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Sample records for learning control jian-xin

  1. Learning from neural control.

    Science.gov (United States)

    Wang, Cong; Hill, David J

    2006-01-01

    One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

  2. Procedural learning during declarative control.

    Science.gov (United States)

    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. (c) 2015 APA, all rights reserved).

  3. Learn How to Control Asthma

    Science.gov (United States)

    ... Guidelines Asthma & Community Health Learn How to Control Asthma Language: English (US) Español (Spanish) Arabic Chinese Français ... Is Asthma Treated? Select a Language What Is Asthma? Asthma is a disease that affects your lungs. ...

  4. Complexity control in statistical learning

    Indian Academy of Sciences (India)

    Then we describe how the method of regularization is used to control complexity in learning. We discuss two examples of regularization, one in which the function space used is finite dimensional, and another in which it is a reproducing kernel Hilbert space. Our exposition follows the formulation of Cucker and Smale.

  5. Repetitive learning control of continuous chaotic systems

    International Nuclear Information System (INIS)

    Chen Maoyin; Shang Yun; Zhou Donghua

    2004-01-01

    Combining a shift method and the repetitive learning strategy, a repetitive learning controller is proposed to stabilize unstable periodic orbits (UPOs) within chaotic attractors in the sense of least mean square. If nonlinear parts in chaotic systems satisfy Lipschitz condition, the proposed controller can be simplified into a simple proportional repetitive learning controller

  6. Statistical learning methods: Basics, control and performance

    Energy Technology Data Exchange (ETDEWEB)

    Zimmermann, J. [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)]. E-mail: zimmerm@mppmu.mpg.de

    2006-04-01

    The basics of statistical learning are reviewed with a special emphasis on general principles and problems for all different types of learning methods. Different aspects of controlling these methods in a physically adequate way will be discussed. All principles and guidelines will be exercised on examples for statistical learning methods in high energy and astrophysics. These examples prove in addition that statistical learning methods very often lead to a remarkable performance gain compared to the competing classical algorithms.

  7. Statistical learning methods: Basics, control and performance

    International Nuclear Information System (INIS)

    Zimmermann, J.

    2006-01-01

    The basics of statistical learning are reviewed with a special emphasis on general principles and problems for all different types of learning methods. Different aspects of controlling these methods in a physically adequate way will be discussed. All principles and guidelines will be exercised on examples for statistical learning methods in high energy and astrophysics. These examples prove in addition that statistical learning methods very often lead to a remarkable performance gain compared to the competing classical algorithms

  8. Reinforcement Learning for Ramp Control: An Analysis of Learning Parameters

    Directory of Open Access Journals (Sweden)

    Chao Lu

    2016-08-01

    Full Text Available Reinforcement Learning (RL has been proposed to deal with ramp control problems under dynamic traffic conditions; however, there is a lack of sufficient research on the behaviour and impacts of different learning parameters. This paper describes a ramp control agent based on the RL mechanism and thoroughly analyzed the influence of three learning parameters; namely, learning rate, discount rate and action selection parameter on the algorithm performance. Two indices for the learning speed and convergence stability were used to measure the algorithm performance, based on which a series of simulation-based experiments were designed and conducted by using a macroscopic traffic flow model. Simulation results showed that, compared with the discount rate, the learning rate and action selection parameter made more remarkable impacts on the algorithm performance. Based on the analysis, some suggestionsabout how to select suitable parameter values that can achieve a superior performance were provided.

  9. Linear System Control Using Stochastic Learning Automata

    Science.gov (United States)

    Ziyad, Nigel; Cox, E. Lucien; Chouikha, Mohamed F.

    1998-01-01

    This paper explains the use of a Stochastic Learning Automata (SLA) to control switching between three systems to produce the desired output response. The SLA learns the optimal choice of the damping ratio for each system to achieve a desired result. We show that the SLA can learn these states for the control of an unknown system with the proper choice of the error criteria. The results of using a single automaton are compared to using multiple automata.

  10. Methods for control over learning individual trajectory

    Science.gov (United States)

    Mitsel, A. A.; Cherniaeva, N. V.

    2015-09-01

    The article discusses models, methods and algorithms of determining student's optimal individual educational trajectory. A new method of controlling the learning trajectory has been developed as a dynamic model of learning trajectory control, which uses score assessment to construct a sequence of studied subjects.

  11. Indirect learning control for nonlinear dynamical systems

    Science.gov (United States)

    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.

