Dynamic Social Community Detection and Its Applications

PubMed Central

Nguyen, Nam P.; Dinh, Thang N.; Shen, Yilin; Thai, My T.

2014-01-01

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods. PMID:24722164

Adaptive Identification and Control of Flow-Induced Cavity Oscillations

NASA Technical Reports Server (NTRS)

Kegerise, M. A.; Cattafesta, L. N.; Ha, C.

2002-01-01

Progress towards an adaptive self-tuning regulator (STR) for the cavity tone problem is discussed in this paper. Adaptive system identification algorithms were applied to an experimental cavity-flow tested as a prerequisite to control. In addition, a simple digital controller and a piezoelectric bimorph actuator were used to demonstrate multiple tone suppression. The control tests at Mach numbers of 0.275, 0.40, and 0.60 indicated approx. = 7dB tone reductions at multiple frequencies. Several different adaptive system identification algorithms were applied at a single freestream Mach number of 0.275. Adaptive finite-impulse response (FIR) filters of orders up to N = 100 were found to be unsuitable for modeling the cavity flow dynamics. Adaptive infinite-impulse response (IIR) filters of comparable order better captured the system dynamics. Two recursive algorithms, the least-mean square (LMS) and the recursive-least square (RLS), were utilized to update the adaptive filter coefficients. Given the sample-time requirements imposed by the cavity flow dynamics, the computational simplicity of the least mean squares (LMS) algorithm is advantageous for real-time control.

Model-Based Adaptive Event-Triggered Control of Strict-Feedback Nonlinear Systems.

PubMed

Li, Yuan-Xin; Yang, Guang-Hong

2018-04-01

This paper is concerned with the adaptive event-triggered control problem of nonlinear continuous-time systems in strict-feedback form. By using the event-sampled neural network (NN) to approximate the unknown nonlinear function, an adaptive model and an associated event-triggered controller are designed by exploiting the backstepping method. In the proposed method, the feedback signals and the NN weights are aperiodically updated only when the event-triggered condition is violated. A positive lower bound on the minimum intersample time is guaranteed to avoid accumulation point. The closed-loop stability of the resulting nonlinear impulsive dynamical system is rigorously proved via Lyapunov analysis under an adaptive event sampling condition. In comparing with the traditional adaptive backstepping design with a fixed sample period, the event-triggered method samples the state and updates the NN weights only when it is necessary. Therefore, the number of transmissions can be significantly reduced. Finally, two simulation examples are presented to show the effectiveness of the proposed control method.

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

NASA Astrophysics Data System (ADS)

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

2016-01-01

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

Adaptive time-sequential binary sensing for high dynamic range imaging

NASA Astrophysics Data System (ADS)

Hu, Chenhui; Lu, Yue M.

2012-06-01

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

Adaptive particle filter for robust visual tracking

NASA Astrophysics Data System (ADS)

Dai, Jianghua; Yu, Shengsheng; Sun, Weiping; Chen, Xiaoping; Xiang, Jinhai

2009-10-01

Object tracking plays a key role in the field of computer vision. Particle filter has been widely used for visual tracking under nonlinear and/or non-Gaussian circumstances. In particle filter, the state transition model for predicting the next location of tracked object assumes the object motion is invariable, which cannot well approximate the varying dynamics of the motion changes. In addition, the state estimate calculated by the mean of all the weighted particles is coarse or inaccurate due to various noise disturbances. Both these two factors may degrade tracking performance greatly. In this work, an adaptive particle filter (APF) with a velocity-updating based transition model (VTM) and an adaptive state estimate approach (ASEA) is proposed to improve object tracking. In APF, the motion velocity embedded into the state transition model is updated continuously by a recursive equation, and the state estimate is obtained adaptively according to the state posterior distribution. The experiment results show that the APF can increase the tracking accuracy and efficiency in complex environments.

NASTRAN computer system level 12.1

NASA Technical Reports Server (NTRS)

Butler, T. G.

1971-01-01

Program uses finite element displacement method for solving linear response of large, three-dimensional structures subject to static, dynamic, thermal, and random loadings. Program adapts to computers of different manufacture, permits up-dating and extention, allows interchange of output and input information between users, and is extensively documented.

Development of force adaptation during childhood.

PubMed

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

2003-03-01

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

A recursive Bayesian updating model of haptic stiffness perception.

PubMed

Wu, Bing; Klatzky, Roberta L

2018-06-01

Stiffness of many materials follows Hooke's Law, but the mechanism underlying the haptic perception of stiffness is not as simple as it seems in the physical definition. The present experiments support a model by which stiffness perception is adaptively updated during dynamic interaction. Participants actively explored virtual springs and estimated their stiffness relative to a reference. The stimuli were simulations of linear springs or nonlinear springs created by modulating a linear counterpart with low-amplitude, half-cycle (Experiment 1) or full-cycle (Experiment 2) sinusoidal force. Experiment 1 showed that subjective stiffness increased (decreased) as a linear spring was positively (negatively) modulated by a half-sinewave force. In Experiment 2, an opposite pattern was observed for full-sinewave modulations. Modeling showed that the results were best described by an adaptive process that sequentially and recursively updated an estimate of stiffness using the force and displacement information sampled over trajectory and time. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Adaptive guidance for an aero-assisted boost vehicle

NASA Astrophysics Data System (ADS)

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

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

Moving vehicles segmentation based on Gaussian motion model

NASA Astrophysics Data System (ADS)

Zhang, Wei; Fang, Xiang Z.; Lin, Wei Y.

2005-07-01

Moving objects segmentation is a challenge in computer vision. This paper focuses on the segmentation of moving vehicles in dynamic scene. We analyses the psychology of human vision and present a framework for segmenting moving vehicles in the highway. The proposed framework consists of two parts. Firstly, we propose an adaptive background update method in which the background is updated according to the change of illumination conditions and thus can adapt to the change of illumination sensitively. Secondly, we construct a Gaussian motion model to segment moving vehicles, in which the motion vectors of the moving pixels are modeled as a Gaussian model and an on-line EM algorithm is used to update the model. The Gaussian distribution of the adaptive model is elevated to determine which moving vectors result from moving vehicles and which from other moving objects such as waving trees. Finally, the pixels with motion vector result from the moving vehicles are segmented. Experimental results of several typical scenes show that the proposed model can detect the moving vehicles correctly and is immune from influence of the moving objects caused by the waving trees and the vibration of camera.

Adaptive fuzzy dynamic surface control of nonlinear systems with input saturation and time-varying output constraints

NASA Astrophysics Data System (ADS)

Edalati, L.; Khaki Sedigh, A.; Aliyari Shooredeli, M.; Moarefianpour, A.

2018-02-01

This paper deals with the design of adaptive fuzzy dynamic surface control for uncertain strict-feedback nonlinear systems with asymmetric time-varying output constraints in the presence of input saturation. To approximate the unknown nonlinear functions and overcome the problem of explosion of complexity, a Fuzzy logic system is combined with the dynamic surface control in the backstepping design technique. To ensure the output constraints satisfaction, an asymmetric time-varying Barrier Lyapunov Function (BLF) is used. Moreover, by applying the minimal learning parameter technique, the number of the online parameters update for each subsystem is reduced to 2. Hence, the semi-globally uniformly ultimately boundedness (SGUUB) of all the closed-loop signals with appropriate tracking error convergence is guaranteed. The effectiveness of the proposed control is demonstrated by two simulation examples.

Adaptive feedback synchronisation of complex dynamical network with discrete-time communications and delayed nodes

NASA Astrophysics Data System (ADS)

Wang, Tong; Ding, Yongsheng; Zhang, Lei; Hao, Kuangrong

2016-08-01

This paper considered the synchronisation of continuous complex dynamical networks with discrete-time communications and delayed nodes. The nodes in the dynamical networks act in the continuous manner, while the communications between nodes are discrete-time; that is, they communicate with others only at discrete time instants. The communication intervals in communication period can be uncertain and variable. By using a piecewise Lyapunov-Krasovskii function to govern the characteristics of the discrete communication instants, we investigate the adaptive feedback synchronisation and a criterion is derived to guarantee the existence of the desired controllers. The globally exponential synchronisation can be achieved by the controllers under the updating laws. Finally, two numerical examples including globally coupled network and nearest-neighbour coupled networks are presented to demonstrate the validity and effectiveness of the proposed control scheme.

A Neural Dynamic Architecture for Reaching and Grasping Integrates Perception and Movement Generation and Enables On-Line Updating.

PubMed

Knips, Guido; Zibner, Stephan K U; Reimann, Hendrik; Schöner, Gregor

2017-01-01

Reaching for objects and grasping them is a fundamental skill for any autonomous robot that interacts with its environment. Although this skill seems trivial to adults, who effortlessly pick up even objects they have never seen before, it is hard for other animals, for human infants, and for most autonomous robots. Any time during movement preparation and execution, human reaching movement are updated if the visual scene changes (with a delay of about 100 ms). The capability for online updating highlights how tightly perception, movement planning, and movement generation are integrated in humans. Here, we report on an effort to reproduce this tight integration in a neural dynamic process model of reaching and grasping that covers the complete path from visual perception to movement generation within a unified modeling framework, Dynamic Field Theory. All requisite processes are realized as time-continuous dynamical systems that model the evolution in time of neural population activation. Population level neural processes bring about the attentional selection of objects, the estimation of object shape and pose, and the mapping of pose parameters to suitable movement parameters. Once a target object has been selected, its pose parameters couple into the neural dynamics of movement generation so that changes of pose are propagated through the architecture to update the performed movement online. Implementing the neural architecture on an anthropomorphic robot arm equipped with a Kinect sensor, we evaluate the model by grasping wooden objects. Their size, shape, and pose are estimated from a neural model of scene perception that is based on feature fields. The sequential organization of a reach and grasp act emerges from a sequence of dynamic instabilities within a neural dynamics of behavioral organization, that effectively switches the neural controllers from one phase of the action to the next. Trajectory formation itself is driven by a dynamical systems version of the potential field approach. We highlight the emergent capacity for online updating by showing that a shift or rotation of the object during the reaching phase leads to the online adaptation of the movement plan and successful completion of the grasp.

A Neural Dynamic Architecture for Reaching and Grasping Integrates Perception and Movement Generation and Enables On-Line Updating

PubMed Central

Knips, Guido; Zibner, Stephan K. U.; Reimann, Hendrik; Schöner, Gregor

2017-01-01

Reaching for objects and grasping them is a fundamental skill for any autonomous robot that interacts with its environment. Although this skill seems trivial to adults, who effortlessly pick up even objects they have never seen before, it is hard for other animals, for human infants, and for most autonomous robots. Any time during movement preparation and execution, human reaching movement are updated if the visual scene changes (with a delay of about 100 ms). The capability for online updating highlights how tightly perception, movement planning, and movement generation are integrated in humans. Here, we report on an effort to reproduce this tight integration in a neural dynamic process model of reaching and grasping that covers the complete path from visual perception to movement generation within a unified modeling framework, Dynamic Field Theory. All requisite processes are realized as time-continuous dynamical systems that model the evolution in time of neural population activation. Population level neural processes bring about the attentional selection of objects, the estimation of object shape and pose, and the mapping of pose parameters to suitable movement parameters. Once a target object has been selected, its pose parameters couple into the neural dynamics of movement generation so that changes of pose are propagated through the architecture to update the performed movement online. Implementing the neural architecture on an anthropomorphic robot arm equipped with a Kinect sensor, we evaluate the model by grasping wooden objects. Their size, shape, and pose are estimated from a neural model of scene perception that is based on feature fields. The sequential organization of a reach and grasp act emerges from a sequence of dynamic instabilities within a neural dynamics of behavioral organization, that effectively switches the neural controllers from one phase of the action to the next. Trajectory formation itself is driven by a dynamical systems version of the potential field approach. We highlight the emergent capacity for online updating by showing that a shift or rotation of the object during the reaching phase leads to the online adaptation of the movement plan and successful completion of the grasp. PMID:28303100

Fixation of competing strategies when interacting agents differ in the time scale of strategy updating

NASA Astrophysics Data System (ADS)

Zhang, Jianlei; Weissing, Franz J.; Cao, Ming

2016-09-01

A commonly used assumption in evolutionary game theory is that natural selection acts on individuals in the same time scale; e.g., players use the same frequency to update their strategies. Variation in learning rates within populations suggests that evolutionary game theory may not necessarily be restricted to uniform time scales associated with the game interaction and strategy adaption evolution. In this study, we remove this restricting assumption by dividing the population into fast and slow groups according to the players' strategy updating frequencies and investigate how different strategy compositions of one group influence the evolutionary outcome of the other's fixation probabilities of strategies within its own group. Analytical analysis and numerical calculations are performed to study the evolutionary dynamics of strategies in typical classes of two-player games (prisoner's dilemma game, snowdrift game, and stag-hunt game). The introduction of the heterogeneity in strategy-update time scales leads to substantial changes in the evolution dynamics of strategies. We provide an approximation formula for the fixation probability of mutant types in finite populations and study the outcome of strategy evolution under the weak selection. We find that although heterogeneity in time scales makes the collective evolutionary dynamics more complicated, the possible long-run evolutionary outcome can be effectively predicted under technical assumptions when knowing the population composition and payoff parameters.

Adaptive relative pose control of spacecraft with model couplings and uncertainties

NASA Astrophysics Data System (ADS)

Sun, Liang; Zheng, Zewei

2018-02-01

The spacecraft pose tracking control problem for an uncertain pursuer approaching to a space target is researched in this paper. After modeling the nonlinearly coupled dynamics for relative translational and rotational motions between two spacecraft, position tracking and attitude synchronization controllers are developed independently by using a robust adaptive control approach. The unknown kinematic couplings, parametric uncertainties, and bounded external disturbances are handled with adaptive updating laws. It is proved via Lyapunov method that the pose tracking errors converge to zero asymptotically. Spacecraft close-range rendezvous and proximity operations are introduced as an example to validate the effectiveness of the proposed control approach.

Incremental update of electrostatic interactions in adaptively restrained particle simulations.

PubMed

Edorh, Semeho Prince A; Redon, Stéphane

2018-04-06

The computation of long-range potentials is one of the demanding tasks in Molecular Dynamics. During the last decades, an inventive panoply of methods was developed to reduce the CPU time of this task. In this work, we propose a fast method dedicated to the computation of the electrostatic potential in adaptively restrained systems. We exploit the fact that, in such systems, only some particles are allowed to move at each timestep. We developed an incremental algorithm derived from a multigrid-based alternative to traditional Fourier-based methods. Our algorithm was implemented inside LAMMPS, a popular molecular dynamics simulation package. We evaluated the method on different systems. We showed that the new algorithm's computational complexity scales with the number of active particles in the simulated system, and is able to outperform the well-established Particle Particle Particle Mesh (P3M) for adaptively restrained simulations. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

Predicting the evolution of complex networks via similarity dynamics

NASA Astrophysics Data System (ADS)

Wu, Tao; Chen, Leiting; Zhong, Linfeng; Xian, Xingping

2017-01-01

Almost all real-world networks are subject to constant evolution, and plenty of them have been investigated empirically to uncover the underlying evolution mechanism. However, the evolution prediction of dynamic networks still remains a challenging problem. The crux of this matter is to estimate the future network links of dynamic networks. This paper studies the evolution prediction of dynamic networks with link prediction paradigm. To estimate the likelihood of the existence of links more accurate, an effective and robust similarity index is presented by exploiting network structure adaptively. Moreover, most of the existing link prediction methods do not make a clear distinction between future links and missing links. In order to predict the future links, the networks are regarded as dynamic systems in this paper, and a similarity updating method, spatial-temporal position drift model, is developed to simulate the evolutionary dynamics of node similarity. Then the updated similarities are used as input information for the future links' likelihood estimation. Extensive experiments on real-world networks suggest that the proposed similarity index performs better than baseline methods and the position drift model performs well for evolution prediction in real-world evolving networks.

Protocol Independent Adaptive Route Update for VANET

PubMed Central

Rasheed, Asim; Qayyum, Amir

2014-01-01

High relative node velocity and high active node density have presented challenges to existing routing approaches within highly scaled ad hoc wireless networks, such as Vehicular Ad hoc Networks (VANET). Efficient routing requires finding optimum route with minimum delay, updating it on availability of a better one, and repairing it on link breakages. Current routing protocols are generally focused on finding and maintaining an efficient route, with very less emphasis on route update. Adaptive route update usually becomes impractical for dense networks due to large routing overheads. This paper presents an adaptive route update approach which can provide solution for any baseline routing protocol. The proposed adaptation eliminates the classification of reactive and proactive by categorizing them as logical conditions to find and update the route. PMID:24723807

A heuristic mathematical model for the dynamics of sensory conflict and motion sickness

NASA Technical Reports Server (NTRS)

Oman, C. M.

1982-01-01

By consideration of the information processing task faced by the central nervous system in estimating body spatial orientation and in controlling active body movement using an internal model referenced control strategy, a mathematical model for sensory conflict generation is developed. The model postulates a major dynamic functional role for sensory conflict signals in movement control, as well as in sensory-motor adaptation. It accounts for the role of active movement in creating motion sickness symptoms in some experimental circumstance, and in alleviating them in others. The relationship between motion sickness produced by sensory rearrangement and that resulting from external motion disturbances is explicitly defined. A nonlinear conflict averaging model is proposed which describes dynamic aspects of experimentally observed subjective discomfort sensation, and suggests resulting behaviours. The model admits several possibilities for adaptive mechanisms which do not involve internal model updating. Further systematic efforts to experimentally refine and validate the model are indicated.

Sensor Spatial Distortion, Visual Latency, and Update Rate Effects on 3D Tracking in Virtual Environments

NASA Technical Reports Server (NTRS)

Ellis, S. R.; Adelstein, B. D.; Baumeler, S.; Jense, G. J.; Jacoby, R. H.; Trejo, Leonard (Technical Monitor)

1998-01-01

Several common defects that we have sought to minimize in immersing virtual environments are: static sensor spatial distortion, visual latency, and low update rates. Human performance within our environments during large amplitude 3D tracking was assessed by objective and subjective methods in the presence and absence of these defects. Results show that 1) removal of our relatively small spatial sensor distortion had minor effects on the tracking activity, 2) an Adapted Cooper-Harper controllability scale proved the most sensitive subjective indicator of the degradation of dynamic fidelity caused by increasing latency and decreasing frame rates, and 3) performance, as measured by normalized RMS tracking error or subjective impressions, was more markedly influenced by changing visual latency than by update rate.

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

PubMed

Fei, Juntao; Lu, Cheng

2018-04-01

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

Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design.

PubMed

Peng, Zhouhua; Wang, Dan; Wang, Wei; Liu, Lu

2015-11-01

This paper investigates the containment control problem of networked autonomous underwater vehicles in the presence of model uncertainty and unknown ocean disturbances. A predictor-based neural dynamic surface control design method is presented to develop the distributed adaptive containment controllers, under which the trajectories of follower vehicles nearly converge to the dynamic convex hull spanned by multiple reference trajectories over a directed network. Prediction errors, rather than tracking errors, are used to update the neural adaptation laws, which are independent of the tracking error dynamics, resulting in two time-scales to govern the entire system. The stability property of the closed-loop network is established via Lyapunov analysis, and transient property is quantified in terms of L2 norms of the derivatives of neural weights, which are shown to be smaller than the classical neural dynamic surface control approach. Comparative studies are given to show the substantial improvements of the proposed new method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Sustainable and Smart City Planning Using Spatial Data in Wallonia

NASA Astrophysics Data System (ADS)

Stephenne, N.; Beaumont, B.; Hallot, E.; Wolff, E.; Poelmans, L.; Baltus, C.

2016-09-01

Simulating population distribution and land use changes in space and time offer opportunities for smart city planning. It provides a holistic and dynamic vision of fast changing urban environment to policy makers. Impacts, such as environmental and health risks or mobility issues, of policies can be assessed and adapted consequently. In this paper, we suppose that "Smart" city developments should be sustainable, dynamic and participative. This paper addresses these three smart objectives in the context of urban risk assessment in Wallonia, Belgium. The sustainable, dynamic and participative solution includes (i) land cover and land use mapping using remote sensing and GIS, (ii) population density mapping using dasymetric mapping, (iii) predictive modelling of land use changes and population dynamics and (iv) risk assessment. The comprehensive and long-term vision of the territory should help to draw sustainable spatial planning policies, to adapt remote sensing acquisition, to update GIS data and to refine risk assessment from regional to city scale.

Adaptive Metropolis Sampling with Product Distributions

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Lee, Chiu Fan

2005-01-01

The Metropolis-Hastings (MH) algorithm is a way to sample a provided target distribution pi(z). It works by repeatedly sampling a separate proposal distribution T(x,x') to generate a random walk {x(t)}. We consider a modification of the MH algorithm in which T is dynamically updated during the walk. The update at time t uses the {x(t' less than t)} to estimate the product distribution that has the least Kullback-Leibler distance to pi. That estimate is the information-theoretically optimal mean-field approximation to pi. We demonstrate through computer experiments that our algorithm produces samples that are superior to those of the conventional MH algorithm.

Anticipation from sensation: using anticipating synchronization to stabilize a system with inherent sensory delay.

PubMed

Eberle, Henry; Nasuto, Slawomir J; Hayashi, Yoshikatsu

2018-03-01

We present a novel way of using a dynamical model for predictive tracking control that can adapt to a wide range of delays without parameter update. This is achieved by incorporating the paradigm of anticipating synchronization (AS), where a 'slave' system predicts a 'master' via delayed self-feedback. By treating the delayed output of the plant as one half of a 'sensory' AS coupling, the plant and an internal dynamical model can be synchronized such that the plant consistently leads the target's motion. We use two simulated robotic systems with differing arrangements of the plant and internal model ('parallel' and 'serial') to demonstrate that this form of control adapts to a wide range of delays without requiring the parameters of the controller to be changed.

Specificity and timescales of cortical adaptation as inferences about natural movie statistics.

PubMed

Snow, Michoel; Coen-Cagli, Ruben; Schwartz, Odelia

2016-10-01

Adaptation is a phenomenological umbrella term under which a variety of temporal contextual effects are grouped. Previous models have shown that some aspects of visual adaptation reflect optimal processing of dynamic visual inputs, suggesting that adaptation should be tuned to the properties of natural visual inputs. However, the link between natural dynamic inputs and adaptation is poorly understood. Here, we extend a previously developed Bayesian modeling framework for spatial contextual effects to the temporal domain. The model learns temporal statistical regularities of natural movies and links these statistics to adaptation in primary visual cortex via divisive normalization, a ubiquitous neural computation. In particular, the model divisively normalizes the present visual input by the past visual inputs only to the degree that these are inferred to be statistically dependent. We show that this flexible form of normalization reproduces classical findings on how brief adaptation affects neuronal selectivity. Furthermore, prior knowledge acquired by the Bayesian model from natural movies can be modified by prolonged exposure to novel visual stimuli. We show that this updating can explain classical results on contrast adaptation. We also simulate the recent finding that adaptation maintains population homeostasis, namely, a balanced level of activity across a population of neurons with different orientation preferences. Consistent with previous disparate observations, our work further clarifies the influence of stimulus-specific and neuronal-specific normalization signals in adaptation.

Specificity and timescales of cortical adaptation as inferences about natural movie statistics

PubMed Central

Snow, Michoel; Coen-Cagli, Ruben; Schwartz, Odelia

2016-01-01

Adaptation is a phenomenological umbrella term under which a variety of temporal contextual effects are grouped. Previous models have shown that some aspects of visual adaptation reflect optimal processing of dynamic visual inputs, suggesting that adaptation should be tuned to the properties of natural visual inputs. However, the link between natural dynamic inputs and adaptation is poorly understood. Here, we extend a previously developed Bayesian modeling framework for spatial contextual effects to the temporal domain. The model learns temporal statistical regularities of natural movies and links these statistics to adaptation in primary visual cortex via divisive normalization, a ubiquitous neural computation. In particular, the model divisively normalizes the present visual input by the past visual inputs only to the degree that these are inferred to be statistically dependent. We show that this flexible form of normalization reproduces classical findings on how brief adaptation affects neuronal selectivity. Furthermore, prior knowledge acquired by the Bayesian model from natural movies can be modified by prolonged exposure to novel visual stimuli. We show that this updating can explain classical results on contrast adaptation. We also simulate the recent finding that adaptation maintains population homeostasis, namely, a balanced level of activity across a population of neurons with different orientation preferences. Consistent with previous disparate observations, our work further clarifies the influence of stimulus-specific and neuronal-specific normalization signals in adaptation. PMID:27699416

Adaptive control of robotic manipulators

NASA Technical Reports Server (NTRS)

Seraji, H.

1987-01-01

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

Voter dynamics on an adaptive network with finite average connectivity

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Abhishek; Schmittmann, Beate

2009-03-01

We study a simple model for voter dynamics in a two-party system. The opinion formation process is implemented in a random network of agents in which interactions are not restricted by geographical distance. In addition, we incorporate the rapidly changing nature of the interpersonal relations in the model. At each time step, agents can update their relationships, so that there is no history dependence in the model. This update is determined by their own opinion, and by their preference to make connections with individuals sharing the same opinion and with opponents. Using simulations and analytic arguments, we determine the final steady states and the relaxation into these states for different system sizes. In contrast to earlier studies, the average connectivity (``degree'') of each agent is constant here, independent of the system size. This has significant consequences for the long-time behavior of the model.

Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Admissibility and Termination Analysis.

PubMed

Wei, Qinglai; Liu, Derong; Lin, Qiao

In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.

Based on interval type-2 fuzzy-neural network direct adaptive sliding mode control for SISO nonlinear systems

NASA Astrophysics Data System (ADS)

Lin, Tsung-Chih

2010-12-01

In this paper, a novel direct adaptive interval type-2 fuzzy-neural tracking control equipped with sliding mode and Lyapunov synthesis approach is proposed to handle the training data corrupted by noise or rule uncertainties for nonlinear SISO nonlinear systems involving external disturbances. By employing adaptive fuzzy-neural control theory, the update laws will be derived for approximating the uncertain nonlinear dynamical system. In the meantime, the sliding mode control method and the Lyapunov stability criterion are incorporated into the adaptive fuzzy-neural control scheme such that the derived controller is robust with respect to unmodeled dynamics, external disturbance and approximation errors. In comparison with conventional methods, the advocated approach not only guarantees closed-loop stability but also the output tracking error of the overall system will converge to zero asymptotically without prior knowledge on the upper bound of the lumped uncertainty. Furthermore, chattering effect of the control input will be substantially reduced by the proposed technique. To illustrate the performance of the proposed method, finally simulation example will be given.

Recent advances in the understanding of the repeated bout effect: the protective effect against muscle damage from a single bout of eccentric exercise.

PubMed

McHugh, Malachy P

2003-04-01

The repeated bout effect refers to the adaptation whereby a single bout of eccentric exercise protects against muscle damage from subsequent eccentric bouts. While the mechanism for this adaptation is poorly understood there have been significant recent advances in the understanding of this phenomenon. The purpose of this review is to provide an update on previously proposed theories and address new theories that have been advanced. The potential adaptations have been categorized as neural, mechanical and cellular. There is some evidence to suggest that the repeated bout effect is associated with a shift toward greater recruitment of slow twitch motor units. However, the repeated bout effect has been demonstrated with electrically stimulated contractions, indicating that a peripheral, non-neural adaptation predominates. With respect to mechanical adaptations there is evidence that both dynamic and passive muscle stiffness increase with eccentric training but there are no studies on passive or dynamic stiffness adaptations to a single eccentric bout. The role of the cytoskeleton in regulating dynamic stiffness is a possible area for future research. With respect to cellular adaptations there is evidence of longitudinal addition of sarcomeres and adaptations in the inflammatory response following an initial bout of eccentric exercise. Addition of sarcomeres is thought to reduce sarcomere strain during eccentric contractions thereby avoiding sarcomere disruption. Inflammatory adaptations are thought to limit the proliferation of damage that typically occurs in the days following eccentric exercise. In conclusion, there have been significant advances in the understanding of the repeated bout effect, however, a unified theory explaining the mechanism or mechanisms for this protective adaptation remains elusive.

Adaptive learning and control for MIMO system based on adaptive dynamic programming.

PubMed

Fu, Jian; He, Haibo; Zhou, Xinmin

2011-07-01

Adaptive dynamic programming (ADP) is a promising research field for design of intelligent controllers, which can both learn on-the-fly and exhibit optimal behavior. Over the past decades, several generations of ADP design have been proposed in the literature, which have demonstrated many successful applications in various benchmarks and industrial applications. While many of the existing researches focus on multiple-inputs-single-output system with steepest descent search, in this paper we investigate a generalized multiple-input-multiple-output (GMIMO) ADP design for online learning and control, which is more applicable to a wide range of practical real-world applications. Furthermore, an improved weight-updating algorithm based on recursive Levenberg-Marquardt methods is presented and embodied in the GMIMO approach to improve its performance. Finally, we test the performance of this approach based on a practical complex system, namely, the learning and control of the tension and height of the looper system in a hot strip mill. Experimental results demonstrate that the proposed approach can achieve effective and robust performance.

Neural adaptive control for vibration suppression in composite fin-tip of aircraft.

PubMed

Suresh, S; Kannan, N; Sundararajan, N; Saratchandran, P

2008-06-01

In this paper, we present a neural adaptive control scheme for active vibration suppression of a composite aircraft fin tip. The mathematical model of a composite aircraft fin tip is derived using the finite element approach. The finite element model is updated experimentally to reflect the natural frequencies and mode shapes very accurately. Piezo-electric actuators and sensors are placed at optimal locations such that the vibration suppression is a maximum. Model-reference direct adaptive neural network control scheme is proposed to force the vibration level within the minimum acceptable limit. In this scheme, Gaussian neural network with linear filters is used to approximate the inverse dynamics of the system and the parameters of the neural controller are estimated using Lyapunov based update law. In order to reduce the computational burden, which is critical for real-time applications, the number of hidden neurons is also estimated in the proposed scheme. The global asymptotic stability of the overall system is ensured using the principles of Lyapunov approach. Simulation studies are carried-out using sinusoidal force functions of varying frequency. Experimental results show that the proposed neural adaptive control scheme is capable of providing significant vibration suppression in the multiple bending modes of interest. The performance of the proposed scheme is better than the H(infinity) control scheme.

Anticipation from sensation: using anticipating synchronization to stabilize a system with inherent sensory delay

PubMed Central

Nasuto, Slawomir J.; Hayashi, Yoshikatsu

2018-01-01

We present a novel way of using a dynamical model for predictive tracking control that can adapt to a wide range of delays without parameter update. This is achieved by incorporating the paradigm of anticipating synchronization (AS), where a ‘slave’ system predicts a ‘master’ via delayed self-feedback. By treating the delayed output of the plant as one half of a ‘sensory’ AS coupling, the plant and an internal dynamical model can be synchronized such that the plant consistently leads the target’s motion. We use two simulated robotic systems with differing arrangements of the plant and internal model (‘parallel’ and ‘serial’) to demonstrate that this form of control adapts to a wide range of delays without requiring the parameters of the controller to be changed. PMID:29657750

Passive mapping and intermittent exploration for mobile robots

NASA Technical Reports Server (NTRS)

Engleson, Sean P.

1994-01-01

An adaptive state space architecture is combined with diktiometric representation to provide the framework for designing a robot mapping system with flexible navigation planning tasks. This involves indexing waypoints described as expectations, geometric indexing, and perceptual indexing. Matching and updating the robot's projected position and sensory inputs with indexing waypoints involves matchers, dynamic priorities, transients, and waypoint restructuring. The robot's map learning can be opganized around the principles of passive mapping.

Uncertainty, learning, and the optimal management of wildlife

USGS Publications Warehouse

Williams, B.K.

2001-01-01

Wildlife management is limited by uncontrolled and often unrecognized environmental variation, by limited capabilities to observe and control animal populations, and by a lack of understanding about the biological processes driving population dynamics. In this paper I describe a comprehensive framework for management that includes multiple models and likelihood values to account for structural uncertainty, along with stochastic factors to account for environmental variation, random sampling, and partial controllability. Adaptive optimization is developed in terms of the optimal control of incompletely understood populations, with the expected value of perfect information measuring the potential for improving control through learning. The framework for optimal adaptive control is generalized by including partial observability and non-adaptive, sample-based updating of model likelihoods. Passive adaptive management is derived as a special case of constrained adaptive optimization, representing a potentially efficient suboptimal alternative that nonetheless accounts for structural uncertainty.

Adaptive Optimal Control Using Frequency Selective Information of the System Uncertainty With Application to Unmanned Aircraft.

PubMed

Maity, Arnab; Hocht, Leonhard; Heise, Christian; Holzapfel, Florian

2018-01-01

A new efficient adaptive optimal control approach is presented in this paper based on the indirect model reference adaptive control (MRAC) architecture for improvement of adaptation and tracking performance of the uncertain system. The system accounts here for both matched and unmatched unknown uncertainties that can act as plant as well as input effectiveness failures or damages. For adaptation of the unknown parameters of these uncertainties, the frequency selective learning approach is used. Its idea is to compute a filtered expression of the system uncertainty using multiple filters based on online instantaneous information, which is used for augmentation of the update law. It is capable of adjusting a sudden change in system dynamics without depending on high adaptation gains and can satisfy exponential parameter error convergence under certain conditions in the presence of structured matched and unmatched uncertainties as well. Additionally, the controller of the MRAC system is designed using a new optimal control method. This method is a new linear quadratic regulator-based optimal control formulation for both output regulation and command tracking problems. It provides a closed-form control solution. The proposed overall approach is applied in a control of lateral dynamics of an unmanned aircraft problem to show its effectiveness.

[Plug-in Based Centralized Control System in Operating Rooms].

PubMed

Wang, Yunlong

2017-05-30

Centralized equipment controls in an operating room (OR) is crucial to an efficient workflow in the OR. To achieve centralized control, an integrative OR needs to focus on designing a control panel that can appropriately incorporate equipment from different manufactures with various connecting ports and controls. Here we propose to achieve equipment integration using plug-in modules. Each OR will be equipped with a dynamic plug-in control panel containing physically removable connecting ports. Matching outlets will be installed onto the control panels of each equipment used at any given time. This dynamic control panel will be backed with a database containing plug-in modules that can connect any two types of connecting ports common among medical equipment manufacturers. The correct connecting ports will be called using reflection dynamics. This database will be updated regularly to include new connecting ports on the market, making it easy to maintain, update, expand and remain relevant as new equipment are developed. Together, the physical panel and the database will achieve centralized equipment controls in the OR that can be easily adapted to any equipment in the OR.

Reinforcement learning neural-network-based controller for nonlinear discrete-time systems with input constraints.

PubMed

He, Pingan; Jagannathan, S

2007-04-01

A novel adaptive-critic-based neural network (NN) controller in discrete time is designed to deliver a desired tracking performance for a class of nonlinear systems in the presence of actuator constraints. The constraints of the actuator are treated in the controller design as the saturation nonlinearity. The adaptive critic NN controller architecture based on state feedback includes two NNs: the critic NN is used to approximate the "strategic" utility function, whereas the action NN is employed to minimize both the strategic utility function and the unknown nonlinear dynamic estimation errors. The critic and action NN weight updates are derived by minimizing certain quadratic performance indexes. Using the Lyapunov approach and with novel weight updates, the uniformly ultimate boundedness of the closed-loop tracking error and weight estimates is shown in the presence of NN approximation errors and bounded unknown disturbances. The proposed NN controller works in the presence of multiple nonlinearities, unlike other schemes that normally approximate one nonlinearity. Moreover, the adaptive critic NN controller does not require an explicit offline training phase, and the NN weights can be initialized at zero or random. Simulation results justify the theoretical analysis.

Adaptive nearly optimal control for a class of continuous-time nonaffine nonlinear systems with inequality constraints.

PubMed

Fan, Quan-Yong; Yang, Guang-Hong

2017-01-01

The state inequality constraints have been hardly considered in the literature on solving the nonlinear optimal control problem based the adaptive dynamic programming (ADP) method. In this paper, an actor-critic (AC) algorithm is developed to solve the optimal control problem with a discounted cost function for a class of state-constrained nonaffine nonlinear systems. To overcome the difficulties resulting from the inequality constraints and the nonaffine nonlinearities of the controlled systems, a novel transformation technique with redesigned slack functions and a pre-compensator method are introduced to convert the constrained optimal control problem into an unconstrained one for affine nonlinear systems. Then, based on the policy iteration (PI) algorithm, an online AC scheme is proposed to learn the nearly optimal control policy for the obtained affine nonlinear dynamics. Using the information of the nonlinear model, novel adaptive update laws are designed to guarantee the convergence of the neural network (NN) weights and the stability of the affine nonlinear dynamics without the requirement for the probing signal. Finally, the effectiveness of the proposed method is validated by simulation studies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Performance and quality assessment of the recent updated CMEMS global ocean monitoring and forecasting real-time system

NASA Astrophysics Data System (ADS)

Le Galloudec, Olivier; Lellouche, Jean-Michel; Greiner, Eric; Garric, Gilles; Régnier, Charly; Drévillon, Marie; Drillet, Yann

2017-04-01

Since May 2015, Mercator Ocean opened the Copernicus Marine Environment and Monitoring Service (CMEMS) and is in charge of the global eddy resolving ocean analyses and forecast. In this context, Mercator Ocean currently delivers in real-time daily services (weekly analyses and daily forecast) with a global 1/12° high resolution system. The model component is the NEMO platform driven at the surface by the IFS ECMWF atmospheric analyses and forecasts. Observations are assimilated by means of a reduced-order Kalman filter with a 3D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. Along track altimeter data, satellite Sea Surface Temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. A 3D-Var scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity. R&D activities have been conducted at Mercator Ocean these last years to improve the real-time 1/12° global system for recent updated CMEMS version in 2016. The ocean/sea-ice model and the assimilation scheme benefited of the following improvements: large-scale and objective correction of atmospheric quantities with satellite data, new Mean Dynamic Topography taking into account the last version of GOCE geoid, new adaptive tuning of some observational errors, new Quality Control on the assimilated temperature and salinity vertical profiles based on dynamic height criteria, assimilation of satellite sea-ice concentration, new freshwater runoff from ice sheets melting, … This presentation will show the impact of some updates separately, with a particular focus on adaptive tuning experiments of satellite Sea Level Anomaly (SLA) and Sea Surface Temperature (SST) observations errors. For the SLA, the a priori prescribed observation error is globally greatly reduced. The median value of the error changed from 5cm to 2.5cm in a few assimilation cycles. For the SST, we chose to maintain the median value of the error to 0.4°C. The spatial distribution of the SST error follows the model physics and atmospheric variability. Either for SLA or SST, we improve the performances of the system using this adaptive tuning. The overall behavior of the system integrating all updates reporting on the products quality improvements will be also discussed, highlighting the level of performance and the reliability of the new system.

Fast and memory efficient text image compression with JBIG2.

PubMed

Ye, Yan; Cosman, Pamela

2003-01-01

In this paper, we investigate ways to reduce encoding time, memory consumption and substitution errors for text image compression with JBIG2. We first look at page striping where the encoder splits the input image into horizontal stripes and processes one stripe at a time. We propose dynamic dictionary updating procedures for page striping to reduce the bit rate penalty it incurs. Experiments show that splitting the image into two stripes can save 30% of encoding time and 40% of physical memory with a small coding loss of about 1.5%. Using more stripes brings further savings in time and memory but the return diminishes. We also propose an adaptive way to update the dictionary only when it has become out-of-date. The adaptive updating scheme can resolve the time versus bit rate tradeoff and the memory versus bit rate tradeoff well simultaneously. We then propose three speedup techniques for pattern matching, the most time-consuming encoding activity in JBIG2. When combined together, these speedup techniques can save up to 75% of the total encoding time with at most 1.7% of bit rate penalty. Finally, we look at improving reconstructed image quality for lossy compression. We propose enhanced prescreening and feature monitored shape unifying to significantly reduce substitution errors in the reconstructed images.

Error bounds of adaptive dynamic programming algorithms for solving undiscounted optimal control problems.

PubMed

Liu, Derong; Li, Hongliang; Wang, Ding

2015-06-01

In this paper, we establish error bounds of adaptive dynamic programming algorithms for solving undiscounted infinite-horizon optimal control problems of discrete-time deterministic nonlinear systems. We consider approximation errors in the update equations of both value function and control policy. We utilize a new assumption instead of the contraction assumption in discounted optimal control problems. We establish the error bounds for approximate value iteration based on a new error condition. Furthermore, we also establish the error bounds for approximate policy iteration and approximate optimistic policy iteration algorithms. It is shown that the iterative approximate value function can converge to a finite neighborhood of the optimal value function under some conditions. To implement the developed algorithms, critic and action neural networks are used to approximate the value function and control policy, respectively. Finally, a simulation example is given to demonstrate the effectiveness of the developed algorithms.

Methodologies for Adaptive Flight Envelope Estimation and Protection

NASA Technical Reports Server (NTRS)

Tang, Liang; Roemer, Michael; Ge, Jianhua; Crassidis, Agamemnon; Prasad, J. V. R.; Belcastro, Christine

2009-01-01

This paper reports the latest development of several techniques for adaptive flight envelope estimation and protection system for aircraft under damage upset conditions. Through the integration of advanced fault detection algorithms, real-time system identification of the damage/faulted aircraft and flight envelop estimation, real-time decision support can be executed autonomously for improving damage tolerance and flight recoverability. Particularly, a bank of adaptive nonlinear fault detection and isolation estimators were developed for flight control actuator faults; a real-time system identification method was developed for assessing the dynamics and performance limitation of impaired aircraft; online learning neural networks were used to approximate selected aircraft dynamics which were then inverted to estimate command margins. As off-line training of network weights is not required, the method has the advantage of adapting to varying flight conditions and different vehicle configurations. The key benefit of the envelope estimation and protection system is that it allows the aircraft to fly close to its limit boundary by constantly updating the controller command limits during flight. The developed techniques were demonstrated on NASA s Generic Transport Model (GTM) simulation environments with simulated actuator faults. Simulation results and remarks on future work are presented.

Adaptive critic autopilot design of bank-to-turn missiles using fuzzy basis function networks.

PubMed

Lin, Chuan-Kai

2005-04-01

A new adaptive critic autopilot design for bank-to-turn missiles is presented. In this paper, the architecture of adaptive critic learning scheme contains a fuzzy-basis-function-network based associative search element (ASE), which is employed to approximate nonlinear and complex functions of bank-to-turn missiles, and an adaptive critic element (ACE) generating the reinforcement signal to tune the associative search element. In the design of the adaptive critic autopilot, the control law receives signals from a fixed gain controller, an ASE and an adaptive robust element, which can eliminate approximation errors and disturbances. Traditional adaptive critic reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment, however, the proposed tuning algorithm can significantly shorten the learning time by online tuning all parameters of fuzzy basis functions and weights of ASE and ACE. Moreover, the weight updating law derived from the Lyapunov stability theory is capable of guaranteeing both tracking performance and stability. Computer simulation results confirm the effectiveness of the proposed adaptive critic autopilot.

An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments.

PubMed

Cong, Li; Li, Xin; Jin, Tian; Yue, Song; Xue, Rui

2016-01-23

As the weak link in global navigation satellite system (GNSS) signal processing, the phase-locked loop (PLL) is easily influenced with frequent cycle slips and loss of lock as a result of higher vehicle dynamics and lower signal-to-noise ratios. With inertial navigation system (INS) aid, PLLs' tracking performance can be improved. However, for harsh environments with high dynamics and signal attenuation, the traditional INS-aided PLL with fixed loop parameters has some limitations to improve the tracking adaptability. In this paper, an adaptive INS-aided PLL capable of adjusting its noise bandwidth and coherent integration time has been proposed. Through theoretical analysis, the relation between INS-aided PLL phase tracking error and carrier to noise density ratio (C/N₀), vehicle dynamics, aiding information update time, noise bandwidth, and coherent integration time has been built. The relation formulae are used to choose the optimal integration time and bandwidth for a given application under the minimum tracking error criterion. Software and hardware simulation results verify the correctness of the theoretical analysis, and demonstrate that the adaptive tracking method can effectively improve the PLL tracking ability and integrated GNSS/INS navigation performance. For harsh environments, the tracking sensitivity is increased by 3 to 5 dB, velocity errors are decreased by 36% to 50% and position errors are decreased by 6% to 24% when compared with other INS-aided PLL methods.

Optimal and robust control of a class of nonlinear systems using dynamically re-optimised single network adaptive critic design

NASA Astrophysics Data System (ADS)

Tiwari, Shivendra N.; Padhi, Radhakant

2018-01-01

Following the philosophy of adaptive optimal control, a neural network-based state feedback optimal control synthesis approach is presented in this paper. First, accounting for a nominal system model, a single network adaptive critic (SNAC) based multi-layered neural network (called as NN1) is synthesised offline. However, another linear-in-weight neural network (called as NN2) is trained online and augmented to NN1 in such a manner that their combined output represent the desired optimal costate for the actual plant. To do this, the nominal model needs to be updated online to adapt to the actual plant, which is done by synthesising yet another linear-in-weight neural network (called as NN3) online. Training of NN3 is done by utilising the error information between the nominal and actual states and carrying out the necessary Lyapunov stability analysis using a Sobolev norm based Lyapunov function. This helps in training NN2 successfully to capture the required optimal relationship. The overall architecture is named as 'Dynamically Re-optimised single network adaptive critic (DR-SNAC)'. Numerical results for two motivating illustrative problems are presented, including comparison studies with closed form solution for one problem, which clearly demonstrate the effectiveness and benefit of the proposed approach.

An adaptive trajectory tracking control of four rotor hover vehicle using extended normalized radial basis function network

NASA Astrophysics Data System (ADS)

ul Amin, Rooh; Aijun, Li; Khan, Muhammad Umer; Shamshirband, Shahaboddin; Kamsin, Amirrudin

2017-01-01

In this paper, an adaptive trajectory tracking controller based on extended normalized radial basis function network (ENRBFN) is proposed for 3-degree-of-freedom four rotor hover vehicle subjected to external disturbance i.e. wind turbulence. Mathematical model of four rotor hover system is developed using equations of motions and a new computational intelligence based technique ENRBFN is introduced to approximate the unmodeled dynamics of the hover vehicle. The adaptive controller based on the Lyapunov stability approach is designed to achieve tracking of the desired attitude angles of four rotor hover vehicle in the presence of wind turbulence. The adaptive weight update based on the Levenberg-Marquardt algorithm is used to avoid weight drift in case the system is exposed to external disturbances. The closed-loop system stability is also analyzed using Lyapunov stability theory. Simulations and experimental results are included to validate the effectiveness of the proposed control scheme.

Robust Adaptive Thresholder For Document Scanning Applications

NASA Astrophysics Data System (ADS)

Hsing, To R.

1982-12-01

In document scanning applications, thresholding is used to obtain binary data from a scanner. However, due to: (1) a wide range of different color backgrounds; (2) density variations of printed text information; and (3) the shading effect caused by the optical systems, the use of adaptive thresholding to enhance the useful information is highly desired. This paper describes a new robust adaptive thresholder for obtaining valid binary images. It is basically a memory type algorithm which can dynamically update the black and white reference level to optimize a local adaptive threshold function. The results of high image quality from different types of simulate test patterns can be obtained by this algorithm. The software algorithm is described and experiment results are present to describe the procedures. Results also show that the techniques described here can be used for real-time signal processing in the varied applications.

A selective-update affine projection algorithm with selective input vectors

NASA Astrophysics Data System (ADS)

Kong, NamWoong; Shin, JaeWook; Park, PooGyeon

2011-10-01

This paper proposes an affine projection algorithm (APA) with selective input vectors, which based on the concept of selective-update in order to reduce estimation errors and computations. The algorithm consists of two procedures: input- vector-selection and state-decision. The input-vector-selection procedure determines the number of input vectors by checking with mean square error (MSE) whether the input vectors have enough information for update. The state-decision procedure determines the current state of the adaptive filter by using the state-decision criterion. As the adaptive filter is in transient state, the algorithm updates the filter coefficients with the selected input vectors. On the other hand, as soon as the adaptive filter reaches the steady state, the update procedure is not performed. Through these two procedures, the proposed algorithm achieves small steady-state estimation errors, low computational complexity and low update complexity for colored input signals.

PAQ: Persistent Adaptive Query Middleware for Dynamic Environments

NASA Astrophysics Data System (ADS)

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

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

Integrating modeling, monitoring, and management to reduce critical uncertainties in water resource decision making.

PubMed

Peterson, James T; Freeman, Mary C

2016-12-01

Stream ecosystems provide multiple, valued services to society, including water supply, waste assimilation, recreation, and habitat for diverse and productive biological communities. Managers striving to sustain these services in the face of changing climate, land uses, and water demands need tools to assess the potential effectiveness of alternative management actions, and often, the resulting tradeoffs between competing objectives. Integrating predictive modeling with monitoring data in an adaptive management framework provides a process by which managers can reduce model uncertainties and thus improve the scientific bases for subsequent decisions. We demonstrate an integration of monitoring data with a dynamic, metapopulation model developed to assess effects of streamflow alteration on fish occupancy in a southeastern US stream system. Although not extensive (collected over three years at nine sites), the monitoring data allowed us to assess and update support for alternative population dynamic models using model probabilities and Bayes rule. We then use the updated model weights to estimate the effects of water withdrawal on stream fish communities and demonstrate how feedback in the form of monitoring data can be used to improve water resource decision making. We conclude that investment in more strategic monitoring, guided by a priori model predictions under alternative hypotheses and an adaptive sampling design, could substantially improve the information available to guide decision-making and management for ecosystem services from lotic systems. Published by Elsevier Ltd.

Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

PubMed

Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

2017-03-01

A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Shared internal models for feedforward and feedback control.

PubMed

Wagner, Mark J; Smith, Maurice A

2008-10-15

A child often learns to ride a bicycle in the driveway, free of unforeseen obstacles. Yet when she first rides in the street, we hope that if a car suddenly pulls out in front of her, she will combine her innate goal of avoiding an accident with her learned knowledge of the bicycle, and steer away or brake. In general, when we train to perform a new motor task, our learning is most robust if it updates the rules of online error correction to reflect the rules and goals of the new task. Here we provide direct evidence that, after a new feedforward motor adaptation, motor feedback responses to unanticipated errors become precisely task appropriate, even when such errors were never experienced during training. To study this ability, we asked how, if at all, do online responses to occasional, unanticipated force pulses during reaching arm movements change after adapting to altered arm dynamics? Specifically, do they change in a task-appropriate manner? In our task, subjects learned novel velocity-dependent dynamics. However, occasional force-pulse perturbations produced unanticipated changes in velocity. Therefore, after adaptation, task-appropriate responses to unanticipated pulses should compensate corresponding changes in velocity-dependent dynamics. We found that after adaptation, pulse responses precisely compensated these changes, although they were never trained to do so. These results provide evidence for a smart feedback controller which automatically produces responses specific to the learned dynamics of the current task. To accomplish this, the neural processes underlying feedback control must (1) be capable of accurate real-time state prediction for velocity via a forward model and (2) have access to recently learned changes in internal models of limb dynamics.

Dynamic modeling, property investigation, and adaptive controller design of serial robotic manipulators modeled with structural compliance

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.

An Approach to Stable Gradient-Descent Adaptation of Higher Order Neural Units.

PubMed

Bukovsky, Ivo; Homma, Noriyasu

2017-09-01

Stability evaluation of a weight-update system of higher order neural units (HONUs) with polynomial aggregation of neural inputs (also known as classes of polynomial neural networks) for adaptation of both feedforward and recurrent HONUs by a gradient descent method is introduced. An essential core of the approach is based on the spectral radius of a weight-update system, and it allows stability monitoring and its maintenance at every adaptation step individually. Assuring the stability of the weight-update system (at every single adaptation step) naturally results in the adaptation stability of the whole neural architecture that adapts to the target data. As an aside, the used approach highlights the fact that the weight optimization of HONU is a linear problem, so the proposed approach can be generally extended to any neural architecture that is linear in its adaptable parameters.

On the number of different dynamics in Boolean networks with deterministic update schedules.

PubMed

Aracena, J; Demongeot, J; Fanchon, E; Montalva, M

2013-04-01

Deterministic Boolean networks are a type of discrete dynamical systems widely used in the modeling of genetic networks. The dynamics of such systems is characterized by the local activation functions and the update schedule, i.e., the order in which the nodes are updated. In this paper, we address the problem of knowing the different dynamics of a Boolean network when the update schedule is changed. We begin by proving that the problem of the existence of a pair of update schedules with different dynamics is NP-complete. However, we show that certain structural properties of the interaction diagraph are sufficient for guaranteeing distinct dynamics of a network. In [1] the authors define equivalence classes which have the property that all the update schedules of a given class yield the same dynamics. In order to determine the dynamics associated to a network, we develop an algorithm to efficiently enumerate the above equivalence classes by selecting a representative update schedule for each class with a minimum number of blocks. Finally, we run this algorithm on the well known Arabidopsis thaliana network to determine the full spectrum of its different dynamics. Copyright © 2013 Elsevier Inc. All rights reserved.

Dynamic model updating based on strain mode shape and natural frequency using hybrid pattern search technique

NASA Astrophysics Data System (ADS)

Guo, Ning; Yang, Zhichun; Wang, Le; Ouyang, Yan; Zhang, Xinping

2018-05-01

Aiming at providing a precise dynamic structural finite element (FE) model for dynamic strength evaluation in addition to dynamic analysis. A dynamic FE model updating method is presented to correct the uncertain parameters of the FE model of a structure using strain mode shapes and natural frequencies. The strain mode shape, which is sensitive to local changes in structure, is used instead of the displacement mode for enhancing model updating. The coordinate strain modal assurance criterion is developed to evaluate the correlation level at each coordinate over the experimental and the analytical strain mode shapes. Moreover, the natural frequencies which provide the global information of the structure are used to guarantee the accuracy of modal properties of the global model. Then, the weighted summation of the natural frequency residual and the coordinate strain modal assurance criterion residual is used as the objective function in the proposed dynamic FE model updating procedure. The hybrid genetic/pattern-search optimization algorithm is adopted to perform the dynamic FE model updating procedure. Numerical simulation and model updating experiment for a clamped-clamped beam are performed to validate the feasibility and effectiveness of the present method. The results show that the proposed method can be used to update the uncertain parameters with good robustness. And the updated dynamic FE model of the beam structure, which can correctly predict both the natural frequencies and the local dynamic strains, is reliable for the following dynamic analysis and dynamic strength evaluation.

Finite-horizon differential games for missile-target interception system using adaptive dynamic programming with input constraints

NASA Astrophysics Data System (ADS)

Sun, Jingliang; Liu, Chunsheng

2018-01-01

In this paper, the problem of intercepting a manoeuvring target within a fixed final time is posed in a non-linear constrained zero-sum differential game framework. The Nash equilibrium solution is found by solving the finite-horizon constrained differential game problem via adaptive dynamic programming technique. Besides, a suitable non-quadratic functional is utilised to encode the control constraints into a differential game problem. The single critic network with constant weights and time-varying activation functions is constructed to approximate the solution of associated time-varying Hamilton-Jacobi-Isaacs equation online. To properly satisfy the terminal constraint, an additional error term is incorporated in a novel weight-updating law such that the terminal constraint error is also minimised over time. By utilising Lyapunov's direct method, the closed-loop differential game system and the estimation weight error of the critic network are proved to be uniformly ultimately bounded. Finally, the effectiveness of the proposed method is demonstrated by using a simple non-linear system and a non-linear missile-target interception system, assuming first-order dynamics for the interceptor and target.

Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion.

PubMed

Kumar, Rajesh; Srivastava, Smriti; Gupta, J R P

2017-03-01

In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

In-Flight Suppression of an Unstable F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

NASA Technical Reports Server (NTRS)

VanZwieten, Tannen S.; Gilligan, Eric T.; Wall, John H.; Miller, Christopher J.; Hanson, Curtis E.; Orr, Jeb S.

2015-01-01

NASA's Space Launch System (SLS) Flight Control System (FCS) includes an Adaptive Augmenting Control (AAC) component which employs a multiplicative gain update law to enhance the performance and robustness of the baseline control system for extreme off-nominal scenarios. The SLS FCS algorithm including AAC has been flight tested utilizing a specially outfitted F/A-18 fighter jet in which the pitch axis control of the aircraft was performed by a Non-linear Dynamic Inversion (NDI) controller, SLS reference models, and the SLS flight software prototype. This paper describes test cases from the research flight campaign in which the fundamental F/A-18 airframe structural mode was identified using post-flight frequency-domain reconstruction, amplified to result in closed loop instability, and suppressed in-flight by the SLS adaptive control system.

Adaptive control for a class of nonlinear complex dynamical systems with uncertain complex parameters and perturbations

PubMed Central

Liu, Jian; Liu, Kexin; Liu, Shutang

2017-01-01

In this paper, adaptive control is extended from real space to complex space, resulting in a new control scheme for a class of n-dimensional time-dependent strict-feedback complex-variable chaotic (hyperchaotic) systems (CVCSs) in the presence of uncertain complex parameters and perturbations, which has not been previously reported in the literature. In detail, we have developed a unified framework for designing the adaptive complex scalar controller to ensure this type of CVCSs asymptotically stable and for selecting complex update laws to estimate unknown complex parameters. In particular, combining Lyapunov functions dependent on complex-valued vectors and back-stepping technique, sufficient criteria on stabilization of CVCSs are derived in the sense of Wirtinger calculus in complex space. Finally, numerical simulation is presented to validate our theoretical results. PMID:28467431

Adaptive control for a class of nonlinear complex dynamical systems with uncertain complex parameters and perturbations.

PubMed

Liu, Jian; Liu, Kexin; Liu, Shutang

2017-01-01

In this paper, adaptive control is extended from real space to complex space, resulting in a new control scheme for a class of n-dimensional time-dependent strict-feedback complex-variable chaotic (hyperchaotic) systems (CVCSs) in the presence of uncertain complex parameters and perturbations, which has not been previously reported in the literature. In detail, we have developed a unified framework for designing the adaptive complex scalar controller to ensure this type of CVCSs asymptotically stable and for selecting complex update laws to estimate unknown complex parameters. In particular, combining Lyapunov functions dependent on complex-valued vectors and back-stepping technique, sufficient criteria on stabilization of CVCSs are derived in the sense of Wirtinger calculus in complex space. Finally, numerical simulation is presented to validate our theoretical results.

Neural basis for dynamic updating of object representation in visual working memory.

