; for strengthening local capacity; and for strategies to diversify economic activities in order to increase the resilience of the region.
An Evidence-Based Public Health Approach to Climate Change Adaptation
Eidson, Millicent; Tlumak, Jennifer E.; Raab, Kristin K.; Luber, George
2014-01-01
Background: Public health is committed to evidence-based practice, yet there has been minimal discussion of how to apply an evidence-based practice framework to climate change adaptation. Objectives: Our goal was to review the literature on evidence-based public health (EBPH), to determine whether it can be applied to climate change adaptation, and to consider how emphasizing evidence-based practice may influence research and practice decisions related to public health adaptation to climate change. Methods: We conducted a substantive review of EBPH, identified a consensus EBPH framework, and modified it to support an EBPH approach to climate change adaptation. We applied the framework to an example and considered implications for stakeholders. Discussion: A modified EBPH framework can accommodate the wide range of exposures, outcomes, and modes of inquiry associated with climate change adaptation and the variety of settings in which adaptation activities will be pursued. Several factors currently limit application of the framework, including a lack of higher-level evidence of intervention efficacy and a lack of guidelines for reporting climate change health impact projections. To enhance the evidence base, there must be increased attention to designing, evaluating, and reporting adaptation interventions; standardized health impact projection reporting; and increased attention to knowledge translation. This approach has implications for funders, researchers, journal editors, practitioners, and policy makers. Conclusions: The current approach to EBPH can, with modifications, support climate change adaptation activities, but there is little evidence regarding interventions and knowledge translation, and guidelines for projecting health impacts are lacking. Realizing the goal of an evidence-based approach will require systematic, coordinated efforts among various stakeholders. Citation: Hess JJ, Eidson M, Tlumak JE, Raab KK, Luber G. 2014. An evidence-based public
Adaptation in hindsight: dynamics and drivers shaping urban wastewater systems.
Neumann, Marc B; Rieckermann, Jörg; Hug, Thomas; Gujer, Willi
2015-03-15
Well-planned urban infrastructure should meet critical loads during its design lifetime. In order to proceed with design, engineers are forced to make numerous assumptions with very little supporting information about the development of various drivers. For the wastewater sector, these drivers include the future amount and composition of the generated wastewater, effluent requirements, technologies, prices of inputs such as energy or chemicals, and the value of outputs produced such as nutrients for fertilizer use. When planning wastewater systems, there is a lack of methods to address discrepancies between the timescales at which fundamental changes in these drivers can occur, and the long physical life expectancy of infrastructure (on the order of 25-80 years). To explore these discrepancies, we take a hindsight perspective of the long-term development of wastewater infrastructure and assess the stability of assumptions made during previous designs. Repeatedly we find that the drivers influencing wastewater loads, environmental requirements or technological innovation can change at smaller timescales than the infrastructure design lifetime, often in less than a decade. Our analysis shows that i) built infrastructure is continuously confronted with challenges it was not conceived for, ii) significant adaptation occurs during a structure's lifetime, and iii) "muddling-through" is the pre-dominant strategy for adaptive management. As a consequence, we argue, there is a need to explore robust design strategies which require the systematic use of scenario planning methods and instruments to increase operational, structural, managerial, institutional and financial flexibility. Hindsight studies, such as this one, may inform the development of robust design strategies and assist in the transition to more explicit forms of adaptive management for urban infrastructures.
Uncertain dynamical systems: A differential game approach
NASA Technical Reports Server (NTRS)
Gutman, S.
1976-01-01
A class of dynamical systems in a conflict situation is formulated and discussed, and the formulation is applied to the study of an important class of systems in the presence of uncertainty. The uncertainty is deterministic and the only assumption is that its value belongs to a known compact set. Asymptotic stability is fully discussed with application to variable structure and model reference control systems.
Assessing the Dynamic Behavior of Online Q&A Knowledge Markets: A System Dynamics Approach
ERIC Educational Resources Information Center
Jafari, Mostafa; Hesamamiri, Roozbeh; Sadjadi, Jafar; Bourouni, Atieh
2012-01-01
Purpose: The objective of this paper is to propose a holistic dynamic model for understanding the behavior of a complex and internet-based kind of knowledge market by considering both social and economic interactions. Design/methodology/approach: A system dynamics (SD) model is formulated in this study to investigate the dynamic characteristics of…
Quantum electron-vibrational dynamics at finite temperature: Thermo field dynamics approach.
Borrelli, Raffaele; Gelin, Maxim F
2016-12-14
Quantum electron-vibrational dynamics in molecular systems at finite temperature is described using an approach based on the thermo field dynamics theory. This formulation treats temperature effects in the Hilbert space without introducing the Liouville space. A comparison with the theoretically equivalent density matrix formulation shows the key numerical advantages of the present approach. The solution of thermo field dynamics equations with a novel technique for the propagation of tensor trains (matrix product states) is discussed. Numerical applications to model spin-boson systems show that the present approach is a promising tool for the description of quantum dynamics of complex molecular systems at finite temperature.
An adaptive deep learning approach for PPG-based identification.
Jindal, V; Birjandtalab, J; Pouyan, M Baran; Nourani, M
2016-08-01
Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.
Systems and Methods for Parameter Dependent Riccati Equation Approaches to Adaptive Control
NASA Technical Reports Server (NTRS)
Kim, Kilsoo (Inventor); Yucelen, Tansel (Inventor); Calise, Anthony J. (Inventor)
2015-01-01
Systems and methods for adaptive control are disclosed. The systems and methods can control uncertain dynamic systems. The control system can comprise a controller that employs a parameter dependent Riccati equation. The controller can produce a response that causes the state of the system to remain bounded. The control system can control both minimum phase and non-minimum phase systems. The control system can augment an existing, non-adaptive control design without modifying the gains employed in that design. The control system can also avoid the use of high gains in both the observer design and the adaptive control law.
Adaptive optimal spectral range for dynamically changing scene
NASA Astrophysics Data System (ADS)
Pinsky, Ephi; Siman-tov, Avihay; Peles, David
2012-06-01
A novel multispectral video system that continuously optimizes both its spectral range channels and the exposure time of each channel autonomously, under dynamic scenes, varying from short range-clear scene to long range-poor visibility, is currently being developed. Transparency and contrast of high scattering medium of channels with spectral ranges in the near infrared is superior to the visible channels, particularly to the blue range. Longer wavelength spectral ranges that induce higher contrast are therefore favored. Images of 3 spectral channels are fused and displayed for (pseudo) color visualization, as an integrated high contrast video stream. In addition to the dynamic optimization of the spectral channels, optimal real-time exposure time is adjusted simultaneously and autonomously for each channel. A criterion of maximum average signal, derived dynamically from previous frames of the video stream is used (Patent Application - International Publication Number: WO2009/093110 A2, 30.07.2009). This configuration enables dynamic compatibility with the optimal exposure time of a dynamically changing scene. It also maximizes the signal to noise ratio and compensates each channel for the specified value of daylight reflections and sensors response for each spectral range. A possible implementation is a color video camera based on 4 synchronized, highly responsive, CCD imaging detectors, attached to a 4CCD dichroic prism and combined with a common, color corrected, lens. Principal Components Analysis (PCA) technique is then applied for real time "dimensional collapse" in color space, in order to select and fuse, for clear color visualization, the 3 most significant principal channels out of at least 4 characterized by high contrast and rich details in the image data.
Lafaye, Murielle; Sall, Baba; Ndiaye, Youssou; Vignolles, Cecile; Tourre, Yves M; Borchi, Franc Ois; Soubeyroux, Jean-Michel; Diallo, Mawlouth; Dia, Ibrahima; Ba, Yamar; Faye, Abdoulaye; Ba, Taibou; Ka, Alioune; Ndione, Jacques-André; Gauthier, Hélène; Lacaux, Jean-Pierre
2013-11-01
The multi-disciplinary French project "Adaptation à la Fiévre de la Vallée du Rift" (AdaptFVR) has concluded a 10-year constructive interaction between many scientists/partners involved with the Rift Valley fever (RVF) dynamics in Senegal. The three targeted objectives reached were (i) to produce--in near real-time--validated risk maps for parked livestock exposed to RVF mosquitoes/vectors bites; (ii) to assess the impacts on RVF vectors from climate variability at different time-scales including climate change; and (iii) to isolate processes improving local livestock management and animal health. Based on these results, concrete, pro-active adaptive actions were taken on site, which led to the establishment of a RVF early warning system (RVFews). Bulletins were released in a timely fashion during the project, tested and validated in close collaboration with the local populations, i.e. the primary users. Among the strategic, adaptive methods developed, conducted and evaluated in terms of cost/benefit analyses are the larvicide campaigns and the coupled bio-mathematical (hydrological and entomological) model technologies, which are being transferred to the staff of the "Centre de Suivi Ecologique" (CSE) in Dakar during 2013. Based on the results from the AdaptFVR project, other projects with similar conceptual and modelling approaches are currently being implemented, e.g. for urban and rural malaria and dengue in the French Antilles.
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
A qualitative approach for recovering relative depths in dynamic scenes
NASA Technical Reports Server (NTRS)
Haynes, S. M.; Jain, R.
1987-01-01
This approach to dynamic scene analysis is a qualitative one. It computes relative depths using very general rules. The depths calculated are qualitative in the sense that the only information obtained is which object is in front of which others. The motion is qualitative in the sense that the only required motion data is whether objects are moving toward or away from the camera. Reasoning, which takes into account the temporal character of the data and the scene, is qualitative. This approach to dynamic scene analysis can tolerate imprecise data because in dynamic scenes the data are redundant.
System identification based approach to dynamic weighing revisited
NASA Astrophysics Data System (ADS)
Niedźwiecki, Maciej; Meller, Michał; Pietrzak, Przemysław
2016-12-01
Dynamic weighing, i.e., weighing of objects in motion, without stopping them on the weighing platform, allows one to increase the rate of operation of automatic weighing systems, used in industrial production processes, without compromising their accuracy. Since the classical identification-based approach to dynamic weighing, based on the second-order mass-spring-damper model of the weighing system, does not yield satisfactory results when applied to conveyor belt type checkweighers, several extensions of this technique are examined. Experiments confirm that when appropriately modified the identification-based approach becomes a reliable tool for dynamic mass measurement in checkweighers.
Adaptation of a weighted regression approach to evaluate water quality trends in anestuary
To improve the description of long-term changes in water quality, a weighted regression approach developed to describe trends in pollutant transport in rivers was adapted to analyze a long-term water quality dataset from Tampa Bay, Florida. The weighted regression approach allows...
Adaptation of a Weighted Regression Approach to Evaluate Water Quality Trends in an Estuary
To improve the description of long-term changes in water quality, we adapted a weighted regression approach to analyze a long-term water quality dataset from Tampa Bay, Florida. The weighted regression approach, originally developed to resolve pollutant transport trends in rivers...
A Hybrid Approach for Supporting Adaptivity in E-Learning Environments
ERIC Educational Resources Information Center
Al-Omari, Mohammad; Carter, Jenny; Chiclana, Francisco
2016-01-01
Purpose: The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the event-condition-action (ECA) model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity…
ERIC Educational Resources Information Center
Greenspan, Stanley I.; Lourie, Reginald S.
This paper applies a developmental structuralist approach to the classification of adaptive and pathologic personality organizations and behavior in infancy and early childhood, and it discusses implications of this approach for preventive intervention. In general, as development proceeds, the structural capacity of the developing infant and child…
Adaptation to floods in future climate: a practical approach
NASA Astrophysics Data System (ADS)
Doroszkiewicz, Joanna; Romanowicz, Renata; Radon, Radoslaw; Hisdal, Hege
2016-04-01
In this study some aspects of the application of the 1D hydraulic model are discussed with a focus on its suitability for flood adaptation under future climate conditions. The Biała Tarnowska catchment is used as a case study. A 1D hydraulic model is developed for the evaluation of inundation extent and risk maps in future climatic conditions. We analyse the following flood indices: (i) extent of inundation area; (ii) depth of water on flooded land; (iii) the flood wave duration; (iv) the volume of a flood wave over the threshold value. In this study we derive a model cross-section geometry following the results of primary research based on a 500-year flood inundation extent. We compare two methods of localisation of cross-sections from the point of view of their suitability to the derivation of the most precise inundation outlines. The aim is to specify embankment heights along the river channel that would protect the river valley in the most vulnerable locations under future climatic conditions. We present an experimental design for scenario analysis studies and uncertainty reduction options for future climate projections obtained from the EUROCORDEX project. Acknowledgements: This work was supported by the project CHIHE (Climate Change Impact on Hydrological Extremes), carried out in the Institute of Geophysics Polish Academy of Sciences, funded by Norway Grants (contract No. Pol-Nor/196243/80/2013). The hydro-meteorological observations were provided by the Institute of Meteorology and Water Management (IMGW), Poland.
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
Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation.
Elena, Santiago F; Lenski, Richard E
2003-06-01
Microorganisms have been mutating and evolving on Earth for billions of years. Now, a field of research has developed around the idea of using microorganisms to study evolution in action. Controlled and replicated experiments are using viruses, bacteria and yeast to investigate how their genomes and phenotypic properties evolve over hundreds and even thousands of generations. Here, we examine the dynamics of evolutionary adaptation, the genetic bases of adaptation, tradeoffs and the environmental specificity of adaptation, the origin and evolutionary consequences of mutators, and the process of drift decay in very small populations.
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.
Transformational Education for Psychotherapy and Counselling: A Relational Dynamic Approach
ERIC Educational Resources Information Center
Macaskie, Jane; Meekums, Bonnie; Nolan, Greg
2013-01-01
An evolving relational dynamic approach to psychotherapy and counselling education is described. Key themes integrated within the approach are the learning community and transformational relationships. Learning is a reciprocal change process involving students, teachers, supervisors and therapists in overlapping learning communities. Drawing on…
Dynamic Assessment: An Approach Toward Reducing Test Bias.
ERIC Educational Resources Information Center
Carlson, Jerry S.; Wiedl, Karl Heinz
Through dynamic testing (the notion that tailored testing can be extended to the use of a learning oriented approach to assessment), analysis were made of how motivational, personality, and cognitive style factors interact with assessment approaches to yield performance data. Testing procedures involving simple feedback, elaborated feedback, and…
von Thiele Schwarz, Ulrica; Lundmark, Robert; Hasson, Henna
2016-10-01
Recently, there have been calls to develop ways of using a participatory approach when conducting interventions, including evaluating the process and context to improve and adapt the intervention as it evolves over time. The need to integrate interventions into daily organizational practices, thereby increasing the likelihood of successful implementation and sustainable changes, has also been highlighted. We propose an evaluation model-the Dynamic Integrated Evaluation Model (DIEM)-that takes this into consideration. In the model, evaluation is fitted into a co-created iterative intervention process, in which the intervention activities can be continuously adapted based on collected data. By explicitly integrating process and context factors, DIEM also considers the dynamic sustainability of the intervention over time. It emphasizes the practical value of these evaluations for organizations, as well as the importance of their rigorousness for research purposes. Copyright © 2016 John Wiley & Sons, Ltd.
Design and realization of dynamic self-adaptive technology based on disc quality
NASA Astrophysics Data System (ADS)
Zhou, Gongye; Wang, Jingqi; Fang, Xiaojing; Xie, Changsheng; Liu, Tong
2003-04-01
Dynamic self-adaptive technology makes it possible to adjust the spindle motor speed of optical disc drive based on the different quality of optical discs. It guarantees the read process have the optimal speed to read data smoothly and protect the optical-head components. This paper presents a dynamic self-adaptive technology based on disc quality which uses fussy logic control to make the speed adjusting process fast. It is applied to the servo system of high-speed CD-ROM driver system and good results are obtained.
Nanostructural self-organization and dynamic adaptation of metal-polymer tribosystems
NASA Astrophysics Data System (ADS)
Mashkov, Yu. K.
2017-02-01
The results of investigating the effect of nanosize modifiers of a polymer matrix on the nanostructural self-organization of polymer composites and dynamic adaptation of metal-polymer tribosystems, which considerably affect the wear resistance of polymer composite materials, have been analyzed. It has been shown that the physicochemical nanostructural self-organization processes are developed in metal-polymer tribosystems with the formation of thermotropic liquid-crystal structures of the polymer matrix, followed by the transition of the system to the stationary state with a negative feedback that ensures dynamic adaptation of the tribosystem to given operating conditions.
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.
How do Chinese cities grow? A distribution dynamics approach
NASA Astrophysics Data System (ADS)
Wu, Jian-Xin; He, Ling-Yun
2017-03-01
This paper examines the dynamic behavior of city size using a distribution dynamics approach with Chinese city data for the period 1984-2010. Instead of convergence, divergence or paralleled growth, multimodality and persistence are the dominant characteristics in the distribution dynamics of Chinese prefectural cities. Moreover, initial city size matters, initially small and medium-sized cities exhibit strong tendency of convergence, while large cities show significant persistence and multimodality in the sample period. Examination on the regional city groups shows that locational fundamentals have important impact on the distribution dynamics of city size.
Gustatory processing: a dynamic systems approach.
Jones, Lauren M; Fontanini, Alfredo; Katz, Donald B
2006-08-01
Recent gustatory studies have provided a growing body of evidence that taste processing is dynamic and distributed, and the taste system too complex to be adequately described by traditional feed-forward models of taste coding. Current research demonstrates that neuronal responses throughout the gustatory neuroaxis are broad, variable and temporally structured, as a result of the fact that the taste network is extensive and heavily interconnected, containing modulatory pathways, many of which are reciprocal. Multimodal influences (e.g. olfactory and somatosensory) and effects of internal state (e.g. attention and expectation), shown in both behavioral and neuronal responses to taste stimuli, add further complexity to neural taste responses. Future gustatory research should extend to more brain regions, incorporate more connections, and analyze behaviors and neuronal responses in both time- and state-dependent manners.
Cosmic infinity: a dynamical system approach
NASA Astrophysics Data System (ADS)
Bouhmadi-López, Mariam; Marto, João; Morais, João; Silva, César M.