  12. Mosaic model for sensorimotor learning and control.

    Science.gov (United States)

    Haruno, M; Wolpert, D M; Kawato, M

    2001-10-01

    Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. We previously proposed a new modular architecture, the modular selection and identification for control (MOSAIC) model, for motor learning and control based on multiple pairs of forward (predictor) and inverse (controller) models. The architecture simultaneously learns the multiple inverse models necessary for control as well as how to select the set of inverse models appropriate for a given environment. It combines both feedforward and feedback sensorimotor information so that the controllers can be selected both prior to movement and subsequently during movement. This article extends and evaluates the MOSAIC architecture in the following respects. The learning in the architecture was implemented by both the original gradient-descent method and the expectation-maximization (EM) algorithm. Unlike gradient descent, the newly derived EM algorithm is robust to the initial starting conditions and learning parameters. Second, simulations of an object manipulation task prove that the architecture can learn to manipulate multiple objects and switch between them appropriately. Moreover, after learning, the model shows generalization to novel objects whose dynamics lie within the polyhedra of already learned dynamics. Finally, when each of the dynamics is associated with a particular object shape, the model is able to select the appropriate controller before movement execution. When presented with a novel shape-dynamic pairing, inappropriate activation of modules is observed followed by on-line correction.

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

    Science.gov (United States)

    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.

  14. Online reinforcement learning control for aerospace systems

    NARCIS (Netherlands)

    Zhou, Y.

    2018-01-01

    Reinforcement Learning (RL) methods are relatively new in the field of aerospace guidance, navigation, and control. This dissertation aims to exploit RL methods to improve the autonomy and online learning of aerospace systems with respect to the a priori unknown system and environment, dynamical

  15. Learning to Control Advanced Life Support Systems

    Science.gov (United States)

    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

  16. Measuring strategic control in artificial grammar learning.

    Science.gov (United States)

    Norman, Elisabeth; Price, Mark C; Jones, Emma

    2011-12-01

    In response to concerns with existing procedures for measuring strategic control over implicit knowledge in artificial grammar learning (AGL), we introduce a more stringent measurement procedure. After two separate training blocks which each consisted of letter strings derived from a different grammar, participants either judged the grammaticality of novel letter strings with respect to only one of these two grammars (pure-block condition), or had the target grammar varying randomly from trial to trial (novel mixed-block condition) which required a higher degree of conscious flexible control. Random variation in the colour and font of letters was introduced to disguise the nature of the rule and reduce explicit learning. Strategic control was observed both in the pure-block and mixed-block conditions, and even among participants who did not realise the rule was based on letter identity. This indicated detailed strategic control in the absence of explicit learning. Copyright © 2011 Elsevier Inc. All rights reserved.

  17. On equivalence classes in iterative learning control

    NARCIS (Netherlands)

    Verwoerd, M.H.A.; Meinsma, Gjerrit; de Vries, Theodorus J.A.

    2003-01-01

    This paper advocates a new approach to study the relation between causal iterative learning control (ILC) and conventional feedback control. Central to this approach is the introduction of the set of admissible pairs (of operators) defined with respect to a family of iterations. Considered are two

  18. AMYGDALA MICROCIRCUITS CONTROLLING LEARNED FEAR

    Science.gov (United States)

    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

  19. Learning System Center App Controller

    CERN Document Server

    Naeem, Nasir

    2015-01-01

    This book is intended for IT professionals working with Hyper-V, Azure cloud, VMM, and private cloud technologies who are looking for a quick way to get up and running with System Center 2012 R2 App Controller. To get the most out of this book, you should be familiar with Microsoft Hyper-V technology. Knowledge of Virtual Machine Manager is helpful but not mandatory.

  20. Machine Learning for Flapping Wing Flight Control

    NARCIS (Netherlands)

    Goedhart, Menno; van Kampen, E.; Armanini, S.F.; de Visser, C.C.; Chu, Q.

    2018-01-01

    Flight control of Flapping Wing Micro Air Vehicles is challenging, because of their complex dynamics and variability due to manufacturing inconsistencies. Machine Learning algorithms can be used to tackle these challenges. A Policy Gradient algorithm is used to tune the gains of a

  1. Approaches to Learning to Control Dynamic Uncertainty

    Directory of Open Access Journals (Sweden)

    Magda Osman

    2015-10-01

    Full Text Available In dynamic environments, when faced with a choice of which learning strategy to adopt, do people choose to mostly explore (maximizing their long term gains or exploit (maximizing their short term gains? More to the point, how does this choice of learning strategy influence one’s later ability to control the environment? In the present study, we explore whether people’s self-reported learning strategies and levels of arousal (i.e., surprise, stress correspond to performance measures of controlling a Highly Uncertain or Moderately Uncertain dynamic environment. Generally, self-reports suggest a preference for exploring the environment to begin with. After which, those in the Highly Uncertain environment generally indicated they exploited more than those in the Moderately Uncertain environment; this difference did not impact on performance on later tests of people’s ability to control the dynamic environment. Levels of arousal were also differentially associated with the uncertainty of the environment. Going beyond behavioral data, our model of dynamic decision-making revealed that, in actual fact, there was no difference in exploitation levels between those in the highly uncertain or moderately uncertain environments, but there were differences based on sensitivity to negative reinforcement. We consider the implications of our findings with respect to learning and strategic approaches to controlling dynamic uncertainty.