PubMed

Takahama, Sachiko; Miyauchi, Satoru; Saiki, Jun

2010-02-15

In real world, objects have multiple features and change dynamically. Thus, object representations must satisfy dynamic updating and feature binding. Previous studies have investigated the neural activity of dynamic updating or feature binding alone, but not both simultaneously. We investigated the neural basis of feature-bound object representation in a dynamically updating situation by conducting a multiple object permanence tracking task, which required observers to simultaneously process both the maintenance and dynamic updating of feature-bound objects. Using an event-related design, we separated activities during memory maintenance and change detection. In the search for regions showing selective activation in dynamic updating of feature-bound objects, we identified a network during memory maintenance that was comprised of the inferior precentral sulcus, superior parietal lobule, and middle frontal gyrus. In the change detection period, various prefrontal regions, including the anterior prefrontal cortex, were activated. In updating object representation of dynamically moving objects, the inferior precentral sulcus closely cooperates with a so-called "frontoparietal network", and subregions of the frontoparietal network can be decomposed into those sensitive to spatial updating and feature binding. The anterior prefrontal cortex identifies changes in object representation by comparing memory and perceptual representations rather than maintaining object representations per se, as previously suggested. Copyright 2009 Elsevier Inc. All rights reserved.

Adaptive Bio-Inspired Wireless Network Routing for Planetary Surface Exploration

NASA Technical Reports Server (NTRS)

Alena, Richard I.; Lee, Charles

2004-01-01

Wireless mobile networks suffer connectivity loss when used in a terrain that has hills, and valleys when line of sight is interrupted or range is exceeded. To resolve this problem and achieve acceptable network performance, we have designed an adaptive, configurable, hybrid system to automatically route network packets along the best path between multiple geographically dispersed modules. This is very useful in planetary surface exploration, especially for ad-hoc mobile networks, where computational devices take an active part in creating a network infrastructure, and can actually be used to route data dynamically and even store data for later transmission between networks. Using inspiration from biological systems, this research proposes to use ant trail algorithms with multi-layered information maps (topographic maps, RF coverage maps) to determine the best route through ad-hoc network at real time. The determination of best route is a complex one, and requires research into the appropriate metrics, best method to identify the best path, optimizing traffic capacity, network performance, reliability, processing capabilities and cost. Real ants are capable of finding the shortest path from their nest to a food source without visual sensing through the use of pheromones. They are also able to adapt to changes in the environment using subtle clues. To use ant trail algorithms, we need to define the probability function. The artificial ant is, in this case, a software agent that moves from node to node on a network graph. The function to calculate the fitness (evaluate the better path) includes: length of the network edge, the coverage index, topology graph index, and pheromone trail left behind by other ant agents. Each agent modifies the environment in two different ways: 1) Local trail updating: As the ant moves between nodes it updates the amount of pheromone on the edge; and 2) Global trail updating: When all ants have completed a tour the ant that found the shortest route updates the edges in its path.

Stress attenuates the flexible updating of aversive value

PubMed Central

Raio, Candace M.; Hartley, Catherine A.; Orederu, Temidayo A.; Li, Jian; Phelps, Elizabeth A.

2017-01-01

In a dynamic environment, sources of threat or safety can unexpectedly change, requiring the flexible updating of stimulus−outcome associations that promote adaptive behavior. However, aversive contexts in which we are required to update predictions of threat are often marked by stress. Acute stress is thought to reduce behavioral flexibility, yet its influence on the modulation of aversive value has not been well characterized. Given that stress exposure is a prominent risk factor for anxiety and trauma-related disorders marked by persistent, inflexible responses to threat, here we examined how acute stress affects the flexible updating of threat responses. Participants completed an aversive learning task, in which one stimulus was probabilistically associated with an electric shock, while the other stimulus signaled safety. A day later, participants underwent an acute stress or control manipulation before completing a reversal learning task during which the original stimulus−outcome contingencies switched. Skin conductance and neuroendocrine responses provided indices of sympathetic arousal and stress responses, respectively. Despite equivalent initial learning, stressed participants showed marked impairments in reversal learning relative to controls. Additionally, reversal learning deficits across participants were related to heightened levels of alpha-amylase, a marker of noradrenergic activity. Finally, fitting arousal data to a computational reinforcement learning model revealed that stress-induced reversal learning deficits emerged from stress-specific changes in the weight assigned to prediction error signals, disrupting the adaptive adjustment of learning rates. Our findings provide insight into how stress renders individuals less sensitive to changes in aversive reinforcement and have implications for understanding clinical conditions marked by stress-related psychopathology. PMID:28973957

Three-dimensional flow about penguin wings

NASA Astrophysics Data System (ADS)

Noca, Flavio; Sudki, Bassem; Lauria, Michel

2012-11-01

Penguins, contrary to airborne birds, do not need to compensate for gravity. Yet, the kinematics of their wings is highly three-dimensional and seems exceedingly complex for plain swimming. Is such kinematics the result of an evolutionary optimization or is it just a forced adaptation of an airborne flying apparatus to underwater swimming? Some answers will be provided based on flow dynamics around robotic penguin wings. Updates will also be presented on the development of a novel robotic arm intended to simulate penguin swimming and enable novel propulsion devices.

Soft sensor modelling by time difference, recursive partial least squares and adaptive model updating

NASA Astrophysics Data System (ADS)

Fu, Y.; Yang, W.; Xu, O.; Zhou, L.; Wang, J.

2017-04-01

To investigate time-variant and nonlinear characteristics in industrial processes, a soft sensor modelling method based on time difference, moving-window recursive partial least square (PLS) and adaptive model updating is proposed. In this method, time difference values of input and output variables are used as training samples to construct the model, which can reduce the effects of the nonlinear characteristic on modelling accuracy and retain the advantages of recursive PLS algorithm. To solve the high updating frequency of the model, a confidence value is introduced, which can be updated adaptively according to the results of the model performance assessment. Once the confidence value is updated, the model can be updated. The proposed method has been used to predict the 4-carboxy-benz-aldehyde (CBA) content in the purified terephthalic acid (PTA) oxidation reaction process. The results show that the proposed soft sensor modelling method can reduce computation effectively, improve prediction accuracy by making use of process information and reflect the process characteristics accurately.

Novel method to form adaptive internal impedance profiles in walkers.

PubMed

Escudero Morland, Maximilano F; Althoefer, Kaspar; Nanayakkara, Thrishantha

2015-01-01

This paper proposes a novel approach to improve walking in prosthetics, orthotics and robotics without closed loop controllers. The approach requires impedance profiles to be formed in a walker and uses state feedback to update the profiles in real-time via a simple policy. This approach is open loop and inherently copes with the challenge of uncertain environment. In application it could be used either online for a walker to adjust its impedance profiles in real-time to compensate for environmental changes, or offline to learn suitable profiles for specific environments. So far we have conducted simulations and experiments to investigate the transient and steady state gaits obtained using two simple update policies to form damping profiles in a passive dynamic walker known as the rimless wheel (RW). The damping profiles are formed in the motor that moves the RW vertically along a rail, analogous to a knee joint, and the two update equations were designed to a) control the angular velocity profile and b) minimise peak collision forces. Simulation results show that the velocity update equation works within limits and can cope with varying ground conditions. Experiment results show the angular velocity average reaching the target as well as the peak force update equation reducing peak collision forces in real-time.

A meta-learning system based on genetic algorithms

NASA Astrophysics Data System (ADS)

Pellerin, Eric; Pigeon, Luc; Delisle, Sylvain

2004-04-01

The design of an efficient machine learning process through self-adaptation is a great challenge. The goal of meta-learning is to build a self-adaptive learning system that is constantly adapting to its specific (and dynamic) environment. To that end, the meta-learning mechanism must improve its bias dynamically by updating the current learning strategy in accordance with its available experiences or meta-knowledge. We suggest using genetic algorithms as the basis of an adaptive system. In this work, we propose a meta-learning system based on a combination of the a priori and a posteriori concepts. A priori refers to input information and knowledge available at the beginning in order to built and evolve one or more sets of parameters by exploiting the context of the system"s information. The self-learning component is based on genetic algorithms and neural Darwinism. A posteriori refers to the implicit knowledge discovered by estimation of the future states of parameters and is also applied to the finding of optimal parameters values. The in-progress research presented here suggests a framework for the discovery of knowledge that can support human experts in their intelligence information assessment tasks. The conclusion presents avenues for further research in genetic algorithms and their capability to learn to learn.

A class of optimum digital phase locked loops

NASA Technical Reports Server (NTRS)

Kumar, R.; Hurd, W. J.

1986-01-01

This paper presents a class of optimum digital filters for digital phase locked loops, for the important case in which the maximum update rate of the loop filter and numerically controlled oscillator (NCO) is limited. This case is typical when the loop filter is implemented in a microprocessor. In these situations, pure delay is encountered in the loop transfer function and thus the stability and gain margin of the loop are of crucial interest. The optimum filters designed for such situations are evaluated in terms of their gain margin for stability, dynamic error, and steady-state error performance. For situations involving considerably high phase dynamics an adaptive and programmable implementation is also proposed to obtain an overall optimum strategy.

Ancient village fire escape path planning based on improved ant colony algorithm

NASA Astrophysics Data System (ADS)

Xia, Wei; Cao, Kang; Hu, QianChuan

2017-06-01

The roadways are narrow and perplexing in ancient villages, it brings challenges and difficulties for people to choose route to escape when a fire occurs. In this paper, a fire escape path planning method based on ant colony algorithm is presented according to the problem. The factors in the fire environment which influence the escape speed is introduced to improve the heuristic function of the algorithm, optimal transfer strategy, and adjustment pheromone volatile factor to improve pheromone update strategy adaptively, improve its dynamic search ability and search speed. Through simulation, the dynamic adjustment of the optimal escape path is obtained, and the method is proved to be feasible.

IIR filtering based adaptive active vibration control methodology with online secondary path modeling using PZT actuators

NASA Astrophysics Data System (ADS)

Boz, Utku; Basdogan, Ipek

2015-12-01

Structural vibrations is a major cause for noise problems, discomfort and mechanical failures in aerospace, automotive and marine systems, which are mainly composed of plate-like structures. In order to reduce structural vibrations on these structures, active vibration control (AVC) is an effective approach. Adaptive filtering methodologies are preferred in AVC due to their ability to adjust themselves for varying dynamics of the structure during the operation. The filtered-X LMS (FXLMS) algorithm is a simple adaptive filtering algorithm widely implemented in active control applications. Proper implementation of FXLMS requires availability of a reference signal to mimic the disturbance and model of the dynamics between the control actuator and the error sensor, namely the secondary path. However, the controller output could interfere with the reference signal and the secondary path dynamics may change during the operation. This interference problem can be resolved by using an infinite impulse response (IIR) filter which considers feedback of the one or more previous control signals to the controller output and the changing secondary path dynamics can be updated using an online modeling technique. In this paper, IIR filtering based filtered-U LMS (FULMS) controller is combined with online secondary path modeling algorithm to suppress the vibrations of a plate-like structure. The results are validated through numerical and experimental studies. The results show that the FULMS with online secondary path modeling approach has more vibration rejection capabilities with higher convergence rate than the FXLMS counterpart.

Wireless rake-receiver using adaptive filter with a family of partial update algorithms in noise cancellation applications

NASA Astrophysics Data System (ADS)

Fayadh, Rashid A.; Malek, F.; Fadhil, Hilal A.; Aldhaibani, Jaafar A.; Salman, M. K.; Abdullah, Farah Salwani

2015-05-01

For high data rate propagation in wireless ultra-wideband (UWB) communication systems, the inter-symbol interference (ISI), multiple-access interference (MAI), and multiple-users interference (MUI) are influencing the performance of the wireless systems. In this paper, the rake-receiver was presented with the spread signal by direct sequence spread spectrum (DS-SS) technique. The adaptive rake-receiver structure was shown with adjusting the receiver tap weights using least mean squares (LMS), normalized least mean squares (NLMS), and affine projection algorithms (APA) to support the weak signals by noise cancellation and mitigate the interferences. To minimize the data convergence speed and to reduce the computational complexity by the previous algorithms, a well-known approach of partial-updates (PU) adaptive filters were employed with algorithms, such as sequential-partial, periodic-partial, M-max-partial, and selective-partial updates (SPU) in the proposed system. The simulation results of bit error rate (BER) versus signal-to-noise ratio (SNR) are illustrated to show the performance of partial-update algorithms that have nearly comparable performance with the full update adaptive filters. Furthermore, the SPU-partial has closed performance to the full-NLMS and full-APA while the M-max-partial has closed performance to the full-LMS updates algorithms.

Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula

NASA Astrophysics Data System (ADS)

Sarhadi, Ali; Burn, Donald H.; Concepción Ausín, María.; Wiper, Michael P.

2016-03-01

A time-varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time-varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time-varying joint return period for multivariate time-dependent risk analysis in nonstationary environments. The results demonstrate that the nature and the risk of extreme-climate multidimensional processes are changed over time under the impact of climate change, and accordingly the long-term decision making strategies should be updated based on the anomalies of the nonstationary environment.

Novel approaches to pin cluster synchronization on complex dynamical networks in Lur'e forms

NASA Astrophysics Data System (ADS)

Tang, Ze; Park, Ju H.; Feng, Jianwen

2018-04-01

This paper investigates the cluster synchronization of complex dynamical networks consisted of identical or nonidentical Lur'e systems. Due to the special topology structure of the complex networks and the existence of stochastic perturbations, a kind of randomly occurring pinning controller is designed which not only synchronizes all Lur'e systems in the same cluster but also decreases the negative influence among different clusters. Firstly, based on an extended integral inequality, the convex combination theorem and S-procedure, the conditions for cluster synchronization of identical Lur'e networks are derived in a convex domain. Secondly, randomly occurring adaptive pinning controllers with two independent Bernoulli stochastic variables are designed and then sufficient conditions are obtained for the cluster synchronization on complex networks consisted of nonidentical Lur'e systems. In addition, suitable control gains for successful cluster synchronization of nonidentical Lur'e networks are acquired by designing some adaptive updating laws. Finally, we present two numerical examples to demonstrate the validity of the control scheme and the theoretical analysis.

No Need for Conspiracy: Self-Organized Cartel Formation in a Modified Trust Game

NASA Astrophysics Data System (ADS)

Peixoto, Tiago P.; Bornholdt, Stefan

2012-05-01

We investigate the dynamics of a trust game on a mixed population, where individuals with the role of buyers are forced to play against a predetermined number of sellers whom they choose dynamically. Agents with the role of sellers are also allowed to adapt the level of value for money of their products, based on payoff. The dynamics undergoes a transition at a specific value of the strategy update rate, above which an emergent cartel organization is observed, where sellers have similar values of below-optimal value for money. This cartel organization is not due to an explicit collusion among agents; instead, it arises spontaneously from the maximization of the individual payoffs. This dynamics is marked by large fluctuations and a high degree of unpredictability for most of the parameter space and serves as a plausible qualitative explanation for observed elevated levels and fluctuations of certain commodity prices.

Sequential updating of multimodal hydrogeologic parameter fields using localization and clustering techniques

NASA Astrophysics Data System (ADS)

Sun, Alexander Y.; Morris, Alan P.; Mohanty, Sitakanta

2009-07-01

Estimated parameter distributions in groundwater models may contain significant uncertainties because of data insufficiency. Therefore, adaptive uncertainty reduction strategies are needed to continuously improve model accuracy by fusing new observations. In recent years, various ensemble Kalman filters have been introduced as viable tools for updating high-dimensional model parameters. However, their usefulness is largely limited by the inherent assumption of Gaussian error statistics. Hydraulic conductivity distributions in alluvial aquifers, for example, are usually non-Gaussian as a result of complex depositional and diagenetic processes. In this study, we combine an ensemble Kalman filter with grid-based localization and a Gaussian mixture model (GMM) clustering techniques for updating high-dimensional, multimodal parameter distributions via dynamic data assimilation. We introduce innovative strategies (e.g., block updating and dimension reduction) to effectively reduce the computational costs associated with these modified ensemble Kalman filter schemes. The developed data assimilation schemes are demonstrated numerically for identifying the multimodal heterogeneous hydraulic conductivity distributions in a binary facies alluvial aquifer. Our results show that localization and GMM clustering are very promising techniques for assimilating high-dimensional, multimodal parameter distributions, and they outperform the corresponding global ensemble Kalman filter analysis scheme in all scenarios considered.

Forecasting Geomagnetic Activity Using Kalman Filters

NASA Astrophysics Data System (ADS)

Veeramani, T.; Sharma, A.

2006-05-01

The coupling of energy from the solar wind to the magnetosphere leads to the geomagnetic activity in the form of storms and substorms and are characterized by indices such as AL, Dst and Kp. The geomagnetic activity has been predicted near-real time using local linear filter models of the system dynamics wherein the time series of the input solar wind and the output magnetospheric response were used to reconstruct the phase space of the system by a time-delay embedding technique. Recently, the radiation belt dynamics have been studied using a adaptive linear state space model [Rigler et al. 2004]. This was achieved by assuming a linear autoregressive equation for the underlying process and an adaptive identification of the model parameters using a Kalman filter approach. We use such a model for predicting the geomagnetic activity. In the case of substorms, the Bargatze et al [1985] data set yields persistence like behaviour when a time resolution of 2.5 minutes was used to test the model for the prediction of the AL index. Unlike the local linear filters, which are driven by the solar wind input without feedback from the observations, the Kalman filter makes use of the observations as and when available to optimally update the model parameters. The update procedure requires the prediction intervals to be long enough so that the forecasts can be used in practice. The time resolution of the data suitable for such forecasting is studied by taking averages over different durations.

Real-time prediction of respiratory motion based on a local dynamic model in an augmented space

NASA Astrophysics Data System (ADS)

Hong, S.-M.; Jung, B.-H.; Ruan, D.

2011-03-01

Motion-adaptive radiotherapy aims to deliver ablative radiation dose to the tumor target with minimal normal tissue exposure, by accounting for real-time target movement. In practice, prediction is usually necessary to compensate for system latency induced by measurement, communication and control. This work focuses on predicting respiratory motion, which is most dominant for thoracic and abdominal tumors. We develop and investigate the use of a local dynamic model in an augmented space, motivated by the observation that respiratory movement exhibits a locally circular pattern in a plane augmented with a delayed axis. By including the angular velocity as part of the system state, the proposed dynamic model effectively captures the natural evolution of respiratory motion. The first-order extended Kalman filter is used to propagate and update the state estimate. The target location is predicted by evaluating the local dynamic model equations at the required prediction length. This method is complementary to existing work in that (1) the local circular motion model characterizes 'turning', overcoming the limitation of linear motion models; (2) it uses a natural state representation including the local angular velocity and updates the state estimate systematically, offering explicit physical interpretations; (3) it relies on a parametric model and is much less data-satiate than the typical adaptive semiparametric or nonparametric method. We tested the performance of the proposed method with ten RPM traces, using the normalized root mean squared difference between the predicted value and the retrospective observation as the error metric. Its performance was compared with predictors based on the linear model, the interacting multiple linear models and the kernel density estimator for various combinations of prediction lengths and observation rates. The local dynamic model based approach provides the best performance for short to medium prediction lengths under relatively low observation rate. Sensitivity analysis indicates its robustness toward the choice of parameters. Its simplicity, robustness and low computation cost makes the proposed local dynamic model an attractive tool for real-time prediction with system latencies below 0.4 s.

Real-time prediction of respiratory motion based on a local dynamic model in an augmented space.

PubMed

Hong, S-M; Jung, B-H; Ruan, D

2011-03-21

Motion-adaptive radiotherapy aims to deliver ablative radiation dose to the tumor target with minimal normal tissue exposure, by accounting for real-time target movement. In practice, prediction is usually necessary to compensate for system latency induced by measurement, communication and control. This work focuses on predicting respiratory motion, which is most dominant for thoracic and abdominal tumors. We develop and investigate the use of a local dynamic model in an augmented space, motivated by the observation that respiratory movement exhibits a locally circular pattern in a plane augmented with a delayed axis. By including the angular velocity as part of the system state, the proposed dynamic model effectively captures the natural evolution of respiratory motion. The first-order extended Kalman filter is used to propagate and update the state estimate. The target location is predicted by evaluating the local dynamic model equations at the required prediction length. This method is complementary to existing work in that (1) the local circular motion model characterizes 'turning', overcoming the limitation of linear motion models; (2) it uses a natural state representation including the local angular velocity and updates the state estimate systematically, offering explicit physical interpretations; (3) it relies on a parametric model and is much less data-satiate than the typical adaptive semiparametric or nonparametric method. We tested the performance of the proposed method with ten RPM traces, using the normalized root mean squared difference between the predicted value and the retrospective observation as the error metric. Its performance was compared with predictors based on the linear model, the interacting multiple linear models and the kernel density estimator for various combinations of prediction lengths and observation rates. The local dynamic model based approach provides the best performance for short to medium prediction lengths under relatively low observation rate. Sensitivity analysis indicates its robustness toward the choice of parameters. Its simplicity, robustness and low computation cost makes the proposed local dynamic model an attractive tool for real-time prediction with system latencies below 0.4 s.

In-Flight Suppression of a Destabilized F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

NASA Technical Reports Server (NTRS)

Wall, John H.; VanZwieten, Tannen S.; Gilligan, Eric T.; Miller, Christopher J.; Hanson, Curtis E.; Orr, Jeb S.

2015-01-01

NASA's Space Launch System (SLS) Flight Control System (FCS) includes an Adaptive Augmenting Control (AAC) component which employs a multiplicative gain update law to enhance the performance and robustness of the baseline control system for extreme off nominal scenarios. The SLS FCS algorithm including AAC has been flight tested utilizing a specially outfitted F/A-18 fighter jet in which the pitch axis control of the aircraft was performed by a Non-linear Dynamic Inversion (NDI) controller, SLS reference models, and the SLS flight software prototype. This paper describes test cases from the research flight campaign in which the fundamental F/A-18 airframe structural mode was identified using frequency-domain reconstruction of flight data, amplified to result in closed loop instability, and suppressed in-flight by the SLS adaptive control system.

Study flow diagrams in Cochrane systematic review updates: an adapted PRISMA flow diagram.

PubMed

Stovold, Elizabeth; Beecher, Deirdre; Foxlee, Ruth; Noel-Storr, Anna

2014-05-29

Cochrane systematic reviews are conducted and reported according to rigorous standards. A study flow diagram must be included in a new review, and there is clear guidance from the PRISMA statement on how to do this. However, for a review update, there is currently no guidance on how study flow diagrams should be presented. To address this, a working group was formed to find a solution and produce guidance on how to use these diagrams in review updates.A number of different options were devised for how these flow diagrams could be used in review updates, and also in cases where multiple searches for a review or review update have been conducted. These options were circulated to the Cochrane information specialist community for consultation and feedback. Following the consultation period, the working group refined the guidance and made the recommendation that for review updates an adapted PRISMA flow diagram should be used, which includes an additional box with the number of previously included studies feeding into the total. Where multiple searches have been conducted, the results should be added together and treated as one set of results.There is no existing guidance for using study flow diagrams in review updates. Our adapted diagram is a simple and pragmatic solution for showing the flow of studies in review updates.

Event-Based Robust Control for Uncertain Nonlinear Systems Using Adaptive Dynamic Programming.

PubMed

Zhang, Qichao; Zhao, Dongbin; Wang, Ding

2018-01-01

In this paper, the robust control problem for a class of continuous-time nonlinear system with unmatched uncertainties is investigated using an event-based control method. First, the robust control problem is transformed into a corresponding optimal control problem with an augmented control and an appropriate cost function. Under the event-based mechanism, we prove that the solution of the optimal control problem can asymptotically stabilize the uncertain system with an adaptive triggering condition. That is, the designed event-based controller is robust to the original uncertain system. Note that the event-based controller is updated only when the triggering condition is satisfied, which can save the communication resources between the plant and the controller. Then, a single network adaptive dynamic programming structure with experience replay technique is constructed to approach the optimal control policies. The stability of the closed-loop system with the event-based control policy and the augmented control policy is analyzed using the Lyapunov approach. Furthermore, we prove that the minimal intersample time is bounded by a nonzero positive constant, which excludes Zeno behavior during the learning process. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed control scheme.

A fast dynamic grid adaption scheme for meteorological flows

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

Fiedler, B.H.; Trapp, R.J.

1993-10-01

The continuous dynamic grid adaption (CDGA) technique is applied to a compressible, three-dimensional model of a rising thermal. The computational cost, per grid point per time step, of using CDGA instead of a fixed, uniform Cartesian grid is about 53% of the total cost of the model with CDGA. The use of general curvilinear coordinates contributes 11.7% to this total, calculating and moving the grid 6.1%, and continually updating the transformation relations 20.7%. Costs due to calculations that involve the gridpoint velocities (as well as some substantial unexplained costs) contribute the remaining 14.5%. A simple way to limit the costmore » of calculating the grid is presented. The grid is adapted by solving an elliptic equation for gridpoint coordinates on a coarse grid and then interpolating the full finite-difference grid. In this application, the additional costs per grid point of CDGA are shown to be easily offset by the savings resulting from the reduction in the required number of grid points. In simulation of the thermal costs are reduced by a factor of 3, as compared with those of a companion model with a fixed, uniform Cartesian grid. 8 refs., 8 figs.« less

Highly undersampled MR image reconstruction using an improved dual-dictionary learning method with self-adaptive dictionaries.

PubMed

Li, Jiansen; Song, Ying; Zhu, Zhen; Zhao, Jun

2017-05-01

Dual-dictionary learning (Dual-DL) method utilizes both a low-resolution dictionary and a high-resolution dictionary, which are co-trained for sparse coding and image updating, respectively. It can effectively exploit a priori knowledge regarding the typical structures, specific features, and local details of training sets images. The prior knowledge helps to improve the reconstruction quality greatly. This method has been successfully applied in magnetic resonance (MR) image reconstruction. However, it relies heavily on the training sets, and dictionaries are fixed and nonadaptive. In this research, we improve Dual-DL by using self-adaptive dictionaries. The low- and high-resolution dictionaries are updated correspondingly along with the image updating stage to ensure their self-adaptivity. The updated dictionaries incorporate both the prior information of the training sets and the test image directly. Both dictionaries feature improved adaptability. Experimental results demonstrate that the proposed method can efficiently and significantly improve the quality and robustness of MR image reconstruction.

Transformation of two and three-dimensional regions by elliptic systems

NASA Technical Reports Server (NTRS)

Mastin, C. Wayne

1991-01-01

A reliable linear system is presented for grid generation in 2-D and 3-D. The method is robust in the sense that convergence is guaranteed but is not as reliable as other nonlinear elliptic methods in generating nonfolding grids. The construction of nonfolding grids depends on having reasonable approximations of cell aspect ratios and an appropriate distribution of grid points on the boundary of the region. Some guidelines are included on approximating the aspect ratios, but little help is offered on setting up the boundary grid other than to say that in 2-D the boundary correspondence should be close to that generated by a conformal mapping. It is assumed that the functions which control the grid distribution depend only on the computational variables and not on the physical variables. Whether this is actually the case depends on how the grid is constructed. In a dynamic adaptive procedure where the grid is constructed in the process of solving a fluid flow problem, the grid is usually updated at fixed iteration counts using the current value of the control function. Since the control function is not being updated during the iteration of the grid equations, the grid construction is a linear procedure. However, in the case of a static adaptive procedure where a trial solution is computed and used to construct an adaptive grid, the control functions may be recomputed at every step of the grid iteration.

An Adaptive Pheromone Updation of the Ant-System using LMS Technique

NASA Astrophysics Data System (ADS)

Paul, Abhishek; Mukhopadhyay, Sumitra

2010-10-01

We propose a modified model of pheromone updation for Ant-System, entitled as Adaptive Ant System (AAS), using the properties of basic Adaptive Filters. Here, we have exploited the properties of Least Mean Square (LMS) algorithm for the pheromone updation to find out the best minimum tour for the Travelling Salesman Problem (TSP). TSP library has been used for the selection of benchmark problem and the proposed AAS determines the minimum tour length for the problems containing large number of cities. Our algorithm shows effective results and gives least tour length in most of the cases as compared to other existing approaches.

On Using Exponential Parameter Estimators with an Adaptive Controller

NASA Technical Reports Server (NTRS)

Patre, Parag; Joshi, Suresh M.

2011-01-01

Typical adaptive controllers are restricted to using a specific update law to generate parameter estimates. This paper investigates the possibility of using any exponential parameter estimator with an adaptive controller such that the system tracks a desired trajectory. The goal is to provide flexibility in choosing any update law suitable for a given application. The development relies on a previously developed concept of controller/update law modularity in the adaptive control literature, and the use of a converse Lyapunov-like theorem. Stability analysis is presented to derive gain conditions under which this is possible, and inferences are made about the tracking error performance. The development is based on a class of Euler-Lagrange systems that are used to model various engineering systems including space robots and manipulators.

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update.

PubMed

Gao, Changxin; Shi, Huizhang; Yu, Jin-Gang; Sang, Nong

2016-04-15

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the "good" models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm.

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update

PubMed Central

Gao, Changxin; Shi, Huizhang; Yu, Jin-Gang; Sang, Nong

2016-01-01

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm. PMID:27092505

Dynamic Task Allocation in Multi-Hop Multimedia Wireless Sensor Networks with Low Mobility

PubMed Central

Jin, Yichao; Vural, Serdar; Gluhak, Alexander; Moessner, Klaus

2013-01-01

This paper presents a task allocation-oriented framework to enable efficient in-network processing and cost-effective multi-hop resource sharing for dynamic multi-hop multimedia wireless sensor networks with low node mobility, e.g., pedestrian speeds. The proposed system incorporates a fast task reallocation algorithm to quickly recover from possible network service disruptions, such as node or link failures. An evolutional self-learning mechanism based on a genetic algorithm continuously adapts the system parameters in order to meet the desired application delay requirements, while also achieving a sufficiently long network lifetime. Since the algorithm runtime incurs considerable time delay while updating task assignments, we introduce an adaptive window size to limit the delay periods and ensure an up-to-date solution based on node mobility patterns and device processing capabilities. To the best of our knowledge, this is the first study that yields multi-objective task allocation in a mobile multi-hop wireless environment under dynamic conditions. Simulations are performed in various settings, and the results show considerable performance improvement in extending network lifetime compared to heuristic mechanisms. Furthermore, the proposed framework provides noticeable reduction in the frequency of missing application deadlines. PMID:24135992

Object Recognition using Feature- and Color-Based Methods

NASA Technical Reports Server (NTRS)

Duong, Tuan; Duong, Vu; Stubberud, Allen

2008-01-01

An improved adaptive method of processing image data in an artificial neural network has been developed to enable automated, real-time recognition of possibly moving objects under changing (including suddenly changing) conditions of illumination and perspective. The method involves a combination of two prior object-recognition methods one based on adaptive detection of shape features and one based on adaptive color segmentation to enable recognition in situations in which either prior method by itself may be inadequate. The chosen prior feature-based method is known as adaptive principal-component analysis (APCA); the chosen prior color-based method is known as adaptive color segmentation (ACOSE). These methods are made to interact with each other in a closed-loop system to obtain an optimal solution of the object-recognition problem in a dynamic environment. One of the results of the interaction is to increase, beyond what would otherwise be possible, the accuracy of the determination of a region of interest (containing an object that one seeks to recognize) within an image. Another result is to provide a minimized adaptive step that can be used to update the results obtained by the two component methods when changes of color and apparent shape occur. The net effect is to enable the neural network to update its recognition output and improve its recognition capability via an adaptive learning sequence. In principle, the improved method could readily be implemented in integrated circuitry to make a compact, low-power, real-time object-recognition system. It has been proposed to demonstrate the feasibility of such a system by integrating a 256-by-256 active-pixel sensor with APCA, ACOSE, and neural processing circuitry on a single chip. It has been estimated that such a system on a chip would have a volume no larger than a few cubic centimeters, could operate at a rate as high as 1,000 frames per second, and would consume in the order of milliwatts of power.

Teaching Adaptability of Object-Oriented Programming Language Curriculum

ERIC Educational Resources Information Center

Zhu, Xiao-dong

2012-01-01

The evolution of object-oriented programming languages includes update of their own versions, update of development environments, and reform of new languages upon old languages. In this paper, the evolution analysis of object-oriented programming languages is presented in term of the characters and development. The notion of adaptive teaching upon…

Study flow diagrams in Cochrane systematic review updates: an adapted PRISMA flow diagram

PubMed Central

2014-01-01

Cochrane systematic reviews are conducted and reported according to rigorous standards. A study flow diagram must be included in a new review, and there is clear guidance from the PRISMA statement on how to do this. However, for a review update, there is currently no guidance on how study flow diagrams should be presented. To address this, a working group was formed to find a solution and produce guidance on how to use these diagrams in review updates. A number of different options were devised for how these flow diagrams could be used in review updates, and also in cases where multiple searches for a review or review update have been conducted. These options were circulated to the Cochrane information specialist community for consultation and feedback. Following the consultation period, the working group refined the guidance and made the recommendation that for review updates an adapted PRISMA flow diagram should be used, which includes an additional box with the number of previously included studies feeding into the total. Where multiple searches have been conducted, the results should be added together and treated as one set of results. There is no existing guidance for using study flow diagrams in review updates. Our adapted diagram is a simple and pragmatic solution for showing the flow of studies in review updates. PMID:24886533

Fast but fleeting: adaptive motor learning processes associated with aging and cognitive decline.

PubMed

Trewartha, Kevin M; Garcia, Angeles; Wolpert, Daniel M; Flanagan, J Randall

2014-10-01

Motor learning has been shown to depend on multiple interacting learning processes. For example, learning to adapt when moving grasped objects with novel dynamics involves a fast process that adapts and decays quickly-and that has been linked to explicit memory-and a slower process that adapts and decays more gradually. Each process is characterized by a learning rate that controls how strongly motor memory is updated based on experienced errors and a retention factor determining the movement-to-movement decay in motor memory. Here we examined whether fast and slow motor learning processes involved in learning novel dynamics differ between younger and older adults. In addition, we investigated how age-related decline in explicit memory performance influences learning and retention parameters. Although the groups adapted equally well, they did so with markedly different underlying processes. Whereas the groups had similar fast processes, they had different slow processes. Specifically, the older adults exhibited decreased retention in their slow process compared with younger adults. Within the older group, who exhibited considerable variation in explicit memory performance, we found that poor explicit memory was associated with reduced retention in the fast process, as well as the slow process. These findings suggest that explicit memory resources are a determining factor in impairments in the both the fast and slow processes for motor learning but that aging effects on the slow process are independent of explicit memory declines. Copyright © 2014 the authors 0270-6474/14/3413411-11$15.00/0.

The Propulsive Small Expendable Deployer System (ProSEDS)

NASA Technical Reports Server (NTRS)

Lorenzini, Enrico C.; Cosmo, Mario L.; Estes, Robert D.; Sanmartin, Juan; Pelaez, Jesus; Ruiz, Manuel

2003-01-01

This Final Report covers the following main topics: 1) Brief Description of ProSEDS; 2) Mission Analysis; 3) Dynamics Reference Mission; 4) Dynamics Stability; 5) Deployment Control; 6) Updated System Performance; 7) Updated Mission Analysis; 8) Updated Dynamics Reference Mission; 9) Updated Deployment Control Profiles and Simulations; 10) Updated Reference Mission; 11) Evaluation of Power Delivered by the Tether; 12) Deployment Control Profile Ref. #78 and Simulations; 13) Kalman Filters for Mission Estimation; 14) Analysis/Estimation of Deployment Flight Data; 15) Comparison of ED Tethers and Electrical Thrusters; 16) Dynamics Analysis for Mission Starting at a Lower Altitude; 17) Deployment Performance at a Lower Altitude; 18) Satellite Orbit after a Tether Cut; 19) Deployment with Shorter Dyneema Tether Length; 20) Interactive Software for ED Tethers.

Policy tree optimization for adaptive management of water resources systems

NASA Astrophysics Data System (ADS)

Herman, Jonathan; Giuliani, Matteo

2017-04-01

Water resources systems must cope with irreducible uncertainty in supply and demand, requiring policy alternatives capable of adapting to a range of possible future scenarios. Recent studies have developed adaptive policies based on "signposts" or "tipping points" that suggest the need of updating the policy. However, there remains a need for a general method to optimize the choice of the signposts to be used and their threshold values. This work contributes a general framework and computational algorithm to design adaptive policies as a tree structure (i.e., a hierarchical set of logical rules) using a simulation-optimization approach based on genetic programming. Given a set of feature variables (e.g., reservoir level, inflow observations, inflow forecasts), the resulting policy defines both the optimal reservoir operations and the conditions under which such operations should be triggered. We demonstrate the approach using Folsom Reservoir (California) as a case study, in which operating policies must balance the risk of both floods and droughts. Numerical results show that the tree-based policies outperform the ones designed via Dynamic Programming. In addition, they display good adaptive capacity to the changing climate, successfully adapting the reservoir operations across a large set of uncertain climate scenarios.

Adaptive Correlation Model for Visual Tracking Using Keypoints Matching and Deep Convolutional Feature.

PubMed

Li, Yuankun; Xu, Tingfa; Deng, Honggao; Shi, Guokai; Guo, Jie

2018-02-23

Although correlation filter (CF)-based visual tracking algorithms have achieved appealing results, there are still some problems to be solved. When the target object goes through long-term occlusions or scale variation, the correlation model used in existing CF-based algorithms will inevitably learn some non-target information or partial-target information. In order to avoid model contamination and enhance the adaptability of model updating, we introduce the keypoints matching strategy and adjust the model learning rate dynamically according to the matching score. Moreover, the proposed approach extracts convolutional features from a deep convolutional neural network (DCNN) to accurately estimate the position and scale of the target. Experimental results demonstrate that the proposed tracker has achieved satisfactory performance in a wide range of challenging tracking scenarios.

Adaptive critic neural network-based object grasping control using a three-finger gripper.

PubMed

Jagannathan, S; Galan, Gustavo

2004-03-01

Grasping of objects has been a challenging task for robots. The complex grasping task can be defined as object contact control and manipulation subtasks. In this paper, object contact control subtask is defined as the ability to follow a trajectory accurately by the fingers of a gripper. The object manipulation subtask is defined in terms of maintaining a predefined applied force by the fingers on the object. A sophisticated controller is necessary since the process of grasping an object without a priori knowledge of the object's size, texture, softness, gripper, and contact dynamics is rather difficult. Moreover, the object has to be secured accurately and considerably fast without damaging it. Since the gripper, contact dynamics, and the object properties are not typically known beforehand, an adaptive critic neural network (NN)-based hybrid position/force control scheme is introduced. The feedforward action generating NN in the adaptive critic NN controller compensates the nonlinear gripper and contact dynamics. The learning of the action generating NN is performed on-line based on a critic NN output signal. The controller ensures that a three-finger gripper tracks a desired trajectory while applying desired forces on the object for manipulation. Novel NN weight tuning updates are derived for the action generating and critic NNs so that Lyapunov-based stability analysis can be shown. Simulation results demonstrate that the proposed scheme successfully allows fingers of a gripper to secure objects without the knowledge of the underlying gripper and contact dynamics of the object compared to conventional schemes.

Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters

PubMed Central

Zhang, Sirou; Qiao, Xiaoya

2017-01-01

In recent years, visual object tracking has been widely used in military guidance, human-computer interaction, road traffic, scene monitoring and many other fields. The tracking algorithms based on correlation filters have shown good performance in terms of accuracy and tracking speed. However, their performance is not satisfactory in scenes with scale variation, deformation, and occlusion. In this paper, we propose a scene-aware adaptive updating mechanism for visual tracking via a kernel correlation filter (KCF). First, a low complexity scale estimation method is presented, in which the corresponding weight in five scales is employed to determine the final target scale. Then, the adaptive updating mechanism is presented based on the scene-classification. We classify the video scenes as four categories by video content analysis. According to the target scene, we exploit the adaptive updating mechanism to update the kernel correlation filter to improve the robustness of the tracker, especially in scenes with scale variation, deformation, and occlusion. We evaluate our tracker on the CVPR2013 benchmark. The experimental results obtained with the proposed algorithm are improved by 33.3%, 15%, 6%, 21.9% and 19.8% compared to those of the KCF tracker on the scene with scale variation, partial or long-time large-area occlusion, deformation, fast motion and out-of-view. PMID:29140311

Fairness emergence from zero-intelligence agents

NASA Astrophysics Data System (ADS)

Duan, Wen-Qi; Stanley, H. Eugene

2010-02-01

Fairness plays a key role in explaining the emergence and maintenance of cooperation. Opponent-oriented social utility models were often proposed to explain the origins of fairness preferences in which agents take into account not only their own outcomes but are also concerned with the outcomes of their opponents. Here, we propose a payoff-oriented mechanism in which agents update their beliefs only based on the payoff signals of the previous ultimatum game, regardless of the behaviors and outcomes of the opponents themselves. Employing adaptive ultimatum game, we show that (1) fairness behaviors can emerge out even under such minimalist assumptions, provided that agents are capable of responding to their payoff signals, (2) the average game payoff per agent per round decreases with the increasing discrepancy rate between the average giving rate and the average asking rate, and (3) the belief update process will lead to 50%-50% fair split provided that there is no mutation in the evolutionary dynamics.

An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks

PubMed Central

Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang

2016-01-01

To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It’s theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods. PMID:27669250

An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks.

PubMed

Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang

2016-09-22

To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It's theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods.

Continuous-time adaptive critics.

PubMed

Hanselmann, Thomas; Noakes, Lyle; Zaknich, Anthony

2007-05-01

A continuous-time formulation of an adaptive critic design (ACD) is investigated. Connections to the discrete case are made, where backpropagation through time (BPTT) and real-time recurrent learning (RTRL) are prevalent. Practical benefits are that this framework fits in well with plant descriptions given by differential equations and that any standard integration routine with adaptive step-size does an adaptive sampling for free. A second-order actor adaptation using Newton's method is established for fast actor convergence for a general plant and critic. Also, a fast critic update for concurrent actor-critic training is introduced to immediately apply necessary adjustments of critic parameters induced by actor updates to keep the Bellman optimality correct to first-order approximation after actor changes. Thus, critic and actor updates may be performed at the same time until some substantial error build up in the Bellman optimality or temporal difference equation, when a traditional critic training needs to be performed and then another interval of concurrent actor-critic training may resume.

Optimal updating magnitude in adaptive flat-distribution sampling

NASA Astrophysics Data System (ADS)

Zhang, Cheng; Drake, Justin A.; Ma, Jianpeng; Pettitt, B. Montgomery

2017-11-01

We present a study on the optimization of the updating magnitude for a class of free energy methods based on flat-distribution sampling, including the Wang-Landau (WL) algorithm and metadynamics. These methods rely on adaptive construction of a bias potential that offsets the potential of mean force by histogram-based updates. The convergence of the bias potential can be improved by decreasing the updating magnitude with an optimal schedule. We show that while the asymptotically optimal schedule for the single-bin updating scheme (commonly used in the WL algorithm) is given by the known inverse-time formula, that for the Gaussian updating scheme (commonly used in metadynamics) is often more complex. We further show that the single-bin updating scheme is optimal for very long simulations, and it can be generalized to a class of bandpass updating schemes that are similarly optimal. These bandpass updating schemes target only a few long-range distribution modes and their optimal schedule is also given by the inverse-time formula. Constructed from orthogonal polynomials, the bandpass updating schemes generalize the WL and Langfeld-Lucini-Rago algorithms as an automatic parameter tuning scheme for umbrella sampling.

Optimal updating magnitude in adaptive flat-distribution sampling.

PubMed

Zhang, Cheng; Drake, Justin A; Ma, Jianpeng; Pettitt, B Montgomery

2017-11-07

We present a study on the optimization of the updating magnitude for a class of free energy methods based on flat-distribution sampling, including the Wang-Landau (WL) algorithm and metadynamics. These methods rely on adaptive construction of a bias potential that offsets the potential of mean force by histogram-based updates. The convergence of the bias potential can be improved by decreasing the updating magnitude with an optimal schedule. We show that while the asymptotically optimal schedule for the single-bin updating scheme (commonly used in the WL algorithm) is given by the known inverse-time formula, that for the Gaussian updating scheme (commonly used in metadynamics) is often more complex. We further show that the single-bin updating scheme is optimal for very long simulations, and it can be generalized to a class of bandpass updating schemes that are similarly optimal. These bandpass updating schemes target only a few long-range distribution modes and their optimal schedule is also given by the inverse-time formula. Constructed from orthogonal polynomials, the bandpass updating schemes generalize the WL and Langfeld-Lucini-Rago algorithms as an automatic parameter tuning scheme for umbrella sampling.

Full Gradient Solution to Adaptive Hybrid Control

NASA Technical Reports Server (NTRS)

Bean, Jacob; Schiller, Noah H.; Fuller, Chris

2017-01-01

This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filtered reference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a derivation of the proper weight vector gradient for hybrid (or feedback) controllers that takes into account the presence of feedback. In this formulation, a single weight vector is updated rather than two individually. An internal model structure is assumed for the feedback part of the controller. The full gradient is equivalent to that used in the standard FxLMS algorithm with the addition of a recursive term that is a function of the modeling error. Some simulations are provided to highlight the advantages of using the full gradient in the weight vector update rather than the approximation.

Full Gradient Solution to Adaptive Hybrid Control

NASA Technical Reports Server (NTRS)

Bean, Jacob; Schiller, Noah H.; Fuller, Chris

2016-01-01

This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filtered-reference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a derivation of the proper weight vector gradient for hybrid (or feedback) controllers that takes into account the presence of feedback. In this formulation, a single weight vector is updated rather than two individually. An internal model structure is assumed for the feedback part of the controller. The full gradient is equivalent to that used in the standard FxLMS algorithm with the addition of a recursive term that is a function of the modeling error. Some simulations are provided to highlight the advantages of using the full gradient in the weight vector update rather than the approximation.

Reorganization of finger coordination patterns during adaptation to rotation and scaling of a newly learned sensorimotor transformation.

PubMed

Liu, Xiaolin; Mosier, Kristine M; Mussa-Ivaldi, Ferdinando A; Casadio, Maura; Scheidt, Robert A

2011-01-01

We examined how people organize redundant kinematic control variables (finger joint configurations) while learning to make goal-directed movements of a virtual object (a cursor) within a low-dimensional task space (a computer screen). Subjects participated in three experiments performed on separate days. Learning progressed rapidly on day 1, resulting in reduced target capture error and increased cursor trajectory linearity. On days 2 and 3, one group of subjects adapted to a rotation of the nominal map, imposed either stepwise or randomly over trials. Another group experienced a scaling distortion. We report two findings. First, adaptation rates and memory-dependent motor command updating depended on distortion type. Stepwise application and removal of the rotation induced a marked increase in finger motion variability but scaling did not, suggesting that the rotation initiated a more exhaustive search through the space of viable finger motions to resolve the target capture task than did scaling. Indeed, subjects formed new coordination patterns in compensating the rotation but relied on patterns established during baseline practice to compensate the scaling. These findings support the idea that the brain compensates direction and extent errors separately and in computationally distinct ways, but are inconsistent with the idea that once a task is learned, command updating is limited to those degrees of freedom contributing to performance (thereby minimizing energetic or similar costs of control). Second, we report that subjects who learned a scaling while moving to just one target generalized more narrowly across directions than those who learned a rotation. This contrasts with results from whole-arm reaching studies, where a learned scaling generalizes more broadly across direction than rotation. Based on inverse- and forward-dynamics analyses of reaching with the arm, we propose the difference in results derives from extensive exposure in reaching with familiar arm dynamics versus the novelty of the manual task.

Active contour-based visual tracking by integrating colors, shapes, and motions.

PubMed

Hu, Weiming; Zhou, Xue; Li, Wei; Luo, Wenhan; Zhang, Xiaoqin; Maybank, Stephen

2013-05-01

In this paper, we present a framework for active contour-based visual tracking using level sets. The main components of our framework include contour-based tracking initialization, color-based contour evolution, adaptive shape-based contour evolution for non-periodic motions, dynamic shape-based contour evolution for periodic motions, and the handling of abrupt motions. For the initialization of contour-based tracking, we develop an optical flow-based algorithm for automatically initializing contours at the first frame. For the color-based contour evolution, Markov random field theory is used to measure correlations between values of neighboring pixels for posterior probability estimation. For adaptive shape-based contour evolution, the global shape information and the local color information are combined to hierarchically evolve the contour, and a flexible shape updating model is constructed. For the dynamic shape-based contour evolution, a shape mode transition matrix is learnt to characterize the temporal correlations of object shapes. For the handling of abrupt motions, particle swarm optimization is adopted to capture the global motion which is applied to the contour in the current frame to produce an initial contour in the next frame.

Online Denoising Based on the Second-Order Adaptive Statistics Model.

PubMed

Yi, Sheng-Lun; Jin, Xue-Bo; Su, Ting-Li; Tang, Zhen-Yun; Wang, Fa-Fa; Xiang, Na; Kong, Jian-Lei

2017-07-20

Online denoising is motivated by real-time applications in the industrial process, where the data must be utilizable soon after it is collected. Since the noise in practical process is usually colored, it is quite a challenge for denoising techniques. In this paper, a novel online denoising method was proposed to achieve the processing of the practical measurement data with colored noise, and the characteristics of the colored noise were considered in the dynamic model via an adaptive parameter. The proposed method consists of two parts within a closed loop: the first one is to estimate the system state based on the second-order adaptive statistics model and the other is to update the adaptive parameter in the model using the Yule-Walker algorithm. Specifically, the state estimation process was implemented via the Kalman filter in a recursive way, and the online purpose was therefore attained. Experimental data in a reinforced concrete structure test was used to verify the effectiveness of the proposed method. Results show the proposed method not only dealt with the signals with colored noise, but also achieved a tradeoff between efficiency and accuracy.

Chaos Suppression in Fractional order Permanent Magnet Synchronous Generator in Wind Turbine Systems

NASA Astrophysics Data System (ADS)

Rajagopal, Karthikeyan; Karthikeyan, Anitha; Duraisamy, Prakash

2017-06-01

In this paper we investigate the control of three-dimensional non-autonomous fractional-order uncertain model of a permanent magnet synchronous generator (PMSG) via a adaptive control technique. We derive a dimensionless fractional order model of the PMSM from the integer order presented in the literatures. Various dynamic properties of the fractional order model like eigen values, Lyapunov exponents, bifurcation and bicoherence are investigated. The system chaotic behavior for various orders of fractional calculus are presented. An adaptive controller is derived to suppress the chaotic oscillations of the fractional order model. As the direct Lyapunov stability analysis of the robust controller is difficult for a fractional order first derivative, we have derived a new lemma to analyze the stability of the system. Numerical simulations of the proposed chaos suppression methodology are given to prove the analytical results derived through which we show that for the derived adaptive controller and the parameter update law, the origin of the system for any bounded initial conditions is asymptotically stable.

Real-Time Adaptive Control of Flow-Induced Cavity Tones

NASA Technical Reports Server (NTRS)

Kegerise, Michael A.; Cabell, Randolph H.; Cattafesta, Louis N.

2004-01-01

An adaptive generalized predictive control (GPC) algorithm was formulated and applied to the cavity flow-tone problem. The algorithm employs gradient descent to update the GPC coefficients at each time step. The adaptive control algorithm demonstrated multiple Rossiter mode suppression at fixed Mach numbers ranging from 0.275 to 0.38. The algorithm was also able t o maintain suppression of multiple cavity tones as the freestream Mach number was varied over a modest range (0.275 to 0.29). Controller performance was evaluated with a measure of output disturbance rejection and an input sensitivity transfer function. The results suggest that disturbances entering the cavity flow are colocated with the control input at the cavity leading edge. In that case, only tonal components of the cavity wall-pressure fluctuations can be suppressed and arbitrary broadband pressure reduction is not possible. In the control-algorithm development, the cavity dynamics are treated as linear and time invariant (LTI) for a fixed Mach number. The experimental results lend support this treatment.

Neural network-based model reference adaptive control system.

PubMed

Patino, H D; Liu, D

2000-01-01

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

Dust-wall and dust-plasma interaction in the MIGRAINe code

NASA Astrophysics Data System (ADS)

Vignitchouk, L.; Tolias, P.; Ratynskaia, S.

2014-09-01

The physical models implemented in the recently developed dust dynamics code MIGRAINe are described. A major update of the treatment of secondary electron emission, stemming from models adapted to typical scrape-off layer temperatures, is reported. Sputtering and plasma species backscattering are introduced from fits of available experimental data and their relative importance to dust charging and heating is assessed in fusion-relevant scenarios. Moreover, the description of collisions between dust particles and plasma-facing components, based on the approximation of elastic-perfectly plastic adhesive spheres, has been upgraded to take into account the effects of particle size and temperature.

Object Tracking Using Adaptive Covariance Descriptor and Clustering-Based Model Updating for Visual Surveillance

PubMed Central

Qin, Lei; Snoussi, Hichem; Abdallah, Fahed

2014-01-01

We propose a novel approach for tracking an arbitrary object in video sequences for visual surveillance. The first contribution of this work is an automatic feature extraction method that is able to extract compact discriminative features from a feature pool before computing the region covariance descriptor. As the feature extraction method is adaptive to a specific object of interest, we refer to the region covariance descriptor computed using the extracted features as the adaptive covariance descriptor. The second contribution is to propose a weakly supervised method for updating the object appearance model during tracking. The method performs a mean-shift clustering procedure among the tracking result samples accumulated during a period of time and selects a group of reliable samples for updating the object appearance model. As such, the object appearance model is kept up-to-date and is prevented from contamination even in case of tracking mistakes. We conducted comparing experiments on real-world video sequences, which confirmed the effectiveness of the proposed approaches. The tracking system that integrates the adaptive covariance descriptor and the clustering-based model updating method accomplished stable object tracking on challenging video sequences. PMID:24865883

Vertical Scan (V-SCAN) for 3-D Grid Adaptive Mesh Refinement for an atmospheric Model Dynamical Core

NASA Astrophysics Data System (ADS)

Andronova, N. G.; Vandenberg, D.; Oehmke, R.; Stout, Q. F.; Penner, J. E.

2009-12-01

One of the major building blocks of a rigorous representation of cloud evolution in global atmospheric models is a parallel adaptive grid MPI-based communication library (an Adaptive Blocks for Locally Cartesian Topologies library -- ABLCarT), which manages the block-structured data layout, handles ghost cell updates among neighboring blocks and splits a block as refinements occur. The library has several modules that provide a layer of abstraction for adaptive refinement: blocks, which contain individual cells of user data; shells - the global geometry for the problem, including a sphere, reduced sphere, and now a 3D sphere; a load balancer for placement of blocks onto processors; and a communication support layer which encapsulates all data movement. A major performance concern with adaptive mesh refinement is how to represent calculations that have need to be sequenced in a particular order in a direction, such as calculating integrals along a specific path (e.g. atmospheric pressure or geopotential in the vertical dimension). This concern is compounded if the blocks have varying levels of refinement, or are scattered across different processors, as can be the case in parallel computing. In this paper we describe an implementation in ABLCarT of a vertical scan operation, which allows computing along vertical paths in the correct order across blocks transparent to their resolution and processor location. We test this functionality on a 2D and a 3D advection problem, which tests the performance of the model’s dynamics (transport) and physics (sources and sinks) for different model resolutions needed for inclusion of cloud formation.

An improved multi-domain convolution tracking algorithm

NASA Astrophysics Data System (ADS)

Sun, Xin; Wang, Haiying; Zeng, Yingsen

2018-04-01

Along with the wide application of the Deep Learning in the field of Computer vision, Deep learning has become a mainstream direction in the field of object tracking. The tracking algorithm in this paper is based on the improved multidomain convolution neural network, and the VOT video set is pre-trained on the network by multi-domain training strategy. In the process of online tracking, the network evaluates candidate targets sampled from vicinity of the prediction target in the previous with Gaussian distribution, and the candidate target with the highest score is recognized as the prediction target of this frame. The Bounding Box Regression model is introduced to make the prediction target closer to the ground-truths target box of the test set. Grouping-update strategy is involved to extract and select useful update samples in each frame, which can effectively prevent over fitting. And adapt to changes in both target and environment. To improve the speed of the algorithm while maintaining the performance, the number of candidate target succeed in adjusting dynamically with the help of Self-adaption parameter Strategy. Finally, the algorithm is tested by OTB set, compared with other high-performance tracking algorithms, and the plot of success rate and the accuracy are drawn. which illustrates outstanding performance of the tracking algorithm in this paper.

Rao-Blackwellization for Adaptive Gaussian Sum Nonlinear Model Propagation

NASA Technical Reports Server (NTRS)

Semper, Sean R.; Crassidis, John L.; George, Jemin; Mukherjee, Siddharth; Singla, Puneet

2015-01-01

When dealing with imperfect data and general models of dynamic systems, the best estimate is always sought in the presence of uncertainty or unknown parameters. In many cases, as the first attempt, the Extended Kalman filter (EKF) provides sufficient solutions to handling issues arising from nonlinear and non-Gaussian estimation problems. But these issues may lead unacceptable performance and even divergence. In order to accurately capture the nonlinearities of most real-world dynamic systems, advanced filtering methods have been created to reduce filter divergence while enhancing performance. Approaches, such as Gaussian sum filtering, grid based Bayesian methods and particle filters are well-known examples of advanced methods used to represent and recursively reproduce an approximation to the state probability density function (pdf). Some of these filtering methods were conceptually developed years before their widespread uses were realized. Advanced nonlinear filtering methods currently benefit from the computing advancements in computational speeds, memory, and parallel processing. Grid based methods, multiple-model approaches and Gaussian sum filtering are numerical solutions that take advantage of different state coordinates or multiple-model methods that reduced the amount of approximations used. Choosing an efficient grid is very difficult for multi-dimensional state spaces, and oftentimes expensive computations must be done at each point. For the original Gaussian sum filter, a weighted sum of Gaussian density functions approximates the pdf but suffers at the update step for the individual component weight selections. In order to improve upon the original Gaussian sum filter, Ref. [2] introduces a weight update approach at the filter propagation stage instead of the measurement update stage. This weight update is performed by minimizing the integral square difference between the true forecast pdf and its Gaussian sum approximation. By adaptively updating each component weight during the nonlinear propagation stage an approximation of the true pdf can be successfully reconstructed. Particle filtering (PF) methods have gained popularity recently for solving nonlinear estimation problems due to their straightforward approach and the processing capabilities mentioned above. The basic concept behind PF is to represent any pdf as a set of random samples. As the number of samples increases, they will theoretically converge to the exact, equivalent representation of the desired pdf. When the estimated qth moment is needed, the samples are used for its construction allowing further analysis of the pdf characteristics. However, filter performance deteriorates as the dimension of the state vector increases. To overcome this problem Ref. [5] applies a marginalization technique for PF methods, decreasing complexity of the system to one linear and another nonlinear state estimation problem. The marginalization theory was originally developed by Rao and Blackwell independently. According to Ref. [6] it improves any given estimator under every convex loss function. The improvement comes from calculating a conditional expected value, often involving integrating out a supportive statistic. In other words, Rao-Blackwellization allows for smaller but separate computations to be carried out while reaching the main objective of the estimator. In the case of improving an estimator's variance, any supporting statistic can be removed and its variance determined. Next, any other information that dependents on the supporting statistic is found along with its respective variance. A new approach is developed here by utilizing the strengths of the adaptive Gaussian sum propagation in Ref. [2] and a marginalization approach used for PF methods found in Ref. [7]. In the following sections a modified filtering approach is presented based on a special state-space model within nonlinear systems to reduce the dimensionality of the optimization problem in Ref. [2]. First, the adaptive Gaussian sum propagation is explained and then the new marginalized adaptive Gaussian sum propagation is derived. Finally, an example simulation is presented.