2017-03-01
Dynamical system techniques are extremely useful to study cosmology. It turns out that in most of the cases, we deal with finite isolated fixed points corresponding to a given cosmological epoch. However, it is equally important to analyse the asymptotic behaviour of the universe. On this paper, we show how this can be carried out for 3-form models. In fact, we show that there are fixed points at infinity mainly by introducing appropriate compactifications and defining a new time variable that washes away any potential divergence of the system. The richness of 3-form models allows us as well to identify normally hyperbolic non-isolated fixed points. We apply this analysis to three physically interesting situations: (i) a pre-inflationary era; (ii) an inflationary era; (iii) the late-time dark matter/dark energy epoch.
NASA Astrophysics Data System (ADS)
Tjahyadi, H.; He, F.; Sammut, K.
2008-08-01
A hybrid multi-model-multi-mode adaptive resonant control (M4ARC) approach is proposed for dynamically loaded flexible beam structures to provide superior vibration damping performance as compared to its fixed-model and adaptive counterparts. The proposed approach uses a configurable controller, the parameters of which are updated using a fast and accurate on-line frequency identification method for N modes of interest. This method incorporates a simple supervision scheme that selects between the output of an N-mode filter bank (representing the multiple-fixed-model set) and the output of an estimator bank (representing the accurate model of the plant). The estimator bank comprises a multi-rate set of parallel N second-order recursive-least-squares estimators to achieve rapid parameter convergence. While the estimators are still in transition, the supervisor provides the configurable controller with an intermediate set of frequencies that correspond to the closest fixed model. Once the estimators converge, the supervisor selects the estimated frequency set to provide the configurable controller with an accurate representation of the current plant. This supervisor scheme significantly reduces the computational complexity as compared with existing counterparts. Experiments reveal that the proposed M4ARC approach offers the best compromise in terms of adapting to sudden and highly variable loading condition changes (within a bounded domain) while, at the same time, achieving fast transient performance.
NASA Astrophysics Data System (ADS)
Jia, Ying-Hong; Hu, Quan; Xu, Shi-Jie
2014-02-01
A dynamics-based adaptive control approach is proposed for a planar dual-arm space robot in the presence of closed-loop constraints and uncertain inertial parameters of the payload. The controller is capable of controlling the position and attitude of both the satellite base and the payload grasped by the manipulator end effectors. The equations of motion in reduced-order form for the constrained system are derived by incorporating the constraint equations in terms of accelerations into Kane's equations of the unconstrained system. Model analysis shows that the resulting equations perfectly meet the requirement of adaptive controller design. Consequently, by using an indirect approach, an adaptive control scheme is proposed to accomplish position/attitude trajectory tracking control with the uncertain parameters being estimated on-line. The actuator redundancy due to the closed-loop constraints is utilized to minimize a weighted norm of the joint torques. Global asymptotic stability is proven by using Lyapunov's method, and simulation results are also presented to demonstrate the effectiveness of the proposed approach. [Figure not available: see fulltext.
A neural network approach to dynamic task assignment of multirobots.
Zhu, Anmin; Yang, Simon X
2006-09-01
In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.
NASA Astrophysics Data System (ADS)
Cao, Zhengcai; Yin, Longjie; Fu, Yili
2013-01-01
Vision-based pose stabilization of nonholonomic mobile robots has received extensive attention. At present, most of the solutions of the problem do not take the robot dynamics into account in the controller design, so that these controllers are difficult to realize satisfactory control in practical application. Besides, many of the approaches suffer from the initial speed and torque jump which are not practical in the real world. Considering the kinematics and dynamics, a two-stage visual controller for solving the stabilization problem of a mobile robot is presented, applying the integration of adaptive control, sliding-mode control, and neural dynamics. In the first stage, an adaptive kinematic stabilization controller utilized to generate the command of velocity is developed based on Lyapunov theory. In the second stage, adopting the sliding-mode control approach, a dynamic controller with a variable speed function used to reduce the chattering is designed, which is utilized to generate the command of torque to make the actual velocity of the mobile robot asymptotically reach the desired velocity. Furthermore, to handle the speed and torque jump problems, the neural dynamics model is integrated into the above mentioned controllers. The stability of the proposed control system is analyzed by using Lyapunov theory. Finally, the simulation of the control law is implemented in perturbed case, and the results show that the control scheme can solve the stabilization problem effectively. The proposed control law can solve the speed and torque jump problems, overcome external disturbances, and provide a new solution for the vision-based stabilization of the mobile robot.
Farms adaptation to changes in flood risk: a management approach
NASA Astrophysics Data System (ADS)
Pivot, Jean-Marc; Martin, Philippe
2002-10-01
Creating flood expansion areas e.g. for the protection of urban areas from flooding involves a localised increase in risk which may require farmers to be compensated for crop damage or other losses. With this in mind, the paper sets out the approach used to study the problem and gives results obtained from a survey of farms liable to flooding in central France. The approach is based on a study of decisions made by farmers in situations of uncertainty, using the concept of 'model of action'. The results show that damage caused to farming areas by flooding should be considered both at field level and at farm level. The damage caused to the field depends on the flood itself, the fixed characteristics of the field, and the plant species cultivated. However, the losses to the farm taken as a whole can differ considerably from those for the flooded field, due to 'knock-on' effects on farm operations which depend on the internal organization, the availability of production resources, and the farmer's objectives, both for the farm as a whole and for its individual enterprises. Three main strategies regarding possible flood events were identified. Reasons for choosing one of these include the way the farmer perceives the risk and the size of the area liable to flooding. Finally, the formalisation of farm system management in the face of uncertainty, especially due to flooding, enables compensation to be calculated for farmers whose land is affected by the creation of flood expansion areas.
Fast Dynamical Coupling Enhances Frequency Adaptation of Oscillators for Robotic Locomotion Control
Nachstedt, Timo; Tetzlaff, Christian; Manoonpong, Poramate
2017-01-01
Rhythmic neural signals serve as basis of many brain processes, in particular of locomotion control and generation of rhythmic movements. It has been found that specific neural circuits, named central pattern generators (CPGs), are able to autonomously produce such rhythmic activities. In order to tune, shape and coordinate the produced rhythmic activity, CPGs require sensory feedback, i.e., external signals. Nonlinear oscillators are a standard model of CPGs and are used in various robotic applications. A special class of nonlinear oscillators are adaptive frequency oscillators (AFOs). AFOs are able to adapt their frequency toward the frequency of an external periodic signal and to keep this learned frequency once the external signal vanishes. AFOs have been successfully used, for instance, for resonant tuning of robotic locomotion control. However, the choice of parameters for a standard AFO is characterized by a trade-off between the speed of the adaptation and its precision and, additionally, is strongly dependent on the range of frequencies the AFO is confronted with. As a result, AFOs are typically tuned such that they require a comparably long time for their adaptation. To overcome the problem, here, we improve the standard AFO by introducing a novel adaptation mechanism based on dynamical coupling strengths. The dynamical adaptation mechanism enhances both the speed and precision of the frequency adaptation. In contrast to standard AFOs, in this system, the interplay of dynamics on short and long time scales enables fast as well as precise adaptation of the oscillator for a wide range of frequencies. Amongst others, a very natural implementation of this mechanism is in terms of neural networks. The proposed system enables robotic applications which require fast retuning of locomotion control in order to react to environmental changes or conditions. PMID:28377710
Broom, Donald M
2006-01-01
The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and
ERIC Educational Resources Information Center
Luk, HingKwan
This study examined whether an expert system approach involving intelligent selection of items (EXSPRT-I) is as efficient as item response theory (IRT) based three-parameter adaptive mastery testing (AMT) when there are enough subjects to estimate the three IRT item parameters for all items in the test and when subjects in the item parameter…
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.
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
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.
Adaptive Thouless-Anderson-Palmer approach to inverse Ising problems with quenched random fields
NASA Astrophysics Data System (ADS)
Huang, Haiping; Kabashima, Yoshiyuki
2013-06-01
The adaptive Thouless-Anderson-Palmer equation is derived for inverse Ising problems in the presence of quenched random fields. We test the proposed scheme on Sherrington-Kirkpatrick, Hopfield, and random orthogonal models and find that the adaptive Thouless-Anderson-Palmer approach allows accurate inference of quenched random fields whose distribution can be either Gaussian or bimodal. In particular, another competitive method for inferring external fields, namely, the naive mean field method with diagonal weights, is compared and discussed.
Compressible magma/mantle dynamics: 3-D, adaptive simulations in ASPECT
NASA Astrophysics Data System (ADS)
Dannberg, Juliane; Heister, Timo
2016-12-01
Melt generation and migration are an important link between surface processes and the thermal and chemical evolution of the Earth's interior. However, their vastly different timescales make it difficult to study mantle convection and melt migration in a unified framework, especially for 3-D global models. And although experiments suggest an increase in melt volume of up to 20 per cent from the depth of melt generation to the surface, previous computations have neglected the individual compressibilities of the solid and the fluid phase. Here, we describe our extension of the finite element mantle convection code ASPECT that adds melt generation and migration. We use the original compressible formulation of the McKenzie equations, augmented by an equation for the conservation of energy. Applying adaptive mesh refinement to this type of problems is particularly advantageous, as the resolution can be increased in areas where melt is present and viscosity gradients are high, whereas a lower resolution is sufficient in regions without melt. Together with a high-performance, massively parallel implementation, this allows for high-resolution, 3-D, compressible, global mantle convection simulations coupled with melt migration. We evaluate the functionality and potential of this method using a series of benchmarks and model setups, compare results of the compressible and incompressible formulation, and show the effectiveness of adaptive mesh refinement when applied to melt migration. Our model of magma dynamics provides a framework for modelling processes on different scales and investigating links between processes occurring in the deep mantle and melt generation and migration. This approach could prove particularly useful applied to modelling the generation of komatiites or other melts originating in greater depths. The implementation is available in the Open Source ASPECT repository.
The role of idiotypic interactions in the adaptive immune system: a belief-propagation approach
NASA Astrophysics Data System (ADS)
Bartolucci, Silvia; Mozeika, Alexander; Annibale, Alessia
2016-08-01
In this work we use belief-propagation techniques to study the equilibrium behaviour of a minimal model for the immune system comprising interacting T and B clones. We investigate the effect of the so-called idiotypic interactions among complementary B clones on the system’s activation. Our results show that B-B interactions increase the system’s resilience to noise, making clonal activation more stable, while increasing the cross-talk between different clones. We derive analytically the noise level at which a B clone gets activated, in the absence of cross-talk, and find that this increases with the strength of idiotypic interactions and with the number of T cells sending signals to the B clones. We also derive, analytically and numerically, via population dynamics, the critical line where clonal cross-talk arises. Our approach allows us to derive the B clone size distribution, which can be experimentally measured and gives important information about the adaptive immune system response to antigens and vaccination.
Magnetospheric structure and dynamics: A multisatellite approach
Hughes, W.J.
1991-03-20
This report reviews progress during the first year of a contract to study magnetospheric structure and dynamics. Four areas of scientific investigation are highlighted. Pressure gradients form in the geotail because ions drift preferentially toward the dusk flank. These pressure gradients drive field aligned currents that close in the ionosphere and which provide a natural explanation of the Harang discontinuity when the full electrodynamics are modelled. Observations made during a passage by DE 2 through the dayside cusp at a time when the IMF was directed northwards are consistent with magnetic merging occurring on field line that map to the poleward cusp boundary. The authors infer that tail lobe field lines were merging with magnetosheath field lines at the magnetopause tailward of the external cusp. During the March 1989 magnetic storm, the DMSP F9 spacecraft observed extensive substantial decreases in equatorial ion density in the post-dusk sector. Modelling calculations show that the depletions were caused by unusually large upward flows moving the equatorial F region peak above 850 km. Calculations of ion cyclotron wave group velocities show that they are sensitive to both the hot and cold plasma populations. Calculated group delays agree with their earlier observations.
Inverse dynamics of adaptive space cranes with tip point adjustment
NASA Technical Reports Server (NTRS)
Das, S. K.; Utku, S.; Wada, B. K.
1990-01-01
The 'space crane', which resembles a conventional solid-link robot but employs truss sections in place of links and length-adjustable bars in place of torque-generating motors, is presently characterized by means of two different inverse-dynamics schemes. While in the first of these the nominal angles are maintained between the links constituting the crane, the second scheme adjusts the nominal angles as a function of time in order to always maintain the tip of the crane along the desired (nomical) trajectory. Attention is given to the second scheme, and to a tip-adjustment method which keeps the high frequency flexibility vibration within limits and ensures numerical stability.
Adaptive model of plankton dynamics for the North Atlantic
NASA Astrophysics Data System (ADS)
Pahlow, Markus; Vézina, Alain F.; Casault, Benoit; Maass, Heidi; Malloch, Louise; Wright, Daniel G.; Lu, Youyu
2008-02-01
Plankton ecosystems in the North Atlantic display strong regional and interannual variability in productivity and trophic structure, which cannot be captured by simple plankton models. Additional compartments subdividing functional groups can increase predictive power, but the high number of parameters tends to compromise portability and robustness of model predictions. An alternative strategy is to use property state variables, such as cell size, normally considered constant parameters in ecosystem models, to define the structure of functional groups in terms of both behaviour and response to physical forcing. This strategy may allow us to simulate realistically regional and temporal differences among plankton communities while keeping model complexity at a minimum. We fit a model of plankton and DOM dynamics globally and individually to observed climatologies at three diverse locations in the North Atlantic. Introducing additional property state variables is shown to improve the model fit both locally and globally, make the model more portable, and help identify model deficiencies. The zooplankton formulation exerts strong control on model performance. Our results suggest that the current paradigm on zooplankton allometric functional relationships might be at odds with observed plankton dynamics. Our parameter estimation resulted in more realistic estimates of parameters important for primary production than previous data assimilation studies. Property state variables generate complex emergent functional relationships, and might be used like tracers to differentiate between locally produced and advected biomass. The model results suggest that the observed temperature dependence of heterotrophic growth efficiency [Rivkin, R.B., Legendre, L., 2001. Biogenic carbon cycling in the upper ocean: effects of microbial respiration. Science 291 (5512) 2398-2400] could be an emergent relation due to intercorrelations among temperature, nutrient concentration and growth
Dynamic Portfolio Strategy Using Clustering Approach
Lu, Ya-Nan; Li, Sai-Ping; Jiang, Xiong-Fei; Zhong, Li-Xin; Qiu, Tian
2017-01-01
The problem of portfolio optimization is one of the most important issues in asset management. We here propose a new dynamic portfolio strategy based on the time-varying structures of MST networks in Chinese stock markets, where the market condition is further considered when using the optimal portfolios for investment. A portfolio strategy comprises two stages: First, select the portfolios by choosing central and peripheral stocks in the selection horizon using five topological parameters, namely degree, betweenness centrality, distance on degree criterion, distance on correlation criterion and distance on distance criterion. Second, use the portfolios for investment in the investment horizon. The optimal portfolio is chosen by comparing central and peripheral portfolios under different combinations of market conditions in the selection and investment horizons. Market conditions in our paper are identified by the ratios of the number of trading days with rising index to the total number of trading days, or the sum of the amplitudes of the trading days with rising index to the sum of the amplitudes of the total trading days. We find that central portfolios outperform peripheral portfolios when the market is under a drawup condition, or when the market is stable or drawup in the selection horizon and is under a stable condition in the investment horizon. We also find that peripheral portfolios gain more than central portfolios when the market is stable in the selection horizon and is drawdown in the investment horizon. Empirical tests are carried out based on the optimal portfolio strategy. Among all possible optimal portfolio strategies based on different parameters to select portfolios and different criteria to identify market conditions, 65% of our optimal portfolio strategies outperform the random strategy for the Shanghai A-Share market while the proportion is 70% for the Shenzhen A-Share market. PMID:28129333
Dynamic Portfolio Strategy Using Clustering Approach.
Ren, Fei; Lu, Ya-Nan; Li, Sai-Ping; Jiang, Xiong-Fei; Zhong, Li-Xin; Qiu, Tian
2017-01-01
The problem of portfolio optimization is one of the most important issues in asset management. We here propose a new dynamic portfolio strategy based on the time-varying structures of MST networks in Chinese stock markets, where the market condition is further considered when using the optimal portfolios for investment. A portfolio strategy comprises two stages: First, select the portfolios by choosing central and peripheral stocks in the selection horizon using five topological parameters, namely degree, betweenness centrality, distance on degree criterion, distance on correlation criterion and distance on distance criterion. Second, use the portfolios for investment in the investment horizon. The optimal portfolio is chosen by comparing central and peripheral portfolios under different combinations of market conditions in the selection and investment horizons. Market conditions in our paper are identified by the ratios of the number of trading days with rising index to the total number of trading days, or the sum of the amplitudes of the trading days with rising index to the sum of the amplitudes of the total trading days. We find that central portfolios outperform peripheral portfolios when the market is under a drawup condition, or when the market is stable or drawup in the selection horizon and is under a stable condition in the investment horizon. We also find that peripheral portfolios gain more than central portfolios when the market is stable in the selection horizon and is drawdown in the investment horizon. Empirical tests are carried out based on the optimal portfolio strategy. Among all possible optimal portfolio strategies based on different parameters to select portfolios and different criteria to identify market conditions, 65% of our optimal portfolio strategies outperform the random strategy for the Shanghai A-Share market while the proportion is 70% for the Shenzhen A-Share market.
Unsupervised learning approach to adaptive differential pulse code modulation.
Griswold, N C; Sayood, K
1982-04-01
This research is concerned with investigating the problem of data compression utilizing an unsupervised estimation algorithm. This extends previous work utilizing a hybrid source coder which combines an orthogonal transformation with differential pulse code modulation (DPCM). The data compression is achieved in the DPCM loop, and it is the quantizer of this scheme which is approached from an unsupervised learning procedure. The distribution defining the quantizer is represented as a set of separable Laplacian mixture densities for two-dimensional images. The condition of identifiability is shown for the Laplacian case and a decision directed estimate of both the active distribution parameters and the mixing parameters are discussed in view of a Bayesian structure. The decision directed estimators, although not optimum, provide a realizable structure for estimating the parameters which define a distribution which has become active. These parameters are then used to scale the optimum (in the mean square error sense) Laplacian quantizer. The decision criteria is modified to prevent convergence to a single distribution which in effect is the default condition for a variance estimator. This investigation was applied to a test image and the resulting data demonstrate improvement over other techniques using fixed bit assignments and ideal channel conditions.
