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Sample records for iterative learning control

  1. Iterative learning control algorithm for spiking behavior of neuron model

    NASA Astrophysics Data System (ADS)

    Li, Shunan; Li, Donghui; Wang, Jiang; Yu, Haitao

    2016-11-01

    Controlling neurons to generate a desired or normal spiking behavior is the fundamental building block of the treatment of many neurologic diseases. The objective of this work is to develop a novel control method-closed-loop proportional integral (PI)-type iterative learning control (ILC) algorithm to control the spiking behavior in model neurons. In order to verify the feasibility and effectiveness of the proposed method, two single-compartment standard models of different neuronal excitability are specifically considered: Hodgkin-Huxley (HH) model for class 1 neural excitability and Morris-Lecar (ML) model for class 2 neural excitability. ILC has remarkable advantages for the repetitive processes in nature. To further highlight the superiority of the proposed method, the performances of the iterative learning controller are compared to those of classical PI controller. Either in the classical PI control or in the PI control combined with ILC, appropriate background noises are added in neuron models to approach the problem under more realistic biophysical conditions. Simulation results show that the controller performances are more favorable when ILC is considered, no matter which neuronal excitability the neuron belongs to and no matter what kind of firing pattern the desired trajectory belongs to. The error between real and desired output is much smaller under ILC control signal, which suggests ILC of neuron’s spiking behavior is more accurate.

  2. Fast calculation of the `ILC norm' in iterative learning control

    NASA Astrophysics Data System (ADS)

    Rice, Justin K.; van Wingerden, Jan-Willem

    2013-06-01

    In this paper, we discuss and demonstrate a method for the exploitation of matrix structure in computations for iterative learning control (ILC). In Barton, Bristow, and Alleyne [International Journal of Control, 83(2), 1-8 (2010)], a special insight into the structure of the lifted convolution matrices involved in ILC is used along with a modified Lanczos method to achieve very fast computational bounds on the learning convergence, by calculating the 'ILC norm' in ? computational complexity. In this paper, we show how their method is equivalent to a special instance of the sequentially semi-separable (SSS) matrix arithmetic, and thus can be extended to many other computations in ILC, and specialised in some cases to even faster methods. Our SSS-based methodology will be demonstrated on two examples: a linear time-varying example resulting in the same ? complexity as in Barton et al., and a linear time-invariant example where our approach reduces the computational complexity to ?, thus decreasing the computation time, for an example, from the literature by a factor of almost 100. This improvement is achieved by transforming the norm computation via a linear matrix inequality into a check of positive definiteness - which allows us to further exploit the almost-Toeplitz properties of the matrix, and additionally provides explicit upper and lower bounds on the norm of the matrix, instead of the indirect Ritz estimate. These methods are now implemented in a MATLAB toolbox, freely available on the Internet.

  3. H∞ iterative learning controller design for a class of discrete-time systems with data dropouts

    NASA Astrophysics Data System (ADS)

    Bu, Xuhui; Hou, Zhongsheng; Yu, Fashan; Wang, Fuzhong

    2014-09-01

    In this paper, the issue of H∞ iterative learning controller design is considered for a class of discrete-time systems with data dropouts. With the super-vector formulation of iterative learning control (ILC), such a system can be formulated as a linear discrete-time stochastic system in the iteration domain, and then a sufficient condition guaranteeing both stability of the ILC process and the desired H∞ performance in the iteration domain is presented. The condition can be derived in terms of linear matrix inequalities that can be solved by using existing numerical techniques. A numerical simulation example is also included to validate the theoretical results.

  4. Adaptive iterative learning control for a class of non-linearly parameterised systems with input saturations

    NASA Astrophysics Data System (ADS)

    Zhang, Ruikun; Hou, Zhongsheng; Ji, Honghai; Yin, Chenkun

    2016-04-01

    In this paper, an adaptive iterative learning control scheme is proposed for a class of non-linearly parameterised systems with unknown time-varying parameters and input saturations. By incorporating a saturation function, a new iterative learning control mechanism is presented which includes a feedback term and a parameter updating term. Through the use of parameter separation technique, the non-linear parameters are separated from the non-linear function and then a saturated difference updating law is designed in iteration domain by combining the unknown parametric term of the local Lipschitz continuous function and the unknown time-varying gain into an unknown time-varying function. The analysis of convergence is based on a time-weighted Lyapunov-Krasovskii-like composite energy function which consists of time-weighted input, state and parameter estimation information. The proposed learning control mechanism warrants a L2[0, T] convergence of the tracking error sequence along the iteration axis. Simulation results are provided to illustrate the effectiveness of the adaptive iterative learning control scheme.

  5. Implementation of a new iterative learning control algorithm on real data

    NASA Astrophysics Data System (ADS)

    Zamanian, Hamed; Koohi, Ardavan

    2016-02-01

    In this paper, a newly presented approach is proposed for closed-loop automatic tuning of a proportional integral derivative (PID) controller based on iterative learning control (ILC) algorithm. A modified ILC scheme iteratively changes the control signal by adjusting it. Once a satisfactory performance is achieved, a linear compensator is identified in the ILC behavior using casual relationship between the closed loop signals. This compensator is approximated by a PD controller which is used to tune the original PID controller. Results of implementing this approach presented on the experimental data of Damavand tokamak and are consistent with simulation outcome.

  6. Control of a pneumatic power active lower-limb orthosis with filter-based iterative learning control

    NASA Astrophysics Data System (ADS)

    Huang, Chia-En; Chen, Jian-Shiang

    2014-05-01

    A filter-based iterative learning control (FILC) scheme is developed in this paper, which consists in a proportional-derivative (PD) feedback controller and a feedforward filter. Moreover, based on two-dimensional system theory, the stability of the FILC system is proven. The design criteria for a wavelet transform filter (WTF) - chosen as the feedforward filter - and the PD feedback controller are also given. Finally, using a pneumatic power active lower-limb orthosis (PPALO) as the controlled plant, the wavelet-based iterative learning control (WILC) implementation and the orchestration of a trajectory tracking control simulation are given in detail and the overall tracking performance is validated.

  7. An iterative learning control method with application for CNC machine tools

    SciTech Connect

    Kim, D.I.; Kim, S.

    1996-01-01

    A proportional, integral, and derivative (PID) type iterative learning controller is proposed for precise tracking control of industrial robots and computer numerical controller (CNC) machine tools performing repetitive tasks. The convergence of the output error by the proposed learning controller is guaranteed under a certain condition even when the system parameters are not known exactly and unknown external disturbances exist. As the proposed learning controller is repeatedly applied to the industrial robot or the CNC machine tool with the path-dependent repetitive task, the distance difference between the desired path and the actual tracked or machined path, which is one of the most significant factors in the evaluation of control performance, is progressively reduced. The experimental results demonstrate that the proposed learning controller can improve machining accuracy when the CNC machine tool performs repetitive machining tasks.

  8. Distributed adaptive fuzzy iterative learning control of coordination problems for higher order multi-agent systems

    NASA Astrophysics Data System (ADS)

    Li, Jinsha; Li, Junmin

    2016-07-01

    In this paper, the adaptive fuzzy iterative learning control scheme is proposed for coordination problems of Mth order (M ≥ 2) distributed multi-agent systems. Every follower agent has a higher order integrator with unknown nonlinear dynamics and input disturbance. The dynamics of the leader are a higher order nonlinear systems and only available to a portion of the follower agents. With distributed initial state learning, the unified distributed protocols combined time-domain and iteration-domain adaptive laws guarantee that the follower agents track the leader uniformly on [0, T]. Then, the proposed algorithm extends to achieve the formation control. A numerical example and a multiple robotic system are provided to demonstrate the performance of the proposed approach.

  9. Task-Space Iterative Learning for Redundant Robotic Systems: Existence of a Task-Space Control and Convergence of Learning

    NASA Astrophysics Data System (ADS)

    Arimoto, Suguru; Sekimoto, Masahiro; Kawamura, Sadao

    This paper presents a feasibility study of iterative learning control for a class of redundant multi-joint robotic systems when a desired motion trajectory is specified in task-space with less dimension than that of joint space. First, it is shown that if the desired trajectory described in task-space for a time interval t ∈ [0,T] is twice continuously differentiable then a unique control signal describable in task-space exists despite of the system joint-redundancy. Second, a learning control update law is constructed through transpose of the Jacobian matrix of task-space coordinates with respect to joint coordinates by using measured data of motion trajectories in task-space. Third, the convergence of trajectory trackings through iterative learning is proved theoretically on the basis of original nonlinear robot dynamics in joint space.

  10. Application of a repetitive process setting to design of monotonically convergent iterative learning control

    NASA Astrophysics Data System (ADS)

    Boski, Marcin; Paszke, Wojciech

    2015-11-01

    This paper deals with the problem of designing an iterative learning control algorithm for discrete linear systems using repetitive process stability theory. The resulting design produces a stabilizing output feedback controller in the time domain and a feedforward controller that guarantees monotonic convergence in the trial-to-trial domain. The results are also extended to limited frequency range design specification. New design procedure is introduced in terms of linear matrix inequality (LMI) representations, which guarantee the prescribed performances of ILC scheme. A simulation example is given to illustrate the theoretical developments.

  11. Iterative learning control with applications in energy generation, lasers and health care

    PubMed Central

    Tutty, O. R.

    2016-01-01

    Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability. PMID:27713654

  12. Iterative learning control with applications in energy generation, lasers and health care

    NASA Astrophysics Data System (ADS)

    Rogers, E.; Tutty, O. R.

    2016-09-01

    Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability.

  13. Networked iterative learning control approach for nonlinear systems with random communication delay

    NASA Astrophysics Data System (ADS)

    Liu, Jian; Ruan, Xiaoe

    2016-12-01

    This paper constructs a proportional-type networked iterative learning control (NILC) scheme for a class of discrete-time nonlinear systems with the stochastic data communication delay within one operation duration and being subject to Bernoulli-type distribution. In the scheme, the communication delayed data is replaced by successfully captured one at the concurrent sampling moment of the latest iteration. The tracking performance of the addressed NILC algorithm is analysed by statistic technique in virtue of mathematical expectation. The analysis shows that, under certain conditions, the expectation of the tracking error measured in the form of 1-norm is asymptotically convergent to zero. Numerical experiments are carried out to illustrate the validity and effectiveness.

  14. Model-based iterative learning control of Parkinsonian state in thalamic relay neuron

    NASA Astrophysics Data System (ADS)

    Liu, Chen; Wang, Jiang; Li, Huiyan; Xue, Zhiqin; Deng, Bin; Wei, Xile

    2014-09-01

    Although the beneficial effects of chronic deep brain stimulation on Parkinson's disease motor symptoms are now largely confirmed, the underlying mechanisms behind deep brain stimulation remain unclear and under debate. Hence, the selection of stimulation parameters is full of challenges. Additionally, due to the complexity of neural system, together with omnipresent noises, the accurate model of thalamic relay neuron is unknown. Thus, the iterative learning control of the thalamic relay neuron's Parkinsonian state based on various variables is presented. Combining the iterative learning control with typical proportional-integral control algorithm, a novel and efficient control strategy is proposed, which does not require any particular knowledge on the detailed physiological characteristics of cortico-basal ganglia-thalamocortical loop and can automatically adjust the stimulation parameters. Simulation results demonstrate the feasibility of the proposed control strategy to restore the fidelity of thalamic relay in the Parkinsonian condition. Furthermore, through changing the important parameter—the maximum ionic conductance densities of low-threshold calcium current, the dominant characteristic of the proposed method which is independent of the accurate model can be further verified.

  15. Realization of Comfortable Massage by Using Iterative Learning Control Based on EEG

    NASA Astrophysics Data System (ADS)

    Teramae, Tatsuya; Kushida, Daisuke; Takemori, Fumiaki; Kitamura, Akira

    Recently the massage chair is used by a lot of people because they are able to use it easily at home. However a present massage chair only realizes the massage motion. Moreover the massage chair can not consider the user’s condition and massage force. On the other hand, the professional masseur is according to presume the mental condition by patient’s reaction. Then this paper proposes the method of applying masseur’s procedure for the massage chair using iterative learning control based on EEG. And massage force is estimated by acceleration sensor. The realizability of the proposed method is verified by the experimental works using the massage chair.

  16. Multivariable norm optimal and parameter optimal iterative learning control: a unified formulation

    NASA Astrophysics Data System (ADS)

    Owens, D. H.

    2012-08-01

    This article investigates the two paradigms of norm optimal iterative learning control (NOILC) and parameter optimal iterative learning control (POILC) for multivariable (MIMO) ℓ-input, m-output linear discrete-time systems. The main result is a proof that, despite their algebraic and conceptual differences, they can be unified using linear quadratic multi-parameter optimisation techniques. In particular, whilst POILC has been naturally regarded as an approximation to NOILC, it is shown that the NOILC control law can be generated from a suitable choice of control law parameterisation and objective function in a multi-parameter MIMO POILC problem. The form of this equivalence is used to propose a new general approach to the construction of POILC problems for MIMO systems that approximates the solution of a given NOILC problem. An infinite number of such approximations exist. This great diversity is illustrated by the derivation of new convergent algorithms based on time interval and gradient partition that extend previously published work.

  17. Iterative Learning Control Systems Based on Inverse Systems and Interactor Matrix for Linear Discrete-time Systems

    NASA Astrophysics Data System (ADS)

    Kase, Wataru

    In this paper, it will be clear the structure of the Iterative Learning Control (ILC) based on the inverse system. Moore-Penrose pseudo-inverse of a Toeplitz matrix will be investigated to analyze the learning gain matrix and will be derived the cascade controller transfer function matrix. From these investigations, the critical points of ILC based on the gradient will be issued.

  18. Effects of iterative learning based signal control strategies on macroscopic fundamental diagrams of urban road networks

    NASA Astrophysics Data System (ADS)

    Yan, Fei; Tian, Fuli; Shi, Zhongke

    2016-10-01

    Urban traffic flows are inherently repeated on a daily or weekly basis. This repeatability can help improve the traffic conditions if it is used properly by the control system. In this paper, we propose a novel iterative learning control (ILC) strategy for traffic signals of urban road networks using the repeatability feature of traffic flow. To improve the control robustness, the ILC strategy is further integrated with an error feedback control law in a complementary manner. Theoretical analysis indicates that the ILC-based traffic signal control methods can guarantee the asymptotic learning convergence, despite the presence of modeling uncertainties and exogenous disturbances. Finally, the impacts of the ILC-based signal control strategies on the network macroscopic fundamental diagram (MFD) are examined. The results show that the proposed ILC-based control strategies can homogenously distribute the network accumulation by controlling the vehicle numbers in each link to the desired levels under different traffic demands, which can result in the network with high capacity and mobility.

  19. A 2D systems approach to iterative learning control for discrete linear processes with zero Markov parameters

    NASA Astrophysics Data System (ADS)

    Hladowski, Lukasz; Galkowski, Krzysztof; Cai, Zhonglun; Rogers, Eric; Freeman, Chris T.; Lewin, Paul L.

    2011-07-01

    In this article a new approach to iterative learning control for the practically relevant case of deterministic discrete linear plants with uniform rank greater than unity is developed. The analysis is undertaken in a 2D systems setting that, by using a strong form of stability for linear repetitive processes, allows simultaneous consideration of both trial-to-trial error convergence and along the trial performance, resulting in design algorithms that can be computed using linear matrix inequalities (LMIs). Finally, the control laws are experimentally verified on a gantry robot that replicates a pick and place operation commonly found in a number of applications to which iterative learning control is applicable.

  20. Indirect iterative learning control for a discrete visual servo without a camera-robot model.

    PubMed

    Jiang, Ping; Bamforth, Leon C A; Feng, Zuren; Baruch, John E F; Chen, YangQuan

    2007-08-01

    This paper presents a discrete learning controller for vision-guided robot trajectory imitation with no prior knowledge of the camera-robot model. A teacher demonstrates a desired movement in front of a camera, and then, the robot is tasked to replay it by repetitive tracking. The imitation procedure is considered as a discrete tracking control problem in the image plane, with an unknown and time-varying image Jacobian matrix. Instead of updating the control signal directly, as is usually done in iterative learning control (ILC), a series of neural networks are used to approximate the unknown Jacobian matrix around every sample point in the demonstrated trajectory, and the time-varying weights of local neural networks are identified through repetitive tracking, i.e., indirect ILC. This makes repetitive segmented training possible, and a segmented training strategy is presented to retain the training trajectories solely within the effective region for neural network approximation. However, a singularity problem may occur if an unmodified neural-network-based Jacobian estimation is used to calculate the robot end-effector velocity. A new weight modification algorithm is proposed which ensures invertibility of the estimation, thus circumventing the problem. Stability is further discussed, and the relationship between the approximation capability of the neural network and the tracking accuracy is obtained. Simulations and experiments are carried out to illustrate the validity of the proposed controller for trajectory imitation of robot manipulators with unknown time-varying Jacobian matrices.

  1. Height control of laser metal-wire deposition based on iterative learning control and 3D scanning

    NASA Astrophysics Data System (ADS)

    Heralić, Almir; Christiansson, Anna-Karin; Lennartson, Bengt

    2012-09-01

    Laser Metal-wire Deposition is an additive manufacturing technique for solid freeform fabrication of fully dense metal structures. The technique is based on robotized laser welding and wire filler material, and the structures are built up layer by layer. The deposition process is, however, sensitive to disturbances and thus requires continuous monitoring and adjustments. In this work a 3D scanning system is developed and integrated with the robot control system for automatic in-process control of the deposition. The goal is to ensure stable deposition, by means of choosing a correct offset of the robot in the vertical direction, and obtaining a flat surface, for each deposited layer. The deviations in the layer height are compensated by controlling the wire feed rate on next deposition layer, based on the 3D scanned data, by means of iterative learning control. The system is tested through deposition of bosses, which is expected to be a typical application for this technique in the manufacture of jet engine components. The results show that iterative learning control including 3D scanning is a suitable method for automatic deposition of such structures. This paper presents the equipment, the control strategy and demonstrates the proposed approach with practical experiments.

  2. Design of robust iterative learning control schemes for systems with polytopic uncertainties and sector-bounded nonlinearities

    NASA Astrophysics Data System (ADS)

    Boski, Marcin; Paszke, Wojciech

    2017-01-01

    This paper deals with designing of iterative learning control schemes for uncertain systems with static nonlinearities. More specifically, the nonlinear part is supposed to be sector bounded and system matrices are assumed to range in the polytope of matrices. For systems with such nonlinearities and uncertainties the repetitive process setting is exploited to develop a linear matrix inequality based conditions for computing the feedback and feedforward (learning) controllers. These controllers guarantee acceptable dynamics along the trials and ensure convergence of the trial-to-trial error dynamics, respectively. Numerical examples illustrate the theoretical results and confirm effectiveness of the designed control scheme.

  3. An inverse-model approach to multivariable norm optimal iterative learning control with auxiliary optimisation

    NASA Astrophysics Data System (ADS)

    Owens, David H.; Freeman, Chris T.; Chu, Bing

    2014-08-01

    Motivated by the commonly encountered problem in which tracking is only required at selected intermediate points within the time interval, a general optimisation-based iterative learning control (ILC) algorithm is derived that ensures convergence of tracking errors to zero whilst simultaneously minimising a specified quadratic objective function of the input signals and chosen auxiliary (state) variables. In practice, the proposed solutions enable a repeated tracking task to be accurately completed whilst simultaneously reducing undesirable effects such as payload spillage, vibration tendencies and actuator wear. The theory is developed using the well-known norm optimal ILC (NOILC) framework, using general linear, functional operators between real Hilbert spaces. Solutions are derived using feedforward action, convergence is proved and robustness bounds are presented using both norm bounds and positivity conditions. Algorithms are specified for both continuous and discrete-time state-space representations, with the latter including application to multi-rate sampled systems. Experimental results using a robotic manipulator confirm the practical utility of the algorithms and the closeness with which observed results match theoretical predictions.

  4. Model-Free Primitive-Based Iterative Learning Control Approach to Trajectory Tracking of MIMO Systems With Experimental Validation.

    PubMed

    Radac, Mircea-Bogdan; Precup, Radu-Emil; Petriu, Emil M

    2015-11-01

    This paper proposes a novel model-free trajectory tracking of multiple-input multiple-output (MIMO) systems by the combination of iterative learning control (ILC) and primitives. The optimal trajectory tracking solution is obtained in terms of previously learned solutions to simple tasks called primitives. The library of primitives that are stored in memory consists of pairs of reference input/controlled output signals. The reference input primitives are optimized in a model-free ILC framework without using knowledge of the controlled process. The guaranteed convergence of the learning scheme is built upon a model-free virtual reference feedback tuning design of the feedback decoupling controller. Each new complex trajectory to be tracked is decomposed into the output primitives regarded as basis functions. The optimal reference input for the control system to track the desired trajectory is next recomposed from the reference input primitives. This is advantageous because the optimal reference input is computed straightforward without the need to learn from repeated executions of the tracking task. In addition, the optimization problem specific to trajectory tracking of square MIMO systems is decomposed in a set of optimization problems assigned to each separate single-input single-output control channel that ensures a convenient model-free decoupling. The new model-free primitive-based ILC approach is capable of planning, reasoning, and learning. A case study dealing with the model-free control tuning for a nonlinear aerodynamic system is included to validate the new approach. The experimental results are given.

  5. Using Functional Electrical Stimulation Mediated by Iterative Learning Control and Robotics to Improve Arm Movement for People With Multiple Sclerosis.

    PubMed

    Sampson, Patrica; Freeman, Chris; Coote, Susan; Demain, Sara; Feys, Peter; Meadmore, Katie; Hughes, Ann-Marie

    2016-02-01

    Few interventions address multiple sclerosis (MS) arm dysfunction but robotics and functional electrical stimulation (FES) appear promising. This paper investigates the feasibility of combining FES with passive robotic support during virtual reality (VR) training tasks to improve upper limb function in people with multiple sclerosis (pwMS). The system assists patients in following a specified trajectory path, employing an advanced model-based paradigm termed iterative learning control (ILC) to adjust the FES to improve accuracy and maximise voluntary effort. Reaching tasks were repeated six times with ILC learning the optimum control action from previous attempts. A convenience sample of five pwMS was recruited from local MS societies, and the intervention comprised 18 one-hour training sessions over 10 weeks. The accuracy of tracking performance without FES and the amount of FES delivered during training were analyzed using regression analysis. Clinical functioning of the arm was documented before and after treatment with standard tests. Statistically significant results following training included: improved accuracy of tracking performance both when assisted and unassisted by FES; reduction in maximum amount of FES needed to assist tracking; and less impairment in the proximal arm that was trained. The system was well tolerated by all participants with no increase in muscle fatigue reported. This study confirms the feasibility of FES combined with passive robot assistance as a potentially effective intervention to improve arm movement and control in pwMS and provides the basis for a follow-up study.

  6. ITER Plasma Control System Development

    NASA Astrophysics Data System (ADS)

    Snipes, Joseph; ITER PCS Design Team

    2015-11-01

    The development of the ITER Plasma Control System (PCS) continues with the preliminary design phase for 1st plasma and early plasma operation in H/He up to Ip = 15 MA in L-mode. The design is being developed through a contract between the ITER Organization and a consortium of plasma control experts from EU and US fusion laboratories, which is expected to be completed in time for a design review at the end of 2016. This design phase concentrates on breakdown including early ECH power and magnetic control of the poloidal field null, plasma current, shape, and position. Basic kinetic control of the heating (ECH, ICH, NBI) and fueling systems is also included. Disruption prediction, mitigation, and maintaining stable operation are also included because of the high magnetic and kinetic stored energy present already for early plasma operation. Support functions for error field topology and equilibrium reconstruction are also required. All of the control functions also must be integrated into an architecture that will be capable of the required complexity of all ITER scenarios. A database is also being developed to collect and manage PCS functional requirements from operational scenarios that were defined in the Conceptual Design with links to proposed event handling strategies and control algorithms for initial basic control functions. A brief status of the PCS development will be presented together with a proposed schedule for design phases up to DT operation.

  7. Iterative learning control for synchronization of reticle stage and wafer stage in step-and-scan lithographic equipment

    NASA Astrophysics Data System (ADS)

    Li, Lan-lan; Hu, Song; Zhao, Li-xin; Ma, Ping

    2013-08-01

    Lithographic equipments are highly complex machines used to manufacture integrated circuits (ICs). To make larger ICs, a larger lens is required, which, however, is prohibitively expensive. The solution to this problem is to expose a chip not in one flash but in a scanning fashion. For step-and-scan lithographic equipment (wafer scanner), the image quality is decided by many factors, in which synchronization of reticle stage and wafer stage during exposure is a key one. In this paper, the principle of reticle stage and wafer stage was analyzed through investigating the structure of scanners, firstly. While scanning, the reticle stage and wafer stage should scan simultaneously at a high speed and the speed ratio is 1:4. Secondly, an iterative learning controller (ILC) for synchronization of reticle stage and wafer stage is presented. In the controller, a master-slave structure is used, with the wafer stage acting as the master, and the reticle stage as the slave. Since the scanning process of scanner is repetitive, ILC is used to improve tracking performance. A simple design procedure is presented which allows design of the ILC system for the reticle stage and wafer stage independently. Finally, performance of the algorithm is illustrated by simulated on the virtual stages (the reticle stage and wafer stage).The results of simulation experiments and theory analyzing demonstrate that using the proposed controller better synchronization performance can be obtained for the reticle stage and wafer stage in scanner. Theory analysis and experiment shows the method is reasonable and efficient.

  8. Iterated learning and the evolution of language.

    PubMed

    Kirby, Simon; Griffiths, Tom; Smith, Kenny

    2014-10-01

    Iterated learning describes the process whereby an individual learns their behaviour by exposure to another individual's behaviour, who themselves learnt it in the same way. It can be seen as a key mechanism of cultural evolution. We review various methods for understanding how behaviour is shaped by the iterated learning process: computational agent-based simulations; mathematical modelling; and laboratory experiments in humans and non-human animals. We show how this framework has been used to explain the origins of structure in language, and argue that cultural evolution must be considered alongside biological evolution in explanations of language origins.

  9. Language Evolution by Iterated Learning with Bayesian Agents

    ERIC Educational Resources Information Center

    Griffiths, Thomas L.; Kalish, Michael L.

    2007-01-01

    Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute…

  10. Learning to improve iterative repair scheduling

    NASA Technical Reports Server (NTRS)

    Zweben, Monte; Davis, Eugene

    1992-01-01

    This paper presents a general learning method for dynamically selecting between repair heuristics in an iterative repair scheduling system. The system employs a version of explanation-based learning called Plausible Explanation-Based Learning (PEBL) that uses multiple examples to confirm conjectured explanations. The basic approach is to conjecture contradictions between a heuristic and statistics that measure the quality of the heuristic. When these contradictions are confirmed, a different heuristic is selected. To motivate the utility of this approach we present an empirical evaluation of the performance of a scheduling system with respect to two different repair strategies. We show that the scheduler that learns to choose between the heuristics outperforms the same scheduler with any one of two heuristics alone.

  11. Novel aspects of plasma control in ITER

    NASA Astrophysics Data System (ADS)

    Humphreys, D.; Ambrosino, G.; de Vries, P.; Felici, F.; Kim, S. H.; Jackson, G.; Kallenbach, A.; Kolemen, E.; Lister, J.; Moreau, D.; Pironti, A.; Raupp, G.; Sauter, O.; Schuster, E.; Snipes, J.; Treutterer, W.; Walker, M.; Welander, A.; Winter, A.; Zabeo, L.

    2015-02-01

    ITER plasma control design solutions and performance requirements are strongly driven by its nuclear mission, aggressive commissioning constraints, and limited number of operational discharges. In addition, high plasma energy content, heat fluxes, neutron fluxes, and very long pulse operation place novel demands on control performance in many areas ranging from plasma boundary and divertor regulation to plasma kinetics and stability control. Both commissioning and experimental operations schedules provide limited time for tuning of control algorithms relative to operating devices. Although many aspects of the control solutions required by ITER have been well-demonstrated in present devices and even designed satisfactorily for ITER application, many elements unique to ITER including various crucial integration issues are presently under development. We describe selected novel aspects of plasma control in ITER, identifying unique parts of the control problem and highlighting some key areas of research remaining. Novel control areas described include control physics understanding (e.g., current profile regulation, tearing mode (TM) suppression), control mathematics (e.g., algorithmic and simulation approaches to high confidence robust performance), and integration solutions (e.g., methods for management of highly subscribed control resources). We identify unique aspects of the ITER TM suppression scheme, which will pulse gyrotrons to drive current within a magnetic island, and turn the drive off following suppression in order to minimize use of auxiliary power and maximize fusion gain. The potential role of active current profile control and approaches to design in ITER are discussed. Issues and approaches to fault handling algorithms are described, along with novel aspects of actuator sharing in ITER.

  12. Novel aspects of plasma control in ITER

    DOE PAGES

    Humphreys, David; Ambrosino, G.; de Vries, Peter; ...

    2015-02-12

    ITER plasma control design solutions and performance requirements are strongly driven by its nuclear mission, aggressive commissioning constraints, and limited number of operational discharges. In addition, high plasma energy content, heat fluxes, neutron fluxes, and very long pulse operation place novel demands on control performance in many areas ranging from plasma boundary and divertor regulation to plasma kinetics and stability control. Both commissioning and experimental operations schedules provide limited time for tuning of control algorithms relative to operating devices. Although many aspects of the control solutions required by ITER have been well-demonstrated in present devices and even designed satisfactorily formore » ITER application, many elements unique to ITER including various crucial integration issues are presently under development. We describe selected novel aspects of plasma control in ITER, identifying unique parts of the control problem and highlighting some key areas of research remaining. Novel control areas described include control physics understanding (e.g. current profile regulation, tearing mode suppression (TM)), control mathematics (e.g. algorithmic and simulation approaches to high confidence robust performance), and integration solutions (e.g. methods for management of highly-subscribed control resources). We identify unique aspects of the ITER TM suppression scheme, which will pulse gyrotrons to drive current within a magnetic island, and turn the drive off following suppression in order to minimize use of auxiliary power and maximize fusion gain. The potential role of active current profile control and approaches to design in ITER are discussed. Finally, issues and approaches to fault handling algorithms are described, along with novel aspects of actuator sharing in ITER.« less

  13. ITER Shape Controller and Transport Simulations

    SciTech Connect

    Casper, T A; Meyer, W H; Pearlstein, L D; Portone, A

    2007-05-31

    We currently use the CORSICA integrated modeling code for scenario studies for both the DIII-D and ITER experiments. In these simulations, free- or fixed-boundary equilibria are simultaneously converged with thermal evolution determined from transport models providing temperature and current density profiles. Using a combination of fixed boundary evolution followed by free-boundary calculation to determine the separatrix and coil currents. In the free-boundary calculation, we use the state-space controller representation with transport simulations to provide feedback modeling of shape, vertical stability and profile control. In addition to a tightly coupled calculation with simulator and controller imbedded inside CORSICA, we also use a remote procedure call interface to couple the CORSICA non-linear plasma simulations to the controller environments developed within the Mathworks Matlab/Simulink environment. We present transport simulations using full shape and vertical stability control with evolution of the temperature profiles to provide simulations of the ITER controller and plasma response.

  14. Quantized Iterative Learning Consensus Tracking of Digital Networks With Limited Information Communication.

    PubMed

    Xiong, Wenjun; Yu, Xinghuo; Chen, Yao; Gao, Jie

    2016-03-03

    This brief investigates the quantized iterative learning problem for digital networks with time-varying topologies. The information is first encoded as symbolic data and then transmitted. After the data are received, a decoder is used by the receiver to get an estimate of the sender's state. Iterative learning quantized communication is considered in the process of encoding and decoding. A sufficient condition is then presented to achieve the consensus tracking problem in a finite interval using the quantized iterative learning controllers. Finally, simulation results are given to illustrate the usefulness of the developed criterion.

  15. Iter

    NASA Astrophysics Data System (ADS)

    Iotti, Robert

    2015-04-01

    ITER is an international experimental facility being built by seven Parties to demonstrate the long term potential of fusion energy. The ITER Joint Implementation Agreement (JIA) defines the structure and governance model of such cooperation. There are a number of necessary conditions for such international projects to be successful: a complete design, strong systems engineering working with an agreed set of requirements, an experienced organization with systems and plans in place to manage the project, a cost estimate backed by industry, and someone in charge. Unfortunately for ITER many of these conditions were not present. The paper discusses the priorities in the JIA which led to setting up the project with a Central Integrating Organization (IO) in Cadarache, France as the ITER HQ, and seven Domestic Agencies (DAs) located in the countries of the Parties, responsible for delivering 90%+ of the project hardware as Contributions-in-Kind and also financial contributions to the IO, as ``Contributions-in-Cash.'' Theoretically the Director General (DG) is responsible for everything. In practice the DG does not have the power to control the work of the DAs, and there is not an effective management structure enabling the IO and the DAs to arbitrate disputes, so the project is not really managed, but is a loose collaboration of competing interests. Any DA can effectively block a decision reached by the DG. Inefficiencies in completing design while setting up a competent organization from scratch contributed to the delays and cost increases during the initial few years. So did the fact that the original estimate was not developed from industry input. Unforeseen inflation and market demand on certain commodities/materials further exacerbated the cost increases. Since then, improvements are debatable. Does this mean that the governance model of ITER is a wrong model for international scientific cooperation? I do not believe so. Had the necessary conditions for success

  16. Eliminating Unpredictable Variation through Iterated Learning

    ERIC Educational Resources Information Center

    Smith, Kenny; Wonnacott, Elizabeth

    2010-01-01

    Human languages may be shaped not only by the (individual psychological) processes of language acquisition, but also by population-level processes arising from repeated language learning and use. One prevalent feature of natural languages is that they avoid unpredictable variation. The current work explores whether linguistic predictability might…

  17. Iterative learning-based decentralized adaptive tracker for large-scale systems: a digital redesign approach.

    PubMed

    Tsai, Jason Sheng-Hong; Du, Yan-Yi; Huang, Pei-Hsiang; Guo, Shu-Mei; Shieh, Leang-San; Chen, Yuhua

    2011-07-01

    In this paper, a digital redesign methodology of the iterative learning-based decentralized adaptive tracker is proposed to improve the dynamic performance of sampled-data linear large-scale control systems consisting of N interconnected multi-input multi-output subsystems, so that the system output will follow any trajectory which may not be presented by the analytic reference model initially. To overcome the interference of each sub-system and simplify the controller design, the proposed model reference decentralized adaptive control scheme constructs a decoupled well-designed reference model first. Then, according to the well-designed model, this paper develops a digital decentralized adaptive tracker based on the optimal analog control and prediction-based digital redesign technique for the sampled-data large-scale coupling system. In order to enhance the tracking performance of the digital tracker at specified sampling instants, we apply the iterative learning control (ILC) to train the control input via continual learning. As a result, the proposed iterative learning-based decentralized adaptive tracker not only has robust closed-loop decoupled property but also possesses good tracking performance at both transient and steady state. Besides, evolutionary programming is applied to search for a good learning gain to speed up the learning process of ILC.

  18. Color Image Denoising via Discriminatively Learned Iterative Shrinkage.

    PubMed

    Sun, Jian; Sun, Jian; Xu, Zingben

    2015-11-01

    In this paper, we propose a novel model, a discriminatively learned iterative shrinkage (DLIS) model, for color image denoising. The DLIS is a generalization of wavelet shrinkage by iteratively performing shrinkage over patch groups and whole image aggregation. We discriminatively learn the shrinkage functions and basis from the training pairs of noisy/noise-free images, which can adaptively handle different noise characteristics in luminance/chrominance channels, and the unknown structured noise in real-captured color images. Furthermore, to remove the splotchy real color noises, we design a Laplacian pyramid-based denoising framework to progressively recover the clean image from the coarsest scale to the finest scale by the DLIS model learned from the real color noises. Experiments show that our proposed approach can achieve the state-of-the-art denoising results on both synthetic denoising benchmark and real-captured color images.

  19. Decentralized control of sound radiation using iterative loop recovery.

    PubMed

    Schiller, Noah H; Cabell, Randolph H; Fuller, Chris R

    2010-10-01

    A decentralized model-based control strategy is designed to reduce low-frequency sound radiation from periodically stiffened panels. While decentralized control systems tend to be scalable, performance can be limited due to modeling error introduced by the unmodeled interaction between neighboring control units. Since bounds on modeling error are not known in advance, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is evaluated numerically using a model of a stiffened aluminum panel that is representative of the sidewall of an aircraft. Simulations demonstrate that the iterative approach can achieve significant reductions in radiated sound power from the stiffened panel without destabilizing neighboring control units.

  20. Decentralized Control of Sound Radiation Using Iterative Loop Recovery

    NASA Technical Reports Server (NTRS)

    Schiller, Noah H.; Cabell, Randolph H.; Fuller, Chris R.

    2009-01-01

    A decentralized model-based control strategy is designed to reduce low-frequency sound radiation from periodically stiffened panels. While decentralized control systems tend to be scalable, performance can be limited due to modeling error introduced by the unmodeled interaction between neighboring control units. Since bounds on modeling error are not known in advance, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is evaluated numerically using a model of a stiffened aluminum panel that is representative of the sidewall of an aircraft. Simulations demonstrate that the iterative approach can achieve significant reductions in radiated sound power from the stiffened panel without destabilizing neighboring control units.

  1. Iterative LQG Controller Design Through Closed-Loop Identification

    NASA Technical Reports Server (NTRS)

    Hsiao, Min-Hung; Huang, Jen-Kuang; Cox, David E.

    1996-01-01

    This paper presents an iterative Linear Quadratic Gaussian (LQG) controller design approach for a linear stochastic system with an uncertain open-loop model and unknown noise statistics. This approach consists of closed-loop identification and controller redesign cycles. In each cycle, the closed-loop identification method is used to identify an open-loop model and a steady-state Kalman filter gain from closed-loop input/output test data obtained by using a feedback LQG controller designed from the previous cycle. Then the identified open-loop model is used to redesign the state feedback. The state feedback and the identified Kalman filter gain are used to form an updated LQC controller for the next cycle. This iterative process continues until the updated controller converges. The proposed controller design is demonstrated by numerical simulations and experiments on a highly unstable large-gap magnetic suspension system.

  2. Upper limb stroke rehabilitation: the effectiveness of Stimulation Assistance through Iterative Learning (SAIL).

    PubMed

    Meadmore, Katie L; Cai, Zhonglun; Tong, Daisy; Hughes, Ann-Marie; Freeman, Chris T; Rogers, Eric; Burridge, Jane H

    2011-01-01

    A novel system has been developed which combines robotic therapy with electrical stimulation (ES) for upper limb stroke rehabilitation. This technology, termed SAIL: Stimulation Assistance through Iterative Learning, employs advanced model-based iterative learning control (ILC) algorithms to precisely assist participant's completion of 3D tracking tasks with their impaired arm. Data is reported from a preliminary study with unimpaired participants, and also from a single hemiparetic stroke participant with reduced upper limb function who has used the system in a clinical trial. All participants completed tasks which involved moving their (impaired) arm to follow an image of a slowing moving sphere along a trajectory. The participants' arm was supported by a robot and ES was applied to the triceps brachii and anterior deltoid muscles. During each task, the same tracking trajectory was repeated 6 times and ILC was used to compute the stimulation signals to be applied on the next iteration. Unimpaired participants took part in a single, one hour training session and the stroke participant undertook 18, 1 hour treatment sessions composed of tracking tasks varying in length, orientation and speed. The results reported describe changes in tracking ability and demonstrate feasibility of the SAIL system for upper limb rehabilitation.

  3. EC power management and NTM control in ITER

    NASA Astrophysics Data System (ADS)

    Poli, Francesca; Fredrickson, E.; Henderson, M.; Bertelli, N.; Farina, D.; Figini, L.; Nowak, S.; Poli, E.; Sauter, O.

    2016-10-01

    The suppression of Neoclassical Tearing Modes (NTMs) is an essential requirement for the achievement of the demonstration baseline in ITER. The Electron Cyclotron upper launcher is specifically designed to provide highly localized heating and current drive for NTM stabilization. In order to assess the power management for shared applications, we have performed time-dependent simulations for ITER scenarios covering operation from half to full field. The free-boundary TRANSP simulations evolve the magnetic equilibrium and the pressure profiles in response to the heating and current drive sources and are interfaced with a GRE for the evolution of size and frequency of the magnetic islands. Combined with a feedback control of the EC power and the steering angle, these simulations are used to model the plasma response to NTM control, accounting for the misalignment of the EC deposition with the resonant surfaces, uncertainties in the magnetic equilibrium reconstruction and in the magnetic island detection threshold. Simulations indicate that the threshold for detection of the island should not exceed 2-3cm, that pre-emptive control is a preferable option, and that for safe operation the power needed for NTM control should be reserved, rather than shared with other applications. Work supported by ITER under IO/RFQ/13/9550/JTR and by DOE under DE-AC02-09CH11466.

  4. Iterative exponential growth of stereo- and sequence-controlled polymers

    NASA Astrophysics Data System (ADS)

    Barnes, Jonathan C.; Ehrlich, Deborah J. C.; Gao, Angela X.; Leibfarth, Frank A.; Jiang, Yivan; Zhou, Erica; Jamison, Timothy F.; Johnson, Jeremiah A.

    2015-10-01

    Chemists have long sought sequence-controlled synthetic polymers that mimic nature's biopolymers, but a practical synthetic route that enables absolute control over polymer sequence and structure remains a key challenge. Here, we report an iterative exponential growth plus side-chain functionalization (IEG+) strategy that begins with enantiopure epoxides and facilitates the efficient synthesis of a family of uniform >3 kDa macromolecules of varying sequence and stereoconfiguration that are coupled to produce unimolecular polymers (>6 kDa) with sequences and structures that cannot be obtained using traditional polymerization techniques. Selective side-chain deprotection of three hexadecamers is also demonstrated, which imbues each compound with the ability to dissolve in water. We anticipate that these new macromolecules and the general IEG+ strategy will find broad application as a versatile platform for the scalable synthesis of sequence-controlled polymers.

  5. Fixed Point Transformations Based Iterative Control of a Polymerization Reaction

    NASA Astrophysics Data System (ADS)

    Tar, József K.; Rudas, Imre J.

    As a paradigm of strongly coupled non-linear multi-variable dynamic systems the mathematical model of the free-radical polymerization of methyl-metachrylate with azobis (isobutyro-nitrile) as an initiator and toluene as a solvent taking place in a jacketed Continuous Stirred Tank Reactor (CSTR) is considered. In the adaptive control of this system only a single input variable is used as the control signal (the process input, i.e. dimensionless volumetric flow rate of the initiator), and a single output variable is observed (the process output, i.e. the number-average molecular weight of the polymer). Simulation examples illustrate that on the basis of a very rough and primitive model consisting of two scalar variables various fixed-point transformations based convergent iterations result in a novel, sophisticated adaptive control.

  6. Error control of iterative linear solvers for integrated groundwater models.

    PubMed

    Dixon, Matthew F; Bai, Zhaojun; Brush, Charles F; Chung, Francis I; Dogrul, Emin C; Kadir, Tariq N

    2011-01-01

    An open problem that arises when using modern iterative linear solvers, such as the preconditioned conjugate gradient method or Generalized Minimum RESidual (GMRES) method, is how to choose the residual tolerance in the linear solver to be consistent with the tolerance on the solution error. This problem is especially acute for integrated groundwater models, which are implicitly coupled to another model, such as surface water models, and resolve both multiple scales of flow and temporal interaction terms, giving rise to linear systems with variable scaling. This article uses the theory of "forward error bound estimation" to explain the correspondence between the residual error in the preconditioned linear system and the solution error. Using examples of linear systems from models developed by the US Geological Survey and the California State Department of Water Resources, we observe that this error bound guides the choice of a practical measure for controlling the error in linear systems. We implemented a preconditioned GMRES algorithm and benchmarked it against the Successive Over-Relaxation (SOR) method, the most widely known iterative solver for nonsymmetric coefficient matrices. With forward error control, GMRES can easily replace the SOR method in legacy groundwater modeling packages, resulting in the overall simulation speedups as large as 7.74×. This research is expected to broadly impact groundwater modelers through the demonstration of a practical and general approach for setting the residual tolerance in line with the solution error tolerance and presentation of GMRES performance benchmarking results.

  7. Process control strategy for ITER central solenoid operation

    NASA Astrophysics Data System (ADS)

    Maekawa, R.; Takami, S.; Iwamoto, A.; Chang, H.-S.; Forgeas, A.; Chalifour, M.

    2016-12-01

    ITER Central Solenoid (CS) pulse operation induces significant flow disturbance in the forced-flow Supercritical Helium (SHe) cooling circuit, which could impact primarily on the operation of cold circulator (SHe centrifugal pump) in Auxiliary Cold Box (ACB). Numerical studies using Venecia®, SUPERMAGNET and 4C have identified reverse flow at the CS module inlet due to the substantial thermal energy deposition at the inner-most winding. To assess the reliable operation of ACB-CS (dedicated ACB for CS), the process analyses have been conducted with a dynamic process simulation model developed by Cryogenic Process REal-time SimulaTor (C-PREST). As implementing process control of hydrodynamic instability, several strategies have been applied to evaluate their feasibility. The paper discusses control strategy to protect the centrifugal type cold circulator/compressor operations and its impact on the CS cooling.

  8. Remix as Professional Learning: Educators' Iterative Literacy Practice in CLMOOC

    ERIC Educational Resources Information Center

    Smith, Anna; West-Puckett, Stephanie; Cantrill, Christina; Zamora, Mia

    2016-01-01

    The Connected Learning Massive Open Online Collaboration (CLMOOC) is an online professional development experience designed as an openly networked, production-centered, participatory learning collaboration for educators. Addressing the paucity of research that investigates learning processes in MOOC experiences, this paper examines the situated…

  9. Sawtooth control in JET with ITER relevant low field side resonance ion cyclotron resonance heating and ITER-like wall

    NASA Astrophysics Data System (ADS)

    Graves, J. P.; Lennholm, M.; Chapman, I. T.; Lerche, E.; Reich, M.; Alper, B.; Bobkov, V.; Dumont, R.; Faustin, J. M.; Jacquet, P.; Jaulmes, F.; Johnson, T.; Keeling, D. L.; Liu, Yueqiang; Nicolas, T.; Tholerus, S.; Blackman, T.; Carvalho, I. S.; Coelho, R.; Van Eester, D.; Felton, R.; Goniche, M.; Kiptily, V.; Monakhov, I.; Nave, M. F. F.; Perez von Thun, C.; Sabot, R.; Sozzi, C.; Tsalas, M.

    2015-01-01

    New experiments at JET with the ITER-like wall show for the first time that ITER-relevant low field side resonance first harmonic ion cyclotron resonance heating (ICRH) can be used to control sawteeth that have been initially lengthened by fast particles. In contrast to previous (Graves et al 2012 Nat. Commun. 3 624) high field side resonance sawtooth control experiments undertaken at JET, it is found that the sawteeth of L-mode plasmas can be controlled with less accurate alignment between the resonance layer and the sawtooth inversion radius. This advantage, as well as the discovery that sawteeth can be shortened with various antenna phasings, including dipole, indicates that ICRH is a particularly effective and versatile tool that can be used in future fusion machines for controlling sawteeth. Without sawtooth control, neoclassical tearing modes (NTMs) and locked modes were triggered at very low normalised beta. High power H-mode experiments show the extent to which ICRH can be tuned to control sawteeth and NTMs while simultaneously providing effective electron heating with improved flushing of high Z core impurities. Dedicated ICRH simulations using SELFO, SCENIC and EVE, including wide drift orbit effects, explain why sawtooth control is effective with various antenna phasings and show that the sawtooth control mechanism cannot be explained by enhancement of the magnetic shear. Hybrid kinetic-magnetohydrodynamic stability calculations using MISHKA and HAGIS unravel the optimal sawtooth control regimes in these ITER relevant plasma conditions.

  10. A strategy for sequence control in vinyl polymers via iterative controlled radical cyclization

    PubMed Central

    Hibi, Yusuke; Ouchi, Makoto; Sawamoto, Mitsuo

    2016-01-01

    There is a growing interest in sequence-controlled polymers toward advanced functional materials. However, control of side-chain order for vinyl polymers has been lacking feasibility in the field of polymer synthesis because of the inherent feature of chain-growth propagation. Here we show a general and versatile strategy to control sequence in vinyl polymers through iterative radical cyclization with orthogonally cleavable and renewable bonds. The proposed methodology employs a repetitive and iterative intramolecular cyclization via a radical intermediate in a one-time template with a radical-generating site at one end and an alkene end at the other, each of which is connected to a linker via independently cleavable and renewable bonds. The unique design specifically allowed control of radical addition reaction although inherent chain-growth intermediate (radical species) was used, as well as the iterative cycle and functionalization for resultant side chains, to lead to sequence-controlled vinyl polymers (or oligomers). PMID:26996881

  11. Nonlinear Burn Control and Operating Point Optimization in ITER

    NASA Astrophysics Data System (ADS)

    Boyer, Mark; Schuster, Eugenio

    2013-10-01

    Control of the fusion power through regulation of the plasma density and temperature will be essential for achieving and maintaining desired operating points in fusion reactors and burning plasma experiments like ITER. In this work, a volume averaged model for the evolution of the density of energy, deuterium and tritium fuel ions, alpha-particles, and impurity ions is used to synthesize a multi-input multi-output nonlinear feedback controller for stabilizing and modulating the burn condition. Adaptive control techniques are used to account for uncertainty in model parameters, including particle confinement times and recycling rates. The control approach makes use of the different possible methods for altering the fusion power, including adjusting the temperature through auxiliary heating, modulating the density and isotopic mix through fueling, and altering the impurity density through impurity injection. Furthermore, a model-based optimization scheme is proposed to drive the system as close as possible to desired fusion power and temperature references. Constraints are considered in the optimization scheme to ensure that, for example, density and beta limits are avoided, and that optimal operation is achieved even when actuators reach saturation. Supported by the NSF CAREER award program (ECCS-0645086).

  12. Magnetic Control of Locked Modes in Present Devices and ITER

    NASA Astrophysics Data System (ADS)

    Volpe, F. A.; Sabbagh, S.; Sweeney, R.; Hender, T.; Kirk, A.; La Haye, R. J.; Strait, E. J.; Ding, Y. H.; Rao, B.; Fietz, S.; Maraschek, M.; Frassinetti, L.; in, Y.; Jeon, Y.; Sakakihara, S.

    2014-10-01

    The toroidal phase of non-rotating (``locked'') neoclassical tearing modes was controlled in several devices by means of applied magnetic perturbations. Evidence is presented from various tokamaks (ASDEX Upgrade, DIII-D, JET, J-TEXT, KSTAR), spherical tori (MAST, NSTX) and a reversed field pinch (EXTRAP-T2R). Furthermore, the phase of interchange modes was controlled in the LHD helical device. These results share a common interpretation in terms of torques acting on the mode. Based on this interpretation, it is predicted that control-coil currents will be sufficient to control the phase of locking in ITER. This will be possible both with the internal coils and with the external error-field-correction coils, and might have promising consequences for disruption avoidance (by aiding the electron cyclotron current drive stabilization of locked modes), as well as for spatially distributing heat loads during disruptions. This work was supported in part by the US Department of Energy under DE-SC0008520, DE-FC-02-04ER54698 and DE-AC02-09CH11466.

  13. Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems.

    PubMed

    Wei, Qinglai; Liu, Derong; Lin, Hanquan

    2016-03-01

    In this paper, a value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon undiscounted optimal control problems for discrete-time nonlinear systems. The present value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize the algorithm. A novel convergence analysis is developed to guarantee that the iterative value function converges to the optimal performance index function. Initialized by different initial functions, it is proven that the iterative value function will be monotonically nonincreasing, monotonically nondecreasing, or nonmonotonic and will converge to the optimum. In this paper, for the first time, the admissibility properties of the iterative control laws are developed for value iteration algorithms. It is emphasized that new termination criteria are established to guarantee the effectiveness of the iterative control laws. Neural networks are used to approximate the iterative value function and compute the iterative control law, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.

  14. Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach

    NASA Astrophysics Data System (ADS)

    Wei, Qing-Lai; Liu, De-Rong; Xu, Yan-Cai

    2015-03-01

    A policy iteration algorithm of adaptive dynamic programming (ADP) is developed to solve the optimal tracking control for a class of discrete-time chaotic systems. By system transformations, the optimal tracking problem is transformed into an optimal regulation one. The policy iteration algorithm for discrete-time chaotic systems is first described. Then, the convergence and admissibility properties of the developed policy iteration algorithm are presented, which show that the transformed chaotic system can be stabilized under an arbitrary iterative control law and the iterative performance index function simultaneously converges to the optimum. By implementing the policy iteration algorithm via neural networks, the developed optimal tracking control scheme for chaotic systems is verified by a simulation. Project supported by the National Natural Science Foundation of China (Grant Nos. 61034002, 61233001, 61273140, 61304086, and 61374105) and the Beijing Natural Science Foundation, China (Grant No. 4132078).

  15. Studio Based Learning: Proposing, Critiquing, Iterating Our Way to Person-Centeredness for Better Classroom Management

    ERIC Educational Resources Information Center

    Brocato, Kay

    2009-01-01

    This article relates how the proposing, critiquing, iterating process of studio-based learning (SBL) provides for person-centered classroom management. SBL is defined in connection to how the pedagogy works within a school of architecture. Then, a description of how the approach is applied to one course in a teacher education program is offered.…

  16. Multimodal and Adaptive Learning Management: An Iterative Design

    ERIC Educational Resources Information Center

    Squires, David R.; Orey, Michael A.

    2015-01-01

    The purpose of this study is to measure the outcome of a comprehensive learning management system implemented at a Spinal Cord Injury (SCI) hospital in the Southeast United States. Specifically this SCI hospital has been experiencing an evident volume of patients returning seeking more information about the nature of their injuries. Recognizing…

  17. Quantum-Inspired Multidirectional Associative Memory With a Self-Convergent Iterative Learning.

    PubMed

    Masuyama, Naoki; Loo, Chu Kiong; Seera, Manjeevan; Kubota, Naoyuki

    2017-02-06

    Quantum-inspired computing is an emerging research area, which has significantly improved the capabilities of conventional algorithms. In general, quantum-inspired hopfield associative memory (QHAM) has demonstrated quantum information processing in neural structures. This has resulted in an exponential increase in storage capacity while explaining the extensive memory, and it has the potential to illustrate the dynamics of neurons in the human brain when viewed from quantum mechanics perspective although the application of QHAM is limited as an autoassociation. We introduce a quantum-inspired multidirectional associative memory (QMAM) with a one-shot learning model, and QMAM with a self-convergent iterative learning model (IQMAM) based on QHAM in this paper. The self-convergent iterative learning enables the network to progressively develop a resonance state, from inputs to outputs. The simulation experiments demonstrate the advantages of QMAM and IQMAM, especially the stability to recall reliability.