Adaptive optics scanning laser ophthalmoscopy in fundus imaging, a review and update.

PubMed

Zhang, Bing; Li, Ni; Kang, Jie; He, Yi; Chen, Xiao-Ming

2017-01-01

Adaptive optics scanning laser ophthalmoscopy (AO-SLO) has been a promising technique in funds imaging with growing popularity. This review firstly gives a brief history of adaptive optics (AO) and AO-SLO. Then it compares AO-SLO with conventional imaging methods (fundus fluorescein angiography, fundus autofluorescence, indocyanine green angiography and optical coherence tomography) and other AO techniques (adaptive optics flood-illumination ophthalmoscopy and adaptive optics optical coherence tomography). Furthermore, an update of current research situation in AO-SLO is made based on different fundus structures as photoreceptors (cones and rods), fundus vessels, retinal pigment epithelium layer, retinal nerve fiber layer, ganglion cell layer and lamina cribrosa. Finally, this review indicates possible research directions of AO-SLO in future.

Adaptive optics scanning laser ophthalmoscopy in fundus imaging, a review and update

PubMed Central

Zhang, Bing; Li, Ni; Kang, Jie; He, Yi; Chen, Xiao-Ming

2017-01-01

Adaptive optics scanning laser ophthalmoscopy (AO-SLO) has been a promising technique in funds imaging with growing popularity. This review firstly gives a brief history of adaptive optics (AO) and AO-SLO. Then it compares AO-SLO with conventional imaging methods (fundus fluorescein angiography, fundus autofluorescence, indocyanine green angiography and optical coherence tomography) and other AO techniques (adaptive optics flood-illumination ophthalmoscopy and adaptive optics optical coherence tomography). Furthermore, an update of current research situation in AO-SLO is made based on different fundus structures as photoreceptors (cones and rods), fundus vessels, retinal pigment epithelium layer, retinal nerve fiber layer, ganglion cell layer and lamina cribrosa. Finally, this review indicates possible research directions of AO-SLO in future. PMID:29181321

Essential energy space random walks to accelerate molecular dynamics simulations: Convergence improvements via an adaptive-length self-healing strategy

NASA Astrophysics Data System (ADS)

Zheng, Lianqing; Yang, Wei

2008-07-01

Recently, accelerated molecular dynamics (AMD) technique was generalized to realize essential energy space random walks so that further sampling enhancement and effective localized enhanced sampling could be achieved. This method is especially meaningful when essential coordinates of the target events are not priori known; moreover, the energy space metadynamics method was also introduced so that biasing free energy functions can be robustly generated. Despite the promising features of this method, due to the nonequilibrium nature of the metadynamics recursion, it is challenging to rigorously use the data obtained at the recursion stage to perform equilibrium analysis, such as free energy surface mapping; therefore, a large amount of data ought to be wasted. To resolve such problem so as to further improve simulation convergence, as promised in our original paper, we are reporting an alternate approach: the adaptive-length self-healing (ALSH) strategy for AMD simulations; this development is based on a recent self-healing umbrella sampling method. Here, the unit simulation length for each self-healing recursion is increasingly updated based on the Wang-Landau flattening judgment. When the unit simulation length for each update is long enough, all the following unit simulations naturally run into the equilibrium regime. Thereafter, these unit simulations can serve for the dual purposes of recursion and equilibrium analysis. As demonstrated in our model studies, by applying ALSH, both fast recursion and short nonequilibrium data waste can be compromised. As a result, combining all the data obtained from all the unit simulations that are in the equilibrium regime via the weighted histogram analysis method, efficient convergence can be robustly ensured, especially for the purpose of free energy surface mapping.

Adaptive classifier for steel strip surface defects

NASA Astrophysics Data System (ADS)

Jiang, Mingming; Li, Guangyao; Xie, Li; Xiao, Mang; Yi, Li

2017-01-01

Surface defects detection system has been receiving increased attention as its precision, speed and less cost. One of the most challenges is reacting to accuracy deterioration with time as aged equipment and changed processes. These variables will make a tiny change to the real world model but a big impact on the classification result. In this paper, we propose a new adaptive classifier with a Bayes kernel (BYEC) which update the model with small sample to it adaptive for accuracy deterioration. Firstly, abundant features were introduced to cover lots of information about the defects. Secondly, we constructed a series of SVMs with the random subspace of the features. Then, a Bayes classifier was trained as an evolutionary kernel to fuse the results from base SVMs. Finally, we proposed the method to update the Bayes evolutionary kernel. The proposed algorithm is experimentally compared with different algorithms, experimental results demonstrate that the proposed method can be updated with small sample and fit the changed model well. Robustness, low requirement for samples and adaptive is presented in the experiment.

An analysis of neural receptive field plasticity by point process adaptive filtering

PubMed Central

Brown, Emery N.; Nguyen, David P.; Frank, Loren M.; Wilson, Matthew A.; Solo, Victor

2001-01-01

Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of receptive field plasticity from experimental measurements is crucial for understanding how neural systems adapt their representations of relevant biological information. Current analysis methods using histogram estimates of spike rate functions in nonoverlapping temporal windows do not track the evolution of receptive field plasticity on a fine time scale. Adaptive signal processing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity. We derive an instantaneous steepest descent algorithm by using as the criterion function the instantaneous log likelihood of a point process spike train model. We apply the point process adaptive filter algorithm in a study of spatial (place) receptive field properties of simulated and actual spike train data from rat CA1 hippocampal neurons. A stability analysis of the algorithm is sketched in the Appendix. The adaptive algorithm can update the place field parameter estimates on a millisecond time scale. It reliably tracked the migration, changes in scale, and changes in maximum firing rate characteristic of hippocampal place fields in a rat running on a linear track. Point process adaptive filtering offers an analytic method for studying the dynamics of neural receptive fields. PMID:11593043

Multimodel Kalman filtering for adaptive nonuniformity correction in infrared sensors.

PubMed

Pezoa, Jorge E; Hayat, Majeed M; Torres, Sergio N; Rahman, Md Saifur

2006-06-01

We present an adaptive technique for the estimation of nonuniformity parameters of infrared focal-plane arrays that is robust with respect to changes and uncertainties in scene and sensor characteristics. The proposed algorithm is based on using a bank of Kalman filters in parallel. Each filter independently estimates state variables comprising the gain and the bias matrices of the sensor, according to its own dynamic-model parameters. The supervising component of the algorithm then generates the final estimates of the state variables by forming a weighted superposition of all the estimates rendered by each Kalman filter. The weights are computed and updated iteratively, according to the a posteriori-likelihood principle. The performance of the estimator and its ability to compensate for fixed-pattern noise is tested using both simulated and real data obtained from two cameras operating in the mid- and long-wave infrared regime.

BARTER: Behavior Profile Exchange for Behavior-Based Admission and Access Control in MANETs

NASA Astrophysics Data System (ADS)

Frias-Martinez, Vanessa; Stolfo, Salvatore J.; Keromytis, Angelos D.

Mobile Ad-hoc Networks (MANETs) are very dynamic networks with devices continuously entering and leaving the group. The highly dynamic nature of MANETs renders the manual creation and update of policies associated with the initial incorporation of devices to the MANET (admission control) as well as with anomaly detection during communications among members (access control) a very difficult task. In this paper, we present BARTER, a mechanism that automatically creates and updates admission and access control policies for MANETs based on behavior profiles. BARTER is an adaptation for fully distributed environments of our previously introduced BB-NAC mechanism for NAC technologies. Rather than relying on a centralized NAC enforcer, MANET members initially exchange their behavior profiles and compute individual local definitions of normal network behavior. During admission or access control, each member issues an individual decision based on its definition of normalcy. Individual decisions are then aggregated via a threshold cryptographic infrastructure that requires an agreement among a fixed amount of MANET members to change the status of the network. We present experimental results using content and volumetric behavior profiles computed from the ENRON dataset. In particular, we show that the mechanism achieves true rejection rates of 95% with false rejection rates of 9%.

An object-oriented framework for distributed hydrologic and geomorphic modeling using triangulated irregular networks

NASA Astrophysics Data System (ADS)

Tucker, Gregory E.; Lancaster, Stephen T.; Gasparini, Nicole M.; Bras, Rafael L.; Rybarczyk, Scott M.

2001-10-01

We describe a new set of data structures and algorithms for dynamic terrain modeling using a triangulated irregular network (TINs). The framework provides an efficient method for storing, accessing, and updating a Delaunay triangulation and its associated Voronoi diagram. The basic data structure consists of three interconnected data objects: triangles, nodes, and directed edges. Encapsulating each of these geometric elements within a data object makes it possible to essentially decouple the TIN representation from the modeling applications that make use of it. Both the triangulation and its corresponding Voronoi diagram can be rapidly retrieved or updated, making these methods well suited to adaptive remeshing schemes. We develop a set of algorithms for defining drainage networks and identifying closed depressions (e.g., lakes) for hydrologic and geomorphic modeling applications. We also outline simple numerical algorithms for solving network routing and 2D transport equations within the TIN framework. The methods are illustrated with two example applications, a landscape evolution model and a distributed rainfall-runoff model.

A novel model of motor learning capable of developing an optimal movement control law online from scratch.

PubMed

Shimansky, Yury P; Kang, Tao; He, Jiping

2004-02-01

A computational model of a learning system (LS) is described that acquires knowledge and skill necessary for optimal control of a multisegmental limb dynamics (controlled object or CO), starting from "knowing" only the dimensionality of the object's state space. It is based on an optimal control problem setup different from that of reinforcement learning. The LS solves the optimal control problem online while practicing the manipulation of CO. The system's functional architecture comprises several adaptive components, each of which incorporates a number of mapping functions approximated based on artificial neural nets. Besides the internal model of the CO's dynamics and adaptive controller that computes the control law, the LS includes a new type of internal model, the minimal cost (IM(mc)) of moving the controlled object between a pair of states. That internal model appears critical for the LS's capacity to develop an optimal movement trajectory. The IM(mc) interacts with the adaptive controller in a cooperative manner. The controller provides an initial approximation of an optimal control action, which is further optimized in real time based on the IM(mc). The IM(mc) in turn provides information for updating the controller. The LS's performance was tested on the task of center-out reaching to eight randomly selected targets with a 2DOF limb model. The LS reached an optimal level of performance in a few tens of trials. It also quickly adapted to movement perturbations produced by two different types of external force field. The results suggest that the proposed design of a self-optimized control system can serve as a basis for the modeling of motor learning that includes the formation and adaptive modification of the plan of a goal-directed movement.

Citation Discovery Tools for Conducting Adaptive Meta-analyses to Update Systematic Reviews.

PubMed

Bae, Jong-Myon; Kim, Eun Hee

2016-03-01

The systematic review (SR) is a research methodology that aims to synthesize related evidence. Updating previously conducted SRs is necessary when new evidence has been produced, but no consensus has yet emerged on the appropriate update methodology. The authors have developed a new SR update method called 'adaptive meta-analysis' (AMA) using the 'cited by', 'similar articles', and 'related articles' citation discovery tools in the PubMed and Scopus databases. This study evaluates the usefulness of these citation discovery tools for updating SRs. Lists were constructed by applying the citation discovery tools in the two databases to the articles analyzed by a published SR. The degree of overlap between the lists and distribution of excluded results were evaluated. The articles ultimately selected for the SR update meta-analysis were found in the lists obtained from the 'cited by' and 'similar' tools in PubMed. Most of the selected articles appeared in both the 'cited by' lists in Scopus and PubMed. The Scopus 'related' tool did not identify the appropriate articles. The AMA, which involves using both citation discovery tools in PubMed, and optionally, the 'related' tool in Scopus, was found to be useful for updating an SR.

Accurate and efficient integration for molecular dynamics simulations at constant temperature and pressure

NASA Astrophysics Data System (ADS)

Lippert, Ross A.; Predescu, Cristian; Ierardi, Douglas J.; Mackenzie, Kenneth M.; Eastwood, Michael P.; Dror, Ron O.; Shaw, David E.

2013-10-01

In molecular dynamics simulations, control over temperature and pressure is typically achieved by augmenting the original system with additional dynamical variables to create a thermostat and a barostat, respectively. These variables generally evolve on timescales much longer than those of particle motion, but typical integrator implementations update the additional variables along with the particle positions and momenta at each time step. We present a framework that replaces the traditional integration procedure with separate barostat, thermostat, and Newtonian particle motion updates, allowing thermostat and barostat updates to be applied infrequently. Such infrequent updates provide a particularly substantial performance advantage for simulations parallelized across many computer processors, because thermostat and barostat updates typically require communication among all processors. Infrequent updates can also improve accuracy by alleviating certain sources of error associated with limited-precision arithmetic. In addition, separating the barostat, thermostat, and particle motion update steps reduces certain truncation errors, bringing the time-average pressure closer to its target value. Finally, this framework, which we have implemented on both general-purpose and special-purpose hardware, reduces software complexity and improves software modularity.

Evaluating a multispecies adaptive management framework: Must uncertainty impede effective decision-making?

USGS Publications Warehouse

Smith, David R.; McGowan, Conor P.; Daily, Jonathan P.; Nichols, James D.; Sweka, John A.; Lyons, James E.

2013-01-01

Application of adaptive management to complex natural resource systems requires careful evaluation to ensure that the process leads to improved decision-making. As part of that evaluation, adaptive policies can be compared with alternative nonadaptive management scenarios. Also, the value of reducing structural (ecological) uncertainty to achieving management objectives can be quantified.A multispecies adaptive management framework was recently adopted by the Atlantic States Marine Fisheries Commission for sustainable harvest of Delaware Bay horseshoe crabs Limulus polyphemus, while maintaining adequate stopover habitat for migrating red knots Calidris canutus rufa, the focal shorebird species. The predictive model set encompassed the structural uncertainty in the relationships between horseshoe crab spawning, red knot weight gain and red knot vital rates. Stochastic dynamic programming was used to generate a state-dependent strategy for harvest decisions given that uncertainty. In this paper, we employed a management strategy evaluation approach to evaluate the performance of this adaptive management framework. Active adaptive management was used by including model weights as state variables in the optimization and reducing structural uncertainty by model weight updating.We found that the value of information for reducing structural uncertainty is expected to be low, because the uncertainty does not appear to impede effective management. Harvest policy responded to abundance levels of both species regardless of uncertainty in the specific relationship that generated those abundances. Thus, the expected horseshoe crab harvest and red knot abundance were similar when the population generating model was uncertain or known, and harvest policy was robust to structural uncertainty as specified.Synthesis and applications. The combination of management strategy evaluation with state-dependent strategies from stochastic dynamic programming was an informative approach to evaluate adaptive management performance and value of learning. Although natural resource decisions are characterized by uncertainty, not all uncertainty will cause decisions to be altered substantially, as we found in this case. It is important to incorporate uncertainty into the decision framing and evaluate the effect of reducing that uncertainty on achieving the desired outcomes

On the role of model-based monitoring for adaptive planning under uncertainty

NASA Astrophysics Data System (ADS)

Raso, Luciano; Kwakkel, Jan; Timmermans, Jos; Haasnoot, Mariolijn

2016-04-01

Adaptive plans, designed to anticipate and respond to an unfolding uncertain future, have found a fertile application domain in the planning of deltas that are exposed to rapid socioeconomic development and climate change. Adaptive planning, under the moniker of adaptive delta management, is used in the Dutch Delta Program for developing a nation-wide plan to prepare for uncertain climate change and socio-economic developments. Scientifically, adaptive delta management relies heavily on Dynamic Adaptive Policy Pathways. Currently, in the Netherlands the focus is shifting towards implementing the adaptive delta plan. This shift is especially relevant because the efficacy of adaptive plans hinges on monitoring on-going developments and ensuring that actions are indeed taken if and when necessary. In the design of an effective monitoring system for an adaptive plan, three challenges have to be confronted: • Shadow of the past: The development of adaptive plans and the design of their monitoring system relies heavily on current knowledge of the system, and current beliefs about plausible future developments. A static monitoring system is therefore exposed to the exact same uncertainties one tries to address through adaptive planning. • Inhibition of learning: Recent applications of adaptive planning tend to overlook the importance of learning and new information, and fail to account for this explicitly in the design of adaptive plans. • Challenge of surprise: Adaptive policies are designed in light of the current foreseen uncertainties. However, developments that are not considered during the design phase as being plausible could still substantially affect the performance of adaptive policies. The shadow of the past, the inhibition of learning, and the challenge of surprise taken together suggest that there is a need for redesigning the concepts of monitoring and evaluation to support the implementation of adaptive plans. Innovations from control theory, triggered by the challenge of uncertainty in operational control, may offer solutions from which monitoring for adaptive planning can benefit. Specifically: (i) in control, observations are incorporated into the model through data assimilation, updating the present state, boundary conditions, and parameters based on new observations, diminishing the shadow of the past; (ii) adaptive control is a way to modify the characteristics of the internal model, incorporating new knowledge on the system, countervailing the inhibition of learning; and (iii) in closed-loop control, a continuous system update equips the controller with "inherent robustness", i.e. to capacity to adapts to new conditions even when these were not initially considered. We aim to explore how inherent robustness addresses the challenge of surprise. Innovations in model-based control might help to improve and adapt the models used to support adaptive delta management to new information (reducing uncertainty). Moreover, this would offer a starting point for using these models not only in the design of adaptive plans, but also as part of the monitoring. The proposed research requires multidisciplinary cooperation between control theory, the policy sciences, and integrated assessment modeling.

A model-updating procedure to stimulate piezoelectric transducers accurately.

PubMed

Piranda, B; Ballandras, S; Steichen, W; Hecart, B

2001-09-01

The use of numerical calculations based on finite element methods (FEM) has yielded significant improvements in the simulation and design of piezoelectric transducers piezoelectric transducer utilized in acoustic imaging. However, the ultimate precision of such models is directly controlled by the accuracy of material characterization. The present work is dedicated to the development of a model-updating technique adapted to the problem of piezoelectric transducer. The updating process is applied using the experimental admittance of a given structure for which a finite element analysis is performed. The mathematical developments are reported and then applied to update the entries of a FEM of a two-layer structure (a PbZrTi-PZT-ridge glued on a backing) for which measurements were available. The efficiency of the proposed approach is demonstrated, yielding the definition of a new set of constants well adapted to predict the structure response accurately. Improvement of the proposed approach, consisting of the updating of material coefficients not only on the admittance but also on the impedance data, is finally discussed.

Optimal structure of metaplasticity for adaptive learning

PubMed Central

2017-01-01

Learning from reward feedback in a changing environment requires a high degree of adaptability, yet the precise estimation of reward information demands slow updates. In the framework of estimating reward probability, here we investigated how this tradeoff between adaptability and precision can be mitigated via metaplasticity, i.e. synaptic changes that do not always alter synaptic efficacy. Using the mean-field and Monte Carlo simulations we identified ‘superior’ metaplastic models that can substantially overcome the adaptability-precision tradeoff. These models can achieve both adaptability and precision by forming two separate sets of meta-states: reservoirs and buffers. Synapses in reservoir meta-states do not change their efficacy upon reward feedback, whereas those in buffer meta-states can change their efficacy. Rapid changes in efficacy are limited to synapses occupying buffers, creating a bottleneck that reduces noise without significantly decreasing adaptability. In contrast, more-populated reservoirs can generate a strong signal without manifesting any observable plasticity. By comparing the behavior of our model and a few competing models during a dynamic probability estimation task, we found that superior metaplastic models perform close to optimally for a wider range of model parameters. Finally, we found that metaplastic models are robust to changes in model parameters and that metaplastic transitions are crucial for adaptive learning since replacing them with graded plastic transitions (transitions that change synaptic efficacy) reduces the ability to overcome the adaptability-precision tradeoff. Overall, our results suggest that ubiquitous unreliability of synaptic changes evinces metaplasticity that can provide a robust mechanism for mitigating the tradeoff between adaptability and precision and thus adaptive learning. PMID:28658247

Dynamic adaptive chemistry for turbulent flame simulations

NASA Astrophysics Data System (ADS)

Yang, Hongtao; Ren, Zhuyin; Lu, Tianfeng; Goldin, Graham M.

2013-02-01

The use of large chemical mechanisms in flame simulations is computationally expensive due to the large number of chemical species and the wide range of chemical time scales involved. This study investigates the use of dynamic adaptive chemistry (DAC) for efficient chemistry calculations in turbulent flame simulations. DAC is achieved through the directed relation graph (DRG) method, which is invoked for each computational fluid dynamics cell/particle to obtain a small skeletal mechanism that is valid for the local thermochemical condition. Consequently, during reaction fractional steps, one needs to solve a smaller set of ordinary differential equations governing chemical kinetics. Test calculations are performed in a partially-stirred reactor (PaSR) involving both methane/air premixed and non-premixed combustion with chemistry described by the 53-species GRI-Mech 3.0 mechanism and the 129-species USC-Mech II mechanism augmented with recently updated NO x pathways, respectively. Results show that, in the DAC approach, the DRG reduction threshold effectively controls the incurred errors in the predicted temperature and species concentrations. The computational saving achieved by DAC increases with the size of chemical kinetic mechanisms. For the PaSR simulations, DAC achieves a speedup factor of up to three for GRI-Mech 3.0 and up to six for USC-Mech II in simulation time, while at the same time maintaining good accuracy in temperature and species concentration predictions.

Distributed Synchronization in Networks of Agent Systems With Nonlinearities and Random Switchings.

PubMed

Tang, Yang; Gao, Huijun; Zou, Wei; Kurths, Jürgen

2013-02-01

In this paper, the distributed synchronization problem of networks of agent systems with controllers and nonlinearities subject to Bernoulli switchings is investigated. Controllers and adaptive updating laws injected in each vertex of networks depend on the state information of its neighborhood. Three sets of Bernoulli stochastic variables are introduced to describe the occurrence probabilities of distributed adaptive controllers, updating laws and nonlinearities, respectively. By the Lyapunov functions method, we show that the distributed synchronization of networks composed of agent systems with multiple randomly occurring nonlinearities, multiple randomly occurring controllers, and multiple randomly occurring updating laws can be achieved in mean square under certain criteria. The conditions derived in this paper can be solved by semi-definite programming. Moreover, by mathematical analysis, we find that the coupling strength, the probabilities of the Bernoulli stochastic variables, and the form of nonlinearities have great impacts on the convergence speed and the terminal control strength. The synchronization criteria and the observed phenomena are demonstrated by several numerical simulation examples. In addition, the advantage of distributed adaptive controllers over conventional adaptive controllers is illustrated.

Updating beliefs and combining evidence in adaptive forest management under climate change: a case study of Norway spruce (Picea abies L. Karst) in the Black Forest, Germany.

PubMed

Yousefpour, Rasoul; Temperli, Christian; Bugmann, Harald; Elkin, Che; Hanewinkel, Marc; Meilby, Henrik; Jacobsen, Jette Bredahl; Thorsen, Bo Jellesmark

2013-06-15

We study climate uncertainty and how managers' beliefs about climate change develop and influence their decisions. We develop an approach for updating knowledge and beliefs based on the observation of forest and climate variables and illustrate its application for the adaptive management of an even-aged Norway spruce (Picea abies L. Karst) forest in the Black Forest, Germany. We simulated forest development under a range of climate change scenarios and forest management alternatives. Our analysis used Bayesian updating and Dempster's rule of combination to simulate how observations of climate and forest variables may influence a decision maker's beliefs about climate development and thereby management decisions. While forest managers may be inclined to rely on observed forest variables to infer climate change and impacts, we found that observation of climate state, e.g. temperature or precipitation is superior for updating beliefs and supporting decision-making. However, with little conflict among information sources, the strongest evidence would be offered by a combination of at least two informative variables, e.g., temperature and precipitation. The success of adaptive forest management depends on when managers switch to forward-looking management schemes. Thus, robust climate adaptation policies may depend crucially on a better understanding of what factors influence managers' belief in climate change. Copyright © 2013 Elsevier Ltd. All rights reserved.

Model-Free Adaptive Control for Unknown Nonlinear Zero-Sum Differential Game.

PubMed

Zhong, Xiangnan; He, Haibo; Wang, Ding; Ni, Zhen

2018-05-01

In this paper, we present a new model-free globalized dual heuristic dynamic programming (GDHP) approach for the discrete-time nonlinear zero-sum game problems. First, the online learning algorithm is proposed based on the GDHP method to solve the Hamilton-Jacobi-Isaacs equation associated with optimal regulation control problem. By setting backward one step of the definition of performance index, the requirement of system dynamics, or an identifier is relaxed in the proposed method. Then, three neural networks are established to approximate the optimal saddle point feedback control law, the disturbance law, and the performance index, respectively. The explicit updating rules for these three neural networks are provided based on the data generated during the online learning along the system trajectories. The stability analysis in terms of the neural network approximation errors is discussed based on the Lyapunov approach. Finally, two simulation examples are provided to show the effectiveness of the proposed method.

Protein bio-corona: critical issue in immune nanotoxicology.

PubMed

Neagu, Monica; Piperigkou, Zoi; Karamanou, Konstantina; Engin, Ayse Basak; Docea, Anca Oana; Constantin, Carolina; Negrei, Carolina; Nikitovic, Dragana; Tsatsakis, Aristidis

2017-03-01

With the expansion of the nanomedicine field, the knowledge focusing on the behavior of nanoparticles in the biological milieu has rapidly escalated. Upon introduction to a complex biological system, nanomaterials dynamically interact with all the encountered biomolecules and form the protein "bio-corona." The decoration with these surface biomolecules endows nanoparticles with new properties. The present review will address updates of the protein bio-corona characteristics as influenced by nanoparticle's physicochemical properties and by the particularities of the encountered biological milieu. Undeniably, bio-corona generation influences the efficacy of the nanodrug and guides the actions of innate and adaptive immunity. Exploiting the dynamic process of protein bio-corona development in combination with the new engineered horizons of drugs linked to nanoparticles could lead to innovative functional nanotherapies. Therefore, bio-medical nanotechnologies should focus on the interactions of nanoparticles with the immune system for both safety and efficacy reasons.

Catheter tracking via online learning for dynamic motion compensation in transcatheter aortic valve implantation.

PubMed

Wang, Peng; Zheng, Yefeng; John, Matthias; Comaniciu, Dorin

2012-01-01

Dynamic overlay of 3D models onto 2D X-ray images has important applications in image guided interventions. In this paper, we present a novel catheter tracking for motion compensation in the Transcatheter Aortic Valve Implantation (TAVI). To address such challenges as catheter shape and appearance changes, occlusions, and distractions from cluttered backgrounds, we present an adaptive linear discriminant learning method to build a measurement model online to distinguish catheters from background. An analytic solution is developed to effectively and efficiently update the discriminant model and to minimize the classification errors between the tracking object and backgrounds. The online learned discriminant model is further combined with an offline learned detector and robust template matching in a Bayesian tracking framework. Quantitative evaluations demonstrate the advantages of this method over current state-of-the-art tracking methods in tracking catheters for clinical applications.

Nonlinear and Digital Man-machine Control Systems Modeling

NASA Technical Reports Server (NTRS)

Mekel, R.

1972-01-01

An adaptive modeling technique is examined by which controllers can be synthesized to provide corrective dynamics to a human operator's mathematical model in closed loop control systems. The technique utilizes a class of Liapunov functions formulated for this purpose, Liapunov's stability criterion and a model-reference system configuration. The Liapunov function is formulated to posses variable characteristics to take into consideration the identification dynamics. The time derivative of the Liapunov function generate the identification and control laws for the mathematical model system. These laws permit the realization of a controller which updates the human operator's mathematical model parameters so that model and human operator produce the same response when subjected to the same stimulus. A very useful feature is the development of a digital computer program which is easily implemented and modified concurrent with experimentation. The program permits the modeling process to interact with the experimentation process in a mutually beneficial way.

Using Consumer Electronics and Apps in Industrial Environments - Development of a Framework for Dynamic Feature Deployment and Extension by Using Apps on Field Devices

NASA Astrophysics Data System (ADS)

Schmitt, Mathias

2014-12-01

The aim of this paper is to give a preliminary insight regarding the current work in the field of mobile interaction in industrial environments by using established interaction technologies and metaphors from the consumer goods industry. The major objective is the development and implementation of a holistic app-framework, which enables dynamic feature deployment and extension by using mobile apps on industrial field devices. As a result, field device functionalities can be updated and adapted effectively in accordance with well-known appconcepts from consumer electronics to comply with the urgent requirements of more flexible and changeable factory systems of the future. In addition, a much more user-friendly and utilizable interaction with field devices can be realized. Proprietary software solutions and device-stationary user interfaces can be overcome and replaced by uniform, cross-vendor solutions

A Novel Extreme Learning Control Framework of Unmanned Surface Vehicles.

PubMed

Wang, Ning; Sun, Jing-Chao; Er, Meng Joo; Liu, Yan-Cheng

2016-05-01

In this paper, an extreme learning control (ELC) framework using the single-hidden-layer feedforward network (SLFN) with random hidden nodes for tracking an unmanned surface vehicle suffering from unknown dynamics and external disturbances is proposed. By combining tracking errors with derivatives, an error surface and transformed states are defined to encapsulate unknown dynamics and disturbances into a lumped vector field of transformed states. The lumped nonlinearity is further identified accurately by an extreme-learning-machine-based SLFN approximator which does not require a priori system knowledge nor tuning input weights. Only output weights of the SLFN need to be updated by adaptive projection-based laws derived from the Lyapunov approach. Moreover, an error compensator is incorporated to suppress approximation residuals, and thereby contributing to the robustness and global asymptotic stability of the closed-loop ELC system. Simulation studies and comprehensive comparisons demonstrate that the ELC framework achieves high accuracy in both tracking and approximation.

A Novel Dynamic Update Framework for Epileptic Seizure Prediction

PubMed Central

Wang, Minghui; Hong, Xiaojun; Han, Jie

2014-01-01

Epileptic seizure prediction is a difficult problem in clinical applications, and it has the potential to significantly improve the patients' daily lives whose seizures cannot be controlled by either drugs or surgery. However, most current studies of epileptic seizure prediction focus on high sensitivity and low false-positive rate only and lack the flexibility for a variety of epileptic seizures and patients' physical conditions. Therefore, a novel dynamic update framework for epileptic seizure prediction is proposed in this paper. In this framework, two basic sample pools are constructed and updated dynamically. Furthermore, the prediction model can be updated to be the most appropriate one for the prediction of seizures' arrival. Mahalanobis distance is introduced in this part to solve the problem of side information, measuring the distance between two data sets. In addition, a multichannel feature extraction method based on Hilbert-Huang transform and extreme learning machine is utilized to extract the features of a patient's preseizure state against the normal state. At last, a dynamic update epileptic seizure prediction system is built up. Simulations on Freiburg database show that the proposed system has a better performance than the one without update. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices. PMID:25050381

A novel dynamic update framework for epileptic seizure prediction.

PubMed

Han, Min; Ge, Sunan; Wang, Minghui; Hong, Xiaojun; Han, Jie

2014-01-01

Epileptic seizure prediction is a difficult problem in clinical applications, and it has the potential to significantly improve the patients' daily lives whose seizures cannot be controlled by either drugs or surgery. However, most current studies of epileptic seizure prediction focus on high sensitivity and low false-positive rate only and lack the flexibility for a variety of epileptic seizures and patients' physical conditions. Therefore, a novel dynamic update framework for epileptic seizure prediction is proposed in this paper. In this framework, two basic sample pools are constructed and updated dynamically. Furthermore, the prediction model can be updated to be the most appropriate one for the prediction of seizures' arrival. Mahalanobis distance is introduced in this part to solve the problem of side information, measuring the distance between two data sets. In addition, a multichannel feature extraction method based on Hilbert-Huang transform and extreme learning machine is utilized to extract the features of a patient's preseizure state against the normal state. At last, a dynamic update epileptic seizure prediction system is built up. Simulations on Freiburg database show that the proposed system has a better performance than the one without update. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices.

Nonlinear neural control with power systems applications

NASA Astrophysics Data System (ADS)

Chen, Dingguo

1998-12-01

Extensive studies have been undertaken on the transient stability of large interconnected power systems with flexible ac transmission systems (FACTS) devices installed. Varieties of control methodologies have been proposed to stabilize the postfault system which would otherwise eventually lose stability without a proper control. Generally speaking, regular transient stability is well understood, but the mechanism of load-driven voltage instability or voltage collapse has not been well understood. The interaction of generator dynamics and load dynamics makes synthesis of stabilizing controllers even more challenging. There is currently increasing interest in the research of neural networks as identifiers and controllers for dealing with dynamic time-varying nonlinear systems. This study focuses on the development of novel artificial neural network architectures for identification and control with application to dynamic electric power systems so that the stability of the interconnected power systems, following large disturbances, and/or with the inclusion of uncertain loads, can be largely enhanced, and stable operations are guaranteed. The latitudinal neural network architecture is proposed for the purpose of system identification. It may be used for identification of nonlinear static/dynamic loads, which can be further used for static/dynamic voltage stability analysis. The properties associated with this architecture are investigated. A neural network methodology is proposed for dealing with load modeling and voltage stability analysis. Based on the neural network models of loads, voltage stability analysis evolves, and modal analysis is performed. Simulation results are also provided. The transient stability problem is studied with consideration of load effects. The hierarchical neural control scheme is developed. Trajectory-following policy is used so that the hierarchical neural controller performs as almost well for non-nominal cases as they do for the nominal cases. The adaptive hierarchical neural control scheme is also proposed to deal with the time-varying nature of loads. Further, adaptive neural control, which is based on the on-line updating of the weights and biases of the neural networks, is studied. Simulations provided on the faulted power systems with unknown loads suggest that the proposed adaptive hierarchical neural control schemes should be useful for practical power applications.

Static and Dynamic Model Update of an Inflatable/Rigidizable Torus Structure

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, mercedes C.

2006-01-01

The present work addresses the development of an experimental and computational procedure for validating finite element models. A torus structure, part of an inflatable/rigidizable Hexapod, is used to demonstrate the approach. Because of fabrication, materials, and geometric uncertainties, a statistical approach combined with optimization is used to modify key model parameters. Static test results are used to update stiffness parameters and dynamic test results are used to update the mass distribution. Updated parameters are computed using gradient and non-gradient based optimization algorithms. Results show significant improvements in model predictions after parameters are updated. Lessons learned in the areas of test procedures, modeling approaches, and uncertainties quantification are presented.

Recommendations for kidney disease guideline updating: a report by the KDIGO Methods Committee

PubMed Central

Uhlig, Katrin; Berns, Jeffrey S.; Carville, Serena; Chan, Wiley; Cheung, Michael; Guyatt, Gordon H.; Hart, Allyson; Lewis, Sandra Zelman; Tonelli, Marcello; Webster, Angela C.; Wilt, Timothy J.; Kasiske, Bertram L.

2017-01-01

Updating rather than de novo guideline development now accounts for the majority of guideline activities for many guideline development organizations, including Kidney Disease: Improving Global Outcomes (KDIGO), an international kidney disease guideline development entity that has produced guidelines on kidney diseases since 2008. Increasingly, guideline developers are moving away from updating at fixed intervals in favor of more flexible approaches that use periodic expert assessment of guideline currency (with or without an updated systematic review) to determine the need for updating. Determining the need for guideline updating in an efficient, transparent, and timely manner is challenging, and updating of systematic reviews and guidelines is labor intensive. Ideally, guidelines should be updated dynamically when new evidence indicates a need for a substantive change in the guideline based on a priori criteria. This dynamic updating (sometimes referred to as a living guideline model) can be facilitated with the use of integrated electronic platforms that allow updating of specific recommendations. This report summarizes consensus-based recommendations from a panel of guideline methodology professionals on how to keep KDIGO guidelines up to date. PMID:26994574

Estimation of beam material random field properties via sensitivity-based model updating using experimental frequency response functions

NASA Astrophysics Data System (ADS)

Machado, M. R.; Adhikari, S.; Dos Santos, J. M. C.; Arruda, J. R. F.

2018-03-01

Structural parameter estimation is affected not only by measurement noise but also by unknown uncertainties which are present in the system. Deterministic structural model updating methods minimise the difference between experimentally measured data and computational prediction. Sensitivity-based methods are very efficient in solving structural model updating problems. Material and geometrical parameters of the structure such as Poisson's ratio, Young's modulus, mass density, modal damping, etc. are usually considered deterministic and homogeneous. In this paper, the distributed and non-homogeneous characteristics of these parameters are considered in the model updating. The parameters are taken as spatially correlated random fields and are expanded in a spectral Karhunen-Loève (KL) decomposition. Using the KL expansion, the spectral dynamic stiffness matrix of the beam is expanded as a series in terms of discretized parameters, which can be estimated using sensitivity-based model updating techniques. Numerical and experimental tests involving a beam with distributed bending rigidity and mass density are used to verify the proposed method. This extension of standard model updating procedures can enhance the dynamic description of structural dynamic models.

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

PubMed Central

Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

2017-01-01

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). PMID:28608824

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition.

PubMed

Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

2017-06-13

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle).

Adaptive reference update (ARU) algorithm. A stochastic search algorithm for efficient optimization of multi-drug cocktails

PubMed Central

2012-01-01

Background Multi-target therapeutics has been shown to be effective for treating complex diseases, and currently, it is a common practice to combine multiple drugs to treat such diseases to optimize the therapeutic outcomes. However, considering the huge number of possible ways to mix multiple drugs at different concentrations, it is practically difficult to identify the optimal drug combination through exhaustive testing. Results In this paper, we propose a novel stochastic search algorithm, called the adaptive reference update (ARU) algorithm, that can provide an efficient and systematic way for optimizing multi-drug cocktails. The ARU algorithm iteratively updates the drug combination to improve its response, where the update is made by comparing the response of the current combination with that of a reference combination, based on which the beneficial update direction is predicted. The reference combination is continuously updated based on the drug response values observed in the past, thereby adapting to the underlying drug response function. To demonstrate the effectiveness of the proposed algorithm, we evaluated its performance based on various multi-dimensional drug functions and compared it with existing algorithms. Conclusions Simulation results show that the ARU algorithm significantly outperforms existing stochastic search algorithms, including the Gur Game algorithm. In fact, the ARU algorithm can more effectively identify potent drug combinations and it typically spends fewer iterations for finding effective combinations. Furthermore, the ARU algorithm is robust to random fluctuations and noise in the measured drug response, which makes the algorithm well-suited for practical drug optimization applications. PMID:23134742

Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks.

PubMed

Shen, Lin; Yang, Weitao

2018-03-13

Direct molecular dynamics (MD) simulation with ab initio quantum mechanical and molecular mechanical (QM/MM) methods is very powerful for studying the mechanism of chemical reactions in a complex environment but also very time-consuming. The computational cost of QM/MM calculations during MD simulations can be reduced significantly using semiempirical QM/MM methods with lower accuracy. To achieve higher accuracy at the ab initio QM/MM level, a correction on the existing semiempirical QM/MM model is an attractive idea. Recently, we reported a neural network (NN) method as QM/MM-NN to predict the potential energy difference between semiempirical and ab initio QM/MM approaches. The high-level results can be obtained using neural network based on semiempirical QM/MM MD simulations, but the lack of direct MD samplings at the ab initio QM/MM level is still a deficiency that limits the applications of QM/MM-NN. In the present paper, we developed a dynamic scheme of QM/MM-NN for direct MD simulations on the NN-predicted potential energy surface to approximate ab initio QM/MM MD. Since some configurations excluded from the database for NN training were encountered during simulations, which may cause some difficulties on MD samplings, an adaptive procedure inspired by the selection scheme reported by Behler [ Behler Int. J. Quantum Chem. 2015 , 115 , 1032 ; Behler Angew. Chem., Int. Ed. 2017 , 56 , 12828 ] was employed with some adaptions to update NN and carry out MD iteratively. We further applied the adaptive QM/MM-NN MD method to the free energy calculation and transition path optimization on chemical reactions in water. The results at the ab initio QM/MM level can be well reproduced using this method after 2-4 iteration cycles. The saving in computational cost is about 2 orders of magnitude. It demonstrates that the QM/MM-NN with direct MD simulations has great potentials not only for the calculation of thermodynamic properties but also for the characterization of reaction dynamics, which provides a useful tool to study chemical or biochemical systems in solution or enzymes.

Cerebellum, temporal predictability and the updating of a mental model.

PubMed

Kotz, Sonja A; Stockert, Anika; Schwartze, Michael

2014-12-19

We live in a dynamic and changing environment, which necessitates that we adapt to and efficiently respond to changes of stimulus form ('what') and stimulus occurrence ('when'). Consequently, behaviour is optimal when we can anticipate both the 'what' and 'when' dimensions of a stimulus. For example, to perceive a temporally expected stimulus, a listener needs to establish a fairly precise internal representation of its external temporal structure, a function ascribed to classical sensorimotor areas such as the cerebellum. Here we investigated how patients with cerebellar lesions and healthy matched controls exploit temporal regularity during auditory deviance processing. We expected modulations of the N2b and P3b components of the event-related potential in response to deviant tones, and also a stronger P3b response when deviant tones are embedded in temporally regular compared to irregular tone sequences. We further tested to what degree structural damage to the cerebellar temporal processing system affects the N2b and P3b responses associated with voluntary attention to change detection and the predictive adaptation of a mental model of the environment, respectively. Results revealed that healthy controls and cerebellar patients display an increased N2b response to deviant tones independent of temporal context. However, while healthy controls showed the expected enhanced P3b response to deviant tones in temporally regular sequences, the P3b response in cerebellar patients was significantly smaller in these sequences. The current data provide evidence that structural damage to the cerebellum affects the predictive adaptation to the temporal structure of events and the updating of a mental model of the environment under voluntary attention. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Cerebellum, temporal predictability and the updating of a mental model

PubMed Central

Kotz, Sonja A.; Stockert, Anika; Schwartze, Michael

2014-01-01

We live in a dynamic and changing environment, which necessitates that we adapt to and efficiently respond to changes of stimulus form (‘what’) and stimulus occurrence (‘when’). Consequently, behaviour is optimal when we can anticipate both the ‘what’ and ‘when’ dimensions of a stimulus. For example, to perceive a temporally expected stimulus, a listener needs to establish a fairly precise internal representation of its external temporal structure, a function ascribed to classical sensorimotor areas such as the cerebellum. Here we investigated how patients with cerebellar lesions and healthy matched controls exploit temporal regularity during auditory deviance processing. We expected modulations of the N2b and P3b components of the event-related potential in response to deviant tones, and also a stronger P3b response when deviant tones are embedded in temporally regular compared to irregular tone sequences. We further tested to what degree structural damage to the cerebellar temporal processing system affects the N2b and P3b responses associated with voluntary attention to change detection and the predictive adaptation of a mental model of the environment, respectively. Results revealed that healthy controls and cerebellar patients display an increased N2b response to deviant tones independent of temporal context. However, while healthy controls showed the expected enhanced P3b response to deviant tones in temporally regular sequences, the P3b response in cerebellar patients was significantly smaller in these sequences. The current data provide evidence that structural damage to the cerebellum affects the predictive adaptation to the temporal structure of events and the updating of a mental model of the environment under voluntary attention. PMID:25385781

Past and future detector arrays for complete event reconstruction in heavy-ion reactions

NASA Astrophysics Data System (ADS)

Cardella, G.; Acosta, L.; Auditore, L.; Boiano, C.; Castoldi, A.; D'Andrea, M.; De Filippo, E.; Dell'Aquila, D.; De Luca, S.; Fichera, F.; Giudice, N.; Gnoffo, B.; Grimaldi, A.; Guazzoni, C.; Lanzalone, G.; Librizzi, F.; Lombardo, I.; Maiolino, C.; Maffesanti, S.; Martorana, N. S.; Norella, S.; Pagano, A.; Pagano, E. V.; Papa, M.; Parsani, T.; Passaro, G.; Pirrone, S.; Politi, G.; Previdi, F.; Quattrocchi, L.; Rizzo, F.; Russotto, P.; Saccà, G.; Salemi, G.; Sciliberto, D.; Trifirò, A.; Trimarchi, M.; Vigilante, M.

2017-11-01

Complex and more and more complete detector arrays have been developed in the last two decades, or are in advanced design stage, in different laboratories. Such arrays are necessary to fully characterize nuclear reactions induced by stable and exotic beams. The need for contemporary detection of charged particles, and/or γ -rays, and/or neutrons, has been stressed in many fields of nuclear structure and reaction dynamics, with particular attention to the improvement of both high angular and energy resolution. Some examples of detection systems adapted to various energy ranges is discussed. Emphasis is given to the possible update of relatively old 4π detectors with new electronics and new detection methods.

A comprehensive Reputation mechanism for ubiquitous healthcare environment exploiting cloud model.

PubMed

Athanasiou, Georgia; Lymberopoulos, Dimitrios

2016-08-01

Absence of trust foundations may outweigh benefits of ubiquitous and personalized mental healthcare supervision provided within a Ubiquitous Healthcare environment (UH). Trust is composed by patient's Personal Interaction Experience (PIE) and social entourage accumulated PIE, i.e. Reputation (R). In this paper, a cloud-based Reputation mechanism is proposed. Since PIE is the elementary trust information source, also an Updating mechanism of PIE, is introduced, in this paper. Cloud materialization of combined mechanisms provides adaptability to UH Providers' dynamic behavior, facilitates detection of milking behaviors and complex malicious attacks while meets the challenge of limited accuracy in case of data sparseness. The effectiveness of the proposed mechanisms is verified via simulation in MATLAB.

Adaptation, evaluation, and updating of guidelines: article 14 in Integrating and coordinating efforts in COPD guideline development. An official ATS/ERS workshop report.

PubMed

Burgers, Jako S; Anzueto, Antonio; Black, Peter N; Cruz, Alvaro A; Fervers, Béatrice; Graham, Ian D; Metersky, Mark; Woodhead, Mark; Yawn, Barbara P

2012-12-01

Professional societies, like many other organizations, have recognized the need to use more rigorous processes to ensure that health care recommendations are informed by the best available research evidence. This is the last of a series of 14 articles that methodologists and researchers from around the world have prepared to advise guideline developers in respiratory and other diseases on how to achieve this. We updated a review of the literature on guideline adaptation, evaluation, and updating, focusing on four key questions. In this review we addressed the following questions. (1) Which high-quality guidelines on chronic obstructive pulmonary disease (COPD) are available? (2) How should guidelines be adapted to the user's context and culture? (3) How should the use of guidelines be evaluated in clinical practice? and (4) How should guidelines be efficiently kept up-to-date? We did not conduct systematic reviews ourselves. We relied on a literature review published in 2006 and on a manual produced by the ADAPTE Collaboration to inform our judgments, as well as our collective experience and workshop discussions. Guideline adaptation can be seen as an alternative to de novo development and as part of an implementation process, taking into consideration the user's own context. A systematic approach should be followed to ensure high quality of the resulting guidance. On the topic of COPD, many guidelines are available. Guidelines of the Global Initiative for Chronic Obstructive Lung Disease and of the American Thoracic Society and European Respiratory Society are particularly well-suited for adaptation. The adaptation process includes (1) definition of specific questions that need to be answered by the guideline; (2) assessment of guideline quality; (3) assessment of the clinical content, validity, acceptability, applicability, and transferability of the recommendations; and (4) decisions about adoption or adaptation of the recommendations. The use of the guidelines in practice can be measured with performance indicators. Adverse effects of strict adherence to guideline recommendations should be prevented, in particular when the improvement of patient outcomes is unclear. COPD guidelines should be updated at least every 2 years. Collaboration between COPD guideline developers is recommended to prevent duplication of effort.

Adapting to change: The role of the right hemisphere in mental model building and updating.

PubMed

Filipowicz, Alex; Anderson, Britt; Danckert, James

2016-09-01

We recently proposed that the right hemisphere plays a crucial role in the processes underlying mental model building and updating. Here, we review the evidence we and others have garnered to support this novel account of right hemisphere function. We begin by presenting evidence from patient work that suggests a critical role for the right hemisphere in the ability to learn from the statistics in the environment (model building) and adapt to environmental change (model updating). We then provide a review of neuroimaging research that highlights a network of brain regions involved in mental model updating. Next, we outline specific roles for particular regions within the network such that the anterior insula is purported to maintain the current model of the environment, the medial prefrontal cortex determines when to explore new or alternative models, and the inferior parietal lobule represents salient and surprising information with respect to the current model. We conclude by proposing some future directions that address some of the outstanding questions in the field of mental model building and updating. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton.

PubMed

Koller, Jeffrey R; Jacobs, Daniel A; Ferris, Daniel P; Remy, C David

2015-11-04

Robotic ankle exoskeletons can provide assistance to users and reduce metabolic power during walking. Our research group has investigated the use of proportional myoelectric control for controlling robotic ankle exoskeletons. Previously, these controllers have relied on a constant gain to map user's muscle activity to actuation control signals. A constant gain may act as a constraint on the user, so we designed a controller that dynamically adapts the gain to the user's myoelectric amplitude. We hypothesized that an adaptive gain proportional myoelectric controller would reduce metabolic energy expenditure compared to walking with the ankle exoskeleton unpowered because users could choose their preferred control gain. We tested eight healthy subjects walking with the adaptive gain proportional myoelectric controller with bilateral ankle exoskeletons. The adaptive gain was updated each stride such that on average the user's peak muscle activity was mapped to maximal power output of the exoskeleton. All subjects participated in three identical training sessions where they walked on a treadmill for 50 minutes (30 minutes of which the exoskeleton was powered) at 1.2 ms(-1). We calculated and analyzed metabolic energy consumption, muscle recruitment, inverse kinematics, inverse dynamics, and exoskeleton mechanics. Using our controller, subjects achieved a metabolic reduction similar to that seen in previous work in about a third of the training time. The resulting controller gain was lower than that seen in previous work (β=1.50±0.14 versus a constant β=2). The adapted gain allowed users more total ankle joint power than that of unassisted walking, increasing ankle power in exchange for a decrease in hip power. Our findings indicate that humans prefer to walk with greater ankle mechanical power output than their unassisted gait when provided with an ankle exoskeleton using an adaptive controller. This suggests that robotic assistance from an exoskeleton can allow humans to adopt gait patterns different from their normal choices for locomotion. In our specific experiment, subjects increased ankle power and decreased hip power to walk with a reduction in metabolic cost. Future exoskeleton devices that rely on proportional myolectric control are likely to demonstrate improved performance by including an adaptive gain.

Update schemes of multi-velocity floor field cellular automaton for pedestrian dynamics

NASA Astrophysics Data System (ADS)

Luo, Lin; Fu, Zhijian; Cheng, Han; Yang, Lizhong

2018-02-01

Modeling pedestrian movement is an interesting problem both in statistical physics and in computational physics. Update schemes of cellular automaton (CA) models for pedestrian dynamics govern the schedule of pedestrian movement. Usually, different update schemes make the models behave in different ways, which should be carefully recalibrated. Thus, in this paper, we investigated the influence of four different update schemes, namely parallel/synchronous scheme, random scheme, order-sequential scheme and shuffled scheme, on pedestrian dynamics. The multi-velocity floor field cellular automaton (FFCA) considering the changes of pedestrians' moving properties along walking paths and heterogeneity of pedestrians' walking abilities was used. As for parallel scheme only, the collisions detection and resolution should be considered, resulting in a great difference from any other update schemes. For pedestrian evacuation, the evacuation time is enlarged, and the difference in pedestrians' walking abilities is better reflected, under parallel scheme. In face of a bottleneck, for example a exit, using a parallel scheme leads to a longer congestion period and a more dispersive density distribution. The exit flow and the space-time distribution of density and velocity have significant discrepancies under four different update schemes when we simulate pedestrian flow with high desired velocity. Update schemes may have no influence on pedestrians in simulation to create tendency to follow others, but sequential and shuffled update scheme may enhance the effect of pedestrians' familiarity with environments.

Advances in adaptive control theory: Gradient- and derivative-free approaches

NASA Astrophysics Data System (ADS)

Yucelen, Tansel

In this dissertation, we present new approaches to improve standard designs in adaptive control theory, and novel adaptive control architectures. We first present a novel Kalman filter based approach for approximately enforcing a linear constraint in standard adaptive control design. One application is that this leads to alternative forms for well known modification terms such as e-modification. In addition, it leads to smaller tracking errors without incurring significant oscillations in the system response and without requiring high modification gain. We derive alternative forms of e- and adaptive loop recovery (ALR-) modifications. Next, we show how to use Kalman filter optimization to derive a novel adaptation law. This results in an optimization-based time-varying adaptation gain that reduces the need for adaptation gain tuning. A second major contribution of this dissertation is the development of a novel derivative-free, delayed weight update law for adaptive control. The assumption of constant unknown ideal weights is relaxed to the existence of time-varying weights, such that fast and possibly discontinuous variation in weights are allowed. This approach is particulary advantageous for applications to systems that can undergo a sudden change in dynamics, such as might be due to reconfiguration, deployment of a payload, docking, or structural damage, and for rejection of external disturbance processes. As a third and final contribution, we develop a novel approach for extending all the methods developed in this dissertation to the case of output feedback. The approach is developed only for the case of derivative-free adaptive control, and the extension of the other approaches developed previously for the state feedback case to output feedback is left as a future research topic. The proposed approaches of this dissertation are illustrated in both simulation and flight test.

Adaptive sliding control of non-autonomous active suspension systems with time-varying loadings

NASA Astrophysics Data System (ADS)

Chen, Po-Chang; Huang, An-Chyau

2005-04-01

An adaptive sliding controller is proposed in this paper for controlling a non-autonomous quarter-car suspension system with time-varying loadings. The bound of the car-body loading is assumed to be available. Then, the reference coordinate is placed at the static position under the nominal loading so that the system dynamic equation is derived. Due to spring nonlinearities, the system property becomes asymmetric after coordinate transformation. Besides, in practical cases, system parameters are not easy to be obtained precisely for controller design. Therefore, in this paper, system uncertainties are lumped into two unknown time-varying functions. Since the variation bound of one of the unknown functions is not available, conventional adaptive schemes and robust designs are not applicable. To deal with this problem, the function approximation technique is employed to represent the unknown function as a finite combination of basis functions. The Lyapunov direct method can thus be used to find adaptive laws for updating coefficients in the approximating series and to prove stability of the closed-loop system. Since the position and velocity measurements of the unsprung mass are lumped into the unknown function, there is no need to install sensors on the axle and wheel assembly in the actual implementation. Simulation results are presented to show the performance of the proposed strategy.

Improving emergency medical dispatching with emphasis on mass-casualty incidents.

PubMed

Kleinoscheg, Gabriel; Burgsteiner, Harald; Bernroider, Martin; Kiechle, Günter; Obermayer, Maria

2014-01-01

Dispatching ambulances is a demanding and stressful task for dispatchers. This is especially true in case of mass-casualty incidents. Therefore, the aim of this work was to investigate if and to what extent the dispatch operation of the Red Cross Salzburg can be optimized on such occasions with a computerized system. The basic problem of a dynamic multi-vehicle Dial-a-Ride Problem with time windows was enhanced according to the requirements of the Red Cross Salzburg. The general objective was to minimize the total mileage covered by ambulances and the waiting time of patients. Furthermore, in case of emergencies suitable adaptions to a plan should be carried out automatically. Consequently, the problem is solved by using the Adaptive Large Neighborhood Search. Evaluation results indicate that the system outperforms a human dispatcher by between 2.5% and 36% within 1 minute of runtime concerning total costs. Moreover, the system's response time in case that a plan has to be updated is less than 1 minute on average.

Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation.

PubMed

Hu, Weiming; Li, Wei; Zhang, Xiaoqin; Maybank, Stephen

2015-04-01

In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation

PubMed Central

Kong, Zehui; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control. PMID:28671967

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

PubMed

Kong, Zehui; Zou, Yuan; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

Load-adaptive practical multi-channel communications in wireless sensor networks.

PubMed

Islam, Md Shariful; Alam, Muhammad Mahbub; Hong, Choong Seon; Lee, Sungwon

2010-01-01

In recent years, a significant number of sensor node prototypes have been designed that provide communications in multiple channels. This multi-channel feature can be effectively exploited to increase the overall capacity and performance of wireless sensor networks (WSNs). In this paper, we present a multi-channel communications system for WSNs that is referred to as load-adaptive practical multi-channel communications (LPMC). LPMC estimates the active load of a channel at the sink since it has a more comprehensive view of the network behavior, and dynamically adds or removes channels based on the estimated load. LPMC updates the routing path to balance the loads of the channels. The nodes in a path use the same channel; therefore, they do not need to switch channels to receive or forward packets. LPMC has been evaluated through extensive simulations, and the results demonstrate that it can effectively increase the delivery ratio, network throughput, and channel utilization, and that it can decrease the end-to-end delay and energy consumption.

MBGD update 2013: the microbial genome database for exploring the diversity of microbial world.

PubMed

Uchiyama, Ikuo; Mihara, Motohiro; Nishide, Hiroyo; Chiba, Hirokazu

2013-01-01

The microbial genome database for comparative analysis (MBGD, available at http://mbgd.genome.ad.jp/) is a platform for microbial genome comparison based on orthology analysis. As its unique feature, MBGD allows users to conduct orthology analysis among any specified set of organisms; this flexibility allows MBGD to adapt to a variety of microbial genomic study. Reflecting the huge diversity of microbial world, the number of microbial genome projects now becomes several thousands. To efficiently explore the diversity of the entire microbial genomic data, MBGD now provides summary pages for pre-calculated ortholog tables among various taxonomic groups. For some closely related taxa, MBGD also provides the conserved synteny information (core genome alignment) pre-calculated using the CoreAligner program. In addition, efficient incremental updating procedure can create extended ortholog table by adding additional genomes to the default ortholog table generated from the representative set of genomes. Combining with the functionalities of the dynamic orthology calculation of any specified set of organisms, MBGD is an efficient and flexible tool for exploring the microbial genome diversity.

Bilinear modeling and nonlinear estimation

NASA Technical Reports Server (NTRS)

Dwyer, Thomas A. W., III; Karray, Fakhreddine; Bennett, William H.