Algebraic approach to electronic spectroscopy and dynamics.
Toutounji, Mohamad
2008-04-28
Lie algebra, Zassenhaus, and parameter differentiation techniques are utilized to break up the exponential of a bilinear Hamiltonian operator into a product of noncommuting exponential operators by the virtue of the theory of Wei and Norman [J. Math. Phys. 4, 575 (1963); Proc. Am. Math. Soc., 15, 327 (1964)]. There are about three different ways to find the Zassenhaus exponents, namely, binomial expansion, Suzuki formula, and q-exponential transformation. A fourth, and most reliable method, is provided. Since linearly displaced and distorted (curvature change upon excitation/emission) Hamiltonian and spin-boson Hamiltonian may be classified as bilinear Hamiltonians, the presented algebraic algorithm (exponential operator disentanglement exploiting six-dimensional Lie algebra case) should be useful in spin-boson problems. The linearly displaced and distorted Hamiltonian exponential is only treated here. While the spin-boson model is used here only as a demonstration of the idea, the herein approach is more general and powerful than the specific example treated. The optical linear dipole moment correlation function is algebraically derived using the above mentioned methods and coherent states. Coherent states are eigenvectors of the bosonic lowering operator a and not of the raising operator a(+). While exp(a(+)) translates coherent states, exp(a(+)a(+)) operation on coherent states has always been a challenge, as a(+) has no eigenvectors. Three approaches, and the results, of that operation are provided. Linear absorption spectra are derived, calculated, and discussed. The linear dipole moment correlation function for the pure quadratic coupling case is expressed in terms of Legendre polynomials to better show the even vibronic transitions in the absorption spectrum. Comparison of the present line shapes to those calculated by other methods is provided. Franck-Condon factors for both linear and quadratic couplings are exactly accounted for by the herein calculated
NASA Astrophysics Data System (ADS)
Kust, German; Andreeva, Olga
2015-04-01
A number of new concepts and paradigms appeared during last decades, such as sustainable land management (SLM), climate change (CC) adaptation, environmental services, ecosystem health, and others. All of these initiatives still not having the common scientific platform although some agreements in terminology were reached, schemes of links and feedback loops created, and some models developed. Nevertheless, in spite of all these scientific achievements, the land related issues are still not in the focus of CC adaptation and mitigation. The last did not grow much beyond the "greenhouse gases" (GHG) concept, which makes land degradation as the "forgotten side of climate change" The possible decision to integrate concepts of climate and desertification/land degradation could be consideration of the "GHG" approach providing global solution, and "land" approach providing local solution covering other "locally manifesting" issues of global importance (biodiversity conservation, food security, disasters and risks, etc.) to serve as a central concept among those. SLM concept is a land-based approach, which includes the concepts of both ecosystem-based approach (EbA) and community-based approach (CbA). SLM can serve as in integral CC adaptation strategy, being based on the statement "the more healthy and resilient the system is, the less vulnerable and more adaptive it will be to any external changes and forces, including climate" The biggest scientific issue is the methods to evaluate the SLM and results of the SLM investments. We suggest using the approach based on the understanding of the balance or equilibrium of the land and nature components as the major sign of the sustainable system. Prom this point of view it is easier to understand the state of the ecosystem stress, size of the "health", range of adaptive capacity, drivers of degradation and SLM nature, as well as the extended land use, and the concept of environmental land management as the improved SLM approach
Robustness of continuous-time adaptive control algorithms in the presence of unmodeled dynamics
NASA Technical Reports Server (NTRS)
Rohrs, C. E.; Valavani, L.; Athans, M.; Stein, G.
1985-01-01
This paper examines the robustness properties of existing adaptive control algorithms to unmodeled plant high-frequency dynamics and unmeasurable output disturbances. It is demonstrated that there exist two infinite-gain operators in the nonlinear dynamic system which determines the time-evolution of output and parameter errors. The pragmatic implications of the existence of such infinite-gain operators is that: (1) sinusoidal reference inputs at specific frequencies and/or (2) sinusoidal output disturbances at any frequency (including dc), can cause the loop gain to increase without bound, thereby exciting the unmodeled high-frequency dynamics, and yielding an unstable control system. Hence, it is concluded that existing adaptive control algorithms as they are presented in the literature referenced in this paper, cannot be used with confidence in practical designs where the plant contains unmodeled dynamics because instability is likely to result. Further understanding is required to ascertain how the currently implemented adaptive systems differ from the theoretical systems studied here and how further theoretical development can improve the robustness of adaptive controllers.
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.
Design implementation and control of MRAS error dynamics. [Model-Reference Adaptive System
NASA Technical Reports Server (NTRS)
Colburn, B. K.; Boland, J. S., III
1974-01-01
Use is made of linearized error characteristic equation for model-reference adaptive systems to determine a parameter adjustment rule for obtaining time-invariant error dynamics. Theoretical justification of error stability is given and an illustrative example included to demonstrate the utility of the proposed technique.
Adaptive evolution of cooperation through Darwinian dynamics in Public Goods games.
Deng, Kuiying; Chu, Tianguang
2011-01-01
The linear or threshold Public Goods game (PGG) is extensively accepted as a paradigmatic model to approach the evolution of cooperation in social dilemmas. Here we explore the significant effect of nonlinearity of the structures of public goods on the evolution of cooperation within the well-mixed population by adopting Darwinian dynamics, which simultaneously consider the evolution of populations and strategies on a continuous adaptive landscape, and extend the concept of evolutionarily stable strategy (ESS) as a coalition of strategies that is both convergent-stable and resistant to invasion. Results show (i) that in the linear PGG contributing nothing is an ESS, which contradicts experimental data, (ii) that in the threshold PGG contributing the threshold value is a fragile ESS, which cannot resist the invasion of contributing nothing, and (iii) that there exists a robust ESS of contributing more than half in the sigmoid PGG if the return rate is relatively high. This work reveals the significant effect of the nonlinearity of the structures of public goods on the evolution of cooperation, and suggests that, compared with the linear or threshold PGG, the sigmoid PGG might be a more proper model for the evolution of cooperation within the well-mixed population.
Bhatnagar, Navendu; Kamath, Ganesh; Chelst, Issac; Potoff, Jeffrey J
2012-07-07
The 1-octanol-water partition coefficient log K(ow) of a solute is a key parameter used in the prediction of a wide variety of complex phenomena such as drug availability and bioaccumulation potential of trace contaminants. In this work, adaptive biasing force molecular dynamics simulations are used to determine absolute free energies of hydration, solvation, and 1-octanol-water partition coefficients for n-alkanes from methane to octane. Two approaches are evaluated; the direct transfer of the solute from 1-octanol to water phase, and separate transfers of the solute from the water or 1-octanol phase to vacuum, with both methods yielding statistically indistinguishable results. Calculations performed with the TIP4P and SPC∕E water models and the TraPPE united-atom force field for n-alkanes show that the choice of water model has a negligible effect on predicted free energies of transfer and partition coefficients for n-alkanes. A comparison of calculations using wet and dry octanol phases shows that the predictions for log K(ow) using wet octanol are 0.2-0.4 log units lower than for dry octanol, although this is within the statistical uncertainty of the calculation.
A semiclassical hybrid approach to many particle quantum dynamics
NASA Astrophysics Data System (ADS)
Grossmann, Frank
2006-07-01
We analytically derive a correlated approach for a mixed semiclassical many particle dynamics, treating a fraction of the degrees of freedom by the multitrajectory semiclassical initial value method of Herman and Kluk [Chem. Phys. 91, 27 (1984)] while approximately treating the dynamics of the remaining degrees of freedom with fixed initial phase space variables, analogously to the thawed Gaussian wave packet dynamics of Heller [J. Chem. Phys. 62, 1544 (1975)]. A first application of this hybrid approach to the well studied Secrest-Johnson [J. Chem. Phys. 45, 4556 (1966)] model of atom-diatomic collisions is promising. Results close to the quantum ones for correlation functions as well as scattering probabilities could be gained with considerably reduced numerical effort as compared to the full semiclassical Herman-Kluk approach. Furthermore, the harmonic nature of the different degrees of freedom can be determined a posteriori by comparing results with and without the additional approximation.
A semiclassical hybrid approach to many particle quantum dynamics.
Grossmann, Frank
2006-07-07
We analytically derive a correlated approach for a mixed semiclassical many particle dynamics, treating a fraction of the degrees of freedom by the multitrajectory semiclassical initial value method of Herman and Kluk [Chem. Phys. 91, 27 (1984)] while approximately treating the dynamics of the remaining degrees of freedom with fixed initial phase space variables, analogously to the thawed Gaussian wave packet dynamics of Heller [J. Chem. Phys. 62, 1544 (1975)]. A first application of this hybrid approach to the well studied Secrest-Johnson [J. Chem. Phys. 45, 4556 (1966)] model of atom-diatomic collisions is promising. Results close to the quantum ones for correlation functions as well as scattering probabilities could be gained with considerably reduced numerical effort as compared to the full semiclassical Herman-Kluk approach. Furthermore, the harmonic nature of the different degrees of freedom can be determined a posteriori by comparing results with and without the additional approximation.
Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors.
Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Chen, Bing; Lin, Chong
2015-03-01
This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.
An Analytical Dynamics Approach to the Control of Mechanical Systems
NASA Astrophysics Data System (ADS)
Mylapilli, Harshavardhan
A new and novel approach to the control of nonlinear mechanical systems is presented in this study. The approach is inspired by recent results in analytical dynamics that deal with the theory of constrained motion. The control requirements on the dynamical system are viewed from an analytical dynamics perspective and the theory of constrained motion is used to recast these control requirements as constraints on the dynamical system. Explicit closed form expressions for the generalized nonlinear control forces are obtained by using the fundamental equation of mechanics. The control so obtained is optimal at each instant of time and causes the constraints to be exactly satisfied. No linearizations and/or approximations of the nonlinear dynamical system are made, and no a priori structure is imposed on the nature of nonlinear controller. Three examples dealing with highly nonlinear complex dynamical systems that are chosen from diverse areas of discrete and continuum mechanics are presented to demonstrate the control approach. The first example deals with the energy control of underactuated inhomogeneous nonlinear lattices (or chains), the second example deals with the synchronization of the motion of multiple coupled slave gyros with that of a master gyro, and the final example deals with the control of incompressible hyperelastic rubber-like thin cantilever beams. Numerical simulations accompanying these examples show the ease, simplicity and the efficacy with which the control methodology can be applied and the accuracy with which the desired control objectives can be met.
A reduced adaptive observer for multivariable systems. [using reduced dynamic ordering
NASA Technical Reports Server (NTRS)
Carroll, R. L.; Lindorff, D. P.
1973-01-01
An adaptive observer for multivariable systems is presented for which the dynamic order of the observer is reduced, subject to mild restrictions. The observer structure depends directly upon the multivariable structure of the system rather than a transformation to a single-output system. The number of adaptive gains is at most the sum of the order of the system and the number of input parameters being adapted. Moreover, for the relatively frequent specific cases for which the number of required adaptive gains is less than the sum of system order and input parameters, the number of these gains is easily determined by inspection of the system structure. This adaptive observer possesses all the properties ascribed to the single-input single-output adpative observer. Like the other adaptive observers some restriction is required of the allowable system command input to guarantee convergence of the adaptive algorithm, but the restriction is more lenient than that required by the full-order multivariable observer. This reduced observer is not restricted to cycle systems.
Quantum-information approach to dynamical diffraction theory
NASA Astrophysics Data System (ADS)
Nsofini, J.; Ghofrani, K.; Sarenac, D.; Cory, D. G.; Pushin, D. A.
2016-12-01
We present a simplified model for dynamical diffraction of particles through a periodic thick perfect crystal based on repeated application of a coherent beam-splitting unitary at coarse-grained lattice sites. By demanding translational invariance and a computationally tractable number of sites in the coarse graining, we show how this approach reproduces many results typical of dynamical diffraction theory and experiments. This approach has the benefit of being applicable in the thick, thin, and intermediate crystal regimes. The method is applied to a three-blade neutron interferometer to predict the output beam profiles, interference patterns, and contrast variation.
Stock market networks: The dynamic conditional correlation approach
NASA Astrophysics Data System (ADS)
Lyócsa, Štefan; Výrost, Tomáš; Baumöhl, Eduard
2012-08-01
We demonstrate the economic relevance of minimum spanning trees (MSTs) constructed from dynamic conditional correlations (DCC) for a sample of S&P 100 constituents. An empirical comparison of MST properties shows that using the standard approach of rolling (or sliding-window) correlations yields trees that are more robust, have higher densities and exhibit higher industry clustering than MSTs based on DCC. Our results suggest that these properties are achieved at the expense of the smoothing of market dynamics, which is better preserved by DCC. The DCC approach offers a new perspective for the analysis of complex systems such as stock markets.
A decoupled recursive approach for constrained flexible multibody system dynamics
NASA Technical Reports Server (NTRS)
Lai, Hao-Jan; Kim, Sung-Soo; Haug, Edward J.; Bae, Dae-Sung
1989-01-01
A variational-vector calculus approach is employed to derive a recursive formulation for dynamic analysis of flexible multibody systems. Kinematic relationships for adjacent flexible bodies are derived in a companion paper, using a state vector notation that represents translational and rotational components simultaneously. Cartesian generalized coordinates are assigned for all body and joint reference frames, to explicitly formulate deformation kinematics under small deformation kinematics and an efficient flexible dynamics recursive algorithm is developed. Dynamic analysis of a closed loop robot is performed to illustrate efficiency of the algorithm.
Allaby, Robin G.; Gutaker, Rafal; Clarke, Andrew C.; Pearson, Neil; Ware, Roselyn; Palmer, Sarah A.; Kitchen, James L.; Smith, Oliver
2015-01-01
Our understanding of the evolution of domestication has changed radically in the past 10 years, from a relatively simplistic rapid origin scenario to a protracted complex process in which plants adapted to the human environment. The adaptation of plants continued as the human environment changed with the expansion of agriculture from its centres of origin. Using archaeogenomics and computational models, we can observe genome evolution directly and understand how plants adapted to the human environment and the regional conditions to which agriculture expanded. We have applied various archaeogenomics approaches as exemplars to study local adaptation of barley to drought resistance at Qasr Ibrim, Egypt. We show the utility of DNA capture, ancient RNA, methylation patterns and DNA from charred remains of archaeobotanical samples from low latitudes where preservation conditions restrict ancient DNA research to within a Holocene timescale. The genomic level of analyses that is now possible, and the complexity of the evolutionary process of local adaptation means that plant studies are set to move to the genome level, and account for the interaction of genes under selection in systems-level approaches. This way we can understand how plants adapted during the expansion of agriculture across many latitudes with rapidity. PMID:25487329
A User-Centered Approach to Adaptive Hypertext Based on an Information Relevance Model
NASA Technical Reports Server (NTRS)
Mathe, Nathalie; Chen, James
1994-01-01
Rapid and effective to information in large electronic documentation systems can be facilitated if information relevant in an individual user's content can be automatically supplied to this user. However most of this knowledge on contextual relevance is not found within the contents of documents, it is rather established incrementally by users during information access. We propose a new model for interactively learning contextual relevance during information retrieval, and incrementally adapting retrieved information to individual user profiles. The model, called a relevance network, records the relevance of references based on user feedback for specific queries and user profiles. It also generalizes such knowledge to later derive relevant references for similar queries and profiles. The relevance network lets users filter information by context of relevance. Compared to other approaches, it does not require any prior knowledge nor training. More importantly, our approach to adaptivity is user-centered. It facilitates acceptance and understanding by users by giving them shared control over the adaptation without disturbing their primary task. Users easily control when to adapt and when to use the adapted system. Lastly, the model is independent of the particular application used to access information, and supports sharing of adaptations among users.
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.
Mu, Chaoxu; Ni, Zhen; Sun, Changyin; He, Haibo
2016-04-22
A data-driven adaptive tracking control approach is proposed for a class of continuous-time nonlinear systems using a recent developed goal representation heuristic dynamic programming (GrHDP) architecture. The major focus of this paper is on designing a multivariable tracking scheme, including the filter-based action network (FAN) architecture, and the stability analysis in continuous-time fashion. In this design, the FAN is used to observe the system function, and then generates the corresponding control action together with the reference signals. The goal network will provide an internal reward signal adaptively based on the current system states and the control action. This internal reward signal is assigned as the input for the critic network, which approximates the cost function over time. We demonstrate its improved tracking performance in comparison with the existing heuristic dynamic programming (HDP) approach under the same parameter and environment settings. The simulation results of the multivariable tracking control on two examples have been presented to show that the proposed scheme can achieve better control in terms of learning speed and overall performance.
NASA Astrophysics Data System (ADS)
Ball, David A.; Moody, Stephen E.; Peccoud, Jean
2010-02-01
We have developed a fundamentally new type of cytometer to track the statistics of dynamic molecular interactions in hundreds of individual live cells within a single experiment. This entirely new high-throughput experimental system, which we have named Cyto•IQ, reports statistical, rather than image-based data for a large cellular population. Like a flow cytometer, Cyto•IQ rapidly measures several fluorescent probes in a large population of cells to yield a reduced statistical model that is matched to the experimental goals set by the user. However, Cyto•IQ moves beyond flow cytometry by tracking multiple probes in individual cells over time. Using adaptive learning algorithms, we process data in real time to maximize the convergence of the statistical model parameter estimators. Software controlling Cyto•IQ integrates existing open source applications to interface hardware components, process images, and adapt the data acquisition strategy based on previously acquired data. These innovations allow the study of larger populations of cells, and molecular interactions with more complex dynamics, than is possible with traditional microscope-based approaches. Cyto•IQ supports research to characterize the noisy dynamics of molecular interactions controlling biological processes.