  18. Near-Optimal Controller for Nonlinear Continuous-Time Systems With Unknown Dynamics Using Policy Iteration.

    PubMed

    Dutta, Samrat; Patchaikani, Prem Kumar; Behera, Laxmidhar

    2016-07-01

    This paper presents a single-network adaptive critic-based controller for continuous-time systems with unknown dynamics in a policy iteration (PI) framework. It is assumed that the unknown dynamics can be estimated using the Takagi-Sugeno-Kang fuzzy model with arbitrary precision. The successful implementation of a PI scheme depends on the effective learning of critic network parameters. Network parameters must stabilize the system in each iteration in addition to approximating the critic and the cost. It is found that the critic updates according to the Hamilton-Jacobi-Bellman formulation sometimes lead to the instability of the closed-loop systems. In the proposed work, a novel critic network parameter update scheme is adopted, which not only approximates the critic at current iteration but also provides feasible solutions that keep the policy stable in the next step of training by combining a Lyapunov-based linear matrix inequalities approach with PI. The critic modeling technique presented here is the first of its kind to address this issue. Though multiple literature exists discussing the convergence of PI, however, to the best of our knowledge, there exists no literature, which focuses on the effect of critic network parameters on the convergence. Computational complexity in the proposed algorithm is reduced to the order of (Fz)(n-1) , where n is the fuzzy state dimensionality and Fz is the number of fuzzy zones in the states space. A genetic algorithm toolbox of MATLAB is used for searching stable parameters while minimizing the training error. The proposed algorithm also provides a way to solve for the initial stable control policy in the PI scheme. The algorithm is validated through real-time experiment on a commercial robotic manipulator. Results show that the algorithm successfully finds stable critic network parameters in real time for a highly nonlinear system.

  19. Active learning framework with iterative clustering for bioimage classification.

    PubMed

    Kutsuna, Natsumaro; Higaki, Takumi; Matsunaga, Sachihiro; Otsuki, Tomoshi; Yamaguchi, Masayuki; Fujii, Hirofumi; Hasezawa, Seiichiro

    2012-01-01

    Advances in imaging systems have yielded a flood of images into the research field. A semi-automated facility can reduce the laborious task of classifying this large number of images. Here we report the development of a novel framework, CARTA (Clustering-Aided Rapid Training Agent), applicable to bioimage classification that facilitates annotation and selection of features. CARTA comprises an active learning algorithm combined with a genetic algorithm and self-organizing map. The framework provides an easy and interactive annotation method and accurate classification. The CARTA framework enables classification of subcellular localization, mitotic phases and discrimination of apoptosis in images of plant and human cells with an accuracy level greater than or equal to annotators. CARTA can be applied to classification of magnetic resonance imaging of cancer cells or multicolour time-course images after surgery. Furthermore, CARTA can support development of customized features for classification, high-throughput phenotyping and application of various classification schemes dependent on the user's purpose.

  20. The evolution of frequency distributions: relating regularization to inductive biases through iterated learning.

    PubMed

    Reali, Florencia; Griffiths, Thomas L

    2009-06-01

    The regularization of linguistic structures by learners has played a key role in arguments for strong innate constraints on language acquisition, and has important implications for language evolution. However, relating the inductive biases of learners to regularization behavior in laboratory tasks can be challenging without a formal model. In this paper we explore how regular linguistic structures can emerge from language evolution by iterated learning, in which one person's linguistic output is used to generate the linguistic input provided to the next person. We use a model of iterated learning with Bayesian agents to show that this process can result in regularization when learners have the appropriate inductive biases. We then present three experiments demonstrating that simulating the process of language evolution in the laboratory can reveal biases towards regularization that might not otherwise be obvious, allowing weak biases to have strong effects. The results of these experiments suggest that people tend to regularize inconsistent word-meaning mappings, and that even a weak bias towards regularization can allow regular languages to be produced via language evolution by iterated learning.

  1. Iterative volume morphing and learning for mobile tumor based on 4DCT

    NASA Astrophysics Data System (ADS)

    Mao, Songan; Wu, Huanmei; Sandison, George; Fang, Shiaofen

    2017-02-01

    During image-guided cancer radiation treatment, three-dimensional (3D) tumor volumetric information is important for treatment success. However, it is typically not feasible to image a patient’s 3D tumor continuously in real time during treatment due to concern over excessive patient radiation dose. We present a new iterative morphing algorithm to predict the real-time 3D tumor volume based on time-resolved computed tomography (4DCT) acquired before treatment. An offline iterative learning process has been designed to derive a target volumetric deformation function from one breathing phase to another. Real-time volumetric prediction is performed to derive the target 3D volume during treatment delivery. The proposed iterative deformable approach for tumor volume morphing and prediction based on 4DCT is innovative because it makes three major contributions: (1) a novel approach to landmark selection on 3D tumor surfaces using a minimum bounding box; (2) an iterative morphing algorithm to generate the 3D tumor volume using mapped landmarks; and (3) an online tumor volume prediction strategy based on previously trained deformation functions utilizing 4DCT. The experimental performance showed that the maximum morphing deviations are 0.27% and 1.25% for original patient data and artificially generated data, which is promising. This newly developed algorithm and implementation will have important applications for treatment planning, dose calculation and treatment validation in cancer radiation treatment.

  2. Iterative volume morphing and learning for mobile tumor based on 4DCT.

    PubMed

    Mao, Songan; Wu, Huanmei; Sandison, George; Fang, Shiaofen

    2017-02-21

    During image-guided cancer radiation treatment, three-dimensional (3D) tumor volumetric information is important for treatment success. However, it is typically not feasible to image a patient's 3D tumor continuously in real time during treatment due to concern over excessive patient radiation dose. We present a new iterative morphing algorithm to predict the real-time 3D tumor volume based on time-resolved computed tomography (4DCT) acquired before treatment. An offline iterative learning process has been designed to derive a target volumetric deformation function from one breathing phase to another. Real-time volumetric prediction is performed to derive the target 3D volume during treatment delivery. The proposed iterative deformable approach for tumor volume morphing and prediction based on 4DCT is innovative because it makes three major contributions: (1) a novel approach to landmark selection on 3D tumor surfaces using a minimum bounding box; (2) an iterative morphing algorithm to generate the 3D tumor volume using mapped landmarks; and (3) an online tumor volume prediction strategy based on previously trained deformation functions utilizing 4DCT. The experimental performance showed that the maximum morphing deviations are 0.27% and 1.25% for original patient data and artificially generated data, which is promising. This newly developed algorithm and implementation will have important applications for treatment planning, dose calculation and treatment validation in cancer radiation treatment.

  3. Analysis of Distribution of Time Scores in Iterative Learning Type Courseware Using Fourier Transform

    NASA Astrophysics Data System (ADS)

    Watanabe, Hiroyuki

    In this research, an iterative learning type courseware was made, the distribution of time scores in the courseware is gotten by the learning management system. It is a proposed method by which the distribution of time scores is changed to frequency and to power spectrum using Fourier Transform. The learning process continues until students get the passing scores and are classified by using these values, which are related to average time and the average of scores‧ square. Furthermore, the cross-correlation coefficients between the standard student and students are calculated, and delay times are analyzed. Finally, the transfer functions of some students are calculated, and the characteristics of the learning processes are analyzed.

  4. Learning to Teach Elementary Science Through Iterative Cycles of Enactment in Culturally and Linguistically Diverse Contexts

    NASA Astrophysics Data System (ADS)

    Bottoms, SueAnn I.; Ciechanowski, Kathryn M.; Hartman, Brian

    2015-12-01

    Iterative cycles of enactment embedded in culturally and linguistically diverse contexts provide rich opportunities for preservice teachers (PSTs) to enact core practices of science. This study is situated in the larger Families Involved in Sociocultural Teaching and Science, Technology, Engineering and Mathematics (FIESTAS) project, which weaves together cycles of enactment, core practices in science education and culturally relevant pedagogies. The theoretical foundation draws upon situated learning theory and communities of practice. Using video analysis by PSTs and course artifacts, the authors studied how the iterative process of these cycles guided PSTs development as teachers of elementary science. Findings demonstrate how PSTs were drawing on resources to inform practice, purposefully noticing their practice, renegotiating their roles in teaching, and reconsidering "professional blindness" through cultural practice.

  5. A theoretical analysis of temporal difference learning in the iterated prisoner's dilemma game.

    PubMed

    Masuda, Naoki; Ohtsuki, Hisashi

    2009-11-01

    Direct reciprocity is a chief mechanism of mutual cooperation in social dilemma. Agents cooperate if future interactions with the same opponents are highly likely. Direct reciprocity has been explored mostly by evolutionary game theory based on natural selection. Our daily experience tells, however, that real social agents including humans learn to cooperate based on experience. In this paper, we analyze a reinforcement learning model called temporal difference learning and study its performance in the iterated Prisoner's Dilemma game. Temporal difference learning is unique among a variety of learning models in that it inherently aims at increasing future payoffs, not immediate ones. It also has a neural basis. We analytically and numerically show that learners with only two internal states properly learn to cooperate with retaliatory players and to defect against unconditional cooperators and defectors. Four-state learners are more capable of achieving a high payoff against various opponents. Moreover, we numerically show that four-state learners can learn to establish mutual cooperation for sufficiently small learning rates.

  6. A policy iteration approach to online optimal control of continuous-time constrained-input systems.

    PubMed

    Modares, Hamidreza; Naghibi Sistani, Mohammad-Bagher; Lewis, Frank L

    2013-09-01

    This paper is an effort towards developing an online learning algorithm to find the optimal control solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on the policy iteration (PI) technique which has recently evolved as a major technique for solving optimal control problems. Although a number of online PI algorithms have been developed for CT systems, none of them take into account the input constraints caused by actuator saturation. In practice, however, ignoring these constraints leads to performance degradation or even system instability. In this paper, to deal with the input constraints, a suitable nonquadratic functional is employed to encode the constraints into the optimization formulation. Then, the proposed PI algorithm is implemented on an actor-critic structure to solve the Hamilton-Jacobi-Bellman (HJB) equation associated with this nonquadratic cost functional in an online fashion. That is, two coupled neural network (NN) approximators, namely an actor and a critic are tuned online and simultaneously for approximating the associated HJB solution and computing the optimal control policy. The critic is used to evaluate the cost associated with the current policy, while the actor is used to find an improved policy based on information provided by the critic. Convergence to a close approximation of the HJB solution as well as stability of the proposed feedback control law are shown. Simulation results of the proposed method on a nonlinear CT system illustrate the effectiveness of the proposed approach.

  7. Nuclear Safety Functions of ITER Gas Injection System Instrumentation and Control and the Concept Design

    NASA Astrophysics Data System (ADS)

    Yang, Yu; Maruyama, S.; Fossen, A.; Villers, F.; Kiss, G.; Zhang, Bo; Li, Bo; Jiang, Tao; Huang, Xiangmei

    2016-08-01

    The ITER Gas Injection System (GIS) plays an important role on fueling, wall conditioning and distribution for plasma operation. Besides that, to support the safety function of ITER, GIS needs to implement three nuclear safety Instrumentation and Control (I&C) functions. In this paper, these three functions are introduced with the emphasis on their latest safety classifications. The nuclear I&C design concept is briefly discussed at the end.

  8. Iterative development of visual control systems in a research vivarium.

    PubMed

    Bassuk, James A; Washington, Ida M

    2014-01-01

    The goal of this study was to test the hypothesis that reintroduction of Continuous Performance Improvement (CPI) methodology, a lean approach to management at Seattle Children's (Hospital, Research Institute, Foundation), would facilitate engagement of vivarium employees in the development and sustainment of a daily management system and a work-in-process board. Such engagement was implemented through reintroduction of aspects of the Toyota Production System. Iterations of a Work-In-Process Board were generated using Shewhart's Plan-Do-Check-Act process improvement cycle. Specific attention was given to the importance of detecting and preventing errors through assessment of the following 5 levels of quality: Level 1, customer inspects; Level 2, company inspects; Level 3, work unit inspects; Level 4, self-inspection; Level 5, mistake proofing. A functioning iteration of a Mouse Cage Work-In-Process Board was eventually established using electronic data entry, an improvement that increased the quality level from 1 to 3 while reducing wasteful steps, handoffs and queues. A visual workplace was realized via a daily management system that included a Work-In-Process Board, a problem solving board and two Heijunka boards. One Heijunka board tracked cage changing as a function of a biological kanban, which was validated via ammonia levels. A 17% reduction in cage changing frequency provided vivarium staff with additional time to support Institute researchers in their mutual goal of advancing cures for pediatric diseases. Cage washing metrics demonstrated an improvement in the flow continuum in which a traditional batch and queue push system was replaced with a supermarket-type pull system. Staff engagement during the improvement process was challenging and is discussed. The collective data indicate that the hypothesis was found to be true. The reintroduction of CPI into daily work in the vivarium is consistent with the 4P Model of the Toyota Way and selected Principles

  9. Iterative Development of Visual Control Systems in a Research Vivarium

    PubMed Central

    Bassuk, James A.; Washington, Ida M.

    2014-01-01

    The goal of this study was to test the hypothesis that reintroduction of Continuous Performance Improvement (CPI) methodology, a lean approach to management at Seattle Children’s (Hospital, Research Institute, Foundation), would facilitate engagement of vivarium employees in the development and sustainment of a daily management system and a work-in-process board. Such engagement was implemented through reintroduction of aspects of the Toyota Production System. Iterations of a Work-In-Process Board were generated using Shewhart’s Plan-Do-Check-Act process improvement cycle. Specific attention was given to the importance of detecting and preventing errors through assessment of the following 5 levels of quality: Level 1, customer inspects; Level 2, company inspects; Level 3, work unit inspects; Level 4, self-inspection; Level 5, mistake proofing. A functioning iteration of a Mouse Cage Work-In-Process Board was eventually established using electronic data entry, an improvement that increased the quality level from 1 to 3 while reducing wasteful steps, handoffs and queues. A visual workplace was realized via a daily management system that included a Work-In-Process Board, a problem solving board and two Heijunka boards. One Heijunka board tracked cage changing as a function of a biological kanban, which was validated via ammonia levels. A 17% reduction in cage changing frequency provided vivarium staff with additional time to support Institute researchers in their mutual goal of advancing cures for pediatric diseases. Cage washing metrics demonstrated an improvement in the flow continuum in which a traditional batch and queue push system was replaced with a supermarket-type pull system. Staff engagement during the improvement process was challenging and is discussed. The collective data indicate that the hypothesis was found to be true. The reintroduction of CPI into daily work in the vivarium is consistent with the 4P Model of the Toyota Way and selected

  10. Efficient decentralized iterative learning tracker for unknown sampled-data interconnected large-scale state-delay system with closed-loop decoupling property.

    PubMed

    Tsai, Jason Sheng-Hong; Chen, Fu-Ming; Yu, Tze-Yu; Guo, Shu-Mei; Shieh, Leang-San

    2012-01-01

    In this paper, an efficient decentralized iterative learning tracker is proposed to improve the dynamic performance of the unknown controllable and observable sampled-data interconnected large-scale state-delay system, which consists of N multi-input multi-output (MIMO) subsystems, with the closed-loop decoupling property. The off-line observer/Kalman filter identification (OKID) method is used to obtain the decentralized linear models for subsystems in the interconnected large-scale system. In order to get over the effect of modeling error on the identified linear model of each subsystem, an improved observer with the high-gain property based on the digital redesign approach is developed to replace the observer identified by OKID. Then, the iterative learning control (ILC) scheme is integrated with the high-gain tracker design for the decentralized models. To significantly reduce the iterative learning epochs, a digital-redesign linear quadratic digital tracker with the high-gain property is proposed as the initial control input of ILC. The high-gain property controllers can suppress uncertain errors such as modeling errors, nonlinear perturbations, and external disturbances (Guo et al., 2000) [18]. Thus, the system output can quickly and accurately track the desired reference in one short time interval after all drastically-changing points of the specified reference input with the closed-loop decoupling property.

  11. An iterative algorithm combining model reduction and control design

    NASA Technical Reports Server (NTRS)

    Hsieh, C.; Kim, J. H.; Zhu, G.; Liu, K.; Skelton, R. E.

    1990-01-01

    A design strategy which integrates model reduction by modal cost analysis and a multiobjective controller design is proposed. The necessary modeling and control algorithms are easily programmed in Matlab standard software. Hence, this method is very practical for controller design for large space structures. The design algorithm also solves the very important problem of tuning multiple loop controllers (multi-input, multi-output, or MIMO). Instead of the single gain change that is used in standard root locus and gain and phase margin theories, this method tunes multiple loop controllers from low to high gain in a systematic way in the design procedure. This design strategy is applied to NASA's Mini-Mast system.

  12. Learning Iteration-wise Generalized Shrinkage-Thresholding Operators for Blind Deconvolution.

    PubMed

    Wangmeng Zuo; Dongwei Ren; Zhang, David; Shuhang Gu; Lei Zhang

    2016-04-01

    Salient edge selection and time-varying regularization are two crucial techniques to guarantee the success of maximum a posteriori (MAP)-based blind deconvolution. However, the existing approaches usually rely on carefully designed regularizers and handcrafted parameter tuning to obtain satisfactory estimation of the blur kernel. Many regularizers exhibit the structure-preserving smoothing capability, but fail to enhance salient edges. In this paper, under the MAP framework, we propose the iteration-wise ℓp-norm regularizers together with data-driven strategy to address these issues. First, we extend the generalized shrinkage-thresholding (GST) operator for ℓp-norm minimization with negative p value, which can sharpen salient edges while suppressing trivial details. Then, the iteration-wise GST parameters are specified to allow dynamical salient edge selection and time-varying regularization. Finally, instead of handcrafted tuning, a principled discriminative learning approach is proposed to learn the iterationwise GST operators from the training dataset. Furthermore, the multi-scale scheme is developed to improve the efficiency of the algorithm. Experimental results show that, negative p value is more effective in estimating the coarse shape of blur kernel at the early stage, and the learned GST operators can be well generalized to other dataset and real world blurry images. Compared with the state-of-the-art methods, our method achieves better deblurring results in terms of both quantitative metrics and visual quality, and it is much faster than the state-of-the-art patch-based blind deconvolution method.

  13. e-Learning Application for Machine Maintenance Process using Iterative Method in XYZ Company

    NASA Astrophysics Data System (ADS)

    Nurunisa, Suaidah; Kurniawati, Amelia; Pramuditya Soesanto, Rayinda; Yunan Kurnia Septo Hediyanto, Umar

    2016-02-01

    XYZ Company is a company based on manufacturing part for airplane, one of the machine that is categorized as key facility in the company is Millac 5H6P. As a key facility, the machines should be assured to work well and in peak condition, therefore, maintenance process is needed periodically. From the data gathering, it is known that there are lack of competency from the maintenance staff to maintain different type of machine which is not assigned by the supervisor, this indicate that knowledge which possessed by maintenance staff are uneven. The purpose of this research is to create knowledge-based e-learning application as a realization from externalization process in knowledge transfer process to maintain the machine. The application feature are adjusted for maintenance purpose using e-learning framework for maintenance process, the content of the application support multimedia for learning purpose. QFD is used in this research to understand the needs from user. The application is built using moodle with iterative method for software development cycle and UML Diagram. The result from this research is e-learning application as sharing knowledge media for maintenance staff in the company. From the test, it is known that the application make maintenance staff easy to understand the competencies.

  14. Multiagent reinforcement learning: spiking and nonspiking agents in the iterated Prisoner's Dilemma.

    PubMed

    Vassiliades, Vassilis; Cleanthous, Aristodemos; Christodoulou, Chris

    2011-04-01

    This paper investigates multiagent reinforcement learning (MARL) in a general-sum game where the payoffs' structure is such that the agents are required to exploit each other in a way that benefits all agents. The contradictory nature of these games makes their study in multiagent systems quite challenging. In particular, we investigate MARL with spiking and nonspiking agents in the Iterated Prisoner's Dilemma by exploring the conditions required to enhance its cooperative outcome. The spiking agents are neural networks with leaky integrate-and-fire neurons trained with two different learning algorithms: 1) reinforcement of stochastic synaptic transmission, or 2) reward-modulated spike-timing-dependent plasticity with eligibility trace. The nonspiking agents use a tabular representation and are trained with Q- and SARSA learning algorithms, with a novel reward transformation process also being applied to the Q-learning agents. According to the results, the cooperative outcome is enhanced by: 1) transformed internal reinforcement signals and a combination of a high learning rate and a low discount factor with an appropriate exploration schedule in the case of non-spiking agents, and 2) having longer eligibility trace time constant in the case of spiking agents. Moreover, it is shown that spiking and nonspiking agents have similar behavior and therefore they can equally well be used in a multiagent interaction setting. For training the spiking agents in the case where more than one output neuron competes for reinforcement, a novel and necessary modification that enhances competition is applied to the two learning algorithms utilized, in order to avoid a possible synaptic saturation. This is done by administering to the networks additional global reinforcement signals for every spike of the output neurons that were not "responsible" for the preceding decision.

  15. US NDC Modernization Iteration E1 Prototyping Report: Processing Control Framework

    SciTech Connect

    Prescott, Ryan; Hamlet, Benjamin R.

    2014-12-01

    During the first iteration of the US NDC Modernization Elaboration phase (E1), the SNL US NDC modernization project team developed an initial survey of applicable COTS solutions, and established exploratory prototyping related to the processing control framework in support of system architecture definition. This report summarizes these activities and discusses planned follow-on work.

  16. Single-Iteration Learning Algorithm for Feed-Forward Neural Networks

    SciTech Connect

    Barhen, J.; Cogswell, R.; Protopopescu, V.

    1999-07-31

    A new methodology for neural learning is presented, whereby only a single iteration is required to train a feed-forward network with near-optimal results. To this aim, a virtual input layer is added to the multi-layer architecture. The virtual input layer is connected to the nominal input layer by a specird nonlinear transfer function, and to the fwst hidden layer by regular (linear) synapses. A sequence of alternating direction singular vrdue decompositions is then used to determine precisely the inter-layer synaptic weights. This algorithm exploits the known separability of the linear (inter-layer propagation) and nonlinear (neuron activation) aspects of information &ansfer within a neural network.

  17. Co-Simulation Research of the Mechanical-Hydraulic-Control Coupling System of ITER Tractor

    NASA Astrophysics Data System (ADS)

    Yang, Xiuqing; Luo, Minzhou; Mei, Tao; Yao, Damao

    2009-06-01

    The virtual prototyping models of the mechanical, hydraulic and control system of the ITER tractor were built with CATIA, ADAMS and MATLAB/Simulink respectively according to its heavy load and high precision characteristics, and the data transfer between the different models was accomplished by the integration interface between different software. Consequently the virtual experimental platform for the multi-disciplinary co-simulation was established. A co-simulation study of the mechanical-hydraulic-control coupling system of the ITER tractor was carried out. The synchronization servo control of parallel hydraulic cylinders was implemented, and the tracking control of the preconcerted trajectory of the hydraulic cylinders was realized on the established experimental platform. This paper presents the optimization design and technology rebuilding for the complicated coupling system with its theoretic foundation and co-simulation virtual experimental platform.

  18. Foucauldian Iterative Learning Conversations--An Example of Organisational Change: Developing Conjoint-Work between EPS and Social Workers

    ERIC Educational Resources Information Center

    Apter, Brian

    2014-01-01

    An organisational change-process in a UK local authority (LA) over two years is examined using transcribed excerpts from three meetings. The change-process is analysed using a Foucauldian analytical tool--Iterative Learning Conversations (ILCS). An Educational Psychology Service was changed from being primarily an education-focussed…

  19. How to Combine Objectives and Methods of Evaluation in Iterative ILE Design: Lessons Learned from Designing Ambre-Add

    ERIC Educational Resources Information Center

    Nogry, S.; Jean-Daubias, S.; Guin, N.

    2012-01-01

    This article deals with evaluating an interactive learning environment (ILE) during the iterative-design process. Various aspects of the system must be assessed and a number of evaluation methods are available. In designing the ILE Ambre-add, several techniques were combined to test and refine the system. In particular, we point out the merits of…

  20. An iterative investigation into the implementation of handheld computers as learning tools in a science museum

    NASA Astrophysics Data System (ADS)

    Phipps, Molly E.

    In this study I discuss the state of Free-Choice Learning research, and an investigation into the use of personal ubiquitous technology on visitors' experiences at a science center. The three manuscripts included in this document: (1) Review published research on free-choice learning from 1997-2007 from selected journals (2) Examine visitors' interest in using handheld computers (iPods) for learning in a science museum, and report on refining protocols for this type of research. (3) Investigate the impact of using an iPod with supplementary videos on visitors use and understanding of an exhibit on scientific chaos. This study was approached in two phases, the first phase follows the principles of design research in exploring ways to present the iPods within the most favorable context to encourage learning. These changes were systematically implemented and their impact on visitors' experiences were documented. The second phase of the research focused on one particular exhibit and three accompanying videos on the iPod. This exhibit is well loved, but difficult to understand for visitors and docents alike. Through naturalistic inquiry and iterative open coding, I found visitors interpreted appropriate use of the exhibit in four distinct ways: HOW DOES IT WORK?, WAITING FOR THE SPLASH, INTERACTING, and RESTING. However, iPod users all interpreted appropriate use of the exhibit as HOW DOES IT WORK?. Careful observation of visitors' actions at the "Chaos Wheel" exhibit suggests that the exhibit needs some revision if it is to become more accessible to more visitors. The iPod represents one way to increase the accessibility of the exhibit, but other means should be explored.

  1. Machine learning in motion control

    NASA Technical Reports Server (NTRS)

    Su, Renjeng; Kermiche, Noureddine

    1989-01-01

    The existing methodologies for robot programming originate primarily from robotic applications to manufacturing, where uncertainties of the robots and their task environment may be minimized by repeated off-line modeling and identification. In space application of robots, however, a higher degree of automation is required for robot programming because of the desire of minimizing the human intervention. We discuss a new paradigm of robotic programming which is based on the concept of machine learning. The goal is to let robots practice tasks by themselves and the operational data are used to automatically improve their motion performance. The underlying mathematical problem is to solve the problem of dynamical inverse by iterative methods. One of the key questions is how to ensure the convergence of the iterative process. There have been a few small steps taken into this important approach to robot programming. We give a representative result on the convergence problem.

  2. An iterative approach to the optimal co-design of linear control systems

    NASA Astrophysics Data System (ADS)

    Jiang, Yu; Wang, Yebin; Bortoff, Scott A.; Jiang, Zhong-Ping

    2016-04-01

    This paper investigates the optimal co-design of both physical plants and control policies for a class of continuous-time linear control systems. The optimal co-design of a specific linear control system is commonly formulated as a nonlinear non-convex optimisation problem (NNOP), and solved by using iterative techniques, where the plant parameters and the control policy are updated iteratively and alternately. This paper proposes a novel iterative approach to solve the NNOP, where the plant parameters are updated by solving a standard semi-definite programming problem, with non-convexity no longer involved. The proposed system design is generally less conservative in terms of the system performance compared to the conventional system-equivalence-based design, albeit the range of applicability is slightly reduced. A practical optimisation algorithm is proposed to compute a sub-optimal solution ensuring the system stability, and the convergence of the algorithm is established. The effectiveness of the proposed algorithm is illustrated by its application to the optimal co-design of a physical load positioning system.

  3. Iterated non-linear model predictive control based on tubes and contractive constraints.

    PubMed

    Murillo, M; Sánchez, G; Giovanini, L

    2016-05-01

    This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle.

  4. Decentralized Control of Sound Radiation from an Aircraft-Style Panel Using Iterative Loop Recovery

    NASA Technical Reports Server (NTRS)

    Schiller, Noah H.; Cabell, Randolph H.; Fuller, Chris R.

    2008-01-01

    A decentralized LQG-based control strategy is designed to reduce low-frequency sound transmission through periodically stiffened panels. While modern control strategies have been used to reduce sound radiation from relatively simple structural acoustic systems, significant implementation issues have to be addressed before these control strategies can be extended to large systems such as the fuselage of an aircraft. For instance, centralized approaches typically require a high level of connectivity and are computationally intensive, while decentralized strategies face stability problems caused by the unmodeled interaction between neighboring control units. Since accurate uncertainty bounds are not known a priori, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is validated using real-time control experiments performed on a built-up aluminum test structure representative of the fuselage of an aircraft. Experiments demonstrate that the iterative approach is capable of achieving 12 dB peak reductions and a 3.6 dB integrated reduction in radiated sound power from the stiffened panel.

  5. Current Control in ITER Steady State Plasmas With Neutral Beam Steering

    SciTech Connect

    R.V. Budny

    2009-09-10

    Predictions of quasi steady state DT plasmas in ITER are generated using the PTRANSP code. The plasma temperatures, densities, boundary shape, and total current (9 - 10 MA) anticipated for ITER steady state plasmas are specified. Current drive by negative ion neutral beam injection, lower-hybrid, and electron cyclotron resonance are calculated. Four modes of operation with different combinations of current drive are studied. For each mode, scans with the NNBI aimed at differing heights in the plasma are performed to study effects of current control on the q profile. The timeevolution of the currents and q are calculated to evaluate long duration transients. Quasi steady state, strongly reversed q profiles are predicted for some beam injection angles if the current drive and bootstrap currents are sufficiently large.

  6. Burn control of an ITER-like fusion reactor using fuzzy logic

    NASA Astrophysics Data System (ADS)

    Garcia-Amador, A. Sair; Martinell, Julio J.

    2016-10-01

    The fuel burn in a fusion reactor has to be kept at a nearly constant rate in order to have a steady power exhaust. Here, we develop a control system based on a fuzzy logic controller in order that adjusts external parameters to keep the plasma temperature and density at the design values of a reactor of the characteristics of ITER. The control parameters chosen are the D-T refueling rate, the auxiliary heating power and a neutral helium beam. We use a fuzzy controller of the Mamdani type that uses a number of membership functions appropriate to produce a response to parameter deviations that minimizes the response time. The inference rules are determined in a way to provide stabilization to all perturbations of the temperature, density and alpha particle fraction. The dynamical response of the reactor is simulated with a 0D model that uses confinement times provided by the ITER scaling. We show that the system is feedback stabilized for a large range of parameters around the nominal values. The recovery time after a departure from the steady values is of the order of one second. We compare the results with another control system based on neural networks that was developed previously. Funded by projects PAPIIT IN109115 and Conacyt 152905.

  7. Procedural Learning during Declarative Control

    ERIC Educational Resources Information Center

    Crossley, Matthew J.; Ashby, F. Gregory

    2015-01-01

    There is now abundant evidence that human learning and memory are governed by multiple systems. As a result, research is now turning to the next question of how these putative systems interact. For instance, how is overall control of behavior coordinated, and does learning occur independently within systems regardless of what system is in control?…

  8. A Tale of Two Chambers: Iterative Approaches and Lessons Learned from Life Support Systems Testing in Altitude Chambers

    NASA Technical Reports Server (NTRS)

    Callini, Gianluca

    2016-01-01

    The drive for the journey to Mars is in a higher gear than ever before. We are developing new spacecraft and life support systems to take humans to the Red Planet. The journey that development hardware takes before its final incarnation in a fully integrated spacecraft can take years, as is the case for the Orion environmental control and life support system (ECLSS). Through the Pressure Integrated Suit Test (PIST) series, NASA personnel at Johnson Space Center have been characterizing the behavior of a closed loop ECLSS in the event of cabin depressurization. This kind of testing - one of the most hazardous activities performed at JSC - requires an iterative approach, increasing in complexity and hazards). The PIST series, conducted in the Crew and Thermal Systems Division (CTSD) 11-ft Chamber, started with unmanned test precursors before moving to a human-in-the-loop phase, and continues to evolve with the eventual goal of a qualification test for the final system that will be installed on Orion. Meanwhile, the Human Exploration Spacecraft Testbed for Integration and Advancement (HESTIA) program is an effort to research and develop technologies that will work in concert to support habitation on Mars. September 2015 marked the first unmanned HESTIA test, with the goal of characterizing how ECLSS technologies work together in a closed environment. HESTIA will culminate in crewed testing, but it can benefit from the lessons learned from another test that is farther ahead in its development and life cycle. Discussing PIST and HESTIA, this paper illustrates how we approach testing, the kind of information that facility teams need to ensure efficient collaborations and successful testing, and how we can apply what we learn to execute future tests.

  9. Learning fuzzy logic control system

    NASA Technical Reports Server (NTRS)

    Lung, Leung Kam

    1994-01-01

    The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the

  10. Performance analysis of model based iterative reconstruction with dictionary learning in transportation security CT

    NASA Astrophysics Data System (ADS)

    Haneda, Eri; Luo, Jiajia; Can, Ali; Ramani, Sathish; Fu, Lin; De Man, Bruno

    2016-05-01

    In this study, we implement and compare model based iterative reconstruction (MBIR) with dictionary learning (DL) over MBIR with pairwise pixel-difference regularization, in the context of transportation security. DL is a technique of sparse signal representation using an over complete dictionary which has provided promising results in image processing applications including denoising,1 as well as medical CT reconstruction.2 It has been previously reported that DL produces promising results in terms of noise reduction and preservation of structural details, especially for low dose and few-view CT acquisitions.2 A distinguishing feature of transportation security CT is that scanned baggage may contain items with a wide range of material densities. While medical CT typically scans soft tissues, blood with and without contrast agents, and bones, luggage typically contains more high density materials (i.e. metals and glass), which can produce severe distortions such as metal streaking artifacts. Important factors of security CT are the emphasis on image quality such as resolution, contrast, noise level, and CT number accuracy for target detection. While MBIR has shown exemplary performance in the trade-off of noise reduction and resolution preservation, we demonstrate that DL may further improve this trade-off. In this study, we used the KSVD-based DL3 combined with the MBIR cost-minimization framework and compared results to Filtered Back Projection (FBP) and MBIR with pairwise pixel-difference regularization. We performed a parameter analysis to show the image quality impact of each parameter. We also investigated few-view CT acquisitions where DL can show an additional advantage relative to pairwise pixel difference regularization.

  11. Dual-Dictionary Learning-Based Iterative Image Reconstruction for Spectral Computed Tomography Application

    PubMed Central

    Zhao, Bo; Ding, Huanjun; Lu, Yang; Wang, Ge; Zhao, Jun; Molloi, Sabee

    2015-01-01

    In this study, we investigated the effectiveness of a novel Iterative Reconstruction (IR) method coupled with Dual-Dictionary Learning (DDL) for image reconstruction in a dedicated breast Computed Tomography (CT) system based on a Cadmium-Zinc-Telluride (CZT) photon-counting detector and compared it to the Filtered-Back-Projection (FBP) method with the ultimate goal of reducing the number of projections necessary for reconstruction without sacrificing image quality. Postmortem breast samples were scanned in a fan-beam CT system and were reconstructed from 100–600 projections with both IR and FBP methods. The Contrast-to-Noise Ratio (CNR) between the glandular and adipose tissues of the postmortem breast samples was calculated to compare the quality of images reconstructed from IR and FBP. The spatial resolution of the two reconstruction techniques was evaluated using Aluminum (Al) wires with diameters of 643, 813, 1020, 1290 and 1630 µm in a plastic epoxy resin phantom with diameter of 13 cm. Both the spatial resolution and the CNR were improved with IR compared to FBP for the images reconstructed from the same number of projections. In comparison with FBP reconstruction, the CNR was improved from 3.4 to 7.5 by using the IR method with 6-fold fewer projections while maintaining the same spatial resolution. The study demonstrated that the IR method coupled with DDL could significantly reduce the required number of projections for a CT reconstruction compared to FBP method while achieving a much better CNR and maintaining the same spatial resolution. From this, the radiation dose and scanning time can potentially be reduced by a factor of approximately 6 by using this IR method for image reconstruction in a CZT-based breast CT system. PMID:23192234

  12. eNOSHA, a Free, Open and Flexible Learning Object Repository--An Iterative Development Process for Global User-Friendliness

    ERIC Educational Resources Information Center

    Mozelius, Peter; Hettiarachchi, Enosha

    2012-01-01

    This paper describes the iterative development process of a Learning Object Repository (LOR), named eNOSHA. Discussions on a project for a LOR started at the e-Learning Centre (eLC) at The University of Colombo, School of Computing (UCSC) in 2007. The eLC has during the last decade been developing learning content for a nationwide e-learning…

  13. Procedural learning during declarative control.

    PubMed

    Crossley, Matthew J; Ashby, F Gregory

    2015-09-01

    There is now abundant evidence that human learning and memory are governed by multiple systems. As a result, research is now turning to the next question of how these putative systems interact. For instance, how is overall control of behavior coordinated, and does learning occur independently within systems regardless of what system is in control? Behavioral, neuroimaging, and neuroscience data are somewhat mixed with respect to these questions. Human neuroimaging and animal lesion studies suggest independent learning and are mostly agnostic with respect to control. Human behavioral studies suggest active inhibition of behavioral output but have little to say regarding learning. The results of two perceptual category-learning experiments are described that strongly suggest that procedural learning does occur while the explicit system is in control of behavior and that this learning might be just as good as if the procedural system was controlling the response. These results are consistent with the idea that declarative memory systems inhibit the ability of the procedural system to access motor output systems but do not prevent procedural learning.

  14. Robust Control Feedback and Learning

    DTIC Science & Technology

    2002-11-30

    98-1-0026 5b. GRANT NUMBER Robust Control, Feedback and Learning F49620-98-1-0026 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER Michael G...Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std. Z39.18 Final Report: ROBUST CONTROL FEEDBACK AND LEARNING AFOSR Grant F49620-98-1-0026 October 1...Philadelphia, PA, 2000. [16] M. G. Safonov. Recent advances in robust control, feedback and learning . In S. 0. R. Moheimani, editor, Perspectives in Robust

  15. Numerical analysis of a reinforcement learning model with the dynamic aspiration level in the iterated Prisoner's dilemma.

    PubMed

    Masuda, Naoki; Nakamura, Mitsuhiro

    2011-06-07

    Humans and other animals can adapt their social behavior in response to environmental cues including the feedback obtained through experience. Nevertheless, the effects of the experience-based learning of players in evolution and maintenance of cooperation in social dilemma games remain relatively unclear. Some previous literature showed that mutual cooperation of learning players is difficult or requires a sophisticated learning model. In the context of the iterated Prisoner's dilemma, we numerically examine the performance of a reinforcement learning model. Our model modifies those of Karandikar et al. (1998), Posch et al. (1999), and Macy and Flache (2002) in which players satisfy if the obtained payoff is larger than a dynamic threshold. We show that players obeying the modified learning mutually cooperate with high probability if the dynamics of threshold is not too fast and the association between the reinforcement signal and the action in the next round is sufficiently strong. The learning players also perform efficiently against the reactive strategy. In evolutionary dynamics, they can invade a population of players adopting simpler but competitive strategies. Our version of the reinforcement learning model does not complicate the previous model and is sufficiently simple yet flexible. It may serve to explore the relationships between learning and evolution in social dilemma situations.

  16. Hybrid learning control for improving suppression of hand tremor.

    PubMed

    As'arry, Azizan; Md Zain, Mohd Zarhamdy; Mailah, Musa; Hussein, Mohamed

    2013-11-01

    Patients with hand tremors may find routine activities such as writing and holding objects affected. In response to this problem, an active control technique has been examined in order to lessen the severity of tremors. In this article, an online method of a hybrid proportional-integral control with active force control strategy for tremor attenuation is presented. An intelligent mechanism using iterative learning control is incorporated into the active force control loop to approximate the estimation mass parameter. Experiments were conducted on a dummy hand model placed horizontally in a tremor test rig. When activated by a shaker in the vertical direction, this resembles a postural tremor condition. In the proportional-integral plus active force control, a linear voice coil actuator is used as the main active tremor suppressive element. A sensitivity analysis is presented to investigate the robustness of the proposed controller in a real-time control environment. The findings of this study demonstrate that the intelligent active force control and iterative learning controller show excellent performance in reducing tremor error compared to classic pure proportional, proportional-integral and hybrid proportional-integral plus active force control controllers.

  17. Real-time sawtooth control and neoclassical tearing mode preemption in ITER

    NASA Astrophysics Data System (ADS)

    Kim, D.; Goodman, T. P.; Sauter, O.

    2014-06-01

    Real-time control of multiple plasma actuators is a requirement in advanced tokamaks; for example, for burn control, plasma current profile control and MHD stabilization—electron cyclotron (EC) wave absorption is ideally suited especially for the latter. On ITER, 24 EC sources can be switched between 56 inputs at the torus. In the torus, 5 launchers direct the power to various locations across the plasma profile via 11 steerable mirrors. For optimal usage of the available power, the aiming and polarization of the beams must be adapted to the plasma configuration and the needs of the scenario. Since the EC system performs many competing tasks, present day systems should demonstrate the ability of an EC plant to deal with several targets in parallel and/or to switch smoothly between goals to attain overall satisfaction. Based on pacing and locking experiments performed on TCV (Tokamak à Configuration Variable), the real-time sawtooth control of ITER with this complex set of actuators is analyzed, as an example. It is shown that sawtooth locking and pacing are possible with various levels of powers, leading to different time delays between the end of the EC power phase and the next sawtooth crash. This timing is important since it allows use of the same launchers for neoclassical tearing mode (NTM) preemption at the q = 1.5 or 2 surface, avoiding the need to switch power between launchers. These options are presented. It is also demonstrated that increasing the total EC power does not necessarily increase the range of control because of the geometry of the launchers.

  18. Real-time sawtooth control and neoclassical tearing mode preemption in ITER

    SciTech Connect

    Kim, D. Goodman, T. P.; Sauter, O.

    2014-06-15

    Real-time control of multiple plasma actuators is a requirement in advanced tokamaks; for example, for burn control, plasma current profile control and MHD stabilization—electron cyclotron (EC) wave absorption is ideally suited especially for the latter. On ITER, 24 EC sources can be switched between 56 inputs at the torus. In the torus, 5 launchers direct the power to various locations across the plasma profile via 11 steerable mirrors. For optimal usage of the available power, the aiming and polarization of the beams must be adapted to the plasma configuration and the needs of the scenario. Since the EC system performs many competing tasks, present day systems should demonstrate the ability of an EC plant to deal with several targets in parallel and/or to switch smoothly between goals to attain overall satisfaction. Based on pacing and locking experiments performed on TCV (Tokamak à Configuration Variable), the real-time sawtooth control of ITER with this complex set of actuators is analyzed, as an example. It is shown that sawtooth locking and pacing are possible with various levels of powers, leading to different time delays between the end of the EC power phase and the next sawtooth crash. This timing is important since it allows use of the same launchers for neoclassical tearing mode (NTM) preemption at the q = 1.5 or 2 surface, avoiding the need to switch power between launchers. These options are presented. It is also demonstrated that increasing the total EC power does not necessarily increase the range of control because of the geometry of the launchers.

  19. 17th Workshop on MHD Stability Control: addressing the disruption challenge for ITER

    NASA Astrophysics Data System (ADS)

    Buttery, Richard

    2013-08-01

    This annual workshop on magnetohydrodynamic stability control was held on 5-7 November 2012 at Columbia University in the city of New York, in the aftermath of a violent hydrodynamic instability event termed 'Hurricane Sandy'. Despite these challenging circumstances, Columbia University managed an excellent meeting, enabling the full participation of the community. This Workshop has been held since 1996 to help in the development of understanding and control of magnetohydrodynamic (MHD) instabilities for future fusion reactors. It covers a wide range of stability topics—from disruptions, to tearing modes, error fields, edge-localized modes (ELMs), resistive wall modes (RWMs) and ideal MHD—spanning many device types (tokamaks, stellarators and reversed field pinches) to identify commonalities in the physics and a means of control. The theme for 2012 was 'addressing the disruption challenge for ITER', and thus the first day had a heavy focus on both the avoidance and mitigation of disruptions in ITER. Key elements included understanding how to apply 3D fields to maintain stability, as well as managing the disruption process itself through mitigating loads in the thermal quench and handling so called 'runaway electrons'. This culminated in a panel discussion on the disruption mitigation strategy for ITER, which noted that heat load asymmetries during the thermal quench appear to be an artifact of MHD processes, and that runaway electron generation may be inevitable, suggesting research should focus on control and dissipation of the runaway beam. The workshop was combined this year with the annual US-Japan MHD Workshop, with a special section looking more deeply at 'Fundamentals of 3D Perturbed Equilibrium Control', with interesting sessions on 3D equilibrium reconstruction, RWM physics, novel control concepts such as non-magnetic sensing, adaptive control, q < 2 tokamak operation, and the effects of flow. The final day turned to tearing mode interactions

  20. Aircraft adaptive learning control

    NASA Technical Reports Server (NTRS)

    Lee, P. S. T.; Vanlandingham, H. F.

    1979-01-01

    The optimal control theory of stochastic linear systems is discussed in terms of the advantages of distributed-control systems, and the control of randomly-sampled systems. An optimal solution to longitudinal control is derived and applied to the F-8 DFBW aircraft. A randomly-sampled linear process model with additive process and noise is developed.

  1. Examination of the Entry to Burn and Burn Control for the ITER 15 MA Baseline and Other Scenarios

    SciTech Connect

    Kesse, Charles E.; Kim, S-H.; Koechl, F.

    2014-09-01

    The entry to burn and flattop burn control in ITER will be a critical need from the first DT experiments. Simulations are used to address time-dependent behavior under a range of possible conditions that include injected power level, impurity content (W, Ar, Be), density evolution, H-mode regimes, controlled parameter (Wth, Pnet, Pfusion), and actuator (Paux, fueling, fAr), with a range of transport models. A number of physics issues at the L-H transition require better understanding to project to ITER, however, simulations indicate viable control with sufficient auxiliary power (up to 73 MW), while lower powers become marginal (as low as 43 MW).

  2. Reinforcement learning for robot control

    NASA Astrophysics Data System (ADS)

    Smart, William D.; Pack Kaelbling, Leslie

    2002-02-01

    Writing control code for mobile robots can be a very time-consuming process. Even for apparently simple tasks, it is often difficult to specify in detail how the robot should accomplish them. Robot control code is typically full of magic numbers that must be painstakingly set for each environment that the robot must operate in. The idea of having a robot learn how to accomplish a task, rather than being told explicitly is an appealing one. It seems easier and much more intuitive for the programmer to specify what the robot should be doing, and let it learn the fine details of how to do it. In this paper, we describe JAQL, a framework for efficient learning on mobile robots, and present the results of using it to learn control policies for simple tasks.

  3. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning

    PubMed Central

    Heffernan, Rhys; Paliwal, Kuldip; Lyons, James; Dehzangi, Abdollah; Sharma, Alok; Wang, Jihua; Sattar, Abdul; Yang, Yuedong; Zhou, Yaoqi

    2015-01-01

    Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking. PMID:26098304

  4. Learning to Teach Elementary Science through Iterative Cycles of Enactment in Culturally and Linguistically Diverse Contexts

    ERIC Educational Resources Information Center

    Bottoms, SueAnn I.; Ciechanowski, Kathryn M.; Hartman, Brian

    2015-01-01

    Iterative cycles of enactment embedded in culturally and linguistically diverse contexts provide rich opportunities for preservice teachers (PSTs) to enact core practices of science. This study is situated in the larger Families Involved in Sociocultural Teaching and Science, Technology, Engineering and Mathematics (FIESTAS) project, which weaves…

  5. On the Sequential Control of ITER Poloidal Field Converters for Reactive Power Reduction

    NASA Astrophysics Data System (ADS)

    Yuan, Hongwen; Fu, Peng; Gao, Ge; Huang, Liansheng; Song, Zhiquan; He, Shiying; Wu, Yanan; Dong, Lin; Wang, Min; Fang, Tongzhen

    2014-12-01

    Sequential control applied to the International Thermonuclear Experimental Reactor (ITER) poloidal field converter system for the purpose of reactive power reduction is the subject of this investigation. Due to the inherent characteristics of thyristor-based phase-controlled converter, the poloidal field converter system consumes a huge amount of reactive power from the grid, which subsequently results in a voltage drop at the 66 kV busbar if no measure is taken. The installation of a static var compensator rated for 750 MVar at the 66 kV busbar is an essential way to compensate reactive power to the grid, which is the most effective measure to solve the problem. However, sequential control of the multi-series converters provides an additional method to improve the natural power factor and thus alleviate the pressure of reactive power demand of the converter system without any additional cost. In the present paper, by comparing with the symmetrical control technique, the advantage of sequential control in reactive power consumption is highlighted. Simulation results based on SIMULINK are found in agreement with the theoretical analysis.

  6. SVM-Hustle - An iterative semi-supervised machine learning approach for pairwise protein remote homology detection

    SciTech Connect

    Shah, Anuj R.; Oehmen, Chris S.; Webb-Robertson, Bobbie-Jo M.

    2008-03-15

    Motivation: As the amount of biological sequence data continues to grow exponentially we face the increasing challenge of assigning function to this enormous molecular ‘parts list’. The most popular approaches to this challenge make use of the simplifying assumption that similar functional molecules, or proteins, sometimes have similar composition, or sequence. However, these algorithms often fail to identify remote homologs (proteins with similar function but dissimilar sequence) which often are a significant fraction of the total homolog collection for a given sequence. We introduce a Support Vector Machine (SVM)-based tool to detect Homology Using Semisupervised iTerative LEarning (SVM-HUSTLE) that detects significantly more remote homologs than current state-of-the-art sequence or cluster-based methods. As opposed to building profiles or position specific scoring matrices, SVM-HUSTLE builds an SVM classifier for a query sequence by training on a collection of representative highconfidence training sets. SVM-HUSTLE combines principles of semi-supervised learning theory with statistical sampling to create many concurrent classifiers to iteratively detect and refine on-the-fly patterns indicating homology. Results: When compared against existing methods for identifying protein homologs (BLASTp, PSI-BLAST, RANKPROP, MOTIFPROP and their variants) on the SCOP 1.59 benchmark dataset consisting of 7329 protein sequences, SVM-HUSTLE significantly outperforms each of the above methods using the most stringent ROC1 statistic with p-values less than 1e-20.

  7. Progress on the application of ELM control schemes to ITER scenarios from the non-active phase to DT operation

    NASA Astrophysics Data System (ADS)

    Loarte, A.; Huijsmans, G.; Futatani, S.; Baylor, L. R.; Evans, T. E.; Orlov, D. M.; Schmitz, O.; Becoulet, M.; Cahyna, P.; Gribov, Y.; Kavin, A.; Sashala Naik, A.; Campbell, D. J.; Casper, T.; Daly, E.; Frerichs, H.; Kischner, A.; Laengner, R.; Lisgo, S.; Pitts, R. A.; Saibene, G.; Wingen, A.

    2014-03-01

    Progress in the definition of the requirements for edge localized mode (ELM) control and the application of ELM control methods both for high fusion performance DT operation and non-active low-current operation in ITER is described. Evaluation of the power fluxes for low plasma current H-modes in ITER shows that uncontrolled ELMs will not lead to damage to the tungsten (W) divertor target, unlike for high-current H-modes in which divertor damage by uncontrolled ELMs is expected. Despite the lack of divertor damage at lower currents, ELM control is found to be required in ITER under these conditions to prevent an excessive contamination of the plasma by W, which could eventually lead to an increased disruptivity. Modelling with the non-linear MHD code JOREK of the physics processes determining the flow of energy from the confined plasma onto the plasma-facing components during ELMs at the ITER scale shows that the relative contribution of conductive and convective losses is intrinsically linked to the magnitude of the ELM energy loss. Modelling of the triggering of ELMs by pellet injection for DIII-D and ITER has identified the minimum pellet size required to trigger ELMs and, from this, the required fuel throughput for the application of this technique to ITER is evaluated and shown to be compatible with the installed fuelling and tritium re-processing capabilities in ITER. The evaluation of the capabilities of the ELM control coil system in ITER for ELM suppression is carried out (in the vacuum approximation) and found to have a factor of ˜2 margin in terms of coil current to achieve its design criterion, although such a margin could be substantially reduced when plasma shielding effects are taken into account. The consequences for the spatial distribution of the power fluxes at the divertor of ELM control by three-dimensional (3D) fields are evaluated and found to lead to substantial toroidal asymmetries in zones of the divertor target away from the separatrix

  8. Metacognitive control and optimal learning.

    PubMed

    Son, Lisa K; Sethi, Rajiv

    2006-07-08

    The notion of optimality is often invoked informally in the literature on metacognitive control. We provide a precise formulation of the optimization problem and show that optimal time allocation strategies depend critically on certain characteristics of the learning environment, such as the extent of time pressure, and the nature of the uptake function. When the learning curve is concave, optimality requires that items at lower levels of initial competence be allocated greater time. On the other hand, with logistic learning curves, optimal allocations vary with time availability in complex and surprising ways. Hence there are conditions under which optimal strategies will be relatively easy to uncover, and others in which suboptimal time allocation might be expected. The model can therefore be used to address the question of whether and when learners should be able to exercise good metacognitive control in practice.

  9. Performance improvement of robots using a learning control scheme

    NASA Technical Reports Server (NTRS)

    Krishna, Ramuhalli; Chiang, Pen-Tai; Yang, Jackson C. S.

    1987-01-01

    Many applications of robots require that the same task be repeated a number of times. In such applications, the errors associated with one cycle are also repeated every cycle of the operation. An off-line learning control scheme is used here to modify the command function which would result in smaller errors in the next operation. The learning scheme is based on a knowledge of the errors and error rates associated with each cycle. Necessary conditions for the iterative scheme to converge to zero errors are derived analytically considering a second order servosystem model. Computer simulations show that the errors are reduced at a faster rate if the error rate is included in the iteration scheme. The results also indicate that the scheme may increase the magnitude of errors if the rate information is not included in the iteration scheme. Modification of the command input using a phase and gain adjustment is also proposed to reduce the errors with one attempt. The scheme is then applied to a computer model of a robot system similar to PUMA 560. Improved performance of the robot is shown by considering various cases of trajectory tracing. The scheme can be successfully used to improve the performance of actual robots within the limitations of the repeatability and noise characteristics of the robot.