1989-01-01

New methods are illustrated for online nonlinear estimation applied to the lateral deflection of an elastic beam on board measurements of angular rates and angular accelerations. The development of the filter equations, together with practical issues of their numerical solution as developed from global linearization by nonlinear output injection are contrasted with the usual method of the extended Kalman filter (EKF). It is shown how nonlinear estimation due to gyroscopic coupling can be implemented as an adaptive covariance filter using off-the-shelf Kalman filter algorithms. The effect of the global linearization by nonlinear output injection is to introduce a change of coordinates in which only the process noise covariance is to be updated in online implementation. This is in contrast to the computational approach which arises in EKF methods arising by local linearization with respect to the current conditional mean. Processing refinements for nonlinear estimation based on optimal, nonlinear interpolation between observations are also highlighted. In these methods the extrapolation of the process dynamics between measurement updates is obtained by replacing a transition matrix with an operator spline that is optimized off-line from responses to selected test inputs.

Long-Range Correlations and Memory in the Dynamics of Internet Interdomain Routing

PubMed Central

Havlin, Shlomo; Krioukov, Dmitri

2015-01-01

Data transfer is one of the main functions of the Internet. The Internet consists of a large number of interconnected subnetworks or domains, known as Autonomous Systems (ASes). Due to privacy and other reasons the information about what route to use to reach devices within other ASes is not readily available to any given AS. The Border Gateway Protocol (BGP) is responsible for discovering and distributing this reachability information to all ASes. Since the topology of the Internet is highly dynamic, all ASes constantly exchange and update this reachability information in small chunks, known as routing control packets or BGP updates. In the view of the quick growth of the Internet there are significant concerns with the scalability of the BGP updates and the efficiency of the BGP routing in general. Motivated by these issues we conduct a systematic time series analysis of BGP update rates. We find that BGP update time series are extremely volatile, exhibit long-term correlations and memory effects, similar to seismic time series, or temperature and stock market price fluctuations. The presented statistical characterization of BGP update dynamics could serve as a basis for validation of existing and developing better models of Internet interdomain routing. PMID:26529312

Long-Range Correlations and Memory in the Dynamics of Internet Interdomain Routing.

PubMed

Kitsak, Maksim; Elmokashfi, Ahmed; Havlin, Shlomo; Krioukov, Dmitri

2015-01-01

Data transfer is one of the main functions of the Internet. The Internet consists of a large number of interconnected subnetworks or domains, known as Autonomous Systems (ASes). Due to privacy and other reasons the information about what route to use to reach devices within other ASes is not readily available to any given AS. The Border Gateway Protocol (BGP) is responsible for discovering and distributing this reachability information to all ASes. Since the topology of the Internet is highly dynamic, all ASes constantly exchange and update this reachability information in small chunks, known as routing control packets or BGP updates. In the view of the quick growth of the Internet there are significant concerns with the scalability of the BGP updates and the efficiency of the BGP routing in general. Motivated by these issues we conduct a systematic time series analysis of BGP update rates. We find that BGP update time series are extremely volatile, exhibit long-term correlations and memory effects, similar to seismic time series, or temperature and stock market price fluctuations. The presented statistical characterization of BGP update dynamics could serve as a basis for validation of existing and developing better models of Internet interdomain routing.

Update on optical design of adaptive optics system at Lick Observatory

NASA Astrophysics Data System (ADS)

Bauman, Brian J.; Gavel, Donald T.; Waltjen, Kenneth E.; Freeze, Gary J.; Hurd, Randall L.; Gates, Elinor L.; Max, Claire E.; Olivier, Scot S.; Pennington, Deanna M.

2002-02-01

In 1999, we presented our plan to upgrade the adaptive optics (AO) system on the Lick Observatory Shane telescope (3m) from a prototype instrument pressed into field service to a facility instrument. This paper updates the progress of that plan and details several important improvements in the alignment and calibration of the AO bench. The paper also includes a discussion of the problems seen in the original design of the tip/tilt (t/t) sensor used in laser guide star mode, and how these problems were corrected with excellent results.

Update on Optical Design of Adaptive Optics System at Lick Observatory

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

Bauman, B J; Gavel, D T; Waltjen, K E

2001-07-31

In 1999, we presented our plan to upgrade the adaptive optics (AO) system on the Lick Observatory Shane telescope (3m) from a prototype instrument pressed into field service to a facility instrument. This paper updates the progress of that plan and details several important improvements in the alignment and calibration of the AO bench. The paper also includes a discussion of the problems seen in the original design of the tip/tilt (t/t) sensor used in laser guide star mode, and how these problems were corrected with excellent results.

Swarming UAS II

DTIC Science & Technology

2010-05-05

employed biomimicry to model a swarm of UAS as a colony of ants, where each UAS dynamically updates a global memory map, allowing pheromone-like...matter of design, DSE-R-0808 employed biomimicry to model a swarm of UAS as a colony of ants, where each UAS dynamically updates a global memory map

Probabilistic Multi-Person Tracking Using Dynamic Bayes Networks

NASA Astrophysics Data System (ADS)

Klinger, T.; Rottensteiner, F.; Heipke, C.

2015-08-01

Tracking-by-detection is a widely used practice in recent tracking systems. These usually rely on independent single frame detections that are handled as observations in a recursive estimation framework. If these observations are imprecise the generated trajectory is prone to be updated towards a wrong position. In contrary to existing methods our novel approach uses a Dynamic Bayes Network in which the state vector of a recursive Bayes filter, as well as the location of the tracked object in the image are modelled as unknowns. These unknowns are estimated in a probabilistic framework taking into account a dynamic model, and a state-of-the-art pedestrian detector and classifier. The classifier is based on the Random Forest-algorithm and is capable of being trained incrementally so that new training samples can be incorporated at runtime. This allows the classifier to adapt to the changing appearance of a target and to unlearn outdated features. The approach is evaluated on a publicly available benchmark. The results confirm that our approach is well suited for tracking pedestrians over long distances while at the same time achieving comparatively good geometric accuracy.

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

PubMed

Fu, Yue; Chai, Tianyou

2016-12-01

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

Performance and Quality Assessment of the Forthcoming Copernicus Marine Service Global Ocean Monitoring and Forecasting Real-Time System

NASA Astrophysics Data System (ADS)

Lellouche, J. M.; Le Galloudec, O.; Greiner, E.; Garric, G.; Regnier, C.; Drillet, Y.

2016-02-01

Mercator Ocean currently delivers in real-time daily services (weekly analyses and daily forecast) with a global 1/12° high resolution system. The model component is the NEMO platform driven at the surface by the IFS ECMWF atmospheric analyses and forecasts. Observations are assimilated by means of a reduced-order Kalman filter with a 3D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. Along track altimeter data, satellite Sea Surface Temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. A 3D-Var scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.Since May 2015, Mercator Ocean opened the Copernicus Marine Service (CMS) and is in charge of the global ocean analyses and forecast, at eddy resolving resolution. In this context, R&D activities have been conducted at Mercator Ocean these last years in order to improve the real-time 1/12° global system for the next CMS version in 2016. The ocean/sea-ice model and the assimilation scheme benefit among others from the following improvements: large-scale and objective correction of atmospheric quantities with satellite data, new Mean Dynamic Topography taking into account the last version of GOCE geoid, new adaptive tuning of some observational errors, new Quality Control on the assimilated temperature and salinity vertical profiles based on dynamic height criteria, assimilation of satellite sea-ice concentration, new freshwater runoff from ice sheets melting …This presentation doesn't focus on the impact of each update, but rather on the overall behavior of the system integrating all updates. This assessment reports on the products quality improvements, highlighting the level of performance and the reliability of the new system.

Reduced adaptability, but no fundamental disruption, of norm-based face coding following early visual deprivation from congenital cataracts.

PubMed

Rhodes, Gillian; Nishimura, Mayu; de Heering, Adelaide; Jeffery, Linda; Maurer, Daphne

2017-05-01

Faces are adaptively coded relative to visual norms that are updated by experience, and this adaptive coding is linked to face recognition ability. Here we investigated whether adaptive coding of faces is disrupted in individuals (adolescents and adults) who experience face recognition difficulties following visual deprivation from congenital cataracts in infancy. We measured adaptive coding using face identity aftereffects, where smaller aftereffects indicate less adaptive updating of face-coding mechanisms by experience. We also examined whether the aftereffects increase with adaptor identity strength, consistent with norm-based coding of identity, as in typical populations, or whether they show a different pattern indicating some more fundamental disruption of face-coding mechanisms. Cataract-reversal patients showed significantly smaller face identity aftereffects than did controls (Experiments 1 and 2). However, their aftereffects increased significantly with adaptor strength, consistent with norm-based coding (Experiment 2). Thus we found reduced adaptability but no fundamental disruption of norm-based face-coding mechanisms in cataract-reversal patients. Our results suggest that early visual experience is important for the normal development of adaptive face-coding mechanisms. © 2016 John Wiley & Sons Ltd.

Adapting an Ant Colony Metaphor for Multi-Robot Chemical Plume Tracing

PubMed Central

Meng, Qing-Hao; Yang, Wei-Xing; Wang, Yang; Li, Fei; Zeng, Ming

2012-01-01

We consider chemical plume tracing (CPT) in time-varying airflow environments using multiple mobile robots. The purpose of CPT is to approach a gas source with a previously unknown location in a given area. Therefore, the CPT could be considered as a dynamic optimization problem in continuous domains. The traditional ant colony optimization (ACO) algorithm has been successfully used for combinatorial optimization problems in discrete domains. To adapt the ant colony metaphor to the multi-robot CPT problem, the two-dimension continuous search area is discretized into grids and the virtual pheromone is updated according to both the gas concentration and wind information. To prevent the adapted ACO algorithm from being prematurely trapped in a local optimum, the upwind surge behavior is adopted by the robots with relatively higher gas concentration in order to explore more areas. The spiral surge (SS) algorithm is also examined for comparison. Experimental results using multiple real robots in two indoor natural ventilated airflow environments show that the proposed CPT method performs better than the SS algorithm. The simulation results for large-scale advection-diffusion plume environments show that the proposed method could also work in outdoor meandering plume environments. PMID:22666056

Adapting an ant colony metaphor for multi-robot chemical plume tracing.

PubMed

Meng, Qing-Hao; Yang, Wei-Xing; Wang, Yang; Li, Fei; Zeng, Ming

2012-01-01

We consider chemical plume tracing (CPT) in time-varying airflow environments using multiple mobile robots. The purpose of CPT is to approach a gas source with a previously unknown location in a given area. Therefore, the CPT could be considered as a dynamic optimization problem in continuous domains. The traditional ant colony optimization (ACO) algorithm has been successfully used for combinatorial optimization problems in discrete domains. To adapt the ant colony metaphor to the multi-robot CPT problem, the two-dimension continuous search area is discretized into grids and the virtual pheromone is updated according to both the gas concentration and wind information. To prevent the adapted ACO algorithm from being prematurely trapped in a local optimum, the upwind surge behavior is adopted by the robots with relatively higher gas concentration in order to explore more areas. The spiral surge (SS) algorithm is also examined for comparison. Experimental results using multiple real robots in two indoor natural ventilated airflow environments show that the proposed CPT method performs better than the SS algorithm. The simulation results for large-scale advection-diffusion plume environments show that the proposed method could also work in outdoor meandering plume environments.

Evolving learning rules and emergence of cooperation in spatial prisoner's dilemma.

PubMed

Moyano, Luis G; Sánchez, Angel

2009-07-07

In the evolutionary Prisoner's dilemma (PD) game, agents play with each other and update their strategies in every generation according to some microscopic dynamical rule. In its spatial version, agents do not play with every other but, instead, interact only with their neighbours, thus mimicking the existing of a social or contact network that defines who interacts with whom. In this work, we explore evolutionary, spatial PD systems consisting of two types of agents, each with a certain update (reproduction, learning) rule. We investigate two different scenarios: in the first case, update rules remain fixed for the entire evolution of the system; in the second case, agents update both strategy and update rule in every generation. We show that in a well-mixed population the evolutionary outcome is always full defection. We subsequently focus on two-strategy competition with nearest-neighbour interactions on the contact network and synchronised update of strategies. Our results show that, for an important range of the parameters of the game, the final state of the system is largely different from that arising from the usual setup of a single, fixed dynamical rule. Furthermore, the results are also very different if update rules are fixed or evolve with the strategies. In these respect, we have studied representative update rules, finding that some of them may become extinct while others prevail. We describe the new and rich variety of final outcomes that arise from this co-evolutionary dynamics. We include examples of other neighbourhoods and asynchronous updating that confirm the robustness of our conclusions. Our results pave the way to an evolutionary rationale for modelling social interactions through game theory with a preferred set of update rules.

A family of variable step-size affine projection adaptive filter algorithms using statistics of channel impulse response

NASA Astrophysics Data System (ADS)

Shams Esfand Abadi, Mohammad; AbbasZadeh Arani, Seyed Ali Asghar

2011-12-01

This paper extends the recently introduced variable step-size (VSS) approach to the family of adaptive filter algorithms. This method uses prior knowledge of the channel impulse response statistic. Accordingly, optimal step-size vector is obtained by minimizing the mean-square deviation (MSD). The presented algorithms are the VSS affine projection algorithm (VSS-APA), the VSS selective partial update NLMS (VSS-SPU-NLMS), the VSS-SPU-APA, and the VSS selective regressor APA (VSS-SR-APA). In VSS-SPU adaptive algorithms the filter coefficients are partially updated which reduce the computational complexity. In VSS-SR-APA, the optimal selection of input regressors is performed during the adaptation. The presented algorithms have good convergence speed, low steady state mean square error (MSE), and low computational complexity features. We demonstrate the good performance of the proposed algorithms through several simulations in system identification scenario.

Adaptive Output-Feedback Neural Control of Switched Uncertain Nonlinear Systems With Average Dwell Time.

PubMed

Long, Lijun; Zhao, Jun

2015-07-01

This paper investigates the problem of adaptive neural tracking control via output-feedback for a class of switched uncertain nonlinear systems without the measurements of the system states. The unknown control signals are approximated directly by neural networks. A novel adaptive neural control technique for the problem studied is set up by exploiting the average dwell time method and backstepping. A switched filter and different update laws are designed to reduce the conservativeness caused by adoption of a common observer and a common update law for all subsystems. The proposed controllers of subsystems guarantee that all closed-loop signals remain bounded under a class of switching signals with average dwell time, while the output tracking error converges to a small neighborhood of the origin. As an application of the proposed design method, adaptive output feedback neural tracking controllers for a mass-spring-damper system are constructed.

An Adaptive Cultural Algorithm with Improved Quantum-behaved Particle Swarm Optimization for Sonar Image Detection.

PubMed

Wang, Xingmei; Hao, Wenqian; Li, Qiming

2017-12-18

This paper proposes an adaptive cultural algorithm with improved quantum-behaved particle swarm optimization (ACA-IQPSO) to detect the underwater sonar image. In the population space, to improve searching ability of particles, iterative times and the fitness value of particles are regarded as factors to adaptively adjust the contraction-expansion coefficient of the quantum-behaved particle swarm optimization algorithm (QPSO). The improved quantum-behaved particle swarm optimization algorithm (IQPSO) can make particles adjust their behaviours according to their quality. In the belief space, a new update strategy is adopted to update cultural individuals according to the idea of the update strategy in shuffled frog leaping algorithm (SFLA). Moreover, to enhance the utilization of information in the population space and belief space, accept function and influence function are redesigned in the new communication protocol. The experimental results show that ACA-IQPSO can obtain good clustering centres according to the grey distribution information of underwater sonar images, and accurately complete underwater objects detection. Compared with other algorithms, the proposed ACA-IQPSO has good effectiveness, excellent adaptability, a powerful searching ability and high convergence efficiency. Meanwhile, the experimental results of the benchmark functions can further demonstrate that the proposed ACA-IQPSO has better searching ability, convergence efficiency and stability.

Adaptive Reception for Underwater Communications

DTIC Science & Technology

2011-06-01

Experimental results prove the effectiveness of the receiver. 14. SUBJECT TERMS Underwater acoustic communications, adaptive algorithms , Kalman filter...the update algorithm design and the value of the spatial diversity are addressed. In this research, an adaptive multichannel equalizer made up of a...for the time-varying nature of the channel is to use an Adaptive Decision Feedback Equalizer based on either the RLS or LMS algorithm . Although this

Signaling and sensory adaptation in Escherichia coli chemoreceptors: 2015 update

PubMed Central

Parkinson, John S.; Hazelbauer, Gerald L.; Falke, Joseph J.

2015-01-01

Motile Escherichia coli cells track gradients of attractant and repellent chemicals in their environment with transmembrane chemoreceptor proteins. These receptors operate in cooperative arrays to produce large changes in the activity of a signaling kinase CheA in response to small changes in chemoeffector concentration. Recent research has provided much deeper understanding of the structure and function of core receptor signaling complexes and the architecture of higher-order receptor arrays, which in turn has led to new insights into the molecular signaling mechanisms of chemoreceptor networks. Current evidence supports a new view of receptor signaling in which stimulus information travels within receptor molecules through shifts in the dynamic properties of adjoining structural elements rather than through a few discrete conformational states. PMID:25834953

Research on measurement method of optical camouflage effect of moving object

NASA Astrophysics Data System (ADS)

Wang, Juntang; Xu, Weidong; Qu, Yang; Cui, Guangzhen

2016-10-01

Camouflage effectiveness measurement as an important part of the camouflage technology, which testing and measuring the camouflage effect of the target and the performance of the camouflage equipment according to the tactical and technical requirements. The camouflage effectiveness measurement of current optical band is mainly aimed at the static target which could not objectively reflect the dynamic camouflage effect of the moving target. This paper synthetical used technology of dynamic object detection and camouflage effect detection, the digital camouflage of the moving object as the research object, the adaptive background update algorithm of Surendra was improved, a method of optical camouflage effect detection using Lab-color space in the detection of moving-object was presented. The binary image of moving object is extracted by this measurement technology, in the sequence diagram, the characteristic parameters such as the degree of dispersion, eccentricity, complexity and moment invariants are constructed to construct the feature vector space. The Euclidean distance of moving target which through digital camouflage was calculated, the results show that the average Euclidean distance of 375 frames was 189.45, which indicated that the degree of dispersion, eccentricity, complexity and moment invariants of the digital camouflage graphics has a great difference with the moving target which not spray digital camouflage. The measurement results showed that the camouflage effect was good. Meanwhile with the performance evaluation module, the correlation coefficient of the dynamic target image range 0.1275 from 0.0035, and presented some ups and down. Under the dynamic condition, the adaptability of target and background was reflected. In view of the existing infrared camouflage technology, the next step, we want to carry out the camouflage effect measurement technology of the moving target based on infrared band.

Fifty-sixth Christmas Bird Count. 147. Southern Dorchester County, Md

USGS Publications Warehouse

Johnson, F.A.; Williams, B.K.; Nichols, J.D.; Hines, J.E.; Kendall, W.L.; Smith, G.W.; Caithamer, David F.

1956-01-01

Summary and Recommendations: We suggest that managers are approaching the limits of their ability to improve waterfowl harvest management, primarily because the information needed to make better decisions is being sacrificed by the current approach to setting regulations. We propose an actively adaptive management strategy in which regulatory decisions play a dominant role in reducing uncertainty about population dynamics. The proposed strategy recognizes 'value' in acquiring knowledge only to the extent that it contributes to the objective of optimizing harvests. To implement this strategy, managers will need: (1) a set of regulatory options, with possible constraints on their use; (2) quantifiable harvest management objectives; (3) a set of models that represent an array of meaningful hypotheses about the effects of regulations on populations; and (4) a measure of credibility (or likelihood) for each model, which can be updated regularly using information from waterfowl monitoring programs. Adaptive optimization is an iterative process in which the harvest-management policy converges over time to one that maximizes harvest under the most appropriate model. At each time step, an optimal regulatory decision is identified based on the state of the system and the model likelihoods. In the next time step, predicted population changes from the alternative models are compared with the actual changes provided by the monitoring program, The likelihoods are increased or decreased to the extent that predicted and actual population changes correspond. These updated likelihoods then are used in setting regulations in the next cycle and the process begins again. This iterative process produces the most informative regulations when uncertainty is prevalent and produces maximum sustainable yields as uncertainty is eliminated. We see no major obstacles to implementing this adaptive strategy, although there are a number of practical considerations. First and foremost, managers should assess the 'value' of learning. Only when there is a high degree of uncertainty about the effects of hunting regulations on population dynamics will the merit of our proposed strategy be evident. We suggest that this almost always will be true given our current understanding of the relationship between annual regulations, survival and population growth in waterfowl. Nonetheless, careful consideration should be given to formulating the set of alternative models. There is no value in distinguishing between models which differ in their mathematical formulation or biological realism, but which suggest similar harvest strategies. We suspect that 'mechanistic' models (i.e., those that attempt to capture the essence of biological processes) will make better candidates for model sets than so-called 'phenomenological' models. Assuming that all model sets include a good approximation of reality, learning rates will be dependent on the quality of monitoring programs. Fortunately, a variety of high-quality monitoring plans for many duck and goose populations of North America, when used with our adaptive approach, should provide new knowledge about population dynamics and response to hunting, and, thus, lead to improved management.

The Dynamic Planner: The Sequencer, Scheduler, and Runway Allocator for Air Traffic Control Automation

NASA Technical Reports Server (NTRS)

Wong, Gregory L.; Denery, Dallas (Technical Monitor)

2000-01-01

The Dynamic Planner (DP) has been designed, implemented, and integrated into the Center-TRACON Automation System (CTAS) to assist Traffic Management Coordinators (TMCs), in real time, with the task of planning and scheduling arrival traffic approximately 35 to 200 nautical miles from the destination airport. The TMC may input to the DP a series of current and future scheduling constraints that reflect the operation and environmental conditions of the airspace. Under these constraints, the DP uses flight plans, track updates, and Estimated Time of Arrival (ETA) predictions to calculate optimal runway assignments and arrival schedules that help ensure an orderly, efficient, and conflict-free flow of traffic into the terminal area. These runway assignments and schedules can be shown directly to controllers or they can be used by other CTAS tools to generate advisories to the controllers. Additionally, the TMC and controllers may override the decisions made by the DP for tactical considerations. The DP will adapt to computations to accommodate these manual inputs.

Automation of temperature control for large-array microwave surface applicators.

PubMed

Zhou, L; Fessenden, P

1993-01-01

An adaptive temperature control system has been developed for the microstrip antenna array applicators used for large area superficial hyperthermia. A recursive algorithm which allows rapid power updating even for large antenna arrays and accounts for coupling between neighbouring antennas has been developed, based on a first-order difference equation model. Surface temperatures from the centre of each antenna element are the primary feedback information. Also used are temperatures from additional surface probes placed within the treatment field to protect locations vulnerable to excessive temperatures. In addition, temperatures at depth are observed by mappers and utilized to restrain power to reduce treatment-related complications. Experiments on a tissue-equivalent phantom capable of dynamic differential cooling have successfully verified this temperature control system. The results with the 25 (5 x 5) antenna array have demonstrated that during dynamic water cooling changes and other experimentally simulated disturbances, the controlled temperatures converge to desired temperature patterns with a precision close to the resolution of the thermometry system (0.1 degree C).

The advantage of flexible neuronal tunings in neural network models for motor learning

PubMed Central

Marongelli, Ellisha N.; Thoroughman, Kurt A.

2013-01-01

Human motor adaptation to novel environments is often modeled by a basis function network that transforms desired movement properties into estimated forces. This network employs a layer of nodes that have fixed broad tunings that generalize across the input domain. Learning is achieved by updating the weights of these nodes in response to training experience. This conventional model is unable to account for rapid flexibility observed in human spatial generalization during motor adaptation. However, added plasticity in the widths of the basis function tunings can achieve this flexibility, and several neurophysiological experiments have revealed flexibility in tunings of sensorimotor neurons. We found a model, Locally Weighted Projection Regression (LWPR), which uniquely possesses the structure of a basis function network in which both the weights and tuning widths of the nodes are updated incrementally during adaptation. We presented this LWPR model with training functions of different spatial complexities and monitored incremental updates to receptive field widths. An inverse pattern of dependence of receptive field adaptation on experienced error became evident, underlying both a relationship between generalization and complexity, and a unique behavior in which generalization always narrows after a sudden switch in environmental complexity. These results implicate a model that is flexible in both basis function widths and weights, like LWPR, as a viable alternative model for human motor adaptation that can account for previously observed plasticity in spatial generalization. This theory can be tested by using the behaviors observed in our experiments as novel hypotheses in human studies. PMID:23888141

Learning Rate Updating Methods Applied to Adaptive Fuzzy Equalizers for Broadband Power Line Communications

NASA Astrophysics Data System (ADS)

Ribeiro, Moisés V.

2004-12-01

This paper introduces adaptive fuzzy equalizers with variable step size for broadband power line (PL) communications. Based on delta-bar-delta and local Lipschitz estimation updating rules, feedforward, and decision feedback approaches, we propose singleton and nonsingleton fuzzy equalizers with variable step size to cope with the intersymbol interference (ISI) effects of PL channels and the hardness of the impulse noises generated by appliances and nonlinear loads connected to low-voltage power grids. The computed results show that the convergence rates of the proposed equalizers are higher than the ones attained by the traditional adaptive fuzzy equalizers introduced by J. M. Mendel and his students. Additionally, some interesting BER curves reveal that the proposed techniques are efficient for mitigating the above-mentioned impairments.

Crucial role of strategy updating for coexistence of strategies in interaction networks.

PubMed

Zhang, Jianlei; Zhang, Chunyan; Cao, Ming; Weissing, Franz J

2015-04-01

Network models are useful tools for studying the dynamics of social interactions in a structured population. After a round of interactions with the players in their local neighborhood, players update their strategy based on the comparison of their own payoff with the payoff of one of their neighbors. Here we show that the assumptions made on strategy updating are of crucial importance for the strategy dynamics. In the first step, we demonstrate that seemingly small deviations from the standard assumptions on updating have major implications for the evolutionary outcome of two cooperation games: cooperation can more easily persist in a Prisoner's Dilemma game, while it can go more easily extinct in a Snowdrift game. To explain these outcomes, we develop a general model for the updating of states in a network that allows us to derive conditions for the steady-state coexistence of states (or strategies). The analysis reveals that coexistence crucially depends on the number of agents consulted for updating. We conclude that updating rules are as important for evolution on a network as network structure and the nature of the interaction.

Crucial role of strategy updating for coexistence of strategies in interaction networks

NASA Astrophysics Data System (ADS)

Zhang, Jianlei; Zhang, Chunyan; Cao, Ming; Weissing, Franz J.

2015-04-01

Network models are useful tools for studying the dynamics of social interactions in a structured population. After a round of interactions with the players in their local neighborhood, players update their strategy based on the comparison of their own payoff with the payoff of one of their neighbors. Here we show that the assumptions made on strategy updating are of crucial importance for the strategy dynamics. In the first step, we demonstrate that seemingly small deviations from the standard assumptions on updating have major implications for the evolutionary outcome of two cooperation games: cooperation can more easily persist in a Prisoner's Dilemma game, while it can go more easily extinct in a Snowdrift game. To explain these outcomes, we develop a general model for the updating of states in a network that allows us to derive conditions for the steady-state coexistence of states (or strategies). The analysis reveals that coexistence crucially depends on the number of agents consulted for updating. We conclude that updating rules are as important for evolution on a network as network structure and the nature of the interaction.

Designing an Adaptive Web-Based Learning System Based on Students' Cognitive Styles Identified Online

ERIC Educational Resources Information Center

Lo, Jia-Jiunn; Chan, Ya-Chen; Yeh, Shiou-Wen

2012-01-01

This study developed an adaptive web-based learning system focusing on students' cognitive styles. The system is composed of a student model and an adaptation model. It collected students' browsing behaviors to update the student model for unobtrusively identifying student cognitive styles through a multi-layer feed-forward neural network (MLFF).…

The Construct of Adaptive Behavior: Its Conceptualization, Measurement, and Use in the Field of Intellectual Disability

ERIC Educational Resources Information Center

Tasse, Marc J.; Schalock, Robert L.; Balboni, Giulia; Bersani, Hank, Jr.; Borthwick-Duffy, Sharon A.; Spreat, Scott; Thissen, David; Widaman, Keith F.; Zhang, Dalun

2012-01-01

This article updates the current conceptualization, measurement, and use of the adaptive behavior construct. Major sections of the article address an understanding of the construct, the current approaches to its measurement, four assessment issues and challenges related to the use of adaptive behavior information for the diagnosis of intellectual…

Criticality and Chaos in Systems of Communities

NASA Astrophysics Data System (ADS)

Ostilli, Massimo; Figueiredo, Wagner

2016-01-01

We consider a simple model of communities interacting via bilinear terms. After analyzing the thermal equilibrium case, which can be described by an Hamiltonian, we introduce the dynamics that, for Ising-like variables, reduces to a Glauber-like dynamics. We analyze and compare four different versions of the dynamics: flow (differential equations), map (discretetime dynamics), local-time update flow, and local-time update map. The presence of only bilinear interactions prevent the flow cases to develop any dynamical instability, the system converging always to the thermal equilibrium. The situation is different for the map when unfriendly couplings are involved, where period-two oscillations arise. In the case of the map with local-time updates, oscillations of any period and chaos can arise as a consequence of the reciprocal “tension” accumulated among the communities during their sleeping time interval. The resulting chaos can be of two kinds: true chaos characterized by positive Lyapunov exponent and bifurcation cascades, or marginal chaos characterized by zero Lyapunov exponent and critical continuous regions.

Conditions and limitations on learning in the adaptive management of mallard harvests

USGS Publications Warehouse

Johnson, F.A.; Kendall, W.L.; Dubovsky, J.A.

2002-01-01

In 1995, the United States Fish and Wildlife Service adopted a protocol for the adaptive management of waterfowl hunting regulations (AHM) to help reduce uncertainty about the magnitude of sustainable harvests. To date, the AHM process has focused principally on the midcontinent population of mallards (Anas platyrhynchos), whose dynamics are described by 4 alternative models. Collectively, these models express uncertainty (or disagreement) about whether harvest is an additive or a compensatory form of mortality and whether the reproductive process is weakly or strongly density-dependent. Each model is associated with a probability or 'weight,' which describes its relative ability to predict changes in population size. These Bayesian probabilities are updated annually using a comparison of population size predicted under each model with that observed by a monitoring program. The current AHM process is passively adaptive, in the sense that there is no a priori consideration of how harvest decisions might affect discrimination among models. We contrast this approach with an actively adaptive approach, in which harvest decisions are used in part to produce the learning needed to increase long-term management performance. Our investigation suggests that the passive approach is expected to perform nearly as well as an optimal actively adaptive approach, particularly considering the nature of the model set, management objectives and constraints, and current regulatory alternatives. We offer some comments about the nature of the biological hypotheses being tested and describe some of the inherent limitations on learning in the AHM process.

Computational model for behavior shaping as an adaptive health intervention strategy.

PubMed

Berardi, Vincent; Carretero-González, Ricardo; Klepeis, Neil E; Ghanipoor Machiani, Sahar; Jahangiri, Arash; Bellettiere, John; Hovell, Melbourne

2018-03-01

Adaptive behavioral interventions that automatically adjust in real-time to participants' changing behavior, environmental contexts, and individual history are becoming more feasible as the use of real-time sensing technology expands. This development is expected to improve shortcomings associated with traditional behavioral interventions, such as the reliance on imprecise intervention procedures and limited/short-lived effects. JITAI adaptation strategies often lack a theoretical foundation. Increasing the theoretical fidelity of a trial has been shown to increase effectiveness. This research explores the use of shaping, a well-known process from behavioral theory for engendering or maintaining a target behavior, as a JITAI adaptation strategy. A computational model of behavior dynamics and operant conditioning was modified to incorporate the construct of behavior shaping by adding the ability to vary, over time, the range of behaviors that were reinforced when emitted. Digital experiments were performed with this updated model for a range of parameters in order to identify the behavior shaping features that optimally generated target behavior. Narrowing the range of reinforced behaviors continuously in time led to better outcomes compared with a discrete narrowing of the reinforcement window. Rapid narrowing followed by more moderate decreases in window size was more effective in generating target behavior than the inverse scenario. The computational shaping model represents an effective tool for investigating JITAI adaptation strategies. Model parameters must now be translated from the digital domain to real-world experiments so that model findings can be validated.

Discriminative clustering on manifold for adaptive transductive classification.

PubMed

Zhang, Zhao; Jia, Lei; Zhang, Min; Li, Bing; Zhang, Li; Li, Fanzhang

2017-10-01

In this paper, we mainly propose a novel adaptive transductive label propagation approach by joint discriminative clustering on manifolds for representing and classifying high-dimensional data. Our framework seamlessly combines the unsupervised manifold learning, discriminative clustering and adaptive classification into a unified model. Also, our method incorporates the adaptive graph weight construction with label propagation. Specifically, our method is capable of propagating label information using adaptive weights over low-dimensional manifold features, which is different from most existing studies that usually predict the labels and construct the weights in the original Euclidean space. For transductive classification by our formulation, we first perform the joint discriminative K-means clustering and manifold learning to capture the low-dimensional nonlinear manifolds. Then, we construct the adaptive weights over the learnt manifold features, where the adaptive weights are calculated through performing the joint minimization of the reconstruction errors over features and soft labels so that the graph weights can be joint-optimal for data representation and classification. Using the adaptive weights, we can easily estimate the unknown labels of samples. After that, our method returns the updated weights for further updating the manifold features. Extensive simulations on image classification and segmentation show that our proposed algorithm can deliver the state-of-the-art performance on several public datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

An approximately Bayesian delta-rule model explains the dynamics of belief updating in a changing environment.

PubMed

Nassar, Matthew R; Wilson, Robert C; Heasly, Benjamin; Gold, Joshua I

2010-09-15

Maintaining appropriate beliefs about variables needed for effective decision making can be difficult in a dynamic environment. One key issue is the amount of influence that unexpected outcomes should have on existing beliefs. In general, outcomes that are unexpected because of a fundamental change in the environment should carry more influence than outcomes that are unexpected because of persistent environmental stochasticity. Here we use a novel task to characterize how well human subjects follow these principles under a range of conditions. We show that the influence of an outcome depends on both the error made in predicting that outcome and the number of similar outcomes experienced previously. We also show that the exact nature of these tendencies varies considerably across subjects. Finally, we show that these patterns of behavior are consistent with a computationally simple reduction of an ideal-observer model. The model adjusts the influence of newly experienced outcomes according to ongoing estimates of uncertainty and the probability of a fundamental change in the process by which outcomes are generated. A prior that quantifies the expected frequency of such environmental changes accounts for individual variability, including a positive relationship between subjective certainty and the degree to which new information influences existing beliefs. The results suggest that the brain adaptively regulates the influence of decision outcomes on existing beliefs using straightforward updating rules that take into account both recent outcomes and prior expectations about higher-order environmental structure.

Diversity in plant hydraulic traits explains seasonal and inter-annual variations of vegetation dynamics in seasonally dry tropical forests.

PubMed

Xu, Xiangtao; Medvigy, David; Powers, Jennifer S; Becknell, Justin M; Guan, Kaiyu

2016-10-01

We assessed whether diversity in plant hydraulic traits can explain the observed diversity in plant responses to water stress in seasonally dry tropical forests (SDTFs). The Ecosystem Demography model 2 (ED2) was updated with a trait-driven mechanistic plant hydraulic module, as well as novel drought-phenology and plant water stress schemes. Four plant functional types were parameterized on the basis of meta-analysis of plant hydraulic traits. Simulations from both the original and the updated ED2 were evaluated against 5 yr of field data from a Costa Rican SDTF site and remote-sensing data over Central America. The updated model generated realistic plant hydraulic dynamics, such as leaf water potential and stem sap flow. Compared with the original ED2, predictions from our novel trait-driven model matched better with observed growth, phenology and their variations among functional groups. Most notably, the original ED2 produced unrealistically small leaf area index (LAI) and underestimated cumulative leaf litter. Both of these biases were corrected by the updated model. The updated model was also better able to simulate spatial patterns of LAI dynamics in Central America. Plant hydraulic traits are intercorrelated in SDTFs. Mechanistic incorporation of plant hydraulic traits is necessary for the simulation of spatiotemporal patterns of vegetation dynamics in SDTFs in vegetation models. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Urn models for response-adaptive randomized designs: a simulation study based on a non-adaptive randomized trial.

PubMed

Ghiglietti, Andrea; Scarale, Maria Giovanna; Miceli, Rosalba; Ieva, Francesca; Mariani, Luigi; Gavazzi, Cecilia; Paganoni, Anna Maria; Edefonti, Valeria

2018-03-22

Recently, response-adaptive designs have been proposed in randomized clinical trials to achieve ethical and/or cost advantages by using sequential accrual information collected during the trial to dynamically update the probabilities of treatment assignments. In this context, urn models-where the probability to assign patients to treatments is interpreted as the proportion of balls of different colors available in a virtual urn-have been used as response-adaptive randomization rules. We propose the use of Randomly Reinforced Urn (RRU) models in a simulation study based on a published randomized clinical trial on the efficacy of home enteral nutrition in cancer patients after major gastrointestinal surgery. We compare results with the RRU design with those previously published with the non-adaptive approach. We also provide a code written with the R software to implement the RRU design in practice. In detail, we simulate 10,000 trials based on the RRU model in three set-ups of different total sample sizes. We report information on the number of patients allocated to the inferior treatment and on the empirical power of the t-test for the treatment coefficient in the ANOVA model. We carry out a sensitivity analysis to assess the effect of different urn compositions. For each sample size, in approximately 75% of the simulation runs, the number of patients allocated to the inferior treatment by the RRU design is lower, as compared to the non-adaptive design. The empirical power of the t-test for the treatment effect is similar in the two designs.

Learning and adaptation in the management of waterfowl harvests

USGS Publications Warehouse

Johnson, Fred A.

2011-01-01

A formal framework for the adaptive management of waterfowl harvests was adopted by the U.S. Fish and Wildlife Service in 1995. The process admits competing models of waterfowl population dynamics and harvest impacts, and relies on model averaging to compute optimal strategies for regulating harvest. Model weights, reflecting the relative ability of the alternative models to predict changes in population size, are used in the model averaging and are updated each year based on a comparison of model predictions and observations of population size. Since its inception the adaptive harvest program has focused principally on mallards (Anas platyrhynchos), which constitute a large portion of the U.S. waterfowl harvest. Four competing models, derived from a combination of two survival and two reproductive hypotheses, were originally assigned equal weights. In the last year of available information (2007), model weights favored the weakly density-dependent reproductive hypothesis over the strongly density-dependent one, and the additive mortality hypothesis over the compensatory one. The change in model weights led to a more conservative harvesting policy than what was in effect in the early years of the program. Adaptive harvest management has been successful in many ways, but nonetheless has exposed the difficulties in defining management objectives, in predicting and regulating harvests, and in coping with the tradeoffs inherent in managing multiple waterfowl stocks exposed to a common harvest. The key challenge now facing managers is whether adaptive harvest management as an institution can be sufficiently adaptive, and whether the knowledge and experience gained from the process can be reflected in higher-level policy decisions.

Avionics Tether Operations Control

NASA Technical Reports Server (NTRS)

Glaese, John R.

2001-01-01

The activities described in this Final Report were authorized and performed under Purchase Order Number H32835D, issued as part of NASA contract number NAS8-00114. The period of performance of this PO was from March 1 to September 30, 2001. The primary work activity was the continued development and updating of the tether dynamic simulation tools GTOSS (Generalized Tethered Object System Simulation) and TSSIM (Tethered Satellite System) and use of these and other tools in the analysis of various tether dynamics problems. Several updated versions of GTOSS were delivered during the period of performance by the author of the simulation, Lang Associates' David Lang. These updates had mainly to do with updated documentation and an updated coordinate system definition to the J2000 standards. This Final Report is organized by the months in which the activities described were performed. The following sections review the Statement of Work (SOW) and activities performed to satisfy it.

Electronic Education System Model-2

ERIC Educational Resources Information Center

Güllü, Fatih; Kuusik, Rein; Laanpere, Mart

2015-01-01

In this study we presented new EES Model-2 extended from EES model for more productive implementation in e-learning process design and modelling in higher education. The most updates were related to uppermost instructional layer. We updated learning processes object of the layer for adaptation of educational process for young and old people,…

Adaptive Management and the Value of Information: Learning Via Intervention in Epidemiology

PubMed Central

Shea, Katriona; Tildesley, Michael J.; Runge, Michael C.; Fonnesbeck, Christopher J.; Ferrari, Matthew J.

2014-01-01

Optimal intervention for disease outbreaks is often impeded by severe scientific uncertainty. Adaptive management (AM), long-used in natural resource management, is a structured decision-making approach to solving dynamic problems that accounts for the value of resolving uncertainty via real-time evaluation of alternative models. We propose an AM approach to design and evaluate intervention strategies in epidemiology, using real-time surveillance to resolve model uncertainty as management proceeds, with foot-and-mouth disease (FMD) culling and measles vaccination as case studies. We use simulations of alternative intervention strategies under competing models to quantify the effect of model uncertainty on decision making, in terms of the value of information, and quantify the benefit of adaptive versus static intervention strategies. Culling decisions during the 2001 UK FMD outbreak were contentious due to uncertainty about the spatial scale of transmission. The expected benefit of resolving this uncertainty prior to a new outbreak on a UK-like landscape would be £45–£60 million relative to the strategy that minimizes livestock losses averaged over alternate transmission models. AM during the outbreak would be expected to recover up to £20.1 million of this expected benefit. AM would also recommend a more conservative initial approach (culling of infected premises and dangerous contact farms) than would a fixed strategy (which would additionally require culling of contiguous premises). For optimal targeting of measles vaccination, based on an outbreak in Malawi in 2010, AM allows better distribution of resources across the affected region; its utility depends on uncertainty about both the at-risk population and logistical capacity. When daily vaccination rates are highly constrained, the optimal initial strategy is to conduct a small, quick campaign; a reduction in expected burden of approximately 10,000 cases could result if campaign targets can be updated on the basis of the true susceptible population. Formal incorporation of a policy to update future management actions in response to information gained in the course of an outbreak can change the optimal initial response and result in significant cost savings. AM provides a framework for using multiple models to facilitate public-health decision making and an objective basis for updating management actions in response to improved scientific understanding. PMID:25333371

Adaptive management and the value of information: learning via intervention in epidemiology

USGS Publications Warehouse

Shea, Katriona; Tildesley, Michael J.; Runge, Michael C.; Fonnesbeck, Christopher J.; Ferrari, Matthew J.

2014-01-01

Optimal intervention for disease outbreaks is often impeded by severe scientific uncertainty. Adaptive management (AM), long-used in natural resource management, is a structured decision-making approach to solving dynamic problems that accounts for the value of resolving uncertainty via real-time evaluation of alternative models. We propose an AM approach to design and evaluate intervention strategies in epidemiology, using real-time surveillance to resolve model uncertainty as management proceeds, with foot-and-mouth disease (FMD) culling and measles vaccination as case studies. We use simulations of alternative intervention strategies under competing models to quantify the effect of model uncertainty on decision making, in terms of the value of information, and quantify the benefit of adaptive versus static intervention strategies. Culling decisions during the 2001 UK FMD outbreak were contentious due to uncertainty about the spatial scale of transmission. The expected benefit of resolving this uncertainty prior to a new outbreak on a UK-like landscape would be £45–£60 million relative to the strategy that minimizes livestock losses averaged over alternate transmission models. AM during the outbreak would be expected to recover up to £20.1 million of this expected benefit. AM would also recommend a more conservative initial approach (culling of infected premises and dangerous contact farms) than would a fixed strategy (which would additionally require culling of contiguous premises). For optimal targeting of measles vaccination, based on an outbreak in Malawi in 2010, AM allows better distribution of resources across the affected region; its utility depends on uncertainty about both the at-risk population and logistical capacity. When daily vaccination rates are highly constrained, the optimal initial strategy is to conduct a small, quick campaign; a reduction in expected burden of approximately 10,000 cases could result if campaign targets can be updated on the basis of the true susceptible population. Formal incorporation of a policy to update future management actions in response to information gained in the course of an outbreak can change the optimal initial response and result in significant cost savings. AM provides a framework for using multiple models to facilitate public-health decision making and an objective basis for updating management actions in response to improved scientific understanding.

Dynamic circuitry for updating spatial representations. II. Physiological evidence for interhemispheric transfer in area LIP of the split-brain macaque.

PubMed

Heiser, Laura M; Berman, Rebecca A; Saunders, Richard C; Colby, Carol L

2005-11-01

With each eye movement, a new image impinges on the retina, yet we do not notice any shift in visual perception. This perceptual stability indicates that the brain must be able to update visual representations to take our eye movements into account. Neurons in the lateral intraparietal area (LIP) update visual representations when the eyes move. The circuitry that supports these updated representations remains unknown, however. In this experiment, we asked whether the forebrain commissures are necessary for updating in area LIP when stimulus representations must be updated from one visual hemifield to the other. We addressed this question by recording from LIP neurons in split-brain monkeys during two conditions: stimulus traces were updated either across or within hemifields. Our expectation was that across-hemifield updating activity in LIP would be reduced or abolished after transection of the forebrain commissures. Our principal finding is that LIP neurons can update stimulus traces from one hemifield to the other even in the absence of the forebrain commissures. This finding provides the first evidence that representations in parietal cortex can be updated without the use of direct cortico-cortical links. The second main finding is that updating activity in LIP is modified in the split-brain monkey: across-hemifield signals are reduced in magnitude and delayed in onset compared with within-hemifield signals, which indicates that the pathways for across-hemifield updating are less effective in the absence of the forebrain commissures. Together these findings reveal a dynamic circuit that contributes to updating spatial representations.

Attractor-Based Obstructions to Growth in Homogeneous Cyclic Boolean Automata.

PubMed

Khan, Bilal; Cantor, Yuri; Dombrowski, Kirk

2015-11-01

We consider a synchronous Boolean organism consisting of N cells arranged in a circle, where each cell initially takes on an independently chosen Boolean value. During the lifetime of the organism, each cell updates its own value by responding to the presence (or absence) of diversity amongst its two neighbours' values. We show that if all cells eventually take a value of 0 (irrespective of their initial values) then the organism necessarily has a cell count that is a power of 2. In addition, the converse is also proved: if the number of cells in the organism is a proper power of 2, then no matter what the initial values of the cells are, eventually all cells take on a value of 0 and then cease to change further. We argue that such an absence of structure in the dynamical properties of the organism implies a lack of adaptiveness, and so is evolutionarily disadvantageous. It follows that as the organism doubles in size (say from m to 2m) it will necessarily encounter an intermediate size that is a proper power of 2, and suffers from low adaptiveness. Finally we show, through computational experiments, that one way an organism can grow to more than twice its size and still avoid passing through intermediate sizes that lack structural dynamics, is for the organism to depart from assumptions of homogeneity at the cellular level.

Attractor-Based Obstructions to Growth in Homogeneous Cyclic Boolean Automata

PubMed Central

Khan, Bilal; Cantor, Yuri; Dombrowski, Kirk

2016-01-01

We consider a synchronous Boolean organism consisting of N cells arranged in a circle, where each cell initially takes on an independently chosen Boolean value. During the lifetime of the organism, each cell updates its own value by responding to the presence (or absence) of diversity amongst its two neighbours’ values. We show that if all cells eventually take a value of 0 (irrespective of their initial values) then the organism necessarily has a cell count that is a power of 2. In addition, the converse is also proved: if the number of cells in the organism is a proper power of 2, then no matter what the initial values of the cells are, eventually all cells take on a value of 0 and then cease to change further. We argue that such an absence of structure in the dynamical properties of the organism implies a lack of adaptiveness, and so is evolutionarily disadvantageous. It follows that as the organism doubles in size (say from m to 2m) it will necessarily encounter an intermediate size that is a proper power of 2, and suffers from low adaptiveness. Finally we show, through computational experiments, that one way an organism can grow to more than twice its size and still avoid passing through intermediate sizes that lack structural dynamics, is for the organism to depart from assumptions of homogeneity at the cellular level. PMID:27660398

Hurricane Sandy: Caught in the eye of the storm and a city's adaptation response

NASA Astrophysics Data System (ADS)

Orton, P. M.; Horton, R. M.; Blumberg, A. F.; Rosenzweig, C.; Solecki, W.; Bader, D.

2015-12-01

The NOAA RISA program has funded the seven-institution Consortium for Climate Risk in the Urban Northeast (CCRUN) for the past five years to serve stakeholder needs in assessing and managing risks from climate variability and change. When Hurricane Sandy struck, we were in an ideal position, making flood forecasts and communicating NOAA forecasts to the public with dozens of media placements, translating the poorly understood flood forecasts into human dimensions. In 2013 and 2015, by request of New York City (NYC), we worked through the NYC Panel on Climate Change to deliver updated climate risk assessment reports, to be used in the post-Sandy rebuilding and resiliency efforts. These utilized innovative methodologies for probabilistic local and regional sea level change projections, and contrasted methods of dynamic versus (the more common) static flood mapping. We participated in a federal-academic partnership that developed a Sea Level Tool for Sandy Recovery that integrates CCRUN sea level rise projections with policy-relevant FEMA flood maps, and now several updated flood maps and coastal flood mapping tools (NOAA, FEMA, and USACE) incorporate our projections. For the adaptation response, we helped develop NYC's $20 billion flood adaptation plan, and we were on a winning team under the Housing and Urban Development Rebuild By Design (RBD) competition, a few of the many opportunities that arose with negligible additional funding and which CCRUN funds supported. Our work at times disrupted standard lines of thinking, but NYC showed an openness to altering course. In one case we showed that an NYC plan of wetland restoration in Jamaica Bay would provide no reduction in flooding unless deep-dredged channels circumventing them were shallowed or narrowed. In another, the lead author's RBD team challenged the notion at one location that levees were the solution to accelerating sea level rise, developing a plan to use ecological breakwaters and layered components of physical and social resilience. CCRUN has succeeded in winning another five years of RISA funding, and this will enable us to continue our climate risk and adaptation work for the entire Urban Northeast.

Scale-adaptive compressive tracking with feature integration

NASA Astrophysics Data System (ADS)

Liu, Wei; Li, Jicheng; Chen, Xiao; Li, Shuxin

2016-05-01

Numerous tracking-by-detection methods have been proposed for robust visual tracking, among which compressive tracking (CT) has obtained some promising results. A scale-adaptive CT method based on multifeature integration is presented to improve the robustness and accuracy of CT. We introduce a keypoint-based model to achieve the accurate scale estimation, which can additionally give a prior location of the target. Furthermore, by the high efficiency of data-independent random projection matrix, multiple features are integrated into an effective appearance model to construct the naïve Bayes classifier. At last, an adaptive update scheme is proposed to update the classifier conservatively. Experiments on various challenging sequences demonstrate substantial improvements by our proposed tracker over CT and other state-of-the-art trackers in terms of dealing with scale variation, abrupt motion, deformation, and illumination changes.

Adaptable Iterative and Recursive Kalman Filter Schemes

NASA Technical Reports Server (NTRS)

Zanetti, Renato

2014-01-01

Nonlinear filters are often very computationally expensive and usually not suitable for real-time applications. Real-time navigation algorithms are typically based on linear estimators, such as the extended Kalman filter (EKF) and, to a much lesser extent, the unscented Kalman filter. The Iterated Kalman filter (IKF) and the Recursive Update Filter (RUF) are two algorithms that reduce the consequences of the linearization assumption of the EKF by performing N updates for each new measurement, where N is the number of recursions, a tuning parameter. This paper introduces an adaptable RUF algorithm to calculate N on the go, a similar technique can be used for the IKF as well.

The Colorado Climate Preparedness Project: A Systematic Approach to Assessing Efforts Supporting State-Level Adaptation

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. Project results can also inform numerous other ongoing database efforts connected to the U.S. National Assessment of Climate Change.

Attitude determination using an adaptive multiple model filtering Scheme

NASA Technical Reports Server (NTRS)

Lam, Quang; Ray, Surendra N.

1995-01-01

Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown quantities such as spacecraft dynamics parameters, gyro biases, dynamic disturbances, or environment variations.

Attitude determination using an adaptive multiple model filtering Scheme

NASA Astrophysics Data System (ADS)

Lam, Quang; Ray, Surendra N.

1995-05-01

Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown quantities such as spacecraft dynamics parameters, gyro biases, dynamic disturbances, or environment variations.

An Energy- and Charge-conserving, Implicit, Electrostatic Particle-in-Cell Algorithm in curvilinear geometry

NASA Astrophysics Data System (ADS)

Chen, G.; Chacón, L.; Barnes, D. C.

2012-03-01

A recent proof-of-principle study proposes an energy- and charge-conserving, fully implicit particle-in-cell algorithm in one dimension [1], which is able to use timesteps comparable to the dynamical timescale of interest. Here, we generalize the method to employ non-uniform meshes via a curvilinear map. The key enabling technology is a hybrid particle pusher [2], with particle positions updated in logical space and particle velocities updated in physical space. The self-adaptive, charge-conserving particle mover of Ref. [1] is extended to the non-uniform mesh case. The fully implicit implementation, using a Jacobian-free Newton-Krylov iterative solver, remains exactly charge- and energy-conserving. The extension of the formulation to multiple dimensions will be discussed. We present numerical experiments of 1D electrostatic, long-timescale ion-acoustic wave and ion-acoustic shock wave simulations, demonstrating that charge and energy are conserved to round-off for arbitrary mesh non-uniformity, and that the total momentum remains well conserved.[4pt] [1] Chen, Chac'on, Barnes, J. Comput. Phys. 230 (2011). [0pt] [2] Camporeale and Delzanno, Bull. Am. Phys. Soc. 56(6) (2011); Wang, et al., J. Plasma Physics, 61 (1999).

A Unified Point Process Probabilistic Framework to Assess Heartbeat Dynamics and Autonomic Cardiovascular Control

PubMed Central

Chen, Zhe; Purdon, Patrick L.; Brown, Emery N.; Barbieri, Riccardo

2012-01-01

In recent years, time-varying inhomogeneous point process models have been introduced for assessment of instantaneous heartbeat dynamics as well as specific cardiovascular control mechanisms and hemodynamics. Assessment of the model’s statistics is established through the Wiener-Volterra theory and a multivariate autoregressive (AR) structure. A variety of instantaneous cardiovascular metrics, such as heart rate (HR), heart rate variability (HRV), respiratory sinus arrhythmia (RSA), and baroreceptor-cardiac reflex (baroreflex) sensitivity (BRS), are derived within a parametric framework and instantaneously updated with adaptive and local maximum likelihood estimation algorithms. Inclusion of second-order non-linearities, with subsequent bispectral quantification in the frequency domain, further allows for definition of instantaneous metrics of non-linearity. We here present a comprehensive review of the devised methods as applied to experimental recordings from healthy subjects during propofol anesthesia. Collective results reveal interesting dynamic trends across the different pharmacological interventions operated within each anesthesia session, confirming the ability of the algorithm to track important changes in cardiorespiratory elicited interactions, and pointing at our mathematical approach as a promising monitoring tool for an accurate, non-invasive assessment in clinical practice. We also discuss the limitations and other alternative modeling strategies of our point process approach. PMID:22375120

Gating based on internal/external signals with dynamic correlation updates.

PubMed

Wu, Huanmei; Zhao, Qingya; Berbeco, Ross I; Nishioka, Seiko; Shirato, Hiroki; Jiang, Steve B

2008-12-21

Precise localization of mobile tumor positions in real time is critical to the success of gated radiotherapy. Tumor positions are usually derived from either internal or external surrogates. Fluoroscopic gating based on internal surrogates, such as implanted fiducial markers, is accurate however requiring a large amount of imaging dose. Gating based on external surrogates, such as patient abdominal surface motion, is non-invasive however less accurate due to the uncertainty in the correlation between tumor location and external surrogates. To address these complications, we propose to investigate an approach based on hybrid gating with dynamic internal/external correlation updates. In this approach, the external signal is acquired at high frequency (such as 30 Hz) while the internal signal is sparsely acquired (such as 0.5 Hz or less). The internal signal is used to validate and update the internal/external correlation during treatment. Tumor positions are derived from the external signal based on the newly updated correlation. Two dynamic correlation updating algorithms are introduced. One is based on the motion amplitude and the other is based on the motion phase. Nine patients with synchronized internal/external motion signals are simulated retrospectively to evaluate the effectiveness of hybrid gating. The influences of different clinical conditions on hybrid gating, such as the size of gating windows, the optimal timing for internal signal acquisition and the acquisition frequency are investigated. The results demonstrate that dynamically updating the internal/external correlation in or around the gating window will reduce false positive with relatively diminished treatment efficiency. This improvement will benefit patients with mobile tumors, especially greater for early stage lung cancers, for which the tumors are less attached or freely floating in the lung.

A Continuous Square Root in Formation Filter-Swoother with Discrete Data Update

NASA Technical Reports Server (NTRS)

Miller, J. K.

1994-01-01

A differential equation for the square root information matrix is derived and adapted to the problems of filtering and smoothing. The resulting continuous square root information filter (SRIF) performs the mapping of state and process noise by numerical integration of the SRIF matrix and admits data via a discrete least square update.

Robust/optimal temperature profile control of a high-speed aerospace vehicle using neural networks.

PubMed

Yadav, Vivek; Padhi, Radhakant; Balakrishnan, S N

2007-07-01

An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A 1-D distributed parameter model of a fin is developed from basic thermal physics principles. "Snapshot" solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the "proper orthogonal decomposition" (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.

Aspiration dynamics in structured population acts as if in a well-mixed one.

PubMed

Du, Jinming; Wu, Bin; Wang, Long

2015-01-26

Understanding the evolution of human interactive behaviors is important. Recent experimental results suggest that human cooperation in spatial structured population is not enhanced as predicted in previous works, when payoff-dependent imitation updating rules are used. This constraint opens up an avenue to shed light on how humans update their strategies in real life. Studies via simulations show that, instead of comparison rules, self-evaluation driven updating rules may explain why spatial structure does not alter the evolutionary outcome. Though inspiring, there is a lack of theoretical result to show the existence of such evolutionary updating rule. Here we study the aspiration dynamics, and show that it does not alter the evolutionary outcome in various population structures. Under weak selection, by analytical approximation, we find that the favored strategy in regular graphs is invariant. Further, we show that this is because the criterion under which a strategy is favored is the same as that of a well-mixed population. By simulation, we show that this holds for random networks. Although how humans update their strategies is an open question to be studied, our results provide a theoretical foundation of the updating rules that may capture the real human updating rules.