Wilkinson, Simon R
2016-01-01
The major challenge for a clinician is integration of the wisdom available in the wide range of therapeutic paradigms available. I have found the principles guiding dialectic behaviour therapy (DBT; see Miller, Rathus, & Linehan, 2007, for applying DBT to adolescents) extremely useful in my practice running a general adolescent unit; similarly, the understanding of the different information processing and learning principles associated with each of the Type A and C attachment strategies, as understood in dynamic maturational model (DMM), has guided me through the dark corners of treatment. Specifically, how does DMM inform practice of DBT? As a 'DBTer' might say, 'Where is the wisdom in both points of view?' Nevertheless, DMM is not primarily about treatment. It concerns how different ways of adapting to developmental contingencies bias perceptual propensities, and hence the information available for reflective brain function. Recognition of these twists to knowing what is going on can then be used to inform a variety of therapeutic approaches. The purpose of this article is to look for the signposts in DBT and DMM which together help navigate the comprehensive approach necessary in complicated therapy. In the process, hopefully some more general principles for addressing discomfited adolescents arise for informing future practice. Although many steer shy of using personality disorder diagnoses for adolescents, clinicians are nevertheless addressing, directly or indirectly, the personality development of all adolescents in treatment, regardless of their classical axis I diagnoses, including both those with developing emotional instability and a group of avoidant over-controlled adolescents, which in Norway is growing in prominence.
Evaluating adaptive governance approaches to sustainable water management in north-west Thailand.
Clark, Julian R A; Semmahasak, Chutiwalanch
2013-04-01
Adaptive governance is advanced as a potent means of addressing institutional fit of natural resource systems with prevailing modes of political-administrative management. Its advocates also argue that it enhances participatory and learning opportunities for stakeholders over time. Yet an increasing number of studies demonstrate real difficulties in implementing adaptive governance 'solutions'. This paper builds on these debates by examining the introduction of adaptive governance to water management in Chiang Mai province, north-west Thailand. The paper considers, first, the limitations of current water governance modes at the provincial scale, and the rationale for implementation of an adaptive approach. The new approach is then critically examined, with its initial performance and likely future success evaluated by (i) analysis of water stakeholders' opinions of its first year of operation; and (ii) comparison of its governance attributes against recent empirical accounts of implementation difficulty and failure of adaptive governance of natural resource management more generally. The analysis confirms the potentially significant role that the new approach can play in brokering and resolving the underlying differences in stakeholder representation and knowledge construction at the heart of the prevailing water governance modes in north-west Thailand.
An Enhanced Adaptive Management Approach for Remediation of Legacy Mercury in the South River
Foran, Christy M.; Baker, Kelsie M.; Grosso, Nancy R.; Linkov, Igor
2015-01-01
Uncertainties about future conditions and the effects of chosen actions, as well as increasing resource scarcity, have been driving forces in the utilization of adaptive management strategies. However, many applications of adaptive management have been criticized for a number of shortcomings, including a limited ability to learn from actions and a lack of consideration of stakeholder objectives. To address these criticisms, we supplement existing adaptive management approaches with a decision-analytical approach that first informs the initial selection of management alternatives and then allows for periodic re-evaluation or phased implementation of management alternatives based on monitoring information and incorporation of stakeholder values. We describe the application of this enhanced adaptive management (EAM) framework to compare remedial alternatives for mercury in the South River, based on an understanding of the loading and behavior of mercury in the South River near Waynesboro, VA. The outcomes show that the ranking of remedial alternatives is influenced by uncertainty in the mercury loading model, by the relative importance placed on different criteria, and by cost estimates. The process itself demonstrates that a decision model can link project performance criteria, decision-maker preferences, environmental models, and short- and long-term monitoring information with management choices to help shape a remediation approach that provides useful information for adaptive, incremental implementation. PMID:25665032
A flexible low-complexity device adaptation approach for data presentation
NASA Astrophysics Data System (ADS)
Rosenbaum, René; Gimenez, Alfredo; Schumann, Heidrun; Hamann, Bernd
2011-01-01
Visual data presentations require adaptation for appropriate display on a viewing device that is limited in re- sources such as computing power, screen estate, and/or bandwidth. Due to the complexity of suitable adaptation, the few proposed solutions available are either too resource-intensive or in exible to be applied broadly. Eective use and acceptance of data visualization on constrained viewing devices require adaptation approaches that are tailored to the requirements of the user and the capabilities of the viewing device. We propose a predictive device adaptation approach that takes advantage of progressive data renement. The approach relies on hierarchical data structures that are created once and used multiple times. By incrementally reconstructing the visual presentation on the client with increasing levels of detail and resource utilization, we can determine when to truncate the renement of detail so as to use the resources of the device to their full capacities. To determine when to nish the renement for a particular device, we introduce a prole-based strategy which also considers user preferences. We discuss the whole adaptation process from the storage of the data into a scalable structure to the presentation on the respective viewing device. This particular implementation is shown for two common data visualization methods, and empirical results we obtained from our experiments are presented and discussed.
An enhanced adaptive management approach for remediation of legacy mercury in the South River.
Foran, Christy M; Baker, Kelsie M; Grosso, Nancy R; Linkov, Igor
2015-01-01
Uncertainties about future conditions and the effects of chosen actions, as well as increasing resource scarcity, have been driving forces in the utilization of adaptive management strategies. However, many applications of adaptive management have been criticized for a number of shortcomings, including a limited ability to learn from actions and a lack of consideration of stakeholder objectives. To address these criticisms, we supplement existing adaptive management approaches with a decision-analytical approach that first informs the initial selection of management alternatives and then allows for periodic re-evaluation or phased implementation of management alternatives based on monitoring information and incorporation of stakeholder values. We describe the application of this enhanced adaptive management (EAM) framework to compare remedial alternatives for mercury in the South River, based on an understanding of the loading and behavior of mercury in the South River near Waynesboro, VA. The outcomes show that the ranking of remedial alternatives is influenced by uncertainty in the mercury loading model, by the relative importance placed on different criteria, and by cost estimates. The process itself demonstrates that a decision model can link project performance criteria, decision-maker preferences, environmental models, and short- and long-term monitoring information with management choices to help shape a remediation approach that provides useful information for adaptive, incremental implementation.
Sariyar, Murat; Schumacher, Martin; Binder, Harald
2014-06-01
Risk prediction models can link high-dimensional molecular measurements, such as DNA methylation, to clinical endpoints. For biological interpretation, often a sparse fit is desirable. Different molecular aggregation levels, such as considering DNA methylation at the CpG, gene, or chromosome level, might demand different degrees of sparsity. Hence, model building and estimation techniques should be able to adapt their sparsity according to the setting. Additionally, underestimation of coefficients, which is a typical problem of sparse techniques, should also be addressed. We propose a comprehensive approach, based on a boosting technique that allows a flexible adaptation of model sparsity and addresses these problems in an integrative way. The main motivation is to have an automatic sparsity adaptation. In a simulation study, we show that this approach reduces underestimation in sparse settings and selects more adequate model sizes than the corresponding non-adaptive boosting technique in non-sparse settings. Using different aggregation levels of DNA methylation data from a study in kidney carcinoma patients, we illustrate how automatically selected values of the sparsity tuning parameter can reflect the underlying structure of the data. In addition to that, prediction performance and variable selection stability is compared to the non-adaptive boosting approach.
A Constraint Generation Approach to Learning Stable Linear Dynamical Systems
2008-01-01
and † denotes the Moore - Penrose inverse . Eq. (3) asks Â to minimize the error in predicting the state at time t + 1 from the state at time t. Given...A Constraint Generation Approach to Learning Stable Linear Dynamical Systems Sajid M. Siddiqi Byron Boots Geoffrey J. Gordon January 2008...REPORT DATE JAN 2008 2. REPORT TYPE 3. DATES COVERED 00-00-2008 to 00-00-2008 4. TITLE AND SUBTITLE A Constraint Generation Approach to Learning
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.
NASA Astrophysics Data System (ADS)
Krasichkov, Alexander S.; Grigoriev, Eugene B.; Bogachev, Mikhail I.; Nifontov, Eugene M.
2015-10-01
We suggest an analytical approach to the adaptive thresholding in a shape anomaly detection problem. We find an analytical expression for the distribution of the cosine similarity score between a reference shape and an observational shape hindered by strong measurement noise that depends solely on the noise level and is independent of the particular shape analyzed. The analytical treatment is also confirmed by computer simulations and shows nearly perfect agreement. Using this analytical solution, we suggest an improved shape anomaly detection approach based on adaptive thresholding. We validate the noise robustness of our approach using typical shapes of normal and pathological electrocardiogram cycles hindered by additive white noise. We show explicitly that under high noise levels our approach considerably outperforms the conventional tactic that does not take into account variations in the noise level.
NASA Astrophysics Data System (ADS)
Steger, Sebastian; Jung, Florian; Wesarg, Stefan
2014-03-01
This paper presents a novel segmentation method for the joint segmentation of individual bones in CT- or CT/MR- head and neck images. It is based on an articulated atlas for CT images that learned the shape and appearance of the individual bones along with the articulation between them from annotated training instances. First, a novel dynamic adaptation strategy for the atlas is presented in order to increase the rate of successful adaptations. Then, if a corresponding CT image is available the atlas can be enriched with personalized information about shape, appearance and size of the individual bones from that image. Using mutual information, this personalized atlas is adapted to an MR image in order to propagate segmentations. For evaluation, a head and neck bone atlas created from 15 manually annotated training images was adapted to 58 clinically acquired head andneck CT datasets. Visual inspection showed that the automatic dynamic adaptation strategy was successful for all bones in 86% of the cases. This is a 22% improvement compared to the traditional gradient descent based approach. In leave-one-out cross validation manner the average surface distance of the correctly adapted items was found to be 0.6 8mm. In 20 cases corresponding CT/MR image pairs were available and the atlas could be personalized and adapted to the MR image. This was successful in 19 cases.
NASA Astrophysics Data System (ADS)
Ogden, D. E.; Wohletz, K. H.; Glatzmaier, G. A.
2004-12-01
The shape and size of volcanic eruption conduits play a large role in determining the character of eruption phenomena. Conduits for explosive eruptions evolve with time during the eruption greatly affecting mass fluxes and the fate of degassed volatile constituents. Previous modeling has focused on compressible fluid dynamics of gas and solid particle mixtures moving within a cylindrical conduit and expanding into the atmosphere. However, it is clear that evolution of the conduit itself is influenced by the erupting mixture and the flow field of the mixture is controlled in part by the conduit. In order to address this important aspect of eruption dynamics, both compressible fluid flow and solid mechanics must be solved simultaneously. Two different three-dimensional state of the art computer codes have been developed at Los Alamos National Laboratories that are capable of simulating these dynamics together, CFDLib and SAGE. CFDLib is a compilation of well-tested computational fluid dynamics approaches suited for a wide range of fluid and solid dynamics, using well-known Marker-And-Cell (MAC) and Implicit Continuous-fluid Eulerian (ICE) techniques. SAGE employs adaptive grid Eulerian techniques to provide local areas of high resolution for dynamics of complex materials. We are benchmarking and validating these codes for geophysical application as a preliminary step toward modeling the three-dimensional dynamics of volcanic conduits.
Neural Network Aided Adaptive Extended Kalman Filtering Approach for DGPS Positioning
NASA Astrophysics Data System (ADS)
Jwo, Dah-Jing; Huang, Hung-Chih
2004-09-01
The extended Kalman filter, when employed in the GPS receiver as the navigation state estimator, provides optimal solutions if the noise statistics for the measurement and system are completely known. In practice, the noise varies with time, which results in performance degradation. The covariance matching method is a conventional adaptive approach for estimation of noise covariance matrices. The technique attempts to make the actual filter residuals consistent with their theoretical covariance. However, this innovation-based adaptive estimation shows very noisy results if the window size is small. To resolve the problem, a multilayered neural network is trained to identify the measurement noise covariance matrix, in which the back-propagation algorithm is employed to iteratively adjust the link weights using the steepest descent technique. Numerical simulations show that based on the proposed approach the adaptation performance is substantially enhanced and the positioning accuracy is substantially improved.
Sharif, Behzad; Derbyshire, J. Andrew; Faranesh, Anthony Z.; Bresler, Yoram
2010-01-01
MR imaging of the human heart without explicit cardiac synchronization promises to extend the applicability of cardiac MR to a larger patient population and potentially expand its diagnostic capabilities. However, conventional non-gated imaging techniques typically suffer from low image quality or inadequate spatio-temporal resolution and fidelity. Patient-Adaptive Reconstruction and Acquisition in Dynamic Imaging with Sensitivity Encoding (PARADISE) is a highly-accelerated non-gated dynamic imaging method that enables artifact-free imaging with high spatio-temporal resolutions by utilizing novel computational techniques to optimize the imaging process. In addition to using parallel imaging, the method gains acceleration from a physiologically-driven spatio-temporal support model; hence, it is doubly accelerated. The support model is patient-adaptive, i.e., its geometry depends on dynamics of the imaged slice, e.g., subject’s heart-rate and heart location within the slice. The proposed method is also doubly adaptive as it adapts both the acquisition and reconstruction schemes. Based on the theory of time-sequential sampling, the proposed framework explicitly accounts for speed limitations of gradient encoding and provides performance guarantees on achievable image quality. The presented in-vivo results demonstrate the effectiveness and feasibility of the PARADISE method for high resolution non-gated cardiac MRI during a short breath-hold. PMID:20665794
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.
Fonteyn, Ella M R; Heeren, Anita; Engels, Jasper-Jan C; Boer, Jasper J Den; van de Warrenburg, Bart P C; Weerdesteyn, Vivian
2014-01-01
Balance and gait problems in patients with cerebellar degeneration lead to reduced mobility, loss of independence, and frequent falls. It is currently unclear, however, whether balance and gait capacities can be improved by training in this group of patients. Therefore, the aim of this study was to examine the effects of gait adaptability training on obstacle avoidance and dynamic stability during adaptive gait. Ten patients with degenerative cerebellar ataxia received 10 protocolized gait adaptability training sessions of 1 h each during 5 weeks. Training was performed on a treadmill with visual stepping targets and obstacles projected on the belt's surface. As the primary outcome, we used an obstacle avoidance task while walking on a treadmill. We determined avoidance success rates, as well as dynamic stability during the avoidance manoeuvre. Clinical ratings included the scale for the assessment of ataxia (SARA), 10 m walking test, timed up-and-go test, berg balance scale, and the obstacle subtask of the emory functional ambulation profile (EFAP). Following the intervention, success rates on the obstacle avoidance task had significantly improved compared to pre-intervention. For successful avoidance, participants allowed themselves smaller stability margins in the sagittal plane in the (shortened) pre-crossing step. However, in the subsequent steps they returned to baseline stability values more effectively than before training. SARA scores and the EFAP obstacle subtask improved significantly as well. This pilot study provides preliminary evidence of a beneficial effect of gait adaptability training on obstacle avoidance capacity and dynamic stability in patients with cerebellar degeneration.
A multi-band environment-adaptive approach to noise suppression for cochlear implants.
Saki, Fatemeh; Mirzahasanloo, Taher; Kehtarnavaz, Nasser
2014-01-01
This paper presents an improved environment-adaptive noise suppression solution for the cochlear implants speech processing pipeline. This improvement is achieved by using a multi-band data-driven approach in place of a previously developed single-band data-driven approach. Seven commonly encountered noisy environments of street, car, restaurant, mall, bus, pub and train are considered to quantify the improvement. The results obtained indicate about 10% improvement in speech quality measures.
Development of an Assistance Environment for Tutors Based on a Co-Adaptive Design Approach
ERIC Educational Resources Information Center
Lavoue, Elise; George, Sebastien; Prevot, Patrick
2012-01-01
In this article, we present a co-adaptive design approach named TE-Cap (Tutoring Experience Capitalisation) that we applied for the development of an assistance environment for tutors. Since tasks assigned to tutors in educational contexts are not well defined, we are developing an environment which responds to needs which are not precisely…
ERIC Educational Resources Information Center
Southam-Gerow, Michael A.; Hourigan, Shannon E.; Allin, Robert B., Jr.
2009-01-01
This article describes the application of a university-community partnership model to the problem of adapting evidence-based treatment approaches in a community mental health setting. Background on partnership research is presented, with consideration of methodological and practical issues related to this kind of research. Then, a rationale for…
An Enhanced Approach to Combine Item Response Theory with Cognitive Diagnosis in Adaptive Testing
ERIC Educational Resources Information Center
Wang, Chun; Zheng, Chanjin; Chang, Hua-Hua
2014-01-01
Computerized adaptive testing offers the possibility of gaining information on both the overall ability and cognitive profile in a single assessment administration. Some algorithms aiming for these dual purposes have been proposed, including the shadow test approach, the dual information method (DIM), and the constraint weighted method. The…
ERIC Educational Resources Information Center
Rule, Audrey C.; Barrera, Manuel T., III
2008-01-01
Integration of subject areas with technology and thinking skills is a way to help teachers cope with today's overloaded curriculum and to help students see the connectedness of different curriculum areas. This study compares three authentic approaches to teaching a science unit on bird adaptations for habitat that integrate thinking skills and…
A Monte Carlo Approach to the Design, Assembly, and Evaluation of Multistage Adaptive Tests
ERIC Educational Resources Information Center
Belov, Dmitry I.; Armstrong, Ronald D.
2008-01-01
This article presents an application of Monte Carlo methods for developing and assembling multistage adaptive tests (MSTs). A major advantage of the Monte Carlo assembly over other approaches (e.g., integer programming or enumerative heuristics) is that it provides a uniform sampling from all MSTs (or MST paths) available from a given item pool.…
EXSPRT: An Expert Systems Approach to Computer-Based Adaptive Testing.
ERIC Educational Resources Information Center
Frick, Theodore W.; And Others
Expert systems can be used to aid decision making. A computerized adaptive test (CAT) is one kind of expert system, although it is not commonly recognized as such. A new approach, termed EXSPRT, was devised that combines expert systems reasoning and sequential probability ratio test stopping rules. EXSPRT-R uses random selection of test items,…
Project Adapt: A Developmental Approach to Psycho-Motor Transfer. A Guide to Movement and Learning.