  10. Fast and automatic depth control of iterative bone ablation based on optical coherence tomography data

    NASA Astrophysics Data System (ADS)

    Fuchs, Alexander; Pengel, Steffen; Bergmeier, Jan; Kahrs, Lüder A.; Ortmaier, Tobias

    2015-07-01

    Laser surgery is an established clinical procedure in dental applications, soft tissue ablation, and ophthalmology. The presented experimental set-up for closed-loop control of laser bone ablation addresses a feedback system and enables safe ablation towards anatomical structures that usually would have high risk of damage. This study is based on combined working volumes of optical coherence tomography (OCT) and Er:YAG cutting laser. High level of automation in fast image data processing and tissue treatment enables reproducible results and shortens the time in the operating room. For registration of the two coordinate systems a cross-like incision is ablated with the Er:YAG laser and segmented with OCT in three distances. The resulting Er:YAG coordinate system is reconstructed. A parameter list defines multiple sets of laser parameters including discrete and specific ablation rates as ablation model. The control algorithm uses this model to plan corrective laser paths for each set of laser parameters and dynamically adapts the distance of the laser focus. With this iterative control cycle consisting of image processing, path planning, ablation, and moistening of tissue the target geometry and desired depth are approximated until no further corrective laser paths can be set. The achieved depth stays within the tolerances of the parameter set with the smallest ablation rate. Specimen trials with fresh porcine bone have been conducted to prove the functionality of the developed concept. Flat bottom surfaces and sharp edges of the outline without visual signs of thermal damage verify the feasibility of automated, OCT controlled laser bone ablation with minimal process time.

  11. Reflections on the Use of Iterative, Agile and Collaborative Approaches for Blended Flipped Learning Development

    ERIC Educational Resources Information Center

    Owen, Hazel; Dunham, Nicola

    2015-01-01

    E-learning experiences are widely becoming common practice in many schools, tertiary institutions and other organisations. However despite this increased use of technology to enhance learning and the associated investment involved the result does not always equate to more engaged, knowledgeable and skilled learners. We have observed two key…

  12. Online Supplementary ADP Learning Controller Design and Application to Power System Frequency Control With Large-Scale Wind Energy Integration.

    PubMed

    Guo, Wentao; Liu, Feng; Si, Jennie; He, Dawei; Harley, Ronald; Mei, Shengwei

    2016-08-01

    The emergence of smart grids has posed great challenges to traditional power system control given the multitude of new risk factors. This paper proposes an online supplementary learning controller (OSLC) design method to compensate the traditional power system controllers for coping with the dynamic power grid. The proposed OSLC is a supplementary controller based on approximate dynamic programming, which works alongside an existing power system controller. By introducing an action-dependent cost function as the optimization objective, the proposed OSLC is a nonidentifier-based method to provide an online optimal control adaptively as measurement data become available. The online learning of the OSLC enjoys the policy-search efficiency during policy iteration and the data efficiency of the least squares method. For the proposed OSLC, the stability of the controlled system during learning, the monotonic nature of the performance measure of the iterative supplementary controller, and the convergence of the iterative supplementary controller are proved. Furthermore, the efficacy of the proposed OSLC is demonstrated in a challenging power system frequency control problem in the presence of high penetration of wind generation.

  13. An Optimization of Maximal Invariance in a Class of Multiple Valued Iterative Dynamics Models of Nonlinear Disturbed Control Systems

    NASA Astrophysics Data System (ADS)

    Kahng, Byungik

    2016-11-01

    We discuss an optimization problem on the maximal invariance of a class of multiple-valued iterative dynamics (MVID) models of discrete-time nonlinear control dynamical systems with singular disturbance. We study the inner and outer bounds of the maximal invariance, between which all noninterfering MVID models of nonlinear discrete-time control dynamical systems with singular disturbance reside. We also study the invariant fractal structure and an optimization of Lyapunov multipliers associated to it.

  14. Circuit model of the ITER-like antenna for JET and simulation of its control algorithms

    SciTech Connect

    Durodié, Frédéric Křivská, Alena; Helou, Walid; Collaboration: EUROfusion Consortium

    2015-12-10

    The ITER-like Antenna (ILA) for JET [1] is a 2 toroidal by 2 poloidal array of Resonant Double Loops (RDL) featuring in-vessel matching capacitors feeding RF current straps in conjugate-T manner, a low impedance quarter-wave impedance transformer, a service stub allowing hydraulic actuator and water cooling services to reach the aforementioned capacitors and a 2nd stage phase-shifter-stub matching circuit allowing to correct/choose the conjugate-T working impedance. Toroidally adjacent RDLs are fed from a 3dB hybrid splitter. It has been operated at 33, 42 and 47MHz on plasma (2008-2009) while it presently estimated frequency range is from 29 to 49MHz. At the time of the design (2001-2004) as well as the experiments the circuit models of the ILA were quite basic. The ILA front face and strap array Topica model was relatively crude and failed to correctly represent the poloidal central septum, Faraday Screen attachment as well as the segmented antenna central septum limiter. The ILA matching capacitors, T-junction, Vacuum Transmission Line (VTL) and Service Stubs were represented by lumped circuit elements and simple transmission line models. The assessment of the ILA results carried out to decide on the repair of the ILA identified that achieving routine full array operation requires a better understanding of the RF circuit, a feedback control algorithm for the 2nd stage matching as well as tighter calibrations of RF measurements. The paper presents the progress in modelling of the ILA comprising a more detailed Topica model of the front face for various plasma Scrape Off Layer profiles, a comprehensive HFSS model of the matching capacitors including internal bellows and electrode cylinders, 3D-EM models of the VTL including vacuum ceramic window, Service stub, a transmission line model of the 2nd stage matching circuit and main transmission lines including the 3dB hybrid splitters. A time evolving simulation using the improved circuit model allowed to design and

  15. Stimulus control and associative learning.

    PubMed Central

    Williams, B A

    1984-01-01

    Interest in operant research on stimulus control has declined at the same time that much interest has burgeoned in nonoperant areas. Several examples of this shift toward traditional learning theory are considered, all of which have sponsored theoretical approaches that attempt to characterize the underlying associative units. These theoretical approaches are defended on the grounds that they have generated a deeper understanding of a variety of often puzzling phenomena. My projection is that future research will be determined even more strongly by theories about the structure of associations. Particular issues for which such discussion will have major impact include (1) whether conditional stimulus control is qualitatively different than simpler forms of stimulus control, (2) whether stimulus control is organized hierarchically, and (3) the origin of categories of stimulus equivalence. PMID:6520579

  16. The Iterative Design of a Mobile Learning Application to Support Scientific Inquiry

    ERIC Educational Resources Information Center

    Marty, Paul F.; Mendenhall, Anne; Douglas, Ian; Southerland, Sherry A.; Sampson, Victor; Kazmer, Michelle M.; Alemanne, Nicole; Clark, Amanda; Schellinger, Jennifer

    2013-01-01

    The ubiquity of mobile devices makes them well suited for field-based learning experiences that require students to gather data as part of the process of developing scientific inquiry practices. The usefulness of these devices, however, is strongly influenced by the nature of the applications students use to collect data in the field. To…

  17. LATUX: An Iterative Workflow for Designing, Validating, and Deploying Learning Analytics Visualizations

    ERIC Educational Resources Information Center

    Martinez-Maldonado, Roberto; Pardo, Abelardo; Mirriahi, Negin; Yacef, Kalina; Kay, Judy; Clayphan, Andrew

    2015-01-01

    Designing, validating, and deploying learning analytics tools for instructors or students is a challenge that requires techniques and methods from different disciplines, such as software engineering, human-computer interaction, computer graphics, educational design, and psychology. Whilst each has established its own design methodologies, we now…

  18. Tests on a mock-up of the feedback controlled matching options of the ITER ICRH system

    SciTech Connect

    Grine, D.; Vervier, M.; Messiaen, A.; Dumortier, P.

    2009-11-26

    Automatic control of the matching of the ITER ICRH antenna array on a reference load is presently developed and tested for optimization on a low-powered scaled (1:5) mock-up. Resilience to fast load variations is obtained either by 4 Conjugate-T (CT) or 4 quadrature hybrid circuits, the latter being the reference option. The main results are (i) for the CT option: successful implementation of the simultaneous feedback control of 11 actuators for the matching of the 4 CT and for the control of the array toroidal phasing; (ii) for the hybrid option: the matching and the array current control via feedback control of the decouplers and double stub tuners. This system is being progressively implemented and the simultaneous control of matching and antenna current has already been successfully tested on half of the array for heating and current drive phasings.

  19. Enhanced Confinement Scenarios Without Large Edge Localized Modes in Tokamaks: Control, Performance, and Extrapolability Issues for ITER

    SciTech Connect

    Maingi, R

    2014-07-01

    Large edge localized modes (ELMs) typically accompany good H-mode confinement in fusion devices, but can present problems for plasma facing components because of high transient heat loads. Here the range of techniques for ELM control deployed in fusion devices is reviewed. The two baseline strategies in the ITER baseline design are emphasized: rapid ELM triggering and peak heat flux control via pellet injection, and the use of magnetic perturbations to suppress or mitigate ELMs. While both of these techniques are moderately well developed, with reasonable physical bases for projecting to ITER, differing observations between multiple devices are also discussed to highlight the needed community R & D. In addition, recent progress in ELM-free regimes, namely Quiescent H-mode, I-mode, and Enhanced Pedestal H-mode is reviewed, and open questions for extrapolability are discussed. Finally progress and outstanding issues in alternate ELM control techniques are reviewed: supersonic molecular beam injection, edge electron cyclotron heating, lower hybrid heating and/or current drive, controlled periodic jogs of the vertical centroid position, ELM pace-making via periodic magnetic perturbations, ELM elimination with lithium wall conditioning, and naturally occurring small ELM regimes.

  20. Enhanced confinement scenarios without large edge localized modes in tokamaks: control, performance, and extrapolability issues for ITER

    NASA Astrophysics Data System (ADS)

    Maingi, R.

    2014-11-01

    Large edge localized modes (ELMs) typically accompany good H-mode confinement in fusion devices, but can present problems for plasma facing components because of high transient heat loads. Here the range of techniques for ELM control deployed in fusion devices is reviewed. Two strategies in the ITER baseline design are emphasized: rapid ELM triggering and peak heat flux control via pellet injection, and the use of magnetic perturbations to suppress or mitigate ELMs. While both of these techniques are moderately well developed, with reasonable physical bases for projecting to ITER, differing observations between multiple devices are also discussed to highlight the needed community R&D. In addition, recent progress in ELM-free regimes, namely quiescent H-mode, I-mode, and enhanced pedestal H-mode is reviewed, and open questions for extrapolability are discussed. Finally progress and outstanding issues in alternate ELM control techniques are reviewed: supersonic molecular beam injection, edge electron cyclotron heating, lower hybrid heating and/or current drive, controlled periodic jogs of the vertical centroid position, ELM pace-making via periodic magnetic perturbations, ELM elimination with lithium wall conditioning, and naturally occurring small ELM regimes.

  1. A potentially robust plasma profile control approach for ITER using real-time estimation of linearized profile response models

    NASA Astrophysics Data System (ADS)

    Kim, S. H.; Lister, J. B.

    2012-07-01

    An active plasma profile control approach for ITER, which is potentially robust by being tolerant to changing and uncertain physics, has been explored in this work, using a technique based on real-time estimation of linearized profile response models. The linearized models approximate static responses of the plasma profiles to power changes in auxiliary heating and current drive systems. These models are updated in real-time, differing from the model-based technique which deduces a dynamic model from identification experiments. The underlying physics is simplified with several assumptions to allow real-time update of the profile response models; however, without significant loss of information necessary for feedback control of the plasma profiles. The response of the electron temperature profile is modelled by simplifying the electron heat transport equation. The response of the safety factor profile is computed by directly relating it to the changes in source current density profiles. The required actuator power changes are directly computed by inverting the response matrix using the singular value decomposition technique. The saturation of the actuator powers is taken into account and the capability of using quantized auxiliary powers is provided. The potential of our active control approach has been tested by applying it to simulations of the ITER hybrid mode operation using CRONOS. In these simulations, either a global transport model or a theory-based local transport model has been used and the electron temperature and safety factor profiles were well controlled either independently or simultaneously.

  2. Towards First-principles Control-oriented Modeling of the Magnetic and Kinetic Plasma Profile Evolutions in ITER

    NASA Astrophysics Data System (ADS)

    Barton, Justin E.; Schuster, Eugenio; Besseghir, Karim; Lister, Jonathan

    2012-10-01

    The ``hybrid'' and ``steady-state'' advanced scenarios are characterized by q profiles higher or equal to one to mitigate plasma instabilities and improve confinement, which are key for ITER to achieve its operational objectives. To achieve these scenarios, active model-based control of the current profile and thermal state of the plasma is required. Towards this goal, two control-oriented, plasma-response models are proposed. First, the poloidal flux diffusion equation is combined with empirical models of the electron density and temperature profiles, plasma resistivity, and non-inductive current drives to obtain a physics-based model of the poloidal flux and stored energy evolutions. Second, the empirical electron temperature model is replaced by the electron heat transport equation, which is combined with empirical models of the electron heat conductivity and heat sources to obtain a physics-based model of the poloidal flux and electron temperature evolutions. Simulation results comparing the evolution of the plasma parameters predicted by the control-oriented, physic-based models and the DINA-CH+CRONOS simulation code are presented for ITER, and the control objectives and challenges are discussed.

  3. Connectionist Learning Control at GTE Laboratories

    NASA Astrophysics Data System (ADS)

    Franklin, Judy A.; Sutton, Richard S.; Anderson, Charles W.; Selfridge, Oliver G.; Schwartz, Daniel B.

    1990-02-01

    At GTE Laboratories, we are advancing the theory of connectionist learning architectures for real-time control while exploring their relationships to animal learning models, applications in manufacturing quality control, and VLSI implementations. We seek connectionist-network architectures with improved convergence rate and scaling properties, as assessed on simulated and actual control problems. Our primary focus is on extensions to reinforcement learning. These include adaptive critics, feature/representation adaptation in multilayer networks, hybrid connectionist/conventional controllers, and modular networks for hierarchical control. We are also extending methods for system identification, or model learning, to include internal models learned using temporal-differences. We propose the integration of reinforcement and model learning based on their relationships to dynamic programming. We are working to resolve how connectionist systems should serve as a total systems concept or as tools in a larger architecture.

  4. Control Theoretic Approach to Iterative Methods for Large-scale Toeplitz-type Systems with Application to Magnetic Field Analysis

    NASA Astrophysics Data System (ADS)

    Oda, Tomohito; Kashima, Kenji; Imura, Jun-Ichi; Miyazaki, Shuji; Morita, Hiroshi

    In this paper, stationary iterative methods for large-scale Toeplitz-type systems are investigated from a control theoretic point of view. We utilize spatially invariant structure of Toeplitz matrices, to avoid the curse of dimensionality arising in analysis and design of the convergence properties. Nonlinearities in the system are theoretically handled within the small gain and stability analysis for Lur'e systems. This theory enables us to achieve the desired global convergence of the proposed numerical scheme. We also evaluate the efficacy of the proposed method through an application to magnetic field analysis.

  5. Physics-based control-oriented modeling and robust feedback control of the plasma safety factor profile and stored energy dynamics in ITER

    NASA Astrophysics Data System (ADS)

    Barton, Justin E.; Besseghir, Karim; Lister, Jo; Schuster, Eugenio

    2015-11-01

    Many challenging plasma control problems still need to be addressed in order for the ITER plasma control system (PCS) to be able to maintain the plasma within a predefined operational space and optimize the plasma state evolution in the tokamak, which will greatly aid in the successful achievement of ITER’s goals. Firstly in this work, a general control-oriented, physics-based modeling approach is developed to obtain first-principles-driven (FPD) models of the plasma magnetic profile and stored energy evolutions valid for high performance, high confinement (H-mode) scenarios, with the goal of developing model-based closed-loop algorithms to control the safety factor profile (q profile) and stored energy evolutions in the tokamak. The FPD model is tailored to H-mode burning plasma scenarios in ITER by employing the DINA-CH & CRONOS free-boundary tokamak simulation code, and the FPD model’s prediction capabilities are demonstrated by comparing the prediction to data obtained from DINA-CH & CRONOS. Secondly, a model-based feedback control algorithm is designed to simultaneously track target q profile and stored energy evolutions in H-mode burning plasma scenarios in ITER by embedding the developed FPD model of the magnetic profile evolution into the control design process. The feedback controller is designed to ensure that the closed-loop system is robust to uncertainties in the electron density, electron temperature and plasma resistivity, and is tested in simulations with the developed FPD model. The effectiveness of the controller is demonstrated by first tracking nominal q profile and stored energy target evolutions, and then modulating the generated fusion power while maintaining the q profile in a stationary condition. In the process, many key practical issues for plasma profile control in ITER are investigated, which will be useful for the development of the ITER PCS that has recently been initiated. Some of the more pertinent investigated issues are the

  6. Tritium inventory control during ITER operation under carbon plasma-facing components by nitrogen-based plasma chemistry: a review

    NASA Astrophysics Data System (ADS)

    Tabarés, F. L.

    2013-06-01

    In spite of being highly suited for advanced plasma performance operation of tokamaks, as demonstrated over at least two decades of fusion plasma research, carbon is not currently considered as an integrating element of the plasma-facing components (PFCs) for the active phase of ITER. The main reason preventing its use under the very challenging scenarios foreseen in this phase, with edge-localized modes delivering several tens of MW m-2 to the divertor target every second or less, is the existing concern about reaching the tritium inventory value of 1000 g used in safety assessments in a time shorter than the projected lifetime of the divertor materials eroded by the plasma, set at 3000 shots. Although several mechanisms of tritium trapping in carbon components have been identified, co-deposition of the carbon radicals arising from chemically eroded chlorofluorocarbons in remote areas appears to play a dominant role. Several possible ways to keep control of the tritium build-up during the full operation of ITER have been put forward, mostly based on the periodic removal of the co-deposits by chemical (thermo-oxidation, plasma chemistry) or physical (laser, flash lamps) methods. In this work, we review the techniques for the inhibition and removal of tritium-rich co-deposits based on the strong chemical reactivity of some N-bearing molecules with carbon. The integration of these techniques into a possible scheme for tritium inventory control in the active phase of ITER under carbon-based PFCs with minimum down-time is discussed and the existing caveats are addressed.

  7. An adaptive learning control system for aircraft

    NASA Technical Reports Server (NTRS)

    Mekel, R.; Nachmias, S.

    1976-01-01

    A learning control system is developed which blends the gain scheduling and adaptive control into a single learning system that has the advantages of both. An important feature of the developed learning control system is its capability to adjust the gain schedule in a prescribed manner to account for changing aircraft operating characteristics. Furthermore, if tests performed by the criteria of the learning system preclude any possible change in the gain schedule, then the overall system becomes an ordinary gain scheduling system. Examples are discussed.

  8. A Reinforcement Learning Approach to Control.

    DTIC Science & Technology

    1997-05-31

    acquisition is inherently a partially observable Markov decision problem. This report describes an efficient, scalable reinforcement learning approach to the...deployment of refined intelligent gaze control techniques. This report first lays a theoretical foundation for reinforcement learning . It then introduces...perform well in both high and low SNR ATR environments. Reinforcement learning coupled with history features appears to be both a sound foundation and a practical scalable base for gaze control.

  9. Vehicle Steering control: A model of learning

    NASA Technical Reports Server (NTRS)

    Smiley, A.; Reid, L.; Fraser, M.

    1978-01-01

    A hierarchy of strategies were postulated to describe the process of learning steering control. Vehicle motion and steering control data were recorded for twelve novices who drove an instrumented car twice a week during and after a driver training course. Car-driver describing functions were calculated, the probable control structure determined, and the driver-alone transfer function modelled. The data suggested that the largest changes in steering control with learning were in the way the driver used the lateral position cue.

  10. An iterative particle filter approach for coupled hydro-geophysical inversion of a controlled infiltration experiment

    SciTech Connect

    Manoli, Gabriele; Rossi, Matteo; Pasetto, Damiano; Deiana, Rita; Ferraris, Stefano; Cassiani, Giorgio; Putti, Mario

    2015-02-15

    The modeling of unsaturated groundwater flow is affected by a high degree of uncertainty related to both measurement and model errors. Geophysical methods such as Electrical Resistivity Tomography (ERT) can provide useful indirect information on the hydrological processes occurring in the vadose zone. In this paper, we propose and test an iterated particle filter method to solve the coupled hydrogeophysical inverse problem. We focus on an infiltration test monitored by time-lapse ERT and modeled using Richards equation. The goal is to identify hydrological model parameters from ERT electrical potential measurements. Traditional uncoupled inversion relies on the solution of two sequential inverse problems, the first one applied to the ERT measurements, the second one to Richards equation. This approach does not ensure an accurate quantitative description of the physical state, typically violating mass balance. To avoid one of these two inversions and incorporate in the process more physical simulation constraints, we cast the problem within the framework of a SIR (Sequential Importance Resampling) data assimilation approach that uses a Richards equation solver to model the hydrological dynamics and a forward ERT simulator combined with Archie's law to serve as measurement model. ERT observations are then used to update the state of the system as well as to estimate the model parameters and their posterior distribution. The limitations of the traditional sequential Bayesian approach are investigated and an innovative iterative approach is proposed to estimate the model parameters with high accuracy. The numerical properties of the developed algorithm are verified on both homogeneous and heterogeneous synthetic test cases based on a real-world field experiment.

  11. An iterative particle filter approach for coupled hydro-geophysical inversion of a controlled infiltration experiment

    NASA Astrophysics Data System (ADS)

    Manoli, Gabriele; Rossi, Matteo; Pasetto, Damiano; Deiana, Rita; Ferraris, Stefano; Cassiani, Giorgio; Putti, Mario

    2015-02-01

    The modeling of unsaturated groundwater flow is affected by a high degree of uncertainty related to both measurement and model errors. Geophysical methods such as Electrical Resistivity Tomography (ERT) can provide useful indirect information on the hydrological processes occurring in the vadose zone. In this paper, we propose and test an iterated particle filter method to solve the coupled hydrogeophysical inverse problem. We focus on an infiltration test monitored by time-lapse ERT and modeled using Richards equation. The goal is to identify hydrological model parameters from ERT electrical potential measurements. Traditional uncoupled inversion relies on the solution of two sequential inverse problems, the first one applied to the ERT measurements, the second one to Richards equation. This approach does not ensure an accurate quantitative description of the physical state, typically violating mass balance. To avoid one of these two inversions and incorporate in the process more physical simulation constraints, we cast the problem within the framework of a SIR (Sequential Importance Resampling) data assimilation approach that uses a Richards equation solver to model the hydrological dynamics and a forward ERT simulator combined with Archie's law to serve as measurement model. ERT observations are then used to update the state of the system as well as to estimate the model parameters and their posterior distribution. The limitations of the traditional sequential Bayesian approach are investigated and an innovative iterative approach is proposed to estimate the model parameters with high accuracy. The numerical properties of the developed algorithm are verified on both homogeneous and heterogeneous synthetic test cases based on a real-world field experiment.

  12. Indirect learning control for nonlinear dynamical systems

    NASA Technical Reports Server (NTRS)

    Ryu, Yeong Soon; Longman, Richard W.

    1993-01-01

    In a previous paper, learning control algorithms were developed based on adaptive control ideas for linear time variant systems. The learning control methods were shown to have certain advantages over their adaptive control counterparts, such as the ability to produce zero tracking error in time varying systems, and the ability to eliminate repetitive disturbances. In recent years, certain adaptive control algorithms have been developed for multi-body dynamic systems such as robots, with global guaranteed convergence to zero tracking error for the nonlinear system euations. In this paper we study the relationship between such adaptive control methods designed for this specific class of nonlinear systems, and the learning control problem for such systems, seeking to converge to zero tracking error in following a specific command repeatedly, starting from the same initial conditions each time. The extension of these methods from the adaptive control problem to the learning control problem is seen to be trivial. The advantages and disadvantages of using learning control based on such adaptive control concepts for nonlinear systems, and the use of other currently available learning control algorithms are discussed.

  13. A Non-iterative Scheme for Orthogonal Grid Generation with Control Function and Specified Boundary Correspondence on Three Sides

    NASA Astrophysics Data System (ADS)

    Oh, H. J.; Kang, I. S.

    1994-05-01

    A new numerical scheme is proposed for generating an orthogonal grid in a simply-connected 2D domain. The scheme is based on the idea of decomposition of a global orthogonal transform into consecutive mappings of a conformal mapping and an auxiliary orthogonal mapping, which was suggested by Kang and Leal (J, Comput. Phys.102, 78 (1992)). The method is non-iterative and flexible in the adjustment of grid spacing The grid spacing can be controlled mainly by specification of the boundary correspondence up to three sides of the boundary. The method is also equipped with a control function that provides further degrees of freedom in the grid spacing adjustment. From a mathematical viewpoint, the proposed scheme can also be regarded as a numerical implementation of the constructive proof for the existence of a solution of the orthogonal mapping problem in an arbitrary simply-connected domain under the condition that the boundary correspondence is specified on three sides.

  14. A non-iterative scheme for orthogonal grid generation with control function and specified boundary correspondence on three sides

    NASA Astrophysics Data System (ADS)

    Oh, H. J.; Kang, I. S.

    1994-05-01

    A new numerical scheme is proposed for generating an orthogonal grid in a simply-connected 2D domain. The scheme is based on the idea of decomposition of a global orthogonal transform into consecutive mappings of a conformal mapping and an auxiliary orthogonal mapping which was suggested by Kang and Leal (J. Comput. Phys. 102, 78 (1992)). The method is non-iterative and flexible in the adjustment of grid spacing. The grid spacing can be controlled mainly by specification of the boundary correspondence up to three sides of the boundary. The method is also equipped with a control function that provides further degrees of freedom in the grid spacing adjustment. From a mathematical viewpoint, the proposed scheme can also be regarded as a numerical implementation of the constructive proof for the existence of a solution of the orthogonal mapping problem in an arbitrary simply-connected domain under the condition that the boundary correspondence is specified on three sides.

  15. Multimachine data–based prediction of high-frequency sensor signal noise for resistive wall mode control in ITER

    DOE PAGES

    Liu, Yueqiang; Sabbagh, S. A.; Chapman, I. T.; ...

    2016-08-12

    The high-frequency noise measured by magnetic sensors, at levels above the typical frequency of resistive wall modes, is analyzed across a range of present tokamak devices including DIII-D, JET, MAST, ASDEX Upgrade, JT-60U, and NSTX. A high-pass filter enables identification of the noise component with Gaussian-like statistics that shares certain common characteristics in all devices considered. A conservative prediction is made for ITER plasma operation of the high-frequency noise component of the sensor signals, to be used for resistive wall mode feedback stabilization, based on the multimachine database. The predicted root-mean-square n = 1 (n is the toroidal mode number)more » noise level is 104 to 105 G/s for the voltage signal, and 0.1 to 1 G for the perturbed magnetic field signal. The lower cutoff frequency of the Gaussian pickup noise scales linearly with the sampling frequency, with a scaling coefficient of about 0.1. As a result, these basic noise characteristics should be useful for the modeling-based design of the feedback control system for the resistive wall mode in ITER.« less

  16. Multimachine data–based prediction of high-frequency sensor signal noise for resistive wall mode control in ITER

    SciTech Connect

    Liu, Yueqiang; Sabbagh, S. A.; Chapman, I. T.; Gerasimov, S.; Gribov, Y.; Hender, T. C.; Igochine, V.; Maraschek, M.; Matsunaga, G.; Okabayashi, M.; Strait, E. J.

    2016-08-12

    The high-frequency noise measured by magnetic sensors, at levels above the typical frequency of resistive wall modes, is analyzed across a range of present tokamak devices including DIII-D, JET, MAST, ASDEX Upgrade, JT-60U, and NSTX. A high-pass filter enables identification of the noise component with Gaussian-like statistics that shares certain common characteristics in all devices considered. A conservative prediction is made for ITER plasma operation of the high-frequency noise component of the sensor signals, to be used for resistive wall mode feedback stabilization, based on the multimachine database. The predicted root-mean-square n = 1 (n is the toroidal mode number) noise level is 104 to 105 G/s for the voltage signal, and 0.1 to 1 G for the perturbed magnetic field signal. The lower cutoff frequency of the Gaussian pickup noise scales linearly with the sampling frequency, with a scaling coefficient of about 0.1. As a result, these basic noise characteristics should be useful for the modeling-based design of the feedback control system for the resistive wall mode in ITER.

  17. Simultaneous gains tuning in boiler/turbine PID-based controller clusters using iterative feedback tuning methodology.

    PubMed

    Zhang, Shu; Taft, Cyrus W; Bentsman, Joseph; Hussey, Aaron; Petrus, Bryan

    2012-09-01

    Tuning a complex multi-loop PID based control system requires considerable experience. In today's power industry the number of available qualified tuners is dwindling and there is a great need for better tuning tools to maintain and improve the performance of complex multivariable processes. Multi-loop PID tuning is the procedure for the online tuning of a cluster of PID controllers operating in a closed loop with a multivariable process. This paper presents the first application of the simultaneous tuning technique to the multi-input-multi-output (MIMO) PID based nonlinear controller in the power plant control context, with the closed-loop system consisting of a MIMO nonlinear boiler/turbine model and a nonlinear cluster of six PID-type controllers. Although simplified, the dynamics and cross-coupling of the process and the PID cluster are similar to those used in a real power plant. The particular technique selected, iterative feedback tuning (IFT), utilizes the linearized version of the PID cluster for signal conditioning, but the data collection and tuning is carried out on the full nonlinear closed-loop system. Based on the figure of merit for the control system performance, the IFT is shown to deliver performance favorably comparable to that attained through the empirical tuning carried out by an experienced control engineer.

  18. Learning styles: The learning methods of air traffic control students

    NASA Astrophysics Data System (ADS)

    Jackson, Dontae L.

    In the world of aviation, air traffic controllers are an integral part in the overall level of safety that is provided. With a number of controllers reaching retirement age, the Air Traffic Collegiate Training Initiative (AT-CTI) was created to provide a stronger candidate pool. However, AT-CTI Instructors have found that a number of AT-CTI students are unable to memorize types of aircraft effectively. This study focused on the basic learning styles (auditory, visual, and kinesthetic) of students and created a teaching method to try to increase memorization in AT-CTI students. The participants were asked to take a questionnaire to determine their learning style. Upon knowing their learning styles, participants attended two classroom sessions. The participants were given a presentation in the first class, and divided into a control and experimental group for the second class. The control group was given the same presentation from the first classroom session while the experimental group had a group discussion and utilized Middle Tennessee State University's Air Traffic Control simulator to learn the aircraft types. Participants took a quiz and filled out a survey, which tested the new teaching method. An appropriate statistical analysis was applied to determine if there was a significant difference between the control and experimental groups. The results showed that even though the participants felt that the method increased their learning, there was no significant difference between the two groups.

  19. Version Control in Project-Based Learning

    ERIC Educational Resources Information Center

    Milentijevic, Ivan; Ciric, Vladimir; Vojinovic, Oliver

    2008-01-01

    This paper deals with the development of a generalized model for version control systems application as a support in a range of project-based learning methods. The model is given as UML sequence diagram and described in detail. The proposed model encompasses a wide range of different project-based learning approaches by assigning a supervisory…

  20. Locus of Control, Social Class, and Learning.

    ERIC Educational Resources Information Center

    Vasquez, James A.

    The relationship between locus of control, social class, and learning processes were reviewed by analyzing: (1) externality as a function of socioeconomic status, i.e., the lower the status, the greater the degree of externality; (2) the impact of the early home environment on the child's learning and development; (3) classroom and teacher…

  1. Nearly data-based optimal control for linear discrete model-free systems with delays via reinforcement learning

    NASA Astrophysics Data System (ADS)

    Zhang, Jilie; Zhang, Huaguang; Wang, Binrui; Cai, Tiaoyang

    2016-05-01

    In this paper, a nearly data-based optimal control scheme is proposed for linear discrete model-free systems with delays. The nearly optimal control can be obtained using only measured input/output data from systems, by reinforcement learning technology, which combines Q-learning with value iterative algorithm. First, we construct a state estimator by using the measured input/output data. Second, the quadratic functional is used to approximate the value function at each point in the state space, and the data-based control is designed by Q-learning method using the obtained state estimator. Then, the paper states the method, that is, how to solve the optimal inner kernel matrix ? in the least-square sense, by value iteration algorithm. Finally, the numerical examples are given to illustrate the effectiveness of our approach.

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

    NASA Astrophysics Data System (ADS)

    Saeed, Bakhtiar I.; Mehrdadi, B.

    2012-05-01

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

  3. Refining fuzzy logic controllers with machine learning

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1994-01-01

    In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.

  4. A learning controller for nonrepetitive robotic operation

    NASA Technical Reports Server (NTRS)

    Miller, W. T., III

    1987-01-01

    A practical learning control system is described which is applicable to complex robotic and telerobotic systems involving multiple feedback sensors and multiple command variables. In the controller, the learning algorithm is used to learn to reproduce the nonlinear relationship between the sensor outputs and the system command variables over particular regions of the system state space, rather than learning the actuator commands required to perform a specific task. The learned information is used to predict the command signals required to produce desired changes in the sensor outputs. The desired sensor output changes may result from automatic trajectory planning or may be derived from interactive input from a human operator. The learning controller requires no a priori knowledge of the relationships between the sensor outputs and the command variables. The algorithm is well suited for real time implementation, requiring only fixed point addition and logical operations. The results of learning experiments using a General Electric P-5 manipulator interfaced to a VAX-11/730 computer are presented. These experiments involved interactive operator control, via joysticks, of the position and orientation of an object in the field of view of a video camera mounted on the end of the robot arm.

  5. Learning to Control Advanced Life Support Systems

    NASA Technical Reports Server (NTRS)

    Subramanian, Devika

    2004-01-01

    Advanced life support systems have many interacting processes and limited resources. Controlling and optimizing advanced life support systems presents unique challenges. In particular, advanced life support systems are nonlinear coupled dynamical systems and it is difficult for humans to take all interactions into account to design an effective control strategy. In this project. we developed several reinforcement learning controllers that actively explore the space of possible control strategies, guided by rewards from a user specified long term objective function. We evaluated these controllers using a discrete event simulation of an advanced life support system. This simulation, called BioSim, designed by Nasa scientists David Kortenkamp and Scott Bell has multiple, interacting life support modules including crew, food production, air revitalization, water recovery, solid waste incineration and power. They are implemented in a consumer/producer relationship in which certain modules produce resources that are consumed by other modules. Stores hold resources between modules. Control of this simulation is via adjusting flows of resources between modules and into/out of stores. We developed adaptive algorithms that control the flow of resources in BioSim. Our learning algorithms discovered several ingenious strategies for maximizing mission length by controlling the air and water recycling systems as well as crop planting schedules. By exploiting non-linearities in the overall system dynamics, the learned controllers easily out- performed controllers written by human experts. In sum, we accomplished three goals. We (1) developed foundations for learning models of coupled dynamical systems by active exploration of the state space, (2) developed and tested algorithms that learn to efficiently control air and water recycling processes as well as crop scheduling in Biosim, and (3) developed an understanding of the role machine learning in designing control systems for

  6. Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

    NASA Astrophysics Data System (ADS)

    Wei, Qing-Lai; Song, Rui-Zhuo; Sun, Qiu-Ye; Xiao, Wen-Dong

    2015-09-01

    This paper estimates an off-policy integral reinforcement learning (IRL) algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton-Jacobi-Bellman (HJB) equation, an off-policy IRL algorithm is proposed. It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method. Project supported by the National Natural Science Foundation of China (Grant Nos. 61304079 and 61374105), the Beijing Natural Science Foundation, China (Grant Nos. 4132078 and 4143065), the China Postdoctoral Science Foundation (Grant No. 2013M530527), the Fundamental Research Funds for the Central Universities, China (Grant No. FRF-TP-14-119A2), and the Open Research Project from State Key Laboratory of Management and Control for Complex Systems, China (Grant No. 20150104).

  7. An adaptive learning control system for aircraft

    NASA Technical Reports Server (NTRS)

    Mekel, R.; Nachmias, S.

    1978-01-01

    A learning control system and its utilization as a flight control system for F-8 Digital Fly-By-Wire (DFBW) research aircraft is studied. The system has the ability to adjust a gain schedule to account for changing plant characteristics and to improve its performance and the plant's performance in the course of its own operation. Three subsystems are detailed: (1) the information acquisition subsystem which identifies the plant's parameters at a given operating condition; (2) the learning algorithm subsystem which relates the identified parameters to predetermined analytical expressions describing the behavior of the parameters over a range of operating conditions; and (3) the memory and control process subsystem which consists of the collection of updated coefficients (memory) and the derived control laws. Simulation experiments indicate that the learning control system is effective in compensating for parameter variations caused by changes in flight conditions.

  8. Analysis of the phase control of the ITER ICRH antenna array. Influence on the load resilience and radiated power spectrum

    SciTech Connect

    Messiaen, A. Ongena, J.; Vervier, M.; Swain, D.

    2015-12-10

    The paper analyses how the phasing of the ITER ICRH 24 strap array evolves from the power sources up to the strap currents of the antenna. The study of the phasing control and coherence through the feeding circuits with prematching and automatic matching and decoupling network is made by modeling starting from the TOPICA matrix of the antenna array for a low coupling plasma profile and for current drive phasing (worst case for mutual coupling effects). The main results of the analysis are: (i) the strap current amplitude is well controlled by the antinode V{sub max} amplitude of the feeding lines, (ii) the best toroidal phasing control is done by the adjustment of the mean phase of V{sub max} of each poloidal straps column, (iii) with well adjusted system the largest strap current phasing error is ±20°, (iv) the effect on load resilience remains well below the maximum affordable VSWR of the generators, (v) the effect on the radiated power spectrum versus k{sub //} computed by means of the coupling code ANTITER II remains small for the considered cases.

  9. Analysis of the phase control of the ITER ICRH antenna array. Influence on the load resilience and radiated power spectrum

    SciTech Connect

    Messiaen, Andre; Swain, David W; Ongena, Jef; Vervier, Michael

    2015-01-01

    The paper analyses how the phasing of the ITER ICRH 24 strap array evolves from the power sources up to the strap currents of the antenna. The study of the phasing control and coherence through the feeding circuits with prematching and automatic matching and decoupling network is made by modeling starting from the TOPICA matrix of the antenna array for a low coupling plasma profile and for current drive phasing (worst case for mutual coupling effects). The main results of the analysis are: (i) the strap current amplitude is well controlled by the antinode V-max amplitude of the feeding lines, (ii) the best toroidal phasing control is done by the adjustment of the mean phase of V-max of each poloidal straps column, (iii) with well adjusted system the largest strap current phasing error is +/- 20 degrees, (iv) the effect on load resilience remains well below the maximum affordable VSWR of the generators, (v) the effect on the radiated power spectrum versus k//computed by means of the coupling code ANTITER II remains small for the considered cases. [GRAPHICS] .

  10. A quasi-analytical method for non-iterative computation of nonlinear controls

    NASA Technical Reports Server (NTRS)

    Junkins, J. L.; Thompson, R. C.; Turner, J. D.

    1987-01-01

    An optimal control solution process was developed for a general class of nonlinear dynamical systems. The method combines control theory, perturbation methods, and Van Loan's recent matrix exponential results. A variety of applications support the practical utility of this method. Nonlinear rigid body optimal maneuvers are routinely solved. Flexible body dynamical systems of an order greater than 40 were solved. The method fails occasionally due to poor convergence of the perturbation expansion or numerical difficulties associated with computing the matrix exponential. The method is attractive because it appears to be a good candidate for semi-automation; no initial guess is required, and it usually converges at 2nd or 3rd order in minutes of machine time.

  11. A Case Study of Modern PLC and LabVIEW Controls: Power Supply Controls for the ORNL ITER ECH Test Stand

    SciTech Connect

    Barker, Alan M; Killough, Stephen M; Bigelow, Tim S; White, John A; Munro Jr, John K

    2011-01-01

    Power Supply Controls are being developed at Oak Ridge National Laboratory (ORNL) to test transmission line components of the Electron Cyclotron Heating (ECH) system, with a focus on gyrotrons and waveguides, in support of the International Thermonuclear Experimental Reactor (ITER). The control is performed by several Programmable Logic Controllers (PLC s) located near the different equipment. A technique of Supervisory Control and Data Acquisition (SCADA) is presented to monitor, control, and log actions of the PLC s on a PC through use of Allen Bradley s Remote I/O communication interface coupled with an Open Process Control/Object Linking and Embedding [OLE] for Process Control (OPC) Server/Client architecture. The OPC data is then linked to a National Instruments (NI) LabVIEW system for monitoring and control. Details of the architecture and insight into applicability to other systems are presented in the rest of this paper. Future integration with an EPICS (Experimental Physics Industrial Control System) based mini-CODAC (Control, Data Access and Communication) SCADA system is under consideration, and integration considerations will be briefly introduced.

  12. AMYGDALA MICROCIRCUITS CONTROLLING LEARNED FEAR

    PubMed Central

    Duvarci, Sevil; Pare, Denis

    2014-01-01

    We review recent work on the role of intrinsic amygdala networks in the regulation of classically conditioned defensive behaviors, commonly known as conditioned fear. These new developments highlight how conditioned fear depends on far more complex networks than initially envisioned. Indeed, multiple parallel inhibitory and excitatory circuits are differentially recruited during the expression versus extinction of conditioned fear. Moreover, shifts between expression and extinction circuits involve coordinated interactions with different regions of the medial prefrontal cortex. However, key areas of uncertainty remain, particularly with respect to the connectivity of the different cell types. Filling these gaps in our knowledge is important because much evidence indicates that human anxiety disorders results from an abnormal regulation of the networks supporting fear learning. PMID:24908482

  13. Air pollution control system research: An iterative approach to developing affordable systems

    SciTech Connect

    Watt, L.C.; Cannon, F.S.; Heinsohn, R.J.; Spaeder, T.A.; Darvin, C.H.

    1993-12-31

    The research will be accomplished on lab scale, pilot scale, and production air pollution control systems (APCS). The production system, to be installed at Marine Corps Logistics Base (MCLB) Barstow, CA, will treat the exhaust from three paint booths which will be modified to recirculate a large percentage of their exhaust. These recirculation systems are, themselves, a critical element in the overall R and D effort. The goal of the program is to conduct an R and D effort which will improve and demonstrate a combination of technologies intended to make VOC treatment both effective and affordable. The US Marine Corps, the other services and industry will each benefit.

  14. Air pollution control system research: An iterative approach to developing affordable systems

    NASA Technical Reports Server (NTRS)

    Watt, Lewis C.; Cannon, Fred S.; Heinsohn, Robert J.; Spaeder, Timothy A.

    1995-01-01

    This paper describes a Strategic Environmental Research and Development Program (SERDP) funded project led jointly by the Marine Corps Multi-Commodity Maintenance Centers, and the Air and Energy Engineering Research Laboratory (AEERL) of the USEPA. The research focuses on paint booth exhaust minimization using recirculation, and on volatile organic compound (VOC) oxidation by the modules of a hybrid air pollution control system. The research team is applying bench, pilot and full scale systems to accomplish the goals of reduced cost and improved effectiveness of air treatment systems for paint booth exhaust.

  15. Apprenticeship Learning for Robotic Control

    DTIC Science & Technology

    2015-10-09

    perturbations, and controllability. We made contributions in handling collisions, handling occlusions, scaling up belief space planning, going beyond unimodal...handling  occlusions,  scaling  up  belief  space  planning,   going  beyond   unimodal  beliefs,  and  hierarchical  belief...perturbations, and controllability. We made contributions in handling collisions, handling occlusions, scaling up belief space planning, going beyond

  16. Filtering sensory information with XCSF: improving learning robustness and robot arm control performance.

    PubMed

    Kneissler, Jan; Stalph, Patrick O; Drugowitsch, Jan; Butz, Martin V

    2014-01-01

    It has been shown previously that the control of a robot arm can be efficiently learned using the XCSF learning classifier system, which is a nonlinear regression system based on evolutionary computation. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we utilize the forward velocity kinematics knowledge of XCSF to alleviate the negative effect of noisy sensors for successful learning and control. We incorporate Kalman filtering for estimating successive arm positions, iteratively combining sensory readings with XCSF-based predictions of hand position changes over time. The filtered arm position is used to improve both trajectory planning and further learning of the forward velocity kinematics. We test the approach on a simulated kinematic robot arm model. The results show that the combination can improve learning and control performance significantly. However, it also shows that variance estimates of XCSF prediction may be underestimated, in which case self-delusional spiraling effects can hinder effective learning. Thus, we introduce a heuristic parameter, which can be motivated by theory, and which limits the influence of XCSF's predictions on its own further learning input. As a result, we obtain drastic improvements in noise tolerance, allowing the system to cope with more than 10 times higher noise levels.

  17. Model learning for robot control: a survey.

    PubMed

    Nguyen-Tuong, Duy; Peters, Jan

    2011-11-01

    Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot's own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.

  18. Reinforcement learning output feedback NN control using deterministic learning technique.

    PubMed

    Xu, Bin; Yang, Chenguang; Shi, Zhongke

    2014-03-01

    In this brief, a novel adaptive-critic-based neural network (NN) controller is investigated for nonlinear pure-feedback systems. The controller design is based on the transformed predictor form, and the actor-critic NN control architecture includes two NNs, whereas the critic NN is used to approximate the strategic utility function, and the action NN is employed to minimize both the strategic utility function and the tracking error. A deterministic learning technique has been employed to guarantee that the partial persistent excitation condition of internal states is satisfied during tracking control to a periodic reference orbit. The uniformly ultimate boundedness of closed-loop signals is shown via Lyapunov stability analysis. Simulation results are presented to demonstrate the effectiveness of the proposed control.

  19. Learned States of Preparatory Attentional Control

    ERIC Educational Resources Information Center

    Sali, Anthony W.; Anderson, Brian A.; Yantis, Steven

    2015-01-01

    Individuals regularly experience fluctuations in the ability to perform cognitive operations. Although previous research has focused on predicting cognitive flexibility from persistent individual traits, as well as from spontaneous fluctuations in neural activity, the role of learning in shaping preparatory attentional control remains poorly…

  20. Real-time control of divertor detachment in H-mode with impurity seeding using Langmuir probe feedback in JET-ITER-like wall

    NASA Astrophysics Data System (ADS)

    Guillemaut, C.; Lennholm, M.; Harrison, J.; Carvalho, I.; Valcarcel, D.; Felton, R.; Griph, S.; Hogben, C.; Lucock, R.; Matthews, G. F.; Perez Von Thun, C.; Pitts, R. A.; Wiesen, S.; contributors, JET

    2017-04-01

    Burning plasmas with 500 MW of fusion power on ITER will rely on partially detached divertor operation to keep target heat loads at manageable levels. Such divertor regimes will be maintained by a real-time control system using the seeding of radiative impurities like nitrogen (N), neon or argon as actuator and one or more diagnostic signals as sensors. Recently, real-time control of divertor detachment has been successfully achieved in Type I ELMy H-mode JET-ITER-like wall discharges by using saturation current (I sat) measurements from divertor Langmuir probes as feedback signals to control the level of N seeding. The degree of divertor detachment is calculated in real-time by comparing the outer target peak I sat measurements to the peak I sat value at the roll-over in order to control the opening of the N injection valve. Real-time control of detachment has been achieved in both fixed and swept strike point experiments. The system has been progressively improved and can now automatically drive the divertor conditions from attached through high recycling and roll-over down to a user-defined level of detachment. Such a demonstration is a successful proof of principle in the context of future operation on ITER which will be extensively equipped with divertor target probes.

  1. Connectionist reinforcement learning of robot control skills

    NASA Astrophysics Data System (ADS)

    Araújo, Rui; Nunes, Urbano; de Almeida, A. T.

    1998-07-01

    Many robot manipulator tasks are difficult to model explicitly and it is difficult to design and program automatic control algorithms for them. The development, improvement, and application of learning techniques taking advantage of sensory information would enable the acquisition of new robot skills and avoid some of the difficulties of explicit programming. In this paper we use a reinforcement learning approach for on-line generation of skills for control of robot manipulator systems. Instead of generating skills by explicit programming of a perception to action mapping they are generated by trial and error learning, guided by a performance evaluation feedback function. The resulting system may be seen as an anticipatory system that constructs an internal representation model of itself and of its environment. This enables it to identify its current situation and to generate corresponding appropriate commands to the system in order to perform the required skill. The method was applied to the problem of learning a force control skill in which the tool-tip of a robot manipulator must be moved from a free space situation, to a contact state with a compliant surface and having a constant interaction force.

  2. Optimal chaos control through reinforcement learning.

    PubMed

    Gadaleta, Sabino; Dangelmayr, Gerhard

    1999-09-01

    A general purpose chaos control algorithm based on reinforcement learning is introduced and applied to the stabilization of unstable periodic orbits in various chaotic systems and to the targeting problem. The algorithm does not require any information about the dynamical system nor about the location of periodic orbits. Numerical tests demonstrate good and fast performance under noisy and nonstationary conditions. (c) 1999 American Institute of Physics.

  3. Safe Exploration Algorithms for Reinforcement Learning Controllers.

    PubMed

    Mannucci, Tommaso; van Kampen, Erik-Jan; de Visser, Cornelis; Chu, Qiping

    2017-02-06

    Self-learning approaches, such as reinforcement learning, offer new possibilities for autonomous control of uncertain or time-varying systems. However, exploring an unknown environment under limited prediction capabilities is a challenge for a learning agent. If the environment is dangerous, free exploration can result in physical damage or in an otherwise unacceptable behavior. With respect to existing methods, the main contribution of this paper is the definition of a new approach that does not require global safety functions, nor specific formulations of the dynamics or of the environment, but relies on interval estimation of the dynamics of the agent during the exploration phase, assuming a limited capability of the agent to perceive the presence of incoming fatal states. Two algorithms are presented with this approach. The first is the Safety Handling Exploration with Risk Perception Algorithm (SHERPA), which provides safety by individuating temporary safety functions, called backups. SHERPA is shown in a simulated, simplified quadrotor task, for which dangerous states are avoided. The second algorithm, denominated OptiSHERPA, can safely handle more dynamically complex systems for which SHERPA is not sufficient through the use of safety metrics. An application of OptiSHERPA is simulated on an aircraft altitude control task.

  4. The Capacity Building programmes of GITEWS - visions, goals, lessons learned, and re-iterated needs and demands

    NASA Astrophysics Data System (ADS)

    Schlurmann, T.; Siebert, M.

    2011-02-01

    hazards is still pending. Local authorities and researchers in tentative affected regions are now trained and enabled to disseminate and apply their knowledge and planning experience to other coastal regions in the area to help facilitating and multiplying effective disaster management plans and strategies. Yet, the Capacity Building framework within GITEWS also elucidated gaps in the early warning chain so that updated and to some extent re-iterated needs and demands in Capacity Building programs in any future research or development cooperation project are presented and discussed.

  5. Tunnel Ventilation Control Using Reinforcement Learning Methodology

    NASA Astrophysics Data System (ADS)

    Chu, Baeksuk; Kim, Dongnam; Hong, Daehie; Park, Jooyoung; Chung, Jin Taek; Kim, Tae-Hyung

    The main purpose of tunnel ventilation system is to maintain CO pollutant concentration and VI (visibility index) under an adequate level to provide drivers with comfortable and safe driving environment. Moreover, it is necessary to minimize power consumption used to operate ventilation system. To achieve the objectives, the control algorithm used in this research is reinforcement learning (RL) method. RL is a goal-directed learning of a mapping from situations to actions without relying on exemplary supervision or complete models of the environment. The goal of RL is to maximize a reward which is an evaluative feedback from the environment. In the process of constructing the reward of the tunnel ventilation system, two objectives listed above are included, that is, maintaining an adequate level of pollutants and minimizing power consumption. RL algorithm based on actor-critic architecture and gradient-following algorithm is adopted to the tunnel ventilation system. The simulations results performed with real data collected from existing tunnel ventilation system and real experimental verification are provided in this paper. It is confirmed that with the suggested controller, the pollutant level inside the tunnel was well maintained under allowable limit and the performance of energy consumption was improved compared to conventional control scheme.

  6. Learning Dynamic Control of Body Roll Orientation

    PubMed Central

    Vimal, Vivekanand Pandey; Lackner, James R.; DiZio, Paul

    2016-01-01

    Our objective was to examine how the control of orientation is learned in a task involving dynamically balancing about an unstable equilibrium point, the gravitational vertical, in the absence of leg reflexes and muscle stiffness. Subjects (n=10) used a joystick to set themselves to the gravitational vertical while seated in a multi-axis rotation system device (MARS) programmed with inverted pendulum dynamics. The MARS is driven by powerful servomotors and can faithfully follow joystick commands up to 2.5 Hz with a 30 ms latency. To make the task extremely difficult, the pendulum constant was set to 600°/sec2. Each subject participated in 5 blocks of 4 trials, with a trial ending after a cumulative 100 s of balancing, excluding reset times when a subject lost control. To characterize performance and learning, we used metrics derived from joystick movements, phase portraits (joystick deflections vs MARS position and MARS velocity vs angular position), and stabilogram diffusion functions. We found that as subjects improved their balancing performance they did so by making fewer destabilizing joystick movements and reducing the number and duration of joystick commands. The control strategy they acquired involved making more persistent short-term joystick movements, waiting longer before making changes to ongoing motion, and only intervening intermittently. PMID:26525709

  7. Space Station Control Moment Gyroscope Lessons Learned

    NASA Technical Reports Server (NTRS)

    Gurrisi, Charles; Seidel, Raymond; Dickerson, Scott; Didziulis, Stephen; Frantz, Peter; Ferguson, Kevin

    2010-01-01

    Four 4760 Nms (3510 ft-lbf-s) Double Gimbal Control Moment Gyroscopes (DGCMG) with unlimited gimbal freedom about each axis were adopted by the International Space Station (ISS) Program as the non-propulsive solution for continuous attitude control. These CMGs with a life expectancy of approximately 10 years contain a flywheel spinning at 691 rad/s (6600 rpm) and can produce an output torque of 258 Nm (190 ft-lbf)1. One CMG unexpectedly failed after approximately 1.3 years and one developed anomalous behavior after approximately six years. Both units were returned to earth for failure investigation. This paper describes the Space Station Double Gimbal Control Moment Gyroscope design, on-orbit telemetry signatures and a summary of the results of both failure investigations. The lessons learned from these combined sources have lead to improvements in the design that will provide CMGs with greater reliability to assure the success of the Space Station. These lessons learned and design improvements are not only applicable to CMGs but can be applied to spacecraft mechanisms in general.