A parallel row-based algorithm with error control for standard-cell replacement on a hypercube multiprocessor

NASA Technical Reports Server (NTRS)

Sargent, Jeff Scott

1988-01-01

A new row-based parallel algorithm for standard-cell placement targeted for execution on a hypercube multiprocessor is presented. Key features of this implementation include a dynamic simulated-annealing schedule, row-partitioning of the VLSI chip image, and two novel new approaches to controlling error in parallel cell-placement algorithms; Heuristic Cell-Coloring and Adaptive (Parallel Move) Sequence Control. Heuristic Cell-Coloring identifies sets of noninteracting cells that can be moved repeatedly, and in parallel, with no buildup of error in the placement cost. Adaptive Sequence Control allows multiple parallel cell moves to take place between global cell-position updates. This feedback mechanism is based on an error bound derived analytically from the traditional annealing move-acceptance profile. Placement results are presented for real industry circuits and the performance is summarized of an implementation on the Intel iPSC/2 Hypercube. The runtime of this algorithm is 5 to 16 times faster than a previous program developed for the Hypercube, while producing equivalent quality placement. An integrated place and route program for the Intel iPSC/2 Hypercube is currently being developed.

An intelligent interface for satellite operations: Your Orbit Determination Assistant (YODA)

NASA Technical Reports Server (NTRS)

Schur, Anne

1988-01-01

An intelligent interface is often characterized by the ability to adapt evaluation criteria as the environment and user goals change. Some factors that impact these adaptations are redefinition of task goals and, hence, user requirements; time criticality; and system status. To implement adaptations affected by these factors, a new set of capabilities must be incorporated into the human-computer interface design. These capabilities include: (1) dynamic update and removal of control states based on user inputs, (2) generation and removal of logical dependencies as change occurs, (3) uniform and smooth interfacing to numerous processes, databases, and expert systems, and (4) unobtrusive on-line assistance to users of concepts were applied and incorporated into a human-computer interface using artificial intelligence techniques to create a prototype expert system, Your Orbit Determination Assistant (YODA). YODA is a smart interface that supports, in real teime, orbit analysts who must determine the location of a satellite during the station acquisition phase of a mission. Also described is the integration of four knowledge sources required to support the orbit determination assistant: orbital mechanics, spacecraft specifications, characteristics of the mission support software, and orbit analyst experience. This initial effort is continuing with expansion of YODA's capabilities, including evaluation of results of the orbit determination task.

Convergence and Efficiency of Adaptive Importance Sampling Techniques with Partial Biasing

NASA Astrophysics Data System (ADS)

Fort, G.; Jourdain, B.; Lelièvre, T.; Stoltz, G.

2018-04-01

We propose a new Monte Carlo method to efficiently sample a multimodal distribution (known up to a normalization constant). We consider a generalization of the discrete-time Self Healing Umbrella Sampling method, which can also be seen as a generalization of well-tempered metadynamics. The dynamics is based on an adaptive importance technique. The importance function relies on the weights (namely the relative probabilities) of disjoint sets which form a partition of the space. These weights are unknown but are learnt on the fly yielding an adaptive algorithm. In the context of computational statistical physics, the logarithm of these weights is, up to an additive constant, the free-energy, and the discrete valued function defining the partition is called the collective variable. The algorithm falls into the general class of Wang-Landau type methods, and is a generalization of the original Self Healing Umbrella Sampling method in two ways: (i) the updating strategy leads to a larger penalization strength of already visited sets in order to escape more quickly from metastable states, and (ii) the target distribution is biased using only a fraction of the free-energy, in order to increase the effective sample size and reduce the variance of importance sampling estimators. We prove the convergence of the algorithm and analyze numerically its efficiency on a toy example.

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 rejection and noise suppression for nonnegative and compartmental dynamical systems with noise and exogenous system disturbances. We then use the developed framework to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for surgery in the face of continuing hemorrhage and hemodilution. Critical care patients, whether undergoing surgery or recovering in intensive care units, require drug administration to regulate physiological variables such as blood pressure, cardiac output, heart rate, and degree of consciousness. The rate of infusion of each administered drug is critical, requiring constant monitoring and frequent adjustments. In this dissertation, we develop a neuroadaptive output feedback control framework for nonlinear uncertain nonnegative and compartmental systems with nonnegative control inputs and noisy measurements. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals. In addition, the neuroadaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state space. Finally, the developed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for surgery in the face of noisy electroencephalographic (EEG) measurements. Clinical trials demonstrate excellent regulation of unconsciousness allowing for a safe and effective administration of the anesthetic agent propofol. Furthermore, a neuroadaptive output feedback control architecture for nonlinear nonnegative dynamical systems with input amplitude and integral constraints is developed. Specifically, the neuroadaptive controller guarantees that the imposed amplitude and integral input constraints are satisfied and the physical system states remain in the nonnegative orthant of the state space. The proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for noncardiac surgery in the face of infusion rate constraints and a drug dosing constraint over a specified period. In addition, the aforementioned control architecture is used to control lung volume and minute ventilation with input pressure constraints that also accounts for spontaneous breathing by the patient. Specifically, we develop a pressure- and work-limited neuroadaptive controller for mechanical ventilation based on a nonlinear multi-compartmental lung model. The control framework does not rely on any averaged data and is designed to automatically adjust the input pressure to the patient's physiological characteristics capturing lung resistance and compliance modeling uncertainty. Moreover, the controller accounts for input pressure constraints as well as work of breathing constraints. The effect of spontaneous breathing is incorporated within the lung model and the control framework. Finally, a neural network hybrid adaptive control framework for nonlinear uncertain hybrid dynamical systems is developed. The proposed hybrid adaptive control framework is Lyapunov-based and guarantees partial asymptotic stability of the closed-loop hybrid system; that is, asymptotic stability with respect to part of the closed-loop system states associated with the hybrid plant states. A numerical example is provided to demonstrate the efficacy of the proposed hybrid adaptive stabilization approach.

Heuristic reusable dynamic programming: efficient updates of local sequence alignment.

PubMed

Hong, Changjin; Tewfik, Ahmed H

2009-01-01

Recomputation of the previously evaluated similarity results between biological sequences becomes inevitable when researchers realize errors in their sequenced data or when the researchers have to compare nearly similar sequences, e.g., in a family of proteins. We present an efficient scheme for updating local sequence alignments with an affine gap model. In principle, using the previous matching result between two amino acid sequences, we perform a forward-backward alignment to generate heuristic searching bands which are bounded by a set of suboptimal paths. Given a correctly updated sequence, we initially predict a new score of the alignment path for each contour to select the best candidates among them. Then, we run the Smith-Waterman algorithm in this confined space. Furthermore, our heuristic alignment for an updated sequence shows that it can be further accelerated by using reusable dynamic programming (rDP), our prior work. In this study, we successfully validate "relative node tolerance bound" (RNTB) in the pruned searching space. Furthermore, we improve the computational performance by quantifying the successful RNTB tolerance probability and switch to rDP on perturbation-resilient columns only. In our searching space derived by a threshold value of 90 percent of the optimal alignment score, we find that 98.3 percent of contours contain correctly updated paths. We also find that our method consumes only 25.36 percent of the runtime cost of sparse dynamic programming (sDP) method, and to only 2.55 percent of that of a normal dynamic programming with the Smith-Waterman algorithm.

Adaptation of clinical prediction models for application in local settings.

PubMed

Kappen, Teus H; Vergouwe, Yvonne; van Klei, Wilton A; van Wolfswinkel, Leo; Kalkman, Cor J; Moons, Karel G M

2012-01-01

When planning to use a validated prediction model in new patients, adequate performance is not guaranteed. For example, changes in clinical practice over time or a different case mix than the original validation population may result in inaccurate risk predictions. To demonstrate how clinical information can direct updating a prediction model and development of a strategy for handling missing predictor values in clinical practice. A previously derived and validated prediction model for postoperative nausea and vomiting was updated using a data set of 1847 patients. The update consisted of 1) changing the definition of an existing predictor, 2) reestimating the regression coefficient of a predictor, and 3) adding a new predictor to the model. The updated model was then validated in a new series of 3822 patients. Furthermore, several imputation models were considered to handle real-time missing values, so that possible missing predictor values could be anticipated during actual model use. Differences in clinical practice between our local population and the original derivation population guided the update strategy of the prediction model. The predictive accuracy of the updated model was better (c statistic, 0.68; calibration slope, 1.0) than the original model (c statistic, 0.62; calibration slope, 0.57). Inclusion of logistical variables in the imputation models, besides observed patient characteristics, contributed to a strategy to deal with missing predictor values at the time of risk calculation. Extensive knowledge of local, clinical processes provides crucial information to guide the process of adapting a prediction model to new clinical practices.

Probiotics as beneficial microbes in aquaculture: an update on their multiple modes of action: a review.

PubMed

Zorriehzahra, Mohammad Jalil; Delshad, Somayeh Torabi; Adel, Milad; Tiwari, Ruchi; Karthik, K; Dhama, Kuldeep; Lazado, Carlo C

2016-12-01

Wide and discriminate use of antibiotics has resulted in serious biological and ecological concerns, especially the emergence of antibiotic resistance. Probiotics, known as beneficial microbes, are being proposed as an effective and eco-friendly alternative to antibiotics. They were first applied in aquaculture species more than three decades ago, but considerable attention had been given only in the early 2000s. Probiotics are defined as live or dead, or even a component of the microorganisms that act under different modes of action in conferring beneficial effects to the host or to its environment. Several probiotics have been characterized and applied in fish and a number of them are of host origin. Unlike some disease control alternatives being adapted and proposed in aquaculture where actions are unilateral, the immense potential of probiotics lies on their multiple mechanisms in conferring benefits to the host fish and the rearing environment. The staggering number of probiotics papers in aquaculture highlights the multitude of advantages from these microorganisms and conspicuously position them in the dynamic search for health-promoting alternatives for cultured fish. This paper provides an update on the use of probiotics in finfish aquaculture, particularly focusing on their modes of action. It explores the contemporary understanding of their spatial and nutritional competitiveness, inhibitory metabolites, environmental modification capability, immunomodulatory potential and stress-alleviating mechanism. This timely update affirms the importance of probiotics in fostering sustainable approaches in aquaculture and provides avenues in furthering its research and development.

One Decade Later: KF Canadian Adaptation Scheme.

ERIC Educational Resources Information Center

Rashid, H.

1984-01-01

Provides background and rationale for formulation and use of the KF Canadian Adaptation Scheme in Canadian law libraries and describes methodological approaches and applications of the scheme to diverse and specific situations. Recent developments in its maintenance and updating and suggestions for its potential use are highlighted. (EJS)

Face Adaptation and Attractiveness Aftereffects in 8-Year-Olds and Adults

ERIC Educational Resources Information Center

Anzures, Gizelle; Mondloch, Catherine J.; Lackner, Christine

2009-01-01

A novel method was used to investigate developmental changes in face processing: attractiveness aftereffects. Consistent with the norm-based coding model, viewing consistently distorted faces shifts adults' attractiveness preferences toward the adapting stimuli. Thus, adults' attractiveness judgments are influenced by a continuously updated face…

Brief Report: Adaptive Behavior and Cognitive Skills for Toddlers on the Autism Spectrum

ERIC Educational Resources Information Center

Ray-Subramanian, Corey E.; Huai, Nan; Weismer, Susan Ellis

2011-01-01

This study examined adaptive behavior and cognitive skills for 125 toddlers on the autism spectrum using the recently updated Vineland-II and Bayley-III. Delays in adaptive skills were apparent at 2 years of age. As a group, toddlers on the autism spectrum had a profile of Vineland-II standard scores in which Motor Skills greater than Daily Living…

The Voronoi spatio-temporal data structure

NASA Astrophysics Data System (ADS)

Mioc, Darka

2002-04-01

Current GIS models cannot integrate the temporal dimension of spatial data easily. Indeed, current GISs do not support incremental (local) addition and deletion of spatial objects, and they can not support the temporal evolution of spatial data. Spatio-temporal facilities would be very useful in many GIS applications: harvesting and forest planning, cadastre, urban and regional planning, and emergency planning. The spatio-temporal model that can overcome these problems is based on a topological model---the Voronoi data structure. Voronoi diagrams are irregular tessellations of space, that adapt to spatial objects and therefore they are a synthesis of raster and vector spatial data models. The main advantage of the Voronoi data structure is its local and sequential map updates, which allows us to automatically record each event and performed map updates within the system. These map updates are executed through map construction commands that are composed of atomic actions (geometric algorithms for addition, deletion, and motion of spatial objects) on the dynamic Voronoi data structure. The formalization of map commands led to the development of a spatial language comprising a set of atomic operations or constructs on spatial primitives (points and lines), powerful enough to define the complex operations. This resulted in a new formal model for spatio-temporal change representation, where each update is uniquely characterized by the numbers of newly created and inactivated Voronoi regions. This is used for the extension of the model towards the hierarchical Voronoi data structure. In this model, spatio-temporal changes induced by map updates are preserved in a hierarchical data structure that combines events and corresponding changes in topology. This hierarchical Voronoi data structure has an implicit time ordering of events visible through changes in topology, and it is equivalent to an event structure that can support temporal data without precise temporal information. This formal model of spatio-temporal change representation is currently applied to retroactive map updates and visualization of map evolution. It offers new possibilities in the domains of temporal GIS, transaction processing, spatio-temporal queries, spatio-temporal analysis, map animation and map visualization.

Space Launch System Implementation of Adaptive Augmenting Control

NASA Technical Reports Server (NTRS)

VanZwieten, Tannen S.; Wall, John H.; Orr, Jeb S.

2014-01-01

Given the complex structural dynamics, challenging ascent performance requirements, and rigorous flight certification constraints owing to its manned capability, the NASA Space Launch System (SLS) launch vehicle requires a proven thrust vector control algorithm design with highly optimized parameters to robustly demonstrate stable and high performance flight. On its development path to preliminary design review (PDR), the stability of the SLS flight control system has been challenged by significant vehicle flexibility, aerodynamics, and sloshing propellant dynamics. While the design has been able to meet all robust stability criteria, it has done so with little excess margin. Through significant development work, an adaptive augmenting control (AAC) algorithm previously presented by Orr and VanZwieten, has been shown to extend the envelope of failures and flight anomalies for which the SLS control system can accommodate while maintaining a direct link to flight control stability criteria (e.g. gain & phase margin). In this paper, the work performed to mature the AAC algorithm as a baseline component of the SLS flight control system is presented. The progress to date has brought the algorithm design to the PDR level of maturity. The algorithm has been extended to augment the SLS digital 3-axis autopilot, including existing load-relief elements, and necessary steps for integration with the production flight software prototype have been implemented. Several updates to the adaptive algorithm to increase its performance, decrease its sensitivity to expected external commands, and safeguard against limitations in the digital implementation are discussed with illustrating results. Monte Carlo simulations and selected stressing case results are shown to demonstrate the algorithm's ability to increase the robustness of the integrated SLS flight control system.

Rugged Metropolis sampling with simultaneous updating of two dynamical variables

NASA Astrophysics Data System (ADS)

Berg, Bernd A.; Zhou, Huan-Xiang

2005-07-01

The rugged Metropolis (RM) algorithm is a biased updating scheme which aims at directly hitting the most likely configurations in a rugged free-energy landscape. Details of the one-variable (RM1) implementation of this algorithm are presented. This is followed by an extension to simultaneous updating of two dynamical variables (RM2) . In a test with the brain peptide Met-Enkephalin in vacuum RM2 improves conventional Metropolis simulations by a factor of about 4. Correlations between three or more dihedral angles appear to prevent larger improvements at low temperatures. We also investigate a multihit Metropolis scheme, which spends more CPU time on variables with large autocorrelation times.

Kalman Filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry.

PubMed

Zhang, Yuxin; Chen, Shuo; Deng, Kexin; Chen, Bingyao; Wei, Xing; Yang, Jiafei; Wang, Shi; Ying, Kui

2017-01-01

To develop a self-adaptive and fast thermometry method by combining the original hybrid magnetic resonance thermometry method and the bio heat transfer equation (BHTE) model. The proposed Kalman filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry, abbreviated as KalBHT hybrid method, introduced the BHTE model to synthesize a window on the regularization term of the hybrid algorithm, which leads to a self-adaptive regularization both spatially and temporally with change of temperature. Further, to decrease the sensitivity to accuracy of the BHTE model, Kalman filter is utilized to update the window at each iteration time. To investigate the effect of the proposed model, computer heating simulation, phantom microwave heating experiment and dynamic in-vivo model validation of liver and thoracic tumor were conducted in this study. The heating simulation indicates that the KalBHT hybrid algorithm achieves more accurate results without adjusting λ to a proper value in comparison to the hybrid algorithm. The results of the phantom heating experiment illustrate that the proposed model is able to follow temperature changes in the presence of motion and the temperature estimated also shows less noise in the background and surrounding the hot spot. The dynamic in-vivo model validation with heating simulation demonstrates that the proposed model has a higher convergence rate, more robustness to susceptibility problem surrounding the hot spot and more accuracy of temperature estimation. In the healthy liver experiment with heating simulation, the RMSE of the hot spot of the proposed model is reduced to about 50% compared to the RMSE of the original hybrid model and the convergence time becomes only about one fifth of the hybrid model. The proposed model is able to improve the accuracy of the original hybrid algorithm and accelerate the convergence rate of MR temperature estimation.

Link Climate Effects to Surface Water Quality and Drinking Water Plant Adaptation - A Update on Hydroclimatic Province and WTP-ccam Model

EPA Science Inventory

Key points in this presentation are: (1) How and why hydroclimatic province can help precipitation projection for water program engineering and management, (2) Implications of initial research results and planned further monitoring / research activities, (3) Five adaptation t...

76 FR 584 - Glen Canyon Dam Adaptive Management Program Work Group (AMWG)

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-05

... 2010 expenditures, (2) updates on High Flow Experimental Protocol and the Non-native Fish Control... Group (AMWG) AGENCY: Bureau of Reclamation, Interior. ACTION: Notice of public meeting. SUMMARY: The... committee, the Adaptive Management Work Group (AMWG), a technical work group (TWG), a Grand Canyon...

Time course of dynamic range adaptation in the auditory nerve

PubMed Central

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

2012-01-01

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

Behavior of Collective Cooperation Yielded by Two Update Rules in Social Dilemmas: Combining Fermi and Moran Rules

NASA Astrophysics Data System (ADS)

Xia, Cheng-Yi; Wang, Lei; Wang, Juan; Wang, Jin-Song

2012-09-01

We combine the Fermi and Moran update rules in the spatial prisoner's dilemma and snowdrift games to investigate the behavior of collective cooperation among agents on the regular lattice. Large-scale simulations indicate that, compared to the model with only one update rule, the cooperation behavior exhibits the richer phenomena, and the role of update dynamics should be paid more attention in the evolutionary game theory. Meanwhile, we also observe that the introduction of Moran rule, which needs to consider all neighbor's information, can markedly promote the aggregate cooperation level, that is, randomly selecting the neighbor proportional to its payoff to imitate will facilitate the cooperation among agents. Current results will contribute to further understand the cooperation dynamics and evolutionary behaviors within many biological, economic and social systems.

SIDECACHE: Information access, management and dissemination framework for web services.

PubMed

Doderer, Mark S; Burkhardt, Cory; Robbins, Kay A

2011-06-14

Many bioinformatics algorithms and data sets are deployed using web services so that the results can be explored via the Internet and easily integrated into other tools and services. These services often include data from other sites that is accessed either dynamically or through file downloads. Developers of these services face several problems because of the dynamic nature of the information from the upstream services. Many publicly available repositories of bioinformatics data frequently update their information. When such an update occurs, the developers of the downstream service may also need to update. For file downloads, this process is typically performed manually followed by web service restart. Requests for information obtained by dynamic access of upstream sources is sometimes subject to rate restrictions. SideCache provides a framework for deploying web services that integrate information extracted from other databases and from web sources that are periodically updated. This situation occurs frequently in biotechnology where new information is being continuously generated and the latest information is important. SideCache provides several types of services including proxy access and rate control, local caching, and automatic web service updating. We have used the SideCache framework to automate the deployment and updating of a number of bioinformatics web services and tools that extract information from remote primary sources such as NCBI, NCIBI, and Ensembl. The SideCache framework also has been used to share research results through the use of a SideCache derived web service.

Just-in-time adaptive classifiers-part II: designing the classifier.

PubMed

Alippi, Cesare; Roveri, Manuel

2008-12-01

Aging effects, environmental changes, thermal drifts, and soft and hard faults affect physical systems by changing their nature and behavior over time. To cope with a process evolution adaptive solutions must be envisaged to track its dynamics; in this direction, adaptive classifiers are generally designed by assuming the stationary hypothesis for the process generating the data with very few results addressing nonstationary environments. This paper proposes a methodology based on k-nearest neighbor (NN) classifiers for designing adaptive classification systems able to react to changing conditions just-in-time (JIT), i.e., exactly when it is needed. k-NN classifiers have been selected for their computational-free training phase, the possibility to easily estimate the model complexity k and keep under control the computational complexity of the classifier through suitable data reduction mechanisms. A JIT classifier requires a temporal detection of a (possible) process deviation (aspect tackled in a companion paper) followed by an adaptive management of the knowledge base (KB) of the classifier to cope with the process change. The novelty of the proposed approach resides in the general framework supporting the real-time update of the KB of the classification system in response to novel information coming from the process both in stationary conditions (accuracy improvement) and in nonstationary ones (process tracking) and in providing a suitable estimate of k. It is shown that the classification system grants consistency once the change targets the process generating the data in a new stationary state, as it is the case in many real applications.

Uncertainty aggregation and reduction in structure-material performance prediction

NASA Astrophysics Data System (ADS)

Hu, Zhen; Mahadevan, Sankaran; Ao, Dan

2018-02-01

An uncertainty aggregation and reduction framework is presented for structure-material performance prediction. Different types of uncertainty sources, structural analysis model, and material performance prediction model are connected through a Bayesian network for systematic uncertainty aggregation analysis. To reduce the uncertainty in the computational structure-material performance prediction model, Bayesian updating using experimental observation data is investigated based on the Bayesian network. It is observed that the Bayesian updating results will have large error if the model cannot accurately represent the actual physics, and that this error will be propagated to the predicted performance distribution. To address this issue, this paper proposes a novel uncertainty reduction method by integrating Bayesian calibration with model validation adaptively. The observation domain of the quantity of interest is first discretized into multiple segments. An adaptive algorithm is then developed to perform model validation and Bayesian updating over these observation segments sequentially. Only information from observation segments where the model prediction is highly reliable is used for Bayesian updating; this is found to increase the effectiveness and efficiency of uncertainty reduction. A composite rotorcraft hub component fatigue life prediction model, which combines a finite element structural analysis model and a material damage model, is used to demonstrate the proposed method.

First Update of the Criteria for Certification of Chest Pain Units in Germany: Facelift or New Model?

PubMed

Breuckmann, Frank; Rassaf, Tienush

2016-03-01

In an effort to provide a systematic and specific standard-of-care for patients with acute chest pain, the German Cardiac Society introduced criteria for certification of specialized chest pain units (CPUs) in 2008, which have been replaced by a recent update published in 2015. We reviewed the development of CPU establishment in Germany during the past 7 years and compared and commented the current update of the certification criteria. As of October 2015, 228 CPUs in Germany have been successfully certified by the German Cardiac Society; 300 CPUs are needed for full coverage closing gaps in rural regions. Current changes of the criteria mainly affect guideline-adherent adaptions of diagnostic work-ups, therapeutic strategies, risk stratification, in-hospital timing and education, and quality measures, whereas the overall structure remained unchanged. Benchmarking by participation within the German CPU registry is encouraged. Even though the history is short, the concept of certified CPUs in Germany is accepted and successful underlined by its recent implementation in national and international guidelines. First registry data demonstrated a high standard of quality-of-care. The current update provides rational adaptions to new guidelines and developments without raising the level for successful certifications. A periodic release of fast-track updates with shorter time frames and an increase of minimum requirements should be considered.

Fast Deep Tracking via Semi-Online Domain Adaptation

NASA Astrophysics Data System (ADS)

Li, Xiaoping; Luo, Wenbing; Zhu, Yi; Li, Hanxi; Wang, Mingwen

2018-04-01

Deep tracking has been illustrating overwhelming superiorities over the shallow methods. Unfortunately, it also suffers from low FPS rates. To alleviate the problem, a number of real-time deep trackers have been proposed via removing the online updating procedure on the CNN model. However, the absent of the online update leads to a significant drop on tracking accuracy. In this work, we propose to perform the domain adaptation for visual tracking in two stages for transferring the information from the visual tracking domain and the instance domain respectively. In this way, the proposed visual tracker achieves comparable tracking accuracy to the state-of-the-art trackers and runs at real-time speed on an average consuming GPU.

Linearly scaling and almost Hamiltonian dielectric continuum molecular dynamics simulations through fast multipole expansions

NASA Astrophysics Data System (ADS)

Lorenzen, Konstantin; Mathias, Gerald; Tavan, Paul

2015-11-01

Hamiltonian Dielectric Solvent (HADES) is a recent method [S. Bauer et al., J. Chem. Phys. 140, 104103 (2014)] which enables atomistic Hamiltonian molecular dynamics (MD) simulations of peptides and proteins in dielectric solvent continua. Such simulations become rapidly impractical for large proteins, because the computational effort of HADES scales quadratically with the number N of atoms. If one tries to achieve linear scaling by applying a fast multipole method (FMM) to the computation of the HADES electrostatics, the Hamiltonian character (conservation of total energy, linear, and angular momenta) may get lost. Here, we show that the Hamiltonian character of HADES can be almost completely preserved, if the structure-adapted fast multipole method (SAMM) as recently redesigned by Lorenzen et al. [J. Chem. Theory Comput. 10, 3244-3259 (2014)] is suitably extended and is chosen as the FMM module. By this extension, the HADES/SAMM forces become exact gradients of the HADES/SAMM energy. Their translational and rotational invariance then guarantees (within the limits of numerical accuracy) the exact conservation of the linear and angular momenta. Also, the total energy is essentially conserved—up to residual algorithmic noise, which is caused by the periodically repeated SAMM interaction list updates. These updates entail very small temporal discontinuities of the force description, because the employed SAMM approximations represent deliberately balanced compromises between accuracy and efficiency. The energy-gradient corrected version of SAMM can also be applied, of course, to MD simulations of all-atom solvent-solute systems enclosed by periodic boundary conditions. However, as we demonstrate in passing, this choice does not offer any serious advantages.

Functional Dissociation of Latency-Variable, Stimulus- and Response-Locked Target P3 Sub-components in Task-Switching.

PubMed

Brydges, Christopher R; Barceló, Francisco

2018-01-01

Cognitive control warrants efficient task performance in dynamic and changing environments through adjustments in executive attention, stimulus and response selection. The well-known P300 component of the human event-related potential (ERP) has long been proposed to index "context-updating"-critical for cognitive control-in simple target detection tasks. However, task switching ERP studies have revealed both target P3 (300-350 ms) and later sustained P3-like potentials (400-1,200 ms) to first targets ensuing transition cues, although it remains unclear whether these target P3-like potentials also reflect context updating operations. To address this question, we applied novel single-trial EEG analyses-residue iteration decomposition (RIDE)-in order to disentangle target P3 sub-components in a sample of 22 young adults while they either repeated or switched (updated) task rules. The rationale was to revise the context updating hypothesis of P300 elicitation in the light of new evidence suggesting that "the context" consists of not only the sensory units of stimulation, but also associated motor units, and intermediate low- and high-order sensorimotor units, all of which may need to be dynamically updated on a trial by trial basis. The results showed functionally distinct target P3-like potentials in stimulus-locked, response-locked, and intermediate RIDE component clusters overlying parietal and frontal regions, implying multiple functionally distinct, though temporarily overlapping context updating operations. These findings support a reformulated version of the context updating hypothesis, and reveal a rich family of distinct target P3-like sub-components during the reactive control of target detection in task-switching, plausibly indexing the complex and dynamic workings of frontoparietal cortical networks subserving cognitive control.

Industrial Computer Codes

NASA Technical Reports Server (NTRS)

Shapiro, Wilbur

1996-01-01

This is an overview of new and updated industrial codes for seal design and testing. GCYLT (gas cylindrical seals -- turbulent), SPIRALI (spiral-groove seals -- incompressible), KTK (knife to knife) Labyrinth Seal Code, and DYSEAL (dynamic seal analysis) are covered. CGYLT uses G-factors for Poiseuille and Couette turbulence coefficients. SPIRALI is updated to include turbulence and inertia, but maintains the narrow groove theory. KTK labyrinth seal code handles straight or stepped seals. And DYSEAL provides dynamics for the seal geometry.

[An update of the obstetrics hemorrhage treatment protocol].

PubMed

Morillas-Ramírez, F; Ortiz-Gómez, J R; Palacio-Abizanda, F J; Fornet-Ruiz, I; Pérez-Lucas, R; Bermejo-Albares, L

2014-04-01

Obstetric hemorrhage is still a major cause of maternal and fetal morbimortality in developed countries. This is an underestimated problem, which usually appears unpredictably. A high proportion of the morbidity of obstetric hemorrhage is considered to be preventable if adequately managed. The major international clinical guidelines recommend producing consensus management protocols, adapted to local characteristics and keep them updated in the light of experience and new scientific publications. We present a protocol updated, according to the latest recommendations, and our own experience, in order to be used as a basis for those anesthesiologists who wish to use and adapt it locally to their daily work. This last aspect is very important to be effective, and is a task to be performed at each center, according to the availability of resources, personnel and architectural features. Copyright © 2013 Sociedad Española de Anestesiología, Reanimación y Terapéutica del Dolor. Published by Elsevier España. All rights reserved.

Normal response function method for mass and stiffness matrix updating using complex FRFs

NASA Astrophysics Data System (ADS)

Pradhan, S.; Modak, S. V.

2012-10-01

Quite often a structural dynamic finite element model is required to be updated so as to accurately predict the dynamic characteristics like natural frequencies and the mode shapes. Since in many situations undamped natural frequencies and mode shapes need to be predicted, it has generally been the practice in these situations to seek updating of only mass and stiffness matrix so as to obtain a reliable prediction model. Updating using frequency response functions (FRFs) has been one of the widely used approaches for updating, including updating of mass and stiffness matrices. However, the problem with FRF based methods, for updating mass and stiffness matrices, is that these methods are based on use of complex FRFs. Use of complex FRFs to update mass and stiffness matrices is not theoretically correct as complex FRFs are not only affected by these two matrices but also by the damping matrix. Therefore, in situations where updating of only mass and stiffness matrices using FRFs is required, the use of complex FRFs based updating formulation is not fully justified and would lead to inaccurate updated models. This paper addresses this difficulty and proposes an improved FRF based finite element model updating procedure using the concept of normal FRFs. The proposed method is a modified version of the existing response function method that is based on the complex FRFs. The effectiveness of the proposed method is validated through a numerical study of a simple but representative beam structure. The effect of coordinate incompleteness and robustness of method under presence of noise is investigated. The results of updating obtained by the improved method are compared with the existing response function method. The performance of the two approaches is compared for cases of light, medium and heavily damped structures. It is found that the proposed improved method is effective in updating of mass and stiffness matrices in all the cases of complete and incomplete data and with all levels and types of damping.

Optimal tracking control for a class of nonlinear discrete-time systems with time delays based on heuristic dynamic programming.

PubMed

Zhang, Huaguang; Song, Ruizhuo; Wei, Qinglai; Zhang, Tieyan

2011-12-01

In this paper, a novel heuristic dynamic programming (HDP) iteration algorithm is proposed to solve the optimal tracking control problem for a class of nonlinear discrete-time systems with time delays. The novel algorithm contains state updating, control policy iteration, and performance index iteration. To get the optimal states, the states are also updated. Furthermore, the "backward iteration" is applied to state updating. Two neural networks are used to approximate the performance index function and compute the optimal control policy for facilitating the implementation of HDP iteration algorithm. At last, we present two examples to demonstrate the effectiveness of the proposed HDP iteration algorithm.

Computer animation challenges for computational fluid dynamics

NASA Astrophysics Data System (ADS)

Vines, Mauricio; Lee, Won-Sook; Mavriplis, Catherine

2012-07-01

Computer animation requirements differ from those of traditional computational fluid dynamics (CFD) investigations in that visual plausibility and rapid frame update rates trump physical accuracy. We present an overview of the main techniques for fluid simulation in computer animation, starting with Eulerian grid approaches, the Lattice Boltzmann method, Fourier transform techniques and Lagrangian particle introduction. Adaptive grid methods, precomputation of results for model reduction, parallelisation and computation on graphical processing units (GPUs) are reviewed in the context of accelerating simulation computations for animation. A survey of current specific approaches for the application of these techniques to the simulation of smoke, fire, water, bubbles, mixing, phase change and solid-fluid coupling is also included. Adding plausibility to results through particle introduction, turbulence detail and concentration on regions of interest by level set techniques has elevated the degree of accuracy and realism of recent animations. Basic approaches are described here. Techniques to control the simulation to produce a desired visual effect are also discussed. Finally, some references to rendering techniques and haptic applications are mentioned to provide the reader with a complete picture of the challenges of simulating fluids in computer animation.

Adaptive dynamic programming for discrete-time linear quadratic regulation based on multirate generalised policy iteration

NASA Astrophysics Data System (ADS)

Chun, Tae Yoon; Lee, Jae Young; Park, Jin Bae; Choi, Yoon Ho

2018-06-01

In this paper, we propose two multirate generalised policy iteration (GPI) algorithms applied to discrete-time linear quadratic regulation problems. The proposed algorithms are extensions of the existing GPI algorithm that consists of the approximate policy evaluation and policy improvement steps. The two proposed schemes, named heuristic dynamic programming (HDP) and dual HDP (DHP), based on multirate GPI, use multi-step estimation (M-step Bellman equation) at the approximate policy evaluation step for estimating the value function and its gradient called costate, respectively. Then, we show that these two methods with the same update horizon can be considered equivalent in the iteration domain. Furthermore, monotonically increasing and decreasing convergences, so called value iteration (VI)-mode and policy iteration (PI)-mode convergences, are proved to hold for the proposed multirate GPIs. Further, general convergence properties in terms of eigenvalues are also studied. The data-driven online implementation methods for the proposed HDP and DHP are demonstrated and finally, we present the results of numerical simulations performed to verify the effectiveness of the proposed methods.

Solvent effect on the folding dynamics and structure of E6-associated protein characterized from ab initio protein folding simulations

NASA Astrophysics Data System (ADS)

Xu, Zhijun; Lazim, Raudah; Sun, Tiedong; Mei, Ye; Zhang, Dawei

2012-04-01

Solvent effect on protein conformation and folding mechanism of E6-associated protein (E6ap) peptide are investigated using a recently developed charge update scheme termed as adaptive hydrogen bond-specific charge (AHBC). On the basis of the close agreement between the calculated helix contents from AHBC simulations and experimental results, we observed based on the presented simulations that the two ends of the peptide may simultaneously take part in the formation of the helical structure at the early stage of folding and finally merge to form a helix with lowest backbone RMSD of about 0.9 Å in 40% 2,2,2-trifluoroethanol solution. However, in pure water, the folding may start at the center of the peptide sequence instead of at the two opposite ends. The analysis of the free energy landscape indicates that the solvent may determine the folding clusters of E6ap, which subsequently leads to the different final folded structure. The current study demonstrates new insight to the role of solvent in the determination of protein structure and folding dynamics.

Adaptation and Assimilation: US Business Responses to Linguistic Diversity in the Workplace.

ERIC Educational Resources Information Center

Dicker, Susan J.

1998-01-01

Provides a historical overview of attitudes toward immigrants and their languages in the United States, gives an update of the Official English movement, and analyzes recent articles in business-related journals. Discussion centers on how businesses have adapted to a multilingual workforce and on the connection between the Official English…

Integrating Linguistic, Motor, and Perceptual Information in Language Production

ERIC Educational Resources Information Center

Frank, Austin F.

2011-01-01

Speakers show remarkable adaptability in updating and correcting their utterances in response to changes in the environment. When an interlocutor raises an eyebrow or the AC kicks on and introduces ambient noise, it seems that speakers are able to quickly integrate this information into their speech plans and adapt appropriately. This ability to…

Adaptive System Modeling for Spacecraft Simulation

NASA Technical Reports Server (NTRS)

Thomas, Justin

2011-01-01

This invention introduces a methodology and associated software tools for automatically learning spacecraft system models without any assumptions regarding system behavior. Data stream mining techniques were used to learn models for critical portions of the International Space Station (ISS) Electrical Power System (EPS). Evaluation on historical ISS telemetry data shows that adaptive system modeling reduces simulation error anywhere from 50 to 90 percent over existing approaches. The purpose of the methodology is to outline how someone can create accurate system models from sensor (telemetry) data. The purpose of the software is to support the methodology. The software provides analysis tools to design the adaptive models. The software also provides the algorithms to initially build system models and continuously update them from the latest streaming sensor data. The main strengths are as follows: Creates accurate spacecraft system models without in-depth system knowledge or any assumptions about system behavior. Automatically updates/calibrates system models using the latest streaming sensor data. Creates device specific models that capture the exact behavior of devices of the same type. Adapts to evolving systems. Can reduce computational complexity (faster simulations).

Low complexity adaptive equalizers for underwater acoustic communications

NASA Astrophysics Data System (ADS)

Soflaei, Masoumeh; Azmi, Paeiz

2014-08-01

Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm (APA), selective regressor APA(SR-APA), family of selective partial update (SPU) algorithms, family of set-membership (SM) algorithms and selective partial update selective regressor APA (SPU-SR-APA) are compared with conventional algorithms such as the least mean square (LMS) in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error (MSE) of SR-APA, SPU-APA, SPU-normalized least mean square (SPU-NLMS), SPU-SR-APA, SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.

Documentation of the dynamic parameter, water-use, stream and lake flow routing, and two summary output modules and updates to surface-depression storage simulation and initial conditions specification options with the Precipitation-Runoff Modeling System (PRMS)

USGS Publications Warehouse

Regan, R. Steve; LaFontaine, Jacob H.

2017-10-05

This report documents seven enhancements to the U.S. Geological Survey (USGS) Precipitation-Runoff Modeling System (PRMS) hydrologic simulation code: two time-series input options, two new output options, and three updates of existing capabilities. The enhancements are (1) new dynamic parameter module, (2) new water-use module, (3) new Hydrologic Response Unit (HRU) summary output module, (4) new basin variables summary output module, (5) new stream and lake flow routing module, (6) update to surface-depression storage and flow simulation, and (7) update to the initial-conditions specification. This report relies heavily upon U.S. Geological Survey Techniques and Methods, book 6, chapter B7, which documents PRMS version 4 (PRMS-IV). A brief description of PRMS is included in this report.

The neural dynamics of updating person impressions

PubMed Central

Cai, Yang; Todorov, Alexander

2013-01-01

Person perception is a dynamic, evolving process. Because other people are an endless source of social information, people need to update their impressions of others based upon new information. We devised an fMRI study to identify brain regions involved in updating impressions. Participants saw faces paired with valenced behavioral information and were asked to form impressions of these individuals. Each face was seen five times in a row, each time with a different behavioral description. Critically, for half of the faces the behaviors were evaluatively consistent, while for the other half they were inconsistent. In line with prior work, dorsomedial prefrontal cortex (dmPFC) was associated with forming impressions of individuals based on behavioral information. More importantly, a whole-brain analysis revealed a network of other regions associated with updating impressions of individuals who exhibited evaluatively inconsistent behaviors, including rostrolateral PFC, superior temporal sulcus, right inferior parietal lobule and posterior cingulate cortex. PMID:22490923

Behavioral Ecology of Captive Species: Using Bibliographic Information to Assess Pet Suitability of Mammal Species

PubMed Central

Koene, Paul; de Mol, Rudi M.; Ipema, Bert

2016-01-01

Which mammal species are suitable to be kept as pet? For answering this question many factors have to be considered. Animals have many adaptations to their natural environment in which they have evolved that may cause adaptation problems and/or risks in captivity. Problems may be visible in behavior, welfare, health, and/or human–animal interaction, resulting, for example, in stereotypies, disease, and fear. A framework is developed in which bibliographic information of mammal species from the wild and captive environment is collected and assessed by three teams of animal scientists. Oneliners from literature about behavioral ecology, health, and welfare and human–animal relationship of 90 mammal species are collected by team 1 in a database and strength of behavioral needs and risks is assessed by team 2. Based on summaries of those strengths the suitability of the mammal species is assessed by team 3. Involvement of stakeholders for supplying bibliographic information and assessments was propagated. Combining the individual and subjective assessments of the scientists using statistical methods makes the final assessment of a rank order of suitability as pet of those species less biased and more objective. The framework is dynamic and produces an initial rank ordered list of the pet suitability of 90 mammal species, methods to add new mammal species to the list or remove animals from the list and a method to incorporate stakeholder assessments. A model is developed that allows for provisional classification of pet suitability. Periodical update of the pet suitability framework is expected to produce an updated list with increased reliability and accuracy. Furthermore, the framework could be further developed to assess the pet suitability of additional species of other animal groups, e.g., birds, reptiles, and amphibians. PMID:27243023

Influence of warning information changes on emergency response

NASA Astrophysics Data System (ADS)

Heisterkamp, Tobias; Ulbrich, Uwe; Glade, Thomas; Tetzlaff, Gerd

2014-05-01

Mitigation and risk reduction of natural hazards is significantly related to the possibility of predicting the actual event. Some hazards can already be forecasted several days in advance. For these hazards, early warning systems have been developed, installed and improved over the years. The formation of winter storms for example can be recognized up to one week before they pass through Central Europe. This relative long early warning time has the advantage that forecasters can concretise the warnings over time. Therefore, warnings can even be adapted to alternating conditions within the process, the observation or changes in its modelling. Emergency managers are one group of warning recipients in the civil protection sector. They have to prepare or initiate prevention or response measures at a specific point of time, depending on the required lead time of the referring actions. At this point of time already, the forecast and its equivalent warning, has to be assumed as a stage of reality, hence the decision-makers have to come to a conclusion. These decisions are based on spatial and temporal knowledge of the forecasted event and the consequential situation of risk. With incoming warning updates, the detailed status of information is permanently being alternated. Consequently, decisions can be influenced by the development of the warning situation and the inherent tendency before a certain point of time. They can also be adapted to updates later on, according to the changing 'decision reality'. The influence of these dynamic hazard situations on operational planning and response by emergency managers is investigated in case studies on winter storms for Berlin, Germany. Therefore, the issued warnings by the weather service and data of operation of Berlin Fire Brigades are analysed and compared. This presentation shows and discusses first results.

Embracing Change: Adapting and Evolving Your Distance Learning Library Services to Meet the New ACRL Distance Learning Library Services Standards

ERIC Educational Resources Information Center

Marcum, Brad

2016-01-01

This article examines the update and revision of the current Association of College and Research Libraries (ACRL) Distance Learning Standards that has been proposed and submitted to the ACRL Standards Committee. An in-depth analysis of the update is included, along with some comparisons between the old and new. Practical advice detailing…

Feedback Blunting: Total Sleep Deprivation Impairs Decision Making that Requires Updating Based on Feedback

PubMed Central

Whitney, Paul; Hinson, John M.; Jackson, Melinda L.; Van Dongen, Hans P.A.

2015-01-01

Study Objectives: To better understand the sometimes catastrophic effects of sleep loss on naturalistic decision making, we investigated effects of sleep deprivation on decision making in a reversal learning paradigm requiring acquisition and updating of information based on outcome feedback. Design: Subjects were randomized to a sleep deprivation or control condition, with performance testing at baseline, after 2 nights of total sleep deprivation (or rested control), and following 2 nights of recovery sleep. Subjects performed a decision task involving initial learning of go and no go response sets followed by unannounced reversal of contingencies, requiring use of outcome feedback for decisions. A working memory scanning task and psychomotor vigilance test were also administered. Setting: Six consecutive days and nights in a controlled laboratory environment with continuous behavioral monitoring. Subjects: Twenty-six subjects (22–40 y of age; 10 women). Interventions: Thirteen subjects were randomized to a 62-h total sleep deprivation condition; the others were controls. Results: Unlike controls, sleep deprived subjects had difficulty with initial learning of go and no go stimuli sets and had profound impairment adapting to reversal. Skin conductance responses to outcome feedback were diminished, indicating blunted affective reactions to feedback accompanying sleep deprivation. Working memory scanning performance was not significantly affected by sleep deprivation. And although sleep deprived subjects showed expected attentional lapses, these could not account for impairments in reversal learning decision making. Conclusions: Sleep deprivation is particularly problematic for decision making involving uncertainty and unexpected change. Blunted reactions to feedback while sleep deprived underlie failures to adapt to uncertainty and changing contingencies. Thus, an error may register, but with diminished effect because of reduced affective valence of the feedback or because the feedback is not cognitively bound with the choice. This has important implications for understanding and managing sleep loss-induced cognitive impairment in emergency response, disaster management, military operations, and other dynamic real-world settings with uncertain outcomes and imperfect information. Citation: Whitney P, Hinson JM, Jackson ML, Van Dongen HPA. Feedback blunting: total sleep deprivation impairs decision making that requires updating based on feedback. SLEEP 2015;38(5):745–754. PMID:25515105

Improved National Response to Climate Change: Aligning USGCRP reports and the U.S. Climate Resilience Toolkit

NASA Astrophysics Data System (ADS)

Lipschultz, F.; Dahlman, L. E.; Herring, D.; Fox, J. F.

2017-12-01

As part of an effort to coordinate production and distribution of scientific climate information across the U.S. Government, and to spur adaptation actions across the nation, the U.S. Global Change Research Program (USGCRP) has worked to better integrate the U.S. Climate Resilience Toolkit (CRT) and its Climate Explorer (CE) tool into USGCRP activities and products. Much of the initial CRT content was based on the Third National Climate Assessment (NCA3). The opportunity to integrate current development of NCA4—scheduled for release in late 2018—with CRT and CE can enhance all three projects and result in a useable and "living" NCA that is part of USGCRP's approach to sustained climate assessment. To coordinate this work, a USGCRP-led science team worked with CRT staff and CE developers to update the set of climate projections displayed in the CE tool. In concert with the USGCRP scenarios effort, the combined team selected the Localized Constructed Analogs (LOCA) dataset for the updated version of CE, based on its capabilities for capturing climate extremes and local climate variations. The team identified 28 variables from the LOCA dataset for display in the CE; many of these variables will also be used in USGCRP reports. In CRT engagements, communities with vulnerable assets have expressed a high value for the ability to integrate climate data available through the CE with data related to non-climate stressors in their locations. Moving forward, the teams intend to serve climate information needs at additional spatial scales by making NCA4 content available via CE's capability for dynamic interaction with climate-relevant datasets. This will permit users to customize the extent of data they access for decision-making, starting with the static NCA4 report. Additionally, NCA4 case studies and other content can be linked to more in-depth content within the CRT site. This capability will enable more frequent content updates than can be managed with quadrennial NCA reports. Overall, enhanced integration between USGCRP and CRT will provide consistent information for communities that are assessing their climate vulnerabilities or considering adaptation options.

Update On the Status of the FLUKA Monte Carlo Transport Code*

NASA Technical Reports Server (NTRS)

Ferrari, A.; Lorenzo-Sentis, M.; Roesler, S.; Smirnov, G.; Sommerer, F.; Theis, C.; Vlachoudis, V.; Carboni, M.; Mostacci, A.; Pelliccioni, M.

2006-01-01

The FLUKA Monte Carlo transport code is a well-known simulation tool in High Energy Physics. FLUKA is a dynamic tool in the sense that it is being continually updated and improved by the authors. We review the progress achieved since the last CHEP Conference on the physics models, some technical improvements to the code and some recent applications. From the point of view of the physics, improvements have been made with the extension of PEANUT to higher energies for p, n, pi, pbar/nbar and for nbars down to the lowest energies, the addition of the online capability to evolve radioactive products and get subsequent dose rates, upgrading of the treatment of EM interactions with the elimination of the need to separately prepare preprocessed files. A new coherent photon scattering model, an updated treatment of the photo-electric effect, an improved pair production model, new photon cross sections from the LLNL Cullen database have been implemented. In the field of nucleus-- nucleus interactions the electromagnetic dissociation of heavy ions has been added along with the extension of the interaction models for some nuclide pairs to energies below 100 MeV/A using the BME approach, as well as the development of an improved QMD model for intermediate energies. Both DPMJET 2.53 and 3 remain available along with rQMD 2.4 for heavy ion interactions above 100 MeV/A. Technical improvements include the ability to use parentheses in setting up the combinatorial geometry, the introduction of pre-processor directives in the input stream. a new random number generator with full 64 bit randomness, new routines for mathematical special functions (adapted from SLATEC). Finally, work is progressing on the deployment of a user-friendly GUI input interface as well as a CAD-like geometry creation and visualization tool. On the application front, FLUKA has been used to extensively evaluate the potential space radiation effects on astronauts for future deep space missions, the activation dose for beam target areas, dose calculations for radiation therapy as well as being adapted for use in the simulation of events in the ALICE detector at the LHC.

Application of firefly algorithm to the dynamic model updating problem

NASA Astrophysics Data System (ADS)

Shabbir, Faisal; Omenzetter, Piotr

2015-04-01

Model updating can be considered as a branch of optimization problems in which calibration of the finite element (FE) model is undertaken by comparing the modal properties of the actual structure with these of the FE predictions. The attainment of a global solution in a multi dimensional search space is a challenging problem. The nature-inspired algorithms have gained increasing attention in the previous decade for solving such complex optimization problems. This study applies the novel Firefly Algorithm (FA), a global optimization search technique, to a dynamic model updating problem. This is to the authors' best knowledge the first time FA is applied to model updating. The working of FA is inspired by the flashing characteristics of fireflies. Each firefly represents a randomly generated solution which is assigned brightness according to the value of the objective function. The physical structure under consideration is a full scale cable stayed pedestrian bridge with composite bridge deck. Data from dynamic testing of the bridge was used to correlate and update the initial model by using FA. The algorithm aimed at minimizing the difference between the natural frequencies and mode shapes of the structure. The performance of the algorithm is analyzed in finding the optimal solution in a multi dimensional search space. The paper concludes with an investigation of the efficacy of the algorithm in obtaining a reference finite element model which correctly represents the as-built original structure.

An updated evolutionary classification of CRISPR–Cas systems

PubMed Central

Makarova, Kira S.; Wolf, Yuri I.; Alkhnbashi, Omer S.; Costa, Fabrizio; Shah, Shiraz A.; Saunders, Sita J.; Barrangou, Rodolphe; Brouns, Stan J. J.; Charpentier, Emmanuelle; Haft, Daniel H.; Horvath, Philippe; Moineau, Sylvain; Mojica, Francisco J. M.; Terns, Rebecca M.; Terns, Michael P.; White, Malcolm F.; Yakunin, Alexander F.; Garrett, Roger A.; van der Oost, John; Backofen, Rolf; Koonin, Eugene V.

2017-01-01

The evolution of CRISPR–cas loci, which encode adaptive immune systems in archaea and bacteria, involves rapid changes, in particular numerous rearrangements of the locus architecture and horizontal transfer of complete loci or individual modules. These dynamics complicate straightforward phylogenetic classification, but here we present an approach combining the analysis of signature protein families and features of the architecture of cas loci that unambiguously partitions most CRISPR–cas loci into distinct classes, types and subtypes. The new classification retains the overall structure of the previous version but is expanded to now encompass two classes, five types and 16 subtypes. The relative stability of the classification suggests that the most prevalent variants of CRISPR–Cas systems are already known. However, the existence of rare, currently unclassifiable variants implies that additional types and subtypes remain to be characterized. PMID:26411297

Dual-thread parallel control strategy for ophthalmic adaptive optics.

PubMed

Yu, Yongxin; Zhang, Yuhua

To improve ophthalmic adaptive optics speed and compensate for ocular wavefront aberration of high temporal frequency, the adaptive optics wavefront correction has been implemented with a control scheme including 2 parallel threads; one is dedicated to wavefront detection and the other conducts wavefront reconstruction and compensation. With a custom Shack-Hartmann wavefront sensor that measures the ocular wave aberration with 193 subapertures across the pupil, adaptive optics has achieved a closed loop updating frequency up to 110 Hz, and demonstrated robust compensation for ocular wave aberration up to 50 Hz in an adaptive optics scanning laser ophthalmoscope.

Dual-thread parallel control strategy for ophthalmic adaptive optics

PubMed Central

Yu, Yongxin; Zhang, Yuhua

2015-01-01

To improve ophthalmic adaptive optics speed and compensate for ocular wavefront aberration of high temporal frequency, the adaptive optics wavefront correction has been implemented with a control scheme including 2 parallel threads; one is dedicated to wavefront detection and the other conducts wavefront reconstruction and compensation. With a custom Shack-Hartmann wavefront sensor that measures the ocular wave aberration with 193 subapertures across the pupil, adaptive optics has achieved a closed loop updating frequency up to 110 Hz, and demonstrated robust compensation for ocular wave aberration up to 50 Hz in an adaptive optics scanning laser ophthalmoscope. PMID:25866498

On the adaptive daily forecasting of seismic aftershock hazard

NASA Astrophysics Data System (ADS)

Ebrahimian, Hossein; Jalayer, Fatemeh; Asprone, Domenico; Lombardi, Anna Maria; Marzocchi, Warner; Prota, Andrea; Manfredi, Gaetano

2013-04-01

Post-earthquake ground motion hazard assessment is a fundamental initial step towards time-dependent seismic risk assessment for buildings in a post main-shock environment. Therefore, operative forecasting of seismic aftershock hazard forms a viable support basis for decision-making regarding search and rescue, inspection, repair, and re-occupation in a post main-shock environment. Arguably, an adaptive procedure for integrating the aftershock occurrence rate together with suitable ground motion prediction relations is key to Probabilistic Seismic Aftershock Hazard Assessment (PSAHA). In the short-term, the seismic hazard may vary significantly (Jordan et al., 2011), particularly after the occurrence of a high magnitude earthquake. Hence, PSAHA requires a reliable model that is able to track the time evolution of the earthquake occurrence rates together with suitable ground motion prediction relations. This work focuses on providing adaptive daily forecasts of the mean daily rate of exceeding various spectral acceleration values (the aftershock hazard). Two well-established earthquake occurrence models suitable for daily seismicity forecasts associated with the evolution of an aftershock sequence, namely, the modified Omori's aftershock model and the Epidemic Type Aftershock Sequence (ETAS) are adopted. The parameters of the modified Omori model are updated on a daily basis using Bayesian updating and based on the data provided by the ongoing aftershock sequence based on the methodology originally proposed by Jalayer et al. (2011). The Bayesian updating is used also to provide sequence-based parameter estimates for a given ground motion prediction model, i.e. the aftershock events in an ongoing sequence are exploited in order to update in an adaptive manner the parameters of an existing ground motion prediction model. As a numerical example, the mean daily rates of exceeding specific spectral acceleration values are estimated adaptively for the L'Aquila 2009 aftershock catalog. The parameters of the modified Omori model are estimated in an adaptive manner using the Bayesian updating based on the aftershock events that had already taken place at each day elapsed and using the Italian generic sequence (Lolli and Gasperini 2003) as prior information. For the ETAS model, the real-time daily forecast of the spatio-temporal evolution of the L'Aquila sequence provided for the Italian Civil Protection for managing the emergency (Marzocchi and Lombardi, 2009) is utilized. Moreover, the parameters of the ground motion prediction relation proposed by Sabetta and Pugliese (1996) are updated adaptively and on a daily basis using Bayesian updating based on the ongoing aftershock sequence. Finally, the forecasted daily rates of exceeding (first-mode) spectral acceleration values are compared with observed rates of exceedance calculated based on the wave-forms that have actually taken place. References Jalayer, F., Asprone, D., Prota, A., Manfredi, G. (2011). A decision support system for post-earthquake reliability assessment of structures subjected to after-shocks: an application to L'Aquila earthquake, 2009. Bull. Earthquake Eng. 9(4) 997-1014. Jordan, T.H., Chen Y-T., Gasparini P., Madariaga R., Main I., Marzocchi W., Papadopoulos G., Sobolev G., Yamaoka K., and J. Zschau (2011). Operational earthquake forecasting: State of knowledge and guidelines for implementation, Ann. Geophys. 54(4) 315-391, doi 10.4401/ag-5350. Lolli, B., and P. Gasperini (2003). Aftershocks hazard in Italy part I: estimation of time-magnitude distribution model parameters and computation of probabilities of occurrence. Journal of Seismology 7(2) 235-257. Marzocchi, W., and A.M. Lombardi (2009). Real-time forecasting following a damaging earthquake, Geophys. Res. Lett. 36, L21302, doi: 10.1029/2009GL040233. Sabetta F., A. Pugliese (1996) Estimation of response spectra and simulation of nonstationary earthquake ground motions. Bull Seismol Soc Am 86(2) 337-352.

Nonlinear structural joint model updating based on instantaneous characteristics of dynamic responses

NASA Astrophysics Data System (ADS)

Wang, Zuo-Cai; Xin, Yu; Ren, Wei-Xin

2016-08-01

This paper proposes a new nonlinear joint model updating method for shear type structures based on the instantaneous characteristics of the decomposed structural dynamic responses. To obtain an accurate representation of a nonlinear system's dynamics, the nonlinear joint model is described as the nonlinear spring element with bilinear stiffness. The instantaneous frequencies and amplitudes of the decomposed mono-component are first extracted by the analytical mode decomposition (AMD) method. Then, an objective function based on the residuals of the instantaneous frequencies and amplitudes between the experimental structure and the nonlinear model is created for the nonlinear joint model updating. The optimal values of the nonlinear joint model parameters are obtained by minimizing the objective function using the simulated annealing global optimization method. To validate the effectiveness of the proposed method, a single-story shear type structure subjected to earthquake and harmonic excitations is simulated as a numerical example. Then, a beam structure with multiple local nonlinear elements subjected to earthquake excitation is also simulated. The nonlinear beam structure is updated based on the global and local model using the proposed method. The results show that the proposed local nonlinear model updating method is more effective for structures with multiple local nonlinear elements. Finally, the proposed method is verified by the shake table test of a real high voltage switch structure. The accuracy of the proposed method is quantified both in numerical and experimental applications using the defined error indices. Both the numerical and experimental results have shown that the proposed method can effectively update the nonlinear joint model.

Structural Finite Element Model Updating Using Vibration Tests and Modal Analysis for NPL footbridge - SHM demonstrator

NASA Astrophysics Data System (ADS)

Barton, E.; Middleton, C.; Koo, K.; Crocker, L.; Brownjohn, J.

2011-07-01

This paper presents the results from collaboration between the National Physical Laboratory (NPL) and the University of Sheffield on an ongoing research project at NPL. A 50 year old reinforced concrete footbridge has been converted to a full scale structural health monitoring (SHM) demonstrator. The structure is monitored using a variety of techniques; however, interrelating results and converting data to knowledge are not possible without a reliable numerical model. During the first stage of the project, the work concentrated on static loading and an FE model of the undamaged bridge was created, and updated, under specified static loading and temperature conditions. This model was found to accurately represent the response under static loading and it was used to identify locations for sensor installation. The next stage involves the evaluation of repair/strengthening patches under both static and dynamic loading. Therefore, before deliberately introducing significant damage, the first set of dynamic tests was conducted and modal properties were estimated. The measured modal properties did not match the modal analysis from the statically updated FE model; it was clear that the existing model required updating. This paper introduces the results of the dynamic testing and model updating. It is shown that the structure exhibits large non-linear, amplitude dependant characteristics. This creates a difficult updating process, but we attempt to produce the best linear representation of the structure. A sensitivity analysis is performed to determine the most sensitive locations for planned damage/repair scenarios and is used to decide whether additional sensors will be necessary.