ERIC Educational Resources Information Center
Steele, Wah-Leeta
Described is Project ADAPT (A Developmental Approach to Psychomotor Transfer), a validated program used with 808 primary grade children, some with learning difficulties, over a 3-year period to enhance academic readiness and self esteem through psychomotor training. An introductory project summary explains program objectives, the needs assessment…
An approach for characterizing coupling in dynamical systems
NASA Astrophysics Data System (ADS)
Janjarasjitt, S.; Loparo, K. A.
2008-10-01
The study of coupling in dynamical systems dates back to Christian Hyugens who, in 1665, discovered that pendulum clocks with the same length pendulum synchronize when they are near to each other. In that case the observed synchronous motion was out of phase. In this paper we propose a new approach for measuring the degree of coupling and synchronization of a dynamical system consisting of interacting subsystems. The measure is based on quantifying the active degrees of freedom (e.g. correlation dimension) of the coupled system and the constituent subsystems. The time-delay embedding scheme is extended to coupled systems and used for attractor reconstruction of the coupled dynamical system. We use the coupled Lorenz, Rossler and Hénon model systems with a coupling strength variable for evaluation of the proposed approach. Results show that we can measure the active degrees of freedom of the coupled dynamical systems and can quantify and distinguish the degree of synchronization or coupling in each of the dynamical systems studied. Furthermore, using this approach the direction of coupling can be determined.
A Hybrid Acoustic and Pronunciation Model Adaptation Approach for Non-native Speech Recognition
NASA Astrophysics Data System (ADS)
Oh, Yoo Rhee; Kim, Hong Kook
In this paper, we propose a hybrid model adaptation approach in which pronunciation and acoustic models are adapted by incorporating the pronunciation and acoustic variabilities of non-native speech in order to improve the performance of non-native automatic speech recognition (ASR). Specifically, the proposed hybrid model adaptation can be performed at either the state-tying or triphone-modeling level, depending at which acoustic model adaptation is performed. In both methods, we first analyze the pronunciation variant rules of non-native speakers and then classify each rule as either a pronunciation variant or an acoustic variant. The state-tying level hybrid method then adapts pronunciation models and acoustic models by accommodating the pronunciation variants in the pronunciation dictionary and by clustering the states of triphone acoustic models using the acoustic variants, respectively. On the other hand, the triphone-modeling level hybrid method initially adapts pronunciation models in the same way as in the state-tying level hybrid method; however, for the acoustic model adaptation, the triphone acoustic models are then re-estimated based on the adapted pronunciation models and the states of the re-estimated triphone acoustic models are clustered using the acoustic variants. From the Korean-spoken English speech recognition experiments, it is shown that ASR systems employing the state-tying and triphone-modeling level adaptation methods can relatively reduce the average word error rates (WERs) by 17.1% and 22.1% for non-native speech, respectively, when compared to a baseline ASR system.
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).
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
Randomized Control Trials on the Dynamic Geometry Approach
ERIC Educational Resources Information Center
Jiang, Zhonghong; White, Alexander; Rosenwasser, Alana
2011-01-01
The project reported here is conducting repeated randomized control trials of an approach to high school geometry that utilizes Dynamic Geometry (DG) software to supplement ordinary instructional practices. It compares effects of that intervention with standard instruction that does not make use of computer drawing/exploration tools. The basic…
Multicultural Minds: A Dynamic Constructivist Approach to Culture and Cognition.
ERIC Educational Resources Information Center
Hong, Ying-yi; Morris, Michael W.; Chiu, Chie-yue; Benet-Martinez, Veronica
2000-01-01
This approach to culture and cognition highlights dynamics through which cultural knowledge becomes operative in guiding the construction of meaning from a stimulus. Cognitive priming experiments simulated how bicultural people switch between cultural frames in response to culturally laden symbols. Results illuminate how cultural constructs are…
The effective field theorist's approach to gravitational dynamics
NASA Astrophysics Data System (ADS)
Porto, Rafael A.
2016-05-01
We review the effective field theory (EFT) approach to gravitational dynamics. We focus on extended objects in long-wavelength backgrounds and gravitational wave emission from spinning binary systems. We conclude with an introduction to EFT methods for the study of cosmological large scale structures.
Improving Quality in Education: Dynamic Approaches to School Improvement
ERIC Educational Resources Information Center
Creemers, Bert P. M.; Kyriakides, Leonidas
2011-01-01
This book explores an approach to school improvement that merges the traditions of educational effectiveness research and school improvement efforts. It displays how the dynamic model, which is theoretical and empirically validated, can be used in both traditions. Each chapter integrates evidence from international and national studies, showing…
Many-body approach to the dynamics of batch learning
NASA Astrophysics Data System (ADS)
Wong, K. Y. Michael; Li, S.; Tong, Y. W.
2000-09-01
Using the cavity method and diagrammatic methods, we model the dynamics of batch learning of restricted sets of examples, widely applicable to general learning cost functions, and fully taking into account the temporal correlations introduced by the recycling of the examples. The approach is illustrated using the Adaline rule learning teacher-generated or random examples.
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
Charting Multidisciplinary Team External Dynamics Using a Systems Thinking Approach
NASA Technical Reports Server (NTRS)
Barthelemy, Jean-Francois; Waszak, Martin R.; Jones, Kenneth M.; Silcox, Richard J.; Silva, Walter A.; Nowaczyk, Ronald H.
1998-01-01
Using the formalism provided by the Systems Thinking approach, the dynamics present when operating multidisciplinary teams are examined in the context of the NASA Langley Research and Technology Group, an R&D organization organized along functional lines. The paper focuses on external dynamics and examines how an organization creates and nurtures the teams and how it disseminates and retains the lessons and expertise created by the multidisciplinary activities. Key variables are selected and the causal relationships between the variables are identified. Five "stories" are told, each of which touches on a different aspect of the dynamics. The Systems Thinking Approach provides recommendations as to interventions that will facilitate the introduction of multidisciplinary teams and that therefore will increase the likelihood of performing successful multidisciplinary developments. These interventions can be carried out either by individual researchers, line management or program management.
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.
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.
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.
Jansen-Osmann, Petra; Richter, Stefanie; Konczak, Jürgen; Kalveram, Karl-Theodor
2002-03-01
When humans perform goal-directed arm movements under the influence of an external damping force, they learn to adapt to these external dynamics. After removal of the external force field, they reveal kinematic aftereffects that are indicative of a neural controller that still compensates the no longer existing force. Such behavior suggests that the adult human nervous system uses a neural representation of inverse arm dynamics to control upper-extremity motion. Central to the notion of an inverse dynamic model (IDM) is that learning generalizes. Consequently, aftereffects should be observable even in untrained workspace regions. Adults have shown such behavior, but the ontogenetic development of this process remains unclear. This study examines the adaptive behavior of children and investigates whether learning a force field in one hemifield of the right arm workspace has an effect on force adaptation in the other hemifield. Thirty children (aged 6-10 years) and ten adults performed 30 degrees elbow flexion movements under two conditions of external damping (negative and null). We found that learning to compensate an external damping force transferred to the opposite hemifield, which indicates that a model of the limb dynamics rather than an association of visited space and experienced force was acquired. Aftereffects were more pronounced in the younger children and readaptation to a null-force condition was prolonged. This finding is consistent with the view that IDMs in children are imprecise neural representations of the actual arm dynamics. It indicates that the acquisition of IDMs is a developmental achievement and that the human motor system is inherently flexible enough to adapt to any novel force within the limits of the organism's biomechanics.
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.
Kwong, C K; Fung, K Y; Jiang, Huimin; Chan, K Y; Siu, Kin Wai Michael
2013-01-01
Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.
Kwong, C. K.; Fung, K. Y.; Jiang, Huimin; Chan, K. Y.
2013-01-01
Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort. PMID:24385884
The role of adaptive management as an operational approach for resource management agencies
Johnson, B.L.
1999-01-01
In making resource management decisions, agencies use a variety of approaches that involve different levels of political concern, historical precedence, data analyses, and evaluation. Traditional decision-making approaches have often failed to achieve objectives for complex problems in large systems, such as the Everglades or the Colorado River. I contend that adaptive management is the best approach available to agencies for addressing this type of complex problem, although its success has been limited thus far. Traditional decision-making approaches have been fairly successful at addressing relatively straightforward problems in small, replicated systems, such as management of trout in small streams or pulp production in forests. However, this success may be jeopardized as more users place increasing demands on these systems. Adaptive management has received little attention from agencies for addressing problems in small-scale systems, but I suggest that it may be a useful approach for creating a holistic view of common problems and developing guidelines that can then be used in simpler, more traditional approaches to management. Although adaptive management may be more expensive to initiate than traditional approaches, it may be less expensive in the long run if it leads to more effective management. The overall goal of adaptive management is not to maintain an optimal condition of the resource, but to develop an optimal management capacity. This is accomplished by maintaining ecological resilience that allows the system to react to inevitable stresses, and generating flexibility in institutions and stakeholders that allows managers to react when conditions change. The result is that, rather than managing for a single, optimal state, we manage within a range of acceptable outcomes while avoiding catastrophes and irreversible negative effects. Copyright ?? 1999 by The Resilience Alliance.
Multivariate Dynamic Modeling to Investigate Human Adaptation to Space Flight: Initial Concepts
NASA Technical Reports Server (NTRS)
Shelhamer, Mark; Mindock, Jennifer; Zeffiro, Tom; Krakauer, David; Paloski, William H.; Lumpkins, Sarah
2014-01-01
The array of physiological changes that occur when humans venture into space for long periods presents a challenge to future exploration. The changes are conventionally investigated independently, but a complete understanding of adaptation requires a conceptual basis founded in integrative physiology, aided by appropriate mathematical modeling. NASA is in the early stages of developing such an approach.
Multivariate Dynamical Modeling to Investigate Human Adaptation to Space Flight: Initial Concepts
NASA Technical Reports Server (NTRS)
Shelhamer, Mark; Mindock, Jennifer; Zeffiro, Tom; Krakauer, David; Paloski, William H.; Lumpkins, Sarah
2014-01-01
The array of physiological changes that occur when humans venture into space for long periods presents a challenge to future exploration. The changes are conventionally investigated independently, but a complete understanding of adaptation requires a conceptual basis founded in intergrative physiology, aided by appropriate mathematical modeling. NASA is in the early stages of developing such an approach.
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
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.
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.
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.
Southam-Gerow, Michael A.; Hourigan, Shannon E.; Allin, Robert B.
2009-01-01
This paper describes the application of a university-community partnership model to the problem of adapting evidence-based treatment approaches in a community mental health setting. Background on partnership research is presented, with consideration of methodological and practical issues related to this kind of research. Then, a rationale for using partnerships as a basis for conducting mental health treatment research is presented. Finally, an ongoing partnership research project concerned with the adaptation of evidence-based mental health treatments for childhood internalizing problems in community settings is presented, with preliminary results of the ongoing effort discussed. PMID:18697917
Gresham, David; Desai, Michael M; Tucker, Cheryl M; Jenq, Harry T; Pai, Dave A; Ward, Alexandra; DeSevo, Christopher G; Botstein, David; Dunham, Maitreya J
2008-12-01
The experimental evolution of laboratory populations of microbes provides an opportunity to observe the evolutionary dynamics of adaptation in real time. Until very recently, however, such studies have been limited by our inability to systematically find mutations in evolved organisms. We overcome this limitation by using a variety of DNA microarray-based techniques to characterize genetic changes -- including point mutations, structural changes, and insertion variation -- that resulted from the experimental adaptation of 24 haploid and diploid cultures of Saccharomyces cerevisiae to growth in either glucose, sulfate, or phosphate-limited chemostats for approximately 200 generations. We identified frequent genomic amplifications and rearrangements as well as novel retrotransposition events associated with adaptation. Global nucleotide variation detection in ten clonal isolates identified 32 point mutations. On the basis of mutation frequencies, we infer that these mutations and the subsequent dynamics of adaptation are determined by the batch phase of growth prior to initiation of the continuous phase in the chemostat. We relate these genotypic changes to phenotypic outcomes, namely global patterns of gene expression, and to increases in fitness by 5-50%. We found that the spectrum of available mutations in glucose- or phosphate-limited environments combined with the batch phase population dynamics early in our experiments allowed several distinct genotypic and phenotypic evolutionary pathways in response to these nutrient limitations. By contrast, sulfate-limited populations were much more constrained in both genotypic and phenotypic outcomes. Thus, the reproducibility of evolution varies with specific selective pressures, reflecting the constraints inherent in the system-level organization of metabolic processes in the cell. We were able to relate some of the observed adaptive mutations (e.g., transporter gene amplifications) to known features of the relevant
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
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.
HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.
Kim, J; Kasabov, N
1999-11-01
This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.
Passivity of Directed and Undirected Complex Dynamical Networks With Adaptive Coupling Weights.
Wang, Jin-Liang; Wu, Huai-Ning; Huang, Tingwen; Ren, Shun-Yan; Wu, Jigang
2016-05-05
A complex dynamical network consisting of $N$ identical neural networks with reaction-diffusion terms is considered in this paper. First, several passivity definitions for the systems with different dimensions of input and output are given. By utilizing some inequality techniques, several criteria are presented, ensuring the passivity of the complex dynamical network under the designed adaptive law. Then, we discuss the relationship between the synchronization and output strict passivity of the proposed network model. Furthermore, these results are extended to the case when the topological structure of the network is undirected. Finally, two examples with numerical simulations are provided to illustrate the correctness and effectiveness of the proposed results.
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.
Fournier-Level, Alexandre; Perry, Emily O.; Wang, Jonathan A.; Braun, Peter T.; Migneault, Andrew; Cooper, Martha D.; Metcalf, C. Jessica E.; Schmitt, Johanna
2016-01-01
Predicting whether and how populations will adapt to rapid climate change is a critical goal for evolutionary biology. To examine the genetic basis of fitness and predict adaptive evolution in novel climates with seasonal variation, we grew a diverse panel of the annual plant Arabidopsis thaliana (multiparent advanced generation intercross lines) in controlled conditions simulating four climates: a present-day reference climate, an increased-temperature climate, a winter-warming only climate, and a poleward-migration climate with increased photoperiod amplitude. In each climate, four successive seasonal cohorts experienced dynamic daily temperature and photoperiod variation over a year. We measured 12 traits and developed a genomic prediction model for fitness evolution in each seasonal environment. This model was used to simulate evolutionary trajectories of the base population over 50 y in each climate, as well as 100-y scenarios of gradual climate change following adaptation to a reference climate. Patterns of plastic and evolutionary fitness response varied across seasons and climates. The increased-temperature climate promoted genetic divergence of subpopulations across seasons, whereas in the winter-warming and poleward-migration climates, seasonal genetic differentiation was reduced. In silico “resurrection experiments” showed limited evolutionary rescue compared with the plastic response of fitness to seasonal climate change. The genetic basis of adaptation and, consequently, the dynamics of evolutionary change differed qualitatively among scenarios. Populations with fewer founding genotypes and populations with genetic diversity reduced by prior selection adapted less well to novel conditions, demonstrating that adaptation to rapid climate change requires the maintenance of sufficient standing variation. PMID:27140640
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.
A chaos detectable and time step-size adaptive numerical scheme for nonlinear dynamical systems
NASA Astrophysics Data System (ADS)
Chen, Yung-Wei; Liu, Chein-Shan; Chang, Jiang-Ren
2007-02-01
The first step in investigation the dynamics of a continuous time system described by ordinary differential equations is to integrate them to obtain trajectories. In this paper, we convert the group-preserving scheme (GPS) developed by Liu [International Journal of Non-Linear Mechanics 36 (2001) 1047-1068] to a time step-size adaptive scheme, x=x+hf(x,t), where x∈R is the system variables we are concerned with, and f(x,t)∈R is a time-varying vector field. The scheme has the form similar to the Euler scheme, x=x+Δtf(x,t), but our step-size h is adaptive automatically. Very interestingly, the ratio h/Δt, which we call the adaptive factor, can forecast the appearance of chaos if the considered dynamical system becomes chaotical. The numerical examples of the Duffing equation, the Lorenz equation and the Rossler equation, which may exhibit chaotic behaviors under certain parameters values, are used to demonstrate these phenomena. Two other non-chaotic examples are included to compare the performance of the GPS and the adaptive one.
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.
NASA Astrophysics Data System (ADS)
Kozdon, J. E.; Wilcox, L.; Aranda, A. R.
2014-12-01
The goal of this work is to develop a new set of simulation tools for earthquake rupture dynamics based on state-of-the-art high-order, adaptive numerical methods capable of handling complex geometries. High-order methods are ideal for earthquake rupture simulations as the problems are wave-dominated and the waves excited in simulations propagate over distance much larger than their fundamental wavelength. When high-order methods are used for such problems significantly fewer degrees of freedom are required as compared with low-order methods. The base numerical method in our new software elements is a discontinuous Galerkin method based on curved, Kronecker product hexahedral elements. We currently use MPI for off-node parallelism and are in the process of exploring strategies for on-node parallelism. Spatial mesh adaptivity is handled using the p4est library and temporal adaptivity is achieved through an Adams-Bashforth based local time stepping method; we are presently in the process of including dynamic spatial adaptivity which we believe will be valuable for capturing the small-scale features around the propagating rupture front. One of the key features of our software elements is that the method is provably stable, even after the inclusion of the nonlinear frictions laws which govern rupture dynamics. In this presentation we will both outline the structure of the software elements as well as validate the rupture dynamics with SCEC benchmark test problems. We are also presently developing several realistic simulation geometries which may also be reported on. Finally, the software elements that we have designed are fully public domain and have been designed with tightly coupled, wave dominated multiphysics applications in mind. This latter design decisions means the software elements are applicable to many other geophysical and non-geophysical applications.