  8. Learning dynamic control of body roll orientation.

    PubMed

    Vimal, Vivekanand Pandey; Lackner, James R; DiZio, Paul

    2016-02-01

    Our objective was to examine how the control of orientation is learned in a task involving dynamically balancing about an unstable equilibrium point, the gravitational vertical, in the absence of leg reflexes and muscle stiffness. Subjects (n = 10) used a joystick to set themselves to the gravitational vertical while seated in a multi-axis rotation system (MARS) device programmed with inverted pendulum dynamics. The MARS is driven by powerful servomotors and can faithfully follow joystick commands up to 2.5 Hz with a 30-ms latency. To make the task extremely difficult, the pendulum constant was set to 600°/s(2). Each subject participated in five blocks of four trials, with a trial ending after a cumulative 100 s of balancing, excluding reset times when a subject lost control. To characterize performance and learning, we used metrics derived from joystick movements, phase portraits (joystick deflections vs MARS position and MARS velocity vs angular position), and stabilogram diffusion functions. We found that as subjects improved their balancing performance, they did so by making fewer destabilizing joystick movements and reducing the number and duration of joystick commands. The control strategy they acquired involved making more persistent short-term joystick movements, waiting longer before making changes to ongoing motion, and only intervening intermittently.

  9. A Tale of Two Chambers: Iterative Approaches and Lessons Learned from Life Support Systems Testing in Altitude Chambers

    NASA Technical Reports Server (NTRS)

    Callini, Gianluca

    2016-01-01

    With a brand new fire set ablaze by a serendipitous convergence of events ranging from a science fiction novel and movie ("The Martian"), to ground-breaking recent discoveries of flowing water on its surface, the drive for the journey to Mars seems to be in a higher gear than ever before. We are developing new spacecraft and support systems to take humans to the Red Planet, while scientists on Earth continue using the International Space Station as a laboratory to evaluate the effects of long duration space flight on the human body. Written from the perspective of a facility test director rather than a researcher, and using past and current life support systems tests as examples, this paper seeks to provide an overview on how facility teams approach testing, the kind of information they need to ensure efficient collaborations and successful tests, and how, together with researchers and principal investigators, we can collectively apply what we learn to execute future tests.

  10. WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

    SciTech Connect

    Bai, T; Yan, H; Shi, F; Jia, X; Jiang, Steve B.; Lou, Y; Xu, Q; Mou, X

    2014-06-15

    Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm in a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential

  11. Language learning without control: the role of the PFC.

    PubMed

    Friederici, Angela D; Mueller, Jutta L; Sehm, Bernhard; Ragert, Patrick

    2013-05-01

    Learning takes place throughout lifetime but differs in infants and adults because of the development of the PFC, a brain region responsible for cognitive control. To test this hypothesis, adults were investigated in a language learning paradigm under inhibitory, cathodal transcranial direct current stimulation over PFC. The experiment included a learning session interspersed with test phases and a test-only session. The stimulus material required the learning of grammatical dependencies between two elements in a novel language. In a parallel design, cathodal transcranial direct current stimulation over the left PFC, right PFC, or sham stimulation was applied during the learning session but not during the test-only session. Event-related brain potentials (ERPs) were recorded during both sessions. Whereas no ERP learning effects were observed during the learning session, different ERP learning effects as a function of prior stimulation type were found during the test-only session, although behavioral learning success was equal across conditions. With sham stimulation, the ERP learning effect was reflected in a centro-parietal N400-like negativity indicating lexical processes. Inhibitory stimulation over the left PFC, but not over the right PFC, led to a late positivity similar to that previously observed in prelinguistic infants indicating associative learning. The present data demonstrate that adults can learn with and without cognitive control using different learning mechanisms. In the presence of cognitive control, adult language learning is lexically guided, whereas it appears to be associative in nature when PFC control is downregulated.

  12. Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)

    NASA Technical Reports Server (NTRS)

    Niewoehner, Kevin R.; Carter, John (Technical Monitor)

    2001-01-01

    The research accomplishments for the cooperative agreement 'Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)' include the following: (1) previous IFC program data collection and analysis; (2) IFC program support site (configured IFC systems support network, configured Tornado/VxWorks OS development system, made Configuration and Documentation Management Systems Internet accessible); (3) Airborne Research Test Systems (ARTS) II Hardware (developed hardware requirements specification, developing environmental testing requirements, hardware design, and hardware design development); (4) ARTS II software development laboratory unit (procurement of lab style hardware, configured lab style hardware, and designed interface module equivalent to ARTS II faceplate); (5) program support documentation (developed software development plan, configuration management plan, and software verification and validation plan); (6) LWR algorithm analysis (performed timing and profiling on algorithm); (7) pre-trained neural network analysis; (8) Dynamic Cell Structures (DCS) Neural Network Analysis (performing timing and profiling on algorithm); and (9) conducted technical interchange and quarterly meetings to define IFC research goals.

  13. US ITER Moving Forward

    ScienceCinema

    US ITER / ORNL

    2016-07-12

    US ITER Project Manager Ned Sauthoff, joined by Wayne Reiersen, Team Leader Magnet Systems, and Jan Berry, Team Leader Tokamak Cooling System, discuss the U.S.'s role in the ITER international collaboration.

  14. Exploring Learner Autonomy: Language Learning Locus of Control in Multilinguals

    ERIC Educational Resources Information Center

    Peek, Ron

    2016-01-01

    By using data from an online language learning beliefs survey (n?=?841), defining language learning experience in terms of participants' multilingualism, and using a domain-specific language learning locus of control (LLLOC) instrument, this article examines whether more experienced language learners can also be seen as more autonomous language…

  15. Iter and Ornl

    NASA Astrophysics Data System (ADS)

    Uckan, N. A.; Milora, S. L.

    2004-11-01

    ITER (means ``the way''), a tokamak burning plasma experiment, is the next step device toward making fusion energy a reality. The programmatic objective of ITER is to demonstrate the scientific and technological feasibility of fusion energy for peaceful purposes. ITER began in 1985 as collaboration between the Russian Federation (former Soviet Union), the USA, European Union, and Japan. ITER conceptual and engineering design activities led to a detailed design in 2001. The USA opted out of the project between 1999-2003, but rejoined in 2004 for site selection and construction negotiations. China and Korea joined the project in 2003. Negotiations are continuing and a decision on the site for ITER construction [France versus Japan] is pending. The ITER international undertaking is an unprecedented scale and the six ITER parties represent 40% of the world population. By 2018, ITER will produce a fusion power of 500 million Watts for time periods up to an hour with one-tenth of the power needed to sustain it. Steady state operation is also possible at lower power levels with higher fraction of circulated power. The ITER parties invested about $1 billion into the research and development (R) and related fusion experiments to establish the ITER's feasibility. ORNL has been a key player in the ITER project and contributed to its physics and engineering design and related R since its inception. Recently, the U.S. DOE selected the PPPL/ORNL partnership to lead the U.S. project office for ITER.

  16. Adaptive Performance Seeking Control Using Fuzzy Model Reference Learning Control and Positive Gradient Control

    NASA Technical Reports Server (NTRS)

    Kopasakis, George

    1997-01-01

    Performance Seeking Control attempts to find the operating condition that will generate optimal performance and control the plant at that operating condition. In this paper a nonlinear multivariable Adaptive Performance Seeking Control (APSC) methodology will be developed and it will be demonstrated on a nonlinear system. The APSC is comprised of the Positive Gradient Control (PGC) and the Fuzzy Model Reference Learning Control (FMRLC). The PGC computes the positive gradients of the desired performance function with respect to the control inputs in order to drive the plant set points to the operating point that will produce optimal performance. The PGC approach will be derived in this paper. The feedback control of the plant is performed by the FMRLC. For the FMRLC, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for the effective tuning of the FMRLC controller.

  17. Active controllers and the time duration to learn a task

    NASA Technical Reports Server (NTRS)

    Repperger, D. W.; Goodyear, C.

    1986-01-01

    An active controller was used to help train naive subjects involved in a compensatory tracking task. The controller is called active in this context because it moves the subject's hand in a direction to improve tracking. It is of interest here to question whether the active controller helps the subject to learn a task more rapidly than the passive controller. Six subjects, inexperienced to compensatory tracking, were run to asymptote root mean square error tracking levels with an active controller or a passive controller. The time required to learn the task was defined several different ways. The results of the different measures of learning were examined across pools of subjects and across controllers using statistical tests. The comparison between the active controller and the passive controller as to their ability to accelerate the learning process as well as reduce levels of asymptotic tracking error is reported here.

  18. ITER safety challenges and opportunities

    SciTech Connect

    Piet, S.J.

    1991-01-01

    Results of the Conceptual Design Activity (CDA) for the International Thermonuclear Experimental Reactor (ITER) suggest challenges and opportunities. ITER is capable of meeting anticipated regulatory dose limits,'' but proof is difficult because of large radioactive inventories needing stringent radioactivity confinement. We need much research and development (R D) and design analysis to establish that ITER meets regulatory requirements. We have a further opportunity to do more to prove more of fusion's potential safety and environmental advantages and maximize the amount of ITER technology on the path toward fusion power plants. To fulfill these tasks, we need to overcome three programmatic challenges and three technical challenges. The first programmatic challenge is to fund a comprehensive safety and environmental ITER R D plan. Second is to strengthen safety and environment work and personnel in the international team. Third is to establish an external consultant group to advise the ITER Joint Team on designing ITER to meet safety requirements for siting by any of the Parties. The first of the three key technical challenges is plasma engineering -- burn control, plasma shutdown, disruptions, tritium burn fraction, and steady state operation. The second is the divertor, including tritium inventory, activation hazards, chemical reactions, and coolant disturbances. The third technical challenge is optimization of design requirements considering safety risk, technical risk, and cost. Some design requirements are now too strict; some are too lax. Fuel cycle design requirements are presently too strict, mandating inappropriate T separation from H and D. Heat sink requirements are presently too lax; they should be strengthened to ensure that maximum loss of coolant accident temperatures drop.

  19. An Iterative Solution to the Nonlinear Time-Discrete TEM Model - The Occurrence of Chaos and a Control Theoretic Algorithmic Approach

    NASA Astrophysics Data System (ADS)

    Pickl, S.

    2002-09-01

    This paper is concerned with a mathematical derivation of the nonlinear time-discrete Technology-Emissions Means (TEM-) model. A detailed introduction to the dynamics modelling a Joint Implementation Program concerning Kyoto Protocol is given at the end of the paper. As the nonlinear time-discrete dynamics tends to chaotic behaviour, the necessary introduction of control parameters in the dynamics of the TEM model leads to new results in the field of time-discrete control systems. Furthermore the numerical results give new insights into a Joint-Implementation Program and herewith, they may improve this important economic tool. The iterative solution presented at the end might be a useful orientation to anticipate and support Kyoto Process.

  20. Microtearing instability in ITER*

    NASA Astrophysics Data System (ADS)

    Wong, King-Lap; Mikkelsen, David; Budny, Robert; Breslau, Joshua

    2010-11-01

    Microtearing modes are found to be unstable in some regions of a simulated ITER H-mode plasma [1] with the GS2 code [2]. Modes with kρs>1 are in the interior (r/a˜0.65-0.85) while longer wavelength modes are in the pedestal region. This instability may keep the pedestal within the peeling-ballooning stability boundary [3]. Microtearing modes can produce stochastic magnetic field similar to RMP coils; they may have similar effects on ELMs by increasing the pedestal width. The possibility of using this technique for ELM mitigation in ITER is explored. We propose to use a deuterium gas jet to control the microtearing instability and the Chirikov parameter at the edge. Preliminary evaluation of its effectiveness will be presented and the limitations of the GS2 code will be discussed based on our understanding from NSTX [4]. *This work is supported by USDoE contract DE-AC02-09CH11466. [4pt] [1] R. V. Budny, Nucl. Fusion (2009)[0pt] [2] W. Dorland et al., Phys. Rev. Lett. (2000).[0pt] [3] P. B. Snyder et al.,Nucl. Fusion (2009).[0pt] [4] K. L. Wong et al., Phys. Rev. Lett. (2007).

  1. A reinforcement learning-based architecture for fuzzy logic control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1992-01-01

    This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

  2. Communal learning within a distributed robotic control system

    NASA Astrophysics Data System (ADS)

    Digney, Bruce L.

    2001-09-01

    It is accepted that the ability to learn and adapt is key to prosperity and survival in both individuals and societies. The same is true of populations of robots. Those robots within a population that are able to learn will outperform, survive longer and perhaps exploit their non-learning co- workers. This paper describes the ongoing results of Communal Learning in the Cognitive Colonies Project (CMU/Robotics and DRES), funded jointly by DARPA ITO- Software for Distributed Robotics and DRDC-DRES. Discussed will be how communal learning fits into the free market architecture for distributed control. Techniques for representing experiences, learned behaviors, maps and computational resources as commodities within the market economy will be presented. Once in a commodity structure, the cycle of speculate, act, receive profits or sustain losses and then learn of the market economy. This allows successful control strategies to emerge and the individuals who discovered them to become established as successful. This paper will discuss: learning to predict costs and make better deals, learning transition confidences, learning causes of death, learning with robot sacrifice and learning model parameters.

  3. The real mission of ITER

    SciTech Connect

    Wurden, G A

    2009-01-01

    For future machines, the plasma stored energy is going up by factors of 20-40x, and plasma currents by 2-3x, while the surface to volume ratio is at the same time decreasing. Therefore the disruption forces, even for constant B, (which scale like IxB), and associated possible localized heating on machine components, are more severe. Notably, Tore Supra has demonstrated removal of more than 1 GJ of input energy, over nearly a 400 second period. However, the instantaneous stored energy in the Tore Supra system (which is most directly related to the potential for disruption damage) is quite small compared to other large tokamaks. The goal of ITER is routinely described as studying DT burning plasmas with a Q {approx} 10. In reality, ITER has a much more important first order mission. In fact, if it fails at this mission, the consequences are that ITER will never get to the eventual stated purpose of studying a burning plasma. The real mission of ITER is to study (and demonstrate successfully) plasma control with {approx}10-17 MA toroidal currents and {approx}100-400 MJ plasma stored energy levels in long-pulse scenarios. Before DT operation is ever given a go-ahead in ITER, the reality is that ITER must demonstrate routine and reliable control of high energy hydrogen (and deuterium) plasmas. The difficulty is that ITER must simultaneously deal with several technical problems: (1) heat removal at the plasma/wall interface, (2) protection of the wall components from off-normal events, and (3) generation of dust/redeposition of first wall materials. All previous tokamaks have encountered hundred's of major disruptions in the course of their operation. The consequences of a few MA of runaway electrons (at 20-50 MeV) being generated in ITER, and then being lost to the walls are simply catastrophic. They will not be deposited globally, but will drift out (up, down, whatever, depending on control system), and impact internal structures, unless 'ameliorated'. Basically, this

  4. Efficient model learning methods for actor-critic control.

    PubMed

    Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

    2012-06-01

    We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

  5. Energetic ions in ITER plasmas

    SciTech Connect

    Pinches, S. D.; Chapman, I. T.; Sharapov, S. E.; Lauber, Ph. W.; Oliver, H. J. C.; Shinohara, K.; Tani, K.

    2015-02-15

    This paper discusses the behaviour and consequences of the expected populations of energetic ions in ITER plasmas. It begins with a careful analytic and numerical consideration of the stability of Alfvén Eigenmodes in the ITER 15 MA baseline scenario. The stability threshold is determined by balancing the energetic ion drive against the dominant damping mechanisms and it is found that only in the outer half of the plasma (r/a>0.5) can the fast ions overcome the thermal ion Landau damping. This is in spite of the reduced numbers of alpha-particles and beam ions in this region but means that any Alfvén Eigenmode-induced redistribution is not expected to influence the fusion burn process. The influence of energetic ions upon the main global MHD phenomena expected in ITER's primary operating scenarios, including sawteeth, neoclassical tearing modes and Resistive Wall Modes, is also reviewed. Fast ion losses due to the non-axisymmetric fields arising from the finite number of toroidal field coils, the inclusion of ferromagnetic inserts, the presence of test blanket modules containing ferromagnetic material, and the fields created by the Edge Localised Mode (ELM) control coils in ITER are discussed. The greatest losses and associated heat loads onto the plasma facing components arise due to the use of the ELM control coils and come from neutral beam ions that are ionised in the plasma edge.

  6. Performances of the fractal iterative method with an internal model control law on the ESO end-to-end ELT adaptive optics simulator

    NASA Astrophysics Data System (ADS)

    Béchet, C.; Le Louarn, M.; Tallon, M.; Thiébaut, É.

    2008-07-01

    Adaptive Optics systems under study for the Extremely Large Telescopes gave rise to a new generation of algorithms for both wavefront reconstruction and the control law. In the first place, the large number of controlled actuators impose the use of computationally efficient methods. Secondly, the performance criterion is no longer solely based on nulling residual measurements. Priors on turbulence must be inserted. In order to satisfy these two requirements, we suggested to associate the Fractal Iterative Method for the estimation step with an Internal Model Control. This combination has now been tested on an end-to-end adaptive optics numerical simulator at ESO, named Octopus. Results are presented here and performance of our method is compared to the classical Matrix-Vector Multiplication combined with a pure integrator. In the light of a theoretical analysis of our control algorithm, we investigate the influence of several errors contributions on our simulations. The reconstruction error varies with the signal-to-noise ratio but is limited by the use of priors. The ratio between the system loop delay and the wavefront coherence time also impacts on the reachable Strehl ratio. Whereas no instabilities are observed, correction quality is obviously affected at low flux, when subapertures extinctions are frequent. Last but not least, the simulations have demonstrated the robustness of the method with respect to sensor modeling errors and actuators misalignments.

  7. Towards autonomous neuroprosthetic control using Hebbian reinforcement learning

    NASA Astrophysics Data System (ADS)

    Mahmoudi, Babak; Pohlmeyer, Eric A.; Prins, Noeline W.; Geng, Shijia; Sanchez, Justin C.

    2013-12-01

    Objective. Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. Approach. Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. Main results. The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. Significance. By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

  8. Reinforcement Learning for the Adaptive Control of Perception and Action

    DTIC Science & Technology

    1992-02-01

    This dissertation applies reinforcement learning to the adaptive control of active sensory-motor systems. Active sensory-motor systems, in addition...distinct states in the external world. This phenomenon, called perceptual aliasing, is shown to destabilize existing reinforcement learning algorithms

  9. Short-Term Memory, Executive Control, and Children's Route Learning

    ERIC Educational Resources Information Center

    Purser, Harry R. M.; Farran, Emily K.; Courbois, Yannick; Lemahieu, Axelle; Mellier, Daniel; Sockeel, Pascal; Blades, Mark

    2012-01-01

    The aim of this study was to investigate route-learning ability in 67 children aged 5 to 11 years and to relate route-learning performance to the components of Baddeley's model of working memory. Children carried out tasks that included measures of verbal and visuospatial short-term memory and executive control and also measures of verbal and…

  10. Learned Helplessness: A Theory for the Age of Personal Control.

    ERIC Educational Resources Information Center

    Peterson, Christopher; And Others

    Experiences with uncontrollable events may lead to the expectation that future events will elude control, resulting in disruptions in motivation, emotion, and learning. This text explores this phenomenon, termed learned helplessness, tracking it from its discovery to its entrenchment in the psychological canon. The volume summarizes and integrates…

  11. Fuzzy self-learning control for magnetic servo system

    NASA Technical Reports Server (NTRS)

    Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

    1994-01-01

    It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

  12. Iteration, Not Induction

    ERIC Educational Resources Information Center

    Dobbs, David E.

    2009-01-01

    The main purpose of this note is to present and justify proof via iteration as an intuitive, creative and empowering method that is often available and preferable as an alternative to proofs via either mathematical induction or the well-ordering principle. The method of iteration depends only on the fact that any strictly decreasing sequence of…

  13. Adaptive vibration suppression system: an iterative control law for a piezoelectric actuator shunted by a negative capacitor.

    PubMed

    Kodejska, Milos; Mokry, Pavel; Linhart, Vaclav; Vaclavik, Jan; Sluka, Tomas

    2012-12-01

    An adaptive system for the suppression of vibration transmission using a single piezoelectric actuator shunted by a negative capacitance circuit is presented. It is known that by using a negative-capacitance shunt, the spring constant of a piezoelectric actuator can be controlled to extreme values of zero or infinity. Because the value of spring constant controls a force transmitted through an elastic element, it is possible to achieve a reduction of transmissibility of vibrations through the use of a piezoelectric actuator by reducing its effective spring constant. Narrow frequency range and broad frequency range vibration isolation systems are analyzed, modeled, and experimentally investigated. The problem of high sensitivity of the vibration control system to varying operational conditions is resolved by applying an adaptive control to the circuit parameters of the negative capacitor. A control law that is based on the estimation of the value of the effective spring constant of a shunted piezoelectric actuator is presented. An adaptive system which achieves a self-adjustment of the negative capacitor parameters is presented. It is shown that such an arrangement allows the design of a simple electronic system which offers a great vibration isolation efficiency under variable vibration conditions.

  14. Learning, attentional control and action video games

    PubMed Central

    Green, C.S.; Bavelier, D.

    2012-01-01

    While humans have an incredible capacity to acquire new skills and alter their behavior as a result of experience, enhancements in performance are typically narrowly restricted to the parameters of the training environment, with little evidence of generalization to different, even seemingly highly related, tasks. Such specificity is a major obstacle for the development of many real-world training or rehabilitation paradigms, which necessarily seek to promote more general learning. In contrast to these typical findings, research over the past decade has shown that training on ‘action video games’ produces learning that transfers well beyond the training task. This has led to substantial interest among those interested in rehabilitation, for instance, after stroke or to treat amblyopia, or training for various precision-demanding jobs, for instance, endoscopic surgery or piloting unmanned aerial drones. Although the predominant focus of the field has been on outlining the breadth of possible action-game-related enhancements, recent work has concentrated on uncovering the mechanisms that underlie these changes, an important first step towards the goal of designing and using video games for more definite purposes. Game playing may not convey an immediate advantage on new tasks (increased performance from the very first trial), but rather the true effect of action video game playing may be to enhance the ability to learn new tasks. Such a mechanism may serve as a signature of training regimens that are likely to produce transfer of learning. PMID:22440805

  15. I-mode for ITER?

    NASA Astrophysics Data System (ADS)

    Whyte, D. G.; Marmar, E.; Hubbard, A.; Hughes, J.; Dominguez, A.; Greenwald, M.

    2011-10-01

    I-mode is a recently explored confinement regime that features a temperature pedestal and H-mode energy confinement, yet with L-mode particle confinement and no density pedestal nor large ELMs. Experiments on Alcator C-Mod and ASDEX-Upgrade show this leads to a stationary collisionless pedestal that inherently does not require ELMs for core impurity and particle control, possibly making I-mode an attractive operating regime for ITER where ELM heat pulses are expected to surpass material limits. We speculate as to how I-mode could be obtained, maintained and exploited for the ITER burning plasma physics mission. Issues examined include I-mode topology and power threshold requirements, pedestal formation, density control, avoiding H-mode, and the response of I-mode to alpha self-heating. Key uncertainties requiring further investigation are identified. Supported by the US DOE Cooperative Agreement DE-FC02-99ER54512.

  16. Learner Control in Computer Assisted Learning.

    ERIC Educational Resources Information Center

    Holmes, N.; And Others

    1985-01-01

    An investigation of how secondary students coped when taught binary arithmetic through a computer assisted instruction program used four treatment groups: learner control, learner control with advice; random program control, and adaptive program control. The random group performed less well, but no differences were found between learner and…

  17. Motor skill learning, retention, and control deficits in Parkinson's disease.

    PubMed

    Pendt, Lisa Katharina; Reuter, Iris; Müller, Hermann

    2011-01-01

    Parkinson's disease, which affects the basal ganglia, is known to lead to various impairments of motor control. Since the basal ganglia have also been shown to be involved in learning processes, motor learning has frequently been investigated in this group of patients. However, results are still inconsistent, mainly due to skill levels and time scales of testing. To bridge across the time scale problem, the present study examined de novo skill learning over a long series of practice sessions that comprised early and late learning stages as well as retention. 19 non-demented, medicated, mild to moderate patients with Parkinson's disease and 19 healthy age and gender matched participants practiced a novel throwing task over five days in a virtual environment where timing of release was a critical element. Six patients and seven control participants came to an additional long-term retention testing after seven to nine months. Changes in task performance were analyzed by a method that differentiates between three components of motor learning prominent in different stages of learning: Tolerance, Noise and Covariation. In addition, kinematic analysis related the influence of skill levels as affected by the specific motor control deficits in Parkinson patients to the process of learning. As a result, patients showed similar learning in early and late stages compared to the control subjects. Differences occurred in short-term retention tests; patients' performance constantly decreased after breaks arising from poorer release timing. However, patients were able to overcome the initial timing problems within the course of each practice session and could further improve their throwing performance. Thus, results demonstrate the intact ability to learn a novel motor skill in non-demented, medicated patients with Parkinson's disease and indicate confounding effects of motor control deficits on retention performance.

  18. The ITER design

    NASA Astrophysics Data System (ADS)

    Aymar, R.; Barabaschi, P.; Shimomura, Y.

    2002-05-01

    In 1998, after six years of joint work originally foreseen under the ITER engineering design activities (EDA) agreement, a design for ITER had been developed fulfilling all objectives and the cost target adopted by the ITER parties in 1992 at the start of the EDA. While accepting this design, the ITER parties recognized the possibility that they might be unable, for financial reasons, to proceed to the construction of the then foreseen device. The focus of effort in the ITER EDA since 1998 has been the development of a new design to meet revised technical objectives and a cost reduction target of about 50% of the previously accepted cost estimate. The rationale for the choice of parameters of the design has been based largely on system analysis drawing on the design solutions already developed and using the latest physics results and outputs from technology R&D projects. In so doing the joint central team and home teams converge towards a new design which will allow the exploration of a range of burning plasma conditions. The new ITER design, whilst having reduced technical objectives from its predecessor, will nonetheless meet the programmatic objective of providing an integrated demonstration of the scientific and technological feasibility of fusion energy. Background, design features, performance, safety features, and R&D and future perspectives of the ITER design are discussed.

  19. Self-controlled practice benefits motor learning in older adults.

    PubMed

    Lessa, Helena Thofehrn; Chiviacowsky, Suzete

    2015-04-01

    Providing learners with the chance to choose over certain aspects of practice has been consistently shown to facilitate the acquisition of motor skills in several populations. However, studies investigating the effects of providing autonomy support during the learning process of older adults remain scarce. The objective of the present study was to investigate the effects of self-controlled amount of practice on the learning of a sequential motor task in older adults. Participants in the self-control group were able to choose when to stop practicing a speed cup stacking task, while the number of practice trials for a yoked group was pre-determined, mirroring the self-control group. The opportunity to choose when stop practicing facilitated motor performance and learning compared to the yoked condition. The findings suggest that letting older adult learners choose the amount of practice, supporting their autonomy needs, has a positive influence on motor learning.

  20. Learning and tuning fuzzy logic controllers through reinforcements

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap

    1992-01-01

    This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  1. Learning and tuning fuzzy logic controllers through reinforcements

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap

    1992-01-01

    A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  2. Infection control in learning disability services.

    PubMed

    Huyton, Rita

    Services for people with a learning disability are provided by many sectors of the health and social care economy. These include: social services, health services, voluntary organisations, charities, private care agencies and family carers. Care interventions can take place in a variety of settings, from the client's own home to day care, respite care, educational establishments, workshops, social clubs, luncheon clubs, shared housing and the acute services.

  3. Facts and fiction of learning systems. [decision making intelligent control

    NASA Technical Reports Server (NTRS)

    Saridis, G. N.

    1975-01-01

    The methodology that will provide the updated precision for the hardware control and the advanced decision making and planning in the software control is called learning systems and intelligent control. It was developed theoretically as an alternative for the nonsystematic heuristic approaches of artificial intelligence experiments and the inflexible formulation of modern optimal control methods. Its basic concepts are discussed and some feasibility studies of some practical applications are presented.

  4. Novel reinforcement learning approach for difficult control problems

    NASA Astrophysics Data System (ADS)

    Becus, Georges A.; Thompson, Edward A.

    1997-09-01

    We review work conducted over the past several years and aimed at developing reinforcement learning architectures for solving difficult control problems and based on and inspired by associative control process (ACP) networks. We briefly review ACP networks able to reproduce many classical instrumental conditioning test results observed in animal research and to engage in real-time, closed-loop, goal-seeking interactions with their environment. Chronologically, our contributions include the ideally interfaced ACP network which is endowed with hierarchical, attention, and failure recognition interface mechanisms which greatly enhanced the capabilities of the original ACP network. When solving the cart-pole problem, it achieves 100 percent reliability and a reduction in training time similar to that of Baird and Klopf's modified ACP network and additionally an order of magnitude reduction in number of failures experienced for successful training. Next we introduced the command and control center/internal drive (Cid) architecture for artificial neural learning systems. It consists of a hierarchy of command and control centers governing motor selection networks. Internal drives, similar hunger, thirst, or reproduction in biological systems, are formed within the controller to facilitate learning. Efficiency, reliability, and adjustability of this architecture were demonstrated on the benchmark cart-pole control problem. A comparison with other artificial learning systems indicates that it learns over 100 times faster than Barto, et al's adaptive search element/adaptive critic element, experiencing less failures by more than an order of magnitude while capable of being fine-tuned by the user, on- line, for improved performance without additional training. Finally we present work in progress on a 'peaks and valleys' scheme which moves away from the one-dimensional learning mechanism currently found in Cid and shows promises in solving even more difficult learning control

  5. Perl Modules for Constructing Iterators

    NASA Technical Reports Server (NTRS)

    Tilmes, Curt

    2009-01-01

    The Iterator Perl Module provides a general-purpose framework for constructing iterator objects within Perl, and a standard API for interacting with those objects. Iterators are an object-oriented design pattern where a description of a series of values is used in a constructor. Subsequent queries can request values in that series. These Perl modules build on the standard Iterator framework and provide iterators for some other types of values. Iterator::DateTime constructs iterators from DateTime objects or Date::Parse descriptions and ICal/RFC 2445 style re-currence descriptions. It supports a variety of input parameters, including a start to the sequence, an end to the sequence, an Ical/RFC 2445 recurrence describing the frequency of the values in the series, and a format description that can refine the presentation manner of the DateTime. Iterator::String constructs iterators from string representations. This module is useful in contexts where the API consists of supplying a string and getting back an iterator where the specific iteration desired is opaque to the caller. It is of particular value to the Iterator::Hash module which provides nested iterations. Iterator::Hash constructs iterators from Perl hashes that can include multiple iterators. The constructed iterators will return all the permutations of the iterations of the hash by nested iteration of embedded iterators. A hash simply includes a set of keys mapped to values. It is a very common data structure used throughout Perl programming. The Iterator:: Hash module allows a hash to include strings defining iterators (parsed and dispatched with Iterator::String) that are used to construct an overall series of hash values.

  6. ITER Cryoplant Infrastructures

    NASA Astrophysics Data System (ADS)

    Fauve, E.; Monneret, E.; Voigt, T.; Vincent, G.; Forgeas, A.; Simon, M.

    2017-02-01

    The ITER Tokamak requires an average 75 kW of refrigeration power at 4.5 K and 600 kW of refrigeration Power at 80 K to maintain the nominal operation condition of the ITER thermal shields, superconducting magnets and cryopumps. This is produced by the ITER Cryoplant, a complex cluster of refrigeration systems including in particular three identical Liquid Helium Plants and two identical Liquid Nitrogen Plants. Beyond the equipment directly part of the Cryoplant, colossal infrastructures are required. These infrastructures account for a large part of the Cryoplants lay-out, budget and engineering efforts. It is ITER Organization responsibility to ensure that all infrastructures are adequately sized and designed to interface with the Cryoplant. This proceeding presents the overall architecture of the cryoplant. It provides order of magnitude related to the cryoplant building and utilities: electricity, cooling water, heating, ventilation and air conditioning (HVAC).

  7. Near-complete elimination of mutant mtDNA by iterative or dynamic dose-controlled treatment with mtZFNs

    PubMed Central

    Gammage, Payam A.; Gaude, Edoardo; Van Haute, Lindsey; Rebelo-Guiomar, Pedro; Jackson, Christopher B.; Rorbach, Joanna; Pekalski, Marcin L.; Robinson, Alan J.; Charpentier, Marine; Concordet, Jean-Paul; Frezza, Christian; Minczuk, Michal

    2016-01-01

    Mitochondrial diseases are frequently associated with mutations in mitochondrial DNA (mtDNA). In most cases, mutant and wild-type mtDNAs coexist, resulting in heteroplasmy. The selective elimination of mutant mtDNA, and consequent enrichment of wild-type mtDNA, can rescue pathological phenotypes in heteroplasmic cells. Use of the mitochondrially targeted zinc finger-nuclease (mtZFN) results in degradation of mutant mtDNA through site-specific DNA cleavage. Here, we describe a substantial enhancement of our previous mtZFN-based approaches to targeting mtDNA, allowing near-complete directional shifts of mtDNA heteroplasmy, either by iterative treatment or through finely controlled expression of mtZFN, which limits off-target catalysis and undesired mtDNA copy number depletion. To demonstrate the utility of this improved approach, we generated an isogenic distribution of heteroplasmic cells with variable mtDNA mutant level from the same parental source without clonal selection. Analysis of these populations demonstrated an altered metabolic signature in cells harbouring decreased levels of mutant m.8993T>G mtDNA, associated with neuropathy, ataxia, and retinitis pigmentosa (NARP). We conclude that mtZFN-based approaches offer means for mtDNA heteroplasmy manipulation in basic research, and may provide a strategy for therapeutic intervention in selected mitochondrial diseases. PMID:27466392

  8. Diagnostics for ITER

    SciTech Connect

    Donne, A. J. H.; Hellermann, M. G. von; Barnsley, R.

    2008-10-22

    After an introduction into the specific challenges in the field of diagnostics for ITER (specifically high level of nuclear radiation, long pulses, high fluxes of particles to plasma facing components, need for reliability and robustness), an overview will be given of the spectroscopic diagnostics foreseen for ITER. The paper will describe both active neutral-beam based diagnostics as well as passive spectroscopic diagnostics operating in the visible, ultra-violet and x-ray spectral regions.

  9. Mode conversion in ITER

    NASA Astrophysics Data System (ADS)

    Jaeger, E. F.; Berry, L. A.; Myra, J. R.

    2006-10-01

    Fast magnetosonic waves in the ion cyclotron range of frequencies (ICRF) can convert to much shorter wavelength modes such as ion Bernstein waves (IBW) and ion cyclotron waves (ICW) [1]. These modes are potentially useful for plasma control through the generation of localized currents and sheared flows. As part of the SciDAC Center for Simulation of Wave-Plasma Interactions project, the AORSA global-wave solver [2] has been ported to the new, dual-core Cray XT-3 (Jaguar) at ORNL where it demonstrates excellent scaling with the number of processors. Preliminary calculations using 4096 processors have allowed the first full-wave simulations of mode conversion in ITER. Mode conversion from the fast wave to the ICW is observed in mixtures of deuterium, tritium and helium3 at 53 MHz. The resulting flow velocity and electric field shear will be calculated. [1] F.W. Perkins, Nucl. Fusion 17, 1197 (1977). [2] E.F. Jaeger, L.A. Berry, J.R. Myra, et al., Phys. Rev. Lett. 90, 195001-1 (2003).

  10. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation

    PubMed Central

    Bauer, Robert; Gharabaghi, Alireza

    2015-01-01

    Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting. PMID:25729347

  11. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation.

    PubMed

    Bauer, Robert; Gharabaghi, Alireza

    2015-01-01

    Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting.

  12. A Robust Cooperated Control Method with Reinforcement Learning and Adaptive H∞ Control

    NASA Astrophysics Data System (ADS)

    Obayashi, Masanao; Uchiyama, Shogo; Kuremoto, Takashi; Kobayashi, Kunikazu

    This study proposes a robust cooperated control method combining reinforcement learning with robust control to control the system. A remarkable characteristic of the reinforcement learning is that it doesn't require model formula, however, it doesn't guarantee the stability of the system. On the other hand, robust control system guarantees stability and robustness, however, it requires model formula. We employ both the actor-critic method which is a kind of reinforcement learning with minimal amount of computation to control continuous valued actions and the traditional robust control, that is, H∞ control. The proposed system was compared method with the conventional control method, that is, the actor-critic only used, through the computer simulation of controlling the angle and the position of a crane system, and the simulation result showed the effectiveness of the proposed method.

  13. A neural fuzzy controller learning by fuzzy error propagation

    NASA Technical Reports Server (NTRS)

    Nauck, Detlef; Kruse, Rudolf

    1992-01-01

    In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

  14. CD process control through machine learning

    NASA Astrophysics Data System (ADS)

    Utzny, Clemens

    2016-10-01

    For the specific requirements of the 14nm and 20nm site applications a new CD map approach was developed at the AMTC. This approach relies on a well established machine learning technique called recursive partitioning. Recursive partitioning is a powerful technique which creates a decision tree by successively testing whether the quantity of interest can be explained by one of the supplied covariates. The test performed is generally a statistical test with a pre-supplied significance level. Once the test indicates significant association between the variable of interest and a covariate a split performed at a threshold value which minimizes the variation within the newly attained groups. This partitioning is recurred until either no significant association can be detected or the resulting sub group size falls below a pre-supplied level.

  15. Neuromorphic learning of continuous-valued mappings from noise-corrupted data. Application to real-time adaptive control

    NASA Technical Reports Server (NTRS)

    Troudet, Terry; Merrill, Walter C.

    1990-01-01

    The ability of feed-forward neural network architectures to learn continuous valued mappings in the presence of noise was demonstrated in relation to parameter identification and real-time adaptive control applications. An error function was introduced to help optimize parameter values such as number of training iterations, observation time, sampling rate, and scaling of the control signal. The learning performance depended essentially on the degree of embodiment of the control law in the training data set and on the degree of uniformity of the probability distribution function of the data that are presented to the net during sequence. When a control law was corrupted by noise, the fluctuations of the training data biased the probability distribution function of the training data sequence. Only if the noise contamination is minimized and the degree of embodiment of the control law is maximized, can a neural net develop a good representation of the mapping and be used as a neurocontroller. A multilayer net was trained with back-error-propagation to control a cart-pole system for linear and nonlinear control laws in the presence of data processing noise and measurement noise. The neurocontroller exhibited noise-filtering properties and was found to operate more smoothly than the teacher in the presence of measurement noise.

  16. Can we (control) Engineer the degree learning process?

    NASA Astrophysics Data System (ADS)

    White, A. S.; Censlive, M.; Neilsen, D.

    2014-07-01

    This paper investigates how control theory could be applied to learning processes in engineering education. The initial point for the analysis is White's Double Loop learning model of human automation control modified for the education process where a set of governing principals is chosen, probably by the course designer. After initial training the student decides unknowingly on a mental map or model. After observing how the real world is behaving, a strategy to achieve the governing variables is chosen and a set of actions chosen. This may not be a conscious operation, it maybe completely instinctive. These actions will cause some consequences but not until a certain time delay. The current model is compared with the work of Hollenbeck on goal setting, Nelson's model of self-regulation and that of Abdulwahed, Nagy and Blanchard at Loughborough who investigated control methods applied to the learning process.

  17. Enhancing Hebbian Learning to Control Brain Oscillatory Activity.

    PubMed

    Soekadar, Surjo R; Witkowski, Matthias; Birbaumer, Niels; Cohen, Leonardo G

    2015-09-01

    Sensorimotor rhythms (SMR, 8-15 Hz) are brain oscillations associated with successful motor performance, imagery, and imitation. Voluntary modulation of SMR can be used to control brain-machine interfaces (BMI) in the absence of any physical movements. The mechanisms underlying acquisition of such skill are unknown. Here, we provide evidence for a causal link between function of the primary motor cortex (M1), active during motor skill learning and retention, and successful acquisition of abstract skills such as control over SMR. Thirty healthy participants were trained on 5 consecutive days to control SMR oscillations. Each participant was randomly assigned to one of 3 groups that received either 20 min of anodal, cathodal, or sham transcranial direct current stimulation (tDCS) over M1. Learning SMR control across training days was superior in the anodal tDCS group relative to the other 2. Cathodal tDCS blocked the beneficial effects of training, as evidenced with sham tDCS. One month later, the newly acquired skill remained superior in the anodal tDCS group. Thus, application of weak electric currents of opposite polarities over M1 differentially modulates learning SMR control, pointing to this primary cortical region as a common substrate for acquisition of physical motor skills and learning to control brain oscillatory activity.

  18. Neural control of dopamine neurotransmission: implications for reinforcement learning.

    PubMed

    Aggarwal, Mayank; Hyland, Brian I; Wickens, Jeffery R

    2012-04-01

    In the past few decades there has been remarkable convergence of machine learning with neurobiological understanding of reinforcement learning mechanisms, exemplified by temporal difference (TD) learning models. The anatomy of the basal ganglia provides a number of potential substrates for instantiation of the TD mechanism. In contrast to the traditional concept of direct and indirect pathway outputs from the striatum, we emphasize that projection neurons of the striatum are branched and individual striatofugal neurons innervate both globus pallidus externa and globus pallidus interna/substantia nigra (GPi/SNr). This suggests that the GPi/SNr has the necessary inputs to operate as the source of a TD signal. We also discuss the mechanism for the timing processes necessary for learning in the TD framework. The TD framework has been particularly successful in analysing electrophysiogical recordings from dopamine (DA) neurons during learning, in terms of reward prediction error. However, present understanding of the neural control of DA release is limited, and hence the neural mechanisms involved are incompletely understood. Inhibition is very conspicuously present among the inputs to the DA neurons, with inhibitory synapses accounting for the majority of synapses on DA neurons. Furthermore, synchronous firing of the DA neuron population requires disinhibition and excitation to occur together in a coordinated manner. We conclude that the inhibitory circuits impinging directly or indirectly on the DA neurons play a central role in the control of DA neuron activity and further investigation of these circuits may provide important insight into the biological mechanisms of reinforcement learning.

  19. Online Learning ARMA Controllers With Guaranteed Closed-Loop Stability.

    PubMed

    Sahin, Savas; Guzelis, Cuneyt

    2016-11-01

    This paper presents a novel online block adaptive learning algorithm for autoregressive moving average (ARMA) controller design based on the real data measured from the plant. The method employs ARMA input-output models both for the plant and the resulting closed-loop system. In a sliding window, the plant model parameters are identified first offline using a supervised learning algorithm minimizing an ε -insensitive and regularized identification error, which is the window average of the distances between the measured plant output and the model output for the input provided by the controller. The optimal controller parameters are then determined again offline for another sliding window as the solution to a constrained optimization problem, where the cost is the ε -insensitive and regularized output tracking error and the constraints that are linear inequalities of the controller parameters are imposed for ensuring the closed-loop system to be Schur stable. Not only the identification phase but also the controller design phase uses the input-output samples measured from the plant during online learning. In the developed online controller design method, the controller parameters can always be kept in a parameter region providing Schur stability for the closed-loop system. The ε -insensitiveness provides robustness against disturbances, so does the regularization better generalization performance in the identification and the control. The method is tested on benchmark plants, including the inverted pendulum and dc motor models. The method is also tested on an emulated and also a real dc motor by online block adaptive learning ARMA controllers, in particular, Proportional-Integral-Derivative controllers.

  20. Learning Switching Control: A Tank Level-Control Exercise

    ERIC Educational Resources Information Center

    Pasamontes, M.; Alvarez, J. D.; Guzman, J. L.; Berenguel, M.

    2012-01-01

    A key topic in multicontroller strategies is the mechanism for switching between controllers, depending on the current operating point. The objective of the switching mechanism is to keep the control action coherent. To help students understand the switching strategy involved in multicontroller schema and the relationship between the system…

  1. Robust iterative methods

    SciTech Connect

    Saadd, Y.

    1994-12-31

    In spite of the tremendous progress achieved in recent years in the general area of iterative solution techniques, there are still a few obstacles to the acceptance of iterative methods in a number of applications. These applications give rise to very indefinite or highly ill-conditioned non Hermitian matrices. Trying to solve these systems with the simple-minded standard preconditioned Krylov subspace methods can be a frustrating experience. With the mathematical and physical models becoming more sophisticated, the typical linear systems which we encounter today are far more difficult to solve than those of just a few years ago. This trend is likely to accentuate. This workshop will discuss (1) these applications and the types of problems that they give rise to; and (2) recent progress in solving these problems with iterative methods. The workshop will end with a hopefully stimulating panel discussion with the speakers.

  2. Active route learning in virtual environments: disentangling movement control from intention, instruction specificity, and navigation control.

    PubMed

    von Stülpnagel, Rul; Steffens, Melanie C

    2013-09-01

    Active navigation research examines how physiological and psychological involvement in navigation benefits spatial learning. However, existing conceptualizations of active navigation comprise separable, distinct factors. This research disentangles the contributions of movement control (i.e., self-contained vs. observed movement) as a central factor from learning intention (Experiment 1), instruction specificity and instruction control (Experiment 2), as well as navigation control (Experiment 3) to spatial learning in virtual environments. We tested the effects of these factors on landmark recognition (landmark knowledge), tour-integration and route navigation (route knowledge). Our findings suggest that movement control leads to robust advantages in landmark knowledge as compared to observed movement. Advantages in route knowledge do not depend on learning intention, but on the need to elaborate spatial information. Whenever the necessary level of elaboration is assured for observed movement, too, the development of route knowledge is not inferior to that for self-contained movement.

  3. Patients with Parkinson's Disease Learn to Control Complex Systems via Procedural as Well as Non-Procedural Learning

    ERIC Educational Resources Information Center

    Osman, Magda; Wilkinson, Leonora; Beigi, Mazda; Castaneda, Cristina Sanchez; Jahanshahi, Marjan

    2008-01-01

    The striatum is considered to mediate some forms of procedural learning. Complex dynamic control (CDC) tasks involve an individual having to make a series of sequential decisions to achieve a specific outcome (e.g. learning to operate and control a car), and they involve procedural learning. The aim of this study was to test the hypothesis that…

  4. Rescheduling with iterative repair

    NASA Technical Reports Server (NTRS)

    Zweben, Monte; Davis, Eugene; Daun, Brian; Deale, Michael

    1992-01-01

    This paper presents a new approach to rescheduling called constraint-based iterative repair. This approach gives our system the ability to satisfy domain constraints, address optimization concerns, minimize perturbation to the original schedule, and produce modified schedules quickly. The system begins with an initial, flawed schedule and then iteratively repairs constraint violations until a conflict-free schedule is produced. In an empirical demonstration, we vary the importance of minimizing perturbation and report how fast the system is able to resolve conflicts in a given time bound. These experiments were performed within the domain of Space Shuttle ground processing.

  5. Rescheduling with iterative repair

    NASA Technical Reports Server (NTRS)

    Zweben, Monte; Davis, Eugene; Daun, Brian; Deale, Michael

    1992-01-01

    This paper presents a new approach to rescheduling called constraint-based iterative repair. This approach gives our system the ability to satisfy domain constraints, address optimization concerns, minimize perturbation to the original schedule, produce modified schedules, quickly, and exhibits 'anytime' behavior. The system begins with an initial, flawed schedule and then iteratively repairs constraint violations until a conflict-free schedule is produced. In an empirical demonstration, we vary the importance of minimizing perturbation and report how fast the system is able to resolve conflicts in a given time bound. We also show the anytime characteristics of the system. These experiments were performed within the domain of Space Shuttle ground processing.

  6. Iterated multidimensional wave conversion

    SciTech Connect

    Brizard, A. J.; Tracy, E. R.; Johnston, D.; Kaufman, A. N.; Richardson, A. S.; Zobin, N.

    2011-12-23

    Mode conversion can occur repeatedly in a two-dimensional cavity (e.g., the poloidal cross section of an axisymmetric tokamak). We report on two novel concepts that allow for a complete and global visualization of the ray evolution under iterated conversions. First, iterated conversion is discussed in terms of ray-induced maps from the two-dimensional conversion surface to itself (which can be visualized in terms of three-dimensional rooms). Second, the two-dimensional conversion surface is shown to possess a symplectic structure derived from Dirac constraints associated with the two dispersion surfaces of the interacting waves.

  7. Combustion Control of Diesel Engine using Feedback Error Learning with Kernel Online Learning Approach

    NASA Astrophysics Data System (ADS)

    Widayaka, Elfady Satya; Ohmori, Hiromitsu

    2016-09-01

    This paper shows how to design Multivariable Model Reference Adaptive Control System (MRACS) for “Tokyo University discrete-time engine model” proposed by Yasuda et al (2014). This controller configuration has the structure of “Feedback error learning (FEL)” and adaptive law is based on kernel method. Simulation results indicate that “kernelized” adaptive controllers can improve the tracking performance, the speed of convergence and the robustness to disturbances.

  8. Beamforming and power control in sensor arrays using reinforcement learning.

    PubMed

    Almeida, Náthalee C; Fernandes, Marcelo A C; Neto, Adrião D D

    2015-03-19

    The use of beamforming and power control, combined or separately, has advantages and disadvantages, depending on the application. The combined use of beamforming and power control has been shown to be highly effective in applications involving the suppression of interference signals from different sources. However, it is necessary to identify efficient methodologies for the combined operation of these two techniques. The most appropriate technique may be obtained by means of the implementation of an intelligent agent capable of making the best selection between beamforming and power control. The present paper proposes an algorithm using reinforcement learning (RL) to determine the optimal combination of beamforming and power control in sensor arrays. The RL algorithm used was Q-learning, employing an ε-greedy policy, and training was performed using the offline method. The simulations showed that RL was effective for implementation of a switching policy involving the different techniques, taking advantage of the positive characteristics of each technique in terms of signal reception.

  9. Language Learning and Control in Monolinguals and Bilinguals

    ERIC Educational Resources Information Center

    Bartolotti, James; Marian, Viorica

    2012-01-01

    Parallel language activation in bilinguals leads to competition between languages. Experience managing this interference may aid novel language learning by improving the ability to suppress competition from known languages. To investigate the effect of bilingualism on the ability to control native-language interference, monolinguals and bilinguals…

  10. Attention control learning in the decision space using state estimation

    NASA Astrophysics Data System (ADS)

    Gharaee, Zahra; Fatehi, Alireza; Mirian, Maryam S.; Nili Ahmadabadi, Majid

    2016-05-01

    The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information.

  11. Multi Car Elevator Control by using Learning Automaton

    NASA Astrophysics Data System (ADS)

    Shiraishi, Kazuaki; Hamagami, Tomoki; Hirata, Hironori

    We study an adaptive control technique for multi car elevators (MCEs) by adopting learning automatons (LAs.) The MCE is a high performance and a near-future elevator system with multi shafts and multi cars. A strong point of the system is that realizing a large carrying capacity in small shaft area. However, since the operation is too complicated, realizing an efficient MCE control is difficult for top-down approaches. For example, “bunching up together" is one of the typical phenomenon in a simple traffic environment like the MCE. Furthermore, an adapting to varying environment in configuration requirement is a serious issue in a real elevator service. In order to resolve these issues, having an autonomous behavior is required to the control system of each car in MCE system, so that the learning automaton, as the solutions for this requirement, is supposed to be appropriate for the simple traffic control. First, we assign a stochastic automaton (SA) to each car control system. Then, each SA varies its stochastic behavior distributions for adapting to environment in which its policy is evaluated with each passenger waiting times. That is LA which learns the environment autonomously. Using the LA based control technique, the MCE operation efficiency is evaluated through simulation experiments. Results show the technique enables reducing waiting times efficiently, and we confirm the system can adapt to the dynamic environment.

  12. An Iterative Learning Control Approach to Improving Fidelity in Internet-Distributed Hardware-in-the-Loop Simulation

    DTIC Science & Technology

    2012-06-15

    converter, transmission, and shift logic. The torque converter model is a static model that takes pump and turbine speeds as inputs and generates...pump and turbine torques according to the equations ( ) ( ) ( ) 2 1 pump pump r r turbine r pump sign ω τ ω κ ω τ α ω τ   = −     = (12...where /r turbine pumpω ω ω= is the speed ratio between turbine and pump speeds, ( )rκ ω is a piecewise function approximating a desired capacity

  13. Human-level control through deep reinforcement learning

    NASA Astrophysics Data System (ADS)

    Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

    2015-02-01

    The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

  14. Human-level control through deep reinforcement learning.

    PubMed

    Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A; Veness, Joel; Bellemare, Marc G; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

    2015-02-26

    The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

  15. Nonlinear Performance Seeking Control using Fuzzy Model Reference Learning Control and the Method of Steepest Descent

    NASA Technical Reports Server (NTRS)

    Kopasakis, George

    1997-01-01

    Performance Seeking Control (PSC) attempts to find and control the process at the operating condition that will generate maximum performance. In this paper a nonlinear multivariable PSC methodology will be developed, utilizing the Fuzzy Model Reference Learning Control (FMRLC) and the method of Steepest Descent or Gradient (SDG). This PSC control methodology employs the SDG method to find the operating condition that will generate maximum performance. This operating condition is in turn passed to the FMRLC controller as a set point for the control of the process. The conventional SDG algorithm is modified in this paper in order for convergence to occur monotonically. For the FMRLC control, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for effective tuning of the FMRLC controller.

  16. Iterative software kernels

    SciTech Connect

    Duff, I.

    1994-12-31

    This workshop focuses on kernels for iterative software packages. Specifically, the three speakers discuss various aspects of sparse BLAS kernels. Their topics are: `Current status of user lever sparse BLAS`; Current status of the sparse BLAS toolkit`; and `Adding matrix-matrix and matrix-matrix-matrix multiply to the sparse BLAS toolkit`.