Dynamic updating atlas for heart segmentation with a nonlinear field-based model.

PubMed

Cai, Ken; Yang, Rongqian; Yue, Hongwei; Li, Lihua; Ou, Shanxing; Liu, Feng

2017-09-01

Segmentation of cardiac computed tomography (CT) images is an effective method for assessing the dynamic function of the heart and lungs. In the atlas-based heart segmentation approach, the quality of segmentation usually relies upon atlas images, and the selection of those reference images is a key step. The optimal goal in this selection process is to have the reference images as close to the target image as possible. This study proposes an atlas dynamic update algorithm using a scheme of nonlinear deformation field. The proposed method is based on the features among double-source CT (DSCT) slices. The extraction of these features will form a base to construct an average model and the created reference atlas image is updated during the registration process. A nonlinear field-based model was used to effectively implement a 4D cardiac segmentation. The proposed segmentation framework was validated with 14 4D cardiac CT sequences. The algorithm achieved an acceptable accuracy (1.0-2.8 mm). Our proposed method that combines a nonlinear field-based model and dynamic updating atlas strategies can provide an effective and accurate way for whole heart segmentation. The success of the proposed method largely relies on the effective use of the prior knowledge of the atlas and the similarity explored among the to-be-segmented DSCT sequences. Copyright © 2016 John Wiley & Sons, Ltd.

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

PubMed

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

2018-05-17

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

Study on application of dynamic monitoring of land use based on mobile GIS technology

NASA Astrophysics Data System (ADS)

Tian, Jingyi; Chu, Jian; Guo, Jianxing; Wang, Lixin

2006-10-01

The land use dynamic monitoring is an important mean to maintain the real-time update of the land use data. Mobile GIS technology integrates GIS, GPS and Internet. It can update the historic al data in real time with site-collected data and realize the data update in large scale with high precision. The Monitoring methods on the land use change data with the mobile GIS technology were discussed. Mobile terminal of mobile GIS has self-developed for this study with GPS-25 OEM and notebook computer. The RTD (real-time difference) operation mode is selected. Mobile GIS system of dynamic monitoring of land use have developed with Visual C++ as operation platform, MapObjects control as graphic platform and MSCmm control as communication platform, which realizes organic integration of GPS, GPRS and GIS. This system has such following basic functions as data processing, graphic display, graphic editing, attribute query and navigation. Qinhuangdao city was selected as the experiential area. Shown by the study result, the mobile GIS integration system of dynamic monitoring of land use developed by this study has practical application value.

The state of electric vehicles in Hawaii: 2016 update.

DOT National Transportation Integrated Search

2016-07-01

This report provides an update to the State of Electric Vehicles in Hawaii released in March 2015, a : synopsis of the dynamic landscape between electrified transportation and renewable energy integration : in Hawaii. Focus is placed on the interacti...

Stability and diversity in collective adaptation

NASA Astrophysics Data System (ADS)

Sato, Yuzuru; Akiyama, Eizo; Crutchfield, James P.

2005-10-01

We derive a class of macroscopic differential equations that describe collective adaptation, starting from a discrete-time stochastic microscopic model. The behavior of each agent is a dynamic balance between adaptation that locally achieves the best action and memory loss that leads to randomized behavior. We show that, although individual agents interact with their environment and other agents in a purely self-interested way, macroscopic behavior can be interpreted as game dynamics. Application to several familiar, explicit game interactions shows that the adaptation dynamics exhibits a diversity of collective behaviors. The simplicity of the assumptions underlying the macroscopic equations suggests that these behaviors should be expected broadly in collective adaptation. We also analyze the adaptation dynamics from an information-theoretic viewpoint and discuss self-organization induced by the dynamics of uncertainty, giving a novel view of collective adaptation.

Adaptive dynamical networks

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

A Coupled Economic and Physical Model of Coastal Adaptation and Abandonment: Are human occupied coastlines a bubble waiting to burst?

NASA Astrophysics Data System (ADS)

McNamara, D.; Keeler, A.

2011-12-01

Policy discussions of adaptation by coastal residents to increasing rates of sea level rise and changing frequency of damaging storms have focused on community land use planning processes. This view neglects the role that market dynamics and climate change expectations play in the way coastal communities choose among risk mitigation options and manage land use decisions in an environment of escalating risks. We use a model coupling physical coastal processes with an agent-based model of behavior in real estate and mitigation markets to examine the interplay of climate-driven coastal hazards, collective mitigation decisions, and individual beliefs. The physical component model simulates barrier island processes that respond to both storms and slow scale dynamics associated with sea level rise. The economic component model is an agent-based model of economic behavior where agents are rational economic actors working off different assessments of future climate-driven events. Agents differentially update their beliefs based on a) how much emphasis they give to observed coastal changes and b) how much weight they give to scientific predictions. In essence, agents differ along a spectrum of how much they believe that the past is the best guide to the future and how quickly they react to new information. We use the coupled model to explore three questions of interest to coastal policy. First, how do the interplay of costal processes, beliefs, and mitigation choices affect the level and stability of real estate prices? Second, how does this interplay affect the incentives for community investments in shoreline protection? Third, how do expectations and reactions to observed events, as well as mitigation investments, affect the built environment in circumstances when climate risks reach very high levels? This last question relates to a key aspect of climate change adaptation on the coast - when does mitigation give way to abandonment as an optimal adaptation strategy? Results suggest that subjective expectations about climate risk and about the effectiveness of mitigation in high-risk environments are critical in determining when the market starts to reflect the possibility that property might no longer be inhabitable. Results will be presented that contrast the dynamics of abandonment over a range of sea level rise and storminess scenarios.

Update rules and interevent time distributions: slow ordering versus no ordering in the voter model.

PubMed

Fernández-Gracia, J; Eguíluz, V M; San Miguel, M

2011-07-01

We introduce a general methodology of update rules accounting for arbitrary interevent time (IET) distributions in simulations of interacting agents. We consider in particular update rules that depend on the state of the agent, so that the update becomes part of the dynamical model. As an illustration we consider the voter model in fully connected, random, and scale-free networks with an activation probability inversely proportional to the time since the last action, where an action can be an update attempt (an exogenous update) or a change of state (an endogenous update). We find that in the thermodynamic limit, at variance with standard updates and the exogenous update, the system orders slowly for the endogenous update. The approach to the absorbing state is characterized by a power-law decay of the density of interfaces, observing that the mean time to reach the absorbing state might be not well defined. The IET distributions resulting from both update schemes show power-law tails.

One night of sleep loss impairs innovative thinking and flexible decision making.

PubMed

Harrison, Y; Horne, J A

1999-05-01

Recent findings with clinically oriented neuropsychological tests suggest that one night without sleep causes particular impairment to tasks requiring flexible thinking and the updating of plans in the light of new information. This relatively little investigated field of sleep deprivation research has real-world implications for decision makers having lost a night's sleep. To explore this latter perspective further, we adapted a dynamic and realistic marketing decision making "game" embodying the need for these skills, and whereby such performance could be measured. As the task relied on the comprehension of a large amount of written information, a critical reasoning test was also administered to ascertain whether any failure at the marketing game might lie with information acquisition rather than with failures in decision making. Ten healthy highly motivated and trained participants underwent two counterbalanced 36 h trials, sleep vs no sleep. The critical reasoning task was unaffected by sleep loss, whereas performance at the game significantly deteri orated after 32-36 h of sleep loss, when sleep deprivation led to more rigid thinking, increased perseverative errors, and marked difficulty in appreciating an updated situation. At this point, and despite the sleep-deprived participants' best efforts to do well, their play collapsed, unlike that of the nonsleep-deprived participants. Copyright 1999 Academic Press.

SCADA-based Operator Support System for Power Plant Equipment Fault Forecasting

NASA Astrophysics Data System (ADS)

Mayadevi, N.; Ushakumari, S. S.; Vinodchandra, S. S.

2014-12-01

Power plant equipment must be monitored closely to prevent failures from disrupting plant availability. Online monitoring technology integrated with hybrid forecasting techniques can be used to prevent plant equipment faults. A self learning rule-based expert system is proposed in this paper for fault forecasting in power plants controlled by supervisory control and data acquisition (SCADA) system. Self-learning utilizes associative data mining algorithms on the SCADA history database to form new rules that can dynamically update the knowledge base of the rule-based expert system. In this study, a number of popular associative learning algorithms are considered for rule formation. Data mining results show that the Tertius algorithm is best suited for developing a learning engine for power plants. For real-time monitoring of the plant condition, graphical models are constructed by K-means clustering. To build a time-series forecasting model, a multi layer preceptron (MLP) is used. Once created, the models are updated in the model library to provide an adaptive environment for the proposed system. Graphical user interface (GUI) illustrates the variation of all sensor values affecting a particular alarm/fault, as well as the step-by-step procedure for avoiding critical situations and consequent plant shutdown. The forecasting performance is evaluated by computing the mean absolute error and root mean square error of the predictions.

NOAA SWPC / NASA CCMC Space Weather Modeling Assessment Project: Toward the Validation of Advancements in Heliospheric Space Weather Prediction Within WSA-Enlil

NASA Astrophysics Data System (ADS)

Adamson, E. T.; Pizzo, V. J.; Biesecker, D. A.; Mays, M. L.; MacNeice, P. J.; Taktakishvili, A.; Viereck, R. A.

2017-12-01

In 2011, NOAA's Space Weather Prediction Center (SWPC) transitioned the world's first operational space weather model into use at the National Weather Service's Weather and Climate Operational Supercomputing System (WCOSS). This operational forecasting tool is comprised of the Wang-Sheeley-Arge (WSA) solar wind model coupled with the Enlil heliospheric MHD model. Relying on daily-updated photospheric magnetograms produced by the National Solar Observatory's Global Oscillation Network Group (GONG), this tool provides critical predictive knowledge of heliospheric dynamics such as high speed streams and coronal mass ejections. With the goal of advancing this predictive model and quantifying progress, SWPC and NASA's Community Coordinated Modeling Center (CCMC) have initiated a collaborative effort to assess improvements in space weather forecasts at Earth by moving from a single daily-updated magnetogram to a sequence of time-dependent magnetograms to drive the ambient inputs for the WSA-Enlil model as well as incorporating the newly developed Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model. We will provide a detailed overview of the scope of this effort and discuss preliminary results from the first phase focusing on the impact of time-dependent magnetogram inputs to the WSA-Enlil model.

Change Semantic Constrained Online Data Cleaning Method for Real-Time Observational Data Stream

NASA Astrophysics Data System (ADS)

Ding, Yulin; Lin, Hui; Li, Rongrong

2016-06-01

Recent breakthroughs in sensor networks have made it possible to collect and assemble increasing amounts of real-time observational data by observing dynamic phenomena at previously impossible time and space scales. Real-time observational data streams present potentially profound opportunities for real-time applications in disaster mitigation and emergency response, by providing accurate and timeliness estimates of environment's status. However, the data are always subject to inevitable anomalies (including errors and anomalous changes/events) caused by various effects produced by the environment they are monitoring. The "big but dirty" real-time observational data streams can rarely achieve their full potential in the following real-time models or applications due to the low data quality. Therefore, timely and meaningful online data cleaning is a necessary pre-requisite step to ensure the quality, reliability, and timeliness of the real-time observational data. In general, a straightforward streaming data cleaning approach, is to define various types of models/classifiers representing normal behavior of sensor data streams and then declare any deviation from this model as normal or erroneous data. The effectiveness of these models is affected by dynamic changes of deployed environments. Due to the changing nature of the complicated process being observed, real-time observational data is characterized by diversity and dynamic, showing a typical Big (Geo) Data characters. Dynamics and diversity is not only reflected in the data values, but also reflected in the complicated changing patterns of the data distributions. This means the pattern of the real-time observational data distribution is not stationary or static but changing and dynamic. After the data pattern changed, it is necessary to adapt the model over time to cope with the changing patterns of real-time data streams. Otherwise, the model will not fit the following observational data streams, which may led to large estimation error. In order to achieve the best generalization error, it is an important challenge for the data cleaning methodology to be able to characterize the behavior of data stream distributions and adaptively update a model to include new information and remove old information. However, the complicated data changing property invalidates traditional data cleaning methods, which rely on the assumption of a stationary data distribution, and drives the need for more dynamic and adaptive online data cleaning methods. To overcome these shortcomings, this paper presents a change semantics constrained online filtering method for real-time observational data. Based on the principle that the filter parameter should vary in accordance to the data change patterns, this paper embeds semantic description, which quantitatively depicts the change patterns in the data distribution to self-adapt the filter parameter automatically. Real-time observational water level data streams of different precipitation scenarios are selected for testing. Experimental results prove that by means of this method, more accurate and reliable water level information can be available, which is prior to scientific and prompt flood assessment and decision-making.

Neural Mechanisms of Updating under Reducible and Irreducible Uncertainty.

PubMed

Kobayashi, Kenji; Hsu, Ming

2017-07-19

Adaptive decision making depends on an agent's ability to use environmental signals to reduce uncertainty. However, because of multiple types of uncertainty, agents must take into account not only the extent to which signals violate prior expectations but also whether uncertainty can be reduced in the first place. Here we studied how human brains of both sexes respond to signals under conditions of reducible and irreducible uncertainty. We show behaviorally that subjects' value updating was sensitive to the reducibility of uncertainty, and could be quantitatively characterized by a Bayesian model where agents ignore expectancy violations that do not update beliefs or values. Using fMRI, we found that neural processes underlying belief and value updating were separable from responses to expectancy violation, and that reducibility of uncertainty in value modulated connections from belief-updating regions to value-updating regions. Together, these results provide insights into how agents use knowledge about uncertainty to make better decisions while ignoring mere expectancy violation. SIGNIFICANCE STATEMENT To make good decisions, a person must observe the environment carefully, and use these observations to reduce uncertainty about consequences of actions. Importantly, uncertainty should not be reduced purely based on how surprising the observations are, particularly because in some cases uncertainty is not reducible. Here we show that the human brain indeed reduces uncertainty adaptively by taking into account the nature of uncertainty and ignoring mere surprise. Behaviorally, we show that human subjects reduce uncertainty in a quasioptimal Bayesian manner. Using fMRI, we characterize brain regions that may be involved in uncertainty reduction, as well as the network they constitute, and dissociate them from brain regions that respond to mere surprise. Copyright © 2017 the authors 0270-6474/17/376972-11$15.00/0.

Neural Mechanisms of Updating under Reducible and Irreducible Uncertainty

PubMed Central

2017-01-01

Adaptive decision making depends on an agent's ability to use environmental signals to reduce uncertainty. However, because of multiple types of uncertainty, agents must take into account not only the extent to which signals violate prior expectations but also whether uncertainty can be reduced in the first place. Here we studied how human brains of both sexes respond to signals under conditions of reducible and irreducible uncertainty. We show behaviorally that subjects' value updating was sensitive to the reducibility of uncertainty, and could be quantitatively characterized by a Bayesian model where agents ignore expectancy violations that do not update beliefs or values. Using fMRI, we found that neural processes underlying belief and value updating were separable from responses to expectancy violation, and that reducibility of uncertainty in value modulated connections from belief-updating regions to value-updating regions. Together, these results provide insights into how agents use knowledge about uncertainty to make better decisions while ignoring mere expectancy violation. SIGNIFICANCE STATEMENT To make good decisions, a person must observe the environment carefully, and use these observations to reduce uncertainty about consequences of actions. Importantly, uncertainty should not be reduced purely based on how surprising the observations are, particularly because in some cases uncertainty is not reducible. Here we show that the human brain indeed reduces uncertainty adaptively by taking into account the nature of uncertainty and ignoring mere surprise. Behaviorally, we show that human subjects reduce uncertainty in a quasioptimal Bayesian manner. Using fMRI, we characterize brain regions that may be involved in uncertainty reduction, as well as the network they constitute, and dissociate them from brain regions that respond to mere surprise. PMID:28626019

Training working memory updating in young adults.

PubMed

Linares, Rocío; Borella, Erika; Lechuga, M Teresa; Carretti, Barbara; Pelegrina, Santiago

2018-05-01

Working memory updating (WMU) is a core mechanism in the human mental architecture and a good predictor of a wide range of cognitive processes. This study analyzed the benefits of two different WMU training procedures, near transfer effects on a working memory measure, and far transfer effects on nonverbal reasoning. Maintenance of any benefits a month later was also assessed. Participants were randomly assigned to: an adaptive training group that performed two numerical WMU tasks during four sessions; a non-adaptive training group that performed the same tasks but on a constant and less demanding level of difficulty; or an active control group that performed other tasks unrelated with working memory. After the training, all three groups showed improvements in most of the tasks, and these benefits were maintained a month later. The gain in one of the two WMU measures was larger for the adaptive and non-adaptive groups than for the control group. This specific gain in a task similar to the one trained would indicate the use of a better strategy for performing the task. Besides this nearest transfer effect, no other transfer effects were found. The adaptability of the training procedure did not produce greater improvements. These results are discussed in terms of the training procedure and the feasibility of training WMU.

A residual based adaptive unscented Kalman filter for fault recovery in attitude determination system of microsatellites

NASA Astrophysics Data System (ADS)

Le, Huy Xuan; Matunaga, Saburo

2014-12-01

This paper presents an adaptive unscented Kalman filter (AUKF) to recover the satellite attitude in a fault detection and diagnosis (FDD) subsystem of microsatellites. The FDD subsystem includes a filter and an estimator with residual generators, hypothesis tests for fault detections and a reference logic table for fault isolations and fault recovery. The recovery process is based on the monitoring of mean and variance values of each attitude sensor behaviors from residual vectors. In the case of normal work, the residual vectors should be in the form of Gaussian white noise with zero mean and fixed variance. When the hypothesis tests for the residual vectors detect something unusual by comparing the mean and variance values with dynamic thresholds, the AUKF with real-time updated measurement noise covariance matrix will be used to recover the sensor faults. The scheme developed in this paper resolves the problem of the heavy and complex calculations during residual generations and therefore the delay in the isolation process is reduced. The numerical simulations for TSUBAME, a demonstration microsatellite of Tokyo Institute of Technology, are conducted and analyzed to demonstrate the working of the AUKF and FDD subsystem.

Performance updates of HAWK-I and preparation for the commissioning of the system GRAAL+HAWK-I

NASA Astrophysics Data System (ADS)

Hibon, Pascale; Paufique, Jerome; Kuntschner, Harald; Dobrzycka, Danuta; Le Louarn, Miska; Valenti, Elena; Neeser, Mark; Pompei, Emanuela; Arsenault, Robin; Siebenmorgen, Ralf; Madec, Pierre-Yves; Petr-Gotzens, Monika; La Fuente, Carlos; Urrutia, Josefina; Valenzuela, Javier; Castillo, Roberto; Baksai, Pedro; Garcia Dabo, Cesar Enrique; Jost, Andreas; Argomedo, Javier; Kolb, Johann; Kiekebusch, Mario; Hubin, Norbert; Duhoux, Philippe; Conzelmann, Ralf Dieter; Donaldson, Robert; Tordo, Sebastien; Huber, Stefan

2016-08-01

The High Acuity Wide field K-band Imager (HAWK-I) instrument is a cryogenic wide field imager operating in the wavelength range 0.9 to 2.5 microns. It has been in operations since 2007 on the UT4 at the Very Large Telescope Observatory in seeing-limited mode. In 2017-2018, GRound Layer Adaptive optics Assisted by Lasers module (GRAAL) will be in operation and the system GRAAL+HAWK-I will be commissioned. It will allow: deeper exposures for nearly point-source objects, or shorter exposure times for reaching the same magnitude, and/or deeper detection limiting magnitude. With GRAAL, HAWK-I will operate more than 80% of the time with an equivalent K-band seeing of 0.55" (instead of 0.7" without GRAAL). GRAAL is already installed and the operations without adaptive optics were commissioned in 2015. We discuss here the latest updates on performance from HAWK-I without Adaptive Optics (AO) and the preparation for the commissioning of the system GRAAL+HAWK-I.

The influence of learning and updating speed on the growth of commercial websites

NASA Astrophysics Data System (ADS)

Wan, Xiaoji; Deng, Guishi; Bai, Yang; Xue, Shaowei

2012-08-01

In this paper, we study the competition model of commercial websites with learning and updating speed, and further analyze the influence of learning and updating speed on the growth of commercial websites from a nonlinear dynamics perspective. Using the center manifold theory and the normal form method, we give the explicit formulas determining the stability and periodic fluctuation of commercial sites. Numerical simulations reveal that sites periodically fluctuate as the speed of learning and updating crosses one threshold. The study provides reference and evidence for website operators to make decisions.

Situation Model Updating in Young and Older Adults: Global versus Incremental Mechanisms

PubMed Central

Bailey, Heather R.; Zacks, Jeffrey M.

2015-01-01

Readers construct mental models of situations described by text. Activity in narrative text is dynamic, so readers must frequently update their situation models when dimensions of the situation change. Updating can be incremental, such that a change leads to updating just the dimension that changed, or global, such that the entire model is updated. Here, we asked whether older and young adults make differential use of incremental and global updating. Participants read narratives containing changes in characters and spatial location and responded to recognition probes throughout the texts. Responses were slower when probes followed a change, suggesting that situation models were updated at changes. When either dimension changed, responses to probes for both dimensions were slowed; this provides evidence for global updating. Moreover, older adults showed stronger evidence of global updating than did young adults. One possibility is that older adults perform more global updating to offset reduced ability to manipulate information in working memory. PMID:25938248

Specifying and Verifying the Correctness of Dynamic Software Updates

DTIC Science & Technology

2011-11-15

additional branching introduced by update points and the need to analyze the state transformer code. As tools become faster and more effective , our...It shows the effectiveness of merging-based verification on practical examples, including Redis [20], a widely deployed server program. 2 Defining...Gupta’s reachability while side -stepping the problem that reachability can leave behavior CS-TR-4997 under-constrained. For example, for the vsftpd update

Functional Dissociation of Latency-Variable, Stimulus- and Response-Locked Target P3 Sub-components in Task-Switching

PubMed Central

Brydges, Christopher R.; Barceló, Francisco

2018-01-01

Cognitive control warrants efficient task performance in dynamic and changing environments through adjustments in executive attention, stimulus and response selection. The well-known P300 component of the human event-related potential (ERP) has long been proposed to index “context-updating”—critical for cognitive control—in simple target detection tasks. However, task switching ERP studies have revealed both target P3 (300–350 ms) and later sustained P3-like potentials (400–1,200 ms) to first targets ensuing transition cues, although it remains unclear whether these target P3-like potentials also reflect context updating operations. To address this question, we applied novel single-trial EEG analyses—residue iteration decomposition (RIDE)—in order to disentangle target P3 sub-components in a sample of 22 young adults while they either repeated or switched (updated) task rules. The rationale was to revise the context updating hypothesis of P300 elicitation in the light of new evidence suggesting that “the context” consists of not only the sensory units of stimulation, but also associated motor units, and intermediate low- and high-order sensorimotor units, all of which may need to be dynamically updated on a trial by trial basis. The results showed functionally distinct target P3-like potentials in stimulus-locked, response-locked, and intermediate RIDE component clusters overlying parietal and frontal regions, implying multiple functionally distinct, though temporarily overlapping context updating operations. These findings support a reformulated version of the context updating hypothesis, and reveal a rich family of distinct target P3-like sub-components during the reactive control of target detection in task-switching, plausibly indexing the complex and dynamic workings of frontoparietal cortical networks subserving cognitive control. PMID:29515383

Neural network based adaptive control for nonlinear dynamic regimes

NASA Astrophysics Data System (ADS)

Shin, Yoonghyun

Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named 'composite model reference adaptive control' is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of 'pseudo-control hedging' techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.

Linearly scaling and almost Hamiltonian dielectric continuum molecular dynamics simulations through fast multipole expansions

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

Lorenzen, Konstantin; Mathias, Gerald; Tavan, Paul, E-mail: tavan@physik.uni-muenchen.de

2015-11-14

Hamiltonian Dielectric Solvent (HADES) is a recent method [S. Bauer et al., J. Chem. Phys. 140, 104103 (2014)] which enables atomistic Hamiltonian molecular dynamics (MD) simulations of peptides and proteins in dielectric solvent continua. Such simulations become rapidly impractical for large proteins, because the computational effort of HADES scales quadratically with the number N of atoms. If one tries to achieve linear scaling by applying a fast multipole method (FMM) to the computation of the HADES electrostatics, the Hamiltonian character (conservation of total energy, linear, and angular momenta) may get lost. Here, we show that the Hamiltonian character of HADESmore » can be almost completely preserved, if the structure-adapted fast multipole method (SAMM) as recently redesigned by Lorenzen et al. [J. Chem. Theory Comput. 10, 3244-3259 (2014)] is suitably extended and is chosen as the FMM module. By this extension, the HADES/SAMM forces become exact gradients of the HADES/SAMM energy. Their translational and rotational invariance then guarantees (within the limits of numerical accuracy) the exact conservation of the linear and angular momenta. Also, the total energy is essentially conserved—up to residual algorithmic noise, which is caused by the periodically repeated SAMM interaction list updates. These updates entail very small temporal discontinuities of the force description, because the employed SAMM approximations represent deliberately balanced compromises between accuracy and efficiency. The energy-gradient corrected version of SAMM can also be applied, of course, to MD simulations of all-atom solvent-solute systems enclosed by periodic boundary conditions. However, as we demonstrate in passing, this choice does not offer any serious advantages.« less

Critical role for the mediodorsal thalamus in permitting rapid reward-guided updating in stochastic reward environments

PubMed Central

Chakraborty, Subhojit; Kolling, Nils; Walton, Mark E; Mitchell, Anna S

2016-01-01

Adaptive decision-making uses information gained when exploring alternative options to decide whether to update the current choice strategy. Magnocellular mediodorsal thalamus (MDmc) supports adaptive decision-making, but its causal contribution is not well understood. Monkeys with excitotoxic MDmc damage were tested on probabilistic three-choice decision-making tasks. They could learn and track the changing values in object-reward associations, but they were severely impaired at updating choices after reversals in reward contingencies or when there were multiple options associated with reward. These deficits were not caused by perseveration or insensitivity to negative feedback though. Instead, monkeys with MDmc lesions exhibited an inability to use reward to promote choice repetition after switching to an alternative option due to a diminished influence of recent past choices and the last outcome to guide future behavior. Together, these data suggest MDmc allows for the rapid discovery and persistence with rewarding options, particularly in uncertain or changing environments. DOI: http://dx.doi.org/10.7554/eLife.13588.001 PMID:27136677

Backstepping Design of Adaptive Neural Fault-Tolerant Control for MIMO Nonlinear Systems.

PubMed

Gao, Hui; Song, Yongduan; Wen, Changyun

In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.

Dynamics of social balance on networks

NASA Astrophysics Data System (ADS)

Antal, T.; Krapivsky, P. L.; Redner, S.

2005-09-01

We study the evolution of social networks that contain both friendly and unfriendly pairwise links between individual nodes. The network is endowed with dynamics in which the sense of a link in an imbalanced triad—a triangular loop with one or three unfriendly links—is reversed to make the triad balanced. With this dynamics, an infinite network undergoes a dynamic phase transition from a steady state to “paradise”—all links are friendly—as the propensity p for friendly links in an update event passes through 1/2 . A finite network always falls into a socially balanced absorbing state where no imbalanced triads remain. If the additional constraint that the number of imbalanced triads in the network not increase in an update is imposed, then the network quickly reaches a balanced final state.

Recent Results from NASA's Morphing Project

NASA Technical Reports Server (NTRS)

McGowan, Anna-Maria R.; Washburn, Anthony E.; Horta, Lucas G.; Bryant, Robert G.; Cox, David E.; Siochi, Emilie J.; Padula, Sharon L.; Holloway, Nancy M.

2002-01-01

The NASA Morphing Project seeks to develop and assess advanced technologies and integrated component concepts to enable efficient, multi-point adaptability in air and space vehicles. In the context of the project, the word "morphing" is defined as "efficient, multi-point adaptability" and may include macro, micro, structural and/or fluidic approaches. The project includes research on smart materials, adaptive structures, micro flow control, biomimetic concepts, optimization and controls. This paper presents an updated overview of the content of the Morphing Project including highlights of recent research results.

A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction.

PubMed

Chen, C P; Wan, J Z

1999-01-01

A fast learning algorithm is proposed to find an optimal weights of the flat neural networks (especially, the functional-link network). Although the flat networks are used for nonlinear function approximation, they can be formulated as linear systems. Thus, the weights of the networks can be solved easily using a linear least-square method. This formulation makes it easier to update the weights instantly for both a new added pattern and a new added enhancement node. A dynamic stepwise updating algorithm is proposed to update the weights of the system on-the-fly. The model is tested on several time-series data including an infrared laser data set, a chaotic time-series, a monthly flour price data set, and a nonlinear system identification problem. The simulation results are compared to existing models in which more complex architectures and more costly training are needed. The results indicate that the proposed model is very attractive to real-time processes.

Enumeration and extension of non-equivalent deterministic update schedules in Boolean networks.

PubMed

Palma, Eduardo; Salinas, Lilian; Aracena, Julio

2016-03-01

Boolean networks (BNs) are commonly used to model genetic regulatory networks (GRNs). Due to the sensibility of the dynamical behavior to changes in the updating scheme (order in which the nodes of a network update their state values), it is increasingly common to use different updating rules in the modeling of GRNs to better capture an observed biological phenomenon and thus to obtain more realistic models.In Aracena et al. equivalence classes of deterministic update schedules in BNs, that yield exactly the same dynamical behavior of the network, were defined according to a certain label function on the arcs of the interaction digraph defined for each scheme. Thus, the interaction digraph so labeled (update digraphs) encode the non-equivalent schemes. We address the problem of enumerating all non-equivalent deterministic update schedules of a given BN. First, we show that it is an intractable problem in general. To solve it, we first construct an algorithm that determines the set of update digraphs of a BN. For that, we use divide and conquer methodology based on the structural characteristics of the interaction digraph. Next, for each update digraph we determine a scheme associated. This algorithm also works in the case where there is a partial knowledge about the relative order of the updating of the states of the nodes. We exhibit some examples of how the algorithm works on some GRNs published in the literature. An executable file of the UpdateLabel algorithm made in Java and the files with the outputs of the algorithms used with the GRNs are available at: www.inf.udec.cl/ ∼lilian/UDE/ CONTACT: lilisalinas@udec.cl Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

An adaptive actuator failure compensation scheme for two linked 2WD mobile robots

NASA Astrophysics Data System (ADS)

Ma, Yajie; Al-Dujaili, Ayad; Cocquempot, Vincent; El Badaoui El Najjar, Maan

2017-01-01

This paper develops a new adaptive compensation control scheme for two linked mobile robots with actuator failurs. A configuration with two linked two-wheel drive (2WD) mobile robots is proposed, and the modelling of its kinematics and dynamics are given. An adaptive failure compensation scheme is developed to compensate actuator failures, consisting of a kinematic controller and a multi-design integration based dynamic controller. The kinematic controller is a virtual one, and based on which, multiple adaptive dynamic control signals are designed which covers all possible failure cases. By combing these dynamic control signals, the dynamic controller is designed, which ensures system stability and asymptotic tracking properties. Simulation results verify the effectiveness of the proposed adaptive failure compensation scheme.

ARED (Advanced-Resistive Exercise Device) Update

NASA Technical Reports Server (NTRS)

Ploutz-Snyder, Lori

2009-01-01

This viewgraph presentation describes ARED which is a new hardware exercise device for use on the International Space Station. Astronaut physiological adaptations, muscle parameters, and cardiovascular parameters are also reviewed.

Large fluctuations in anti-coordination games on scale-free graphs

NASA Astrophysics Data System (ADS)

Sabsovich, Daniel; Mobilia, Mauro; Assaf, Michael

2017-05-01

We study the influence of the complex topology of scale-free graphs on the dynamics of anti-coordination games (e.g. snowdrift games). These reference models are characterized by the coexistence (evolutionary stable mixed strategy) of two competing species, say ‘cooperators’ and ‘defectors’, and, in finite systems, by metastability and large-fluctuation-driven fixation. In this work, we use extensive computer simulations and an effective diffusion approximation (in the weak selection limit) to determine under which circumstances, depending on the individual-based update rules, the topology drastically affects the long-time behavior of anti-coordination games. In particular, we compute the variance of the number of cooperators in the metastable state and the mean fixation time when the dynamics is implemented according to the voter model (death-first/birth-second process) and the link dynamics (birth/death or death/birth at random). For the voter update rule, we show that the scale-free topology effectively renormalizes the population size and as a result the statistics of observables depend on the network’s degree distribution. In contrast, such a renormalization does not occur with the link dynamics update rule and we recover the same behavior as on complete graphs.

Recent advances in photorefractive polymers

NASA Astrophysics Data System (ADS)

Thomas, Jayan; Christenson, C. W.; Lynn, B.; Blanche, P.-A.; Voorakaranam, R.; Norwood, R. A.; Yamamoto, M.; Peyghambarian, N.

2011-10-01

Photorefractive composites derived from conducting polymers offer the advantage of dynamically recording holograms without the need for processing of any kind. Thus, they are the material of choice for many cutting edge applications, such as updatable three-dimensional (3D) displays and 3D telepresence. Using photorefractive polymers, 3D images or holograms can be seen with the unassisted eye and are very similar to how humans see the actual environment surrounding them. Absence of a large-area and dynamically updatable holographic recording medium has prevented realization of the concept. The development of a novel nonlinear optical chromophore doped photoconductive polymer composite as the recording medium for a refreshable holographic display is discussed. Further improvements in the polymer composites could bring applications in telemedicine, advertising, updatable 3D maps and entertainment.

Correlation of ground tests and analyses of a dynamically scaled Space Station model configuration

NASA Technical Reports Server (NTRS)

Javeed, Mehzad; Edighoffer, Harold H.; Mcgowan, Paul E.

1993-01-01

Verification of analytical models through correlation with ground test results of a complex space truss structure is demonstrated. A multi-component, dynamically scaled space station model configuration is the focus structure for this work. Previously established test/analysis correlation procedures are used to develop improved component analytical models. Integrated system analytical models, consisting of updated component analytical models, are compared with modal test results to establish the accuracy of system-level dynamic predictions. Design sensitivity model updating methods are shown to be effective for providing improved component analytical models. Also, the effects of component model accuracy and interface modeling fidelity on the accuracy of integrated model predictions is examined.

Stereoscopic augmented reality with pseudo-realistic global illumination effects

NASA Astrophysics Data System (ADS)

de Sorbier, Francois; Saito, Hideo

2014-03-01

Recently, augmented reality has become very popular and has appeared in our daily life with gaming, guiding systems or mobile phone applications. However, inserting object in such a way their appearance seems natural is still an issue, especially in an unknown environment. This paper presents a framework that demonstrates the capabilities of Kinect for convincing augmented reality in an unknown environment. Rather than pre-computing a reconstruction of the scene like proposed by most of the previous method, we propose a dynamic capture of the scene that allows adapting to live changes of the environment. Our approach, based on the update of an environment map, can also detect the position of the light sources. Combining information from the environment map, the light sources and the camera tracking, we can display virtual objects using stereoscopic devices with global illumination effects such as diffuse and mirror reflections, refractions and shadows in real time.

Interaction between gaze and visual and proprioceptive position judgements.

PubMed

Fiehler, Katja; Rösler, Frank; Henriques, Denise Y P

2010-06-01

There is considerable evidence that targets for action are represented in a dynamic gaze-centered frame of reference, such that each gaze shift requires an internal updating of the target. Here, we investigated the effect of eye movements on the spatial representation of targets used for position judgements. Participants had their hand passively placed to a location, and then judged whether this location was left or right of a remembered visual or remembered proprioceptive target, while gaze direction was varied. Estimates of position of the remembered targets relative to the unseen position of the hand were assessed with an adaptive psychophysical procedure. These positional judgements significantly varied relative to gaze for both remembered visual and remembered proprioceptive targets. Our results suggest that relative target positions may also be represented in eye-centered coordinates. This implies similar spatial reference frames for action control and space perception when positions are coded relative to the hand.

Decision-making dynamics in parasitoids of Drosophila.

PubMed

Thiel, Andra; Hoffmeister, Thomas S

2009-01-01

Drosophilids and their associated parasitoids live in environments that vary in resource availability and quality within and between generations. The use of information to adapt behavior to the current environment is a key feature under such circumstances and Drosophila parasitic wasps are excellent model systems to study learning and information use. They are among the few parasitoid model species that have been tested in a wide array of situations. Moreover, several related species have been tested under similar conditions, allowing the analysis of within and between species variability, the effect of natural selection in a typical environment, the current physiological status, and previous experience of the individual. This holds for host habitat and host location as well as for host choice and search time allocation. Here, we review patterns of learning and memory, of information use and updating mechanisms, and we point out that information use itself is under strong selective pressure and thus, optimized by parasitic wasps.

Machine learning based Intelligent cognitive network using fog computing

NASA Astrophysics Data System (ADS)

Lu, Jingyang; Li, Lun; Chen, Genshe; Shen, Dan; Pham, Khanh; Blasch, Erik

2017-05-01

In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the signal source using fog computing with different types of machine learning techniques. Depending on the computational capabilities of the fog nodes, different features and machine learning techniques are chosen to optimize spectrum allocation. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.

Linear accelerator radiosurgery for arteriovenous malformations: Updated literature review.

PubMed

Yahya, S; Heyes, G; Nightingale, P; Lamin, S; Chavda, S; Geh, I; Spooner, D; Cruickshank, G; Sanghera, P

2017-04-01

Arteriovenous malformations (AVMs) are the leading causing of intra-cerebral haemorrhage. Stereotactic radiosurgery (SRS) is an established treatment for arteriovenous malformations (AVM) and commonly delivered using Gamma Knife within dedicated radiosurgery units. Linear accelerator (LINAC) SRS is increasingly available however debate remains over whether it offers an equivalent outcome. The aim of this project is to evaluate the outcomes using LINAC SRS for AVMs used within a UK neurosciences unit and review the literature to aid decision making across various SRS platforms. Results have shown comparability across platforms and strongly supports that an adapted LINAC based SRS facility within a dynamic regional neuro-oncology department delivers similar outcomes (in terms of obliteration and toxicity) to any other dedicated radio-surgical platform. Locally available facilities can facilitate discussion between options however throughput will inevitably be lower than centrally based dedicated national radiosurgery units. Copyright © 2016. Published by Elsevier Ltd.

NEAMS Update. Quarterly Report for January - March 2014

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

Stan, Marius

2014-08-01

This quarterly report covers the following points: A fully three-dimensional smeared cracking model has been implemented and tested in BISON; DAKOTA-BISON was used to study the parameters that govern heat transfer across the fuel-cladding; Calculations of grain boundary mobility in UO 2 have been extended to high temperatures; Mesh adaptivity is being employed in MARMOT simulations to increase computational efficiency; Molecular dynamics simulations have shown correlation between atomic displacements and the anisotropic thermal conductivity in UO 2; The SHARP team continues to address the application of the toolkit to assembly deformations driven by reactivity feedback; The Nek5000 team has extendedmore » the low-Machnumber capability to mixtures with multiple species; The generalized cross section library has been tested for various fuel assemblies and reactor types; and The subgroup cross-section interface was successfully implemented in PROTEUS-SN (page 6).« less

Event-Triggered Adaptive Dynamic Programming for Continuous-Time Systems With Control Constraints.

PubMed

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

2016-08-31

In this paper, an event-triggered near optimal control structure is developed for nonlinear continuous-time systems with control constraints. Due to the saturating actuators, a nonquadratic cost function is introduced and the Hamilton-Jacobi-Bellman (HJB) equation for constrained nonlinear continuous-time systems is formulated. In order to solve the HJB equation, an actor-critic framework is presented. The critic network is used to approximate the cost function and the action network is used to estimate the optimal control law. In addition, in the proposed method, the control signal is transmitted in an aperiodic manner to reduce the computational and the transmission cost. Both the networks are only updated at the trigger instants decided by the event-triggered condition. Detailed Lyapunov analysis is provided to guarantee that the closed-loop event-triggered system is ultimately bounded. Three case studies are used to demonstrate the effectiveness of the proposed method.

Neural Basis of Reinforcement Learning and Decision Making

PubMed Central

Lee, Daeyeol; Seo, Hyojung; Jung, Min Whan

2012-01-01

Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal’s knowledge of its current environment. Studies have revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action. However, how the nature of a behavioral task affects the neural mechanisms of reinforcement learning remains incompletely understood. Future studies should uncover the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain. PMID:22462543

Hedging Your Bets by Learning Reward Correlations in the Human Brain

PubMed Central

Wunderlich, Klaus; Symmonds, Mkael; Bossaerts, Peter; Dolan, Raymond J.

2011-01-01

Summary Human subjects are proficient at tracking the mean and variance of rewards and updating these via prediction errors. Here, we addressed whether humans can also learn about higher-order relationships between distinct environmental outcomes, a defining ecological feature of contexts where multiple sources of rewards are available. By manipulating the degree to which distinct outcomes are correlated, we show that subjects implemented an explicit model-based strategy to learn the associated outcome correlations and were adept in using that information to dynamically adjust their choices in a task that required a minimization of outcome variance. Importantly, the experimentally generated outcome correlations were explicitly represented neuronally in right midinsula with a learning prediction error signal expressed in rostral anterior cingulate cortex. Thus, our data show that the human brain represents higher-order correlation structures between rewards, a core adaptive ability whose immediate benefit is optimized sampling. PMID:21943609

Evolution and classification of the CRISPR-Cas systems

PubMed Central

S. Makarova, Kira; H. Haft, Daniel; Barrangou, Rodolphe; J. J. Brouns, Stan; Charpentier, Emmanuelle; Horvath, Philippe; Moineau, Sylvain; J. M. Mojica, Francisco; I. Wolf, Yuri; Yakunin, Alexander F.; van der Oost, John; V. Koonin, Eugene

2012-01-01

The CRISPR–Cas (clustered regularly interspaced short palindromic repeats–CRISPR-associated proteins) modules are adaptive immunity systems that are present in many archaea and bacteria. These defence systems are encoded by operons that have an extraordinarily diverse architecture and a high rate of evolution for both the cas genes and the unique spacer content. Here, we provide an updated analysis of the evolutionary relationships between CRISPR–Cas systems and Cas proteins. Three major types of CRISPR–Cas system are delineated, with a further division into several subtypes and a few chimeric variants. Given the complexity of the genomic architectures and the extremely dynamic evolution of the CRISPR–Cas systems, a unified classification of these systems should be based on multiple criteria. Accordingly, we propose a `polythetic' classification that integrates the phylogenies of the most common cas genes, the sequence and organization of the CRISPR repeats and the architecture of the CRISPR–cas loci. PMID:21552286

Dynamic analysis of I cross beam section dissimilar plate joined by TIG welding

NASA Astrophysics Data System (ADS)

Sani, M. S. M.; Nazri, N. A.; Rani, M. N. Abdul; Yunus, M. A.

2018-04-01

In this paper, finite element (FE) joint modelling technique for prediction of dynamic properties of sheet metal jointed by tungsten inert gas (TTG) will be presented. I cross section dissimilar flat plate with different series of aluminium alloy; AA7075 and AA6061 joined by TTG are used. In order to find the most optimum set of TTG welding dissimilar plate, the finite element model with three types of joint modelling were engaged in this study; bar element (CBAR), beam element and spot weld element connector (CWELD). Experimental modal analysis (EMA) was carried out by impact hammer excitation on the dissimilar plates that welding by TTG method. Modal properties of FE model with joints were compared and validated with model testing. CWELD element was chosen to represent weld model for TTG joints due to its accurate prediction of mode shapes and contains an updating parameter for weld modelling compare to other weld modelling. Model updating was performed to improve correlation between EMA and FEA and before proceeds to updating, sensitivity analysis was done to select the most sensitive updating parameter. After perform model updating, average percentage of error of the natural frequencies for CWELD model is improved significantly.

The Feasibility of Adaptive Unstructured Computations On Petaflops Systems

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Oliker, Leonid; Heber, Gerd; Gao, Guang; Saini, Subhash (Technical Monitor)

1999-01-01

This viewgraph presentation covers the advantages of mesh adaptation, unstructured grids, and dynamic load balancing. It illustrates parallel adaptive communications, and explains PLUM (Parallel dynamic load balancing for adaptive unstructured meshes), and PSAW (Proper Self Avoiding Walks).

Dynamic Assessment: A Case of Unfulfilled Potential?

ERIC Educational Resources Information Center

Elliott, Julian G.; Resing, Wilma C. M.; Beckmann, Jens F.

2018-01-01

This paper updates a review of dynamic assessment in education by the first author, published in this journal in 2003. It notes that the original review failed to examine the important conceptual distinction between dynamic testing (DT) and dynamic assessment (DA). While both approaches seek to link assessment and intervention, the former is of…

Cooperation guided by the coexistence of imitation dynamics and aspiration dynamics in structured populations

NASA Astrophysics Data System (ADS)

Xu, Kuangyi; Li, Kun; Cong, Rui; Wang, Long

2017-02-01

In the framework of the evolutionary game theory, two fundamentally different mechanisms, the imitation process and the aspiration-driven dynamics, can be adopted by players to update their strategies. In the former case, individuals imitate the strategy of a more successful peer, while in the latter case individuals change their strategies based on a comparison of payoffs they collect in the game to their own aspiration levels. Here we explore how cooperation evolves for the coexistence of these two dynamics. Intriguingly, cooperation reaches its lowest level when a certain moderate fraction of individuals pick aspiration-level-driven rule while the others choose pairwise comparison rule. Furthermore, when individuals can adjust their update rules besides their strategies, either imitation dynamics or aspiration-driven dynamics will finally take over the entire population, and the stationary cooperation level is determined by the outcome of competition between these two dynamics. We find that appropriate synergetic effects and moderate aspiration level boost the fixation probability of aspiration-driven dynamics most effectively. Our work may be helpful in understanding the cooperative behavior induced by the coexistence of imitation dynamics and aspiration dynamics in the society.

Older adults learn less, but still reduce metabolic cost, during motor adaptation

PubMed Central

Huang, Helen J.

2013-01-01

The ability to learn new movements and dynamics is important for maintaining independence with advancing age. Age-related sensorimotor changes and increased muscle coactivation likely alter the trial-and-error-based process of adapting to new movement demands (motor adaptation). Here, we asked, to what extent is motor adaptation to novel dynamics maintained in older adults (≥65 yr)? We hypothesized that older adults would adapt to the novel dynamics less well than young adults. Because older adults often use muscle coactivation, we expected older adults to use greater muscle coactivation during motor adaptation than young adults. Nevertheless, we predicted that older adults would reduce muscle activity and metabolic cost with motor adaptation, similar to young adults. Seated older (n = 11, 73.8 ± 5.6 yr) and young (n = 15, 23.8 ± 4.7 yr) adults made targeted reaching movements while grasping a robotic arm. We measured their metabolic rate continuously via expired gas analysis. A force field was used to add novel dynamics. Older adults had greater movement deviations and compensated for just 65% of the novel dynamics compared with 84% in young adults. As expected, older adults used greater muscle coactivation than young adults. Last, older adults reduced muscle activity with motor adaptation and had consistent reductions in metabolic cost later during motor adaptation, similar to young adults. These results suggest that despite increased muscle coactivation, older adults can adapt to the novel dynamics, albeit less accurately. These results also suggest that reductions in metabolic cost may be a fundamental feature of motor adaptation. PMID:24133222

Early dynamics of the semantic priming shift

PubMed Central

Lavigne, Frédéric; Chanquoy, Lucile; Dumercy, Laurent; Vitu, Françoise

2013-01-01

Semantic processing of sequences of words requires the cognitive system to keep several word meanings simultaneously activated in working memory with limited capacity. The real- time updating of the sequence of word meanings relies on dynamic changes in the associates to the words that are activated. Protocols involving two sequential primes report a semantic priming shift from larger priming of associates to the first prime to larger priming of associates to the second prime, in a range of long SOAs (stimulus-onset asynchronies) between the second prime and the target. However, the possibility for an early semantic priming shift is still to be tested, and its dynamics as a function of association strength remain unknown. Three multiple priming experiments are proposed that cross-manipulate association strength between each of two successive primes and a target, for different values of short SOAs and prime durations. Results show an early priming shift ranging from priming of associates to the first prime only to priming of strong associates to the first prime and all of the associates to the second prime. We investigated the neural basis of the early priming shift by using a network model of spike frequency adaptive cortical neurons (e.g., Deco & Rolls, 2005), able to code different association strengths between the primes and the target. The cortical network model provides a description of the early dynamics of the priming shift in terms of pro-active and retro-active interferences within populations of excitatory neurons regulated by fast and unselective inhibitory feedback. PMID:23717346

Difficulty Swallowing After Stroke (Dysphagia)

MedlinePlus

... Stories Stroke Heroes Among Us Difficulty Swallowing After Stroke (Dysphagia) Updated:Nov 15,2016 Excerpted and adapted ... stiffness (spasticity), fatigue and more. Let's Talk About Stroke Fact Sheets Our stroke fact sheets cover treatments, ...

Context recognition and situation assessment in autonomous mobile robots

NASA Astrophysics Data System (ADS)

Yavnai, Arie

1993-05-01

The capability to recognize the operating context and to assess the situation in real-time is needed, if a high functionality autonomous mobile robot has to react properly and effectively to continuously changing situations and events, either external or internal, while the robot is performing its assigned tasks. A new approach and architecture for context recognition and situation assessment module (CORSA) is presented in this paper. CORSA is a multi-level information processing module which consists of adaptive decision and classification algorithms. It performs dynamic mapping from the data space to the context space, and dynamically decides on the context class. Learning mechanism is employed to update the decision variables so as to minimize the probability of misclassification. CORSA is embedded within the Mission Manager module of the intelligent autonomous hyper-controller (IAHC) of the mobile robot. The information regarding operating context, events and situation is then communicated to other modules of the IAHC where it is used to: (a) select the appropriate action strategy; (b) support the processes to arbitration and conflict resolution between reflexive behaviors and reasoning-driven behaviors; (c) predict future events and situations; and (d) determine criteria and priorities for planning, replanning, and decision making.

The role of population monitoring in the management of North American waterfowl

USGS Publications Warehouse

Nichols, J.D.; Williams, B.K.; Johnson, F.A.

2000-01-01

Despite the effort and expense devoted to large-scale monitoring programs, few existing programs have been designed with specific objectives in mind and few permit strong inferences about the dynamics of monitored systems. The waterfowl population monitoring programs of the U.S. Fish and Wildlife Service, Canadian Wildlife Service and state and provincial agencies provide a nice example with respect to program objectives, design and implementation. The May Breeding Grounds Survey provides an estimate of system state (population size) that serves two primary purposes in the adaptive management process: identifying the appropriate time-specific management actions and updating the information state (model weights) by providing a basis for evaluating predictions of competing models. Other waterfowl monitoring programs (e.g., banding program, hunter questionnaire survey, parts collection survey, winter survey) provide estimates of vital rates (rates of survival, reproduction and movement) associated with system dynamics and variables associated with management objectives (e.g., harvest). The reliability of estimates resulting from monitoring programs depends strongly on whether considerations about spatial variation and detection probability have been adequately incorporated into program design and implementation. Certain waterfowl surveys again provide nice examples of monitoring programs that incorporate these considerations.

A 3D model for rain-induced landslides based on molecular dynamics with fractal and fractional water diffusion

NASA Astrophysics Data System (ADS)

Martelloni, Gianluca; Bagnoli, Franco; Guarino, Alessio

2017-09-01

We present a three-dimensional model of rain-induced landslides, based on cohesive spherical particles. The rainwater infiltration into the soil follows either the fractional or the fractal diffusion equations. We analytically solve the fractal partial differential equation (PDE) for diffusion with particular boundary conditions to simulate a rainfall event. We developed a numerical integration scheme for the PDE, compared with the analytical solution. We adapt the fractal diffusion equation obtaining the gravimetric water content that we use as input of a triggering scheme based on Mohr-Coulomb limit-equilibrium criterion. This triggering is then complemented by a standard molecular dynamics algorithm, with an interaction force inspired by the Lennard-Jones potential, to update the positions and velocities of particles. We present our results for homogeneous and heterogeneous systems, i.e., systems composed by particles with same or different radius, respectively. Interestingly, in the heterogeneous case, we observe segregation effects due to the different volume of the particles. Finally, we analyze the parameter sensibility both for the triggering and the propagation phases. Our simulations confirm the results of a previous two-dimensional model and therefore the feasible applicability to real cases.

Biomimicry of symbiotic multi-species coevolution for discrete and continuous optimization in RFID networks.

PubMed

Lin, Na; Chen, Hanning; Jing, Shikai; Liu, Fang; Liang, Xiaodan

2017-03-01

In recent years, symbiosis as a rich source of potential engineering applications and computational model has attracted more and more attentions in the adaptive complex systems and evolution computing domains. Inspired by different symbiotic coevolution forms in nature, this paper proposed a series of multi-swarm particle swarm optimizers called PS 2 Os, which extend the single population particle swarm optimization (PSO) algorithm to interacting multi-swarms model by constructing hierarchical interaction topologies and enhanced dynamical update equations. According to different symbiotic interrelationships, four versions of PS 2 O are initiated to mimic mutualism, commensalism, predation, and competition mechanism, respectively. In the experiments, with five benchmark problems, the proposed algorithms are proved to have considerable potential for solving complex optimization problems. The coevolutionary dynamics of symbiotic species in each PS 2 O version are also studied respectively to demonstrate the heterogeneity of different symbiotic interrelationships that effect on the algorithm's performance. Then PS 2 O is used for solving the radio frequency identification (RFID) network planning (RNP) problem with a mixture of discrete and continuous variables. Simulation results show that the proposed algorithm outperforms the reference algorithms for planning RFID networks, in terms of optimization accuracy and computation robustness.

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

ERIC Educational Resources Information Center

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

2017-01-01

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

Comparing Methods for Dynamic Airspace Configuration

NASA Technical Reports Server (NTRS)

Zelinski, Shannon; Lai, Chok Fung

2011-01-01

This paper compares airspace design solutions for dynamically reconfiguring airspace in response to nominal daily traffic volume fluctuation. Airspace designs from seven algorithmic methods and a representation of current day operations in Kansas City Center were simulated with two times today's demand traffic. A three-configuration scenario was used to represent current day operations. Algorithms used projected unimpeded flight tracks to design initial 24-hour plans to switch between three configurations at predetermined reconfiguration times. At each reconfiguration time, algorithms used updated projected flight tracks to update the subsequent planned configurations. Compared to the baseline, most airspace design methods reduced delay and increased reconfiguration complexity, with similar traffic pattern complexity results. Design updates enabled several methods to as much as half the delay from their original designs. Freeform design methods reduced delay and increased reconfiguration complexity the most.

Aspiration dynamics of multi-player games in finite populations

PubMed Central

Du, Jinming; Wu, Bin; Altrock, Philipp M.; Wang, Long

2014-01-01

On studying strategy update rules in the framework of evolutionary game theory, one can differentiate between imitation processes and aspiration-driven dynamics. In the former case, individuals imitate the strategy of a more successful peer. In the latter case, individuals adjust their strategies based on a comparison of their pay-offs from the evolutionary game to a value they aspire, called the level of aspiration. Unlike imitation processes of pairwise comparison, aspiration-driven updates do not require additional information about the strategic environment and can thus be interpreted as being more spontaneous. Recent work has mainly focused on understanding how aspiration dynamics alter the evolutionary outcome in structured populations. However, the baseline case for understanding strategy selection is the well-mixed population case, which is still lacking sufficient understanding. We explore how aspiration-driven strategy-update dynamics under imperfect rationality influence the average abundance of a strategy in multi-player evolutionary games with two strategies. We analytically derive a condition under which a strategy is more abundant than the other in the weak selection limiting case. This approach has a long-standing history in evolutionary games and is mostly applied for its mathematical approachability. Hence, we also explore strong selection numerically, which shows that our weak selection condition is a robust predictor of the average abundance of a strategy. The condition turns out to differ from that of a wide class of imitation dynamics, as long as the game is not dyadic. Therefore, a strategy favoured under imitation dynamics can be disfavoured under aspiration dynamics. This does not require any population structure, and thus highlights the intrinsic difference between imitation and aspiration dynamics. PMID:24598208

Aspiration dynamics of multi-player games in finite populations.

PubMed

Du, Jinming; Wu, Bin; Altrock, Philipp M; Wang, Long

2014-05-06

On studying strategy update rules in the framework of evolutionary game theory, one can differentiate between imitation processes and aspiration-driven dynamics. In the former case, individuals imitate the strategy of a more successful peer. In the latter case, individuals adjust their strategies based on a comparison of their pay-offs from the evolutionary game to a value they aspire, called the level of aspiration. Unlike imitation processes of pairwise comparison, aspiration-driven updates do not require additional information about the strategic environment and can thus be interpreted as being more spontaneous. Recent work has mainly focused on understanding how aspiration dynamics alter the evolutionary outcome in structured populations. However, the baseline case for understanding strategy selection is the well-mixed population case, which is still lacking sufficient understanding. We explore how aspiration-driven strategy-update dynamics under imperfect rationality influence the average abundance of a strategy in multi-player evolutionary games with two strategies. We analytically derive a condition under which a strategy is more abundant than the other in the weak selection limiting case. This approach has a long-standing history in evolutionary games and is mostly applied for its mathematical approachability. Hence, we also explore strong selection numerically, which shows that our weak selection condition is a robust predictor of the average abundance of a strategy. The condition turns out to differ from that of a wide class of imitation dynamics, as long as the game is not dyadic. Therefore, a strategy favoured under imitation dynamics can be disfavoured under aspiration dynamics. This does not require any population structure, and thus highlights the intrinsic difference between imitation and aspiration dynamics.