Dynamic Adaptive Binning: An Improved Quantification Technique for NMR Spectroscopic Data
2010-01-01
adaptive intelligent binning, which recursively identifies bin edges in existing bins (De Meyer et al. 2008). Another dynamic binning method is...43. Cancino-De-Greiff, H. F., Ramos-Garcia, R., & Lorenzo -Ginori, J. V. (2002). Signal de-noising in magnetic resonance spectroscopy using wavelet...for metabolomics data using the undecimated wavelet transform. Chemometrics and Intelligent Laboratory Systems, 85, 144–154. De Meyer , T., Sinnaeve, D
NASA Astrophysics Data System (ADS)
Wang, Dong; Singh, Vijay P.; Shang, Xiaosan; Ding, Hao; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi; Wang, Shicheng; Wang, Zhenlong
2014-07-01
De-noising meteorologic and hydrologic time series is important to improve the accuracy and reliability of extraction, analysis, simulation, and forecasting. A hybrid approach, combining sample entropy and wavelet de-noising method, is developed to separate noise from original series and is named as AWDA-SE (adaptive wavelet de-noising approach using sample entropy). The AWDA-SE approach adaptively determines the threshold for wavelet analysis. Two kinds of meteorologic and hydrologic data sets, synthetic data set and 3 representative field measured data sets (one is the annual rainfall data of Jinan station and the other two are annual streamflow series from two typical stations in China, Yingluoxia station on the Heihe River, which is little affected by human activities, and Lijin station on the Yellow River, which is greatly affected by human activities), are used to illustrate the approach. The AWDA-SE approach is compared with three conventional de-noising methods, including fixed-form threshold algorithm, Stein unbiased risk estimation algorithm, and minimax algorithm. Results show that the AWDA-SE approach separates effectively the signal and noise of the data sets and is found to be better than the conventional methods. Measures of assessment standards show that the developed approach can be employed to investigate noisy and short time series and can also be applied to other areas.
Lineage dynamics and mutation-selection balance in non-adapting asexual populations
NASA Astrophysics Data System (ADS)
Pénisson, Sophie; Sniegowski, Paul D.; Colato, Alexandre; Gerrish, Philip J.
2013-02-01
In classical population genetics, mutation-selection balance refers to the equilibrium frequency of a deleterious allele established and maintained under two opposing forces: recurrent mutation, which tends to increase the frequency of the allele; and selection, which tends to decrease its frequency. In a haploid population, if μ denotes the per capita rate of production of the deleterious allele by mutation and s denotes the selective disadvantage of carrying the allele, then the classical mutation-selection balance frequency of the allele is approximated by μ/s. This calculation assumes that lineages carrying the mutant allele in question—the ‘focal allele’—do not accumulate deleterious mutations linked to the focal allele. In principle, indirect selection against the focal allele caused by such additional mutations can decrease the frequency of the focal allele below the classical mutation-selection balance. This effect of indirect selection will be strongest in an asexual population, in which the entire genome is in linkage. Here, we use an approach based on a multitype branching process to investigate this effect, analyzing lineage dynamics under mutation, direct selection, and indirect selection in a non-adapting asexual population. We find that the equilibrium balance between recurrent mutation to the focal allele and the forces of direct and indirect selection against the focal allele is closely approximated by γμ/(s + U) (s = 0 if the focal allele is neutral), where γ ≈ eθθ-(ω+θ)(ω + θ)(Γ(ω + θ) - Γ(ω + θ,θ)), \\theta =U/\\tilde {s}, and \\omega =s/\\tilde {s}; U denotes the genomic deleterious mutation rate and \\tilde {s} denotes the geometric mean selective disadvantage of deleterious mutations elsewhere on the genome. This mutation-selection balance for asexual populations can remain surprisingly invariant over wide ranges of the mutation rate.
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.
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.
A Direct Adaptive Control Approach in the Presence of Model Mismatch
NASA Technical Reports Server (NTRS)
Joshi, Suresh M.; Tao, Gang; Khong, Thuan
2009-01-01
This paper considers the problem of direct model reference adaptive control when the plant-model matching conditions are violated due to abnormal changes in the plant or incorrect knowledge of the plant's mathematical structure. The approach consists of direct adaptation of state feedback gains for state tracking, and simultaneous estimation of the plant-model mismatch. Because of the mismatch, the plant can no longer track the state of the original reference model, but may be able to track a new reference model that still provides satisfactory performance. The reference model is updated if the estimated plant-model mismatch exceeds a bound that is determined via robust stability and/or performance criteria. The resulting controller is a hybrid direct-indirect adaptive controller that offers asymptotic state tracking in the presence of plant-model mismatch as well as parameter deviations.
A simple and flexible graphical approach for adaptive group-sequential clinical trials.
Sugitani, Toshifumi; Bretz, Frank; Maurer, Willi
2016-01-01
In this article, we introduce a graphical approach to testing multiple hypotheses in group-sequential clinical trials allowing for midterm design modifications. It is intended for structured study objectives in adaptive clinical trials and extends the graphical group-sequential designs from Maurer and Bretz (Statistics in Biopharmaceutical Research 2013; 5: 311-320) to adaptive trial designs. The resulting test strategies can be visualized graphically and performed iteratively. We illustrate the methodology with two examples from our clinical trial practice. First, we consider a three-armed gold-standard trial with the option to reallocate patients to either the test drug or the active control group, while stopping the recruitment of patients to placebo, after having demonstrated superiority of the test drug over placebo at an interim analysis. Second, we consider a confirmatory two-stage adaptive design with treatment selection at interim.
Slave rotor approach to dynamically screened Coulomb interactions in solids
NASA Astrophysics Data System (ADS)
Krivenko, I. S.; Biermann, S.
2015-04-01
Recent studies of dynamical screening of the electronic Coulomb interactions in solids have revived interest in lattice models of correlated fermions coupled to bosonic degrees of freedom (Hubbard-Holstein-type models). We propose a new dynamical mean-field-based approach to dynamically screened Coulomb interactions. In the effective Anderson-Holstein model, a transformation to slave rotors [S. Florens and A. Georges, Phys. Rev. B 66, 165111 (2002), 10.1103/PhysRevB.66.165111] is performed to decouple the dynamical part of the interaction. This transformation allows for a systematic derivation and analysis of recently introduced approximate schemes for the solution of dynamical impurity problems, in particular, the Bose factor ansatz within the dynamic atomic limit approximation (DALA) with and without Lang-Firsov correction. More importantly still, it suggests an optimized choice for a Bose factor in the sense of the variational principle of Feynman and Peierls. We demonstrate the accuracy of our scheme and present a comparison to calculations within the DALA.
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
Dynamic scaling at classical phase transitions approached through nonequilibrium quenching
NASA Astrophysics Data System (ADS)
Liu, Cheng-Wei; Polkovnikov, Anatoli; Sandvik, Anders W.
2014-02-01
We use Monte Carlo simulations to demonstrate generic scaling aspects of classical phase transitions approached through a quench (or annealing) protocol where the temperature changes as a function of time with velocity v. Using a generalized Kibble-Zurek ansatz, we demonstrate dynamic scaling for different types of stochastic dynamics (Metropolis, Swendsen-Wang, and Wolff) on Ising models in two and higher dimensions. We show that there are dual scaling functions governing the dynamic scaling, which together describe the scaling behavior in the entire velocity range v ∈[0,∞). These functions have asymptotics corresponding to the adiabatic and diabatic limits, and close to these limits they are perturbative in v and 1/v, respectively. Away from their perturbative domains, both functions cross over into the same universal power-law scaling form governed by the static and dynamic critical exponents (as well as an exponent characterizing the quench protocol). As a by-product of the scaling studies, we obtain high-precision estimates of the dynamic exponent z for the two-dimensional Ising model subject to the three variants of Monte Carlo dynamics: for single-spin Metropolis updates zM=2.1767(5), for Swendsen-Wang multicluster updates zSW=0.297(3), and for Wolff single-cluster updates zW=0.30(2). For Wolff dynamics, we find an interesting behavior with a nonanalytic breakdown of the quasiadiabatic and diabatic scalings, instead of the generic smooth crossover described by a power law. We interpret this disconnect between the two scaling regimes as a dynamic phase transition of the Wolff algorithm, caused by an effective sudden loss of ergodicity at high velocity.
Successional state dynamics: a novel approach to modeling nonequilibrium foodweb dynamics.
Klausmeier, C A
2010-02-21
Communities and ecosystems are often far from equilibrium, but our understanding of nonequilibrium dynamics has been hampered by a paucity of analytical tools. Here I describe a novel approach to modeling seasonally forced food webs, called "successional state dynamics" (SSD). It is applicable to communities where species dynamics are fast relative to the external forcing, such as plankton and other microbes, diseases, and some insect communities. The approach treats succession as a series of state transitions driven by both the internal dynamics of species interactions and external forcing. First, I motivate the approach with numerical solutions of a seasonally forced predator-prey model. Second, I describe how to set up and analyze an SSD model. Finally, I apply the techniques to three additional models of two-species interactions: resource competition (r-K selection), facilitation, and flip-flop competition (where the competitive hierarchy alternates over time). This approach allows easy and thorough exploration of how dynamics depend on the environmental forcing regime, and uncovers unexpected phenomena such as multiple stable annual trajectories and year-to-year irregularity in successional trajectories (chaos).
A novel similarity comparison approach for dynamic ECG series.
Yin, Hong; Zhu, Xiaoqian; Ma, Shaodong; Yang, Shuqiang; Chen, Liqian
2015-01-01
The heart sound signal is a reflection of heart and vascular system motion. Long-term continuous electrocardiogram (ECG) contains important information which can be helpful to prevent heart failure. A single piece of a long-term ECG recording usually consists of more than one hundred thousand data points in length, making it difficult to derive hidden features that may be reflected through dynamic ECG monitoring, which is also very time-consuming to analyze. In this paper, a Dynamic Time Warping based on MapReduce (MRDTW) is proposed to make prognoses of possible lesions in patients. Through comparison of a real-time ECG of a patient with the reference sets of normal and problematic cardiac waveforms, the experimental results reveal that our approach not only retains high accuracy, but also greatly improves the efficiency of the similarity measure in dynamic ECG series.
Logical Attractors: a Boolean Approach to the Dynamics of Psychosis
NASA Astrophysics Data System (ADS)
Kupper, Z.; Hoffmann, H.
A Boolean modeling approach to attractors in the dynamics of psychosis is presented: Kinetic Logic, originating from R. Thomas, describes systems on an intermediate level between a purely verbal, qualitative description and a description using nonlinear differential equations. With this method we may model impact, feedback and temporal evolution, as well as analyze the resulting attractors. In our previous research the method has been applied to general and more specific questions in the dynamics of psychotic disorders. In this paper a model is introduced that describes different dynamical patterns of chronic psychosis in the context of vocational rehabilitation. It also shows to be useful in formulating and exploring possible treatment strategies. Finally, some of the limitations and benefits of Kinetic Logic as a modeling tool for psychology and psychiatry are discussed.
Traditional Chinese medicine: potential approaches from modern dynamical complexity theories.
Ma, Yan; Zhou, Kehua; Fan, Jing; Sun, Shuchen
2016-03-01
Despite the widespread use of traditional Chinese medicine (TCM) in clinical settings, proving its effectiveness via scientific trials is still a challenge. TCM views the human body as a complex dynamical system, and focuses on the balance of the human body, both internally and with its external environment. Such fundamental concepts require investigations using system-level quantification approaches, which are beyond conventional reductionism. Only methods that quantify dynamical complexity can bring new insights into the evaluation of TCM. In a previous article, we briefly introduced the potential value of Multiscale Entropy (MSE) analysis in TCM. This article aims to explain the existing challenges in TCM quantification, to introduce the consistency of dynamical complexity theories and TCM theories, and to inspire future system-level research on health and disease.
An implicit and adaptive nonlinear frequency domain approach for periodic viscous flows
NASA Astrophysics Data System (ADS)
Mosahebi, A.; Nadarajah, S.
2014-12-01
An implicit nonlinear Lower-Upper symmetric Gauss-Seidel (LU-SGS) solver has been extended to the adaptive Nonlinear Frequency Domain method (adaptive NLFD) for periodic viscous flows. The discretized equations are linearized in both spatial and temporal directions, yielding an innovative segregate approach, where the effects of the neighboring cells are transferred to the right-hand-side and are updated iteratively. This property of the solver is aligned with the adaptive NLFD concept, in which different cells have different number of modes; hence, should be treated individually. The segregate analysis of the modal equations prevents assembling and inversion of a large left-hand-side matrix, when high number of modes are involved. This is an important characteristic for a selected flow solver of the adaptive NLFD method, where a high modal content may be required in highly unsteady parts of the flow field. The implicit nonlinear LU-SGS solver has demonstrated to be both robust and computationally efficient as the number of modes is increased. The developed solver is thoroughly validated for the laminar vortex shedding behind a stationary cylinder, high angle of attack NACA0012 airfoil, and a plunging NACA0012 airfoil. An order of magnitude improvement in the computational time is observed through the developed implicit approach over the classical modified 5-stage Runge-Kutta method.
Skjaerven, Lars; Reuter, Nathalie; Martinez, Aurora
2011-12-01
Biomolecules possess important dynamical properties that enable them to adapt and alternate their conformation as a response to environmental stimuli. Recent advancements in computational resources and methodology allow a higher capability to mimic in vitro conditions and open up the possibility of studying large systems over longer timescales. Here, we describe commonly used computational approaches for studying the dynamic properties of proteins. We review a selected set of simulation studies on ligand-induced changes in the chaperonin GroEL-GroES, a molecular folding machine, maltose-binding protein, a prototypical member of the periplasmic binding proteins, and the bacterial ribosomal A-site, focusing on aminoglycoside antibiotic recognition. We also discuss a recent quantitative reconstruction of the binding process of benzamidine and trypsin. These studies contribute to the understanding and further development of the medicinal regulation of large biomolecular systems.
A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults
NASA Technical Reports Server (NTRS)
Tang, Liang; DeCastro, Jonathan A.; Zhang, Xiaodong; Farfan-Ramos, Luis; Simon, Donald L.
2010-01-01
A challenging problem in aircraft engine health management (EHM) system development is to detect and isolate faults in system components (i.e., compressor, turbine), actuators, and sensors. Existing nonlinear EHM methods often deal with component faults, actuator faults, and sensor faults separately, which may potentially lead to incorrect diagnostic decisions and unnecessary maintenance. Therefore, it would be ideal to address sensor faults, actuator faults, and component faults under one unified framework. This paper presents a systematic and unified nonlinear adaptive framework for detecting and isolating sensor faults, actuator faults, and component faults for aircraft engines. The fault detection and isolation (FDI) architecture consists of a parallel bank of nonlinear adaptive estimators. Adaptive thresholds are appropriately designed such that, in the presence of a particular fault, all components of the residual generated by the adaptive estimator corresponding to the actual fault type remain below their thresholds. If the faults are sufficiently different, then at least one component of the residual generated by each remaining adaptive estimator should exceed its threshold. Therefore, based on the specific response of the residuals, sensor faults, actuator faults, and component faults can be isolated. The effectiveness of the approach was evaluated using the NASA C-MAPSS turbofan engine model, and simulation results are presented.
Localized dynamic light scattering: a new approach to dynamic measurements in optical microscopy.
Meller, A; Bar-Ziv, R; Tlusty, T; Moses, E; Stavans, J; Safran, S A
1998-03-01
We present a new approach to probing single-particle dynamics that uses dynamic light scattering from a localized region. By scattering a focused laser beam from a micron-size particle, we measure its spatial fluctuations via the temporal autocorrelation of the scattered intensity. We demonstrate the applicability of this approach by measuring the three-dimensional force constants of a single bead and a pair of beads trapped by laser tweezers. The scattering equations that relate the scattered intensity autocorrelation to the particle position correlation function are derived. This technique has potential applications for measurement of biomolecular force constants and probing viscoelastic properties of complex media.
Neural network approaches to dynamic collision-free trajectory generation.
Yang, S X; Meng, M
2001-01-01
In this paper, dynamic collision-free trajectory generation in a nonstationary environment is studied using biologically inspired neural network approaches. The proposed neural network is topologically organized, where the dynamics of each neuron is characterized by a shunting equation or an additive equation. The state space of the neural network can be either the Cartesian workspace or the joint space of multi-joint robot manipulators. There are only local lateral connections among neurons. The real-time optimal trajectory is generated through the dynamic activity landscape of the neural network without explicitly searching over the free space nor the collision paths, without explicitly optimizing any global cost functions, without any prior knowledge of the dynamic environment, and without any learning procedures. Therefore the model algorithm is computationally efficient. The stability of the neural network system is guaranteed by the existence of a Lyapunov function candidate. In addition, this model is not very sensitive to the model parameters. Several model variations are presented and the differences are discussed. As examples, the proposed models are applied to generate collision-free trajectories for a mobile robot to solve a maze-type of problem, to avoid concave U-shaped obstacles, to track a moving target and at the same to avoid varying obstacles, and to generate a trajectory for a two-link planar robot with two targets. The effectiveness and efficiency of the proposed approaches are demonstrated through simulation and comparison studies.
A Reduced-frequency Approach for Calculating Dynamic Derivatives
NASA Technical Reports Server (NTRS)
Murman, Scott M.
2005-01-01
Computational Fluid Dynamics (CFD) is increasingly being used to both augment and create an aerodynamic performance database for aircraft configurations. This aerodynamic database contains the response of the aircraft to varying flight conditions and control surface deflections. The current work presents a novel method for calculating dynamic stability derivatives which reduces the computational cost over traditional unsteady CFD approaches by an order of magnitude, while still being applicable to arbitrarily complex geometries over a wide range of flow regimes. The primary thesis of this work is that the response to a forced motion can often be represented with a small, predictable number of frequency components without loss of accuracy. By resolving only those frequencies of interest, the computational effort is significantly reduced so that the routine calculation of dynamic derivatives becomes practical. The current implementation uses this same non-linear, frequency-domain approach and extends the application to the 3-D Euler equations. The current work uses a Cartesian, embedded-boundary method to automate the generation of dynamic stability derivatives.