  17. ITER Fusion Energy

    ScienceCinema

    Dr. Norbert Holtkamp

    2016-07-12

    ITER (in Latin “the way”) is designed to demonstrate the scientific and technological feasibility of fusion energy. Fusion is the process by which two light atomic nuclei combine to form a heavier over one and thus release energy. In the fusion process two isotopes of hydrogen – deuterium and tritium – fuse together to form a helium atom and a neutron. Thus fusion could provide large scale energy production without greenhouse effects; essentially limitless fuel would be available all over the world. The principal goals of ITER are to generate 500 megawatts of fusion power for periods of 300 to 500 seconds with a fusion power multiplication factor, Q, of at least 10. Q ? 10 (input power 50 MW / output power 500 MW). The ITER Organization was officially established in Cadarache, France, on 24 October 2007. The seven members engaged in the project – China, the European Union, India, Japan, Korea, Russia and the United States – represent more than half the world’s population. The costs for ITER are shared by the seven members. The cost for the construction will be approximately 5.5 billion Euros, a similar amount is foreseen for the twenty-year phase of operation and the subsequent decommissioning.

  18. Discrete-Time Nonzero-Sum Games for Multiplayer Using Policy-Iteration-Based Adaptive Dynamic Programming Algorithms.

    PubMed

    Zhang, Huaguang; Jiang, He; Luo, Chaomin; Xiao, Geyang

    2016-10-03

    In this paper, we investigate the nonzero-sum games for a class of discrete-time (DT) nonlinear systems by using a novel policy iteration (PI) adaptive dynamic programming (ADP) method. The main idea of our proposed PI scheme is to utilize the iterative ADP algorithm to obtain the iterative control policies, which not only ensure the system to achieve stability but also minimize the performance index function for each player. This paper integrates game theory, optimal control theory, and reinforcement learning technique to formulate and handle the DT nonzero-sum games for multiplayer. First, we design three actor-critic algorithms, an offline one and two online ones, for the PI scheme. Subsequently, neural networks are employed to implement these algorithms and the corresponding stability analysis is also provided via the Lyapunov theory. Finally, a numerical simulation example is presented to demonstrate the effectiveness of our proposed approach.

  19. Learning and Control Model of the Arm for Loading

    NASA Astrophysics Data System (ADS)

    Kim, Kyoungsik; Kambara, Hiroyuki; Shin, Duk; Koike, Yasuharu

    We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.

  20. A fresh look at electron cyclotron current drive power requirements for stabilization of tearing modes in ITER

    NASA Astrophysics Data System (ADS)

    La Haye, R. J.

    2015-12-01

    ITER is an international project to design and build an experimental fusion reactor based on the "tokamak" concept. ITER relies upon localized electron cyclotron current drive (ECCD) at the rational safety factor q=2 to suppress or stabilize the expected poloidal mode m=2, toroidal mode n=1 neoclassical tearing mode (NTM) islands. Such islands if unmitigated degrade energy confinement, lock to the resistive wall (stop rotating), cause loss of "H-mode" and induce disruption. The International Tokamak Physics Activity (ITPA) on MHD, Disruptions and Magnetic Control joint experiment group MDC-8 on Current Drive Prevention/Stabilization of Neoclassical Tearing Modes started in 2005, after which assessments were made for the requirements for ECCD needed in ITER, particularly that of rf power and alignment on q=2 [1]. Narrow well-aligned rf current parallel to and of order of one percent of the total plasma current is needed to replace the "missing" current in the island O-points and heal or preempt (avoid destabilization by applying ECCD on q=2 in absence of the mode) the island [2-4]. This paper updates the advances in ECCD stabilization on NTMs learned in DIII-D experiments and modeling during the last 5 to 10 years as applies to stabilization by localized ECCD of tearing modes in ITER. This includes the ECCD (inside the q=1 radius) stabilization of the NTM "seeding" instability known as sawteeth (m/n=1/1) [5]. Recent measurements in DIII-D show that the ITER-similar current profile is classically unstable, curvature stabilization must not be neglected, and the small island width stabilization effect from helical ion polarization currents is stronger than was previously thought [6]. The consequences of updated assumptions in ITER modeling of the minimum well-aligned ECCD power needed are all-in-all favorable (and well-within the ITER 24 gyrotron capability) when all effects are included. However, a "wild card" may be broadening of the localized ECCD by the presence of

  1. A fresh look at electron cyclotron current drive power requirements for stabilization of tearing modes in ITER

    SciTech Connect

    La Haye, R. J.

    2015-12-10

    ITER is an international project to design and build an experimental fusion reactor based on the “tokamak” concept. ITER relies upon localized electron cyclotron current drive (ECCD) at the rational safety factor q=2 to suppress or stabilize the expected poloidal mode m=2, toroidal mode n=1 neoclassical tearing mode (NTM) islands. Such islands if unmitigated degrade energy confinement, lock to the resistive wall (stop rotating), cause loss of “H-mode” and induce disruption. The International Tokamak Physics Activity (ITPA) on MHD, Disruptions and Magnetic Control joint experiment group MDC-8 on Current Drive Prevention/Stabilization of Neoclassical Tearing Modes started in 2005, after which assessments were made for the requirements for ECCD needed in ITER, particularly that of rf power and alignment on q=2 [1]. Narrow well-aligned rf current parallel to and of order of one percent of the total plasma current is needed to replace the “missing” current in the island O-points and heal or preempt (avoid destabilization by applying ECCD on q=2 in absence of the mode) the island [2-4]. This paper updates the advances in ECCD stabilization on NTMs learned in DIII-D experiments and modeling during the last 5 to 10 years as applies to stabilization by localized ECCD of tearing modes in ITER. This includes the ECCD (inside the q=1 radius) stabilization of the NTM “seeding” instability known as sawteeth (m/n=1/1) [5]. Recent measurements in DIII-D show that the ITER-similar current profile is classically unstable, curvature stabilization must not be neglected, and the small island width stabilization effect from helical ion polarization currents is stronger than was previously thought [6]. The consequences of updated assumptions in ITER modeling of the minimum well-aligned ECCD power needed are all-in-all favorable (and well-within the ITER 24 gyrotron capability) when all effects are included. However, a “wild card” may be broadening of the localized

  2. Design and Control of Large Collections of Learning Agents

    NASA Technical Reports Server (NTRS)

    Agogino, Adrian

    2001-01-01

    The intelligent control of multiple autonomous agents is an important yet difficult task. Previous methods used to address this problem have proved to be either too brittle, too hard to use, or not scalable to large systems. The 'Collective Intelligence' project at NASA/Ames provides an elegant, machine-learning approach to address these problems. This approach mathematically defines some essential properties that a reward system should have to promote coordinated behavior among reinforcement learners. This work has focused on creating additional key properties and algorithms within the mathematics of the Collective Intelligence framework. One of the additions will allow agents to learn more quickly, in a more coordinated manner. The other will let agents learn with less knowledge of their environment. These additions will allow the framework to be applied more easily, to a much larger domain of multi-agent problems.

  3. The Effectiveness of E-Learning Systems: A Review of the Empirical Literature on Learner Control

    ERIC Educational Resources Information Center

    Sorgenfrei, Christian; Smolnik, Stefan

    2016-01-01

    E-learning systems are considerably changing education and organizational training. With the advancement of online-based learning systems, learner control over the instructional process has emerged as a decisive factor in technology-based forms of learning. However, conceptual work on the role of learner control in e-learning has not advanced…

  4. CORSICA modelling of ITER hybrid operation scenarios

    NASA Astrophysics Data System (ADS)

    Kim, S. H.; Bulmer, R. H.; Campbell, D. J.; Casper, T. A.; LoDestro, L. L.; Meyer, W. H.; Pearlstein, L. D.; Snipes, J. A.

    2016-12-01

    The hybrid operating mode observed in several tokamaks is characterized by further enhancement over the high plasma confinement (H-mode) associated with reduced magneto-hydro-dynamic (MHD) instabilities linked to a stationary flat safety factor (q ) profile in the core region. The proposed ITER hybrid operation is currently aiming at operating for a long burn duration (>1000 s) with a moderate fusion power multiplication factor, Q , of at least 5. This paper presents candidate ITER hybrid operation scenarios developed using a free-boundary transport modelling code, CORSICA, taking all relevant physics and engineering constraints into account. The ITER hybrid operation scenarios have been developed by tailoring the 15 MA baseline ITER inductive H-mode scenario. Accessible operation conditions for ITER hybrid operation and achievable range of plasma parameters have been investigated considering uncertainties on the plasma confinement and transport. ITER operation capability for avoiding the poloidal field coil current, field and force limits has been examined by applying different current ramp rates, flat-top plasma currents and densities, and pre-magnetization of the poloidal field coils. Various combinations of heating and current drive (H&CD) schemes have been applied to study several physics issues, such as the plasma current density profile tailoring, enhancement of the plasma energy confinement and fusion power generation. A parameterized edge pedestal model based on EPED1 added to the CORSICA code has been applied to hybrid operation scenarios. Finally, fully self-consistent free-boundary transport simulations have been performed to provide information on the poloidal field coil voltage demands and to study the controllability with the ITER controllers. Extended from Proc. 24th Int. Conf. on Fusion Energy (San Diego, 2012) IT/P1-13.

  5. Design issues of a reinforcement-based self-learning fuzzy controller for petrochemical process control

    NASA Technical Reports Server (NTRS)

    Yen, John; Wang, Haojin; Daugherity, Walter C.

    1992-01-01

    Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller.

  6. Effects of Locus of Control and Learner-Control on Web-Based Language Learning

    ERIC Educational Resources Information Center

    Chang, Mei-Mei; Ho, Chiung-Mei

    2009-01-01

    The study explored the effects of students' locus of control and types of control over instruction on their self-efficacy and performance in a web-based language learning environment. A web-based interactive instructional program focusing on the comprehension of news articles for English language learners was developed in two versions: learner-…

  7. Cryogenic instrumentation for ITER magnets

    NASA Astrophysics Data System (ADS)

    Poncet, J.-M.; Manzagol, J.; Attard, A.; André, J.; Bizel-Bizellot, L.; Bonnay, P.; Ercolani, E.; Luchier, N.; Girard, A.; Clayton, N.; Devred, A.; Huygen, S.; Journeaux, J.-Y.

    2017-02-01

    Accurate measurements of the helium flowrate and of the temperature of the ITER magnets is of fundamental importance to make sure that the magnets operate under well controlled and reliable conditions, and to allow suitable helium flow distribution in the magnets through the helium piping. Therefore, the temperature and flow rate measurements shall be reliable and accurate. In this paper, we present the thermometric chains as well as the venturi flow meters installed in the ITER magnets and their helium piping. The presented thermometric block design is based on the design developed by CERN for the LHC, which has been further optimized via thermal simulations carried out by CEA. The electronic part of the thermometric chain was entirely developed by the CEA and will be presented in detail: it is based on a lock-in measurement and small signal amplification, and also provides a web interface and software to an industrial PLC. This measuring device provides a reliable, accurate, electromagnetically immune, and fast (up to 100 Hz bandwidth) system for resistive temperature sensors between a few ohms to 100 kΩ. The flowmeters (venturi type) which make up part of the helium mass flow measurement chain have been completely designed, and manufacturing is on-going. The behaviour of the helium gas has been studied in detailed thanks to ANSYS CFX software in order to obtain the same differential pressure for all types of flowmeters. Measurement uncertainties have been estimated and the influence of input parameters has been studied. Mechanical calculations have been performed to guarantee the mechanical strength of the venturis required for pressure equipment operating in nuclear environment. In order to complete the helium mass flow measurement chain, different technologies of absolute and differential pressure sensors have been tested in an applied magnetic field to identify equipment compatible with the ITER environment.

  8. Exploratory learning with a computer simulation for control theory: Learning processes and instructional support

    NASA Astrophysics Data System (ADS)

    Njoo, Melanie; de Jong, Ton

    Computer simulations create a context that is well fitted for exploratory or discovery learning. The aim of the present two studies was to gain deeper insight into what constitutes exploratory learning and to assess the effects of a number of instructional support measures. The domain involved was control theory at the university level. In the first study we made an inventory of exploratory learning processes by observing 17 students working with a computer simulation and analyzing students' thinking-aloud protocols. Subjects received a structured assignment with hints as an instructional support measure. In the second study, 91 students received an open-ended assignment with instructional support that consisted of an information sheet and a set of fill-in forms. On both sheets and forms, six cells were presented. A cell was given for each of the following six learning processes: identifying variables and parameters, generating hypotheses, designing an experiment, predicting, interpreting data, and drawing of conclusions. Information sheets were either of a domain specific or of a general nature. The set of fill-in forms were either free or had the cell HYPOTHESIS already filled in. The statements of the students on the fill-in forms were analyzed in a stepwise order. Twenty-two detailed learning processes were identified and classified. Two of the main classes of processes are transformative and regulative. Both studies showed that students were reluctant to apply learning processes that are considered characteristic for exploratory learning. Furthermore, students had problems with the exploratory learning processes, especially with the processes of generating hypotheses, interpreting data, and drawing conclusions. Effects of the instructional support measures were not conclusive. Hints did not result in significant improvements of the study process. Supporting learning processes with information sheets appeared to help students in performing learning processes

  9. The Iterate Manual

    DTIC Science & Technology

    1990-10-01

    is probably a bad idea. A better versica would use a temporary: (defmacro sum-of-squares (expr) (let ((temp ( gensym ))) ’(lot (,temp ,expr)) (sum...val ( gensym )) (tempi ( gensym )) (temp2 ( gensym )) (winner (or var iterate::*result-var*))) ’(progn (with ,max-val - nil) (with ,winner = nil) (cond ((null...the elements of a vector (disregards fill-pointer)" (let ((vect ( gensym )) (end ( gensym )) (index ( gensym ))) ’(progn (with ,vect - v) (with ,end = (array

  10. Hierarchical control of procedural and declarative category-learning systems.

    PubMed

    Turner, Benjamin O; Crossley, Matthew J; Ashby, F Gregory

    2017-04-15

    Substantial evidence suggests that human category learning is governed by the interaction of multiple qualitatively distinct neural systems. In this view, procedural memory is used to learn stimulus-response associations, and declarative memory is used to apply explicit rules and test hypotheses about category membership. However, much less is known about the interaction between these systems: how is control passed between systems as they interact to influence motor resources? Here, we used fMRI to elucidate the neural correlates of switching between procedural and declarative categorization systems. We identified a key region of the cerebellum (left Crus I) whose activity was bidirectionally modulated depending on switch direction. We also identified regions of the default mode network (DMN) that were selectively connected to left Crus I during switching. We propose that the cerebellum-in coordination with the DMN-serves a critical role in passing control between procedural and declarative memory systems.

  11. E-Learning System for Learning Virtual Circuit Making with a Microcontroller and Programming to Control a Robot

    ERIC Educational Resources Information Center

    Takemura, Atsushi

    2015-01-01

    This paper proposes a novel e-Learning system for learning electronic circuit making and programming a microcontroller to control a robot. The proposed e-Learning system comprises a virtual-circuit-making function for the construction of circuits with a versatile, Arduino microcontroller and an educational system that can simulate behaviors of…

  12. Neutron activation for ITER

    SciTech Connect

    Barnes, C.W.; Loughlin, M.J.; Nishitani, Takeo

    1996-04-29

    There are three primary goals for the Neutron Activation system for ITER: maintain a robust relative measure of fusion power with stability and high dynamic range (7 orders of magnitude); allow an absolute calibration of fusion power (energy); and provide a flexible and reliable system for materials testing. The nature of the activation technique is such that stability and high dynamic range can be intrinsic properties of the system. It has also been the technique that demonstrated (on JET and TFTR) the highest accuracy neutron measurements in DT operation. Since the gamma-ray detectors are not located on the tokamak and are therefore amenable to accurate characterization, and if material foils are placed very close to the ITER plasma with minimum scattering or attenuation, high overall accuracy in the fusion energy production (7--10%) should be achievable on ITER. In the paper, a conceptual design is presented. A system is shown to be capable of meeting these three goals, also detailed design issues remain to be solved.

  13. Neutron cameras for ITER

    SciTech Connect

    Johnson, L.C.; Barnes, C.W.; Batistoni, P.

    1998-12-31

    Neutron cameras with horizontal and vertical views have been designed for ITER, based on systems used on JET and TFTR. The cameras consist of fan-shaped arrays of collimated flight tubes, with suitably chosen detectors situated outside the biological shield. The sight lines view the ITER plasma through slots in the shield blanket and penetrate the vacuum vessel, cryostat, and biological shield through stainless steel windows. This paper analyzes the expected performance of several neutron camera arrangements for ITER. In addition to the reference designs, the authors examine proposed compact cameras, in which neutron fluxes are inferred from {sup 16}N decay gammas in dedicated flowing water loops, and conventional cameras with fewer sight lines and more limited fields of view than in the reference designs. It is shown that the spatial sampling provided by the reference designs is sufficient to satisfy target measurement requirements and that some reduction in field of view may be permissible. The accuracy of measurements with {sup 16}N-based compact cameras is not yet established, and they fail to satisfy requirements for parameter range and time resolution by large margins.

  14. Self-learning fuzzy controllers based on temporal back propagation

    NASA Technical Reports Server (NTRS)

    Jang, Jyh-Shing R.

    1992-01-01

    This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules if human experts, or automatically derive the fuzzy if-then rules obtained from human experts are not available. The inverted pendulum system is employed as a test-bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.

  15. Tuning fuzzy PD and PI controllers using reinforcement learning.

    PubMed

    Boubertakh, Hamid; Tadjine, Mohamed; Glorennec, Pierre-Yves; Labiod, Salim

    2010-10-01

    In this paper, we propose a new auto-tuning fuzzy PD and PI controllers using reinforcement Q-learning (QL) algorithm for SISO (single-input single-output) and TITO (two-input two-output) systems. We first, investigate the design parameters and settings of a typical class of Fuzzy PD (FPD) and Fuzzy PI (FPI) controllers: zero-order Takagi-Sugeno controllers with equidistant triangular membership functions for inputs, equidistant singleton membership functions for output, Larsen's implication method, and average sum defuzzification method. Secondly, the analytical structures of these typical fuzzy PD and PI controllers are compared to their classical counterpart PD and PI controllers. Finally, the effectiveness of the proposed method is proven through simulation examples.

  16. Reinforcement learning techniques for controlling resources in power networks

    NASA Astrophysics Data System (ADS)

    Kowli, Anupama Sunil

    As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. There is a critical need for control techniques that recognize the unique characteristics of the different resources and exploit the flexibility afforded by them to provide ancillary services to the grid. The work presented in this dissertation addresses these needs. Specifically, new algorithms are proposed, which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be "learned" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints such as capacity and ramping limits on generation resources, state-of-charge constraints on storage resources, comfort-related limitations on demand response resources and power flow limits on transmission lines. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed. Results demonstrate how the proposed control algorithms can be used to improve the performance and reduce the computational complexity of the economic dispatch mechanism in a power network. We argue that the proposed algorithms are eminently suitable to develop operational decision-making tools for large power grids with many resources and many sources of uncertainty.

  17. Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

    PubMed

    Pan, Yongping; Yu, Haoyong

    2016-03-30

    This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference traj- ectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

  18. Emotional Learning Based Intelligent Controllers for Rotor Flux Oriented Control of Induction Motor

    NASA Astrophysics Data System (ADS)

    Abdollahi, Rohollah; Farhangi, Reza; Yarahmadi, Ali

    2014-08-01

    This paper presents design and evaluation of a novel approach based on emotional learning to improve the speed control system of rotor flux oriented control of induction motor. The controller includes a neuro-fuzzy system with speed error and its derivative as inputs. A fuzzy critic evaluates the present situation, and provides the emotional signal (stress). The controller modifies its characteristics so that the critics stress is reduced. The comparative simulation results show that the proposed controller is more robust and hence found to be a suitable replacement of the conventional PI controller for the high performance industrial drive applications.

  19. Learning to push and learning to move: the adaptive control of contact forces

    PubMed Central

    Casadio, Maura; Pressman, Assaf; Mussa-Ivaldi, Ferdinando A.

    2015-01-01

    To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in “compatible pairs” connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e., when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and motions

  20. Learning to push and learning to move: the adaptive control of contact forces.

    PubMed

    Casadio, Maura; Pressman, Assaf; Mussa-Ivaldi, Ferdinando A

    2015-01-01

    To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in "compatible pairs" connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e., when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and motions.

  1. Force and position control of robot manipulators: Learning and repetitive control approach

    NASA Astrophysics Data System (ADS)

    Jeon, Doyoung

    When a robot performs the same task repeatedly, a learning or repetitive controller can enhance the performance of the system significantly. Learning or repetitive control, however, has not been studied in the force control of a robot manipulator as extensively as in the position control of a robot manipulator. In this dissertation, learning control is applied to hybrid force and position control of robot manipulators. Also, repetitive control is applied to the control of a spring loaded end effector. When the geometry and position of the constraint surface is known, the hybrid force and position controller and the feedforward compensator can be designed in the constraint coordinates. When the operation is periodic, the learning hybrid force and position control enhances the control performance as the feedforward compensator is updated in each cycle by the force and position error in the preceding trials. This scheme is proved to be stable in the sense of Lyapunov. In the experiments, a two degree of freedom SCARA-type direct-drive robot manipulator is used to test the feasibility of the learning hybrid force and position control. The deburring tool mounted on the upper link of the robot could follow a flat, tilted flat, and curved 1/4 inch aluminum plate with a desired contact force of 10 N (within the robot-mean-square force error of 1.95 N) and with desired tangential velocity. Considering the loss of contact observed at the initial trial, the performance of the system improved significantly. A spring loaded end effector is useful in assembly operations such as mounting an electronics package on a board. With the known stiffness of the spring, the desired contact force tracking is accomplished by controlling the spring displacement. Uncertainties in the environment such as the stiffness of the board make it difficult to program the required control force. As the operation repeats, the tracking errors for the spring displacement, i.e., the contact force, are

  2. Optimal control in microgrid using multi-agent reinforcement learning.

    PubMed

    Li, Fu-Dong; Wu, Min; He, Yong; Chen, Xin

    2012-11-01

    This paper presents an improved reinforcement learning method to minimize electricity costs on the premise of satisfying the power balance and generation limit of units in a microgrid with grid-connected mode. Firstly, the microgrid control requirements are analyzed and the objective function of optimal control for microgrid is proposed. Then, a state variable "Average Electricity Price Trend" which is used to express the most possible transitions of the system is developed so as to reduce the complexity and randomicity of the microgrid, and a multi-agent architecture including agents, state variables, action variables and reward function is formulated. Furthermore, dynamic hierarchical reinforcement learning, based on change rate of key state variable, is established to carry out optimal policy exploration. The analysis shows that the proposed method is beneficial to handle the problem of "curse of dimensionality" and speed up learning in the unknown large-scale world. Finally, the simulation results under JADE (Java Agent Development Framework) demonstrate the validity of the presented method in optimal control for a microgrid with grid-connected mode.

  3. Learning a Novel Myoelectric-Controlled Interface Task

    PubMed Central

    Radhakrishnan, Saritha M.; Baker, Stuart N.; Jackson, Andrew

    2008-01-01

    Control of myoelectric prostheses and brain–machine interfaces requires learning abstract neuromotor transformations. To investigate the mechanisms underlying this ability, we trained subjects to move a two-dimensional cursor using a myoelectric-controlled interface. With the upper limb immobilized, an electromyogram from multiple hand and arm muscles moved the cursor in directions that were either intuitive or nonintuitive and with high or low variability. We found that subjects could learn even nonintuitive arrangements to a high level of performance. Muscle-tuning functions were cosine shaped and modulated so as to reduce cursor variability. Subjects exhibited an additional preference for using hand muscles over arm muscles, which resulted from a greater capacity of these to form novel, task-specific synergies. In a second experiment, nonvisual feedback from the hand was degraded with amplitude- and frequency-modulated vibration. Although vibration impaired task performance, it did not affect the rate at which learning occurred. We therefore conclude that the motor system can acquire internal models of novel, abstract neuromotor mappings even in the absence of overt movements or accurate proprioceptive signals, but that the distal motor system may be better suited to provide flexible control signals for neuromotor prostheses than structures related to the arm. PMID:18667540

  4. Intelligent control of an IPMC actuated manipulator using emotional learning-based controller

    NASA Astrophysics Data System (ADS)

    Shariati, Azadeh; Meghdari, Ali; Shariati, Parham

    2008-08-01

    In this research an intelligent emotional learning controller, Takagi- Sugeno- Kang (TSK) is applied to govern the dynamics of a novel Ionic-Polymer Metal Composite (IPMC) actuated manipulator. Ionic-Polymer Metal Composites are active actuators that show very large deformation in existence of low applied voltage. In this research, a new IPMC actuator is considered and applied to a 2-dof miniature manipulator. This manipulator is designed for miniature tasks. The control system consists of a set of neurofuzzy controller whose parameters are adapted according to the emotional learning rules, and a critic with task to assess the present situation resulted from the applied control action in terms of satisfactory achievement of the control goals and provides the emotional signal (the stress). The controller modifies its characteristics so that the critic's stress decreased.

  5. Energetic particle physics issues for ITER

    SciTech Connect

    Cheng, C.Z.; Budny, R.; Fu, G.Y.

    1996-12-31

    This paper summarizes our present understanding of the following energetic/alpha particle physics issues for the 21 MA, 20 TF coil ITER Interim Design configuration and operational scenarios: (a) toroidal field ripple effects on alpha particle confinement, (b) energetic particle interaction with low frequency MHD modes, (c) energetic particle excitation of toroidal Alfven eigenmodes, and (d) energetic particle transport due to MHD modes. TF ripple effects on alpha loss in ITER under a number of different operating conditions are found to be small with a maximum loss of 1%. With careful plasma control in ITER reversed-shear operation, TF ripple induced alpha loss can be reduced to below the nominal ITER design limit of 5%. Fishbone modes are expected to be unstable for {beta}{sub {alpha}} > 1%, and sawtooth stabilization is lost if the ideal kink growth rate exceeds 10% of the deeply trapped alpha precessional drift frequency evaluated at the q = 1 surface. However, it is expected that the fishbone modes will lead only to a local flattening of the alpha profile due to small banana size. MHD modes observed during slow decrease of stored energy after fast partial electron temperature collapse in JT-60U reversed-shear experiments may be resonant type instabilities; they may have implications on the energetic particle confinement in ITER reversed-shear operation. From the results of various TAE stability code calculations, ITER equilibria appear to lie close to TAE linear stability thresholds. However, the prognosis depends strongly on q profile and profiles of alpha and other high energy particles species. If TAE modes are unstable in ITER, the stochastic diffusion is the main loss mechanism, which scales with ({delta}B{sub r}/B){sup 2}, because of the relatively small alpha particle banana orbit size. For isolated TAE modes the particle loss is very small, and TAE modes saturate via the resonant wave-particle trapping process at very small amplitude.

  6. Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning

    PubMed Central

    Jarvers, Christian; Brosch, Tobias; Brechmann, André; Woldeit, Marie L.; Schulz, Andreas L.; Ohl, Frank W.; Lommerzheim, Marcel; Neumann, Heiko

    2016-01-01

    Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden

  7. Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning.

    PubMed

    Jarvers, Christian; Brosch, Tobias; Brechmann, André; Woldeit, Marie L; Schulz, Andreas L; Ohl, Frank W; Lommerzheim, Marcel; Neumann, Heiko

    2016-01-01

    Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden

  8. ETR/ITER systems code

    SciTech Connect

    Barr, W.L.; Bathke, C.G.; Brooks, J.N.; Bulmer, R.H.; Busigin, A.; DuBois, P.F.; Fenstermacher, M.E.; Fink, J.; Finn, P.A.; Galambos, J.D.; Gohar, Y.; Gorker, G.E.; Haines, J.R.; Hassanein, A.M.; Hicks, D.R.; Ho, S.K.; Kalsi, S.S.; Kalyanam, K.M.; Kerns, J.A.; Lee, J.D.; Miller, J.R.; Miller, R.L.; Myall, J.O.; Peng, Y-K.M.; Perkins, L.J.; Spampinato, P.T.; Strickler, D.J.; Thomson, S.L.; Wagner, C.E.; Willms, R.S.; Reid, R.L.

    1988-04-01

    A tokamak systems code capable of modeling experimental test reactors has been developed and is described in this document. The code, named TETRA (for Tokamak Engineering Test Reactor Analysis), consists of a series of modules, each describing a tokamak system or component, controlled by an optimizer/driver. This code development was a national effort in that the modules were contributed by members of the fusion community and integrated into a code by the Fusion Engineering Design Center. The code has been checked out on the Cray computers at the National Magnetic Fusion Energy Computing Center and has satisfactorily simulated the Tokamak Ignition/Burn Experimental Reactor II (TIBER) design. A feature of this code is the ability to perform optimization studies through the use of a numerical software package, which iterates prescribed variables to satisfy a set of prescribed equations or constraints. This code will be used to perform sensitivity studies for the proposed International Thermonuclear Experimental Reactor (ITER). 22 figs., 29 tabs.

  9. Applications of Adaptive Learning Controller to Synthetic Aperture Radar.

    DTIC Science & Technology

    1985-02-01

    FIGURE 37. Location of Two Sub- Phase Histories to be Utilized in Estimating Misfocus Coefficients A and C . . . A8 FIGURES 38.-94. ALC Learning Curves ...FIGURES (Concl uded) FIGURE 23. ALC Learning Curve .... .................. ... 45 .- " FIGURE 24. ALC Learning Curve ......... ................. 47 FIGURE...25. ALC Learning Curve .... .................. ... 48 FIGURE 26. ALC Learning Curve ....... .... ... .... 50 FIGURE 27. ALC Learning Curve

  10. Beamforming and Power Control in Sensor Arrays Using Reinforcement Learning

    PubMed Central

    Almeida, Náthalee C.; Fernandes, Marcelo A.C.; Neto, Adrião D.D.

    2015-01-01

    The use of beamforming and power control, combined or separately, has advantages and disadvantages, depending on the application. The combined use of beamforming and power control has been shown to be highly effective in applications involving the suppression of interference signals from different sources. However, it is necessary to identify efficient methodologies for the combined operation of these two techniques. The most appropriate technique may be obtained by means of the implementation of an intelligent agent capable of making the best selection between beamforming and power control. The present paper proposes an algorithm using reinforcement learning (RL) to determine the optimal combination of beamforming and power control in sensor arrays. The RL algorithm used was Q-learning, employing an ε-greedy policy, and training was performed using the offline method. The simulations showed that RL was effective for implementation of a switching policy involving the different techniques, taking advantage of the positive characteristics of each technique in terms of signal reception. PMID:25808769

  11. An adaptive learning control system for large flexible structures

    NASA Technical Reports Server (NTRS)

    Thau, F. E.

    1985-01-01

    The objective of the research has been to study the design of adaptive/learning control systems for the control of large flexible structures. In the first activity an adaptive/learning control methodology for flexible space structures was investigated. The approach was based on using a modal model of the flexible structure dynamics and an output-error identification scheme to identify modal parameters. In the second activity, a least-squares identification scheme was proposed for estimating both modal parameters and modal-to-actuator and modal-to-sensor shape functions. The technique was applied to experimental data obtained from the NASA Langley beam experiment. In the third activity, a separable nonlinear least-squares approach was developed for estimating the number of excited modes, shape functions, modal parameters, and modal amplitude and velocity time functions for a flexible structure. In the final research activity, a dual-adaptive control strategy was developed for regulating the modal dynamics and identifying modal parameters of a flexible structure. A min-max approach was used for finding an input to provide modal parameter identification while not exceeding reasonable bounds on modal displacement.

  12. Searching with iterated maps

    PubMed Central

    Elser, V.; Rankenburg, I.; Thibault, P.

    2007-01-01

    In many problems that require extensive searching, the solution can be described as satisfying two competing constraints, where satisfying each independently does not pose a challenge. As an alternative to tree-based and stochastic searching, for these problems we propose using an iterated map built from the projections to the two constraint sets. Algorithms of this kind have been the method of choice in a large variety of signal-processing applications; we show here that the scope of these algorithms is surprisingly broad, with applications as diverse as protein folding and Sudoku. PMID:17202267

  13. Iterative Magnetometer Calibration

    NASA Technical Reports Server (NTRS)

    Sedlak, Joseph

    2006-01-01

    This paper presents an iterative method for three-axis magnetometer (TAM) calibration that makes use of three existing utilities recently incorporated into the attitude ground support system used at NASA's Goddard Space Flight Center. The method combines attitude-independent and attitude-dependent calibration algorithms with a new spinning spacecraft Kalman filter to solve for biases, scale factors, nonorthogonal corrections to the alignment, and the orthogonal sensor alignment. The method is particularly well-suited to spin-stabilized spacecraft, but may also be useful for three-axis stabilized missions given sufficient data to provide observability.

  14. Universal learning network and its application to chaos control.

    PubMed

    Hirasawa, K; Wang, X; Murata, J; Hu, J; Jin, C

    2000-03-01

    Universal Learning Networks (ULNs) are proposed and their application to chaos control is discussed. ULNs provide a generalized framework to model and control complex systems. They consist of a number of inter-connected nodes where the nodes may have any continuously differentiable nonlinear functions in them and each pair of nodes can be connected by multiple branches with arbitrary time delays. Therefore, physical systems, which can be described by differential or difference equations and also their controllers, can be modeled in a unified way, and so ULNs may form a super set of neural networks and fuzzy neural networks. In order to optimize the ULNs, a generalized learning algorithm is derived, in which both the first order derivatives (gradients) and the higher order derivatives are incorporated. The derivatives are calculated by using forward or backward propagation schemes. These algorithms for calculating the derivatives are extended versions of Back Propagation Through Time (BPTT) and Real Time Recurrent Learning (RTRL) of Williams in the sense that generalized node functions, generalized network connections with multi-branch of arbitrary time delays, generalized criterion functions and higher order derivatives can be deal with. As an application of ULNs, a chaos control method using maximum Lyapunov exponent of ULNs is proposed. Maximum Lyapunov exponent of ULNs can be formulated by using higher order derivatives of ULNs, and the parameters of ULNs can be adjusted so that the maximum Lyapunov exponent approaches the target value. From the simulation results, it has been shown that a fully connected ULN with three nodes is able to display chaotic behaviors.

  15. Machine learning of parameter control doctrine for sensor and communication systems. Final report

    SciTech Connect

    Kamen, R.B.; Dillard, R.A.

    1988-07-01

    Artificial-intelligence approaches to learning were reviewed for their potential contributions to the construction of a system to learn parameter-control doctrine. Separate learning tasks were isolated and several levels of related problems were distinguished. Formulas for providing the learning system with measures of its performance were derived for four kinds of targets.

  16. Effects of Dispositional Mindfulness on the Self-Controlled Learning of a Novel Motor Task

    ERIC Educational Resources Information Center

    Kee, Ying Hwa; Liu, Yeou-Teh

    2011-01-01

    Current literature suggests that mindful learning is beneficial to learning but its links with motor learning is seldom examined. In the present study, we examine the effects of learners' mindfulness disposition on the self-controlled learning of a novel motor task. Thirty-two participants undertook five practice sessions, in addition to a pre-,…

  17. Humans and Monkeys Exert Metacognitive Control Based on Learning Difficulty in a Perceptual Categorization Task

    ERIC Educational Resources Information Center

    Redford, Joshua S.

    2010-01-01

    Recently, Redford (2010) found that monkeys seemed to exert metacognitive control in a category-learning paradigm. Specifically, they selected more trials to view as the difficulty of the category-learning task increased. However, category-learning difficulty was determined by manipulating the family resemblance across the to-be-learned exemplars.…

  18. Model-based hierarchical reinforcement learning and human action control

    PubMed Central

    Botvinick, Matthew; Weinstein, Ari

    2014-01-01

    Recent work has reawakened interest in goal-directed or ‘model-based’ choice, where decisions are based on prospective evaluation of potential action outcomes. Concurrently, there has been growing attention to the role of hierarchy in decision-making and action control. We focus here on the intersection between these two areas of interest, considering the topic of hierarchical model-based control. To characterize this form of action control, we draw on the computational framework of hierarchical reinforcement learning, using this to interpret recent empirical findings. The resulting picture reveals how hierarchical model-based mechanisms might play a special and pivotal role in human decision-making, dramatically extending the scope and complexity of human behaviour. PMID:25267822

  19. Model-based hierarchical reinforcement learning and human action control.

    PubMed

    Botvinick, Matthew; Weinstein, Ari

    2014-11-05

    Recent work has reawakened interest in goal-directed or 'model-based' choice, where decisions are based on prospective evaluation of potential action outcomes. Concurrently, there has been growing attention to the role of hierarchy in decision-making and action control. We focus here on the intersection between these two areas of interest, considering the topic of hierarchical model-based control. To characterize this form of action control, we draw on the computational framework of hierarchical reinforcement learning, using this to interpret recent empirical findings. The resulting picture reveals how hierarchical model-based mechanisms might play a special and pivotal role in human decision-making, dramatically extending the scope and complexity of human behaviour.

  20. Overview on Experiments On ITER-like Antenna On JET And ICRF Antenna Design For ITER

    SciTech Connect

    Nightingale, M. P. S.; Blackman, T.; Edwards, D.; Fanthome, J.; Graham, M.; Hamlyn-Harris, C.; Hancock, D.; Jacquet, P.; Mayoral, M.-L.; Monakhov, I.; Nicholls, K.; Stork, D.; Whitehurst, A.; Wilson, D.; Wooldridge, E.

    2009-11-26

    Following an overview of the ITER Ion Cyclotron Resonance Frequency (ICRF) system, the JET ITER-like antenna (ILA) will be described. The ILA was designed to test the following ITER issues: (a) reliable operation at power densities of order 8 MW/m{sup 2} at voltages up to 45 kV using a close-packed array of straps; (b) powering through ELMs using an internal (in-vacuum) conjugate-T junction; (c) protection from arcing in a conjugate-T configuration, using both existing and novel systems; and (d) resilience to disruption forces. ITER-relevant results have been achieved: operation at high coupled power density; control of the antenna matching elements in the presence of high inter-strap coupling, use of four conjugate-T systems (as would be used in ITER, should a conjugate-T approach be used); operation with RF voltages on the antenna structures up to 42 kV; achievement of ELM tolerance with a conjugate-T configuration by operating at 3{omega} real impedance at the conjugate-T point; and validation of arc detection systems on conjugate-T configurations in ELMy H-mode plasmas. The impact of these results on the predicted performance and design of the ITER antenna will be reviewed. In particular, the implications of the RF coupling measured on JET will be discussed.

  1. Runaway electrons and ITER

    NASA Astrophysics Data System (ADS)

    Boozer, Allen

    2016-10-01

    ITER planning for avoiding runaway damage depends on magnetic surface breakup in fast relaxations. These arise in thermal quenches and in the spreading of impurities from massive gas injection or shattered pellets. Surface breakup would prevent a runaway to relativistic energies were it not for non-intercepting flux tubes, which contain magnetic field lines that do not intercept the walls. Such tubes persist near the magnetic axis and in the cores of islands but must dissipate before any confining surfaces re-form. Otherwise, a highly dangerous situation arises. Electrons that were trapped and accelerated in these flux tubes can fill a large volume of stochastic field lines and serve as a seed for the transfer of the full plasma current to runaways. If the outer confining surfaces are punctured, as by a drift into the wall, then the full runaway inventory will be lost in a short pulse along a narrow flux tube. Although not part of ITER planning, currents induced in the walls by the fast magnetic relaxation could be used to passively prevent outer surfaces re-forming. If magnetic surface breakup can be avoided during impurity injection, the plasma current could be terminated in tens of milliseconds by plasma cooling with no danger of runaway. Support by DoE Office of Fusion Energy Science Grant De-FG02-03ER54696.

  2. Modes of Executive Control in Sequence Learning: From Stimulus-Based to Plan-Based Control

    ERIC Educational Resources Information Center

    Tubau, Elisabet; Hommel, Bernhard; Lopez-Moliner, Joan

    2007-01-01

    The authors argue that human sequential learning is often but not always characterized by a shift from stimulus- to plan-based action control. To diagnose this shift, they manipulated the frequency of 1st-order transitions in a repeated manual left-right sequence, assuming that performance is sensitive to frequency-induced biases under stimulus-…

  3. Extortion can outperform generosity in the iterated prisoner's dilemma.

    PubMed

    Wang, Zhijian; Zhou, Yanran; Lien, Jaimie W; Zheng, Jie; Xu, Bin

    2016-04-12

    Zero-determinant (ZD) strategies, as discovered by Press and Dyson, can enforce a linear relationship between a pair of players' scores in the iterated prisoner's dilemma. Particularly, the extortionate ZD strategies can enforce and exploit cooperation, providing a player with a score advantage, and consequently higher scores than those from either mutual cooperation or generous ZD strategies. In laboratory experiments in which human subjects were paired with computer co-players, we demonstrate that both the generous and the extortionate ZD strategies indeed enforce a unilateral control of the reward. When the experimental setting is sufficiently long and the computerized nature of the opponent is known to human subjects, the extortionate strategy outperforms the generous strategy. Human subjects' cooperation rates when playing against extortionate and generous ZD strategies are similar after learning has occurred. More than half of extortionate strategists finally obtain an average score higher than that from mutual cooperation.

  4. Extortion can outperform generosity in the iterated prisoner's dilemma

    PubMed Central

    Wang, Zhijian; Zhou, Yanran; Lien, Jaimie W.; Zheng, Jie; Xu, Bin

    2016-01-01

    Zero-determinant (ZD) strategies, as discovered by Press and Dyson, can enforce a linear relationship between a pair of players' scores in the iterated prisoner's dilemma. Particularly, the extortionate ZD strategies can enforce and exploit cooperation, providing a player with a score advantage, and consequently higher scores than those from either mutual cooperation or generous ZD strategies. In laboratory experiments in which human subjects were paired with computer co-players, we demonstrate that both the generous and the extortionate ZD strategies indeed enforce a unilateral control of the reward. When the experimental setting is sufficiently long and the computerized nature of the opponent is known to human subjects, the extortionate strategy outperforms the generous strategy. Human subjects' cooperation rates when playing against extortionate and generous ZD strategies are similar after learning has occurred. More than half of extortionate strategists finally obtain an average score higher than that from mutual cooperation. PMID:27067513

  5. Patients with Parkinson's disease learn to control complex systems via procedural as well as non-procedural learning.

    PubMed

    Osman, Magda; Wilkinson, Leonora; Beigi, Mazda; Castaneda, Cristina Sanchez; Jahanshahi, Marjan

    2008-01-01

    The striatum is considered to mediate some forms of procedural learning. Complex dynamic control (CDC) tasks involve an individual having to make a series of sequential decisions to achieve a specific outcome (e.g. learning to operate and control a car), and they involve procedural learning. The aim of this study was to test the hypothesis that patients with Parkinson's disease who have striatal dysfunction, are impaired on CDC tasks only when learning involves procedural learning. 26 patients with Parkinson's disease (PD) and 26 age-matched controls performed two CDC tasks, one in which training was observation-based (non-procedural), and a second in which training was action-based (procedural). Both groups were able to control the system to a specific criterion equally well, regardless of the training condition. However, when reporting their knowledge of the underlying structure of the system, both groups showed poorer accuracy when learning took place through observation-based compared with action-based training. Moreover, the controls' accuracy in reporting the underlying structure of the systems was superior to that of PD patients. The findings suggest that the striatal dysfunction in Parkinson's disease is not associated with impairment of procedural learning, regardless of whether the task involved procedural learning or not. It is possible that the learning and performance on CDC tasks are mediated by perceptual priming mechanisms in the neocortex.

  6. Projection learning algorithm for threshold - controlled neural networks

    SciTech Connect

    Reznik, A.M.

    1995-03-01

    The projection learning algorithm proposed in [1, 2] and further developed in [3] substantially improves the efficiency of memorizing information and accelerates the learning process in neural networks. This algorithm is compatible with the completely connected neural network architecture (the Hopfield network [4]), but its application to other networks involves a number of difficulties. The main difficulties include constraints on interconnection structure and the need to eliminate the state uncertainty of latent neurons if such are present in the network. Despite the encouraging preliminary results of [3], further extension of the applications of the projection algorithm therefore remains problematic. In this paper, which is a continuation of the work begun in [3], we consider threshold-controlled neural networks. Networks of this type are quite common. They represent the receptor neuron layers in some neurocomputer designs. A similar structure is observed in the lower divisions of biological sensory systems [5]. In multilayer projection neural networks with lateral interconnections, the neuron layers or parts of these layers may also have the structure of a threshold-controlled completely connected network. Here the thresholds are the potentials delivered through the projection connections from other parts of the network. The extension of the projection algorithm to the class of threshold-controlled networks may accordingly prove to be useful both for extending its technical applications and for better understanding of the operation of the nervous system in living organisms.

  7. Memory and cognitive control circuits in mathematical cognition and learning.

    PubMed

    Menon, V

    2016-01-01

    Numerical cognition relies on interactions within and between multiple functional brain systems, including those subserving quantity processing, working memory, declarative memory, and cognitive control. This chapter describes recent advances in our understanding of memory and control circuits in mathematical cognition and learning. The working memory system involves multiple parietal-frontal circuits which create short-term representations that allow manipulation of discrete quantities over several seconds. In contrast, hippocampal-frontal circuits underlying the declarative memory system play an important role in formation of associative memories and binding of new and old information, leading to the formation of long-term memories that allow generalization beyond individual problem attributes. The flow of information across these systems is regulated by flexible cognitive control systems which facilitate the integration and manipulation of quantity and mnemonic information. The implications of recent research for formulating a more comprehensive systems neuroscience view of the neural basis of mathematical learning and knowledge acquisition in both children and adults are discussed.

  8. Thalamic control of human attention driven by memory and learning.

    PubMed

    de Bourbon-Teles, José; Bentley, Paul; Koshino, Saori; Shah, Kushal; Dutta, Agneish; Malhotra, Paresh; Egner, Tobias; Husain, Masud; Soto, David

    2014-05-05

    The role of the thalamus in high-level cognition-attention, working memory (WM), rule-based learning, and decision making-remains poorly understood, especially in comparison to that of cortical frontoparietal networks [1-3]. Studies of visual thalamus have revealed important roles for pulvinar and lateral geniculate nucleus in visuospatial perception and attention [4-10] and for mediodorsal thalamus in oculomotor control [11]. Ventrolateral thalamus contains subdivisions devoted to action control as part of a circuit involving the basal ganglia [12, 13] and motor, premotor, and prefrontal cortices [14], whereas anterior thalamus forms a memory network in connection with the hippocampus [15]. This connectivity profile suggests that ventrolateral and anterior thalamus may represent a nexus between mnemonic and control functions, such as action or attentional selection. Here, we characterize the role of thalamus in the interplay between memory and visual attention. We show that ventrolateral lesions impair the influence of WM representations on attentional deployment. A subsequent fMRI study in healthy volunteers demonstrates involvement of ventrolateral and, notably, anterior thalamus in biasing attention through WM contents. To further characterize the memory types used by the thalamus to bias attention, we performed a second fMRI study that involved learning of stimulus-stimulus associations and their retrieval from long-term memory to optimize attention in search. Responses in ventrolateral and anterior thalamic nuclei tracked learning of the predictiveness of these abstract associations and their use in directing attention. These findings demonstrate a key role for human thalamus in higher-level cognition, notably, in mnemonic biasing of attention.

  9. Preliminary consideration of CFETR ITER-like case diagnostic system.

    PubMed

    Li, G S; Yang, Y; Wang, Y M; Ming, T F; Han, X; Liu, S C; Wang, E H; Liu, Y K; Yang, W J; Li, G Q; Hu, Q S; Gao, X

    2016-11-01

    Chinese Fusion Engineering Test Reactor (CFETR) is a new superconducting tokamak device being designed in China, which aims at bridging the gap between ITER and DEMO, where DEMO is a tokamak demonstration fusion reactor. Two diagnostic cases, ITER-like case and towards DEMO case, have been considered for CFETR early and later operating phases, respectively. In this paper, some preliminary consideration of ITER-like case will be presented. Based on ITER diagnostic system, three versions of increased complexity and coverage of the ITER-like case diagnostic system have been developed with different goals and functions. Version A aims only machine protection and basic control. Both of version B and version C are mainly for machine protection, basic and advanced control, but version C has an increased level of redundancy necessary for improved measurements capability. The performance of these versions and needed R&D work are outlined.

  10. Preliminary consideration of CFETR ITER-like case diagnostic system

    NASA Astrophysics Data System (ADS)

    Li, G. S.; Yang, Y.; Wang, Y. M.; Ming, T. F.; Han, X.; Liu, S. C.; Wang, E. H.; Liu, Y. K.; Yang, W. J.; Li, G. Q.; Hu, Q. S.; Gao, X.

    2016-11-01

    Chinese Fusion Engineering Test Reactor (CFETR) is a new superconducting tokamak device being designed in China, which aims at bridging the gap between ITER and DEMO, where DEMO is a tokamak demonstration fusion reactor. Two diagnostic cases, ITER-like case and towards DEMO case, have been considered for CFETR early and later operating phases, respectively. In this paper, some preliminary consideration of ITER-like case will be presented. Based on ITER diagnostic system, three versions of increased complexity and coverage of the ITER-like case diagnostic system have been developed with different goals and functions. Version A aims only machine protection and basic control. Both of version B and version C are mainly for machine protection, basic and advanced control, but version C has an increased level of redundancy necessary for improved measurements capability. The performance of these versions and needed R&D work are outlined.

  11. Lessons learned on the Ground Test Accelerator control system

    SciTech Connect

    Kozubal, A.J.; Weiss, R.E.

    1994-09-01

    When we initiated the control system design for the Ground Test Accelerator (GTA), we envisioned a system that would be flexible enough to handle the changing requirements of an experimental project. This control system would use a developers` toolkit to reduce the cost and time to develop applications for GTA, and through the use of open standards, the system would accommodate unforeseen requirements as they arose. Furthermore, we would attempt to demonstrate on GTA a level of automation far beyond that achieved by existing accelerator control systems. How well did we achieve these goals? What were the stumbling blocks to deploying the control system, and what assumptions did we make about requirements that turned out to be incorrect? In this paper we look at the process of developing a control system that evolved into what is now the ``Experimental Physics and Industrial Control System`` (EPICS). Also, we assess the impact of this system on the GTA project, as well as the impact of GTA on EPICS. The lessons learned on GTA will be valuable for future projects.

  12. Robot Cognitive Control with a Neurophysiologically Inspired Reinforcement Learning Model

    PubMed Central

    Khamassi, Mehdi; Lallée, Stéphane; Enel, Pierre; Procyk, Emmanuel; Dominey, Peter F.

    2011-01-01

    A major challenge in modern robotics is to liberate robots from controlled industrial settings, and allow them to interact with humans and changing environments in the real-world. The current research attempts to determine if a neurophysiologically motivated model of cortical function in the primate can help to address this challenge. Primates are endowed with cognitive systems that allow them to maximize the feedback from their environment by learning the values of actions in diverse situations and by adjusting their behavioral parameters (i.e., cognitive control) to accommodate unexpected events. In such contexts uncertainty can arise from at least two distinct sources – expected uncertainty resulting from noise during sensory-motor interaction in a known context, and unexpected uncertainty resulting from the changing probabilistic structure of the environment. However, it is not clear how neurophysiological mechanisms of reinforcement learning and cognitive control integrate in the brain to produce efficient behavior. Based on primate neuroanatomy and neurophysiology, we propose a novel computational model for the interaction between lateral prefrontal and anterior cingulate cortex reconciling previous models dedicated to these two functions. We deployed the model in two robots and demonstrate that, based on adaptive regulation of a meta-parameter β that controls the exploration rate, the model can robustly deal with the two kinds of uncertainties in the real-world. In addition the model could reproduce monkey behavioral performance and neurophysiological data in two problem-solving tasks. A last experiment extends this to human–robot interaction with the iCub humanoid, and novel sources of uncertainty corresponding to “cheating” by the human. The combined results provide concrete evidence for the ability of neurophysiologically inspired cognitive systems to control advanced robots in the real-world. PMID:21808619

  13. Robot cognitive control with a neurophysiologically inspired reinforcement learning model.

    PubMed

    Khamassi, Mehdi; Lallée, Stéphane; Enel, Pierre; Procyk, Emmanuel; Dominey, Peter F

    2011-01-01

    A major challenge in modern robotics is to liberate robots from controlled industrial settings, and allow them to interact with humans and changing environments in the real-world. The current research attempts to determine if a neurophysiologically motivated model of cortical function in the primate can help to address this challenge. Primates are endowed with cognitive systems that allow them to maximize the feedback from their environment by learning the values of actions in diverse situations and by adjusting their behavioral parameters (i.e., cognitive control) to accommodate unexpected events. In such contexts uncertainty can arise from at least two distinct sources - expected uncertainty resulting from noise during sensory-motor interaction in a known context, and unexpected uncertainty resulting from the changing probabilistic structure of the environment. However, it is not clear how neurophysiological mechanisms of reinforcement learning and cognitive control integrate in the brain to produce efficient behavior. Based on primate neuroanatomy and neurophysiology, we propose a novel computational model for the interaction between lateral prefrontal and anterior cingulate cortex reconciling previous models dedicated to these two functions. We deployed the model in two robots and demonstrate that, based on adaptive regulation of a meta-parameter β that controls the exploration rate, the model can robustly deal with the two kinds of uncertainties in the real-world. In addition the model could reproduce monkey behavioral performance and neurophysiological data in two problem-solving tasks. A last experiment extends this to human-robot interaction with the iCub humanoid, and novel sources of uncertainty corresponding to "cheating" by the human. The combined results provide concrete evidence for the ability of neurophysiologically inspired cognitive systems to control advanced robots in the real-world.

  14. Comparative study of a learning fuzzy PID controller and a self-tuning controller.

    PubMed

    Kazemian, H B

    2001-01-01

    The self-organising fuzzy controller is an extension of the rule-based fuzzy controller with an additional learning capability. The self-organising fuzzy (SOF) is used as a master controller to readjust conventional PID gains at the actuator level during the system operation, copying the experience of a human operator. The application of the self-organising fuzzy PID (SOF-PID) controller to a 2-link non-linear revolute-joint robot-arm is studied using path tracking trajectories at the setpoint. For the purpose of comparison, the same experiments are repeated by using the self-tuning controller subject to the same data supplied at the setpoint. For the path tracking experiments, the output trajectories of the SOF-PID controller followed the specified path closer and smoother than the self-tuning controller.