An adaptive reentry guidance method considering the influence of blackout zone

NASA Astrophysics Data System (ADS)

Wu, Yu; Yao, Jianyao; Qu, Xiangju

2018-01-01

Reentry guidance has been researched as a popular topic because it is critical for a successful flight. In view that the existing guidance methods do not take into account the accumulated navigation error of Inertial Navigation System (INS) in the blackout zone, in this paper, an adaptive reentry guidance method is proposed to obtain the optimal reentry trajectory quickly with the target of minimum aerodynamic heating rate. The terminal error in position and attitude can be also reduced with the proposed method. In this method, the whole reentry guidance task is divided into two phases, i.e., the trajectory updating phase and the trajectory planning phase. In the first phase, the idea of model predictive control (MPC) is used, and the receding optimization procedure ensures the optimal trajectory in the next few seconds. In the trajectory planning phase, after the vehicle has flown out of the blackout zone, the optimal reentry trajectory is obtained by online planning to adapt to the navigation information. An effective swarm intelligence algorithm, i.e. pigeon inspired optimization (PIO) algorithm, is applied to obtain the optimal reentry trajectory in both of the two phases. Compared to the trajectory updating method, the proposed method can reduce the terminal error by about 30% considering both the position and attitude, especially, the terminal error of height has almost been eliminated. Besides, the PIO algorithm performs better than the particle swarm optimization (PSO) algorithm both in the trajectory updating phase and the trajectory planning phases.

Application of Adaptive Autopilot Designs for an Unmanned Aerial Vehicle

NASA Technical Reports Server (NTRS)

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

2005-01-01

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

Structural Model Tuning Capability in an Object-Oriented Multidisciplinary Design, Analysis, and Optimization Tool

NASA Technical Reports Server (NTRS)

Lung, Shun-fat; Pak, Chan-gi

2008-01-01

Updating the finite element model using measured data is a challenging problem in the area of structural dynamics. The model updating process requires not only satisfactory correlations between analytical and experimental results, but also the retention of dynamic properties of structures. Accurate rigid body dynamics are important for flight control system design and aeroelastic trim analysis. Minimizing the difference between analytical and experimental results is a type of optimization problem. In this research, a multidisciplinary design, analysis, and optimization (MDAO) tool is introduced to optimize the objective function and constraints such that the mass properties, the natural frequencies, and the mode shapes are matched to the target data as well as the mass matrix being orthogonalized.

Structural Model Tuning Capability in an Object-Oriented Multidisciplinary Design, Analysis, and Optimization Tool

NASA Technical Reports Server (NTRS)

Lung, Shun-fat; Pak, Chan-gi

2008-01-01

Updating the finite element model using measured data is a challenging problem in the area of structural dynamics. The model updating process requires not only satisfactory correlations between analytical and experimental results, but also the retention of dynamic properties of structures. Accurate rigid body dynamics are important for flight control system design and aeroelastic trim analysis. Minimizing the difference between analytical and experimental results is a type of optimization problem. In this research, a multidisciplinary design, analysis, and optimization [MDAO] tool is introduced to optimize the objective function and constraints such that the mass properties, the natural frequencies, and the mode shapes are matched to the target data as well as the mass matrix being orthogonalized.

Adaptive pseudolinear compensators of dynamic characteristics of automatic control systems

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

How Stuttering Develops: The Multifactorial Dynamic Pathways Theory

ERIC Educational Resources Information Center

Smith, Anne; Weber, Christine

2017-01-01

Purpose: We advanced a multifactorial, dynamic account of the complex, nonlinear interactions of motor, linguistic, and emotional factors contributing to the development of stuttering. Our purpose here is to update our account as the multifactorial dynamic pathways theory. Method: We review evidence related to how stuttering develops, including…

77 FR 19408 - Dynamic Mobility Applications and Data Capture Management Programs; Notice of Public Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-30

... DEPARTMENT OF TRANSPORTATION Dynamic Mobility Applications and Data Capture Management Programs...) Intelligent Transportation System Joint Program Office (ITS JPO) will host a free public meeting to provide stakeholders an update on the Data Capture and Management (DCM) and Dynamic Mobility Applications (DMA...

The Propulsive Small Expendable Deployer System (ProSEDS)

NASA Technical Reports Server (NTRS)

Lorenzini, Enrico C.; Estes, Robert D.; Cosmo, Mario L.

2001-01-01

This is the Annual Report #2 entitled "The Propulsive Small Expendable Deployer System (ProSEDS)" prepared by the Smithsonian Astrophysical Observatory for NASA Marshall Space Flight Center. This report covers the period of activity from 1 August 2000 through 30 July 2001. The topics include: 1) Updated System Performance; 2) Mission Analysis; 3) Updated Dynamics Reference Mission; 4) Updated Deployment Control Profiles and Simulations; 5) Comparison of ED tethers and electrical thrusters; 6) Kalman filters for mission estimation; and 7) Delivery of interactive software for ED tethers.

Opinion evolution in open community

NASA Astrophysics Data System (ADS)

Pan, Qiuhui; Qin, Yao; Xu, Yiqun; Tong, Mengfei; He, Mingfeng

We consider a dynamic group composed with a constant number of people and the people will change periodically. Every member in the community owns a value of confidence — a mechanism that measures the agent’s coherence to his or her own attitude. Based on Cellular Automata, the opinions of all agents are synchronously updated. As long as the updating frequency and updating proportion are appropriate, the open system can reach a democracy-like steady state. The majority of agents in the community will hold the same opinion.

Parametric optimization and design validation based on finite element analysis of hybrid socket adapter for transfemoral prosthetic knee.

PubMed

Kumar, Neelesh

2014-10-01

Finite element analysis has been universally employed for the stress and strain analysis in lower extremity prosthetics. The socket adapter was the principal subject of interest due to its importance in deciding the knee motion range. This article focused on the static and dynamic stress analysis of the designed hybrid adapter developed by the authors. A standard mechanical design validation approach using von Mises was followed. Four materials were considered for the analysis, namely, carbon fiber, oil-filled nylon, Al-6061, and mild steel. The paper analyses the static and dynamic stress on designed hybrid adapter which incorporates features of conventional male and female socket adapters. The finite element analysis was carried out for possible different angles of knee flexion simulating static and dynamic gait situation. Research was carried out on available design of socket adapter. Mechanical design of hybrid adapter was conceptualized and a CAD model was generated using Inventor modelling software. Static and dynamic stress analysis was carried out on different materials for optimization. The finite element analysis was carried out on the software Autodesk Inventor Professional Ver. 2011. The peak value of von Mises stress occurred in the neck region of the adapter and in the lower face region at rod eye-adapter junction in static and dynamic analyses, respectively. Oil-filled nylon was found to be the best material among the four with respect to strength, weight, and cost. Research investigations on newer materials for development of improved prosthesis will immensely benefit the amputees. The study analyze the static and dynamic stress on the knee joint adapter to provide better material used for hybrid design of adapter. © The International Society for Prosthetics and Orthotics 2013.

The Impact of Binary Companions on Planetary Systems

NASA Astrophysics Data System (ADS)

Kraus, Adam L.; Ireland, Michael; Dupuy, Trent; Mann, Andrew; Huber, Daniel

2018-01-01

The majority of solar-type stars are found in binary systems, and the dynamical influence of binary companions is expected to profoundly influence planetary systems. However, the difficulty of identifying planets in binary systems has left the magnitude of this effect uncertain; despite numerous theoretical hurdles to their formation and survival, at least some binary systems clearly host planets. We present high-resolution imaging of nearly 500 Kepler Objects of Interest (KOIs) obtained using adaptive-optics imaging and nonredundant aperture-mask interferometry on the Keck II telescope. We super-resolve some binary systems to projected separations of under 5 AU, showing that planets might form in these dynamically active environments. However, the full distribution of projected separations for our planet-host sample more broadly reveals a deep paucity of binary companions at solar-system scales. Our results demonstrate that a fifth of all solar-type stars in the Milky Way are disallowed from hosting planetary systems due to the influence of a binary companion. We now update these results with multi-epoch imaging to reject non-comoving background stars and securely identify even the least massive stellar companions, as well as tracing out the orbital motion of stellar companions. These results are beginning to reveal not just the fraction of binaries that do not host planets, but also potential explanations for planet survival even in some very close, dynamically active binary systems.

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'…

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

PubMed

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

2016-08-03

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

Social imitation versus strategic choice, or consensus versus cooperation, in the networked Prisoner's Dilemma

NASA Astrophysics Data System (ADS)

Vilone, Daniele; Ramasco, José J.; Sánchez, Angel; Miguel, Maxi San

2014-08-01

The interplay of social and strategic motivations in human interactions is a largely unexplored topic in collective social phenomena. Whether individuals' decisions are taken in a purely strategic basis or due to social pressure without a rational background crucially influences the model outcome. Here we study a networked Prisoner's Dilemma in which decisions are made either based on the replication of the most successful neighbor's strategy (unconditional imitation) or by pure social imitation following an update rule inspired by the voter model. The main effects of the voter dynamics are an enhancement of the final consensus, i.e., asymptotic states are generally uniform, and a promotion of cooperation in certain regions of the parameter space as compared to the outcome of purely strategic updates. Thus, voter dynamics acts as an interface noise and has a similar effect as a pure random noise; furthermore, its influence is mostly independent of the network heterogeneity. When strategic decisions are made following other update rules such as the replicator or Moran processes, the dynamic mixed state found under unconditional imitation for some parameters disappears, but an increase of cooperation in certain parameter regions is still observed. Comparing our results with recent experiments on the Prisoner's Dilemma, we conclude that such a mixed dynamics may explain moody conditional cooperation among the agents.

Social imitation versus strategic choice, or consensus versus cooperation, in the networked Prisoner's Dilemma.

PubMed

Vilone, Daniele; Ramasco, José J; Sánchez, Angel; San Miguel, Maxi

2014-08-01

The interplay of social and strategic motivations in human interactions is a largely unexplored topic in collective social phenomena. Whether individuals' decisions are taken in a purely strategic basis or due to social pressure without a rational background crucially influences the model outcome. Here we study a networked Prisoner's Dilemma in which decisions are made either based on the replication of the most successful neighbor's strategy (unconditional imitation) or by pure social imitation following an update rule inspired by the voter model. The main effects of the voter dynamics are an enhancement of the final consensus, i.e., asymptotic states are generally uniform, and a promotion of cooperation in certain regions of the parameter space as compared to the outcome of purely strategic updates. Thus, voter dynamics acts as an interface noise and has a similar effect as a pure random noise; furthermore, its influence is mostly independent of the network heterogeneity. When strategic decisions are made following other update rules such as the replicator or Moran processes, the dynamic mixed state found under unconditional imitation for some parameters disappears, but an increase of cooperation in certain parameter regions is still observed. Comparing our results with recent experiments on the Prisoner's Dilemma, we conclude that such a mixed dynamics may explain moody conditional cooperation among the agents.

Asynchronous updates can promote the evolution of cooperation on multiplex networks

NASA Astrophysics Data System (ADS)

Allen, James M.; Hoyle, Rebecca B.

2017-04-01

We study the importance to the frequency of cooperation of the choice of updating strategies in a game played asynchronously or synchronously across layers in a multiplex network. Updating asynchronously in the public goods game leads to higher frequencies of cooperation compared to synchronous updates. How large this effect is depends on the sensitivity of the game dynamics to changes in the number of cooperators surrounding a player, with the largest effect observed when players payoffs are small. The discovery of this effect enhances understanding of cooperation on multiplex networks, and demonstrates a new way to maintain cooperation in these systems.

The Design and Product of National 1:1000000 Cartographic Data of Topographic Map

NASA Astrophysics Data System (ADS)

Wang, Guizhi

2016-06-01

National administration of surveying, mapping and geoinformation started to launch the project of national fundamental geographic information database dynamic update in 2012. Among them, the 1:50000 database was updated once a year, furthermore the 1:250000 database was downsized and linkage-updated on the basis. In 2014, using the latest achievements of 1:250000 database, comprehensively update the 1:1000000 digital line graph database. At the same time, generate cartographic data of topographic map and digital elevation model data. This article mainly introduce national 1:1000000 cartographic data of topographic map, include feature content, database structure, Database-driven Mapping technology, workflow and so on.

An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture.

PubMed

Doulamis, A; Doulamis, N; Ntalianis, K; Kollias, S

2003-01-01

In this paper, an unsupervised video object (VO) segmentation and tracking algorithm is proposed based on an adaptable neural-network architecture. The proposed scheme comprises: 1) a VO tracking module and 2) an initial VO estimation module. Object tracking is handled as a classification problem and implemented through an adaptive network classifier, which provides better results compared to conventional motion-based tracking algorithms. Network adaptation is accomplished through an efficient and cost effective weight updating algorithm, providing a minimum degradation of the previous network knowledge and taking into account the current content conditions. A retraining set is constructed and used for this purpose based on initial VO estimation results. Two different scenarios are investigated. The first concerns extraction of human entities in video conferencing applications, while the second exploits depth information to identify generic VOs in stereoscopic video sequences. Human face/ body detection based on Gaussian distributions is accomplished in the first scenario, while segmentation fusion is obtained using color and depth information in the second scenario. A decision mechanism is also incorporated to detect time instances for weight updating. Experimental results and comparisons indicate the good performance of the proposed scheme even in sequences with complicated content (object bending, occlusion).

Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter.

PubMed

Rastetter, Edward B; Williams, Mathew; Griffin, Kevin L; Kwiatkowski, Bonnie L; Tomasky, Gabrielle; Potosnak, Mark J; Stoy, Paul C; Shaver, Gaius R; Stieglitz, Marc; Hobbie, John E; Kling, George W

2010-07-01

Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal processing filter like the ensemble Kalman filter (EnKF). The EnKF compares each measurement in the time series to a model prediction and updates the NEE estimate by weighting the measurement and model prediction relative to a specified measurement error estimate and an estimate of the model-prediction error that is continuously updated based on model predictions of earlier measurements in the time series. Because of the covariance among model variables, the EnKF can also update estimates of variables for which there is no direct measurement. The resulting estimates evolve through time, enabling the EnKF to be used to estimate dynamic variables like changes in leaf phenology. The evolving estimates can also serve as a means to test the embedded model and reconcile persistent deviations between observations and model predictions. We embedded a simple arctic NEE model into the EnKF and filtered data from an eddy covariance tower located in tussock tundra on the northern foothills of the Brooks Range in northern Alaska, USA. The model predicts NEE based only on leaf area, irradiance, and temperature and has been well corroborated for all the major vegetation types in the Low Arctic using chamber-based data. This is the first application of the model to eddy covariance data. We modified the EnKF by adding an adaptive noise estimator that provides a feedback between persistent model data deviations and the noise added to the ensemble of Monte Carlo simulations in the EnKF. We also ran the EnKF with both a specified leaf-area trajectory and with the EnKF sequentially recalibrating leaf-area estimates to compensate for persistent model-data deviations. When used together, adaptive noise estimation and sequential recalibration substantially improved filter performance, but it did not improve performance when used individually. The EnKF estimates of leaf area followed the expected springtime canopy phenology. However, there were also diel fluctuations in the leaf-area estimates; these are a clear indication of a model deficiency possibly related to vapor pressure effects on canopy conductance.

Changing ideas about others’ intentions: updating prior expectations tunes activity in the human motor system

PubMed Central

Jacquet, Pierre O.; Roy, Alice C.; Chambon, Valérian; Borghi, Anna M.; Salemme, Roméo; Farnè, Alessandro; Reilly, Karen T.

2016-01-01

Predicting intentions from observing another agent’s behaviours is often thought to depend on motor resonance – i.e., the motor system’s response to a perceived movement by the activation of its stored motor counterpart, but observers might also rely on prior expectations, especially when actions take place in perceptually uncertain situations. Here we assessed motor resonance during an action prediction task using transcranial magnetic stimulation to probe corticospinal excitability (CSE) and report that experimentally-induced updates in observers’ prior expectations modulate CSE when predictions are made under situations of perceptual uncertainty. We show that prior expectations are updated on the basis of both biomechanical and probabilistic prior information and that the magnitude of the CSE modulation observed across participants is explained by the magnitude of change in their prior expectations. These findings provide the first evidence that when observers predict others’ intentions, motor resonance mechanisms adapt to changes in their prior expectations. We propose that this adaptive adjustment might reflect a regulatory control mechanism that shares some similarities with that observed during action selection. Such a mechanism could help arbitrate the competition between biomechanical and probabilistic prior information when appropriate for prediction. PMID:27243157

Changing ideas about others' intentions: updating prior expectations tunes activity in the human motor system.

PubMed

Jacquet, Pierre O; Roy, Alice C; Chambon, Valérian; Borghi, Anna M; Salemme, Roméo; Farnè, Alessandro; Reilly, Karen T

2016-05-31

Predicting intentions from observing another agent's behaviours is often thought to depend on motor resonance - i.e., the motor system's response to a perceived movement by the activation of its stored motor counterpart, but observers might also rely on prior expectations, especially when actions take place in perceptually uncertain situations. Here we assessed motor resonance during an action prediction task using transcranial magnetic stimulation to probe corticospinal excitability (CSE) and report that experimentally-induced updates in observers' prior expectations modulate CSE when predictions are made under situations of perceptual uncertainty. We show that prior expectations are updated on the basis of both biomechanical and probabilistic prior information and that the magnitude of the CSE modulation observed across participants is explained by the magnitude of change in their prior expectations. These findings provide the first evidence that when observers predict others' intentions, motor resonance mechanisms adapt to changes in their prior expectations. We propose that this adaptive adjustment might reflect a regulatory control mechanism that shares some similarities with that observed during action selection. Such a mechanism could help arbitrate the competition between biomechanical and probabilistic prior information when appropriate for prediction.

Large Deployable Reflector (LDR) feasibility study update

NASA Technical Reports Server (NTRS)

Alff, W. H.; Banderman, L. W.

1983-01-01

In 1982 a workshop was held to refine the science rationale for large deployable reflectors (LDR) and develop technology requirements that support the science rationale. At the end of the workshop, a set of LDR consensus systems requirements was established. The subject study was undertaken to update the initial LDR study using the new systems requirements. The study included mirror materials selection and configuration, thermal analysis, structural concept definition and analysis, dynamic control analysis and recommendations for further study. The primary emphasis was on the dynamic controls requirements and the sophistication of the controls system needed to meet LDR performance goals.

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.

Predictor-Based Model Reference Adaptive Control

NASA Technical Reports Server (NTRS)

Lavretsky, Eugene; Gadient, Ross; Gregory, Irene M.

2009-01-01

This paper is devoted to robust, Predictor-based Model Reference Adaptive Control (PMRAC) design. The proposed adaptive system is compared with the now-classical Model Reference Adaptive Control (MRAC) architecture. Simulation examples are presented. Numerical evidence indicates that the proposed PMRAC tracking architecture has better than MRAC transient characteristics. In this paper, we presented a state-predictor based direct adaptive tracking design methodology for multi-input dynamical systems, with partially known dynamics. Efficiency of the design was demonstrated using short period dynamics of an aircraft. Formal proof of the reported PMRAC benefits constitute future research and will be reported elsewhere.

Update of the Mexican College of Rheumatology guidelines for the pharmacologic treatment of rheumatoid arthritis.

PubMed

Cardiel, Mario H; Díaz-Borjón, Alejandro; Vázquez del Mercado Espinosa, Mónica; Gámez-Nava, Jorge Iván; Barile Fabris, Leonor A; Pacheco Tena, César; Silveira Torre, Luis H; Pascual Ramos, Virginia; Goycochea Robles, María Victoria; Aguilar Arreola, Jorge Enrique; González Díaz, Verónica; Alvarez Nemegyei, José; González-López, Laura del Carmen; Salazar Páramo, Mario; Portela Hernández, Margarita; Castro Colín, Zully; Xibillé Friedman, Daniel Xavier; Alvarez Hernández, Everardo; Casasola Vargas, Julio; Cortés Hernández, Miguel; Flores-Alvarado, Diana E; Martínez Martínez, Laura A; Vega-Morales, David; Flores-Suárez, Luis Felipe; Medrano Ramírez, Gabriel; Barrera Cruz, Antonio; García González, Adolfo; López López, Susana Marisela; Rosete Reyes, Alejandra; Espinosa Morales, Rolando

2014-01-01

The pharmacologic management of rheumatoid arthritis has progressed substantially over the past years. It is therefore desirable that existing information be periodically updated. There are several published international guidelines for the treatment of rheumatoid arthritis that hardly adapt to the Mexican health system because of its limited healthcare resources. Hence, it is imperative to unify the existing recommendations and to incorporate them to a set of clinical, updated recommendations; the Mexican College of Rheumatology developed these recommendations in order to offer an integral management approach of rheumatoid arthritis according to the resources of the Mexican health system. To review, update and improve the available evidence within clinical practice guidelines on the pharmacological management of rheumatoid arthritis and produce a set of recommendations adapted to the Mexican health system, according to evidence available through December 2012. The working group was composed of 30 trained and experienced rheumatologists with a high quality of clinical knowledge and judgment. Recommendations were based on the highest quality evidence from the previously established treatment guidelines, meta-analysis and controlled clinical trials for the adult population with rheumatoid arthritis. During the conformation of this document, each working group settled the existing evidence from the different topics according to their experience. Finally, all the evidence and decisions were unified into a single document, treatment algorithm and drug standardization tables. This update of the Mexican Guidelines for the Pharmacologic Treatment of Rheumatoid Arthritis provides the highest quality information available at the time the working group undertook this review and contextualizes its use for the complex Mexican health system. Copyright © 2013 Elsevier España, S.L. All rights reserved.

Online adaptation and over-trial learning in macaque visuomotor control.

PubMed

Braun, Daniel A; Aertsen, Ad; Paz, Rony; Vaadia, Eilon; Rotter, Stefan; Mehring, Carsten

2011-01-01

When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e., online adaptation and over-trial learning, in a visuomotor experiment performed by macaques. We show that simple non-adaptive control schemes fail to perform in this task, but that a previously suggested adaptive optimal feedback control model can explain the observed behavior. We also show that over-trial learning as seen in learning and aftereffect curves can be explained by learning in a radial basis function network. Our results suggest that both the process of over-trial learning and the process of online adaptation are crucial to understand visuomotor learning.

Online Adaptation and Over-Trial Learning in Macaque Visuomotor Control

PubMed Central

Braun, Daniel A.; Aertsen, Ad; Paz, Rony; Vaadia, Eilon; Rotter, Stefan; Mehring, Carsten

2011-01-01

When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e., online adaptation and over-trial learning, in a visuomotor experiment performed by macaques. We show that simple non-adaptive control schemes fail to perform in this task, but that a previously suggested adaptive optimal feedback control model can explain the observed behavior. We also show that over-trial learning as seen in learning and aftereffect curves can be explained by learning in a radial basis function network. Our results suggest that both the process of over-trial learning and the process of online adaptation are crucial to understand visuomotor learning. PMID:21720526

Regulation of Dynamical Systems to Optimal Solutions of Semidefinite Programs: Algorithms and Applications to AC Optimal Power Flow

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

Dall'Anese, Emiliano; Dhople, Sairaj V.; Giannakis, Georgios B.

2015-07-01

This paper considers a collection of networked nonlinear dynamical systems, and addresses the synthesis of feedback controllers that seek optimal operating points corresponding to the solution of pertinent network-wide optimization problems. Particular emphasis is placed on the solution of semidefinite programs (SDPs). The design of the feedback controller is grounded on a dual e-subgradient approach, with the dual iterates utilized to dynamically update the dynamical-system reference signals. Global convergence is guaranteed for diminishing stepsize rules, even when the reference inputs are updated at a faster rate than the dynamical-system settling time. The application of the proposed framework to the controlmore » of power-electronic inverters in AC distribution systems is discussed. The objective is to bridge the time-scale separation between real-time inverter control and network-wide optimization. Optimization objectives assume the form of SDP relaxations of prototypical AC optimal power flow problems.« less

An Agent-based Modeling of Water-Food Nexus towards Sustainable Management of Urban Water Resources

NASA Astrophysics Data System (ADS)

Esmaeili, N.; Kanta, L.

2017-12-01

Growing population, urbanization, and climate change have put tremendous stress on water systems in many regions. A shortage in water system not only affects water users of a municipality but also that of food system. About 70% of global water is withdrawn for agriculture; livestock and dairy productions are also dependent on water availability. Although researchers and policy makers have identified and emphasized the water-food (WF) nexus in recent decade, most existing WF models offer strategies to reduce trade-offs and to generate benefits without considering feedback loops and adaptations between those systems. Feedback loops between water and food system can help understand long-term behavioral trends between water users of the integrated WF system which, in turn, can help manage water resources sustainably. An Agent-based modeling approach is applied here to develop a conceptual framework of WF systems. All water users in this system are modeled as agents, who are capable of making decisions and can adapt new behavior based on inputs from other agents in a shared environment through a set of logical and mathematical rules. Residential and commercial/industrial consumers are represented as municipal agents; crop, livestock, and dairy farmers are represented as food agents; and water management officials are represented as policy agent. During the period of water shortage, policy agent will propose/impose various water conservation measures, such as adapting water-efficient technologies, banning outdoor irrigation, implementing supplemental irrigation, using recycled water for livestock/dairy production, among others. Municipal and food agents may adapt conservation strategies and will update their demand accordingly. Emergent properties of the WF nexus will arise through dynamic interactions between various actors of water and food system. This model will be implemented to a case study for resource allocation and future policy development.

Reconfigurable Model Execution in the OpenMDAO Framework

NASA Technical Reports Server (NTRS)

Hwang, John T.

2017-01-01

NASA's OpenMDAO framework facilitates constructing complex models and computing their derivatives for multidisciplinary design optimization. Decomposing a model into components that follow a prescribed interface enables OpenMDAO to assemble multidisciplinary derivatives from the component derivatives using what amounts to the adjoint method, direct method, chain rule, global sensitivity equations, or any combination thereof, using the MAUD architecture. OpenMDAO also handles the distribution of processors among the disciplines by hierarchically grouping the components, and it automates the data transfer between components that are on different processors. These features have made OpenMDAO useful for applications in aircraft design, satellite design, wind turbine design, and aircraft engine design, among others. This paper presents new algorithms for OpenMDAO that enable reconfigurable model execution. This concept refers to dynamically changing, during execution, one or more of: the variable sizes, solution algorithm, parallel load balancing, or set of variables-i.e., adding and removing components, perhaps to switch to a higher-fidelity sub-model. Any component can reconfigure at any point, even when running in parallel with other components, and the reconfiguration algorithm presented here performs the synchronized updates to all other components that are affected. A reconfigurable software framework for multidisciplinary design optimization enables new adaptive solvers, adaptive parallelization, and new applications such as gradient-based optimization with overset flow solvers and adaptive mesh refinement. Benchmarking results demonstrate the time savings for reconfiguration compared to setting up the model again from scratch, which can be significant in large-scale problems. Additionally, the new reconfigurability feature is applied to a mission profile optimization problem for commercial aircraft where both the parametrization of the mission profile and the time discretization are adaptively refined, resulting in computational savings of roughly 10% and the elimination of oscillations in the optimized altitude profile.

Seismic hazard in the eastern United States

USGS Publications Warehouse

Mueller, Charles; Boyd, Oliver; Petersen, Mark D.; Moschetti, Morgan P.; Rezaeian, Sanaz; Shumway, Allison

2015-01-01

The U.S. Geological Survey seismic hazard maps for the central and eastern United States were updated in 2014. We analyze results and changes for the eastern part of the region. Ratio maps are presented, along with tables of ground motions and deaggregations for selected cities. The Charleston fault model was revised, and a new fault source for Charlevoix was added. Background seismicity sources utilized an updated catalog, revised completeness and recurrence models, and a new adaptive smoothing procedure. Maximum-magnitude models and ground motion models were also updated. Broad, regional hazard reductions of 5%–20% are mostly attributed to new ground motion models with stronger near-source attenuation. The revised Charleston fault geometry redistributes local hazard, and the new Charlevoix source increases hazard in northern New England. Strong increases in mid- to high-frequency hazard at some locations—for example, southern New Hampshire, central Virginia, and eastern Tennessee—are attributed to updated catalogs and/or smoothing.

A new approach to adaptive control of manipulators

NASA Technical Reports Server (NTRS)

Seraji, H.

1987-01-01

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

Individual-based models for adaptive diversification in high-dimensional phenotype spaces.

PubMed

Ispolatov, Iaroslav; Madhok, Vaibhav; Doebeli, Michael

2016-02-07

Most theories of evolutionary diversification are based on equilibrium assumptions: they are either based on optimality arguments involving static fitness landscapes, or they assume that populations first evolve to an equilibrium state before diversification occurs, as exemplified by the concept of evolutionary branching points in adaptive dynamics theory. Recent results indicate that adaptive dynamics may often not converge to equilibrium points and instead generate complicated trajectories if evolution takes place in high-dimensional phenotype spaces. Even though some analytical results on diversification in complex phenotype spaces are available, to study this problem in general we need to reconstruct individual-based models from the adaptive dynamics generating the non-equilibrium dynamics. Here we first provide a method to construct individual-based models such that they faithfully reproduce the given adaptive dynamics attractor without diversification. We then show that a propensity to diversify can be introduced by adding Gaussian competition terms that generate frequency dependence while still preserving the same adaptive dynamics. For sufficiently strong competition, the disruptive selection generated by frequency-dependence overcomes the directional evolution along the selection gradient and leads to diversification in phenotypic directions that are orthogonal to the selection gradient. Copyright © 2015 Elsevier Ltd. All rights reserved.

Short-Term Adaptive Modification of Dynamic Ocular Accommodation

PubMed Central

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

2009-01-01

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

A self-adaption compensation control for hysteresis nonlinearity in piezo-actuated stages based on Pi-sigma fuzzy neural network

NASA Astrophysics Data System (ADS)

Xu, Rui; Zhou, Miaolei

2018-04-01

Piezo-actuated stages are widely applied in the high-precision positioning field nowadays. However, the inherent hysteresis nonlinearity in piezo-actuated stages greatly deteriorates the positioning accuracy of piezo-actuated stages. This paper first utilizes a nonlinear autoregressive moving average with exogenous inputs (NARMAX) model based on the Pi-sigma fuzzy neural network (PSFNN) to construct an online rate-dependent hysteresis model for describing the hysteresis nonlinearity in piezo-actuated stages. In order to improve the convergence rate of PSFNN and modeling precision, we adopt the gradient descent algorithm featuring three different learning factors to update the model parameters. The convergence of the NARMAX model based on the PSFNN is analyzed effectively. To ensure that the parameters can converge to the true values, the persistent excitation condition is considered. Then, a self-adaption compensation controller is designed for eliminating the hysteresis nonlinearity in piezo-actuated stages. A merit of the proposed controller is that it can directly eliminate the complex hysteresis nonlinearity in piezo-actuated stages without any inverse dynamic models. To demonstrate the effectiveness of the proposed model and control methods, a set of comparative experiments are performed on piezo-actuated stages. Experimental results show that the proposed modeling and control methods have excellent performance.

Optimizing virtual reality for all users through gaze-contingent and adaptive focus displays.

PubMed

Padmanaban, Nitish; Konrad, Robert; Stramer, Tal; Cooper, Emily A; Wetzstein, Gordon

2017-02-28

From the desktop to the laptop to the mobile device, personal computing platforms evolve over time. Moving forward, wearable computing is widely expected to be integral to consumer electronics and beyond. The primary interface between a wearable computer and a user is often a near-eye display. However, current generation near-eye displays suffer from multiple limitations: they are unable to provide fully natural visual cues and comfortable viewing experiences for all users. At their core, many of the issues with near-eye displays are caused by limitations in conventional optics. Current displays cannot reproduce the changes in focus that accompany natural vision, and they cannot support users with uncorrected refractive errors. With two prototype near-eye displays, we show how these issues can be overcome using display modes that adapt to the user via computational optics. By using focus-tunable lenses, mechanically actuated displays, and mobile gaze-tracking technology, these displays can be tailored to correct common refractive errors and provide natural focus cues by dynamically updating the system based on where a user looks in a virtual scene. Indeed, the opportunities afforded by recent advances in computational optics open up the possibility of creating a computing platform in which some users may experience better quality vision in the virtual world than in the real one.

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

NASA Technical Reports Server (NTRS)

Williams-Hayes, Peggy S.

2004-01-01

The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

Optimizing virtual reality for all users through gaze-contingent and adaptive focus displays

NASA Astrophysics Data System (ADS)

Padmanaban, Nitish; Konrad, Robert; Stramer, Tal; Cooper, Emily A.; Wetzstein, Gordon

2017-02-01

From the desktop to the laptop to the mobile device, personal computing platforms evolve over time. Moving forward, wearable computing is widely expected to be integral to consumer electronics and beyond. The primary interface between a wearable computer and a user is often a near-eye display. However, current generation near-eye displays suffer from multiple limitations: they are unable to provide fully natural visual cues and comfortable viewing experiences for all users. At their core, many of the issues with near-eye displays are caused by limitations in conventional optics. Current displays cannot reproduce the changes in focus that accompany natural vision, and they cannot support users with uncorrected refractive errors. With two prototype near-eye displays, we show how these issues can be overcome using display modes that adapt to the user via computational optics. By using focus-tunable lenses, mechanically actuated displays, and mobile gaze-tracking technology, these displays can be tailored to correct common refractive errors and provide natural focus cues by dynamically updating the system based on where a user looks in a virtual scene. Indeed, the opportunities afforded by recent advances in computational optics open up the possibility of creating a computing platform in which some users may experience better quality vision in the virtual world than in the real one.

From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks

PubMed Central

Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

2016-01-01

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains. PMID:26972968

From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks.

PubMed

Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

2016-03-14

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.

Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices

NASA Astrophysics Data System (ADS)

Chen, Yan; Mabu, Shingo; Shimada, Kaoru; Hirasawa, Kotaro

The key in stock trading model is to take the right actions for trading at the right time, primarily based on the accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied Genetic Network Programming (GNP) to creating a stock trading model. In this paper, we propose a new method called Real Time Updating Genetic Network Programming (RTU-GNP) for adapting to the change of stock prices. There are three important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the candlestick charts according to the real time stock prices. Second, we combine RTU-GNP with a Sarsa learning algorithm to create the programs efficiently. Also, sub-nodes are introduced in each judgment and processing node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. Third, a Real Time Updating system has been firstly introduced in our paper considering the change of the trend of stock prices. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without real time updating. We also compared the experimental results using the proposed method with Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method.

Integration of individual and social information for decision-making in groups of different sizes.

PubMed

Park, Seongmin A; Goïame, Sidney; O'Connor, David A; Dreher, Jean-Claude

2017-06-01

When making judgments in a group, individuals often revise their initial beliefs about the best judgment to make given what others believe. Despite the ubiquity of this phenomenon, we know little about how the brain updates beliefs when integrating personal judgments (individual information) with those of others (social information). Here, we investigated the neurocomputational mechanisms of how we adapt our judgments to those made by groups of different sizes, in the context of jury decisions for a criminal. By testing different theoretical models, we showed that a social Bayesian inference model captured changes in judgments better than 2 other models. Our results showed that participants updated their beliefs by appropriately weighting individual and social sources of information according to their respective credibility. When investigating 2 fundamental computations of Bayesian inference, belief updates and credibility estimates of social information, we found that the dorsal anterior cingulate cortex (dACC) computed the level of belief updates, while the bilateral frontopolar cortex (FPC) was more engaged in individuals who assigned a greater credibility to the judgments of a larger group. Moreover, increased functional connectivity between these 2 brain regions reflected a greater influence of group size on the relative credibility of social information. These results provide a mechanistic understanding of the computational roles of the FPC-dACC network in steering judgment adaptation to a group's opinion. Taken together, these findings provide a computational account of how the human brain integrates individual and social information for decision-making in groups.

Feedback Blunting: Total Sleep Deprivation Impairs Decision Making that Requires Updating Based on Feedback.

PubMed

Whitney, Paul; Hinson, John M; Jackson, Melinda L; Van Dongen, Hans P A

2015-05-01

To better understand the sometimes catastrophic effects of sleep loss on naturalistic decision making, we investigated effects of sleep deprivation on decision making in a reversal learning paradigm requiring acquisition and updating of information based on outcome feedback. Subjects were randomized to a sleep deprivation or control condition, with performance testing at baseline, after 2 nights of total sleep deprivation (or rested control), and following 2 nights of recovery sleep. Subjects performed a decision task involving initial learning of go and no go response sets followed by unannounced reversal of contingencies, requiring use of outcome feedback for decisions. A working memory scanning task and psychomotor vigilance test were also administered. Six consecutive days and nights in a controlled laboratory environment with continuous behavioral monitoring. Twenty-six subjects (22-40 y of age; 10 women). Thirteen subjects were randomized to a 62-h total sleep deprivation condition; the others were controls. Unlike controls, sleep deprived subjects had difficulty with initial learning of go and no go stimuli sets and had profound impairment adapting to reversal. Skin conductance responses to outcome feedback were diminished, indicating blunted affective reactions to feedback accompanying sleep deprivation. Working memory scanning performance was not significantly affected by sleep deprivation. And although sleep deprived subjects showed expected attentional lapses, these could not account for impairments in reversal learning decision making. Sleep deprivation is particularly problematic for decision making involving uncertainty and unexpected change. Blunted reactions to feedback while sleep deprived underlie failures to adapt to uncertainty and changing contingencies. Thus, an error may register, but with diminished effect because of reduced affective valence of the feedback or because the feedback is not cognitively bound with the choice. This has important implications for understanding and managing sleep loss-induced cognitive impairment in emergency response, disaster management, military operations, and other dynamic real-world settings with uncertain outcomes and imperfect information. © 2015 Associated Professional Sleep Societies, LLC.

A dynamic multi-level optimal design method with embedded finite-element modeling for power transformers

NASA Astrophysics Data System (ADS)

Zhang, Yunpeng; Ho, Siu-lau; Fu, Weinong

2018-05-01

This paper proposes a dynamic multi-level optimal design method for power transformer design optimization (TDO) problems. A response surface generated by second-order polynomial regression analysis is updated dynamically by adding more design points, which are selected by Shifted Hammersley Method (SHM) and calculated by finite-element method (FEM). The updating stops when the accuracy requirement is satisfied, and optimized solutions of the preliminary design are derived simultaneously. The optimal design level is modulated through changing the level of error tolerance. Based on the response surface of the preliminary design, a refined optimal design is added using multi-objective genetic algorithm (MOGA). The effectiveness of the proposed optimal design method is validated through a classic three-phase power TDO problem.

Performance evaluation of the atmospheric phase of aeromaneuvering orbital transfer vehicles

NASA Technical Reports Server (NTRS)

Powell, R. W.; Stone, H. W.; Naftel, J. C.

1984-01-01

Studies are underway to design reusable orbital transfer vehicles that would be used to transfer payloads from low-earth orbit to higher orbits and return. One promising concept is to use an atmospheric pass on the return leg to reduce the amount of fuel for the mission. This paper discusses a six-degree-of-freedom simulation analysis for two configurations, a low-lift-to-drag ratio configuration and a medium-lift-to-drag ratio configuration using both a predictive guidance technique and an adaptive guidance technique. Both guidance schemes were evaluated using the 1962 standard atmosphere and three atmospheres that had been derived from three entries of the Space Shuttle. The predictive technique requires less reaction control system activity for both configurations, but because of the limited number of updates and because each update used the 1962 standard atmosphere, the adaptive technique produces more accurate exit conditions.

Cognitive rigidity in unipolar depression and obsessive compulsive disorder: examination of task switching, Stroop, working memory updating and post-conflict adaptation.

PubMed

Meiran, Nachshon; Diamond, Gary M; Toder, Doron; Nemets, Boris

2011-01-30

Obsessive compulsive disorder (OCD) and depressive rumination are both characterized by cognitive rigidity. We examined the performance of 17 patients (9 suffering from unipolar depression [UD] without OCD, and 8 suffering from OCD without UD), and 17 control participants matched on age, gender, language and education, on a battery covering the four main executive functions. Results indicated that, across both disorders, patients required more trials to adjust to single-task conditions after experiencing task switching, reflecting slow disengagement from switching mode, and showed abnormal post-conflict adaptation of processing mode following high conflict Stroop trials in comparison to controls. Rumination, which was elevated in UD and not in OCD, was associated with poor working memory updating and less task preparation. The results show that OCD and UD are associated with similar cognitive rigidity in the presently tested paradigms. Copyright © 2010 Elsevier Ltd. All rights reserved.

ERM model analysis for adaptation to hydrological model errors

NASA Astrophysics Data System (ADS)

Baymani-Nezhad, M.; Han, D.

2018-05-01

Hydrological conditions are changed continuously and these phenomenons generate errors on flood forecasting models and will lead to get unrealistic results. Therefore, to overcome these difficulties, a concept called model updating is proposed in hydrological studies. Real-time model updating is one of the challenging processes in hydrological sciences and has not been entirely solved due to lack of knowledge about the future state of the catchment under study. Basically, in terms of flood forecasting process, errors propagated from the rainfall-runoff model are enumerated as the main source of uncertainty in the forecasting model. Hence, to dominate the exciting errors, several methods have been proposed by researchers to update the rainfall-runoff models such as parameter updating, model state updating, and correction on input data. The current study focuses on investigations about the ability of rainfall-runoff model parameters to cope with three types of existing errors, timing, shape and volume as the common errors in hydrological modelling. The new lumped model, the ERM model, has been selected for this study to evaluate its parameters for its use in model updating to cope with the stated errors. Investigation about ten events proves that the ERM model parameters can be updated to cope with the errors without the need to recalibrate the model.

Enhanced attention amplifies face adaptation.

PubMed

Rhodes, Gillian; Jeffery, Linda; Evangelista, Emma; Ewing, Louise; Peters, Marianne; Taylor, Libby

2011-08-15

Perceptual adaptation not only produces striking perceptual aftereffects, but also enhances coding efficiency and discrimination by calibrating coding mechanisms to prevailing inputs. Attention to simple stimuli increases adaptation, potentially enhancing its functional benefits. Here we show that attention also increases adaptation to faces. In Experiment 1, face identity aftereffects increased when attention to adapting faces was increased using a change detection task. In Experiment 2, figural (distortion) face aftereffects increased when attention was increased using a snap game (detecting immediate repeats) during adaptation. Both were large effects. Contributions of low-level adaptation were reduced using free viewing (both experiments) and a size change between adapt and test faces (Experiment 2). We suggest that attention may enhance adaptation throughout the entire cortical visual pathway, with functional benefits well beyond the immediate advantages of selective processing of potentially important stimuli. These results highlight the potential to facilitate adaptive updating of face-coding mechanisms by strategic deployment of attentional resources. Copyright © 2011 Elsevier Ltd. All rights reserved.

Using Response-Time Constraints in Item Selection To Control for Differential Speededness in Computerized Adaptive Testing. LSAC Research Report Series.

ERIC Educational Resources Information Center

van der Linden, Wim J.; Scrams, David J.; Schnipke, Deborah L.

This paper proposes an item selection algorithm that can be used to neutralize the effect of time limits in computer adaptive testing. The method is based on a statistical model for the response-time distributions of the test takers on the items in the pool that is updated each time a new item has been administered. Predictions from the model are…

IV Brazilian Consensus on Rhinitis - an update on allergic rhinitis.

PubMed

Sakano, Eulalia; Sarinho, Emanuel S C; Cruz, Alvaro A; Pastorino, Antonio C; Tamashiro, Edwin; Kuschnir, Fábio; Castro, Fábio F M; Romano, Fabrizio R; Wandalsen, Gustavo F; Chong-Neto, Herberto J; Mello, João F de; Silva, Luciana R; Rizzo, Maria Cândida; Miyake, Mônica A M; Rosário Filho, Nelson A; Rubini, Norma de Paula M; Mion, Olavo; Camargos, Paulo A; Roithmann, Renato; Godinho, Ricardo N; Pignatari, Shirley Shizue N; Sih, Tania; Anselmo-Lima, Wilma T; Solé, Dirceu

2017-11-02

The guidelines on allergic rhinitis aim to update knowledge about the disease and care for affected patients. The initiative called "Allergic Rhinitis and its Impact on Asthma", initially published in 2001 and updated in 2008 and 2010, has been very successful in disseminating information and evidence, as well as providing a classification of severity and proposing a systemized treatment protocol. In order to include the participation of other medical professionals in the treatment of allergic rhinitis, it is important to develop algorithms that accurately indicate what should and can be done regionally. To update the III Brazilian Consensus on Rhinitis - 2012, with the creation of an algorithm for allergic rhinitis management. We invited 24 experts nominated by the Brazilian Association of Allergy and Immunology, Brazilian Association of Otorhinolaryngology and Head and Neck Surgery and Brazilian Society of Pediatrics to update the 2012 document. The update of the last Brazilian Consensus on Rhinitis incorporated and adapted the relevant information published in all "Allergic Rhinitis and its Impact on Asthma" Initiative documents to the Brazilian scenario, bringing new concepts such as local allergic rhinitis, new drugs and treatment evaluation methods. A flowchart for allergic rhinitis treatment has been proposed. Copyright © 2017. Published by Elsevier Editora Ltda.

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

NASA Technical Reports Server (NTRS)

Rohrs, C. E.

1982-01-01

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

A Conceptual Model of Childhood Adaptation to Type 1 Diabetes

PubMed Central

Whittemore, Robin; Jaser, Sarah; Guo, Jia; Grey, Margaret

2010-01-01

The Childhood Adaptation Model to Chronic Illness: Diabetes Mellitus was developed to identify factors that influence childhood adaptation to type 1 diabetes (T1D). Since this model was proposed, considerable research has been completed. The purpose of this paper is to update the model on childhood adaptation to T1D using research conducted since the original model was proposed. The framework suggests that individual and family characteristics, such as age, socioeconomic status, and in children with T1D, treatment modality (pump vs. injections), psychosocial responses (depressive symptoms and anxiety), and individual and family responses (self-management, coping, self-efficacy, family functioning, social competence) influence the level of adaptation. Adaptation has both physiologic (metabolic control) and psychosocial (QOL) components. This revised model provides greater specificity to the factors that influence adaptation to chronic illness in children. Research and clinical implications are discussed. PMID:20934079

Modeling update for the Thirty Meter Telescope laser guide star dual-conjugate adaptive optics system

NASA Astrophysics Data System (ADS)

Gilles, Luc; Wang, Lianqi; Ellerbroek, Brent

2010-07-01

This paper describes the modeling efforts undertaken in the past couple of years to derive wavefront error (WFE) performance estimates for the Narrow Field Infrared Adaptive Optics System (NFIRAOS), which is the facility laser guide star (LGS) dual-conjugate adaptive optics (AO) system for the Thirty Meter Telescope (TMT). The estimates describe the expected performance of NFIRAOS as a function of seeing on Mauna Kea, zenith angle, and galactic latitude (GL). They have been developed through a combination of integrated AO simulations, side analyses, allocations, lab and lidar experiments.

Iterative-Transform Phase Retrieval Using Adaptive Diversity

NASA Technical Reports Server (NTRS)

Dean, Bruce H.

2007-01-01

A phase-diverse iterative-transform phase-retrieval algorithm enables high spatial-frequency, high-dynamic-range, image-based wavefront sensing. [The terms phase-diverse, phase retrieval, image-based, and wavefront sensing are defined in the first of the two immediately preceding articles, Broadband Phase Retrieval for Image-Based Wavefront Sensing (GSC-14899-1).] As described below, no prior phase-retrieval algorithm has offered both high dynamic range and the capability to recover high spatial-frequency components. Each of the previously developed image-based phase-retrieval techniques can be classified into one of two categories: iterative transform or parametric. Among the modifications of the original iterative-transform approach has been the introduction of a defocus diversity function (also defined in the cited companion article). Modifications of the original parametric approach have included minimizing alternative objective functions as well as implementing a variety of nonlinear optimization methods. The iterative-transform approach offers the advantage of ability to recover low, middle, and high spatial frequencies, but has disadvantage of having a limited dynamic range to one wavelength or less. In contrast, parametric phase retrieval offers the advantage of high dynamic range, but is poorly suited for recovering higher spatial frequency aberrations. The present phase-diverse iterative transform phase-retrieval algorithm offers both the high-spatial-frequency capability of the iterative-transform approach and the high dynamic range of parametric phase-recovery techniques. In implementation, this is a focus-diverse iterative-transform phaseretrieval algorithm that incorporates an adaptive diversity function, which makes it possible to avoid phase unwrapping while preserving high-spatial-frequency recovery. The algorithm includes an inner and an outer loop (see figure). An initial estimate of phase is used to start the algorithm on the inner loop, wherein multiple intensity images are processed, each using a different defocus value. The processing is done by an iterative-transform method, yielding individual phase estimates corresponding to each image of the defocus-diversity data set. These individual phase estimates are combined in a weighted average to form a new phase estimate, which serves as the initial phase estimate for either the next iteration of the iterative-transform method or, if the maximum number of iterations has been reached, for the next several steps, which constitute the outerloop portion of the algorithm. The details of the next several steps must be omitted here for the sake of brevity. The overall effect of these steps is to adaptively update the diversity defocus values according to recovery of global defocus in the phase estimate. Aberration recovery varies with differing amounts as the amount of diversity defocus is updated in each image; thus, feedback is incorporated into the recovery process. This process is iterated until the global defocus error is driven to zero during the recovery process. The amplitude of aberration may far exceed one wavelength after completion of the inner-loop portion of the algorithm, and the classical iterative transform method does not, by itself, enable recovery of multi-wavelength aberrations. Hence, in the absence of a means of off-loading the multi-wavelength portion of the aberration, the algorithm would produce a wrapped phase map. However, a special aberration-fitting procedure can be applied to the wrapped phase data to transfer at least some portion of the multi-wavelength aberration to the diversity function, wherein the data are treated as known phase values. In this way, a multiwavelength aberration can be recovered incrementally by successively applying the aberration-fitting procedure to intermediate wrapped phase maps. During recovery, as more of the aberration is transferred to the diversity function following successive iterations around the ter loop, the estimated phase ceases to wrap in places where the aberration values become incorporated as part of the diversity function. As a result, as the aberration content is transferred to the diversity function, the phase estimate resembles that of a reference flat.

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.

Converted and upgraded maps programmed in the newer speech processor for the first generation of multichannel cochlear implant.

PubMed

Magalhães, Ana Tereza de Matos; Goffi-Gomez, M Valéria Schmidt; Hoshino, Ana Cristina; Tsuji, Robinson Koji; Bento, Ricardo Ferreira; Brito, Rubens

2013-09-01

To identify the technological contributions of the newer version of speech processor to the first generation of multichannel cochlear implant and the satisfaction of users of the new technology. Among the new features available, we focused on the effect of the frequency allocation table, the T-SPL and C-SPL, and the preprocessing gain adjustments (adaptive dynamic range optimization). Prospective exploratory study. Cochlear implant center at hospital. Cochlear implant users of the Spectra processor with speech recognition in closed set. Seventeen patients were selected between the ages of 15 and 82 and deployed for more than 8 years. The technology update of the speech processor for the Nucleus 22. To determine Freedom's contribution, thresholds and speech perception tests were performed with the last map used with the Spectra and the maps created for Freedom. To identify the effect of the frequency allocation table, both upgraded and converted maps were programmed. One map was programmed with 25 dB T-SPL and 65 dB C-SPL and the other map with adaptive dynamic range optimization. To assess satisfaction, SADL and APHAB were used. All speech perception tests and all sound field thresholds were statistically better with the new speech processor; 64.7% of patients preferred maintaining the same frequency table that was suggested for the older processor. The sound field threshold was statistically significant at 500, 1,000, 1,500, and 2,000 Hz with 25 dB T-SPL/65 dB C-SPL. Regarding patient's satisfaction, there was a statistically significant improvement, only in the subscale of speech in noise abilities and phone use. The new technology improved the performance of patients with the first generation of multichannel cochlear implant.

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.

Rapid Structural Design Change Evaluation with AN Experiment Based FEM

NASA Astrophysics Data System (ADS)

Chu, C.-H.; Trethewey, M. W.

1998-04-01

The work in this paper proposes a dynamic structural design model that can be developed in a rapid fashion. The approach endeavours to produce a simplified FEM developed in conjunction with an experimental modal database. The FEM is formulated directly from the geometry and connectivity used in an experimental modal test using beam/frame elements. The model sacrifices fine detail for a rapid development time. The FEM is updated at the element level so the dynamic response replicates the experimental results closely. The physical attributes of the model are retained, making it well suited to evaluate the effect of potential design changes. The capabilities are evaluated in a series of computational and laboratory tests. First, a study is performed with a simulated cantilever beam with a variable mass and stiffness distribution. The modal characteristics serve as the updating target with random noise added to simulate experimental uncertainty. A uniformly distributed FEM is developed and updated. The results show excellent results, all natural frequencies are within 0·001% with MAC values above 0·99. Next, the method is applied to predict the dynamic changes of a hardware portal frame structure for a radical design change. Natural frequency predictions from the original FEM differ by as much as almost 18% with reasonable MAC values. The results predicted from the updated model produce excellent results when compared to the actual hardware changes, the first five modal natural frequency difference is around 5% and the corresponding mode shapes producing MAC values above 0·98.

Technology Enhanced Learning: Best Practices

ERIC Educational Resources Information Center

Lytras, Miltiadis D., Ed.; Gasevic, Dragan, Ed.; Ordonez de Pablos, Patricia, Ed.; Huang, Weihong, Ed.

2008-01-01

With the shift towards the knowledge society, the change of working conditions, and the high-speed evolution of information and communication technologies, peoples' knowledge and skills need continuous updating. Learning based on collaborative working, creativity, multidisciplinarity, adaptiveness, intercultural communication, and problem solving…

Accelerating deep neural network training with inconsistent stochastic gradient descent.

PubMed

Wang, Linnan; Yang, Yi; Min, Renqiang; Chakradhar, Srimat

2017-09-01

Stochastic Gradient Descent (SGD) updates Convolutional Neural Network (CNN) with a noisy gradient computed from a random batch, and each batch evenly updates the network once in an epoch. This model applies the same training effort to each batch, but it overlooks the fact that the gradient variance, induced by Sampling Bias and Intrinsic Image Difference, renders different training dynamics on batches. In this paper, we develop a new training strategy for SGD, referred to as Inconsistent Stochastic Gradient Descent (ISGD) to address this problem. The core concept of ISGD is the inconsistent training, which dynamically adjusts the training effort w.r.t the loss. ISGD models the training as a stochastic process that gradually reduces down the mean of batch's loss, and it utilizes a dynamic upper control limit to identify a large loss batch on the fly. ISGD stays on the identified batch to accelerate the training with additional gradient updates, and it also has a constraint to penalize drastic parameter changes. ISGD is straightforward, computationally efficient and without requiring auxiliary memories. A series of empirical evaluations on real world datasets and networks demonstrate the promising performance of inconsistent training. Copyright © 2017 Elsevier Ltd. All rights reserved.

Balancing exploration and exploitation in population-based sampling improves fragment-based de novo protein structure prediction.

PubMed

Simoncini, David; Schiex, Thomas; Zhang, Kam Y J

2017-05-01

Conformational search space exploration remains a major bottleneck for protein structure prediction methods. Population-based meta-heuristics typically enable the possibility to control the search dynamics and to tune the balance between local energy minimization and search space exploration. EdaFold is a fragment-based approach that can guide search by periodically updating the probability distribution over the fragment libraries used during model assembly. We implement the EdaFold algorithm as a Rosetta protocol and provide two different probability update policies: a cluster-based variation (EdaRose c ) and an energy-based one (EdaRose en ). We analyze the search dynamics of our new Rosetta protocols and show that EdaRose c is able to provide predictions with lower C αRMSD to the native structure than EdaRose en and Rosetta AbInitio Relax protocol. Our software is freely available as a C++ patch for the Rosetta suite and can be downloaded from http://www.riken.jp/zhangiru/software/. Our protocols can easily be extended in order to create alternative probability update policies and generate new search dynamics. Proteins 2017; 85:852-858. © 2016 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

A review of human factors challenges of complex adaptive systems: discovering and understanding chaos in human performance.

PubMed

Karwowski, Waldemar

2012-12-01

In this paper, the author explores a need for a greater understanding of the true nature of human-system interactions from the perspective of the theory of complex adaptive systems, including the essence of complexity, emergent properties of system behavior, nonlinear systems dynamics, and deterministic chaos. Human performance, more often than not, constitutes complex adaptive phenomena with emergent properties that exhibit nonlinear dynamical (chaotic) behaviors. The complexity challenges in the design and management of contemporary work systems, including service systems, are explored. Examples of selected applications of the concepts of nonlinear dynamics to the study of human physical performance are provided. Understanding and applications of the concepts of theory of complex adaptive and dynamical systems should significantly improve the effectiveness of human-centered design efforts of a large system of systems. Performance of many contemporary work systems and environments may be sensitive to the initial conditions and may exhibit dynamic nonlinear properties and chaotic system behaviors. Human-centered design of emergent human-system interactions requires application of the theories of nonlinear dynamics and complex adaptive system. The success of future human-systems integration efforts requires the fusion of paradigms, knowledge, design principles, and methodologies of human factors and ergonomics with those of the science of complex adaptive systems as well as modern systems engineering.

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

PubMed

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

2018-04-12

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

Eye tracking a self-moved target with complex hand-target dynamics

PubMed Central

Landelle, Caroline; Montagnini, Anna; Madelain, Laurent

2016-01-01

Previous work has shown that the ability to track with the eye a moving target is substantially improved when the target is self-moved by the subject's hand compared with when being externally moved. Here, we explored a situation in which the mapping between hand movement and target motion was perturbed by simulating an elastic relationship between the hand and target. Our objective was to determine whether the predictive mechanisms driving eye-hand coordination could be updated to accommodate this complex hand-target dynamics. To fully appreciate the behavioral effects of this perturbation, we compared eye tracking performance when self-moving a target with a rigid mapping (simple) and a spring mapping as well as when the subject tracked target trajectories that he/she had previously generated when using the rigid or spring mapping. Concerning the rigid mapping, our results confirmed that smooth pursuit was more accurate when the target was self-moved than externally moved. In contrast, with the spring mapping, eye tracking had initially similar low spatial accuracy (though shorter temporal lag) in the self versus externally moved conditions. However, within ∼5 min of practice, smooth pursuit improved in the self-moved spring condition, up to a level similar to the self-moved rigid condition. Subsequently, when the mapping unexpectedly switched from spring to rigid, the eye initially followed the expected target trajectory and not the real one, thereby suggesting that subjects used an internal representation of the new hand-target dynamics. Overall, these results emphasize the stunning adaptability of smooth pursuit when self-maneuvering objects with complex dynamics. PMID:27466129

A MATLAB toolbox for the efficient estimation of the psychometric function using the updated maximum-likelihood adaptive procedure.

PubMed

Shen, Yi; Dai, Wei; Richards, Virginia M

2015-03-01

A MATLAB toolbox for the efficient estimation of the threshold, slope, and lapse rate of the psychometric function is described. The toolbox enables the efficient implementation of the updated maximum-likelihood (UML) procedure. The toolbox uses an object-oriented architecture for organizing the experimental variables and computational algorithms, which provides experimenters with flexibility in experimental design and data management. Descriptions of the UML procedure and the UML Toolbox are provided, followed by toolbox use examples. Finally, guidelines and recommendations of parameter configurations are given.