Differential flatness properties and multivariable adaptive control of ovarian system dynamics
NASA Astrophysics Data System (ADS)
Rigatos, Gerasimos
2016-12-01
The ovarian system exhibits nonlinear dynamics which is modeled by a set of coupled nonlinear differential equations. The paper proposes adaptive fuzzy control based on differential flatness theory for the complex dynamics of the ovarian system. It is proven that the dynamic model of the ovarian system, having as state variables the LH and the FSH hormones and their derivatives, is a differentially flat one. This means that all its state variables and its control inputs can be described as differential functions of the flat output. By exploiting differential flatness properties the system's dynamic model is written in the multivariable linear canonical (Brunovsky) form, for which the design of a state feedback controller becomes possible. After this transformation, the new control inputs of the system contain unknown nonlinear parts, which are identified with the use of neurofuzzy approximators. The learning procedure for these estimators is determined by the requirement the first derivative of the closed-loop's Lyapunov function to be a negative one. Moreover, Lyapunov stability analysis shows that H-infinity tracking performance is succeeded for the feedback control loop and this assures improved robustness to the aforementioned model uncertainty as well as to external perturbations. The efficiency of the proposed adaptive fuzzy control scheme is confirmed through simulation experiments.
An Adaptive Particle Filtering Approach to Tracking Modes in a Varying Shallow Ocean Environment
Candy, J V
2011-03-22
The shallow ocean environment is ever changing mostly due to temperature variations in its upper layers (< 100m) directly affecting sound propagation throughout. The need to develop processors that are capable of tracking these changes implies a stochastic as well as an 'adaptive' design. The stochastic requirement follows directly from the multitude of variations created by uncertain parameters and noise. Some work has been accomplished in this area, but the stochastic nature was constrained to Gaussian uncertainties. It has been clear for a long time that this constraint was not particularly realistic leading a Bayesian approach that enables the representation of any uncertainty distribution. Sequential Bayesian techniques enable a class of processors capable of performing in an uncertain, nonstationary (varying statistics), non-Gaussian, variable shallow ocean. In this paper adaptive processors providing enhanced signals for acoustic hydrophonemeasurements on a vertical array as well as enhanced modal function estimates are developed. Synthetic data is provided to demonstrate that this approach is viable.
Barroso, L M A; Teodoro, P E; Nascimento, M; Torres, F E; Dos Santos, A; Corrêa, A M; Sagrilo, E; Corrêa, C C G; Silva, F A; Ceccon, G
2016-03-11
This study aimed to verify that a Bayesian approach could be used for the selection of upright cowpea genotypes with high adaptability and phenotypic stability, and the study also evaluated the efficiency of using informative and minimally informative a priori distributions. Six trials were conducted in randomized blocks, and the grain yield of 17 upright cowpea genotypes was assessed. To represent the minimally informative a priori distributions, a probability distribution with high variance was used, and a meta-analysis concept was adopted to represent the informative a priori distributions. Bayes factors were used to conduct comparisons between the a priori distributions. The Bayesian approach was effective for selection of upright cowpea genotypes with high adaptability and phenotypic stability using the Eberhart and Russell method. Bayes factors indicated that the use of informative a priori distributions provided more accurate results than minimally informative a priori distributions.
A Three-Step Approach with Adaptive Additive Magnitude Selection for the Sharpening of Images
Lee, Tien-Lin
2014-01-01
Aimed to find the additive magnitude automatically and adaptively, we propose a three-step and model-based approach for the sharpening of images in this paper. In the first pass, a Grey prediction model is applied to find a global maximal additive magnitude so that the condition of oversharpening in images to be sharpened can be avoided. During the second pass, edge pixels are picked out with our previously proposed edge detection mechanism. In this pass, a low-pass filter is also applied so that isolated pixels will not be regarded as around an edge. In the final pass, those pixels detected as around an edge are adjusted adaptively based on the local statistics, and those nonedge pixels are kept unaltered. Extensive experiments on natural images as well as medical images with subjective and objective evaluations will be given to demonstrate the usefulness of the proposed approach. PMID:25309951
Insights into nuclear dynamics using live-cell imaging approaches.
Bigley, Rachel B; Payumo, Alexander Y; Alexander, Jeffrey M; Huang, Guo N
2017-03-01
The nucleus contains the genetic blueprint of the cell and myriad interactions within this subcellular structure are required for gene regulation. In the current scientific era, characterization of these gene regulatory networks through biochemical techniques coupled with systems-wide 'omic' approaches has become commonplace. However, these strategies are limited because they represent a mere snapshot of the cellular state. To obtain a holistic understanding of nuclear dynamics, relevant molecules must be studied in their native contexts in living systems. Live-cell imaging approaches are capable of providing quantitative assessment of the dynamics of gene regulatory interactions within the nucleus. We survey recent insights into what live-cell imaging approaches have provided the field of nuclear dynamics. In this review, we focus on interactions of DNA with other DNA loci, proteins, RNA, and the nuclear envelope. WIREs Syst Biol Med 2017, 9:e1372. doi: 10.1002/wsbm.1372 For further resources related to this article, please visit the WIREs website.
Toward a dynamic approach of THA planning based on ultrasound.
Dardenne, Guillaume; Dusseau, Stéphane; Hamitouche, Chafiaâ; Lefèvre, Christian; Stindel, Eric
2009-04-01
The risk of dislocation after THA reportedly is minimized if the acetabular implant is oriented at 45 degrees inclination and 15 degrees anteversion with respect to the anterior pelvic plane. This reference plane now is used in computer-assisted protocols. However, this static approach may lead to postoperative instability because the dynamic variations of the pelvis influence effective cup orientation and are not taken into account in this approach. We propose an ultrasound tool to register the preoperative dynamics of the pelvis for THA planning during computer-assisted surgery. To assess this pelvic behavior and its consequences on implant orientation, we tested a new 2.5-dimensional ultrasound-based approach. The pelvic flexion was registered in sitting, standing, and supine positions in 20 subjects. The mean values were -25.2 degrees +/- 5.8 degrees (standard deviation), 2.4 degrees +/- 5.1 degrees , and 6.8 degrees +/- 3.5 degrees , respectively. The mean functional anteversion varied by 26 degrees and the mean functional inclination by 12 degrees depending on the pelvic flexion. We therefore recommend including dynamic pelvic behavior to minimize dislocation risk. The notion of a safe zone should be revisited and extended to include changes with activity.
Toward a Dynamic Approach of THA Planning Based on Ultrasound
Dusseau, Stéphane; Hamitouche, Chafiaâ; Lefèvre, Christian; Stindel, Eric
2008-01-01
The risk of dislocation after THA reportedly is minimized if the acetabular implant is oriented at 45° inclination and 15° anteversion with respect to the anterior pelvic plane. This reference plane now is used in computer-assisted protocols. However, this static approach may lead to postoperative instability because the dynamic variations of the pelvis influence effective cup orientation and are not taken into account in this approach. We propose an ultrasound tool to register the preoperative dynamics of the pelvis for THA planning during computer-assisted surgery. To assess this pelvic behavior and its consequences on implant orientation, we tested a new 2.5-dimensional ultrasound-based approach. The pelvic flexion was registered in sitting, standing, and supine positions in 20 subjects. The mean values were −25.2° ± 5.8° (standard deviation), 2.4° ± 5.1°, and 6.8° ± 3.5°, respectively. The mean functional anteversion varied by 26° and the mean functional inclination by 12° depending on the pelvic flexion. We therefore recommend including dynamic pelvic behavior to minimize dislocation risk. The notion of a safe zone should be revisited and extended to include changes with activity. PMID:18688691
Dynamic mask: new approach to laser engraving of halftone images
NASA Astrophysics Data System (ADS)
Kadan, Victor N.; Pekarik, Alexander S.; Estrela Liopis, Rafael V.
1997-03-01
New approach to laser engraving of half tone images has been proposed and tested. Combining two basic approaches to laser engraving -- single pulse mask imaging and raster element construction by pack of laser pulses -- the new system constructs every individual raster element by imaging on the workpiece surface a dynamic mask of controlled size. The dynamic mask shape corresponds to the required raster element shape. This approach offers several important advantages over the conventional ones: (1) analog control of the mask shape provides gray level continuum, thus ensuring the image quality, unattainable by other means; (2) raster element marking by single laser pulse provides very good marking rate. It takes only one scan of the writing laser head to mark raster line. Much more powerful laser pulses can be used to engrave complete raster element by single pulse instead of its point-by-point construction by consecutive laser pulses; (3) the influence of laser beam quality parameters, such as beam divergence, and power instabilities on the gray level has been greatly reduced because raster element shape primarily depends on the mask shape and not on the power level and beam divergence. Dynamic mask system can be used both with cw and pulsed laser. Gray scale tones can be reproduced by the linear raster line width in the first case. Advantages of the new device have been demonstrated by engravings on stone, wood, etc. made with 50 W carbon-dioxide laser.
Sun, Tao; Liu, Hongbo; Yu, Hong; Chen, C L Philip
2016-06-28
The central time series crystallizes the common patterns of the set it represents. In this paper, we propose a global constrained degree-pruning dynamic programming (g(dp)²) approach to obtain the central time series through minimizing dynamic time warping (DTW) distance between two time series. The DTW matching path theory with global constraints is proved theoretically for our degree-pruning strategy, which is helpful to reduce the time complexity and computational cost. Our approach can achieve the optimal solution between two time series. An approximate method to the central time series of multiple time series [called as m_g(dp)²] is presented based on DTW barycenter averaging and our g(dp)² approach by considering hierarchically merging strategy. As illustrated by the experimental results, our approaches provide better within-group sum of squares and robustness than other relevant algorithms.
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.
NASA Technical Reports Server (NTRS)
Gupta, Pramod; Guenther, Kurt; Hodgkinson, John; Jacklin, Stephen; Richard, Michael; Schumann, Johann; Soares, Fola
2005-01-01
Modern exploration missions require modern control systems-control systems that can handle catastrophic changes in the system's behavior, compensate for slow deterioration in sustained operations, and support fast system ID. Adaptive controllers, based upon Neural Networks have these capabilities, but they can only be used safely if proper verification & validation (V&V) can be done. In this paper we present our V & V approach and simulation result within NASA's Intelligent Flight Control Systems (IFCS).
NASA Astrophysics Data System (ADS)
Tago, J.; Cruz-Atienza, V. M.; Etienne, V.; Virieux, J.; Benjemaa, M.; Sanchez-Sesma, F. J.
2010-12-01
Simulating any realistic seismic scenario requires incorporating physical basis into the model. Considering both the dynamics of the rupture process and the anelastic attenuation of seismic waves is essential to this purpose and, therefore, we choose to extend the hp-adaptive Discontinuous Galerkin finite-element method to integrate these physical aspects. The 3D elastodynamic equations in an unstructured tetrahedral mesh are solved with a second-order time marching approach in a high-performance computing environment. The first extension incorporates the viscoelastic rheology so that the intrinsic attenuation of the medium is considered in terms of frequency dependent quality factors (Q). On the other hand, the extension related to dynamic rupture is integrated through explicit boundary conditions over the crack surface. For this visco-elastodynamic formulation, we introduce an original discrete scheme that preserves the optimal code performance of the elastodynamic equations. A set of relaxation mechanisms describes the behavior of a generalized Maxwell body. We approximate almost constant Q in a wide frequency range by selecting both suitable relaxation frequencies and anelastic coefficients characterizing these mechanisms. In order to do so, we solve an optimization problem which is critical to minimize the amount of relaxation mechanisms. Two strategies are explored: 1) a least squares method and 2) a genetic algorithm (GA). We found that the improvement provided by the heuristic GA method is negligible. Both optimization strategies yield Q values within the 5% of the target constant Q mechanism. Anelastic functions (i.e. memory variables) are introduced to efficiently evaluate the time convolution terms involved in the constitutive equations and thus to minimize the computational cost. The incorporation of anelastic functions implies new terms with ordinary differential equations in the mathematical formulation. We solve these equations using the same order
Forging tool shape optimization using pseudo inverse approach and adaptive incremental approach
NASA Astrophysics Data System (ADS)
Halouani, A.; Meng, F. J.; Li, Y. M.; Labergère, C.; Abbès, B.; Lafon, P.; Guo, Y. Q.
2013-05-01
This paper presents a simplified finite element method called "Pseudo Inverse Approach" (PIA) for tool shape design and optimization in multi-step cold forging processes. The approach is based on the knowledge of the final part shape. Some intermediate configurations are introduced and corrected by using a free surface method to consider the deformation paths without contact treatment. A robust direct algorithm of plasticity is implemented by using the equivalent stress notion and tensile curve. Numerical tests have shown that the PIA is very fast compared to the incremental approach. The PIA is used in an optimization procedure to automatically design the shapes of the preform tools. Our objective is to find the optimal preforms which minimize the equivalent plastic strain and punch force. The preform shapes are defined by B-Spline curves. A simulated annealing algorithm is adopted for the optimization procedure. The forging results obtained by the PIA are compared to those obtained by the incremental approach to show the efficiency and accuracy of the PIA.
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.
Tsai, Jason Sheng-Hong; Du, Yan-Yi; Huang, Pei-Hsiang; Guo, Shu-Mei; Shieh, Leang-San; Chen, Yuhua
2011-07-01
In this paper, a digital redesign methodology of the iterative learning-based decentralized adaptive tracker is proposed to improve the dynamic performance of sampled-data linear large-scale control systems consisting of N interconnected multi-input multi-output subsystems, so that the system output will follow any trajectory which may not be presented by the analytic reference model initially. To overcome the interference of each sub-system and simplify the controller design, the proposed model reference decentralized adaptive control scheme constructs a decoupled well-designed reference model first. Then, according to the well-designed model, this paper develops a digital decentralized adaptive tracker based on the optimal analog control and prediction-based digital redesign technique for the sampled-data large-scale coupling system. In order to enhance the tracking performance of the digital tracker at specified sampling instants, we apply the iterative learning control (ILC) to train the control input via continual learning. As a result, the proposed iterative learning-based decentralized adaptive tracker not only has robust closed-loop decoupled property but also possesses good tracking performance at both transient and steady state. Besides, evolutionary programming is applied to search for a good learning gain to speed up the learning process of ILC.
An adaptive locally linear embedding manifold learning approach for hyperspectral target detection
NASA Astrophysics Data System (ADS)
Ziemann, Amanda K.; Messinger, David W.
2015-05-01
Algorithms for spectral analysis commonly use parametric or linear models of the data. Research has shown, however, that hyperspectral data -- particularly in materially cluttered scenes -- are not always well-modeled by statistical or linear methods. Here, we propose an approach to hyperspectral target detection that is based on a graph theory model of the data and a manifold learning transformation. An adaptive nearest neighbor (ANN) graph is built on the data, and then used to implement an adaptive version of locally linear embedding (LLE). We artificially induce a target manifold and incorporate it into the adaptive LLE transformation. The artificial target manifold helps to guide the separation of the target data from the background data in the new, transformed manifold coordinates. Then, target detection is performed in the manifold space using Spectral Angle Mapper. This methodology is an improvement over previous iterations of this approach due to the incorporation of ANN, the artificial target manifold, and the choice of detector in the transformed space. We implement our approach in a spatially local way: the image is delineated into square tiles, and the detection maps are normalized across the entire image. Target detection results will be shown using laboratory-measured and scene-derived target data from the SHARE 2012 collect.
NASA Technical Reports Server (NTRS)
Grantham, Katie
2003-01-01
Reusable Launch Vehicles (RLVs) have different mission requirements than the Space Shuttle, which is used for benchmark guidance design. Therefore, alternative Terminal Area Energy Management (TAEM) and Approach and Landing (A/L) Guidance schemes can be examined in the interest of cost reduction. A neural network based solution for a finite horizon trajectory optimization problem is presented in this paper. In this approach the optimal trajectory of the vehicle is produced by adaptive critic based neural networks, which were trained off-line to maintain a gradual glideslope.
Adaptive quasi-Newton algorithm for source extraction via CCA approach.
Zhang, Wei-Tao; Lou, Shun-Tian; Feng, Da-Zheng
2014-04-01
This paper addresses the problem of adaptive source extraction via the canonical correlation analysis (CCA) approach. Based on Liu's analysis of CCA approach, we propose a new criterion for source extraction, which is proved to be equivalent to the CCA criterion. Then, a fast and efficient online algorithm using quasi-Newton iteration is developed. The stability of the algorithm is also analyzed using Lyapunov's method, which shows that the proposed algorithm asymptotically converges to the global minimum of the criterion. Simulation results are presented to prove our theoretical analysis and demonstrate the merits of the proposed algorithm in terms of convergence speed and successful rate for source extraction.
A Neural Network Approach to Intention Modeling for User-Adapted Conversational Agents
Griol, David
2016-01-01
Spoken dialogue systems have been proposed to enable a more natural and intuitive interaction with the environment and human-computer interfaces. In this contribution, we present a framework based on neural networks that allows modeling of the user's intention during the dialogue and uses this prediction to dynamically adapt the dialogue model of the system taking into consideration the user's needs and preferences. We have evaluated our proposal to develop a user-adapted spoken dialogue system that facilitates tourist information and services and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, and the quality perceived by the users. PMID:26819592
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.
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
Saturated Nussbaum Function Based Approach for Robotic Systems With Unknown Actuator Dynamics.
Chen, Ci; Liu, Zhi; Zhang, Yun; Chen, C L Philip; Xie, Shengli
2016-10-01
This paper presents a saturated Nussbaum function based approach for robotic systems with unknown actuator dynamics. To eliminate the effect of the control shock from the traditional Nussbaum function, a new type of the saturated Nussbaum function is developed with the idea of time-elongation. Moreover, by exploiting properties of the proposed Nussbaum function, a promising theorem is established to deal with unknown multiple actuator nonlinearities. In what follows, the proposed theorem is integrated with the adaptive control technique such that the stability analysis of the robotic system is completed. It thus guarantees that the state of the robotic system asymptotically converges to the desired trajectory. Finally, comparative studies are carried out to validate the effectiveness and the superiority of the proposed approach.
NASA Astrophysics Data System (ADS)
Davis, L. C.