  15. A Reactive Blended Learning Proposal for an Introductory Control Engineering Course

    ERIC Educational Resources Information Center

    Mendez, Juan A.; Gonzalez, Evelio J.

    2010-01-01

    As it happens in other fields of engineering, blended learning is widely used to teach process control topics. In this paper, the inclusion of a reactive element--a Fuzzy Logic based controller--is proposed for a blended learning approach in an introductory control engineering course. This controller has been designed in order to regulate the…

  16. Iterated crowdsourcing dilemma game

    PubMed Central

    Oishi, Koji; Cebrian, Manuel; Abeliuk, Andres; Masuda, Naoki

    2014-01-01

    The Internet has enabled the emergence of collective problem solving, also known as crowdsourcing, as a viable option for solving complex tasks. However, the openness of crowdsourcing presents a challenge because solutions obtained by it can be sabotaged, stolen, and manipulated at a low cost for the attacker. We extend a previously proposed crowdsourcing dilemma game to an iterated game to address this question. We enumerate pure evolutionarily stable strategies within the class of so-called reactive strategies, i.e., those depending on the last action of the opponent. Among the 4096 possible reactive strategies, we find 16 strategies each of which is stable in some parameter regions. Repeated encounters of the players can improve social welfare when the damage inflicted by an attack and the cost of attack are both small. Under the current framework, repeated interactions do not really ameliorate the crowdsourcing dilemma in a majority of the parameter space. PMID:24526244

  17. Iterated crowdsourcing dilemma game

    NASA Astrophysics Data System (ADS)

    Oishi, Koji; Cebrian, Manuel; Abeliuk, Andres; Masuda, Naoki

    2014-02-01

    The Internet has enabled the emergence of collective problem solving, also known as crowdsourcing, as a viable option for solving complex tasks. However, the openness of crowdsourcing presents a challenge because solutions obtained by it can be sabotaged, stolen, and manipulated at a low cost for the attacker. We extend a previously proposed crowdsourcing dilemma game to an iterated game to address this question. We enumerate pure evolutionarily stable strategies within the class of so-called reactive strategies, i.e., those depending on the last action of the opponent. Among the 4096 possible reactive strategies, we find 16 strategies each of which is stable in some parameter regions. Repeated encounters of the players can improve social welfare when the damage inflicted by an attack and the cost of attack are both small. Under the current framework, repeated interactions do not really ameliorate the crowdsourcing dilemma in a majority of the parameter space.

  18. Machine Learning for Quantum Metrology and Quantum Control

    NASA Astrophysics Data System (ADS)

    Sanders, Barry; Zahedinejad, Ehsan; Palittapongarnpim, Pantita

    Generating quantum metrological procedures and quantum gate designs, subject to constraints such as temporal or particle-number bounds or limits on the number of control parameters, are typically hard computationally. Although greedy machine learning algorithms are ubiquitous for tackling these problems, the severe constraints listed above limit the efficacy of such approaches. Our aim is to devise heuristic machine learning techniques to generate tractable procedures for adaptive quantum metrology and quantum gate design. In particular we have modified differential evolution to generate adaptive interferometric-phase quantum metrology procedures for up to 100 photons including loss and noise, and we have generated policies for designing single-shot high-fidelity three-qubit gates in superconducting circuits by avoided level crossings. Although quantum metrology and quantum control are regarded as disparate, we have developed a unified framework for these two subjects, and this unification enables us to transfer insights and breakthroughs from one of the topics to the other. Thanks to NSERC, AITF and 1000 Talent Plan.

  19. Effective learning strategies for real-time image-guided adaptive control of multiple-source hyperthermia applicators

    PubMed Central

    Cheng, Kung-Shan; Dewhirst, Mark W.; Stauffer, Paul R.; Das, Shiva

    2010-01-01

    Purpose: This paper investigates overall theoretical requirements for reducing the times required for the iterative learning of a real-time image-guided adaptive control routine for multiple-source heat applicators, as used in hyperthermia and thermal ablative therapy for cancer. Methods: Methods for partial reconstruction of the physical system with and without model reduction to find solutions within a clinically practical timeframe were analyzed. A mathematical analysis based on the Fredholm alternative theorem (FAT) was used to compactly analyze the existence and uniqueness of the optimal heating vector under two fundamental situations: (1) noiseless partial reconstruction and (2) noisy partial reconstruction. These results were coupled with a method for further acceleration of the solution using virtual source (VS) model reduction. The matrix approximation theorem (MAT) was used to choose the optimal vectors spanning the reduced-order subspace to reduce the time for system reconstruction and to determine the associated approximation error. Numerical simulations of the adaptive control of hyperthermia using VS were also performed to test the predictions derived from the theoretical analysis. A thigh sarcoma patient model surrounded by a ten-antenna phased-array applicator was retained for this purpose. The impacts of the convective cooling from blood flow and the presence of sudden increase of perfusion in muscle and tumor were also simulated. Results: By FAT, partial system reconstruction directly conducted in the full space of the physical variables such as phases and magnitudes of the heat sources cannot guarantee reconstructing the optimal system to determine the global optimal setting of the heat sources. A remedy for this limitation is to conduct the partial reconstruction within a reduced-order subspace spanned by the first few maximum eigenvectors of the true system matrix. By MAT, this VS subspace is the optimal one when the goal is to maximize the

  20. Algebraic and adaptive learning in neural control systems

    NASA Astrophysics Data System (ADS)

    Ferrari, Silvia

    A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

  1. Autonomy Supported, Learner-Controlled or System-Controlled Learning in Hypermedia Environments and the Influence of Academic Self-Regulation Style

    ERIC Educational Resources Information Center

    Gorissen, Chantal J. J.; Kester, Liesbeth; Brand-Gruwel, Saskia; Martens, Rob

    2015-01-01

    This study focuses on learning in three different hypermedia environments that either support autonomous learning, learner-controlled learning or system-controlled learning and explores the mediating role of academic self-regulation style (ASRS; i.e. a macro level of motivation) on learning. This research was performed to gain more insight in the…

  2. Experiential learning in control systems laboratories and engineering project management

    NASA Astrophysics Data System (ADS)

    Reck, Rebecca Marie

    Experiential learning is a process by which a student creates knowledge through the insights gained from an experience. Kolb's model of experiential learning is a cycle of four modes: (1) concrete experience, (2) reflective observation, (3) abstract conceptualization, and (4) active experimentation. His model is used in each of the three studies presented in this dissertation. Laboratories are a popular way to apply the experiential learning modes in STEM courses. Laboratory kits allow students to take home laboratory equipment to complete experiments on their own time. Although students like laboratory kits, no previous studies compared student learning outcomes on assignments using laboratory kits with existing laboratory equipment. In this study, we examined the similarities and differences between the experiences of students who used a portable laboratory kit and students who used the traditional equipment. During the 2014- 2015 academic year, we conducted a quasi-experiment to compare students' achievement of learning outcomes and their experiences in the instructional laboratory for an introductory control systems course. Half of the laboratory sections in each semester used the existing equipment, while the other sections used a new kit. We collected both quantitative data and qualitative data. We did not identify any major differences in the student experience based on the equipment they used. Course objectives, like research objectives and product requirements, help provide clarity and direction for faculty and students. Unfortunately, course and laboratory objectives are not always clearly stated. Without a clear set of objectives, it can be hard to design a learning experience and determine whether students are achieving the intended outcomes of the course or laboratory. In this study, I identified a common set of laboratory objectives, concepts, and components of a laboratory apparatus for undergraduate control systems laboratories. During the summer of

  3. A Robust Reinforcement Learning Control Design Method for Nonlinear System with Partially Unknown Structure

    NASA Astrophysics Data System (ADS)

    Nakano, Kazuhiro; Obayashi, Masanao; Kuremoto, Takashi; Kobayashi, Kunikazu

    We propose a robust control system which has robustness for disturbance and can deal with a nonlinear system with partially unknown structure by fusing reinforcement learning and robust control theory. First, we solved an optimal control problem without using unknown part of functions of the system, using neural network and the repetition learning of reinforcement learning algorithm. Second, we built the robust reinforcement learning control system which permits uncertainty and has robustness for disturbance by fusing the idea of H infinity control theory with above system.

  4. RSRA/X-Wing flight control system development - Lessons learned

    NASA Technical Reports Server (NTRS)

    Corliss, Lloyd D.; Dunn, William R.; Morrison, Michael A.

    1989-01-01

    The X-Wing, in concept, marries the efficiencies of a helicopter and fixed wing aircraft through the use of a four-bladed wing/rotor that can be rotated or stopped in flight. The RSRA/X-Wing flight test program was a technology demonstration of this concept which, after three successful flights, was discontinued in late 1987. In spite of many technical challenges in this program, such as the use of circulation control, the fabrication of a large all-composite rotor, the development of an advanced, quadruplex digital flight control system, and the need for higher harmonic control, no major technical problems had been encountered at the time of the stop-work order. This paper addresses the issues of flight control system development and focuses on lessons learned. As with other such programs, software development was the most consuming issue. Other subjects of discussion include the problems of balancing program goals with technical goals, software- and hard-ware-related problems, safety issues, and system testing.

  5. Pupil Diameter May Reflect Motor Control and Learning.

    PubMed

    White, Olivier; French, Robert M

    2017-01-01

    Non-luminance-mediated changes in pupil diameter have been used since the first studies by Darwin in 1872 as indicators of clinical, cognitive, and arousal states. However, the relation between processes involved in motor control and changes in pupil diameter remains largely unknown. Twenty participants attempted to compensate random walks of a cursor with a computer mouse to restrain its trajectory within a target circle while the authors recorded their pupil diameters. Two conditions allowed the authors to experimentally manipulate the motor and cognitive components of the task. First, the step size of the cursor's random walk was either large or small leading to 2 task difficulties (difficult or easy). Second, they instructed participants to imagine controlling the cursor by moving the mouse, but without actually moving it (task modality: imagined movement or real movement condition). Task difficulty and modality allowed the authors to show that pupil diameters reflect processes involved in motor control and in the processing of feedback, respectively. Furthermore, the authors also demonstrate that motor learning can be quantified by pupil size. This noninvasive approach provides a promising method for investigating not only motor control, but also motor imagery, a research field of growing importance in sports and rehabilitation.

  6. Reinforcement learning control with approximation of time-dependent agent dynamics

    NASA Astrophysics Data System (ADS)

    Kirkpatrick, Kenton Conrad

    Reinforcement Learning has received a lot of attention over the years for systems ranging from static game playing to dynamic system control. Using Reinforcement Learning for control of dynamical systems provides the benefit of learning a control policy without needing a model of the dynamics. This opens the possibility of controlling systems for which the dynamics are unknown, but Reinforcement Learning methods like Q-learning do not explicitly account for time. In dynamical systems, time-dependent characteristics can have a significant effect on the control of the system, so it is necessary to account for system time dynamics while not having to rely on a predetermined model for the system. In this dissertation, algorithms are investigated for expanding the Q-learning algorithm to account for the learning of sampling rates and dynamics approximations. For determining a proper sampling rate, it is desired to find the largest sample time that still allows the learning agent to control the system to goal achievement. An algorithm called Sampled-Data Q-learning is introduced for determining both this sample time and the control policy associated with that sampling rate. Results show that the algorithm is capable of achieving a desired sampling rate that allows for system control while not sampling "as fast as possible". Determining an approximation of an agent's dynamics can be beneficial for the control of hierarchical multiagent systems by allowing a high-level supervisor to use the dynamics approximations for task allocation decisions. To this end, algorithms are investigated for learning first- and second-order dynamics approximations. These algorithms are respectively called First-Order Dynamics Learning and Second-Order Dynamics Learning. The dynamics learning algorithms are evaluated on several examples that show their capability to learn accurate approximations of state dynamics. All of these algorithms are then evaluated on hierarchical multiagent systems

  7. The adaptive drop foot stimulator - Multivariable learning control of foot pitch and roll motion in paretic gait.

    PubMed

    Seel, Thomas; Werner, Cordula; Schauer, Thomas

    2016-11-01

    Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift (the lateral and/or medial edge of) the foot and leads to a pathological gait. In this contribution, we consider the treatment of this syndrome via functional electrical stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. A novel three-electrodes setup allows us to manipulate the recruitment of m. tibialis anterior and m. fibularis longus via two independent FES channels without violating the zero-net-current requirement of FES. We characterize the domain of admissible stimulation intensities that results from the nonlinearities in patients' stimulation intensity tolerance. To compensate most of the cross-couplings between the FES intensities and the foot motion, we apply a nonlinear controller output mapping. Gait phase transitions as well as foot pitch and roll angles are assessed in realtime by means of an Inertial Measurement Unit (IMU). A decentralized Iterative Learning Control (ILC) scheme is used to adjust the stimulation to the current needs of the individual patient. We evaluate the effectiveness of this approach in experimental trials with drop foot patients walking on a treadmill and on level ground. Starting from conventional stimulation parameters, the controller automatically determines individual stimulation parameters and thus achieves physiological foot pitch and roll angle trajectories within at most two strides.

  8. ITER (International Thermonuclear Experimental Reactor) current drive and heating physics

    SciTech Connect

    Nevins, W.M.; Lindquist, W. ); Fujisawa, N.; Kimura, H. ); Hopman, H.; Rebuffi, L.; Wegrowe, J.G. . NET Design Team); Parail, V.; Vdovin, V. . Inst. Atomnoj Ehn

    1990-01-01

    The ITER Current Drive and Heating (CD H) systems are required for: Ionization and current initiation; Non-inductive current ramp-up assist; Heating of the plasma; Steady-state operation with full non-inductive current drive; Current profile control; and Burn control by modulation of the auxiliary power. Steady-state current drive is the most demanding requirement, so this has driven the choice of the ITER current drive and heating systems.

  9. Language learning and control in monolinguals and bilinguals.

    PubMed

    Bartolotti, James; Marian, Viorica

    2012-08-01

    Parallel language activation in bilinguals leads to competition between languages. Experience managing this interference may aid novel language learning by improving the ability to suppress competition from known languages. To investigate the effect of bilingualism on the ability to control native-language interference, monolinguals and bilinguals were taught an artificial language designed to elicit between-language competition. Partial activation of interlingual competitors was assessed with eye-tracking and mouse-tracking during a word recognition task in the novel language. Eye-tracking results showed that monolinguals looked at competitors more than bilinguals, and for a longer duration of time. Mouse-tracking results showed that monolinguals' mouse movements were attracted to native-language competitors, whereas bilinguals overcame competitor interference by increasing the activation of target items. Results suggest that bilinguals manage cross-linguistic interference more effectively than monolinguals. We conclude that language interference can affect lexical retrieval, but bilingualism may reduce this interference by facilitating access to a newly learned language.

  10. Perceptual learning and sensomotor flexibility: cortical plasticity under attentional control?

    PubMed Central

    Fahle, Manfred

    2008-01-01

    Recent research reveals long-lasting cortical plasticity of early sensory cortices even in adults. Sensory signals could be modified under top-down control if necessary quite early in order to optimize their signal-to-noise ratio, leading to ‘low level’ or ‘early’ perceptual learning (PL). For easy tasks, such elaborate top-down influences are usually not required, and learning is restricted to late selection of the appropriate signals on higher cortical levels, which seems easier and faster to achieve. But to reach the absolute limits of sensory performance, PL seems to optimize the entire chain of sensory processing. Hence, improvement for these extreme perceptual abilities is quite specific for a number of stimulus parameters, such as the position in the visual field and sometimes even the trained eye, reflecting the specificity of receptive fields in early sensory cortices. Early PL may be just one example—even if a very extensive one—of the mechanisms of neuronal plasticity and sensomotor flexibility that are constantly updating our sensomotor representations as a result of experience. As an illustration, this review contains some new experimental results on PL and sensory flexibility in the context of adaptation to multifocal intraocular lenses. PMID:18977730

  11. The Network Operations Control Center upgrade task: Lessons learned

    NASA Technical Reports Server (NTRS)

    Sherif, J. S.; Tran, T.-L.; Lee, S.

    1994-01-01

    This article synthesizes and describes the lessons learned from the Network Operations Control Center (NOCC) upgrade project, from the requirements phase through development and test and transfer. At the outset, the NOCC upgrade was being performed simultaneously with two other interfacing and dependent upgrades at the Signal Processing Center (SPC) and Ground Communications Facility (GCF), thereby adding a significant measure of complexity to the management and overall coordination of the development and transfer-to-operations (DTO) effort. Like other success stories, this project carried with it the traditional elements of top management support and exceptional dedication of cognizant personnel. Additionally, there were several NOCC-specific reasons for success, such as end-to-end system engineering, adoption of open-system architecture, thorough requirements management, and use of appropriate off-the-shelf technologies. On the other hand, there were several difficulties, such as ill-defined external interfaces, transition issues caused by new communications protocols, ambivalent use of two sets of policies and standards, and mistailoring of the new JPL management standard (due to the lack of practical guidelines). This article highlights the key lessons learned, as a means of constructive suggestions for the benefit of future projects.

  12. Language Learning and Control in Monolinguals and Bilinguals

    PubMed Central

    Bartolotti, James; Marian, Viorica

    2012-01-01

    Parallel language activation in bilinguals leads to competition between languages. Experience managing this interference may aid novel language learning by improving the ability to suppress competition from known languages. To investigate the effect of bilingualism on the ability to control native-language interference, monolinguals and bilinguals were taught an artificial language designed to elicit between-language competition. Partial activation of interlingual competitors was assessed with eye-tracking and mouse-tracking during a word recognition task in the novel language. Eye-tracking results showed that monolinguals looked at competitors more than bilinguals, and for a longer duration of time. Mouse-tracking results showed that monolinguals’ mouse-movements were attracted to native-language competitors, while bilinguals overcame competitor interference by increasing activation of target items. Results suggest that bilinguals manage cross-linguistic interference more effectively than monolinguals. We conclude that language interference can affect lexical retrieval, but bilingualism may reduce this interference by facilitating access to a newly-learned language. PMID:22462514

  13. Allowing Learners to Choose: Self-Controlled Practice Schedules for Learning Multiple Movement Patterns

    ERIC Educational Resources Information Center

    Wu, Will F. W.; Magill, Richard A.

    2011-01-01

    For this study, we investigated the effects of self-controlled practice on learning multiple motor skills. Thirty participants were randomly assigned to self-control or yoked conditions. Participants learned a three-keystroke pattern with three different relative time structures. Those in the self-control group chose one of three relative time…

  14. Efficient exploration through active learning for value function approximation in reinforcement learning.

    PubMed

    Akiyama, Takayuki; Hachiya, Hirotaka; Sugiyama, Masashi

    2010-06-01

    Appropriately designing sampling policies is highly important for obtaining better control policies in reinforcement learning. In this paper, we first show that the least-squares policy iteration (LSPI) framework allows us to employ statistical active learning methods for linear regression. Then we propose a design method of good sampling policies for efficient exploration, which is particularly useful when the sampling cost of immediate rewards is high. The effectiveness of the proposed method, which we call active policy iteration (API), is demonstrated through simulations with a batting robot.

  15. Iterative free-energy optimization for recurrent neural networks (INFERNO).

    PubMed

    Pitti, Alexandre; Gaussier, Philippe; Quoy, Mathias

    2017-01-01

    The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.

  16. Iterative free-energy optimization for recurrent neural networks (INFERNO)

    PubMed Central

    2017-01-01

    The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes’ synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle. PMID:28282439

  17. Iterative marker excision system.

    PubMed

    Myronovskyi, Maksym; Rosenkränzer, Birgit; Luzhetskyy, Andriy

    2014-05-01

    The deletions of large genomic DNA fragments and consecutive gene knockouts are prerequisites for the generation of organisms with improved properties. One of the key issues in this context is the removal of antibiotic resistance markers from engineered organisms without leaving an active recombinase recognition site. Here, we report the establishment of an iterative marker excision system (IMES) that solves this problem. Based on the phiC31 integrase and its mutant att sites, IMES can be used for highly effective deletion of DNA fragments between inversely oriented B-CC and P-GG sites. The B-CC and P-GG sites are derived from attB and attP by substitution of the central core TT dinucleotide with CC and GG, respectively. An unnatural RR site that resides in the chromosome following deletion is the joining product of the right shoulders of B-CC and P-GG. We show that the RR sites do not recombine with each other as well as the RR site recombines with B-CC. The recombination efficiencies between RR and P-GG or RR and LL are only 0.1 % and 1 %, respectively. Thus, IMES can be used for multistep genomic engineering without risking unwanted DNA recombination. The fabrication of multi-purpose antibiotic cassettes and examples of the utilisation of IMES are described.

  18. Nuclear Instrumentation and Control Cyber Testbed Considerations – Lessons Learned

    SciTech Connect

    Jonathan Gray; Robert Anderson; Julio G. Rodriguez; Cheol-Kwon Lee

    2014-08-01

    Abstract: Identifying and understanding digital instrumentation and control (I&C) cyber vulnerabilities within nuclear power plants and other nuclear facilities, is critical if nation states desire to operate nuclear facilities safely, reliably, and securely. In order to demonstrate objective evidence that cyber vulnerabilities have been adequately identified and mitigated, a testbed representing a facility’s critical nuclear equipment must be replicated. Idaho National Laboratory (INL) has built and operated similar testbeds for common critical infrastructure I&C for over ten years. This experience developing, operating, and maintaining an I&C testbed in support of research identifying cyber vulnerabilities has led the Korean Atomic Energy Research Institute of the Republic of Korea to solicit the experiences of INL to help mitigate problems early in the design, development, operation, and maintenance of a similar testbed. The following information will discuss I&C testbed lessons learned and the impact of these experiences to KAERI.

  19. Sustaining Teacher Control in a Blog-Based Personal Learning Environment

    ERIC Educational Resources Information Center

    Tomberg, Vladimir; Laanpere, Mart; Ley, Tobias; Normak, Peeter

    2013-01-01

    Various tools and services based on Web 2.0 (mainly blogs, wikis, social networking tools) are increasingly used in formal education to create personal learning environments, providing self-directed learners with more freedom, choice, and control over their learning. In such distributed and personalized learning environments, the traditional role…

  20. Cognitive Control over Learning: Creating, Clustering, and Generalizing Task-Set Structure

    ERIC Educational Resources Information Center

    Collins, Anne G. E.; Frank, Michael J.

    2013-01-01

    Learning and executive functions such as task-switching share common neural substrates, notably prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for…

  1. A User-Centered Educational Modeling Language Improving the Controllability of Learning Design Quality

    ERIC Educational Resources Information Center

    Zendi, Asma; Bouhadada, Tahar; Bousbia, Nabila

    2016-01-01

    Semiformal EMLs are developed to facilitate the adoption of educational modeling languages (EMLs) and to address practitioners' learning design concerns, such as reusability and readability. In this article, SDLD (Structure Dialogue Learning Design) is presented, which is a semiformal EML that aims to improve controllability of learning design…

  2. The first fusion reactor: ITER

    NASA Astrophysics Data System (ADS)

    Campbell, D. J.

    2016-11-01

    Established by the signature of the ITER Agreement in November 2006 and currently under construction at St Paul-lez-Durance in southern France, the ITER project [1,2] involves the European Union (including Switzerland), China, India, Japan, the Russian Federation, South Korea and the United States. ITER (`the way' in Latin) is a critical step in the development of fusion energy. Its role is to provide an integrated demonstration of the physics and technology required for a fusion power plant based on magnetic confinement.

  3. Hierarchical approximate policy iteration with binary-tree state space decomposition.

    PubMed

    Xu, Xin; Liu, Chunming; Yang, Simon X; Hu, Dewen

    2011-12-01

    In recent years, approximate policy iteration (API) has attracted increasing attention in reinforcement learning (RL), e.g., least-squares policy iteration (LSPI) and its kernelized version, the kernel-based LSPI algorithm. However, it remains difficult for API algorithms to obtain near-optimal policies for Markov decision processes (MDPs) with large or continuous state spaces. To address this problem, this paper presents a hierarchical API (HAPI) method with binary-tree state space decomposition for RL in a class of absorbing MDPs, which can be formulated as time-optimal learning control tasks. In the proposed method, after collecting samples adaptively in the state space of the original MDP, a learning-based decomposition strategy of sample sets was designed to implement the binary-tree state space decomposition process. Then, API algorithms were used on the sample subsets to approximate local optimal policies of sub-MDPs. The original MDP was decomposed into a binary-tree structure of absorbing sub-MDPs, constructed during the learning process, thus, local near-optimal policies were approximated by API algorithms with reduced complexity and higher precision. Furthermore, because of the improved quality of local policies, the combined global policy performed better than the near-optimal policy obtained by a single API algorithm in the original MDP. Three learning control problems, including path-tracking control of a real mobile robot, were studied to evaluate the performance of the HAPI method. With the same setting for basis function selection and sample collection, the proposed HAPI obtained better near-optimal policies than previous API methods such as LSPI and KLSPI.

  4. From Intent to Action: An Iterative Engineering Process

    ERIC Educational Resources Information Center

    Mouton, Patrice; Rodet, Jacques; Vacaresse, Sylvain

    2015-01-01

    Quite by chance, and over the course of a few haphazard meetings, a Master's degree in "E-learning Design" gradually developed in a Faculty of Economics. Its original and evolving design was the result of an iterative process carried out, not by a single Instructional Designer (ID), but by a full ID team. Over the last 10 years it has…

  5. Towards Greater Learner Control: Web Supported Project-Based Learning

    ERIC Educational Resources Information Center

    Guthrie, Cameron

    2010-01-01

    Project-based learning has been suggested as an appropriate pedagogy to prepare students in information systems for the realities of the business world. Web-based resources have been used to support such pedagogy with mixed results. The paper argues that the design of web-based learning support to cater to different learning styles may give…

  6. Learning in the Digital Age: Control or Connection?

    ERIC Educational Resources Information Center

    Van Galen, Jane

    2013-01-01

    In October 2011, 200 state school officers and legislators gathered at a hotel in San Francisco to learn how to "revolutionize" learning by "personalizing" instruction. The occasion was former Florida Gov. Jeb Bush's second annual National Summit on Education Reform. The topic was digital learning. The vision of digitally managed curriculum and…

  7. Tailored, iterative, printed dietary feedback is as effective as group education in improving dietary behaviours: results from a randomised control trial in middle-aged adults with cardiovascular risk factors

    PubMed Central

    2011-01-01

    Background Tailored nutrition interventions have been shown to be more effective than non-tailored materials in changing dietary behaviours, particularly fat intake and fruit and vegetable intake. But further research examining efficacy of tailored nutrition education in comparison to other nutrition education methods and across a wider range of dietary behaviours is needed. The Stages to Healthy Eating Patterns Study (STEPs) was an intervention study, in middle-aged adults with cardiovascular risk factors, to examine the effectiveness of printed, tailored, iterative dietary feedback delivered by mail in improving short-term dietary behaviour in the areas of saturated fat, fruit, vegetable and grain and cereal intake. Methods STEPs was a 3-month randomised controlled trial with a pre and post-test design. There were three experimental conditions: 1) tailored, iterative, printed dietary feedback (TF) with three instalments mail-delivered over a 3-month period that were re-tailored to most recent assessment of dietary intake, intention to change and assessment of self-adequacy of dietary intake. Tailoring for dietary intake was performed on data from a validated 63-item combination FFQ designed for the purpose 2) small group nutrition education sessions (GE): consisting of two 90-minute dietitian-led small group nutrition education sessions and 3) and a wait-listed control (C) group who completed the dietary measures and socio-demographic questionnaires at baseline and 3-months later. Dietary outcome measures in the areas of saturated fat intake (g), and the intake of fruit (serves), vegetables (serves), grain and cereals as total and wholegrain (serves) were collected using 7-day estimated dietary records. Descriptive statistics, paired t-tests and general linear models adjusted for baseline dietary intake, age and gender were used to examine the effectiveness of different nutrition interventions. Results The TF group reported a significantly greater increase in

  8. Active prospective control is required for effective sensorimotor learning.

    PubMed

    Snapp-Childs, Winona; Casserly, Elizabeth; Mon-Williams, Mark; Bingham, Geoffrey P

    2013-01-01

    Passive modeling of movements is often used in movement therapy to overcome disabilities caused by stroke or other disorders (e.g. Developmental Coordination Disorder or Cerebral Palsy). Either a therapist or, recently, a specially designed robot moves or guides the limb passively through the movement to be trained. In contrast, action theory has long suggested that effective skill acquisition requires movements to be actively generated. Is this true? In view of the former, we explicitly tested the latter. Previously, a method was developed that allows children with Developmental Coordination Disorder to produce effective movements actively, so as to improve manual performance to match that of typically developing children. In the current study, we tested practice using such active movements as compared to practice using passive movement. The passive movement employed, namely haptic tracking, provided a strong test of the comparison, one that showed that the mere inaction of the muscles is not the problem. Instead, lack of prospective control was. The result was no effective learning with passive movement while active practice with prospective control yielded significant improvements in performance.

  9. Gaussian Processes for Data-Efficient Learning in Robotics and Control.

    PubMed

    Deisenroth, Marc Peter; Fox, Dieter; Rasmussen, Carl Edward

    2015-02-01

    Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

  10. Rule-based mechanisms of learning for intelligent adaptive flight control

    NASA Technical Reports Server (NTRS)

    Handelman, David A.; Stengel, Robert F.

    1990-01-01

    How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

  11. The ITER project construction status

    NASA Astrophysics Data System (ADS)

    Motojima, O.

    2015-10-01

    The pace of the ITER project in St Paul-lez-Durance, France is accelerating rapidly into its peak construction phase. With the completion of the B2 slab in August 2014, which will support about 400 000 metric tons of the tokamak complex structures and components, the construction is advancing on a daily basis. Magnet, vacuum vessel, cryostat, thermal shield, first wall and divertor structures are under construction or in prototype phase in the ITER member states of China, Europe, India, Japan, Korea, Russia, and the United States. Each of these member states has its own domestic agency (DA) to manage their procurements of components for ITER. Plant systems engineering is being transformed to fully integrate the tokamak and its auxiliary systems in preparation for the assembly and operations phase. CODAC, diagnostics, and the three main heating and current drive systems are also progressing, including the construction of the neutral beam test facility building in Padua, Italy. The conceptual design of the Chinese test blanket module system for ITER has been completed and those of the EU are well under way. Significant progress has been made addressing several outstanding physics issues including disruption load characterization, prediction, avoidance, and mitigation, first wall and divertor shaping, edge pedestal and SOL plasma stability, fuelling and plasma behaviour during confinement transients and W impurity transport. Further development of the ITER Research Plan has included a definition of the required plant configuration for 1st plasma and subsequent phases of ITER operation as well as the major plasma commissioning activities and the needs of the accompanying R&D program to ITER construction by the ITER parties.

  12. Learning-based position control of a closed-kinematic chain robot end-effector

    NASA Technical Reports Server (NTRS)

    Nguyen, Charles C.; Zhou, Zhen-Lei

    1990-01-01

    A trajectory control scheme whose design is based on learning theory, for a six-degree-of-freedom (DOF) robot end-effector built to study robotic assembly of NASA hardwares in space is presented. The control scheme consists of two control systems: the feedback control system and the learning control system. The feedback control system is designed using the concept of linearization about a selected operating point, and the method of pole placement so that the closed-loop linearized system is stabilized. The learning control scheme consisting of PD-type learning controllers, provides additional inputs to improve the end-effector performance after each trial. Experimental studies performed on a 2 DOF end-effector built at CUA, for three tracking cases show that actual trajectories approach desired trajectories as the number of trials increases. The tracking errors are substantially reduced after only five trials.

  13. Motor Learning and Control Foundations of Kinesiology: Defining the Academic Core

    ERIC Educational Resources Information Center

    Fischman, Mark G.

    2007-01-01

    This paper outlines the kinesiological foundations of the motor behavior subdisciplines of motor learning and motor control. After defining the components of motor behavior, the paper addresses the undergraduate major and core knowledge by examining several classic textbooks in motor learning and control, as well as a number of contemporary…

  14. Self-Controlled Amount of Practice Benefits Learning of a Motor Skill

    ERIC Educational Resources Information Center

    Post, Phillip G.; Fairbrother, Jeffrey T.; Barros, Joao A. C.

    2011-01-01

    Self-control over factors involving task-related information (e.g., feedback) can enhance motor learning. It is unknown if these benefits extend to manipulations that do not directly affect such information. The purpose of this study was to determine if self-control over the amount of practice would also facilitate learning. Participants learned…

  15. Permutations of Control: Cognitive Considerations for Agent-Based Learning Environments.

    ERIC Educational Resources Information Center

    Baylor, Amy L.

    2001-01-01

    Discussion of intelligent agents and their use in computer learning environments focuses on cognitive considerations. Presents four dimension of control that should be considered in designing agent-based learning environments: learner control, from constructivist to instructivist; feedback; relationship of learner to agent; and learner confidence…

  16. A Learning Model for Enhancing the Student's Control in Educational Process Using Web 2.0 Personal Learning Environments

    ERIC Educational Resources Information Center

    Rahimi, Ebrahim; van den Berg, Jan; Veen, Wim

    2015-01-01

    In recent educational literature, it has been observed that improving student's control has the potential of increasing his or her feeling of ownership, personal agency and activeness as means to maximize his or her educational achievement. While the main conceived goal for personal learning environments (PLEs) is to increase student's control by…

  17. ITER Central Solenoid Module Fabrication

    SciTech Connect

    Smith, John

    2016-09-23

    The fabrication of the modules for the ITER Central Solenoid (CS) has started in a dedicated production facility located in Poway, California, USA. The necessary tools have been designed, built, installed, and tested in the facility to enable the start of production. The current schedule has first module fabrication completed in 2017, followed by testing and subsequent shipment to ITER. The Central Solenoid is a key component of the ITER tokamak providing the inductive voltage to initiate and sustain the plasma current and to position and shape the plasma. The design of the CS has been a collaborative effort between the US ITER Project Office (US ITER), the international ITER Organization (IO) and General Atomics (GA). GA’s responsibility includes: completing the fabrication design, developing and qualifying the fabrication processes and tools, and then completing the fabrication of the seven 110 tonne CS modules. The modules will be shipped separately to the ITER site, and then stacked and aligned in the Assembly Hall prior to insertion in the core of the ITER tokamak. A dedicated facility in Poway, California, USA has been established by GA to complete the fabrication of the seven modules. Infrastructure improvements included thick reinforced concrete floors, a diesel generator for backup power, along with, cranes for moving the tooling within the facility. The fabrication process for a single module requires approximately 22 months followed by five months of testing, which includes preliminary electrical testing followed by high current (48.5 kA) tests at 4.7K. The production of the seven modules is completed in a parallel fashion through ten process stations. The process stations have been designed and built with most stations having completed testing and qualification for carrying out the required fabrication processes. The final qualification step for each process station is achieved by the successful production of a prototype coil. Fabrication of the first

  18. On the integration of reinforcement learning and approximate reasoning for control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1991-01-01

    The author discusses the importance of strengthening the knowledge representation characteristic of reinforcement learning techniques using methods such as approximate reasoning. The ARIC (approximate reasoning-based intelligent control) architecture is an example of such a hybrid approach in which the fuzzy control rules are modified (fine-tuned) using reinforcement learning. ARIC also demonstrates that it is possible to start with an approximately correct control knowledge base and learn to refine this knowledge through further experience. On the other hand, techniques such as the TD (temporal difference) algorithm and Q-learning establish stronger theoretical foundations for their use in adaptive control and also in stability analysis of hybrid reinforcement learning and approximate reasoning-based controllers.

  19. Progress in LHCD: a tool for advanced regimes on ITER

    NASA Astrophysics Data System (ADS)

    Tuccillo, A. A.; Barbato, E.; Bae, Y. S.; Becoulet, A.; Bernabei, S.; Bibet, P.; Calabrò, G.; Cardinali, A.; Castaldo, C.; Cesario, R.; Cho, M. H.; Cirant, S.; Crisanti, F.; Ekedahl, A.; Eriksson, L.-G.; Farina, D.; Giruzzi, G.; Goniche, M.; Granucci, G.; Ide, S.; Imbeaux, F.; Karttunen, S.; Litaudon, X.; Mailloux, J.; Mazon, D.; Mirizzi, F.; Moreau, D.; Nowak, S.; Namkung, W.; Panaccione, L.; Pericoli-Ridolfini, V.; Peysson, Y.; Petrzilka, V.; Podda, S.; Rantamaki, K.; Santini, F.; Saveliev, A.; Schneider, M.; Sozzi, C.; Suzuki, T.

    2005-12-01

    The recent success in coupling lower hybrid (LH) waves in high performance plasmas at JET together with the first demonstration on FTU of the coupling capability of the new passive active multijunction launcher removed major concerns on the possibility of using LH on ITER. LH exhibits the highest experimental current drive (CD) efficiency at low plasma temperature thus making it the natural candidate for off-axis CD on ITER where current profile control will help in maintaining burning performance on a long-time scale. We review recent LH results: long internal transport barrier obtained in JET with current profile sustained and controlled by LH acting under real time feedback together with first LH control of flat q-profile in a hybrid regime with Te ~ Ti. Minutes long fully non-inductive LH driven discharges on Tore Supra (TS). High CD efficiency with electron cyclotron in synergy with LH obtained in FTU and TS opening the possibility of interesting scenarii on ITER for MHD stabilization. Preliminary results of LH modelling for ITER are also reported. A brief overview of ITER LH system is reported together with some indication of new coming LH experiments, in particular KSTAR where CW klystrons at the foreseen ITER frequency of 5 GHz are being developed.

  20. A parameter control method in reinforcement learning to rapidly follow unexpected environmental changes.

    PubMed

    Murakoshi, Kazushi; Mizuno, Junya

    2004-11-01

    In order to rapidly follow unexpected environmental changes, we propose a parameter control method in reinforcement learning that changes each of learning parameters in appropriate directions. We determine each appropriate direction on the basis of relationships between behaviors and neuromodulators by considering an emergency as a key word. Computer experiments show that the agents using our proposed method could rapidly respond to unexpected environmental changes, not depending on either two reinforcement learning algorithms (Q-learning and actor-critic (AC) architecture) or two learning problems (discontinuous and continuous state-action problems).

  1. Self-Learning Variable Structure Control for a Class of Sensor-Actuator Systems

    PubMed Central

    Chen, Sanfeng; Li, Shuai; Liu, Bo; Lou, Yuesheng; Liang, Yongsheng

    2012-01-01

    Variable structure strategy is widely used for the control of sensor-actuator systems modeled by Euler-Lagrange equations. However, accurate knowledge on the model structure and model parameters are often required for the control design. In this paper, we consider model-free variable structure control of a class of sensor-actuator systems, where only the online input and output of the system are available while the mathematic model of the system is unknown. The problem is formulated from an optimal control perspective and the implicit form of the control law are analytically obtained by using the principle of optimality. The control law and the optimal cost function are explicitly solved iteratively. Simulations demonstrate the effectiveness and the efficiency of the proposed method. PMID:22778633

  2. CREB Selectively Controls Learning-Induced Structural Remodeling of Neurons

    ERIC Educational Resources Information Center

    Middei, Silvia; Spalloni, Alida; Longone, Patrizia; Pittenger, Christopher; O'Mara, Shane M.; Marie, Helene; Ammassari-Teule, Martine

    2012-01-01

    The modulation of synaptic strength associated with learning is post-synaptically regulated by changes in density and shape of dendritic spines. The transcription factor CREB (cAMP response element binding protein) is required for memory formation and in vitro dendritic spine rearrangements, but its role in learning-induced remodeling of neurons…

  3. Remote Labs and Game-Based Learning for Process Control

    ERIC Educational Resources Information Center

    Zualkernan, Imran A.; Husseini, Ghaleb A.; Loughlin, Kevin F.; Mohebzada, Jamshaid G.; El Gaml, Moataz

    2013-01-01

    Social networking platforms and computer games represent a natural informal learning environment for the current generation of learners in higher education. This paper explores the use of game-based learning in the context of an undergraduate chemical engineering remote laboratory. Specifically, students are allowed to manipulate chemical…

  4. Assessing Students' Learning of Internal Controls: Closing the Loop

    ERIC Educational Resources Information Center

    Amer, T. S.; Mohrweis, Lawrence C.

    2009-01-01

    This study describes the multifaceted components of an assessment process. The paper explains a novel approach in which an advisory council participated in a "fun," hands-on activity to rank-order learning outcomes. The top ranked learning competency, as identified by the advisory council, was the need for students to gain a better…

  5. Comparative Learning in Partnerships: Control, Competition or Collaboration?

    ERIC Educational Resources Information Center

    Takahashi, Chie

    2008-01-01

    This paper examines the quality and development of relations between organisations and the ways in which these are informed by incidental learning experiences in two projects. The paper conceptualizes instances of inter-organisational learning (IOL) applying theories such as principal-agent, prisoners' dilemma and women's place in community…

  6. Adaptive versus Learner Control in a Multiple Intelligence Learning Environment

    ERIC Educational Resources Information Center

    Kelly, Declan

    2008-01-01

    Within the field of technology enhanced learning, adaptive educational systems offer an advanced form of learning environment that attempts to meet the needs of different students. Such systems capture and represent, for each student, various characteristics such as knowledge and traits in an individual learner model. Subsequently, using the…

  7. Fusion Power measurement at ITER

    SciTech Connect

    Bertalot, L.; Barnsley, R.; Krasilnikov, V.; Stott, P.; Suarez, A.; Vayakis, G.; Walsh, M.

    2015-07-01

    Nuclear fusion research aims to provide energy for the future in a sustainable way and the ITER project scope is to demonstrate the feasibility of nuclear fusion energy. ITER is a nuclear experimental reactor based on a large scale fusion plasma (tokamak type) device generating Deuterium - Tritium (DT) fusion reactions with emission of 14 MeV neutrons producing up to 700 MW fusion power. The measurement of fusion power, i.e. total neutron emissivity, will play an important role for achieving ITER goals, in particular the fusion gain factor Q related to the reactor performance. Particular attention is given also to the development of the neutron calibration strategy whose main scope is to achieve the required accuracy of 10% for the measurement of fusion power. Neutron Flux Monitors located in diagnostic ports and inside the vacuum vessel will measure ITER total neutron emissivity, expected to range from 1014 n/s in Deuterium - Deuterium (DD) plasmas up to almost 10{sup 21} n/s in DT plasmas. The neutron detection systems as well all other ITER diagnostics have to withstand high nuclear radiation and electromagnetic fields as well ultrahigh vacuum and thermal loads. (authors)

  8. Longitudinal investigation on learned helplessness tested under negative and positive reinforcement involving stimulus control.

    PubMed

    Oliveira, Emileane C; Hunziker, Maria Helena

    2014-07-01

    In this study, we investigated whether (a) animals demonstrating the learned helplessness effect during an escape contingency also show learning deficits under positive reinforcement contingencies involving stimulus control and (b) the exposure to positive reinforcement contingencies eliminates the learned helplessness effect under an escape contingency. Rats were initially exposed to controllable (C), uncontrollable (U) or no (N) shocks. After 24h, they were exposed to 60 escapable shocks delivered in a shuttlebox. In the following phase, we selected from each group the four subjects that presented the most typical group pattern: no escape learning (learned helplessness effect) in Group U and escape learning in Groups C and N. All subjects were then exposed to two phases, the (1) positive reinforcement for lever pressing under a multiple FR/Extinction schedule and (2) a re-test under negative reinforcement (escape). A fourth group (n=4) was exposed only to the positive reinforcement sessions. All subjects showed discrimination learning under multiple schedule. In the escape re-test, the learned helplessness effect was maintained for three of the animals in Group U. These results suggest that the learned helplessness effect did not extend to discriminative behavior that is positively reinforced and that the learned helplessness effect did not revert for most subjects after exposure to positive reinforcement. We discuss some theoretical implications as related to learned helplessness as an effect restricted to aversive contingencies and to the absence of reversion after positive reinforcement. This article is part of a Special Issue entitled: insert SI title.

  9. Metacognitive control and strategy selection: deciding to practice retrieval during learning.

    PubMed

    Karpicke, Jeffrey D

    2009-11-01

    Retrieval practice is a potent technique for enhancing learning, but how often do students practice retrieval when they regulate their own learning? In 4 experiments the subjects learned foreign-language items across multiple study and test periods. When items were assigned to be repeatedly tested, repeatedly studied, or removed after they were recalled, repeated retrieval produced powerful effects on learning and retention. However, when subjects were given control over their own learning and could choose to test, study, or remove items, many subjects chose to remove items rather than practice retrieval, leading to poor retention. In addition, when tests were inserted in the learning phase, attempting retrieval improved learning by enhancing subsequent encoding during study. But when students were given control over their learning they did not attempt retrieval as early or as often as they should to promote the best learning. The experiments identify a compelling metacognitive illusion that occurs during self-regulated learning: Once students can recall an item they tend to believe they have "learned" it. This leads students to terminate practice rather than practice retrieval, a strategy choice that ultimately results in poor retention.

  10. Relaxation Criteria for Iterated Traffic Simulations

    NASA Astrophysics Data System (ADS)

    Kelly, Terence; Nagel, Kai

    Iterative transportation microsimulations adjust traveler route plans by iterating between a microsimulation and a route planner. At each iteration, the route planner adjusts individuals' route choices based on the preceding microsimulations. Empirically, this process yields good results, but it is usually unclear when to stop the iterative process when modeling real-world traffic. This paper investigates several criteria to judge relaxation of the iterative process, emphasizing criteria related to traveler decision-making.

  11. Electron density measurements in the ITER fusion plasma

    NASA Astrophysics Data System (ADS)

    Watts, Christopher; Udintsev, Victor; Andrew, Philip; Vayakis, George; Van Zeeland, Michael; Brower, David; Feder, Russell; Mukhin, Eugene; Tolstyakov, Sergey

    2013-08-01

    The operation of ITER requires high-quality estimates of the plasma electron density over multiple regions in the plasma for plasma evaluation, plasma control and machine protection purposes. Although the density regimes of ITER are not very different from those of existing tokamaks (1018-1021 m-3), the severe conditions of the fusion plasma environment present particular challenges to implementing these density diagnostics. In this paper we present an overview of the array of ITER electron density diagnostics designed to measure over the entire ITER domain: plasma core, pedestal, edge, scrape-off layer and divertor. It will focus on the challenges faced in making these measurements, and the technical solutions of the current designs.

  12. A mathematical theory of learning control for linear discrete multivariable systems

    NASA Technical Reports Server (NTRS)

    Phan, Minh; Longman, Richard W.

    1988-01-01

    When tracking control systems are used in repetitive operations such as robots in various manufacturing processes, the controller will make the same errors repeatedly. Here consideration is given to learning controllers that look at the tracking errors in each repetition of the process and adjust the control to decrease these errors in the next repetition. A general formalism is developed for learning control of discrete-time (time-varying or time-invariant) linear multivariable systems. Methods of specifying a desired trajectory (such that the trajectory can actually be performed by the discrete system) are discussed, and learning controllers are developed. Stability criteria are obtained which are relatively easy to use to insure convergence of the learning process, and proper gain settings are discussed in light of measurement noise and system uncertainties.

  13. Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking

    DTIC Science & Technology

    2015-07-01

    feedback control to generate desired lateral and angular velocities to compensate for vehicle slip rates. Finally, they use the robot’s inverse dynamics to...Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking Chris J. Ostafew Institute for Aerospace Studies...paper presents a Learning-based Nonlinear Model Predictive Control (LB-NMPC) algorithm to achieve high-performance path tracking in challenging off-road

  14. Construction Safety Forecast for ITER

    SciTech Connect

    cadwallader, lee charles

    2006-11-01

    The International Thermonuclear Experimental Reactor (ITER) project is poised to begin its construction activity. This paper gives an estimate of construction safety as if the experiment was being built in the United States. This estimate of construction injuries and potential fatalities serves as a useful forecast of what can be expected for construction of such a major facility in any country. These data should be considered by the ITER International Team as it plans for safety during the construction phase. Based on average U.S. construction rates, ITER may expect a lost workday case rate of < 4.0 and a fatality count of 0.5 to 0.9 persons per year.

  15. Error Field Correction in ITER

    SciTech Connect

    Park, Jong-kyu; Boozer, Allen H.; Menard, Jonathan E.; Schaffer, Michael J.

    2008-05-22

    A new method for correcting magnetic field errors in the ITER tokamak is developed using the Ideal Perturbed Equilibrium Code (IPEC). The dominant external magnetic field for driving islands is shown to be localized to the outboard midplane for three ITER equilibria that represent the projected range of operational scenarios. The coupling matrices between the poloidal harmonics of the external magnetic perturbations and the resonant fields on the rational surfaces that drive islands are combined for different equilibria and used to determine an ordered list of the dominant errors in the external magnetic field. It is found that efficient and robust error field correction is possible with a fixed setting of the correction currents relative to the currents in the main coils across the range of ITER operating scenarios that was considered.

  16. Learning in the tutorial group: a balance between individual freedom and institutional control.

    PubMed

    McAllister, Anita; Aanstoot, Janna; Hammarström, Inger Lundeborg; Samuelsson, Christina; Johannesson, Eva; Sandström, Karin; Berglind, Ulrika

    2014-01-01

    The study investigates factors in problem-based learning tutorial groups which promote or inhibit learning. The informants were tutors and students from speech-language pathology and physiotherapy programmes. Semi-structured focus-group interviews and individual interviews were used. Results revealed three themes: Responsibility. Time and Support. Under responsibility, the delicate balance between individual and institutional responsibility and control was shown. Time included short and long-term perspectives on learning. Under support, supporting documents, activities and personnel resources were mentioned. In summary, an increased control by the program and tutors decreases student's motivation to assume responsibility for learning. Support in tutorial groups needs to adapt to student progression and to be well aligned to tutorial work to have the intended effect. A lifelong learning perspective may help students develop a meta-awareness regarding learning that could make tutorial work more meaningful.

  17. Exploring the relationship between perceptual learning and top-down attentional control.

    PubMed

    Byers, Anna; Serences, John T

    2012-12-01

    Here, we review the role of top-down attention in both the acquisition and the expression of perceptual learning, as well as the role of learning in more efficiently guiding attentional modulations. Although attention often mediates learning at the outset of training, many of the characteristic behavioral and neural changes associated with learning can be observed even when stimuli are task irrelevant and ignored. However, depending on task demands, attention can override the effects of perceptual learning, suggesting that even if top-down factors are not strictly necessary to observe learning, they play a critical role in determining how learning-related changes in behavior and neural activity are ultimately expressed. In turn, training may also act to optimize the effectiveness of top-down attentional control by improving the efficiency of sensory gain modulations, regulating intrinsic noise, and altering the read-out of sensory information.

  18. Iterative Restoration Of Tomosynthetic Slices

    NASA Astrophysics Data System (ADS)

    Ruttimann, U. E.; Groenhuis, R. A.; Webber, R. L.

    1984-08-01

    Tomosynthetic reconstructions suffer from the disadvantage that blurred images of object detail lying outside the plane of interest are superimposed over the desired image of structures in the tomosynthetic plane. It is proposed to selectively reduce these undesired superimpositions by a constrained iterative restoration method. Sufficient conditions are derived ensuring the convergence of the iterations to the exact solution in the absence of noise and constraints. Although in practice the restoration process must be left incomplete because of noise and quantization artifacts, the experimental results demonstrate that for reasons of stability these convergence conditions must be satisfied.

  19. Self-controlled feedback: does it enhance learning because performers get feedback when they need it?

    PubMed

    Chiviacowsky, Suzete; Wulf, Gabriele

    2002-12-01

    This paper examines whether self-controlled feedback schedules enhance learning, because they are more tailored to the performers' needs than externally controlled feedback schedules. Participants practiced a sequential timing task. One group of learners (self-control) was provided with feedback whenever they requested it, whereas another group (yoked) had no influence on the feedback schedule. The self-control group showed learning benefits on a delayed transfer test. Questionnaire results revealed that self-control learners asked for feedback primarily after good trials and yoked learners preferred to receive feedback after good trials. Analyses demonstrated that errors were lower on feedback than no-feedback trials for the self-control group but not for the yoked group. Thus, self-control participants appeared to use a strategy for requesting feedback. This might explain learning advantages of self-controlled practice.

  20. Learning control for minimizing a quadratic cost during repetitions of a task

    NASA Technical Reports Server (NTRS)

    Longman, Richard W.; Chang, Chi-Kuang

    1990-01-01

    In many applications, control systems are asked to perform the same task repeatedly. Learning control laws have been developed over the last few years that allow the controller to improve its performance each repetition, and to converge to zero error in tracking a desired trajectory. This paper generates a new type of learning control law that learns to minimize a quadratic cost function for tracking. Besides being of interest in its own right, this objective alleviates the need to specify a desired trajectory that can actually be performed by the system. The approach used here is to adapt appropriate methods from numerical optimization theory in order to produce learning control algorithms that adjust the system command from repetition to repetition in order to converge to the quadratic cost optimal trajectory.

  1. Learning in Large-Scale Games and Cooperative Control

    DTIC Science & Technology

    2007-01-01

    interested in the field of learning in games largely due to the work of H. Peyton Young . While attending a game theory conference in New York in July...based on past observations, which is ef- fectively equivalent to regret modulo a bias term. A current open question is whether player choices would...Annual ACM Symposium on Principles of Dis- tributed Computing, pp. 276–283, 2005. [FY06] D. P. Foster and H. P. Young . “Regret testing: Learning to

  2. On the Learning of Distractors during Controlled and Automatic Processing.

    DTIC Science & Technology

    1980-02-04

    function of semantic, graphic and syntactic orienting tasks. Journal of Verbal Learning and Verbal Behavior, 1973, 12, 471-480. LaBerge , D. Attention...and the measurement of perceptual learning. Memory and Cognition, 1973, 1, 268-278. LaBerge , D. Acquisition of automatic processing in perceptual and...Univ A. Stevens , Holt Beranek & Newman, Cambridge, 1A D. Stone, SUY, Albany P. Suppes, Stanford Uuiv H. Swaminathan, Univ of Massachusetts K. Tatsuoka

  3. Motor-response learning at a process control panel by an autonomous robot

    SciTech Connect

    Spelt, P.F.; de Saussure, G.; Lyness, E.; Pin, F.G.; Weisbin, C.R.