Blind Backscattering Experimental Data Collected in the Field and an Approximately Globally Convergent Inverse Algorithm

DTIC Science & Technology

2012-01-01

unknown functions q and V . To approximate both of them, we use a predictor / corrector -like scheme. First, given an approximation for V , we update q via...coefficient εr(x). This is our predictor -like step. On the corrector -like step we update the tail function V (x, s) via (5.7). Consider a partition of...10] and figures 5.13–5.16 in [6]. We point out that the adaptivity has used the solution of the approximately globally convergent algorithm as the

Performance analysis of different database in new internet mapping system

NASA Astrophysics Data System (ADS)

Yao, Xing; Su, Wei; Gao, Shuai

2017-03-01

In the Mapping System of New Internet, Massive mapping entries between AID and RID need to be stored, added, updated, and deleted. In order to better deal with the problem when facing a large number of mapping entries update and query request, the Mapping System of New Internet must use high-performance database. In this paper, we focus on the performance of Redis, SQLite, and MySQL these three typical databases, and the results show that the Mapping System based on different databases can adapt to different needs according to the actual situation.

Dynamic mesh adaption for triangular and tetrahedral grids

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Strawn, Roger

1993-01-01

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

Nonlinear dynamics of team performance and adaptability in emergency response.

PubMed

Guastello, Stephen J

2010-04-01

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

Dynamic accommodation responses following adaptation to defocus.

PubMed

Cufflin, Matthew P; Mallen, Edward A H

2008-10-01

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

Software Testbed for Developing and Evaluating Integrated Autonomous Subsystems

NASA Technical Reports Server (NTRS)

Ong, James; Remolina, Emilio; Prompt, Axel; Robinson, Peter; Sweet, Adam; Nishikawa, David

2015-01-01

To implement fault tolerant autonomy in future space systems, it will be necessary to integrate planning, adaptive control, and state estimation subsystems. However, integrating these subsystems is difficult, time-consuming, and error-prone. This paper describes Intelliface/ADAPT, a software testbed that helps researchers develop and test alternative strategies for integrating planning, execution, and diagnosis subsystems more quickly and easily. The testbed's architecture, graphical data displays, and implementations of the integrated subsystems support easy plug and play of alternate components to support research and development in fault-tolerant control of autonomous vehicles and operations support systems. Intelliface/ADAPT controls NASA's Advanced Diagnostics and Prognostics Testbed (ADAPT), which comprises batteries, electrical loads (fans, pumps, and lights), relays, circuit breakers, invertors, and sensors. During plan execution, an experimentor can inject faults into the ADAPT testbed by tripping circuit breakers, changing fan speed settings, and closing valves to restrict fluid flow. The diagnostic subsystem, based on NASA's Hybrid Diagnosis Engine (HyDE), detects and isolates these faults to determine the new state of the plant, ADAPT. Intelliface/ADAPT then updates its model of the ADAPT system's resources and determines whether the current plan can be executed using the reduced resources. If not, the planning subsystem generates a new plan that reschedules tasks, reconfigures ADAPT, and reassigns the use of ADAPT resources as needed to work around the fault. The resource model, planning domain model, and planning goals are expressed using NASA's Action Notation Modeling Language (ANML). Parts of the ANML model are generated automatically, and other parts are constructed by hand using the Planning Model Integrated Development Environment, a visual Eclipse-based IDE that accelerates ANML model development. Because native ANML planners are currently under development and not yet sufficiently capable, the ANML model is translated into the New Domain Definition Language (NDDL) and sent to NASA's EUROPA planning system for plan generation. The adaptive controller executes the new plan, using augmented, hierarchical finite state machines to select and sequence actions based on the state of the ADAPT system. Real-time sensor data, commands, and plans are displayed in information-dense arrays of timelines and graphs that zoom and scroll in unison. A dynamic schematic display uses color to show the real-time fault state and utilization of the system components and resources. An execution manager coordinates the activities of the other subsystems. The subsystems are integrated using the Internet Communications Engine (ICE). an object-oriented toolkit for building distributed applications.

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

PubMed

Yuan, Ruoxi; Geng, Shuo; Li, Liwu

2016-01-01

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

Cooperation and assortativity with dynamic partner updating

PubMed Central

Wang, Jing; Suri, Siddharth; Watts, Duncan J.

2012-01-01

The natural tendency for humans to make and break relationships is thought to facilitate the emergence of cooperation. In particular, allowing conditional cooperators to choose with whom they interact is believed to reinforce the rewards accruing to mutual cooperation while simultaneously excluding defectors. Here we report on a series of human subjects experiments in which groups of 24 participants played an iterated prisoner’s dilemma game where, critically, they were also allowed to propose and delete links to players of their own choosing at some variable rate. Over a wide variety of parameter settings and initial conditions, we found that dynamic partner updating significantly increased the level of cooperation, the average payoffs to players, and the assortativity between cooperators. Even relatively slow update rates were sufficient to produce large effects, while subsequent increases to the update rate had progressively smaller, but still positive, effects. For standard prisoner’s dilemma payoffs, we also found that assortativity resulted predominantly from cooperators avoiding defectors, not by severing ties with defecting partners, and that cooperation correspondingly suffered. Finally, by modifying the payoffs to satisfy two novel conditions, we found that cooperators did punish defectors by severing ties, leading to higher levels of cooperation that persisted for longer. PMID:22904193

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

PubMed

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

2015-01-01

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

Cultural ecologies of adaptive vs. maladaptive traits: A simple nonlinear model

NASA Astrophysics Data System (ADS)

Antoci, Angelo; Russu, Paolo; Sacco, Pier Luigi

2018-05-01

In this paper, we generalize a model by Enquist and Ghirlanda [12] to analyze the "macro" dynamics of cumulative culture in a context where there is a coexistence of adaptive and maladaptive cultural traits. In particular, we introduce a different, nonlinear specification of the main processes at work in the cumulative culture dynamics: imperfect transmission of traits, generation of new traits, and switches from adaptive to maladaptive and vice-versa. We find that the system exhibits a variety of dynamic behaviors where the crucial force is the switching between the adaptive and maladaptive nature of a certain trait, with the other processes playing a modulating role. We identify in particular a number of dynamic regimes with distinctive characteristics.

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

PubMed

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

2006-12-01

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

Adaptive bearing estimation and tracking of multiple targets in a realistic passive sonar scenario

NASA Astrophysics Data System (ADS)

Rajagopal, R.; Challa, Subhash; Faruqi, Farhan A.; Rao, P. R.

1997-06-01

In a realistic passive sonar environment, the received signal consists of multipath arrivals from closely separated moving targets. The signals are contaminated by spatially correlated noise. The differential MUSIC has been proposed to estimate the DOAs in such a scenario. This method estimates the 'noise subspace' in order to estimate the DOAs. However, the 'noise subspace' estimate has to be updated as and when new data become available. In order to save the computational costs, a new adaptive noise subspace estimation algorithm is proposed in this paper. The salient features of the proposed algorithm are: (1) Noise subspace estimation is done by QR decomposition of the difference matrix which is formed from the data covariance matrix. Thus, as compared to standard eigen-decomposition based methods which require O(N3) computations, the proposed method requires only O(N2) computations. (2) Noise subspace is updated by updating the QR decomposition. (3) The proposed algorithm works in a realistic sonar environment. In the second part of the paper, the estimated bearing values are used to track multiple targets. In order to achieve this, the nonlinear system/linear measurement extended Kalman filtering proposed is applied. Computer simulation results are also presented to support the theory.

Space Launch System Implementation of Adaptive Augmenting Control

NASA Technical Reports Server (NTRS)

Wall, John H.; Orr, Jeb S.; VanZwieten, Tannen S.

2014-01-01

Given the complex structural dynamics, challenging ascent performance requirements, and rigorous flight certification constraints owing to its manned capability, the NASA Space Launch System (SLS) launch vehicle requires a proven thrust vector control algorithm design with highly optimized parameters to provide stable and high-performance flight. On its development path to Preliminary Design Review (PDR), the SLS flight control system has been challenged by significant vehicle flexibility, aerodynamics, and sloshing propellant. While the design has been able to meet all robust stability criteria, it has done so with little excess margin. Through significant development work, an Adaptive Augmenting Control (AAC) algorithm has been shown to extend the envelope of failures and flight anomalies the SLS control system can accommodate while maintaining a direct link to flight control stability criteria such as classical gain and phase margin. In this paper, the work performed to mature the AAC algorithm as a baseline component of the SLS flight control system is presented. The progress to date has brought the algorithm design to the PDR level of maturity. The algorithm has been extended to augment the full SLS digital 3-axis autopilot, including existing load-relief elements, and the necessary steps for integration with the production flight software prototype have been implemented. Several updates which have been made to the adaptive algorithm to increase its performance, decrease its sensitivity to expected external commands, and safeguard against limitations in the digital implementation are discussed with illustrating results. Monte Carlo simulations and selected stressing case results are also shown to demonstrate the algorithm's ability to increase the robustness of the integrated SLS flight control system.

Augmented paper maps: Exploring the design space of a mixed reality system

NASA Astrophysics Data System (ADS)

Paelke, Volker; Sester, Monika

Paper maps and mobile electronic devices have complementary strengths and shortcomings in outdoor use. In many scenarios, like small craft sailing or cross-country trekking, a complete replacement of maps is neither useful nor desirable. Paper maps are fail-safe, relatively cheap, offer superior resolution and provide large scale overview. In uses like open-water sailing it is therefore mandatory to carry adequate maps/charts. GPS based mobile devices, on the other hand, offer useful features like automatic positioning and plotting, real-time information update and dynamic adaptation to user requirements. While paper maps are now commonly used in combination with mobile GPS devices, there is no meaningful integration between the two, and the combined use leads to a number of interaction problems and potential safety issues. In this paper we explore the design space of augmented paper maps in which maps are augmented with additional functionality through a mobile device to achieve a meaningful integration between device and map that combines their respective strengths.

Daytime O/N2 Retrieval Algorithm for the Ionospheric Connection Explorer (ICON)

NASA Astrophysics Data System (ADS)

Stephan, Andrew W.; Meier, R. R.; England, Scott L.; Mende, Stephen B.; Frey, Harald U.; Immel, Thomas J.

2018-02-01

The NASA Ionospheric Connection Explorer Far-Ultraviolet spectrometer, ICON FUV, will measure altitude profiles of the daytime far-ultraviolet (FUV) OI 135.6 nm and N2 Lyman-Birge-Hopfield (LBH) band emissions that are used to determine thermospheric density profiles and state parameters related to thermospheric composition; specifically the thermospheric column O/N2 ratio (symbolized as ΣO/N2). This paper describes the algorithm concept that has been adapted and updated from one previously applied with success to limb data from the Global Ultraviolet Imager (GUVI) on the NASA Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) mission. We also describe the requirements that are imposed on the ICON FUV to measure ΣO/N2 over any 500-km sample in daytime with a precision of better than 8.7%. We present results from orbit-simulation testing that demonstrates that the ICON FUV and our thermospheric composition retrieval algorithm can meet these requirements and provide the measurements necessary to address ICON science objectives.

Microbial dynamics in petroleum oilfields and their relationship with physiological properties of petroleum oil reservoirs.

PubMed

Varjani, Sunita J; Gnansounou, Edgard

2017-12-01

Petroleum is produced by thermal decay of buried organic material over millions of years. Petroleum oilfield ecosystems represent resource of reduced carbon which favours microbial growth. Therefore, it is obvious that many microorganisms have adapted to harsh environmental conditions of these ecosystems specifically temperature, oxygen availability and pressure. Knowledge of microorganisms present in ecosystems of petroleum oil reservoirs; their physiological and biological properties help in successful exploration of petroleum. Understanding microbiology of petroleum oilfield(s) can be used to enhance oil recovery, as microorganisms in oil reservoirs produce various metabolites viz. gases, acids, solvents, biopolymers and biosurfactants. The aim of this review is to discuss characteristics of petroleum oil reservoirs. This review also provides an updated literature on microbial ecology of these extreme ecosystems including microbial origin as well as various types of microorganisms such as methanogens; iron, nitrate and sulphate reducing bacteria, and fermentative microbes present in petroleum oilfield ecosystems. Copyright © 2017 Elsevier Ltd. All rights reserved.

Meeting new challenges: The 2014 HUPO-PSI/COSMOS Workshop: 13-15 April 2014, Frankfurt, Germany.

PubMed

Orchard, Sandra; Albar, Juan Pablo; Binz, Pierre-Alain; Kettner, Carsten; Jones, Andrew R; Salek, Reza M; Vizcaino, Juan Antonio; Deutsch, Eric W; Hermjakob, Henning

2014-11-01

The Annual 2014 Spring Workshop of the Proteomics Standards Initiative (PSI) of the Human Proteome Organization (HUPO) was held this year jointly with the metabolomics COordination of Standards in MetabOlomicS (COSMOS) group. The range of existing MS standards (mzML, mzIdentML, mzQuantML, mzTab, TraML) was reviewed and updated in the light of new methodologies and advances in technologies. Adaptations to meet the needs of the metabolomics community were incorporated and a new data format for NMR, nmrML, was presented. The molecular interactions workgroup began work on a new version of the existing XML data interchange format. PSI-MI XML3.0 will enable the capture of more abstract data types such as protein complex topology derived from experimental data, allosteric binding, and dynamic interactions. Further information about the work of the HUPO-PSI can be found at http://www.psidev.info. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Slip-based terrain estimation with a skid-steer vehicle

NASA Astrophysics Data System (ADS)

Reina, Giulio; Galati, Rocco

2016-10-01

In this paper, a novel approach for online terrain characterisation is presented using a skid-steer vehicle. In the context of this research, terrain characterisation refers to the estimation of physical parameters that affects the terrain ability to support vehicular motion. These parameters are inferred from the modelling of the kinematic and dynamic behaviour of a skid-steer vehicle that reveals the underlying relationships governing the vehicle-terrain interaction. The concept of slip track is introduced as a measure of the slippage experienced by the vehicle during turning motion. The proposed terrain estimation system includes common onboard sensors, that is, wheel encoders, electrical current sensors and yaw rate gyroscope. Using these components, the system can characterise terrain online during normal vehicle operations. Experimental results obtained from different surfaces are presented to validate the system in the field showing its effectiveness and potential benefits to implement adaptive driving assistance systems or to automatically update the parameters of onboard control and planning algorithms.

BIM based virtual environment for fire emergency evacuation.

PubMed

Wang, Bin; Li, Haijiang; Rezgui, Yacine; Bradley, Alex; Ong, Hoang N

2014-01-01

Recent building emergency management research has highlighted the need for the effective utilization of dynamically changing building information. BIM (building information modelling) can play a significant role in this process due to its comprehensive and standardized data format and integrated process. This paper introduces a BIM based virtual environment supported by virtual reality (VR) and a serious game engine to address several key issues for building emergency management, for example, timely two-way information updating and better emergency awareness training. The focus of this paper lies on how to utilize BIM as a comprehensive building information provider to work with virtual reality technologies to build an adaptable immersive serious game environment to provide real-time fire evacuation guidance. The innovation lies on the seamless integration between BIM and a serious game based virtual reality (VR) environment aiming at practical problem solving by leveraging state-of-the-art computing technologies. The system has been tested for its robustness and functionality against the development requirements, and the results showed promising potential to support more effective emergency management.

Buffer Management Simulation in ATM Networks

NASA Technical Reports Server (NTRS)

Yaprak, E.; Xiao, Y.; Chronopoulos, A.; Chow, E.; Anneberg, L.

1998-01-01

This paper presents a simulation of a new dynamic buffer allocation management scheme in ATM networks. To achieve this objective, an algorithm that detects congestion and updates the dynamic buffer allocation scheme was developed for the OPNET simulation package via the creation of a new ATM module.

Tribute to the contribution of Gerard Lallenment to structural dynamics

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

Los Alamos National Laboratory

The Society for Experimental Mechanics and the International Modal Analysis Conference recognize the remarkable contribution to experimental mechanics, mechanical engineering and structural dynamics of Professor Gerard Lallement, from the University of Franche-Comte, France. A special session is organized during the IMAC-XX to outline the many achievements of Gerard Lallement in the fields of modal analysis, structural system identification, the theory and practice of structural modification, component mode synthesis and finite element model updating. The purpose of this publication is not to provide an exhaustive account of Gerard Lallement's contribution to structural dynamics. Numerous references are provided that should help themore » interested reader learn more about the many aspects of his work. Instead, the significance of this work is illustrated by discussing the role of structural dynamics in industrial applications and its future challenges. The technical aspects of Gerard Lallement's work are illustrated with a discussion of structural modification, modeling error localization and model updating.« less

Coordinated dynamic encoding in the retina using opposing forms of plasticity

PubMed Central

Kastner, David B.; Baccus, Stephen A.

2011-01-01

The range of natural inputs encoded by a neuron often exceeds its dynamic range. To overcome this limitation, neural populations divide their inputs among different cell classes, as with rod and cone photoreceptors, and adapt by shifting their dynamic range. We report that the dynamic behavior of retinal ganglion cells in salamanders, mice, and rabbits is divided into two opposing forms of short-term plasticity in different cell classes. One population of cells exhibited sensitization—a persistent elevated sensitivity following a strong stimulus. This novel dynamic behavior compensates for the information loss caused by the known process of adaptation occurring in a separate cell population. The two populations divide the dynamic range of inputs, with sensitizing cells encoding weak signals, and adapting cells encoding strong signals. In the two populations, the linear, threshold and adaptive properties are linked to preserve responsiveness when stimulus statistics change, with one population maintaining the ability to respond when the other fails. PMID:21909086

A conceptual model of childhood adaptation to type 1 diabetes.

PubMed

Whittemore, Robin; Jaser, Sarah; Guo, Jia; Grey, Margaret

2010-01-01

The Childhood Adaptation Model to Chronic Illness: Diabetes Mellitus was developed to identify factors that influence childhood adaptation to type 1 diabetes (T1D). Since this model was proposed, considerable research has been completed. The purpose of this article is to update the model on childhood adaptation to T1D using research conducted since the original model was proposed. The framework suggests that, in individuals and families, characteristics such as age and socioeconomic status as well as the individuals' and families' responses (self-management, coping, self-efficacy, family functioning, social competence) influence the level of adaptation; in children with T1D, characteristics such as treatment modality (pump vs injections) and psychosocial responses (depressive symptoms and anxiety) also influence the level of adaptation. Adaptation has both physiologic (metabolic control) and psychosocial (Quality of Life [QOL]) components. This revised model provides greater specificity to the factors that influence adaptation to chronic illness in children. Research and clinical implications are discussed. Copyright © 2010 Mosby, Inc. All rights reserved.

An updated Lagrangian particle hydrodynamics (ULPH) for Newtonian fluids

NASA Astrophysics Data System (ADS)

Tu, Qingsong; Li, Shaofan

2017-11-01

In this work, we have developed an updated Lagrangian particle hydrodynamics (ULPH) for Newtonian fluid. Unlike the smoothed particle hydrodynamics, the non-local particle hydrodynamics formulation proposed here is consistent and convergence. Unlike the state-based peridynamics, the discrete particle dynamics proposed here has no internal material bond between particles, and it is not formulated with respect to initial or a fixed referential configuration. In specific, we have shown that (1) the non-local update Lagrangian particle hydrodynamics formulation converges to the conventional local fluid mechanics formulation; (2) the non-local updated Lagrangian particle hydrodynamics can capture arbitrary flow discontinuities without any changes in the formulation, and (3) the proposed non-local particle hydrodynamics is computationally efficient and robust.

Integration of individual and social information for decision-making in groups of different sizes

PubMed Central

Goïame, Sidney; O'Connor, David A.; Dreher, Jean-Claude

2017-01-01

When making judgments in a group, individuals often revise their initial beliefs about the best judgment to make given what others believe. Despite the ubiquity of this phenomenon, we know little about how the brain updates beliefs when integrating personal judgments (individual information) with those of others (social information). Here, we investigated the neurocomputational mechanisms of how we adapt our judgments to those made by groups of different sizes, in the context of jury decisions for a criminal. By testing different theoretical models, we showed that a social Bayesian inference model captured changes in judgments better than 2 other models. Our results showed that participants updated their beliefs by appropriately weighting individual and social sources of information according to their respective credibility. When investigating 2 fundamental computations of Bayesian inference, belief updates and credibility estimates of social information, we found that the dorsal anterior cingulate cortex (dACC) computed the level of belief updates, while the bilateral frontopolar cortex (FPC) was more engaged in individuals who assigned a greater credibility to the judgments of a larger group. Moreover, increased functional connectivity between these 2 brain regions reflected a greater influence of group size on the relative credibility of social information. These results provide a mechanistic understanding of the computational roles of the FPC-dACC network in steering judgment adaptation to a group’s opinion. Taken together, these findings provide a computational account of how the human brain integrates individual and social information for decision-making in groups. PMID:28658252

A scale self-adapting segmentation approach and knowledge transfer for automatically updating land use/cover change databases using high spatial resolution images

NASA Astrophysics Data System (ADS)

Wang, Zhihua; Yang, Xiaomei; Lu, Chen; Yang, Fengshuo

2018-07-01

Automatic updating of land use/cover change (LUCC) databases using high spatial resolution images (HSRI) is important for environmental monitoring and policy making, especially for coastal areas that connect the land and coast and that tend to change frequently. Many object-based change detection methods are proposed, especially those combining historical LUCC with HSRI. However, the scale parameter(s) segmenting the serial temporal images, which directly determines the average object size, is hard to choose without experts' intervention. And the samples transferred from historical LUCC also need experts' intervention to avoid insufficient or wrong samples. With respect to the scale parameter(s) choosing, a Scale Self-Adapting Segmentation (SSAS) approach based on the exponential sampling of a scale parameter and location of the local maximum of a weighted local variance was proposed to determine the scale selection problem when segmenting images constrained by LUCC for detecting changes. With respect to the samples transferring, Knowledge Transfer (KT), a classifier trained on historical images with LUCC and applied in the classification of updated images, was also proposed. Comparison experiments were conducted in a coastal area of Zhujiang, China, using SPOT 5 images acquired in 2005 and 2010. The results reveal that (1) SSAS can segment images more effectively without intervention of experts. (2) KT can also reach the maximum accuracy of samples transfer without experts' intervention. Strategy SSAS + KT would be a good choice if the temporal historical image and LUCC match, and the historical image and updated image are obtained from the same resource.

Characterizing Decision-Analysis Performances of Risk Prediction Models Using ADAPT Curves.

PubMed

Lee, Wen-Chung; Wu, Yun-Chun

2016-01-01

The area under the receiver operating characteristic curve is a widely used index to characterize the performance of diagnostic tests and prediction models. However, the index does not explicitly acknowledge the utilities of risk predictions. Moreover, for most clinical settings, what counts is whether a prediction model can guide therapeutic decisions in a way that improves patient outcomes, rather than to simply update probabilities.Based on decision theory, the authors propose an alternative index, the "average deviation about the probability threshold" (ADAPT).An ADAPT curve (a plot of ADAPT value against the probability threshold) neatly characterizes the decision-analysis performances of a risk prediction model.Several prediction models can be compared for their ADAPT values at a chosen probability threshold, for a range of plausible threshold values, or for the whole ADAPT curves. This should greatly facilitate the selection of diagnostic tests and prediction models.

Adaptive categorization of ART networks in robot behavior learning using game-theoretic formulation.

PubMed

Fung, Wai-keung; Liu, Yun-hui

2003-12-01

Adaptive Resonance Theory (ART) networks are employed in robot behavior learning. Two of the difficulties in online robot behavior learning, namely, (1) exponential memory increases with time, (2) difficulty for operators to specify learning tasks accuracy and control learning attention before learning. In order to remedy the aforementioned difficulties, an adaptive categorization mechanism is introduced in ART networks for perceptual and action patterns categorization in this paper. A game-theoretic formulation of adaptive categorization for ART networks is proposed for vigilance parameter adaptation for category size control on the categories formed. The proposed vigilance parameter update rule can help improving categorization performance in the aspect of category number stability and solve the problem of selecting initial vigilance parameter prior to pattern categorization in traditional ART networks. Behavior learning using physical robot is conducted to demonstrate the effectiveness of the proposed adaptive categorization mechanism in ART networks.

Inferring topologies via driving-based generalized synchronization of two-layer networks

NASA Astrophysics Data System (ADS)

Wang, Yingfei; Wu, Xiaoqun; Feng, Hui; Lu, Jun-an; Xu, Yuhua

2016-05-01

The interaction topology among the constituents of a complex network plays a crucial role in the network’s evolutionary mechanisms and functional behaviors. However, some network topologies are usually unknown or uncertain. Meanwhile, coupling delays are ubiquitous in various man-made and natural networks. Hence, it is necessary to gain knowledge of the whole or partial topology of a complex dynamical network by taking into consideration communication delay. In this paper, topology identification of complex dynamical networks is investigated via generalized synchronization of a two-layer network. Particularly, based on the LaSalle-type invariance principle of stochastic differential delay equations, an adaptive control technique is proposed by constructing an auxiliary layer and designing proper control input and updating laws so that the unknown topology can be recovered upon successful generalized synchronization. Numerical simulations are provided to illustrate the effectiveness of the proposed method. The technique provides a certain theoretical basis for topology inference of complex networks. In particular, when the considered network is composed of systems with high-dimension or complicated dynamics, a simpler response layer can be constructed, which is conducive to circuit design. Moreover, it is practical to take into consideration perturbations caused by control input. Finally, the method is applicable to infer topology of a subnetwork embedded within a complex system and locate hidden sources. We hope the results can provide basic insight into further research endeavors on understanding practical and economical topology inference of networks.

Predicting dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris

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

Zuniga, Cristal; Levering, Jennifer; Antoniewicz, Maciek R.

Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. We used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of Chlorella vulgaris UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealedmore » an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted fluxes trends and gene expression trends was found for 65% of multi-subunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acids metabolism. Moreover, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of C. vulgaris, iCZ946, thus increasing our knowledgebase by 10% for this model green alga.« less

Predicting dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris

DOE PAGES

Zuniga, Cristal; Levering, Jennifer; Antoniewicz, Maciek R.; ...

2017-09-26

Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. We used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of Chlorella vulgaris UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealedmore » an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted fluxes trends and gene expression trends was found for 65% of multi-subunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acids metabolism. Moreover, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of C. vulgaris, iCZ946, thus increasing our knowledgebase by 10% for this model green alga.« less

Mass Conservation of the Unified Continuous and Discontinuous Element-Based Galerkin Methods on Dynamically Adaptive Grids with Application to Atmospheric Simulations

DTIC Science & Technology

2015-09-01

Discontinuous Element-Based Galerkin Methods on Dynamically Adaptive Grids with Application to Atmospheric Simulations 5a. CONTRACT NUMBER 5b. GRANT NUMBER...Discontinuous Element-Based Galerkin Methods on Dynamically Adaptive Grids with Application to Atmospheric Simulations. Michal A. Koperaa,∗, Francis X...mass conservation, as it is an important feature for many atmospheric applications . We believe this is a good metric because, for smooth solutions

Should the parameters of a BCI translation algorithm be continually adapted?

PubMed

McFarland, Dennis J; Sarnacki, William A; Wolpaw, Jonathan R

2011-07-15

People with or without motor disabilities can learn to control sensorimotor rhythms (SMRs) recorded from the scalp to move a computer cursor in one or more dimensions or can use the P300 event-related potential as a control signal to make discrete selections. Data collected from individuals using an SMR-based or P300-based BCI were evaluated offline to estimate the impact on performance of continually adapting the parameters of the translation algorithm during BCI operation. The performance of the SMR-based BCI was enhanced by adaptive updating of the feature weights or adaptive normalization of the features. In contrast, P300 performance did not benefit from either of these procedures. Copyright © 2011 Elsevier B.V. All rights reserved.

Learning and adaptation: neural and behavioural mechanisms behind behaviour change

NASA Astrophysics Data System (ADS)

Lowe, Robert; Sandamirskaya, Yulia

2018-01-01

This special issue presents perspectives on learning and adaptation as they apply to a number of cognitive phenomena including pupil dilation in humans and attention in robots, natural language acquisition and production in embodied agents (robots), human-robot game play and social interaction, neural-dynamic modelling of active perception and neural-dynamic modelling of infant development in the Piagetian A-not-B task. The aim of the special issue, through its contributions, is to highlight some of the critical neural-dynamic and behavioural aspects of learning as it grounds adaptive responses in robotic- and neural-dynamic systems.

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Functional Description for the Department of the Army Movements Management System. Redesign Phase 1. (DAMMS-R1). Volume 3.

DTIC Science & Technology

1987-12-31

Maintan -.ountry-TbI e CARDINALiii 134; HL~.,N-L.". 1); bL t i 11L h I...MEMO: FCityCd-Memo SOURCE IS: ’TMAS DAlA MODEL’ ATTRIBUTE IS: MEDIA ’DISK’ SEC-CLASS ’UNCLASSIFIED’ volatility ’DYNAMIC’I UPDATE- METHOD ’INTERACTIVE...8217 ATTRIBUTE IS: MEDIA ’DISK’ SEC-CLASS ’UNCLASSIFIED’ volatility ’STATTO’ UPDATE- METHOD ’INTERACTIVE’ UPDATE-FREQUENCY ’AS PER CHANGE’ Tt.LLE-FORM ’COMMAND’

Synthesizing information-update functions using off-line symbolic processing

NASA Technical Reports Server (NTRS)

Rosenschein, Stanley J.

1990-01-01

This paper explores the synthesis of programs that track dynamic conditions in their environment. An approach is proposed in which the designer specifies, in a declarative language, aspects of the environment in which the program will be embedded. This specification is then automatically compiled into a program that, when executed, updates internal data structures so as to maintain as an invariant a desired correspondence between internal data structures and states of the external environment. This approach retains much of the flexibility of declarative programming while guaranteeing a hard bound on the execution time of information-update functions.

Social Media Membership, Browsing, and Profile Updating in a Representative U.S. Sample: Independent and Interdependent Effects of Big Five Traits and Aging and Social Factors

PubMed Central

Bogg, Tim

2017-01-01

Guided by cybernetic perspectives on personality, the present work used a representative sample of U.S. adults (N = 992) to examine Big Five personality traits and social and aging factors as predictors of social media network membership and past-month browsing/searching and profile updating among members. The results showed adults who were less extraverted and less neurotic and who reported greater physical limitations were less likely to be members. Moreover, extraverted adults without partners were more likely to be members than introverted adults without partners. Among members, the results showed extraverted and emotionally stable younger and older adults reported a similar frequency of profile updating. In contrast, older adults with all other combinations of extraversion and neuroticism showed a reduced frequency of profile updating compared to younger adults. The findings are discussed in terms of social media involvement as a response of a self-regulatory system of personality adaptation. PMID:28713320

Orbital frontal cortex updates state-induced value change for decision-making.

PubMed

Baltz, Emily T; Yalcinbas, Ege A; Renteria, Rafael; Gremel, Christina M

2018-06-13

Recent hypotheses have posited that orbital frontal cortex (OFC) is important for using inferred consequences to guide behavior. Less clear is OFC's contribution to goal-directed or model-based behavior, where the decision to act is controlled by previous experience with the consequence or outcome. Investigating OFC's role in learning about changed outcomes separate from decision-making is not trivial and often the two are confounded. Here we adapted an incentive learning task to mice, where we investigated processes controlling experience-based outcome updating independent from inferred action control. We found chemogenetic OFC attenuation did not alter the ability to perceive motivational state-induced changes in outcome value but did prevent the experience-based updating of this change. Optogenetic inhibition of OFC excitatory neuron activity selectively when experiencing an outcome change disrupted the ability to update, leaving mice unable to infer the appropriate behavior. Our findings support a role for OFC in learning that controls decision-making. © 2018, Baltz et al.

A linear recurrent kernel online learning algorithm with sparse updates.

PubMed

Fan, Haijin; Song, Qing

2014-02-01

In this paper, we propose a recurrent kernel algorithm with selectively sparse updates for online learning. The algorithm introduces a linear recurrent term in the estimation of the current output. This makes the past information reusable for updating of the algorithm in the form of a recurrent gradient term. To ensure that the reuse of this recurrent gradient indeed accelerates the convergence speed, a novel hybrid recurrent training is proposed to switch on or off learning the recurrent information according to the magnitude of the current training error. Furthermore, the algorithm includes a data-dependent adaptive learning rate which can provide guaranteed system weight convergence at each training iteration. The learning rate is set as zero when the training violates the derived convergence conditions, which makes the algorithm updating process sparse. Theoretical analyses of the weight convergence are presented and experimental results show the good performance of the proposed algorithm in terms of convergence speed and estimation accuracy. Copyright © 2013 Elsevier Ltd. All rights reserved.

Social Media Membership, Browsing, and Profile Updating in a Representative U.S. Sample: Independent and Interdependent Effects of Big Five Traits and Aging and Social Factors.

PubMed

Bogg, Tim

2017-01-01

Guided by cybernetic perspectives on personality, the present work used a representative sample of U.S. adults ( N = 992) to examine Big Five personality traits and social and aging factors as predictors of social media network membership and past-month browsing/searching and profile updating among members. The results showed adults who were less extraverted and less neurotic and who reported greater physical limitations were less likely to be members. Moreover, extraverted adults without partners were more likely to be members than introverted adults without partners. Among members, the results showed extraverted and emotionally stable younger and older adults reported a similar frequency of profile updating. In contrast, older adults with all other combinations of extraversion and neuroticism showed a reduced frequency of profile updating compared to younger adults. The findings are discussed in terms of social media involvement as a response of a self-regulatory system of personality adaptation.

Seismic source characterization for the 2014 update of the U.S. National Seismic Hazard Model

USGS Publications Warehouse

Moschetti, Morgan P.; Powers, Peter; Petersen, Mark D.; Boyd, Oliver; Chen, Rui; Field, Edward H.; Frankel, Arthur; Haller, Kathleen; Harmsen, Stephen; Mueller, Charles S.; Wheeler, Russell; Zeng, Yuehua

2015-01-01

We present the updated seismic source characterization (SSC) for the 2014 update of the National Seismic Hazard Model (NSHM) for the conterminous United States. Construction of the seismic source models employs the methodology that was developed for the 1996 NSHM but includes new and updated data, data types, source models, and source parameters that reflect the current state of knowledge of earthquake occurrence and state of practice for seismic hazard analyses. We review the SSC parameterization and describe the methods used to estimate earthquake rates, magnitudes, locations, and geometries for all seismic source models, with an emphasis on new source model components. We highlight the effects that two new model components—incorporation of slip rates from combined geodetic-geologic inversions and the incorporation of adaptively smoothed seismicity models—have on probabilistic ground motions, because these sources span multiple regions of the conterminous United States and provide important additional epistemic uncertainty for the 2014 NSHM.

Free Molecular Heat Transfer Programs for Setup and Dynamic Updating the Conductors in Thermal Desktop

NASA Technical Reports Server (NTRS)

Malroy, Eric T.

2007-01-01

The programs, arrays and logic structure were developed to enable the dynamic update of conductors in thermal desktop. The MatLab program FMHTPRE.m processes the Thermal Desktop conductors and sets up the arrays. The user needs to manually copy portions of the output to different input regions in Thermal Desktop. Also, Fortran subroutines are provided that perform the actual updates to the conductors. The subroutines are setup for helium gas, but the equations can be modified for other gases. The maximum number of free molecular conductors allowed is 10,000 for a given radiation task. Additional radiation tasks for FMHT can be generated to account for more conductors. Modifications to the Fortran subroutines may be warranted, when the mode of heat transfer is in the mixed or continuum mode. The FMHT Thermal Desktop model should be activated by using the "Case Set Manager" once the model is setup. Careful setup of the model is needed to avoid excessive solve times.

LESS: Link Estimation with Sparse Sampling in Intertidal WSNs

PubMed Central

Ji, Xiaoyu; Chen, Yi-chao; Li, Xiaopeng; Xu, Wenyuan

2018-01-01

Deploying wireless sensor networks (WSN) in the intertidal area is an effective approach for environmental monitoring. To sustain reliable data delivery in such a dynamic environment, a link quality estimation mechanism is crucial. However, our observations in two real WSN systems deployed in the intertidal areas reveal that link update in routing protocols often suffers from energy and bandwidth waste due to the frequent link quality measurement and updates. In this paper, we carefully investigate the network dynamics using real-world sensor network data and find it feasible to achieve accurate estimation of link quality using sparse sampling. We design and implement a compressive-sensing-based link quality estimation protocol, LESS, which incorporates both spatial and temporal characteristics of the system to aid the link update in routing protocols. We evaluate LESS in both real WSN systems and a large-scale simulation, and the results show that LESS can reduce energy and bandwidth consumption by up to 50% while still achieving more than 90% link quality estimation accuracy. PMID:29494557

Closed-Cycle Engine Program Used to Study Brayton Power Conversion

NASA Technical Reports Server (NTRS)

Johnson, Paul K.

2005-01-01

One form of power conversion under consideration in NASA Glenn Research Center's Thermal Energy Conversion Branch is the closed-Brayton-cycle engine. In the tens-of-kilowatts to multimegawatt class, the Brayton engine lends itself to potential space nuclear power applications such as electric propulsion or surface power. The Thermal Energy Conversion Branch has most recently concentrated its Brayton studies on electric propulsion for Prometheus. One piece of software used for evaluating such designs over a limited tradeoff space has been the Closed Cycle Engine Program (CCEP). The CCEP originated in the mid-1980s from a Fortran aircraft engine code known as the Navy/NASA Engine Program (NNEP). Components such as a solar collector, heat exchangers, ducting, a pumped-loop radiator, a nuclear heat source, and radial turbomachinery were added to NNEP, transforming it into a high-fidelity design and performance tool for closed-Brayton-cycle power conversion and heat rejection. CCEP was used in the 1990s in conjunction with the Solar Dynamic Ground Test Demonstration conducted at Glenn. Over the past year, updates were made to CCEP to adapt it for an electric propulsion application. The pumped-loop radiator coolant can now be n-heptane, water, or sodium-potassium (NaK); liquid-metal pump design tables were added to accommodate the NaK fluid. For the reactor and shield, a user can now elect to calculate a higher fidelity mass estimate. In addition, helium-xenon working-fluid properties were recalculated and updated.

Center Products - National Site for the Regional IPM Centers

Science.gov Websites

IPM eAcademy Impact Assessment Toolkit United States Department of Agriculture - National Institute of Food and Agriculture Website managed by the Southern IPM Center. Design adapted from work by the Agriculture. Last update: March 12, 2014.

Home - National Site for the Regional IPM Centers

Science.gov Websites

Region Northeastern Region Southern Region Western Region United States Department of Agriculture - National Institute of Food and Agriculture Website managed by the Southern IPM Center. Design adapted from of Food and Agriculture. Last update: May 19, 2016.

Model Updating of Complex Structures Using the Combination of Component Mode Synthesis and Kriging Predictor

PubMed Central

Li, Yan; Wang, Dejun; Zhang, Shaoyi

2014-01-01

Updating the structural model of complex structures is time-consuming due to the large size of the finite element model (FEM). Using conventional methods for these cases is computationally expensive or even impossible. A two-level method, which combined the Kriging predictor and the component mode synthesis (CMS) technique, was proposed to ensure the successful implementing of FEM updating of large-scale structures. In the first level, the CMS was applied to build a reasonable condensed FEM of complex structures. In the second level, the Kriging predictor that was deemed as a surrogate FEM in structural dynamics was generated based on the condensed FEM. Some key issues of the application of the metamodel (surrogate FEM) to FEM updating were also discussed. Finally, the effectiveness of the proposed method was demonstrated by updating the FEM of a real arch bridge with the measured modal parameters. PMID:24634612

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

PubMed

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

2016-06-01

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

Dynamical Adaptation in Photoreceptors

PubMed Central

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

2013-01-01

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

The Dynamics of Memory: Context-Dependent Updating

ERIC Educational Resources Information Center

Hupbach, Almut; Hardt, Oliver; Gomez, Rebecca; Nadel, Lynn

2008-01-01

Understanding the dynamics of memory change is one of the current challenges facing cognitive neuroscience. Recent animal work on memory reconsolidation shows that memories can be altered long after acquisition. When reactivated, memories can be modified and require a restabilization (reconsolidation) process. We recently extended this finding to…

Moving target detection method based on improved Gaussian mixture model

NASA Astrophysics Data System (ADS)

Ma, J. Y.; Jie, F. R.; Hu, Y. J.

2017-07-01

Gaussian Mixture Model is often employed to build background model in background difference methods for moving target detection. This paper puts forward an adaptive moving target detection algorithm based on improved Gaussian Mixture Model. According to the graylevel convergence for each pixel, adaptively choose the number of Gaussian distribution to learn and update background model. Morphological reconstruction method is adopted to eliminate the shadow.. Experiment proved that the proposed method not only has good robustness and detection effect, but also has good adaptability. Even for the special cases when the grayscale changes greatly and so on, the proposed method can also make outstanding performance.

System and method for adaptively deskewing parallel data signals relative to a clock

DOEpatents

Jenkins, Philip Nord [Eau Claire, WI; Cornett, Frank N [Chippewa Falls, WI

2008-10-07

A system and method of reducing skew between a plurality of signals transmitted with a transmit clock is described. Skew is detected between the received transmit clock and each of received data signals. Delay is added to the clock or to one or more of the plurality of data signals to compensate for the detected skew. The delay added to each of the plurality of delayed signals is updated to adapt to changes in detected skew.

System and method for adaptively deskewing parallel data signals relative to a clock

DOEpatents

Jenkins, Philip Nord [Redwood Shores, CA; Cornett, Frank N [Chippewa Falls, WI

2011-10-04

A system and method of reducing skew between a plurality of signals transmitted with a transmit clock is described. Skew is detected between the received transmit clock and each of received data signals. Delay is added to the clock or to one or more of the plurality of data signals to compensate for the detected skew. The delay added to each of the plurality of delayed signals is updated to adapt to changes in detected skew.

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

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

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

PubMed

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

2017-11-28

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

Dynamic Courseware Generation on the WWW.

ERIC Educational Resources Information Center

Vassileva, Julita; Deters, Ralph

1998-01-01

The Dynamic Courseware Generator (DCG), which runs on a Web server, was developed for the authoring of adaptive computer-assisted learning courses. It generates an individual course according to the learner's goals and previous knowledge, and dynamically adapts the course according to the learner's success in knowledge acquisition. The tool may be…

Method and system to estimate variables in an integrated gasification combined cycle (IGCC) plant

DOEpatents

Kumar, Aditya; Shi, Ruijie; Dokucu, Mustafa

2013-09-17

System and method to estimate variables in an integrated gasification combined cycle (IGCC) plant are provided. The system includes a sensor suite to measure respective plant input and output variables. An extended Kalman filter (EKF) receives sensed plant input variables and includes a dynamic model to generate a plurality of plant state estimates and a covariance matrix for the state estimates. A preemptive-constraining processor is configured to preemptively constrain the state estimates and covariance matrix to be free of constraint violations. A measurement-correction processor may be configured to correct constrained state estimates and a constrained covariance matrix based on processing of sensed plant output variables. The measurement-correction processor is coupled to update the dynamic model with corrected state estimates and a corrected covariance matrix. The updated dynamic model may be configured to estimate values for at least one plant variable not originally sensed by the sensor suite.

XFEM-based modeling of successive resections for preoperative image updating

NASA Astrophysics Data System (ADS)

Vigneron, Lara M.; Robe, Pierre A.; Warfield, Simon K.; Verly, Jacques G.

2006-03-01

We present a new method for modeling organ deformations due to successive resections. We use a biomechanical model of the organ, compute its volume-displacement solution based on the eXtended Finite Element Method (XFEM). The key feature of XFEM is that material discontinuities induced by every new resection can be handled without remeshing or mesh adaptation, as would be required by the conventional Finite Element Method (FEM). We focus on the application of preoperative image updating for image-guided surgery. Proof-of-concept demonstrations are shown for synthetic and real data in the context of neurosurgery.

2014 Behavioral Health and Performance Standing Review Panel

NASA Technical Reports Server (NTRS)

Steinberg, Susan; Leon, Gloria; Dimsdale, Joel; Balkin, Thomas; Bono, Joyce; Caldwell, John; Joiner, Thomas; Paulus, Martin; Zaccaro, Stephen

2015-01-01

The 2014 Behavioral Health and Performance (BHP) Standing Review Panel (from here on referred to as the SRP) met for a site visit in Houston, TX on December 17 - 18, 2014. The SRP reviewed the updated research plan for the Risk of Performance Errors Due to Fatigue Resulting from Sleep Loss, Circadian Desynchronization, Extended Wakefulness, and Work Overload (Sleep Risk) and also received a status update on the Risk of Adverse Behavioral Conditions and Psychiatric Disorders (BMed Risk) and the Risk of Performance Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team (Team Risk).

A MATLAB toolbox for the efficient estimation of the psychometric function using the updated maximum-likelihood adaptive procedure

PubMed Central

Richards, V. M.; Dai, W.

2014-01-01

A MATLAB toolbox for the efficient estimation of the threshold, slope, and lapse rate of the psychometric function is described. The toolbox enables the efficient implementation of the updated maximum-likelihood (UML) procedure. The toolbox uses an object-oriented architecture for organizing the experimental variables and computational algorithms, which provides experimenters with flexibility in experimental design and data management. Descriptions of the UML procedure and the UML Toolbox are provided, followed by toolbox use examples. Finally, guidelines and recommendations of parameter configurations are given. PMID:24671826

A case against a divide and conquer approach to the nonsymmetric eigenvalue problem

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

Jessup, E.R.

1991-12-01

Divide and conquer techniques based on rank-one updating have proven fast, accurate, and efficient in parallel for the real symmetric tridiagonal and unitary eigenvalue problems and for the bidiagonal singular value problem. Although the divide and conquer mechanism can also be adapted to the real nonsymmetric eigenproblem in a straightforward way, most of the desirable characteristics of the other algorithms are lost. In this paper, we examine the problems of accuracy and efficiency that can stand in the way of a nonsymmetric divide and conquer eigensolver based on low-rank updating. 31 refs., 2 figs.

Vision: a moving hill for spatial updating on the fly.

PubMed

Stanford, Terrence R

2015-02-02

A recent study reveals a dynamic neural map that provides a continuous representation of remembered visual stimulus locations with respect to constantly changing gaze. This finding suggests a new mechanistic framework for understanding the spatiotemporal dynamics of goal-directed action. Copyright © 2015 Elsevier Ltd. All rights reserved.

Volunteer Development. Practice Application Brief.

ERIC Educational Resources Information Center

Kerka, Sandra

Certain practices in volunteer development have proved successful to help organizations make the best use of their volunteers. Development should be a comprehensive, continuous process through which individuals can extend, update, and adapt their knowledge, skills, and abilities to enhance their performance and potential. A model for volunteer…

Adaptability to Changes in Temporal Structure Is Fornix-Dependent

ERIC Educational Resources Information Center

Kwok, Sze Chai; Mitchell, Anna S.; Buckley, Mark J.

2015-01-01

Recognition memory deficits, even after short delays, are sometimes observed following hippocampal damage. One hypothesis links the hippocampus with processes in updating contextual memory representation. Here, we used fornix transection, which partially disconnects the hippocampal system, and compares the performance of fornix-transected monkeys…

Adaptability to Changes Intemporal Structure Is Fornix-Dependent

ERIC Educational Resources Information Center

Kwok, Sze Chai; Mitchell, Anna S.; Buckley, Mark J.

2015-01-01

Recognition memory deficits, even after short delays, are sometimes observed following hippocampal damage. One hypothesis links the hippocampus with processes in updating contextual memory representation. Here, we used fornix transection, which partially disconnects the hippocampal system, and compares the performance of fornix-transected monkeys…

The two sides of adversity: the effect of distant versus recent adversity on updating emotional content in working memory.

PubMed

Levens, Sara M; Armstrong, Laura Marie; Orejuela-Dávila, Ana I; Alverio, Tabitha

2017-09-01

Previous research suggests that adversity can have both adaptive and maladaptive effects, yet the emotional and working memory processes that contribute to more or less adaptive outcomes are unclear. The present study sought to investigate how updating emotional content differs in adolescents who have experienced past, recent, or no adversity. Participants who had experienced distant adversity (N = 53), no adversity (N = 58), or recent adversity only (N = 20) performed an emotion n-back task with emotional facial expressions. Results revealed that the distant adversity group exhibited significantly faster reaction times (RTs) than the no adversity and recent adversity only groups. In contrast, the recent adversity only group exhibited significantly slower RTs and more errors than the distant adversity and no adversity groups. These results suggest an emotion and executive control pathway by which both the benefits and negative effects of adversity may be conferred. Results also highlight the importance of time in assessing the impact of adversity.

Multiphase Interface Tracking with Fast Semi-Lagrangian Contouring.

PubMed

Li, Xiaosheng; He, Xiaowei; Liu, Xuehui; Zhang, Jian J; Liu, Baoquan; Wu, Enhua

2016-08-01

We propose a semi-Lagrangian method for multiphase interface tracking. In contrast to previous methods, our method maintains an explicit polygonal mesh, which is reconstructed from an unsigned distance function and an indicator function, to track the interface of arbitrary number of phases. The surface mesh is reconstructed at each step using an efficient multiphase polygonization procedure with precomputed stencils while the distance and indicator function are updated with an accurate semi-Lagrangian path tracing from the meshes of the last step. Furthermore, we provide an adaptive data structure, multiphase distance tree, to accelerate the updating of both the distance function and the indicator function. In addition, the adaptive structure also enables us to contour the distance tree accurately with simple bisection techniques. The major advantage of our method is that it can easily handle topological changes without ambiguities and preserve both the sharp features and the volume well. We will evaluate its efficiency, accuracy and robustness in the results part with several examples.

Nanoparticle Exposure and Hormetic Dose–Responses: An Update

PubMed Central

Leso, Veruscka; Fontana, Luca; Calabrese, Edward J.

2018-01-01

The concept of hormesis, as an adaptive response of biological systems to moderate environmental challenges, has raised considerable nano-toxicological interests in view of the rapid pace of production and application of even more innovative nanomaterials and the expected increasing likelihood of environmental and human exposure to low-dose concentrations. Therefore, the aim of this review is to provide an update of the current knowledge concerning the biphasic dose–responses induced by nanoparticle exposure. The evidence presented confirmed and extended our previous findings, showing that hormesis is a generalized adaptive response which may be further generalized to nanoscale xenobiotic challenges. Nanoparticle physico-chemical properties emerged as possible features affecting biphasic relationships, although the molecular mechanisms underlining such influences remain to be fully understood, especially in experimental settings resembling long-term and low-dose realistic environmental exposure scenarios. Further investigation is necessary to achieve helpful information for a suitable assessment of nanomaterial risks at the low-dose range for both the ecosystem function and the human health. PMID:29534471

Modeling Gas Dynamics in California Sea Lions

DTIC Science & Technology

2015-09-30

W. and Fahlman, A. (2009). Could beaked whales get the bends?. Effect of diving behaviour and physiology on modelled gas exchange for three species...1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Modeling Gas Dynamics in California Sea Lions Andreas...to update a current gas dynamics model with recently acquired data for respiratory compliance (P-V), and body compartment size estimates in

Design of Low Complexity Model Reference Adaptive Controllers

NASA Technical Reports Server (NTRS)

Hanson, Curt; Schaefer, Jacob; Johnson, Marcus; Nguyen, Nhan

2012-01-01

Flight research experiments have demonstrated that adaptive flight controls can be an effective technology for improving aircraft safety in the event of failures or damage. However, the nonlinear, timevarying nature of adaptive algorithms continues to challenge traditional methods for the verification and validation testing of safety-critical flight control systems. Increasingly complex adaptive control theories and designs are emerging, but only make testing challenges more difficult. A potential first step toward the acceptance of adaptive flight controllers by aircraft manufacturers, operators, and certification authorities is a very simple design that operates as an augmentation to a non-adaptive baseline controller. Three such controllers were developed as part of a National Aeronautics and Space Administration flight research experiment to determine the appropriate level of complexity required to restore acceptable handling qualities to an aircraft that has suffered failures or damage. The controllers consist of the same basic design, but incorporate incrementally-increasing levels of complexity. Derivations of the controllers and their adaptive parameter update laws are presented along with details of the controllers implementations.

Genetic variability and evolutionary dynamics of viruses of the family Closteroviridae

PubMed Central

Rubio, Luis; Guerri, José; Moreno, Pedro

2013-01-01

RNA viruses have a great potential for genetic variation, rapid evolution and adaptation. Characterization of the genetic variation of viral populations provides relevant information on the processes involved in virus evolution and epidemiology and it is crucial for designing reliable diagnostic tools and developing efficient and durable disease control strategies. Here we performed an updated analysis of sequences available in Genbank and reviewed present knowledge on the genetic variability and evolutionary processes of viruses of the family Closteroviridae. Several factors have shaped the genetic structure and diversity of closteroviruses. (I) A strong negative selection seems to be responsible for the high genetic stability in space and time for some viruses. (2) Long distance migration, probably by human transport of infected propagative plant material, have caused that genetically similar virus isolates are found in distant geographical regions. (3) Recombination between divergent sequence variants have generated new genotypes and plays an important role for the evolution of some viruses of the family Closteroviridae. (4) Interaction between virus strains or between different viruses in mixed infections may alter accumulation of certain strains. (5) Host change or virus transmission by insect vectors induced changes in the viral population structure due to positive selection of sequence variants with higher fitness for host-virus or vector-virus interaction (adaptation) or by genetic drift due to random selection of sequence variants during the population bottleneck associated to the transmission process. PMID:23805130

Optimizing virtual reality for all users through gaze-contingent and adaptive focus displays

PubMed Central

Padmanaban, Nitish; Konrad, Robert; Stramer, Tal; Wetzstein, Gordon

2017-01-01

From the desktop to the laptop to the mobile device, personal computing platforms evolve over time. Moving forward, wearable computing is widely expected to be integral to consumer electronics and beyond. The primary interface between a wearable computer and a user is often a near-eye display. However, current generation near-eye displays suffer from multiple limitations: they are unable to provide fully natural visual cues and comfortable viewing experiences for all users. At their core, many of the issues with near-eye displays are caused by limitations in conventional optics. Current displays cannot reproduce the changes in focus that accompany natural vision, and they cannot support users with uncorrected refractive errors. With two prototype near-eye displays, we show how these issues can be overcome using display modes that adapt to the user via computational optics. By using focus-tunable lenses, mechanically actuated displays, and mobile gaze-tracking technology, these displays can be tailored to correct common refractive errors and provide natural focus cues by dynamically updating the system based on where a user looks in a virtual scene. Indeed, the opportunities afforded by recent advances in computational optics open up the possibility of creating a computing platform in which some users may experience better quality vision in the virtual world than in the real one. PMID:28193871

Motion Cueing Algorithm Development: Human-Centered Linear and Nonlinear Approaches

NASA Technical Reports Server (NTRS)

Houck, Jacob A. (Technical Monitor); Telban, Robert J.; Cardullo, Frank M.

2005-01-01

While the performance of flight simulator motion system hardware has advanced substantially, the development of the motion cueing algorithm, the software that transforms simulated aircraft dynamics into realizable motion commands, has not kept pace. Prior research identified viable features from two algorithms: the nonlinear "adaptive algorithm", and the "optimal algorithm" that incorporates human vestibular models. A novel approach to motion cueing, the "nonlinear algorithm" is introduced that combines features from both approaches. This algorithm is formulated by optimal control, and incorporates a new integrated perception model that includes both visual and vestibular sensation and the interaction between the stimuli. Using a time-varying control law, the matrix Riccati equation is updated in real time by a neurocomputing approach. Preliminary pilot testing resulted in the optimal algorithm incorporating a new otolith model, producing improved motion cues. The nonlinear algorithm vertical mode produced a motion cue with a time-varying washout, sustaining small cues for longer durations and washing out large cues more quickly compared to the optimal algorithm. The inclusion of the integrated perception model improved the responses to longitudinal and lateral cues. False cues observed with the NASA adaptive algorithm were absent. The neurocomputing approach was crucial in that the number of presentations of an input vector could be reduced to meet the real time requirement without degrading the quality of the motion cues.

OpenRBC: Redefining the Frontier of Red Blood Cell Simulations at Protein Resolution

NASA Astrophysics Data System (ADS)

Tang, Yu-Hang; Lu, Lu; Li, He; Grinberg, Leopold; Sachdeva, Vipin; Evangelinos, Constantinos; Karniadakis, George

We present a from-scratch development of OpenRBC, a coarse-grained molecular dynamics code, which is capable of performing an unprecedented in silico experiment - simulating an entire mammal red blood cell lipid bilayer and cytoskeleton modeled by 4 million mesoscopic particles - on a single shared memory node. To achieve this, we invented an adaptive spatial searching algorithm to accelerate the computation of short-range pairwise interactions in an extremely sparse 3D space. The algorithm is based on a Voronoi partitioning of the point cloud of coarse-grained particles, and is continuously updated over the course of the simulation. The algorithm enables the construction of a lattice-free cell list, i.e. the key spatial searching data structure in our code, in O (N) time and space space with cells whose position and shape adapts automatically to the local density and curvature. The code implements NUMA/NUCA-aware OpenMP parallelization and achieves perfect scaling with up to hundreds of hardware threads. The code outperforms a legacy solver by more than 8 times in time-to-solution and more than 20 times in problem size, thus providing a new venue for probing the cytomechanics of red blood cells. This work was supported by the Department of Energy (DOE) Collaboratory on Mathematics for Mesoscopic Model- ing of Materials (CM4). YHT acknowledges partial financial support from an IBM Ph.D. Scholarship Award.

Linking Individual and Collective Behavior in Adaptive Social Networks

NASA Astrophysics Data System (ADS)

Pinheiro, Flávio L.; Santos, Francisco C.; Pacheco, Jorge M.

2016-03-01

Adaptive social structures are known to promote the evolution of cooperation. However, up to now the characterization of the collective, population-wide dynamics resulting from the self-organization of individual strategies on a coevolving, adaptive network has remained unfeasible. Here we establish a (reversible) link between individual (micro)behavior and collective (macro)behavior for coevolutionary processes. We demonstrate that an adaptive network transforms a two-person social dilemma locally faced by individuals into a collective dynamics that resembles that associated with an N -person coordination game, whose characterization depends sensitively on the relative time scales between the entangled behavioral and network evolutions. In particular, we show that the faster the relative rate of adaptation of the network, the smaller the critical fraction of cooperators required for cooperation to prevail, thus establishing a direct link between network adaptation and the evolution of cooperation. The framework developed here is general and may be readily applied to other dynamical processes occurring on adaptive networks, notably, the spreading of contagious diseases or the diffusion of innovations.

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

NASA Astrophysics Data System (ADS)

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

2013-08-01

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