2013-09-01
The dynamics of a platoon of adaptive cruise control vehicles is analyzed for a general mechanical response of the vehicle. Effects of acceleration-feedback control that were not previously studied are found. For small acceleration-feedback gain, which produces marginally string-stable behavior, the reduction of a disturbance (with increasing car number n) is found to be faster than for the maximum allowable gain. The asymptotic magnitude of a disturbance is shown to fall off as erf({ct.}/{√n}) when n→∞. For gain approaching the lower limit of stability, oscillations in acceleration associated with a secondary maximum in the transfer function (as a function of frequency) can occur. A frequency-dependent gain that reduces the secondary maximum, but does not affect the transfer function near zero frequency, is proposed. Performance is thereby improved by elimination of the undesirable oscillations while the rapid disturbance reduction is retained.
Zhang, Huaguang; Jiang, He; Luo, Chaomin; Xiao, Geyang
2016-10-03
In this paper, we investigate the nonzero-sum games for a class of discrete-time (DT) nonlinear systems by using a novel policy iteration (PI) adaptive dynamic programming (ADP) method. The main idea of our proposed PI scheme is to utilize the iterative ADP algorithm to obtain the iterative control policies, which not only ensure the system to achieve stability but also minimize the performance index function for each player. This paper integrates game theory, optimal control theory, and reinforcement learning technique to formulate and handle the DT nonzero-sum games for multiplayer. First, we design three actor-critic algorithms, an offline one and two online ones, for the PI scheme. Subsequently, neural networks are employed to implement these algorithms and the corresponding stability analysis is also provided via the Lyapunov theory. Finally, a numerical simulation example is presented to demonstrate the effectiveness of our proposed approach.
Motion-adaptive spatio-temporal regularization for accelerated dynamic MRI.
Asif, M Salman; Hamilton, Lei; Brummer, Marijn; Romberg, Justin
2013-09-01
Accelerated magnetic resonance imaging techniques reduce signal acquisition time by undersampling k-space. A fundamental problem in accelerated magnetic resonance imaging is the recovery of quality images from undersampled k-space data. Current state-of-the-art recovery algorithms exploit the spatial and temporal structures in underlying images to improve the reconstruction quality. In recent years, compressed sensing theory has helped formulate mathematical principles and conditions that ensure recovery of (structured) sparse signals from undersampled, incoherent measurements. In this article, a new recovery algorithm, motion-adaptive spatio-temporal regularization, is presented that uses spatial and temporal structured sparsity of MR images in the compressed sensing framework to recover dynamic MR images from highly undersampled k-space data. In contrast to existing algorithms, our proposed algorithm models temporal sparsity using motion-adaptive linear transformations between neighboring images. The efficiency of motion-adaptive spatio-temporal regularization is demonstrated with experiments on cardiac magnetic resonance imaging for a range of reduction factors. Results are also compared with k-t FOCUSS with motion estimation and compensation-another recently proposed recovery algorithm for dynamic magnetic resonance imaging. .
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.
Abrams, Peter A; Ruokolainen, Lasse
2011-05-21
This article uses simple models to explore the impact of adaptive movement by consumers on the population dynamics of a consumer-resource metacommunity consisting of two identical patches. Consumer-resource interactions within a patch are described by the Rosenzweig-MacArthur predator-prey model, and these dynamics are assumed to be cyclic in the absence of movement. The per capita movement rate from one patch to the other is an increasing function of the difference between the per capita birth minus death rate in the destination patch and that in the currently occupied patch. Several variations on this model are considered. Results show that adaptive movement frequently creates anti-phase cycles in the two patches; these suppress the predator-prey cycle and lead to low temporal variation of the total population sizes of both species. Paradoxically, even when movement is very sensitive to the fitness difference between patches, perfect synchrony of patches is often much less likely than in comparable systems with random movement. Under these circumstances adaptive movement of consumers often generates differences in the average properties of the two patches. In addition, mean global densities and responses to global perturbations often differ greatly from similar systems with no movement or random movement.
ERIC Educational Resources Information Center
Karakostas, A.; Demetriadis, S.
2011-01-01
Research on computer-supported collaborative learning (CSCL) has strongly emphasized the value of providing student support of either fixed (e.g. collaboration scripts) or dynamic form (e.g. adaptive supportive interventions). Currently, however, there is not sufficient evidence corroborating the potential of adaptive support methods to improve…
A statistical state dynamics approach to wall turbulence
NASA Astrophysics Data System (ADS)
Farrell, B. F.; Gayme, D. F.; Ioannou, P. J.
2017-03-01
This paper reviews results obtained using statistical state dynamics (SSD) that demonstrate the benefits of adopting this perspective for understanding turbulence in wall-bounded shear flows. The SSD approach used in this work employs a second-order closure that retains only the interaction between the streamwise mean flow and the streamwise mean perturbation covariance. This closure restricts nonlinearity in the SSD to that explicitly retained in the streamwise constant mean flow together with nonlinear interactions between the mean flow and the perturbation covariance. This dynamical restriction, in which explicit perturbation-perturbation nonlinearity is removed from the perturbation equation, results in a simplified dynamics referred to as the restricted nonlinear (RNL) dynamics. RNL systems, in which a finite ensemble of realizations of the perturbation equation share the same mean flow, provide tractable approximations to the SSD, which is equivalent to an infinite ensemble RNL system. This infinite ensemble system, referred to as the stochastic structural stability theory system, introduces new analysis tools for studying turbulence. RNL systems provide computationally efficient means to approximate the SSD and produce self-sustaining turbulence exhibiting qualitative features similar to those observed in direct numerical simulations despite greatly simplified dynamics. The results presented show that RNL turbulence can be supported by as few as a single streamwise varying component interacting with the streamwise constant mean flow and that judicious selection of this truncated support or `band-limiting' can be used to improve quantitative accuracy of RNL turbulence. These results suggest that the SSD approach provides new analytical and computational tools that allow new insights into wall turbulence.
A statistical state dynamics approach to wall turbulence.
Farrell, B F; Gayme, D F; Ioannou, P J
2017-03-13
This paper reviews results obtained using statistical state dynamics (SSD) that demonstrate the benefits of adopting this perspective for understanding turbulence in wall-bounded shear flows. The SSD approach used in this work employs a second-order closure that retains only the interaction between the streamwise mean flow and the streamwise mean perturbation covariance. This closure restricts nonlinearity in the SSD to that explicitly retained in the streamwise constant mean flow together with nonlinear interactions between the mean flow and the perturbation covariance. This dynamical restriction, in which explicit perturbation-perturbation nonlinearity is removed from the perturbation equation, results in a simplified dynamics referred to as the restricted nonlinear (RNL) dynamics. RNL systems, in which a finite ensemble of realizations of the perturbation equation share the same mean flow, provide tractable approximations to the SSD, which is equivalent to an infinite ensemble RNL system. This infinite ensemble system, referred to as the stochastic structural stability theory system, introduces new analysis tools for studying turbulence. RNL systems provide computationally efficient means to approximate the SSD and produce self-sustaining turbulence exhibiting qualitative features similar to those observed in direct numerical simulations despite greatly simplified dynamics. The results presented show that RNL turbulence can be supported by as few as a single streamwise varying component interacting with the streamwise constant mean flow and that judicious selection of this truncated support or 'band-limiting' can be used to improve quantitative accuracy of RNL turbulence. These results suggest that the SSD approach provides new analytical and computational tools that allow new insights into wall turbulence.This article is part of the themed issue 'Toward the development of high-fidelity models of wall turbulence at large Reynolds number'.
An Integrated Systems Approach to Designing Climate Change Adaptation Policy in Water Resources
NASA Astrophysics Data System (ADS)
Ryu, D.; Malano, H. M.; Davidson, B.; George, B.
2014-12-01
Climate change projections are characterised by large uncertainties with rainfall variability being the key challenge in designing adaptation policies. Climate change adaptation in water resources shows all the typical characteristics of 'wicked' problems typified by cognitive uncertainty as new scientific knowledge becomes available, problem instability, knowledge imperfection and strategic uncertainty due to institutional changes that inevitably occur over time. Planning that is characterised by uncertainties and instability requires an approach that can accommodate flexibility and adaptive capacity for decision-making. An ability to take corrective measures in the event that scenarios and responses envisaged initially derive into forms at some future stage. We present an integrated-multidisciplinary and comprehensive framework designed to interface and inform science and decision making in the formulation of water resource management strategies to deal with climate change in the Musi Catchment of Andhra Pradesh, India. At the core of this framework is a dialogue between stakeholders, decision makers and scientists to define a set of plausible responses to an ensemble of climate change scenarios derived from global climate modelling. The modelling framework used to evaluate the resulting combination of climate scenarios and adaptation responses includes the surface and groundwater assessment models (SWAT & MODFLOW) and the water allocation modelling (REALM) to determine the water security of each adaptation strategy. Three climate scenarios extracted from downscaled climate models were selected for evaluation together with four agreed responses—changing cropping patterns, increasing watershed development, changing the volume of groundwater extraction and improving irrigation efficiency. Water security in this context is represented by the combination of level of water availability and its associated security of supply for three economic activities (agriculture
NASA Astrophysics Data System (ADS)
Roman, Carolina
2010-05-01
Climate change is gaining attention as a significant strategic issue for localities that rely on their business sectors for economic viability. For businesses in the tourism sector, considerable research effort has sought to characterise the vulnerability to the likely impacts of future climate change through scenarios or ‘end-point' approaches (Kelly & Adger, 2000). Whilst useful, there are few demonstrable case studies that complement such work with a ‘start-point' approach that seeks to explore contextual vulnerability (O'Brien et al., 2007). This broader approach is inclusive of climate change as a process operating within a biophysical system and allows recognition of the complex interactions that occur in the coupled human-environmental system. A problem-oriented and interdisciplinary approach was employed at Alpine Shire, in northeast Victoria Australia, to explore the concept of contextual vulnerability and adaptability to stressors that include, but are not limited to climatic change. Using a policy sciences approach, the objective was to identify factors that influence existing vulnerabilities and that might consequently act as barriers to effective adaptation for the Shire's business community involved in the tourism sector. Analyses of results suggest that many threats, including the effects climate change, compete for the resources, strategy and direction of local tourism management bodies. Further analysis of conditioning factors revealed that many complex and interacting factors define the vulnerability and adaptive capacity of the Shire's tourism sector to the challenges of global change, which collectively have more immediate implications for policy and planning than long-term future climate change scenarios. An approximation of the common interest, i.e. enhancing capacity in business acumen amongst tourism operators, would facilitate adaptability and sustainability through the enhancement of social capital in this business community. Kelly, P
NASA Astrophysics Data System (ADS)
Kienberger, S.; Notenbaert, A.; Zeil, P.; Bett, B.; Hagenlocher, M.; Omolo, A.
2012-04-01
Climate change has been stated as being one of the greatest challenges to global health in the current century. Climate change impacts on human health and the socio-economic and related poverty consequences are however still poorly understood. While epidemiological issues are strongly coupled with environmental and climatic parameters, the social and economic circumstances of populations might be of equal or even greater importance when trying to identify vulnerable populations and design appropriate and well-targeted adaptation measures. The inter-linkage between climate change, human health risk and socio-economic impacts remains an important - but largely outstanding - research field. We present an overview on how risk is traditionally being conceptualised in the human health domain and reflect critically on integrated approaches as being currently used in the climate change context. The presentation will also review existing approaches, and how they can be integrated towards adaptation tools. Following this review, an integrated risk concept is being presented, which has been currently adapted under the EC FP7 research project (HEALTHY FUTURES; http://www.healthyfutures.eu/). In this approach, health risk is not only defined through the disease itself (as hazard) but also by the inherent vulnerability of the system, population or region under study. It is in fact the interaction of environment and society that leads to the development of diseases and the subsequent risk of being negatively affected by it. In this conceptual framework vulnerability is being attributed to domains of lack of resilience as well as underlying preconditions determining susceptibilities. To fulfil a holistic picture vulnerability can be associated to social, economic, environmental, institutional, cultural and physical dimensions. The proposed framework also establishes the important nexus to adaptation and how different measures can be related to avoid disease outbreaks, reduce
McCay, Paul; Fuszard, Matthew; Botting, Catherine H.; Abram, Florence; O'Flaherty, Vincent
2013-01-01
Low-temperature anaerobic digestion (LTAD) technology is underpinned by a diverse microbial community. The methanogenic archaea represent a key functional group in these consortia, undertaking CO2 reduction as well as acetate and methylated C1 metabolism with subsequent biogas (40 to 60% CH4 and 30 to 50% CO2) formation. However, the cold adaptation strategies, which allow methanogens to function efficiently in LTAD, remain unclear. Here, a pure-culture proteomic approach was employed to study the functional characteristics of Methanosarcina barkeri (optimum growth temperature, 37°C), which has been detected in LTAD bioreactors. Two experimental approaches were undertaken. The first approach aimed to characterize a low-temperature shock response (LTSR) of M. barkeri DSMZ 800T grown at 37°C with a temperature drop to 15°C, while the second experimental approach aimed to examine the low-temperature adaptation strategies (LTAS) of the same strain when it was grown at 15°C. The latter experiment employed cell viability and growth measurements (optical density at 600 nm [OD600]), which directly compared M. barkeri cells grown at 15°C with those grown at 37°C. During the LTSR experiment, a total of 127 proteins were detected in 37°C and 15°C samples, with 20 proteins differentially expressed with respect to temperature, while in the LTAS experiment 39% of proteins identified were differentially expressed between phases of growth. Functional categories included methanogenesis, cellular information processing, and chaperones. By applying a polyphasic approach (proteomics and growth studies), insights into the low-temperature adaptation capacity of this mesophilically characterized methanogen were obtained which suggest that the metabolically diverse Methanosarcinaceae could be functionally relevant for LTAD systems. PMID:23645201
Sturmberg, Joachim P; Martin, Carmel M
2010-10-01
Health services demonstrate key features of complex adaptive systems (CAS), they are dynamic and unfold in unpredictable ways, and unfolding events are often unique. To better understand the complex adaptive nature of health systems around a core attractor we propose the metaphor of the health care vortex. We also suggest that in an ideal health care system the core attractor would be personal health attainment. Health care reforms around the world offer an opportunity to analyse health system change from a complex adaptive perspective. At large health care reforms have been pursued disregarding the complex adaptive nature of the health system. The paper details some recent reforms and outlines how to understand their strategies and outcomes, and what could be learnt for future efforts, utilising CAS principles. Current health systems show the inherent properties of a CAS driven by a core attractor of disease and cost containment. We content that more meaningful health systems reform requires the delicate task of shifting the core attractor from disease and cost containment towards health attainment.
Ratliff, Eric A.; Kaduri, Pamela; Masao, Frank; Mbwambo, Jessie K.K.; McCurdy, Sheryl A.
2016-01-01
Contrary to popular belief, policies on drug use are not always based on scientific evidence or composed in a rational manner. Rather, decisions concerning drug policies reflect the negotiation of actors’ ambitions, values, and facts as they organize in different ways around the perceived problems associated with illicit drug use. Drug policy is thus best represented as a complex adaptive system (CAS) that is dynamic, self-organizing, and coevolving. In this analysis, we use a CAS framework to examine how harm reduction emerged around heroin trafficking and use in Tanzania over the past thirty years (1985-present). This account is an organizational ethnography based on of the observant participation of the authors as actors within this system. We review the dynamic history and self-organizing nature of harm reduction, noting how interactions among system actors and components have coevolved with patterns of heroin us, policing, and treatment activities over time. Using a CAS framework, we describe harm reduction as a complex process where ambitions, values, facts, and technologies interact in the Tanzanian socio-political environment. We review the dynamic history and self-organizing nature of heroin policies, noting how the interactions within and between competing prohibitionist and harm reduction policies have changed with patterns of heroin use, policing, and treatment activities over time. Actors learn from their experiences to organize with other actors, align their values and facts, and implement new policies. Using a CAS approach provides researchers and policy actors a better understanding of patterns and intricacies in drug policy. This knowledge of how the system works can help improve the policy process through adaptive action to introduce new actors, different ideas, and avenues for communication into the system. PMID:26790689
Ratliff, Eric A; Kaduri, Pamela; Masao, Frank; Mbwambo, Jessie K K; McCurdy, Sheryl A
2016-04-01
Contrary to popular belief, policies on drug use are not always based on scientific evidence or composed in a rational manner. Rather, decisions concerning drug policies reflect the negotiation of actors' ambitions, values, and facts as they organize in different ways around the perceived problems associated with illicit drug use. Drug policy is thus best represented as a complex adaptive system (CAS) that is dynamic, self-organizing, and coevolving. In this analysis, we use a CAS framework to examine how harm reduction emerged around heroin trafficking and use in Tanzania over the past thirty years (1985-present). This account is an organizational ethnography based on of the observant participation of the authors as actors within this system. We review the dynamic history and self-organizing nature of harm reduction, noting how interactions among system actors and components have coevolved with patterns of heroin us, policing, and treatment activities over time. Using a CAS framework, we describe harm reduction as a complex process where ambitions, values, facts, and technologies interact in the Tanzanian sociopolitical environment. We review the dynamic history and self-organizing nature of heroin policies, noting how the interactions within and between competing prohibitionist and harm reduction policies have changed with patterns of heroin use, policing, and treatment activities over time. Actors learn from their experiences to organize with other actors, align their values and facts, and implement new policies. Using a CAS approach provides researchers and policy actors a better understanding of patterns and intricacies in drug policy. This knowledge of how the system works can help improve the policy process through adaptive action to introduce new actors, different ideas, and avenues for communication into the system.
Adaptation of adaptive optics systems.
NASA Astrophysics Data System (ADS)
Xin, Yu; Zhao, Dazun; Li, Chen
1997-10-01
In the paper, a concept of an adaptation of adaptive optical system (AAOS) is proposed. The AAOS has certain real time optimization ability against the variation of the brightness of detected objects m, atmospheric coherence length rO and atmospheric time constant τ by means of changing subaperture number and diameter, dynamic range, and system's temporal response. The necessity of AAOS using a Hartmann-Shack wavefront sensor and some technical approaches are discussed. Scheme and simulation of an AAOS with variable subaperture ability by use of both hardware and software are presented as an example of the system.