    1988-01-01

    The Center for Engineering Systems Advanced Research (CESAR) was founded at Oak Ridge National Laboratory (ORNL) by the Department of Energy's Office of Energy Research/Division of Engineering and Geoscience (DOE-OER/DEG) to conduct basic research in the area of intelligent machines. Therefore, researchers at the CESAR Laboratory are engaged in a variety of research activities in the field of machine learning. In this paper, we describe our approach to a class of machine learning which involves motor response acquisition using feedback from trial-and-error learning. Our formulation is being experimentally validated using an autonomous robot, learning tasks of control panel monitoring and manipulation for effect process control. The CLIPS Expert System and the associated knowledge base used by the robot in the learning process, which reside in a hypercube computer aboard the robot, are described in detail. Benchmark testing of the learning process on a robot/control panel simulation system consisting of two intercommunicating computers is presented, along with results of sample problems used to train and test the expert system. These data illustrate machine learning and the resulting performance improvement in the robot for problems similar to, but not identical with, those on which the robot was trained. Conclusions are drawn concerning the learning problems, and implications for future work on machine learning for autonomous robots are discussed. 16 refs., 4 figs., 1 tab.

  4. The Effects of Instructor Control of Online Learning Environments on Satisfaction and Perceived Learning

    ERIC Educational Resources Information Center

    Costley, Jamie; Lange, Christopher

    2016-01-01

    Instructional design is important as it helps set the discourse, context, and content of learning in an online environment. Specific instructional design decisions do not only play a part in the discourse of the learners, but they can affect the learners' levels of satisfaction and perceived learning as well. Numerous studies have shown the value…

  5. Individual Differences in Learning: Cognitive Control, Cognitive Style, and Learning Style

    ERIC Educational Resources Information Center

    Price, Linda

    2004-01-01

    This paper assesses the value of three learning style tests when used to examine the design of educational materials for teaching computer science at a distance. The paper presents three studies where three different learning styles were used to discriminate preference and performance in different contexts. The studies indicate that the Learning…

  6. Selecting Learning Tasks: Effects of Adaptation and Shared Control on Learning Efficiency and Task Involvement

    ERIC Educational Resources Information Center

    Corbalan, Gemma; Kester, Liesbeth; van Merrienboer, Jeroen J. G.

    2008-01-01

    Complex skill acquisition by performing authentic learning tasks is constrained by limited working memory capacity [Baddeley, A. D. (1992). Working memory. "Science, 255", 556-559]. To prevent cognitive overload, task difficulty and support of each newly selected learning task can be adapted to the learner's competence level and perceived task…

  7. Exploiting Redundancy for Flexible Behavior: Unsupervised Learning in a Modular Sensorimotor Control Architecture

    ERIC Educational Resources Information Center

    Butz, Martin V.; Herbort, Oliver; Hoffmann, Joachim

    2007-01-01

    Autonomously developing organisms face several challenges when learning reaching movements. First, motor control is learned unsupervised or self-supervised. Second, knowledge of sensorimotor contingencies is acquired in contexts in which action consequences unfold in time. Third, motor redundancies must be resolved. To solve all 3 of these…

  8. Students' Learning and Locus of Control in Web-Supplemental Instruction

    ERIC Educational Resources Information Center

    Wang, Danhua

    2005-01-01

    This multicase study investigated the learning experiences of four college students identified respectively as internal and external locus of control. They were taking a basic educational technology course that supplemented classroom teaching with two course web sites. Four categories that characterized their learning experiences suggested some…

  9. Child Predictors of Learning to Control Variables via Instruction or Self-Discovery

    ERIC Educational Resources Information Center

    Wagensveld, Barbara; Segers, Eliane; Kleemans, Tijs; Verhoeven, Ludo

    2015-01-01

    We examined the role child factors on the acquisition and transfer of learning the control of variables strategy (CVS) via instruction or self-discovery. Seventy-six fourth graders and 43 sixth graders were randomly assigned to a group receiving direct CVS instruction or a discovery learning group. Prior to the intervention, cognitive, scientific,…

  10. The Role of Executive Control of Attention and Selective Encoding for Preschoolers' Learning

    ERIC Educational Resources Information Center

    Roderer, Thomas; Krebs, Saskia; Schmid, Corinne; Roebers, Claudia M.

    2012-01-01

    Selectivity in encoding, aspects of attentional control and their contribution to learning performance were explored in a sample of preschoolers. While the children are performing a learning task, their encoding of relevant and attention towards irrelevant information was recorded through an eye-tracking device. Recognition of target items was…

  11. Effectiveness of Adaptive Assessment versus Learner Control in a Multimedia Learning System

    ERIC Educational Resources Information Center

    Chen, Ching-Huei; Chang, Shu-Wei

    2015-01-01

    The purpose of this study was to explore the effectiveness of adaptive assessment versus learner control in a multimedia learning system designed to help secondary students learn science. Unlike other systems, this paper presents a workflow of adaptive assessment following instructional materials that better align with learners' cognitive…

  12. Permutations of Control: Cognitive Considerations for Agent-Based Learning Environments

    ERIC Educational Resources Information Center

    Baylor, Amy L.

    2004-01-01

    While there has been a significant amount of research on technical issues regarding the development of agent-based learning environments (e.g., see the special issue of Journal of "Interactive Learning Research," 10(3/4)), there is less information regarding cognitive foundations for these environments. The management of control is a prime issue…

  13. Iterative method for interferogram processing

    NASA Astrophysics Data System (ADS)

    Kotlyar, Victor V.; Seraphimovich, P. G.; Zalyalov, Oleg K.

    1994-12-01

    We have developed and numerically evaluated an iterative algorithm for interferogram processing including the Fourier-transform method, the Gerchberg-Papoulis algorithm and Wiener's filter-based regularization used in combination. Using a signal-to-noise ratio not less than 1, it has been possible to reconstruct the phase of an object field with accuracy better than 5%.

  14. Networking Theories by Iterative Unpacking

    ERIC Educational Resources Information Center

    Koichu, Boris

    2014-01-01

    An iterative unpacking strategy consists of sequencing empirically-based theoretical developments so that at each step of theorizing one theory serves as an overarching conceptual framework, in which another theory, either existing or emerging, is embedded in order to elaborate on the chosen element(s) of the overarching theory. The strategy is…

  15. Action Control, Motivated Strategies, and Integrative Motivation as Predictors of Language Learning Affect and the Intention to Continue Learning French

    ERIC Educational Resources Information Center

    MacIntyre, Peter D.; Blackie, Rebecca A.

    2012-01-01

    The present study examines the relative ability of variables from three motivational frameworks to predict four non-linguistic outcomes of language learning. The study examines Action Control Theory with its measures of (1) hesitation, (2) volatility and (3) rumination. The study also examined Pintrich's expectancy-value model that uses measures…

  16. Does peer learning or higher levels of e-learning improve learning abilities? A randomized controlled trial.

    PubMed

    Worm, Bjarne Skjødt; Jensen, Kenneth

    2013-01-01

    Background and aims The fast development of e-learning and social forums demands us to update our understanding of e-learning and peer learning. We aimed to investigate if higher, pre-defined levels of e-learning or social interaction in web forums improved students' learning ability. Methods One hundred and twenty Danish medical students were randomized to six groups all with 20 students (eCases level 1, eCases level 2, eCases level 2+, eTextbook level 1, eTextbook level 2, and eTextbook level 2+). All students participated in a pre-test, Group 1 participated in an interactive case-based e-learning program, while Group 2 was presented with textbook material electronically. The 2+ groups were able to discuss the material between themselves in a web forum. The subject was head injury and associated treatment and observation guidelines in the emergency room. Following the e-learning, all students completed a post-test. Pre- and post-tests both consisted of 25 questions randomly chosen from a pool of 50 different questions. Results All students concluded the study with comparable pre-test results. Students at Level 2 (in both groups) improved statistically significant compared to students at level 1 (p>0.05). There was no statistically significant difference between level 2 and level 2+. However, level 2+ was associated with statistically significant greater student's satisfaction than the rest of the students (p>0.05). Conclusions This study applies a new way of comparing different types of e-learning using a pre-defined level division and the possibility of peer learning. Our findings show that higher levels of e-learning does in fact provide better results when compared with the same type of e-learning at lower levels. While social interaction in web forums increase student satisfaction, learning ability does not seem to change. Both findings are relevant when designing new e-learning materials.

  17. Does peer learning or higher levels of e-learning improve learning abilities? A randomized controlled trial

    PubMed Central

    Worm, Bjarne Skjødt; Jensen, Kenneth

    2013-01-01

    Background and aims The fast development of e-learning and social forums demands us to update our understanding of e-learning and peer learning. We aimed to investigate if higher, pre-defined levels of e-learning or social interaction in web forums improved students’ learning ability. Methods One hundred and twenty Danish medical students were randomized to six groups all with 20 students (eCases level 1, eCases level 2, eCases level 2+, eTextbook level 1, eTextbook level 2, and eTextbook level 2+). All students participated in a pre-test, Group 1 participated in an interactive case-based e-learning program, while Group 2 was presented with textbook material electronically. The 2+ groups were able to discuss the material between themselves in a web forum. The subject was head injury and associated treatment and observation guidelines in the emergency room. Following the e-learning, all students completed a post-test. Pre- and post-tests both consisted of 25 questions randomly chosen from a pool of 50 different questions. Results All students concluded the study with comparable pre-test results. Students at Level 2 (in both groups) improved statistically significant compared to students at level 1 (p>0.05). There was no statistically significant difference between level 2 and level 2+. However, level 2+ was associated with statistically significant greater student's satisfaction than the rest of the students (p>0.05). Conclusions This study applies a new way of comparing different types of e-learning using a pre-defined level division and the possibility of peer learning. Our findings show that higher levels of e-learning does in fact provide better results when compared with the same type of e-learning at lower levels. While social interaction in web forums increase student satisfaction, learning ability does not seem to change. Both findings are relevant when designing new e-learning materials. PMID:24229729

  18. Role of the olivo-cerebellar complex in motor learning and control

    PubMed Central

    Schweighofer, Nicolas; Lang, Eric J.; Kawato, Mitsuo

    2013-01-01

    How is the cerebellum capable of efficient motor learning and control despite very low firing of the inferior olive (IO) inputs, which are postulated to carry errors needed for learning and contribute to on-line motor control? IO neurons form the largest electrically coupled network in the adult human brain. Here, we discuss how intermediate coupling strengths can lead to chaotic resonance and increase information transmission of the error signal despite the very low IO firing rate. This increased information transmission can then lead to more efficient learning than with weak or strong coupling. In addition, we argue that a dynamic modulation of IO electrical coupling via the Purkinje cell-deep cerebellar neurons – IO triangle could speed up learning and improve on-line control. Initially strong coupling would allow transmission of large errors to multiple functionally related Purkinje cells, resulting in fast but coarse learning as well as significant effects on deep cerebellar nucleus and on-line motor control. In the late phase of learning decreased coupling would allow desynchronized IO firing, allowing high-fidelity transmission of error, resulting in slower but fine learning, and little on-line motor control effects. PMID:23754983

  19. Influence of self-controlled feedback on learning a serial motor skill.

    PubMed

    Lim, Soowoen; Ali, Asif; Kim, Wonchan; Kim, Jingu; Choi, Sungmook; Radlo, Steven J

    2015-04-01

    Self-controlled feedback on a variety of tasks are well established as effective means of facilitating motor skill learning. This study assessed the effects of self-controlled feedback on the performance of a serial motor skill. The task was to learn the sequence of 18 movements that make up the Taekwondo Poomsae Taegeuk first, which is the first beginner's practice form learned in this martial art. Twenty-four novice female participants (M age=27.2 yr., SD=1.8) were divided into two groups. All participants performed 16 trials in 4 blocks of the acquisition phase and 20 hr. later, 8 trials in 2 blocks of the retention phase. The self-controlled feedback group had significantly higher performance compared to the yoked-feedback group with regard to acquisition and retention. The results of this study may contribute to the literature regarding feedback by extending the usefulness of self-controlled feedback for learning a serial skill.

  20. Machine Learning Control For Highly Reconfigurable High-Order Systems

    DTIC Science & Technology

    2015-01-02

    Rollins, Elizabeth. 2012. Adaptive Dynamic Inversion Control of Linear Plants with Control Position Constraints. IEEE Transactions on Control Systems...developed in this paper. 12. Rollins, Elizabeth, Valasek, John, Muse, Jonathan, and Bolender, Michael, "Nonlinear Adaptive Dynamic Inversion Applied...paper develops a nonlinear adaptive dynamic inversion control architecture with a control allocation scheme to track 8 realistic flight path angle

  1. A Control Systems Concept Inventory Test Design and Assessment

    ERIC Educational Resources Information Center

    Bristow, M.; Erkorkmaz, K.; Huissoon, J. P.; Jeon, Soo; Owen, W. S.; Waslander, S. L.; Stubley, G. D.

    2012-01-01

    Any meaningful initiative to improve the teaching and learning in introductory control systems courses needs a clear test of student conceptual understanding to determine the effectiveness of proposed methods and activities. The authors propose a control systems concept inventory. Development of the inventory was collaborative and iterative. The…

  2. The Effects of the Locus of CAI Control Strategies on the Learning of Mathematics Rules.

    ERIC Educational Resources Information Center

    Goetzfried, Leslie; Hannafin, Michael J.

    1985-01-01

    Effects of the locus of computer assisted instruction (CAI) strategies on low achievers' learning accuracy and efficiency were studied. Externally controlled adaptive, individually based learner control with advisement, and linear control design strategies were used. Effects were examined for CAI strategy, prior achievement, and sex of student.…

  3. Autonomous learning based on cost assumptions: theoretical studies and experiments in robot control.

    PubMed

    Ribeiro, C H; Hemerly, E M

    1999-06-01

    Autonomous learning techniques are based on experience acquisition. In most realistic applications, experience is time-consuming: it implies sensor reading, actuator control and algorithmic update, constrained by the learning system dynamics. The information crudeness upon which classical learning algorithms operate make such problems too difficult and unrealistic. Nonetheless, additional information for facilitating the learning process ideally should be embedded in such a way that the structural, well-studied characteristics of these fundamental algorithms are maintained. We investigate in this article a more general formulation of the Q-learning method that allows for a spreading of information derived from single updates towards a neighbourhood of the instantly visited state and converges to optimality. We show how this new formulation can be used as a mechanism to safely embed prior knowledge about the structure of the state space, and demonstrate it in a modified implementation of a reinforcement learning algorithm in a real robot navigation task.

  4. Autonomous learning based on cost assumptions: theoretical studies and experiments in robot control.

    PubMed

    Ribeiro, C H; Hemerly, E M

    2000-02-01

    Autonomous learning techniques are based on experience acquisition. In most realistic applications, experience is time-consuming: it implies sensor reading, actuator control and algorithmic update, constrained by the learning system dynamics. The information crudeness upon which classical learning algorithms operate make such problems too difficult and unrealistic. Nonetheless, additional information for facilitating the learning process ideally should be embedded in such a way that the structural, well-studied characteristics of these fundamental algorithms are maintained. We investigate in this article a more general formulation of the Q-learning method that allows for a spreading of information derived from single updates towards a neighbourhood of the instantly visited state and converges to optimality. We show how this new formulation can be used as a mechanism to safely embed prior knowledge about the structure of the state space, and demonstrate it in a modified implementation of a reinforcement learning algorithm in a real robot navigation task.

  5. The development of a thermal hydraulic feedback mechanism with a quasi-fixed point iteration scheme for control rod position modeling for the TRIGSIMS-TH application

    NASA Astrophysics Data System (ADS)

    Karriem, Veronica V.

    Nuclear reactor design incorporates the study and application of nuclear physics, nuclear thermal hydraulic and nuclear safety. Theoretical models and numerical methods implemented in computer programs are utilized to analyze and design nuclear reactors. The focus of this PhD study's is the development of an advanced high-fidelity multi-physics code system to perform reactor core analysis for design and safety evaluations of research TRIGA-type reactors. The fuel management and design code system TRIGSIMS was further developed to fulfill the function of a reactor design and analysis code system for the Pennsylvania State Breazeale Reactor (PSBR). TRIGSIMS, which is currently in use at the PSBR, is a fuel management tool, which incorporates the depletion code ORIGEN-S (part of SCALE system) and the Monte Carlo neutronics solver MCNP. The diffusion theory code ADMARC-H is used within TRIGSIMS to accelerate the MCNP calculations. It manages the data and fuel isotopic content and stores it for future burnup calculations. The contribution of this work is the development of an improved version of TRIGSIMS, named TRIGSIMS-TH. TRIGSIMS-TH incorporates a thermal hydraulic module based on the advanced sub-channel code COBRA-TF (CTF). CTF provides the temperature feedback needed in the multi-physics calculations as well as the thermal hydraulics modeling capability of the reactor core. The temperature feedback model is using the CTF-provided local moderator and fuel temperatures for the cross-section modeling for ADMARC-H and MCNP calculations. To perform efficient critical control rod calculations, a methodology for applying a control rod position was implemented in TRIGSIMS-TH, making this code system a modeling and design tool for future core loadings. The new TRIGSIMS-TH is a computer program that interlinks various other functional reactor analysis tools. It consists of the MCNP5, ADMARC-H, ORIGEN-S, and CTF. CTF was coupled with both MCNP and ADMARC-H to provide the

  6. A Real-time Reinforcement Learning Control System with H∞ Tracking Performance Compensator

    NASA Astrophysics Data System (ADS)

    Uchiyama, Shogo; Obayashi, Masanao; Kuremoto, Takashi; Kobayashi, Kunikazu

    Robust control theory generally guarantees robustness and stability of the closed-loop system. It however requires a mathematical model of the system to design the control system. It therefore can't often deal with nonlinear systems due to difficulty of modeling of the system. On the other hand, reinforcement learning methods can deal with nonlinear systems without any mathematical model. It however usually doesn't guarantee the stability of the system control. In this paper, we propose a “Real-time Reinforcement Learning Control System (RRLCS)” through combining reinforcement learning to treat unknown nonlinear systems and robust control theory to guarantee the robustness and stability of the system. Moreover, we analyze the stability of the proposed system using H∞ tracking performance and Lyapunov function. Finally, through the computer simulation for controlling an inverted pendulum system, we show the effectiveness of the proposed method.

  7. Consensus-based distributed cooperative learning from closed-loop neural control systems.

    PubMed

    Chen, Weisheng; Hua, Shaoyong; Zhang, Huaguang

    2015-02-01

    In this paper, the neural tracking problem is addressed for a group of uncertain nonlinear systems where the system structures are identical but the reference signals are different. This paper focuses on studying the learning capability of neural networks (NNs) during the control process. First, we propose a novel control scheme called distributed cooperative learning (DCL) control scheme, by establishing the communication topology among adaptive laws of NN weights to share their learned knowledge online. It is further proved that if the communication topology is undirected and connected, all estimated weights of NNs can converge to small neighborhoods around their optimal values over a domain consisting of the union of all state orbits. Second, as a corollary it is shown that the conclusion on the deterministic learning still holds in the decentralized adaptive neural control scheme where, however, the estimated weights of NNs just converge to small neighborhoods of the optimal values along their own state orbits. Thus, the learned controllers obtained by DCL scheme have the better generalization capability than ones obtained by decentralized learning method. A simulation example is provided to verify the effectiveness and advantages of the control schemes proposed in this paper.

  8. Applying principles of motor learning and control to upper extremity rehabilitation.

    PubMed

    Muratori, Lisa M; Lamberg, Eric M; Quinn, Lori; Duff, Susan V

    2013-01-01

    The purpose of this article is to provide a brief review of the principles of motor control and learning. Different models of motor control from historical to contemporary are presented with emphasis on the Systems model. Concepts of motor learning including skill acquisition, measurement of learning, and methods to promote skill acquisition by examining the many facets of practice scheduling and use of feedback are provided. A fictional client case is introduced and threaded throughout the article to facilitate understanding of these concepts and how they can be applied to clinical practice.

  9. A learning flight control system for the F8-DFBW aircraft. [Digital Fly-By-Wire

    NASA Technical Reports Server (NTRS)

    Montgomery, R. C.; Mekel, R.; Nachmias, S.

    1978-01-01

    This report contains a complete description of a learning control system designed for the F8-DFBW aircraft. The system is parameter-adaptive with the additional feature that it 'learns' the variation of the control system gains needed over the flight envelope. It, thus, generates and modifies its gain schedule when suitable data are available. The report emphasizes the novel learning features of the system: the forms of representation of the flight envelope and the process by which identified parameters are used to modify the gain schedule. It contains data taken during piloted real-time 6 degree-of-freedom simulations that were used to develop and evaluate the system.

  10. Towards unconventional computing through simulated evolution: control of nonlinear media by a learning classifier system.

    PubMed

    Bull, Larry; Budd, Adam; Stone, Christopher; Uroukov, Ivan; de Lacy Costello, Ben; Adamatzky, Andrew

    2008-01-01

    We propose that the behavior of nonlinear media can be controlled automatically through evolutionary learning. By extension, forms of unconventional computing (viz., massively parallel nonlinear computers) can be realized by such an approach. In this initial study a light-sensitive subexcitable Belousov-Zhabotinsky reaction in which a checkerboard image, composed of cells of varying light intensity projected onto the surface of a thin silica gel impregnated with a catalyst and indicator, is controlled using a learning classifier system. Pulses of wave fragments are injected into the checkerboard grid, resulting in rich spatiotemporal behavior, and a learning classifier system is shown to be able to direct the fragments to an arbitrary position through dynamic control of the light intensity within each cell in both simulated and real chemical systems. Similarly, a learning classifier system is shown to be able to control the electrical stimulation of cultured neuronal networks so that they display elementary learning. Results indicate that the learned stimulation protocols identify seemingly fundamental properties of in vitro neuronal networks. Use of another learning scheme presented in the literature confirms that such fundamental behavioral characteristics of a given network must be considered in training experiments.

  11. Distributed learning and multi-objectivity in traffic light control

    NASA Astrophysics Data System (ADS)

    Brys, Tim; Pham, Tong T.; Taylor, Matthew E.

    2014-01-01

    Traffic jams and suboptimal traffic flows are ubiquitous in modern societies, and they create enormous economic losses each year. Delays at traffic lights alone account for roughly 10% of all delays in US traffic. As most traffic light scheduling systems currently in use are static, set up by human experts rather than being adaptive, the interest in machine learning approaches to this problem has increased in recent years. Reinforcement learning (RL) approaches are often used in these studies, as they require little pre-existing knowledge about traffic flows. Distributed constraint optimisation approaches (DCOP) have also been shown to be successful, but are limited to cases where the traffic flows are known. The distributed coordination of exploration and exploitation (DCEE) framework was recently proposed to introduce learning in the DCOP framework. In this paper, we present a study of DCEE and RL techniques in a complex simulator, illustrating the particular advantages of each, comparing them against standard isolated traffic actuated signals. We analyse how learning and coordination behave under different traffic conditions, and discuss the multi-objective nature of the problem. Finally we evaluate several alternative reward signals in the best performing approach, some of these taking advantage of the correlation between the problem-inherent objectives to improve performance.

  12. Motor control: correcting errors and learning from mistakes.

    PubMed

    Miall, Chris

    2010-07-27

    How do we learn from errors during complex movement tasks with redundancy? A new study shows that ambiguous mistakes in bimanual movements are corrected by the non-dominant hand, and responsibility for the error is assumed to fall to the effector with a recent history of poor performance.

  13. Inhibitory Control and Mathematics Learning: Definitional and Operational Considerations

    ERIC Educational Resources Information Center

    Star, Jon R.; Pollack, Courtney

    2015-01-01

    The topic of inhibition in mathematics education is both well timed and important. In this commentary, we reflect on the role of inhibition in mathematics learning through four themes that relate to how inhibition is defined, measured, developed, and applied. First, we consider different characterizations of inhibition and how they may shape the…

  14. Using machine learning to blend human and robot controls for assisted wheelchair navigation.

    PubMed

    Goil, Aditya; Derry, Matthew; Argall, Brenna D

    2013-06-01

    This work presents an algorithm for collaborative control of an assistive semi-autonomous wheelchair. Our approach is based on a statistical machine learning technique to learn task variability from demonstration examples. The algorithm has been developed in the context of shared-control powered wheelchairs that provide assistance to individuals with impairments that affect their control in challenging driving scenarios, like doorway navigation. We validate our algorithm within a simulation environment, and find that with relatively few demonstrations, our approach allows for safe traversal of the doorway while maintaining a high level of user control.

  15. Learning from adaptive neural dynamic surface control of strict-feedback systems.

    PubMed

    Wang, Min; Wang, Cong

    2015-06-01

    Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order strict-feedback systems by adaptive dynamic surface control (DSC) technology, which achieves the human-like ability of learning by doing and doing with learned knowledge. To achieve the learning, this paper first proposes stable adaptive DSC with auxiliary first-order filters, which ensures the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. With the help of DSC, the derivative of the filter output variable is used as the neural network (NN) input instead of traditional intermediate variables. As a result, the proposed adaptive DSC method reduces greatly the dimension of NN inputs, especially for high-order systems. After the stable DSC design, we decompose the stable closed-loop system into a series of linear time-varying perturbed subsystems. Using a recursive design, the recurrent property of NN input variables is easily verified since the complexity is overcome using DSC. Subsequently, the partial persistent excitation condition of the radial basis function NN is satisfied. By combining a state transformation, accurate approximations of the closed-loop system dynamics are recursively achieved in a local region along recurrent orbits. Then, the learning control method using the learned knowledge is proposed to achieve the closed-loop stability and the improved control performance. Simulation studies are performed to demonstrate the proposed scheme can not only reuse the learned knowledge to achieve the better control performance with the faster tracking convergence rate and the smaller tracking error but also greatly alleviate the computational burden because of reducing the number and complexity of NN input variables.

  16. Iterative Mechanism Solutions with Scenario and ADAMS

    NASA Technical Reports Server (NTRS)

    Rhoades, Daren

    2006-01-01

    This slide presentation reviews the use of iterative solutions using Scenario for Motion (UG NX 2 Motion) to assist in designing the Mars Science Laboratory (MSL). The MSL will have very unique design requirements, and in order to meet these requirements the system must have the ability to design for static stability, simulate mechanism kinematics, simulate dynamic behaviour and be capable of reconfiguration, and iterations as designed. The legacy process used on the Mars Exploration rovers worked, but it was cumbersome using multiple tools, limited configuration control, with manual process and communication, and multiple steps. The aim is to develop a mechanism that would reduce turn around time, and make more reiterations possible, to improve the quality and quantity of data, and to enhance configuration control. Currently for NX Scenario for Motion uses are in the articulation studies, the simulations of traverse motions,and subsystem simulations. The design of the Rover landing model requires accurate results, flexible elements, such as beams, and the use of the full ADAMS solver has been used. In order to achieve this, when required, there has been a direct translation from Scenario to ADAMS, with additional data in ascii format. The process that has been designed to move from Scenario to ADAMS is reviewed.

  17. Emergence of Coordinated Neural Dynamics Underlies Neuroprosthetic Learning and Skillful Control.

    PubMed

    Athalye, Vivek R; Ganguly, Karunesh; Costa, Rui M; Carmena, Jose M

    2017-02-22

    During motor learning, movements and underlying neural activity initially exhibit large trial-to-trial variability that decreases over learning. However, it is unclear how task-relevant neural populations coordinate to explore and consolidate activity patterns. Exploration and consolidation could happen for each neuron independently, across the population jointly, or both. We disambiguated among these possibilities by investigating how subjects learned de novo to control a brain-machine interface using neurons from motor cortex. We decomposed population activity into the sum of private and shared signals, which produce uncorrelated and correlated neural variance, respectively, and examined how these signals' evolution causally shapes behavior. We found that initially large trial-to-trial movement and private neural variability reduce over learning. Concomitantly, task-relevant shared variance increases, consolidating a manifold containing consistent neural trajectories that generate refined control. These results suggest that motor cortex acquires skillful control by leveraging both independent and coordinated variance to explore and consolidate neural patterns.

  18. Effects of a National Public Service Information Campaign on Crime Prevention: Perspectives from Social Learning and Social Control Theory.

    ERIC Educational Resources Information Center

    Lordan, Edward J.; Kwon, Joongrok

    This study examined the effects of public service advertising from two theoretical backgrounds: social learning theory and social control theory. Traditional social learning theory assumes that learning occurs by subjects performing responses and experiencing their effects, with reinforcement as the main determinant. Social control theory, as…

  19. Bioinspired iterative synthesis of polyketides

    PubMed Central

    Zheng, Kuan; Xie, Changmin; Hong, Ran

    2015-01-01

    Diverse array of biopolymers and second metabolites (particularly polyketide natural products) has been manufactured in nature through an enzymatic iterative assembly of simple building blocks. Inspired by this strategy, molecules with inherent modularity can be efficiently synthesized by repeated succession of similar reaction sequences. This privileged strategy has been widely adopted in synthetic supramolecular chemistry. Its value also has been reorganized in natural product synthesis. A brief overview of this approach is given with a particular emphasis on the total synthesis of polyol-embedded polyketides, a class of vastly diverse structures and biologically significant natural products. This viewpoint also illustrates the limits of known individual modules in terms of diastereoselectivity and enantioselectivity. More efficient and practical iterative strategies are anticipated to emerge in the future development. PMID:26052510

  20. Spectroscopic problems in ITER diagnostics

    NASA Astrophysics Data System (ADS)

    Lisitsa, V. S.; Bureyeva, L. A.; Kukushkin, A. B.; Kadomtsev, M. B.; Krupin, V. A.; Levashova, M. G.; Medvedev, A. A.; Mukhin, E. E.; Shurygin, V. A.; Tugarinov, S. N.; Vukolov, K. Yu

    2012-12-01

    Problems of spectroscopic diagnostics of ITER plasma are under consideration. Three types of diagnostics are presented: 1) Balmer lines spectroscopy in the edge and divertor plasmas; 2) Thomson scattering, 3) charge exchange recombination spectroscopy. The Zeeman-Stark structure of line shapes is discussed. The overlapping of isotopes H-D-T spectral line shapes are presented for the SOL and divertor conditions. The polarization measurements of H-alpha spectral lines for H-D mixture on T-10 tokamak are shown in order to separate Zeeman splitting in more details. The problem of plasma background radiation emission for Thomson scattering in ITER is discussed in details. The line shape of P-7 hydrogen spectral line having a wave length close to laser one is presented together with continuum radiation. The charge exchange recombination spectroscopy (CXRS) is discussed in details. The data on Dα, HeII and CVI measurements in CXRS experiments on T-10 tokamak are presented.

  1. Design of ITER Relief Lines

    NASA Astrophysics Data System (ADS)

    Shah, N.; Choukekar, K.; Jadon, M.; Sarkar, B.; Joshi, B.; Kanzaria, H.; Gehani, V.; Vyas, H.; Pandya, U.; Panjwani, R.; Badgujar, S.; Monneret, E.

    2017-02-01

    The ITER Cryogenic system is one of the most complex cryogenic systems in the world. It includes roughly 5 km of cryogenic transfer line (cryolines) having large number of layout singularities in terms of bends at odd angles and branches. The relief lines are particularly important cryolines as they collect the helium from outlet of all process safety valves of the cryogenic clients and transfers it back to cryoplant. The total length of ITER relief lines is around 1.6 km with process pipe size varying from DN 50 to DN 200. While some part of relief lines carries warm helium for the recovery system, most part of the relief line is vacuum jacketed cryoline which carries cold helium from the clients. The final detailed design of relief lines has been completed. The paper describes the major input data and constraints for design of relief lines, design steps, flexibility and structural analysis approach and major design outcome.

  2. Language experience differentiates prefrontal and subcortical activation of the cognitive control network in novel word learning

    PubMed Central

    King, Kelly E.; Hernandez, Arturo E.

    2012-01-01

    The purpose of this study was to examine the cognitive control mechanisms in adult English speaking monolinguals compared to early sequential Spanish-English bilinguals during the initial stages of novel word learning. Functional magnetic resonance imaging during a lexico-semantic task after only two hours of exposure to novel German vocabulary flashcards showed that monolinguals activated a broader set of cortical control regions associated with higher-level cognitive processes, including the supplementary motor area (SMA), anterior cingulate (ACC), and dorsolateral prefrontal cortex (DLPFC), as well as the caudate, implicated in cognitive control of language. However, bilinguals recruited a more localized subcortical network that included the putamen, associated more with motor control of language. These results suggest that experience managing multiple languages may differentiate the learning strategy and subsequent neural mechanisms of cognitive control used by bilinguals compared to monolinguals in the early stages of novel word learning. PMID:23194816

  3. Automatic learning rate adjustment for self-supervising autonomous robot control

    NASA Technical Reports Server (NTRS)

    Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

    1992-01-01

    Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.

  4. Learning and Adaptive Hybrid Systems for Nonlinear Control

    DTIC Science & Technology

    1991-05-01

    34 Invention Report, S81-64, File 1, Office of Technology Liscensirig, Stanford University, 1982. [Ros62J Rosenblatt, F., Principles of Neurodynamics ...Explorations in the Microstructure of Cognition , vol. 1, Rumelhart, D., and J. McClelland, ed., MIT Press, Carbnbdge, MA, 1986. [RI-1W86] Rumnelhart, D., 0...Microstructure of Cognition , vol. 1, Rumelhart, D., and J. McClelland, ed., MIT Pres, Cambridge, MA, 1986. [Sain67] Samuel, A., "Some Studies in Machine Learning

  5. Adaptive Neuro-Fuzzy Control of a Spherical Rolling Robot Using Sliding-Mode-Control-Theory-Based Online Learning Algorithm.

    PubMed

    Kayacan, Erkan; Kayacan, Erdal; Ramon, Herman; Saeys, Wouter

    2013-02-01

    As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based on this model. In such cases, a conventional controller cannot remain well tuned. This paper presents the control of a spherical rolling robot by using an adaptive neuro-fuzzy controller in combination with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure consists of a neuro-fuzzy network and a conventional controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only eliminate the steady-state error but also improve the transient response performance of the spherical rolling robot without knowing its dynamic equations.

  6. Neural signatures of second language learning and control.

    PubMed

    Bartolotti, James; Bradley, Kailyn; Hernandez, Arturo E; Marian, Viorica

    2016-04-08

    Experience with multiple languages has unique effects on cortical structure and information processing. Differences in gray matter density and patterns of cortical activation are observed in lifelong bilinguals compared to monolinguals as a result of their experience managing interference across languages. Monolinguals who acquire a second language later in life begin to encounter the same type of linguistic interference as bilinguals, but with a different pre-existing language architecture. The current study used functional magnetic resonance imaging to explore the beginning stages of second language acquisition and cross-linguistic interference in monolingual adults. We found that after English monolinguals learned novel Spanish vocabulary, English and Spanish auditory words led to distinct patterns of cortical activation, with greater recruitment of posterior parietal regions in response to English words and of left hippocampus in response to Spanish words. In addition, cross-linguistic interference from English influenced processing of newly-learned Spanish words, decreasing hippocampus activity. Results suggest that monolinguals may rely on different memory systems to process a newly-learned second language, and that the second language system is sensitive to native language interference.

  7. Episodic Contributions to Sequential Control: Learning from a Typist's Touch

    ERIC Educational Resources Information Center

    Crump, Matthew J. C.; Logan, Gordon D.

    2010-01-01

    Sequential control over routine action is widely assumed to be controlled by stable, highly practiced representations. Our findings demonstrate that the processes controlling routine actions in the domain of skilled typing can be flexibly manipulated by memory processes coding recent experience with typing particular words and letters. In two…

  8. Results of Iterative Standards-Setting Procedures for a Performance-Based System for Renewable Certification.

    ERIC Educational Resources Information Center

    Lofton, Glenda G.; And Others

    This report presents the results of an initial, iterative performance standards-setting (SS) task of a comprehensive on-the-job statewide teacher assessment system--the System for Teaching and Learning Assessment and Review (STAR). The 1990-91 STAR assesses and makes inferences about the quality of teaching and learning on sets of assessment…

  9. A neural model of cerebellar learning for arm movement control: cortico-spino-cerebellar dynamics.

    PubMed

    Contreras-Vidal, J L; Grossberg, S; Bullock, D

    1997-01-01

    A neural network model of opponent cerebellar learning for arm movement control is proposed. The model illustrates how a central pattern generator in cortex and basal ganglia, a neuromuscular force controller in spinal cord, and an adaptive cerebellum cooperate to reduce motor variability during multijoint arm movements using mono- and bi-articular muscles. Cerebellar learning modifies velocity commands to produce phasic antagonist bursts at interpositus nucleus cells whose feed-forward action overcomes inherent limitations of spinal feedback control of tracking. Excitation of alpha motoneuron pools, combined with inhibition of their Renshaw cells by the cerebellum, facilitate movement initiation and optimal execution. Transcerebellar pathways are opened by learning through long-term depression (LTD) of parallel fiber-Purkinje cell synapses in response to conjunctive stimulation of parallel fibers and climbing fiber discharges that signal muscle stretch errors. The cerebellar circuitry also learns to control opponent muscles pairs, allowing cocontraction and reciprocal inhibition of muscles. Learning is stable, exhibits load compensation properties, and generalizes better across movement speeds if motoneuron pools obey the size principle. The intermittency of climbing fiber discharges maintains stable learning. Long-term potentiation (LTP) in response to uncorrelated parallel fiber signals enables previously weakened synapses to recover. Loss of climbing fibers, in the presence of LTP, can erode normal opponent signal processing. Simulated lesions of the cerebellar network reproduce symptoms of cerebellar disease, including sluggish movement onsets, poor execution of multijoint plans, and abnormally prolonged endpoint oscillations.

  10. Simulating the ITER Plasma Startup Scenario in the DIII-D Tokamak

    SciTech Connect

    Jackson, G; Casper, T; Luce, T; Humphreys, D; Ferron, J; Hyatt, A; Petrie, T; West, W

    2008-10-13

    DIII-D experiments have investigated ITER startup scenarios, including an initial phase where the plasma was limited on low field side (LFS) poloidal bumper limiters. Both the original ITER 'small-bore' (constant q{sub 95}) startup and a 'large-bore' lower internal inductance (l{sub i}) startup have been simulated. In addition, l{sub i} feedback control has been tested with the goal of producing discharges at the ITER design value, l{sub i}(3) = 0.85. These discharges have been simulated using the Corsica free boundary equilibrium code. High performance hybrid scenario discharges ({beta}{sub N} = 2.8, H{sub 98,y2} = 1.4) and ITER H-mode baseline discharges ({beta}{sub N} > 1.6, H{sub 98,y2} = 1-1.2) have been obtained experimentally in an ITER similar shape after the ITER-relevant startup.

  11. Robust reinforcement learning control using integral quadratic constraints for recurrent neural networks.

    PubMed

    Anderson, Charles W; Young, Peter Michael; Buehner, Michael R; Knight, James N; Bush, Keith A; Hittle, Douglas C

    2007-07-01

    The applicability of machine learning techniques for feedback control systems is limited by a lack of stability guarantees. Robust control theory offers a framework for analyzing the stability of feedback control loops, but for the integral quadratic constraint (IQC) framework used here, all components are required to be represented as linear, time-invariant systems plus uncertainties with, for IQCs used here, bounded gain. In this paper, the stability of a control loop including a recurrent neural network (NN) is analyzed by replacing the nonlinear and time-varying components of the NN with IQCs on their gain. As a result, a range of the NN's weights is found within which stability is guaranteed. An algorithm is demonstrated for training the recurrent NN using reinforcement learning and guaranteeing stability while learning.

  12. Children with a Learning Disorder Show Prospective Control Impairments during Visuomanual Tracking

    ERIC Educational Resources Information Center

    van Roon, Dominique; Caeyenberghs, Karen; Swinnen, Stephan P.; Smits-Engelsman, Bouwien C. M.

    2010-01-01

    To examine whether children with a learning disorder (LD) are able to use prospective motor control, 30 children with LD (mean age 8 years and 11 months) and an age- and gender-matched control group were asked to smoothly track an accelerating dot presented on a monitor by moving an electronic pen on a digitizer. Children with LD performed worse…

  13. Styles of Exploration in Control, Attention Deficit Disorder with Hyperactivity and Learning Disabled Children.

    ERIC Educational Resources Information Center

    Allen, Terry W.

    1986-01-01

    Results of a study involving control, learning disabled, and attention deficit disorder with hyperactivity (ADD-H) children (14 per group, aged 8-10 years) revealed that LD and ADD-H Ss habituated more rapidly, but they also encoded fewer aspects of the stimulus field than control Ss. (CL)

  14. Implementing Motivational Features in Reactive Blended Learning: Application to an Introductory Control Engineering Course

    ERIC Educational Resources Information Center

    Mendez, J. A.; Gonzalez, E. J.

    2011-01-01

    This paper presents a significant advance in a reactive blended learning methodology applied to an introductory control engineering course. This proposal was based on the inclusion of a reactive element (a fuzzy-logic-based controller) designed to regulate the workload for each student according to his/her activity and performance. The…

  15. Controlled Experiment Replication in Evaluation of E-Learning System's Educational Influence

    ERIC Educational Resources Information Center

    Grubisic, Ani; Stankov, Slavomir; Rosic, Marko; Zitko, Branko

    2009-01-01

    We believe that every effectiveness evaluation should be replicated at least in order to verify the original results and to indicate evaluated e-learning system's advantages or disadvantages. This paper presents the methodology for conducting controlled experiment replication, as well as, results of a controlled experiment and an internal…

  16. Effects of Social Reinforcement, Locus of Control, and Cognitive Style on Concept Learning among Retarded Children.

    ERIC Educational Resources Information Center

    Panda, Kailas C.

    To examine the effects of locus of control (the extent to which an individual feels he has control over his own behavior) and cognitive style variables on learning deficits among mentally handicapped children, 80 mentally retarded boys (IQ 50 to 83, age 160 to 196 months) were administered a battery of tests. Analyses of student performance…

  17. Control and Constraint in E-Learning: Choosing When to Choose

    ERIC Educational Resources Information Center

    Dron, Jon

    2007-01-01

    Every learner is on a trajectory, an individual path that involves choices about what to do next in order to learn, choices that are bounded by intrinsic and extrinsic constraints. In some cases the learner controls those choices, sometimes they are made by someone or something else, sometimes control is negotiated, or it emerges from complex…

  18. Articulatory Control in Childhood Apraxia of Speech in a Novel Word-Learning Task

    ERIC Educational Resources Information Center

    Case, Julie; Grigos, Maria I.

    2016-01-01

    Purpose: Articulatory control and speech production accuracy were examined in children with childhood apraxia of speech (CAS) and typically developing (TD) controls within a novel word-learning task to better understand the influence of planning and programming deficits in the production of unfamiliar words. Method: Participants included 16…

  19. Vibration control of building structures using self-organizing and self-learning neural networks

    NASA Astrophysics Data System (ADS)

    Madan, Alok

    2005-11-01

    Past research in artificial intelligence establishes that artificial neural networks (ANN) are effective and efficient computational processors for performing a variety of tasks including pattern recognition, classification, associative recall, combinatorial problem solving, adaptive control, multi-sensor data fusion, noise filtering and data compression, modelling and forecasting. The paper presents a potentially feasible approach for training ANN in active control of earthquake-induced vibrations in building structures without the aid of teacher signals (i.e. target control forces). A counter-propagation neural network is trained to output the control forces that are required to reduce the structural vibrations in the absence of any feedback on the correctness of the output control forces (i.e. without any information on the errors in output activations of the network). The present study shows that, in principle, the counter-propagation network (CPN) can learn from the control environment to compute the required control forces without the supervision of a teacher (unsupervised learning). Simulated case studies are presented to demonstrate the feasibility of implementing the unsupervised learning approach in ANN for effective vibration control of structures under the influence of earthquake ground motions. The proposed learning methodology obviates the need for developing a mathematical model of structural dynamics or training a separate neural network to emulate the structural response for implementation in practice.

  20. Approximate reasoning-based learning and control for proximity operations and docking in space

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Jani, Yashvant; Lea, Robert N.

    1991-01-01

    A recently proposed hybrid-neutral-network and fuzzy-logic-control architecture is applied to a fuzzy logic controller developed for attitude control of the Space Shuttle. A model using reinforcement learning and learning from past experience for fine-tuning its knowledge base is proposed. Two main components of this approximate reasoning-based intelligent control (ARIC) model - an action-state evaluation network and action selection network are described as well as the Space Shuttle attitude controller. An ARIC model for the controller is presented, and it is noted that the input layer in each network includes three nodes representing the angle error, angle error rate, and bias node. Preliminary results indicate that the controller can hold the pitch rate within its desired deadband and starts to use the jets at about 500 sec in the run.

  1. Transport analysis of tungsten impurity in ITER

    NASA Astrophysics Data System (ADS)

    Murakami, Y.; Amano, T.; Shimizu, K.; Shimada, M.

    2003-03-01

    The radial distribution of tungsten impurity in ITER is calculated by using the 1.5D transport code TOTAL coupled with NCLASS, which can solve the neo-classical impurity flux considering arbitrary aspect ratio and collisionality. An impurity screening effect is observed when the density profile is flat and the line radiation power is smaller than in the case without impurity transport by a factor of 2. It is shown that 90 MW of line radiation power is possible without significant degradation of plasma performance ( HH98( y,2) ˜1) when the fusion power is 700 MW (fusion gain Q=10). The allowable tungsten density is about 7×10 15/m 3, which is 0.01% of the electron density and the increase of the effective ionic charge Zeff is about 0.39. In this case, the total radiation power is more than half of the total heating power 210 MW, and power to the divertor region is less than 100 MW. This operation regime gives an opportunity for high fusion power operation in ITER with acceptable divertor conditions. Simulations for the case with an internal transport barrier (ITB) are also performed and it is found that impurity shielding by an ITB is possible with density profile control.

  2. Adventitious Reinforcement of Maladaptive Stimulus Control Interferes with Learning.

    PubMed

    Saunders, Kathryn J; Hine, Kathleen; Hayashi, Yusuke; Williams, Dean C

    2016-09-01

    Persistent error patterns sometimes develop when teaching new discriminations. These patterns can be adventitiously reinforced, especially during long periods of chance-level responding (including baseline). Such behaviors can interfere with learning a new discrimination. They can also disrupt already learned discriminations, if they re-emerge during teaching procedures that generate errors. We present an example of this process. Our goal was to teach a boy with intellectual disabilities to touch one of two shapes on a computer screen (in technical terms, a simple simultaneous discrimination). We used a size-fading procedure. The correct stimulus was at full size, and the incorrect-stimulus size increased in increments of 10 %. Performance was nearly error free up to and including 60 % of full size. In a probe session with the incorrect stimulus at full size, however, accuracy plummeted. Also, a pattern of switching between choices, which apparently had been established in classroom instruction, re-emerged. The switching pattern interfered with already-learned discriminations. Despite having previously mastered a fading step with the incorrect stimulus up to 60 %, we were unable to maintain consistently high accuracy beyond 20 % of full size. We refined the teaching program such that fading was done in smaller steps (5 %), and decisions to "step back" to a smaller incorrect stimulus were made after every 5-instead of 20-trials. Errors were rare, switching behavior stopped, and he mastered the discrimination. This is a practical example of the importance of designing instruction that prevents adventitious reinforcement of maladaptive discriminated response patterns by reducing errors during acquisition.

  3. Adaptive and learning control of large space structures

    NASA Technical Reports Server (NTRS)

    Montgomery, R. C.; Thau, F. J.

    1980-01-01

    The paper describes the adaptive learning system for space operations which assumes that structural testing can be conducted during deployment and assembly. Simulation results using the solar electric propulsion array and a novel remote sensor are presented; they involve faster scan television coverage of the motions of the array from four cameras on the corners of the Space Shuttle payload bay. The description of the simulation, the filtering algorithm for processing the TV data, the parameter extraction algorithm, and the simulation results are presented.

  4. An H(∞) control approach to robust learning of feedforward neural networks.

    PubMed

    Jing, Xingjian

    2011-09-01

    A novel H(∞) robust control approach is proposed in this study to deal with the learning problems of feedforward neural networks (FNNs). The analysis and design of a desired weight update law for the FNN is transformed into a robust controller design problem for a discrete dynamic system in terms of the estimation error. The drawbacks of some existing learning algorithms can therefore be revealed, especially for the case that the output data is fast changing with respect to the input or the output data is corrupted by noise. Based on this approach, the optimal learning parameters can be found by utilizing the linear matrix inequality (LMI) optimization techniques to achieve a predefined H(∞) "noise" attenuation level. Several existing BP-type algorithms are shown to be special cases of the new H(∞)-learning algorithm. Theoretical analysis and several examples are provided to show the advantages of the new method.

  5. Lessons Learned from the Node 1 Temperature and Humidity Control Subsystem Design

    NASA Technical Reports Server (NTRS)

    Williams, David E.

    2010-01-01

    Node 1 flew to the International Space Station (ISS) on Flight 2A during December 1998. To date the National Aeronautics and Space Administration (NASA) has learned a lot of lessons from this module based on its history of approximately two years of acceptance testing on the ground and currently its twelve years on-orbit. This paper will provide an overview of the ISS Environmental Control and Life Support (ECLS) design of the Node 1 Temperature and Humidity Control (THC) subsystem and it will document some of the lessons that have been learned to date for this subsystem and it will document some of the lessons that have been learned to date for these subsystems based on problems prelaunch, problems encountered on-orbit, and operational problems/concerns. It is hoped that documenting these lessons learned from ISS will help in preventing them in future Programs. 1

  6. Adaptive critic learning techniques for engine torque and air-fuel ratio control.

    PubMed

    Liu, Derong; Javaherian, Hossein; Kovalenko, Olesia; Huang, Ting

    2008-08-01

    A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved.

  7. ITER diagnostic systems in development in Ioffe Institute

    SciTech Connect

    Petrov, M.; Afanasyev, V.; Petrov, S.; Mironov, M.; Mukhin, E.; Tolstyakov, S.; Chugunov, I.; Shevelev, A.

    2014-08-21

    Three diagnostic systems are being developed in Ioffe Institute for ITER. Those are Neutral Particle Analysis (NPA), Thomson Scattering in Divertor (TSD) and Gamma Spectroscopy (GS). The main objective of NPA in ITER is to measure D/T fuel ration in plasma on the basis of measurement of neutralized fluxes of D and T ions [1]. Fuel ratio is one of the key parameters needed by ITER control system to provide the optimal conditions in plasma and the most effective plasma burning. Another objective is to measure the distribution function of fast ions (including alpha particles) generated as a result of the additional heating and nuclear fusion reactions. Thomson Scattering in Divertor (TSD) [2] will be used to measure electron temperature and density in the scrape-off layer in outer leg of ITER divertor. The main task of TSD is to protect the machine from divertor overloading. Gamma Spectroscopy (GS) [3] is based on the measurement of spectral lines of MeV range gammas generated in nuclear reactions in plasma. 2-D gamma-ray emission measurements give valuable information on the confined alpha particles in DT plasma. They also provide important information on the location of MeV range runaway electron beams in ITER plasma. For all three cases the physical basis and instrumentation are presented. The simple NPA version for measurements of D/T ratio in DEMO is also briefly described.

  8. Learning guide for the terminal configured vehicle advanced guidance and control system mode select panel

    NASA Technical Reports Server (NTRS)

    Anderson, M. A.; Callahan, R.

    1981-01-01

    This learning guide is designed to assist pilots in taking the PLATO presimulator training course on the advanced guidance and control system mode select panel. The learning guide is divided into five sections. The first section, the introduction, presents the course goals, prerequisites, definition of PLATO activities, and a suggested approach to completing the course. The remaining four sections present the purpose, learning activities and summary of each lesson of the AGCS PLATO course, which consists of (1) AGCS introduction; (2) lower order modes; (3) higher order modes; and (4) an arrival route exercise.

  9. Adaptive and Energy Efficient Walking in a Hexapod Robot Under Neuromechanical Control and Sensorimotor Learning.

    PubMed

    Xiong, Xiaofeng; Worgotter, Florentin; Manoonpong, Poramate

    2016-11-01

    The control of multilegged animal walking is a neuromechanical process, and to achieve this in an adaptive and energy efficient way is a difficult and challenging problem. This is due to the fact that this process needs in real time: 1) to coordinate very many degrees of freedom of jointed legs; 2) to generate the proper leg stiffness (i.e., compliance); and 3) to determine joint angles that give rise to particular positions at the endpoints of the legs. To tackle this problem for a robotic application, here we present a neuromechanical controller coupled with sensorimotor learning. The controller consists of a modular neural network for coordinating 18 joints and several virtual agonist-antagonist muscle mechanisms (VAAMs) for variable compliant joint motions. In addition, sensorimotor learning, including forward models and dual-rate learning processes, is introduced for predicting foot force feedback and for online tuning the VAAMs' stiffness parameters. The control and learning mechanisms enable the hexapod robot advanced mobility sensor driven-walking device (AMOS) to achieve variable compliant walking that accommodates different gaits and surfaces. As a consequence, AMOS can perform more energy efficient walking, compared to other small legged robots. In addition, this paper also shows that the tight combination of neural control with tunable muscle-like functions, guided by sensory feedback and coupled with sensorimotor learning, is a way forward to better understand and solve adaptive coordination problems in multilegged locomotion.

  10. Perceptual learning and the visual control of braking.

    PubMed

    Fajen, Brett R

    2008-08-01

    Performance on a visually guided action may improve with practice because observers become perceptually attuned to more reliable optical information. Fajen and Devaney (2006) investigated perceptual attunement, using an emergency braking task in which subjects waited until the last possible moment before slamming on the brakes. The subjects in that study learned to use more reliable optical variables with practice, allowing them to perform the task more successfully across changes in the size of the approached object and the speed of approach. In Experiment 1 of the present study, subjects completed blocks of normal, regulated braking before and after practice on emergency braking. Size and speed effects that were present at early stages diminished or were eliminated after practice, suggesting that perceptual attunement resulting from practice on emergency braking transfers to normal, regulated braking. In Experiment 2, practice on regulated braking alone also resulted in perceptual attunement. The findings suggest that braking is not always guided on the basis of an optical invariant and that perceptual attunement plays an important role in learning to perform a visually guided action.

  11. Description of the prototype diagnostic residual gas analyzer for ITER.

    PubMed

    Younkin, T R; Biewer, T M; Klepper, C C; Marcus, C

    2014-11-01

    The diagnostic residual gas analyzer (DRGA) system to be used during ITER tokamak operation is being designed at Oak Ridge National Laboratory to measure fuel ratios (deuterium and tritium), fusion ash (helium), and impurities in the plasma. The eventual purpose of this instrument is for machine protection, basic control, and physics on ITER. Prototyping is ongoing to optimize the hardware setup and measurement capabilities. The DRGA prototype is comprised of a vacuum system and measurement technologies that will overlap to meet ITER measurement requirements. Three technologies included in this diagnostic are a quadrupole mass spectrometer, an ion trap mass spectrometer, and an optical penning gauge that are designed to document relative and absolute gas concentrations.

  12. Description of the prototype diagnostic residual gas analyzer for ITER

    SciTech Connect

    Younkin, T. R.; Biewer, T. M.; Klepper, C. C.; Marcus, C.

    2014-11-15

    The diagnostic residual gas analyzer (DRGA) system to be used during ITER tokamak operation is being designed at Oak Ridge National Laboratory to measure fuel ratios (deuterium and tritium), fusion ash (helium), and impurities in the plasma. The eventual purpose of this instrument is for machine protection, basic control, and physics on ITER. Prototyping is ongoing to optimize the hardware setup and measurement capabilities. The DRGA prototype is comprised of a vacuum system and measurement technologies that will overlap to meet ITER measurement requirements. Three technologies included in this diagnostic are a quadrupole mass spectrometer, an ion trap mass spectrometer, and an optical penning gauge that are designed to document relative and absolute gas concentrations.

  13. Policy iteration adaptive dynamic programming algorithm for discrete-time nonlinear systems.

    PubMed

    Liu, Derong; Wei, Qinglai

    2014-03-01

    This paper is concerned with a new discrete-time policy iteration adaptive dynamic programming (ADP) method for solving the infinite horizon optimal control problem of nonlinear systems. The idea is to use an iterative ADP technique to obtain the iterative control law, which optimizes the iterative performance index function. The main contribution of this paper is to analyze the convergence and stability properties of policy iteration method for discrete-time nonlinear systems for the first time. It shows that the iterative performance index function is nonincreasingly convergent to the optimal solution of the Hamilton-Jacobi-Bellman equation. It is also proven that any of the iterative control laws can stabilize the nonlinear systems. Neural networks are used to approximate the performance index function and compute the optimal control law, respectively, for facilitating the implementation of the iterative ADP algorithm, where the convergence of the weight matrices is analyzed. Finally, the numerical results and analysis are presented to illustrate the performance of the developed method.

  14. Neural substrates of visuomotor learning based on improved feedback control and prediction.

    PubMed

    Grafton, Scott T; Schmitt, Paul; Van Horn, John; Diedrichsen, Jörn

    2008-02-01

    Motor skills emerge from learning feedforward commands as well as improvements in feedback control. These two components of learning were investigated in a compensatory visuomotor tracking task on a trial-by-trial basis. Between-trial learning was characterized with a state-space model to provide smoothed estimates of feedforward and feedback learning, separable from random fluctuations in motor performance and error. The resultant parameters were correlated with brain activity using magnetic resonance imaging. Learning related to the generation of a feedforward command correlated with activity in dorsal premotor cortex, inferior parietal lobule, supplementary motor area and cingulate motor area, supporting a role of these areas in retrieving and executing a predictive motor command. Modulation of feedback control was associated with activity in bilateral posterior superior parietal lobule as well as right ventral premotor cortex. Performance error correlated with activity in a widespread cortical and subcortical network including bilateral parietal, premotor and rostral anterior cingulate cortex as well as the cerebellar cortex. Finally, trial-by-trial changes of kinematics, as measured by mean absolute hand acceleration, correlated with activity in motor cortex and anterior cerebellum. The results demonstrate that incremental, learning-dependent changes can be modeled on a trial-by-trial basis and neural substrates for feedforward control of novel motor programs are localized to secondary motor areas.

  15. Design issues for a reinforcement-based self-learning fuzzy controller

    NASA Technical Reports Server (NTRS)

    Yen, John; Wang, Haojin; Dauherity, Walter

    1993-01-01

    Fuzzy logic controllers have some often cited advantages over conventional techniques such as PID control: easy implementation, its accommodation to natural language, the ability to cover wider range of operating conditions and others. One major obstacle that hinders its broader application is the lack of a systematic way to develop and modify its rules and as result the creation and modification of fuzzy rules often depends on try-error or pure experimentation. One of the proposed approaches to address this issue is self-learning fuzzy logic controllers (SFLC) that use reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of self-learning fuzzy controller is highly contingent on the design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for the application to chemical process are discussed and its performance is compared with that of PID and self-tuning fuzzy logic controller.

  16. Investigation of Drive-Reinforcement Learning and Application of Learning to Flight Control

    DTIC Science & Technology

    1993-08-01

    8217ions in the Microstructure of Cognitions , Vol. P: Foadations, MIT P-•ss, Cornbridge. Rosenblatt, F. (1962). P-inciplks qf Neurodynamics , Spatrn Books...Propagation," in Rumelhart, D. & McClelland, J., eds., Parallel Distributed Processing: Explorations in the Microstructure of Cognition , Vol. 1... Cognition , vol. 1, Rumelhart, D., and J. McClelland, ed., MIT Press, Cambridge, MA, 1986. [IRHW86] Rumelhart, D., G. Hinton, and R. Williams, "Learning

  17. Prediction and uncertainty in associative learning: examining controlled and automatic components of learned attentional biases.

    PubMed

    Luque, David; Vadillo, Miguel A; Le Pelley, Mike E; Beesley, Tom

    2017-08-01

    It has been suggested that attention is guided by two factors that operate during associative learning: a predictiveness principle, by which attention is allocated to the best predictors of outcomes, and an uncertainty principle, by which attention is allocated to learn about the less known features of the environment. Recent studies have shown that predictiveness-driven attention can operate rapidly and in an automatic way to exploit known relationships. The corresponding characteristics of uncertainty-driven attention, on the other hand, remain unexplored. In two experiments we examined whether both predictiveness and uncertainty modulate attentional processing in an adaptation of the dot probe task. This task provides a measure of automatic orientation to cues during associative learning. The stimulus onset asynchrony of the probe display was manipulated in order to explore temporal characteristics of predictiveness- and uncertainty-driven attentional effects. Results showed that the predictive status of cues determined selective attention, with faster attentional capture to predictive than to non-predictive cues. In contrast, the level of uncertainty slowed down responses to the probe regardless of the predictive status of the cues. Both predictiveness- and uncertainty-driven attentional effects were very rapid (at 250 ms from cue onset) and were automatically activated.

  18. How does a specific learning and memory system in the mammalian brain gain control of behavior?

    PubMed

    McDonald, Robert J; Hong, Nancy S

    2013-11-01

    This review addresses a fundamental, yet poorly understood set of issues in systems neuroscience. The issues revolve around conceptualizations of the organization of learning and memory in the mammalian brain. One intriguing, and somewhat popular, conceptualization is the idea that there are multiple learning and memory systems in the mammalian brain and they interact in different ways to influence and/or control behavior. This approach has generated interesting empirical and theoretical work supporting this view. One issue that needs to be addressed is how these systems influence or gain control of voluntary behavior. To address this issue, we clearly specify what we mean by a learning and memory system. We then review two types of processes that might influence which memory system gains control of behavior. One set of processes are external factors that can affect which system controls behavior in a given situation including task parameters like the kind of information available to the subject, types of training experience, and amount of training. The second set of processes are brain mechanisms that might influence what memory system controls behavior in a given situation including executive functions mediated by the prefrontal cortex; switching mechanisms mediated by ascending neurotransmitter systems, the unique role of the hippocampus during learning. The issue of trait differences in control of different learning and memory systems will also be considered in which trait differences in learning and memory function are thought to potentially emerge from differences in level of prefrontal influence, differences in plasticity processes, differences in ascending neurotransmitter control, differential access to effector systems like motivational and motor systems. Finally, we present scenarios in which different mechanisms might interact. This review was conceived to become a jumping off point for new work directed at understanding these issues. The outcome of

  19. Iterates of maps with symmetry

    NASA Technical Reports Server (NTRS)

    Chossat, Pascal; Golubitsky, Martin

    1988-01-01

    Fixed-point bifurcation, period doubling, and Hopf bifurcation (HB) for iterates of equivariant mappings are investigated analytically, with a focus on HB in the presence of symmetry. An algebraic formulation for the hypotheses of the theorem of Ruelle (1973) is derived, and the case of standing waves in a system of ordinary differential equations with O(2) symmetry is considered in detail. In this case, it is shown that HB can lead directly to motion on an invariant 3-torus, with an unexpected third frequency due to drift of standing waves along the torus.

  20. Learning-based controller for biotechnology processing, and method of using

    DOEpatents

    Johnson, John A.; Stoner, Daphne L.; Larsen, Eric D.; Miller, Karen S.; Tolle, Charles R.

    2004-09-14

    The present invention relates to process control where some of the controllable parameters are difficult or impossible to characterize. The present invention relates to process control in biotechnology of such systems, but not limited to. Additionally, the present invention relates to process control in biotechnology minerals processing. In the inventive method, an application of the present invention manipulates a minerals bioprocess to find local exterma (maxima or minima) for selected output variables/process goals by using a learning-based controller for bioprocess oxidation of minerals during hydrometallurgical processing. The learning-based controller operates with or without human supervision and works to find processor optima without previously defined optima due to the non-characterized nature of the process being manipulated.

  1. Self-Control of Task Difficulty During Early Practice Promotes Motor Skill Learning.

    PubMed

    Andrieux, Mathieu; Boutin, Arnaud; Thon, Bernard

    2016-01-01

    This study was designed to determine whether the effect of self-control of task difficulty on motor learning is a function of the period of self-control administration. In a complex anticipation-coincidence task that required participants to intercept 3 targets with a virtual racquet, the task difficulty was either self-controlled or imposed to the participants in the two phases of the acquisition session. First, the results confirmed the beneficial effects of self-control over fully prescribed conditions. Second, the authors also demonstrated that a partial self-control of task difficulty better promotes learning than does a complete self-controlled procedure. Overall, the results revealed that these benefits are increased when this choice is allowed during early practice. The findings are discussed in terms of theoretical and applied perspectives.

  2. Implementations of learning control systems using neural networks

    NASA Technical Reports Server (NTRS)

    Sartori, Michael A.; Antsaklis, Panos J.

    1992-01-01

    The systematic storage in neural networks of prior information to be used in the design of various control subsystems is investigated. Assuming that the prior information is available in a certain form (namely, input/output data points and specifications between the data points), a particular neural network and a corresponding parameter design method are introduced. The proposed neural network addresses the issue of effectively using prior information in the areas of dynamical system (plant and controller) modeling, fault detection and identification, information extraction, and control law scheduling.

  3. Schedule-controlled learning and memory in a regulatory context

    EPA Science Inventory

    Control of behavior by the manipulation of contingencies provides powerful techniques for assessing the hazard of chemical toxicants on the nervous system. When applied to evaluate the consequences of developmental exposure, these techniques are well suited for characterizing per...

  4. Lessons Learned and Flight Results from the F15 Intelligent Flight Control System Project

    NASA Technical Reports Server (NTRS)

    Bosworth, John

    2006-01-01

    A viewgraph presentation on the lessons learned and flight results from the F15 Intelligent Flight Control System (IFCS) project is shown. The topics include: 1) F-15 IFCS Project Goals; 2) Motivation; 3) IFCS Approach; 4) NASA F-15 #837 Aircraft Description; 5) Flight Envelope; 6) Limited Authority System; 7) NN Floating Limiter; 8) Flight Experiment; 9) Adaptation Goals; 10) Handling Qualities Performance Metric; 11) Project Phases; 12) Indirect Adaptive Control Architecture; 13) Indirect Adaptive Experience and Lessons Learned; 14) Gen II Direct Adaptive Control Architecture; 15) Current Status; 16) Effect of Canard Multiplier; 17) Simulated Canard Failure Stab Open Loop; 18) Canard Multiplier Effect Closed Loop Freq. Resp.; 19) Simulated Canard Failure Stab Open Loop with Adaptation; 20) Canard Multiplier Effect Closed Loop with Adaptation; 21) Gen 2 NN Wts from Simulation; 22) Direct Adaptive Experience and Lessons Learned; and 23) Conclusions

  5. Iterated Stretching of Viscoelastic Jets

    NASA Technical Reports Server (NTRS)

    Chang, Hsueh-Chia; Demekhin, Evgeny A.; Kalaidin, Evgeny

    1999-01-01

    We examine, with asymptotic analysis and numerical simulation, the iterated stretching dynamics of FENE and Oldroyd-B jets of initial radius r(sub 0), shear viscosity nu, Weissenberg number We, retardation number S, and capillary number Ca. The usual Rayleigh instability stretches the local uniaxial extensional flow region near a minimum in jet radius into a primary filament of radius [Ca(1 - S)/ We](sup 1/2)r(sub 0) between two beads. The strain-rate within the filament remains constant while its radius (elastic stress) decreases (increases) exponentially in time with a long elastic relaxation time 3We(r(sup 2, sub 0)/nu). Instabilities convected from the bead relieve the tension at the necks during this slow elastic drainage and trigger a filament recoil. Secondary filaments then form at the necks from the resulting stretching. This iterated stretching is predicted to occur successively to generate high-generation filaments of radius r(sub n), (r(sub n)/r(sub 0)) = square root of 2[r(sub n-1)/r(sub 0)](sup 3/2) until finite-extensibility effects set in.

  6. Status of US ITER Diagnostics

    NASA Astrophysics Data System (ADS)

    Stratton, B.; Delgado-Aparicio, L.; Hill, K.; Johnson, D.; Pablant, N.; Barnsley, R.; Bertschinger, G.; de Bock, M. F. M.; Reichle, R.; Udintsev, V. S.; Watts, C.; Austin, M.; Phillips, P.; Beiersdorfer, P.; Biewer, T. M.; Hanson, G.; Klepper, C. C.; Carlstrom, T.; van Zeeland, M. A.; Brower, D.; Doyle, E.; Peebles, A.; Ellis, R.; Levinton, F.; Yuh, H.

    2013-10-01

    The US is providing 7 diagnostics to ITER: the Upper Visible/IR cameras, the Low Field Side Reflectometer, the Motional Stark Effect diagnostic, the Electron Cyclotron Emission diagnostic, the Toroidal Interferometer/Polarimeter, the Core Imaging X-Ray Spectrometer, and the Diagnostic Residual Gas Analyzer. The front-end components of these systems must operate with high reliability in conditions of long pulse operation, high neutron and gamma fluxes, very high neutron fluence, significant neutron heating (up to 7 MW/m3) , large radiant and charge exchange heat flux (0.35 MW/m2) , and high electromagnetic loads. Opportunities for repair and maintenance of these components will be limited. These conditions lead to significant challenges for the design of the diagnostics. Space constraints, provision of adequate radiation shielding, and development of repair and maintenance strategies are challenges for diagnostic integration into the port plugs that also affect diagnostic design. The current status of design of the US ITER diagnostics is presented and R&D needs are identified. Supported by DOE contracts DE-AC02-09CH11466 (PPPL) and DE-AC05-00OR22725 (UT-Battelle, LLC).

  7. Challenges for Cryogenics at Iter

    NASA Astrophysics Data System (ADS)

    Serio, L.

    2010-04-01

    Nuclear fusion of light nuclei is a promising option to provide clean, safe and cost competitive energy in the future. The ITER experimental reactor being designed by seven partners representing more than half of the world population will be assembled at Cadarache, South of France in the next decade. It is a thermonuclear fusion Tokamak that requires high magnetic fields to confine and stabilize the plasma. Cryogenic technology is extensively employed to achieve low-temperature conditions for the magnet and vacuum pumping systems. Efficient and reliable continuous operation shall be achieved despite unprecedented dynamic heat loads due to magnetic field variations and neutron production from the fusion reaction. Constraints and requirements of the largest superconducting Tokamak machine have been analyzed. Safety and technical risks have been initially assessed and proposals to mitigate the consequences analyzed. Industrial standards and components are being investigated to anticipate the requirements of reliable and efficient large scale energy production. After describing the basic features of ITER and its cryogenic system, we shall present the key design requirements, improvements, optimizations and challenges.

  8. ITER Port Interspace Pressure Calculations

    SciTech Connect

    Carbajo, Juan J; Van Hove, Walter A

    2016-01-01

    The ITER Vacuum Vessel (VV) is equipped with 54 access ports. Each of these ports has an opening in the bioshield that communicates with a dedicated port cell. During Tokamak operation, the bioshield opening must be closed with a concrete plug to shield the radiation coming from the plasma. This port plug separates the port cell into a Port Interspace (between VV closure lid and Port Plug) on the inner side and the Port Cell on the outer side. This paper presents calculations of pressures and temperatures in the ITER (Ref. 1) Port Interspace after a double-ended guillotine break (DEGB) of a pipe of the Tokamak Cooling Water System (TCWS) with high temperature water. It is assumed that this DEGB occurs during the worst possible conditions, which are during water baking operation, with water at a temperature of 523 K (250 C) and at a pressure of 4.4 MPa. These conditions are more severe than during normal Tokamak operation, with the water at 398 K (125 C) and 2 MPa. Two computer codes are employed in these calculations: RELAP5-3D Version 4.2.1 (Ref. 2) to calculate the blowdown releases from the pipe break, and MELCOR, Version 1.8.6 (Ref. 3) to calculate the pressures and temperatures in the Port Interspace. A sensitivity study has been performed to optimize some flow areas.

  9. Communication-optimal iterative methods

    NASA Astrophysics Data System (ADS)

    Demmel, J.; Hoemmen, M.; Mohiyuddin, M.; Yelick, K.

    2009-07-01

    Data movement, both within the memory system of a single processor node and between multiple nodes in a system, limits the performance of many Krylov subspace methods that solve sparse linear systems and eigenvalue problems. Here, s iterations of algorithms such as CG, GMRES, Lanczos, and Arnoldi perform s sparse matrix-vector multiplications and Ω(s) vector reductions, resulting in a growth of Ω(s) in both single-node and network communication. By reorganizing the sparse matrix kernel to compute a set of matrix-vector products at once and reorganizing the rest of the algorithm accordingly, we can perform s iterations by sending O(log P) messages instead of Ω(s·log P) messages on a parallel machine, and reading the on-node components of the matrix A from DRAM to cache just once on a single node instead of s times. This reduces communication to the minimum possible. We discuss both algorithms and an implementation of GMRES on a single node of an 8-core Intel Clovertown. Our implementations achieve significant speedups over the conventional algorithms.

  10. Learning.

    ERIC Educational Resources Information Center

    Glaser, Robert

    A report on learning psychology and its relationship to the study of school learning emphasizes the increasing interaction between theorists and educational practitioners, particularly in attempting to learn which variables influence the instructional process and to find an appropriate methodology to measure and evaluate learning. "Learning…

  11. Chaos automata: iterated function systems with memory

    NASA Astrophysics Data System (ADS)

    Ashlock, Dan; Golden, Jim

    2003-07-01

    Transforming biological sequences into fractals in order to visualize them is a long standing technique, in the form of the traditional four-cornered chaos game. In this paper we give a generalization of the standard chaos game visualization for DNA sequences. It incorporates iterated function systems that are called under the control of a finite state automaton, yielding a DNA to fractal transformation system with memory. We term these fractal visualizers chaos automata. The use of memory enables association of widely separated sequence events in the drawing of the fractal, finessing the “forgetfulness” of other fractal visualization methods. We use a genetic algorithm to train chaos automata to distinguish introns and exons in Zea mays (corn). A substantial issue treated here is the creation of a fitness function that leads to good visual separation of distinct data types.

  12. The impact of iterated games on traffic flow at noncontrolled intersections

    NASA Astrophysics Data System (ADS)

    Zhao, Chao; Jia, Ning

    2015-05-01

    Intersections without signal control widely exist in urban road networks. This paper studied the traffic flow in a noncontrolled intersection within an iterated game framework. We assume drivers have learning ability and can repetitively adjust their strategies (to give way or to rush through) in the intersection according to memories. A cellular automata model is applied to investigate the characteristics of the traffic flow. Numerical experiments indicate two main findings. First, the traffic flow experiences a "volcano-shaped" fundamental diagram with three different phases. Second, most drivers choose to give way in the intersection, but the aggressive drivers cannot be completely eliminated, which is coincident with field observations. Analysis are also given out to explain the observed phenomena. These findings allow deeper insight of the real-world bottleneck traffic flow.

  13. IPADE: Iterative prototype adjustment for nearest neighbor classification.

    PubMed

    Triguero, Isaac; Garcia, Salvador; Herrera, Francisco

    2010-12-01

    Nearest prototype methods are a successful trend of many pattern classification tasks. However, they present several shortcomings such as time response, noise sensitivity, and storage requirements. Data reduction techniques are suitable to alleviate these drawbacks. Prototype generation is an appropriate process for data reduction, which allows the fitting of a dataset for nearest neighbor (NN) classification. This brief presents a methodology to learn iteratively the positioning of prototypes using real parameter optimization procedures. Concretely, we propose an iterative prototype adjustment technique based on differential evolution. The results obtained are contrasted with nonparametric statistical tests and show that our proposal consistently outperforms previously proposed methods, thus becoming a suitable tool in the task of enhancing the performance of the NN classifier.

  14. Application of machine learning and expert systems to Statistical Process Control (SPC) chart interpretation

    NASA Technical Reports Server (NTRS)

    Shewhart, Mark

    1991-01-01

    Statistical Process Control (SPC) charts are one of several tools used in quality control. Other tools include flow charts, histograms, cause and effect diagrams, check sheets, Pareto diagrams, graphs, and scatter diagrams. A control chart is simply a graph which indicates process variation over time. The purpose of drawing a control chart is to detect any changes in the process signalled by abnormal points or patterns on the graph. The Artificial Intelligence Support Center (AISC) of the Acquisition Logistics Division has developed a hybrid machine learning expert system prototype which automates the process of constructing and interpreting control charts.

  15. An introduction to stochastic control theory, path integrals and reinforcement learning

    NASA Astrophysics Data System (ADS)

    Kappen, Hilbert J.

    2007-02-01

    Control theory is a mathematical description of how to act optimally to gain future rewards. In this paper I give an introduction to deterministic and stochastic control theory and I give an overview of the possible application of control theory to the modeling of animal behavior and learning. I discuss a class of non-linear stochastic control problems that can be efficiently solved using a path integral or by MC sampling. In this control formalism the central concept of cost-to-go becomes a free energy and methods and concepts from statistical physics can be readily applied.

  16. Learning from adaptive neural network output feedback control of a unicycle-type mobile robot.

    PubMed

    Zeng, Wei; Wang, Qinghui; Liu, Fenglin; Wang, Ying

    2016-03-01

    This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs.

  17. A statistical learning strategy for closed-loop control of fluid flows

    NASA Astrophysics Data System (ADS)

    Guéniat, Florimond; Mathelin, Lionel; Hussaini, M. Yousuff

    2016-12-01

    This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system's dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz'63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well.

  18. Space Stirling Cryocooler Contamination Lessons Learned and Recommended Control Procedures

    NASA Astrophysics Data System (ADS)

    Glaister, D. S.; Price, K.; Gully, W.; Castles, S.; Reilly, J.

    The most important characteristic of a space cryocooler is its reliability over a lifetime typically in excess of 7 years. While design improvements have reduced the probability of mechanical failure, the risk of internal contamination is still significant and has not been addressed in a consistent approach across the industry. A significant fraction of the endurance test and flight units have experienced some performance degradation related to internal contamination. The purpose of this paper is to describe and assess the contamination issues inside long life, space cryocoolers and to recommend procedures to minimize the probability of encountering contamination related failures and degradation. The paper covers the sources of contamination, the degradation and failure mechanisms, the theoretical and observed cryocooler sensitivity, and the recommended prevention procedures and their impact. We begin with a discussion of the contamination sources, both artificial and intrinsic. Next, the degradation and failure mechanisms are discussed in an attempt to arrive at a contaminant susceptibility, from which we can derive a contamination budget for the machine. This theoretical sensitivity is then compared with the observed sensitivity to illustrate the conservative nature of the assumed scenarios. A number of lessons learned on Raytheon, Ball, Air Force Research Laboratory, and NASA GSFC programs are shared to convey the practical aspects of the contamination problem. Then, the materials and processes required to meet the proposed budget are outlined. An attempt is made to present a survey of processes across industry.

  19. Aging affects motor skill learning when the task requires inhibitory control.

    PubMed

    Brosseau, Julie; Potvin, Marie-Julie; Rouleau, Isabelle

    2007-01-01

    Few studies have examined the influence of aging on motor skill learning (MSL) tasks involving different skills and conditions. Two tasks, each including two different conditions (repeated and nonrepeated), were used: (a) the Mirror Tracing task, requiring the inhibition of an overlearned response and the learning of a new visuomotor association, and (b) the Pursuit Tracking task, mainly requiring the processing of visuospatial stimuli. We hypothesized that older participants would benefit as much as younger participants from the stimuli repetition and that they would exhibit a slower learning rate exclusively on the Mirror Tracing task. As expected, older and younger participants' MSL were not differentially affected by task conditions. They also showed a similar learning rate on the Pursuit Tracking task and a subgroup of older participants exhibited MSL difficulties on the Mirror Tracing task. Problems in the inhibitory control of competing motor memories could explain these age-related MSL difficulties.

  20. A neural learning classifier system with self-adaptive constructivism for mobile robot control.

    PubMed

    Hurst, Jacob; Bull, Larry

    2006-01-01

    For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

  1. Effects of Mobile Augmented Reality Learning Compared to Textbook Learning on Medical Students: Randomized Controlled Pilot Study

    PubMed Central

    2013-01-01

    Background By adding new levels of experience, mobile Augmented Reality (mAR) can significantly increase the attractiveness of mobile learning applications in medical education. Objective To compare the impact of the heightened realism of a self-developed mAR blended learning environment (mARble) on learners to textbook material, especially for ethically sensitive subjects such as forensic medicine, while taking into account basic psychological aspects (usability and higher level of emotional involvement) as well as learning outcomes (increased learning efficiency). Methods A prestudy was conducted based on a convenience sample of 10 third-year medical students. The initial emotional status was captured using the “Profile of Mood States” questionnaire (POMS, German variation); previous knowledge about forensic medicine was determined using a 10-item single-choice (SC) test. During the 30-minute learning period, the students were randomized into two groups: the first group consisted of pairs of students, each equipped with one iPhone with a preinstalled copy of mARble, while the second group was provided with textbook material. Subsequently, both groups were asked to once again complete the POMS questionnaire and SC test to measure changes in emotional state and knowledge gain. Usability as well as pragmatic and hedonic qualities of the learning material was captured using AttrakDiff2 questionnaires. Data evaluation was conducted anonymously. Descriptive statistics for the score in total and the subgroups were calculated before and after the intervention. The scores of both groups were tested against each other using paired and unpaired signed-rank tests. An item analysis was performed for the SC test to objectify difficulty and selectivity. Results Statistically significant, the mARble group (6/10) showed greater knowledge gain than the control group (4/10) (Wilcoxon z=2.232, P=.03). The item analysis of the SC test showed a difficulty of P=0.768 (s=0.09) and a

  2. Neuromotor Issues in the Learning and Control of Golf Skill

    ERIC Educational Resources Information Center

    Knight, Christopher A.

    2004-01-01

    Theoretical and practical issues related to the neuromotor control of a golf swing are presented in this paper. The typical strategy for golf training consists of high volume repetition with an emphasis on a large variety of isolated swing characteristics. The student is frequently instructed to maintain consistent performance in each swing with…

  3. Preconditioned iterations to calculate extreme eigenvalues

    SciTech Connect

    Brand, C.W.; Petrova, S.

    1994-12-31

    Common iterative algorithms to calculate a few extreme eigenvalues of a large, sparse matrix are Lanczos methods or power iterations. They converge at a rate proportional to the separation of the extreme eigenvalues from the rest of the spectrum. Appropriate preconditioning improves the separation of the eigenvalues. Davidson`s method and its generalizations exploit this fact. The authors examine a preconditioned iteration that resembles a truncated version of Davidson`s method with a different preconditioning strategy.

  4. Behavioural treatment of urinary incontinence and encopresis in children with learning disabilities: transfer of stimulus control.

    PubMed

    Smith, L; Smith, P; Lee, S K

    2000-04-01

    Urinary and faecal incontinence present a considerable problem in people with learning disabilities, despite the general effectiveness of behavioural techniques in continence training. Children with learning disabilities and obsessional behaviour may be particularly resistant to toilet training, even where relatively cognitively able, and often despite a substantial degree of control over their eliminatory functions. Their resistance may be more appropriately regarded as a challenging behaviour and their incontinence better explained by factors other than a simple failure to learn. A 'stimulus-control' hypothesis proposes that the child's nappy (diaper)/potty/underwear has developed strong stimulus control over the elimination response. This report describes three case studies in which treatment-resistant children, aged between 8 and 12 years, with mild or moderate learning disabilities, were successfully treated for nappy-dependent nocturnal encopresis or diurnal urinary incontinence. The children were routine case referrals for whom previous attempts to train bowel or bladder control had failed. Behavioural techniques, such as 'shaping' (gradually increasing the proximity to the toilet), 'fading' (reducing the presence of the nappy), and rewards for eliminating, effected successful transfer of stimulus control over elimination from nappy to toilet. Treatment times varied, depending on the degree of the child's obsession and resistance to change.

  5. Pavlovian to instrumental transfer of control in a human learning task.

    PubMed

    Nadler, Natasha; Delgado, Mauricio R; Delamater, Andrew R

    2011-10-01

    Pavlovian learning tasks have been widely used as tools to understand basic cognitive and emotional processes in humans. The present studies investigated one particular task, Pavlovian-to-instrumental transfer (PIT), with human participants in an effort to examine potential cognitive and emotional effects of Pavlovian cues upon instrumentally trained performance. In two experiments, subjects first learned two separate instrumental response-outcome relationships (i.e., R1-O1 and R2-O2) and then were exposed to various stimulus-outcome relationships (i.e., S1-O1, S2-O2, S3-O3, and S4-) before the effects of the Pavlovian stimuli on instrumental responding were assessed during a non-reinforced test. In Experiment 1, instrumental responding was established using a positive-reinforcement procedure, whereas in Experiment 2, a quasi-avoidance learning task was used. In both cases, the Pavlovian stimuli exerted selective control over instrumental responding, whereby S1 and S2 selectively elevated the instrumental response with which it shared an outcome. In addition, in Experiment 2, S3 exerted a nonselective transfer of control effect, whereby both responses were elevated over baseline levels. These data identify two ways, one specific and one general, in which Pavlovian processes can exert control over instrumental responding in human learning paradigms, suggesting that this method may serve as a useful tool in the study of basic cognitive and emotional processes in human learning.

  6. Bilingualism and inhibitory control influence statistical learning of novel word forms.

    PubMed

    Bartolotti, James; Marian, Viorica; Schroeder, Scott R; Shook, Anthony

    2011-01-01

    We examined the influence of bilingual experience and inhibitory control on the ability to learn a novel language. Using a statistical learning paradigm, participants learned words in two novel languages that were based on the International Morse Code. First, participants listened to a continuous stream of words in a Morse code language to test their ability to segment words from continuous speech. Since Morse code does not overlap in form with natural languages, interference from known languages was minimized. Next, participants listened to another Morse code language composed of new words that conflicted with the first Morse code language. Interference in this second language was high due to conflict between languages and due to the presence of two colliding cues (compressed pauses between words and statistical regularities) that competed to define word boundaries. Results suggest that bilingual experience can improve word learning when interference from other languages is low, while inhibitory control ability can improve word learning when interference from other languages is high. We conclude that the ability to extract novel words from continuous speech is a skill that is affected both by linguistic factors, such as bilingual experience, and by cognitive abilities, such as inhibitory control.

  7. ERP evidence of adaptive changes in error processing and attentional control during rhythm synchronization learning.

    PubMed

    Padrão, Gonçalo; Penhune, Virginia; de Diego-Balaguer, Ruth; Marco-Pallares, Josep; Rodriguez-Fornells, Antoni

    2014-10-15

    The ability to detect and use information from errors is essential during the acquisition of new skills. There is now a wealth of evidence about the brain mechanisms involved in error processing. However, the extent to which those mechanisms are engaged during the acquisition of new motor skills remains elusive. Here we examined rhythm synchronization learning across 12 blocks of practice in musically naïve individuals and tracked changes in ERP signals associated with error-monitoring and error-awareness across distinct learning stages. Synchronization performance improved with practice, and performance improvements were accompanied by dynamic changes in ERP components related to error-monitoring and error-awareness. Early in learning, when performance was poor and the internal representations of the rhythms were weaker we observed a larger error-related negativity (ERN) following errors compared to later learning. The larger ERN during early learning likely results from greater conflict between competing motor responses, leading to greater engagement of medial-frontal conflict monitoring processes and attentional control. Later in learning, when performance had improved, we observed a smaller ERN accompanied by an enhancement of a centroparietal positive component resembling the P3. This centroparietal positive component was predictive of participant's performance accuracy, suggesting a relation between error saliency, error awareness and the consolidation of internal templates of the practiced rhythms. Moreover, we showed that during rhythm learning errors led to larger auditory evoked responses related to attention orientation which were triggered automatically and which were independent of the learning stage. The present study provides crucial new information about how the electrophysiological signatures related to error-monitoring and error-awareness change during the acquisition of new skills, extending previous work on error processing and cognitive

  8. Distinct discrimination learning strategies and their relation with spatial memory and attentional control in 4- to 14-year-olds.

    PubMed

    Schmittmann, Verena D; van der Maas, Han L J; Raijmakers, Maartje E J

    2012-04-01

    Behavioral, psychophysiological, and neuropsychological studies have revealed large developmental differences in various learning paradigms where learning from positive and negative feedback is essential. The differences are possibly due to the use of distinct strategies that may be related to spatial working memory and attentional control. In this study, strategies in performing a discrimination learning task were distinguished in a cross-sectional sample of 302 children from 4 to 14 years of age. The trial-by-trial accuracy data were analyzed with mathematical learning models. The best-fitting model revealed three learning strategies: hypothesis testing, slow abrupt learning, and nonlearning. The proportion of hypothesis-testing children increased with age. Nonlearners were present only in the youngest age group. Feature preferences for the irrelevant dimension had a detrimental effect on performance in the youngest age group. The executive functions spatial working memory and attentional control significantly predicted posterior learning strategy probabilities after controlling for age.

  9. Cognitive control over learning: Creating, clustering and generalizing task-set structure

    PubMed Central

    Collins, Anne G.E.; Frank, Michael J.

    2013-01-01

    Executive functions and learning share common neural substrates essential for their expression, notably in prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning, but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for cognitive control. We investigate this question from three complementary angles. First, we develop a new computational “C-TS” (context-task-set) model inspired by non-parametric Bayesian methods, specifying how the learner might infer hidden structure and decide whether to re-use that structure in new situations, or to create new structure. Second, we develop a neurobiologically explicit model to assess potential mechanisms of such interactive structured learning in multiple circuits linking frontal cortex and basal ganglia. We systematically explore the link betweens these levels of modeling across multiple task demands. We find that the network provides an approximate implementation of high level C-TS computations, where manipulations of specific neural mechanisms are well captured by variations in distinct C-TS parameters. Third, this synergism across models yields strong predictions about the nature of human optimal and suboptimal choices and response times during learning. In particular, the models suggest that participants spontaneously build task-set structure into a learning problem when not cued to do so, which predicts positive and negative transfer in subsequent generalization tests. We provide evidence for these predictions in two experiments and show that the C-TS model provides a good quantitative fit to human sequences of choices in this task. These findings implicate a strong tendency to interactively engage cognitive control and learning, resulting in structured abstract representations that afford generalization opportunities, and thus potentially long-term rather than short-term optimality. PMID

  10. Real-time Stability Analysis for Disruption Avoidance in ITER

    NASA Astrophysics Data System (ADS)

    Glasser, Alexander; Kolemen, Egemen; Glasser, Alan

    2015-11-01

    ITER is intended to operate at plasma parameters approaching the frontier of achievable stability limits. And yet, plasma disruptions at ITER must be kept to a bare minimum to avoid damage to its plasma-facing structures. These competing goals necessitate real-time plasma stability analysis and feedback control at ITER. This work aims to develop a mechanism for real-time analysis of a large and virulent class of disruptions driven by the rapid growth of ideal MHD unstable modes in tokamak equilibria. Such modes will be identified by a parallelized, low-latency implementation of A.H. Glasser's well-tested DCON (Direct Criterion of Newcomb) code, which measures the energetics of modes in the bulk plasma fluid, as well as M.S. Chance's VACUUM code, which measures the same in the vacuum between the plasma and tokamak chamber wall. Parallelization of these codes is intended to achieve a time-savings of 40x, thereby reducing latency to a timescale of order 100ms and making the codes viable for ideal MHD stability control at ITER. The hardware used to achieve this parallelization will be an Intel Xeon Phi server with 77 cores (308 threads). Supported by the US DOE under DE-AC02-09CH11466.

  11. Neural network learning control of robot manipulators using gradually increasing task difficulty

    SciTech Connect

    Sanger, T.D. )

    1994-06-01

    Trajectory Extension Learning is an incremental method for training an artificial neural network to approximate the inverse dynamics of a robot manipulator. Training data near a desired trajectory is obtained by slowly varying a parameter of the trajectory from a region of easy solvability of the inverse dynamics toward the desired behavior. The parameter can be average speed, path shape, feedback gain, or any other controllable variable. As learning proceeds, an approximate solution to the local inverse dynamics for each value of the parameter is used to guide learning for the next value of the parameter. Convergence conditions are given for two variations on the algorithm. Examples are shown of application to a real 2-joint direct drive robot arm and a simulated 3-joint redundant arm, both using simulated equilibrium point control.

  12. Differential control of learning and anxiety along the dorsoventral axis of the dentate gyrus.

    PubMed

    Kheirbek, Mazen A; Drew, Liam J; Burghardt, Nesha S; Costantini, Daniel O; Tannenholz, Lindsay; Ahmari, Susanne E; Zeng, Hongkui; Fenton, André A; Hen, René

    2013-03-06

    The dentate gyrus (DG), in addition to its role in learning and memory, is increasingly implicated in the pathophysiology of anxiety disorders. Here, we show that, dependent on their position along the dorsoventral axis of the hippocampus, DG granule cells (GCs) control specific features of anxiety and contextual learning. Using optogenetic techniques to either elevate or decrease GC activity, we demonstrate that GCs in the dorsal DG control exploratory drive and encoding, not retrieval, of contextual fear memories. In contrast, elevating the activity of GCs in the ventral DG has no effect on contextual learning but powerfully suppresses innate anxiety. These results suggest that strategies aimed at modulating the excitability of the ventral DG may be beneficial for the treatment of anxiety disorders.

  13. New directions for understanding neural control in swallowing: the potential and promise of motor learning.

    PubMed

    Humbert, Ianessa A; German, Rebecca Z

    2013-03-01

    Oropharyngeal swallowing is a complex sensorimotor phenomenon that has had decades of research dedicated to understanding it more thoroughly. However, the underlying neural mechanisms responsible for normal and disordered swallowing remain very vague. We consider this gap in knowledge the result of swallowing research that has been broad (identifying phenomena) but not deep (identifying what controls the phenomena). The goals of this review are to address the complexity of motor control of oropharyngeal swallowing and to review the principles of motor learning based on limb movements as a model system. We compare this literature on limb motor learning to what is known about oropharyngeal function as a first step toward suggesting the use of motor learning principles in swallowing research.

  14. Iterative phase retrieval without support.

    PubMed

    Wu, J S; Weierstall, U; Spence, J C H; Koch, C T

    2004-12-01

    An iterative phase retrieval method for nonperiodic objects has been developed from the charge-flipping algorithm proposed in crystallography. A combination of the hybrid input-output (HIO) algorithm and the flipping algorithm has greatly improved performance. In this combined algorithm the flipping algorithm serves to find the support (object boundary) dynamically, and the HIO part improves convergence and moves the algorithm out of local minima. It starts with a single intensity measurement in the Fourier domain and does not require a priori knowledge of the support in the image domain. This method is suitable for general image recovery from oversampled diffuse elastic x-ray and electron-diffraction intensities. The relationship between this algorithm and the output-output algorithm is elucidated.

  15. Iterative phase retrieval without support

    NASA Astrophysics Data System (ADS)

    Wu, J. S.; Weierstall, U.; Spence, J. C. H.; Koch, C. T.

    2004-12-01

    An iterative phase retrieval method for nonperiodic objects has been developed from the charge-flipping algorithm proposed in crystallography. A combination of the hybrid input-output (HIO) algorithm and the flipping algorithm has greatly improved performance. In this combined algorithm the flipping algorithm serves to find the support (object boundary) dynamically, and the HIO part improves convergence and moves the algorithm out of local minima. It starts with a single intensity measurement in the Fourier domain and does not require a priori knowledge of the support in the image domain. This method is suitable for general image recovery from oversampled diffuse elastic x-ray and electron-diffraction intensities. The relationship between this algorithm and the output-output algorithm is elucidated.

  16. Planning as an Iterative Process

    NASA Technical Reports Server (NTRS)

    Smith, David E.

    2012-01-01

    Activity planning for missions such as the Mars Exploration Rover mission presents many technical challenges, including oversubscription, consideration of time, concurrency, resources, preferences, and uncertainty. These challenges have all been addressed by the research community to varying degrees, but significant technical hurdles still remain. In addition, the integration of these capabilities into a single planning engine remains largely unaddressed. However, I argue that there is a deeper set of issues that needs to be considered namely the integration of planning into an iterative process that begins before the goals, objectives, and preferences are fully defined. This introduces a number of technical challenges for planning, including the ability to more naturally specify and utilize constraints on the planning process, the ability to generate multiple qualitatively different plans, and the ability to provide deep explanation of plans.

  17. Benchmarking ICRF simulations for ITER

    SciTech Connect

    R. V. Budny, L. Berry, R. Bilato, P. Bonoli, M. Brambilla, R.J. Dumont, A. Fukuyama, R. Harvey, E.F. Jaeger, E. Lerche, C.K. Phillips, V. Vdovin, J. Wright, and members of the ITPA-IOS

    2010-09-28

    Abstract Benchmarking of full-wave solvers for ICRF simulations is performed using plasma profiles and equilibria obtained from integrated self-consistent modeling predictions of four ITER plasmas. One is for a high performance baseline (5.3 T, 15 MA) DT H-mode plasma. The others are for half-field, half-current plasmas of interest for the pre-activation phase with bulk plasma ion species being either hydrogen or He4. The predicted profiles are used by seven groups to predict the ICRF electromagnetic fields and heating profiles. Approximate agreement is achieved for the predicted heating power partitions for the DT and He4 cases. Profiles of the heating powers and electromagnetic fields are compared.

  18. The Impact of Control Belief and Learning Disorientation on Cognitive Load: The Mediating Effect of Academic Emotions in Two Types of Hypermedia Learning Environments

    ERIC Educational Resources Information Center

    Sunawan; Xiong, Junmei

    2017-01-01

    The present study tested the influence of control belief, learning disorientation, and academic emotions on cognitive load in two types of concept-map structures within hypermedia learning environment. Four hundred and eighty-five students were randomly assigned to two groups: 245 students in the hierarchical group and 240 students in the…

  19. An Investigation into the Academic Success of Prospective Teachers in Terms of Learning Strategies, Learning Styles and the Locus of Control

    ERIC Educational Resources Information Center

    Akça, Figen

    2013-01-01

    The present research aims to investigate the relationship between the learning strategies, learning styles, the locus of control and the academic success of prospective teachers. The study group consists of 198 university students in various departments at the Uludag University Faculty of Education. Research data were collected with the Locus of…

  20. Is developing scientific thinking all about learning to control variables?

    PubMed

    Kuhn, Deanna; Dean, David

    2005-11-01

    Academically low-performing urban sixth graders engaged in inquiry activity received a suggestion that they focus their investigation on the role of a single factor. This suggestion had significant effects on their use of a superficially dissimilar strategy--controlling the variation of other factors. This latter strategy has received the lion's share of attention in research on the development of scientific reasoning. These results have implications, we propose, for what undergoes development with respect to scientific thinking and how this development can best be facilitated.

  1. Learning and strain among newcomers: a three-wave study on the effects of job demands and job control.

    PubMed

    Taris, Toon W; Feij, Jan A

    2004-11-01

    The present 3-wave longitudinal study was an examination of job-related learning and strain as a function of job demand and job control. The participants were 311 newcomers to their jobs. On the basis of R. A. Karasek and T. Theorell's (1990) demand-control model, the authors predicted that high demand and high job control would lead to high levels of learning; low demand and low job control should lead to low levels of learning; high demand and low job control should lead to high levels of strain; and low demand and high job control should lead to low levels of strain. The relation between strain and learning was also examined. The authors tested the hypotheses using ANCOVA and structural equation modeling. The results revealed that high levels of strain have an adverse effect on learning; the reverse effect was not confirmed. It appears that Karasek and Theorell's model is very relevant when examining work socialization processes.

  2. Learning Benefits of Self-Controlled Knowledge of Results in 10-Year-Old Children

    ERIC Educational Resources Information Center

    Chiviacowsky, Suzete; Wulf, Gabriele; Laroque de Medeiros, Franklin; Kaefer, Angelica; Tani, Go

    2008-01-01

    The purpose of the present study was to examine whether the learning benefits of self-controlled knowledge of results (KR) would generalize to children. Specifically, the authors chose 10-year-old children representative of late childhood. The authors used a task that required the children to toss beanbags at a target. One group received KR…

  3. Self-Controlled Feedback in 10-Year-Old Children: Higher Feedback Frequencies Enhance Learning

    ERIC Educational Resources Information Center

    Chiviacowsky, Suzete; Wulf, Gabriele; de Medeiros, Franklin Laroque; Kaefer, Angelica; Wally, Raquel

    2008-01-01

    The purpose of the present study was to examine whether learning in 10-year-old children--that is, the age group for which the Chiviacowsky et al. (2006) study found benefits of self-controlled knowledge of results (KR)--would differ depending on the frequency of feedback they chose. The authors surmised that a relatively high feedback frequency…

  4. Service Learning in Medical and Nursing Training: A Randomized Controlled Trial

    ERIC Educational Resources Information Center

    Leung, A. Y. M.; Chan, S. S. C.; Kwan, C. W.; Cheung, M. K. T.; Leung, S. S. K.; Fong, D. Y. T.

    2012-01-01

    The purpose of this study was to explore the long term effect of a service learning project on medical and nursing students' knowledge in aging and their attitudes toward older adults. A total of 124 students were recruited and then randomized to intervention group (IG) and control group (CG). A pre-and-post-intervention design measured students'…

  5. Self-Controlled Practice Enhances Motor Learning in Introverts and Extroverts

    ERIC Educational Resources Information Center

    Kaefer, Angélica; Chiviacowsky, Suzete; Meira, Cassio de Miranda, Jr.; Tani, Go

    2014-01-01

    Purpose: The purpose of the present study was to investigate the effects of self-controlled feedback on the learning of a sequential-timing motor task in introverts and extroverts. Method: Fifty-six university students were selected by the Eysenck Personality Questionnaire. They practiced a motor task consisting of pressing computer keyboard keys…

  6. User Control and Task Authenticity for Spatial Learning in 3D Environments

    ERIC Educational Resources Information Center

    Dalgarno, Barney; Harper, Barry

    2004-01-01

    This paper describes two empirical studies which investigated the importance for spatial learning of view control and object manipulation within 3D environments. A 3D virtual chemistry laboratory was used as the research instrument. Subjects, who were university undergraduate students (34 in the first study and 80 in the second study), undertook…

  7. The Ability of Psychological Flexibility and Job Control to Predict Learning, Job Performance, and Mental Health

    ERIC Educational Resources Information Center

    Bond, Frank W.; Flaxman, Paul E.

    2006-01-01

    This longitudinal study tested the degree to which an individual characteristic, psychological flexibility, and a work organization variable, job control, predicted ability to learn new skills at work, job performance, and mental health, amongst call center workers in the United Kingdom (N = 448). As hypothesized, results indicated that job…

  8. Measuring Learned Resourcefulness in College Students: Factor Structure of the Self-Control Schedule (SCS)

    ERIC Educational Resources Information Center

    McWhirter, Benedict T.; Burrow-Sanchez, Jason J.; Townsend, Katesy C.

    2008-01-01

    Rosenbaum's Self-Control Schedule (SCS) has been used as a unidimensional measure of Learned Resourcefulness (LR) in previous research. In this study we clarified the factor structure of the SCS among college students (N = 583) by conducting a principal axis factor analysis with oblique (Oblimin) rotation on the SCS. Results revealed a…

  9. Effects of Teacher Controlled Segmented-Animation Presentation in Facilitating Learning

    ERIC Educational Resources Information Center

    Mohamad Ali, Ahmad Zamzuri

    2010-01-01

    The aim of this research was to study the effectiveness of teacher controlled segmented-animation presentation on learning achievement of students with lower level of prior knowledge. Segmented-animation and continuous-animation courseware showing cellular signal transmission process were developed for the research purpose. Pre-test and post-test…

  10. An e-Learning System with MR for Experiments Involving Circuit Construction to Control a Robot

    ERIC Educational Resources Information Center

    Takemura, Atsushi

    2016-01-01

    This paper proposes a novel e-Learning system for technological experiments involving electronic circuit-construction and controlling robot motion that are necessary in the field of technology. The proposed system performs automated recognition of circuit images transmitted from individual learners and automatically supplies the learner with…

  11. Computer-Assisted Learning in Elementary Reading: A Randomized Control Trial

    ERIC Educational Resources Information Center

    Shannon, Lisa Cassidy; Styers, Mary Koenig; Wilkerson, Stephanie Baird; Peery, Elizabeth

    2015-01-01

    This study evaluated the efficacy of Accelerated Reader, a computer-based learning program, at improving student reading. Accelerated Reader is a progress-monitoring, assessment, and practice tool that supports classroom instruction and guides independent reading. Researchers used a randomized controlled trial to evaluate the program with 344…

  12. Inhibitory Control in Mathematical Thinking, Learning and Problem Solving: A Survey

    ERIC Educational Resources Information Center

    Van Dooren, Wim; Inglis, Matthew

    2015-01-01

    Inhibitory control--the ability to ignore salient but unhelpful stimuli and responses--seems to be important for learning mathematics. For instance there is now robust evidence that performance on classic measures of inhibition, such as the Stroop Task, correlate with school-level mathematics achievement. At the same time, a great deal of…

  13. Self-Reflection and the Cognitive Control of Behavior: Implications for Learning

    ERIC Educational Resources Information Center

    Marcovitch, Stuart; Jacques, Sophie; Boseovski, Janet J.; Zelazo, Philip David

    2008-01-01

    In this article, we suggest that self-reflection and self-control--studied under the rubric of "executive function" (EF)--have the potential to transform the way in which learning occurs, allowing for the relatively rapid emergence of new behaviors. We describe 2 lines of research that indicate that reflecting on a task and its affordances helps…

  14. Competition and Control: The Impact of Government Regulation on Teaching and Learning in English Schools

    ERIC Educational Resources Information Center

    Elkins, Tom; Elliott, John

    2004-01-01

    This paper outlines the ways in which successive UK governments have regulated and controlled the teaching profession since the 1980s. Key initiatives relating to curriculum, school effectiveness and individual teacher performance are considered in some detail, because these issues have arguably had the greatest impact upon teaching and learning.…

  15. Beyond the Personal Learning Environment: Attachment and Control in the Classroom of the Future

    ERIC Educational Resources Information Center

    Johnson, Mark William; Sherlock, David

    2014-01-01

    The Personal Learning Environment (PLE) has been presented in a number of guises over a period of 10 years as an intervention which seeks the reorganisation of educational technology through shifting the "locus of control" of technology towards the learner. In the intervening period to the present, a number of initiatives have attempted…

  16. Fraction Intervention for Students with Mathematics Difficulties: Lessons Learned from Five Randomized Control Trials

    ERIC Educational Resources Information Center

    Fuchs, Lynn S.; Malone, Amelia S.; Schumacher, Robin F.; Namkung, Jessica; Wang, Amber

    2016-01-01

    The purpose of this article was to summarize results from 5 randomized control trials assessing the effects of intervention to improve the fraction performance of 4th-grade students at-risk for difficulty in learning about fractions. We begin by explaining the importance of competence with fractions and why an instructional focus on fractions…

  17. Feedback Control and Learning To Program with the CMU Lisp Tutor.

    ERIC Educational Resources Information Center

    Corbett, Albert T.; Anderson, John R.

    This study manipulated the timing and control of error feedback in problem solving and examined their effects on skill acquisition by 40 undergraduate students learning to program in the computer language Lisp under four error feedback conditions. These four conditions included two types of symbol-by-symbol feedback that vary in content, a…

  18. Virtual Learning Intervention to Reduce Bullying Victimization in Primary School: A Controlled Trial

    ERIC Educational Resources Information Center

    Sapouna, Maria; Wolke, Dieter; Vannini, Natalie; Watson, Scott; Woods, Sarah; Schneider, Wolfgang; Enz, Sibylle; Hall, Lynne; Paiva, Ana; Andre, Elizabeth; Dautenhahn, Kerstin; Aylett, Ruth

    2010-01-01

    Background: Anti-bullying interventions to date have shown limited success in reducing victimization and have rarely been evaluated using a controlled trial design. This study examined the effects of the FearNot! anti-bullying virtual learning intervention on escaping victimization, and reducing overall victimization rates among primary school…

  19. Learning Control: Sense-Making, CNC Machines, and Changes in Vocational Training for Industrial Work

    ERIC Educational Resources Information Center

    Berner, Boel

    2009-01-01

    The paper explores how novices in school-based vocational training make sense of computerized numerical control (CNC) machines. Based on two ethnographic studies in Swedish schools, one from the early 1980s and one from 2006, it analyses change and continuity in the cognitive, social, and emotional processes of learning how to become a machine…

  20. A Joint Learning Activity in Process Control and Distance Collaboration between Future Engineers and Technicians

    ERIC Educational Resources Information Center

    Deschênes, Jean-Sebastien; Barka, Noureddine; Michaud, Mario; Paradis, Denis; Brousseau, Jean

    2013-01-01

    A joint learning activity in process control is presented, in the context of a distance collaboration between engineering and technical-level students, in a similar fashion as current practices in the industry involving distance coordination and troubleshooting. The necessary infrastructure and the setup used are first detailed, followed by a…