  2. Game-Theoretic Learning in Distributed Control

    KAUST Repository

    Marden, Jason R.

    2018-01-05

    In distributed architecture control problems, there is a collection of interconnected decision-making components that seek to realize desirable collective behaviors through local interactions and by processing local information. Applications range from autonomous vehicles to energy to transportation. One approach to control of such distributed architectures is to view the components as players in a game. In this approach, two design considerations are the components’ incentives and the rules that dictate how components react to the decisions of other components. In game-theoretic language, the incentives are defined through utility functions, and the reaction rules are online learning dynamics. This chapter presents an overview of this approach, covering basic concepts in game theory, special game classes, measures of distributed efficiency, utility design, and online learning rules, all with the interpretation of using game theory as a prescriptive paradigm for distributed control design.

  3. Digital control for nuclear reactors - lessons learned

    International Nuclear Information System (INIS)

    Bernard, J.A.; Aviles, B.N.; Lanning, D.D.

    1992-01-01

    Lessons learned during the course of the now decade-old MIT program on the digital control of nuclear reactors are enumerated. Relative to controller structure, these include the importance of a separate safety system, the need for signal validation, the role of supervisory algorithms, the significance of command validation, and the relevance of automated reasoning. Relative to controller implementation, these include the value of nodal methods to the creation of real-time reactor physics and thermal hydraulic models, the advantages to be gained from the use of real-time system models, and the importance of a multi-tiered structure to the simultaneous achievement of supervisory, global, and local control. Block diagrams are presented of proposed controllers and selected experimental and simulation-study results are shown. In addition, a history is given of the MIT program on reactor digital control

  4. Concurrent Learning of Control in Multi agent Sequential Decision Tasks

    Science.gov (United States)

    2018-04-17

    Concurrent Learning of Control in Multi-agent Sequential Decision Tasks The overall objective of this project was to develop multi-agent reinforcement... learning (MARL) approaches for intelligent agents to autonomously learn distributed control policies in decentral- ized partially observable... learning of policies in Dec-POMDPs, established performance bounds, evaluated these algorithms both theoretically and empirically, The views

  5. Tunnel Ventilation Control Using Reinforcement Learning Methodology

    Science.gov (United States)

    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. Self-learning fuzzy logic controllers based on reinforcement

    International Nuclear Information System (INIS)

    Wang, Z.; Shao, S.; Ding, J.

    1996-01-01

    This paper proposes a new method for learning and tuning Fuzzy Logic Controllers. The self-learning scheme in this paper is composed of Bucket-Brigade and Genetic Algorithm. The proposed method is tested on the cart-pole system. Simulation results show that our approach has good learning and control performance

  7. Iterative learning control an optimization paradigm

    CERN Document Server

    Owens, David H

    2016-01-01

    This book develops a coherent theoretical approach to algorithm design for iterative learning control based on the use of optimization concepts. Concentrating initially on linear, discrete-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately because their relevant algorithm design issues are distinct and give rise to different performance capabilities. Together with algorithm design, the text demonstrates that there are new algorithms that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference signals and also to support new algorithms for local convergence of nonlinear iterative control. Simulation and application studies are used to illustrate algorithm properties and performance in systems like gantry robots and other elect...

  8. Cognitive Models for Learning to Control Dynamic Systems

    National Research Council Canada - National Science Library

    Eberhart, Russ; Hu, Xiaohui; Chen, Yaobin

    2008-01-01

    Report developed under STTR contract for topic "Cognitive models for learning to control dynamic systems" demonstrated a swarm intelligence learning algorithm and its application in unmanned aerial vehicle (UAV) mission planning...

  9. Locus of control and online learning

    Directory of Open Access Journals (Sweden)

    Suretha Esterhuysen

    2004-10-01

    Full Text Available The integration of online learning in university courses is considered to be both inevitable and necessary. Thus there is an increasing need to raise awareness among educators and course designers about the critical issues impacting on online learning. The aim of this study, therefore, was to assess the differences between two groups of first-year Business Sciences learners (online and conventional learners in terms of biographic and demographic characteristics and locus of control. The study population consisted of 586 first-year learners of whom 185 completed the Locus of Control Inventory (LCI. The results show that the two groups of learners do not differ statistically significantly from each other with respect to locus of control. The findings and their implications are also discussed. Opsomming Die integrasie van aanlyn-leer in universiteitskursusse word beskou as sowel onafwendbaar as noodsaaklik. Daar is dus ’n toenemende behoefte om bewustheid onder opvoedkundiges en kursusontwerpers te kweek oor die kritiese aspekte wat ’n impak op aanlyn-leer het (Morgan, 1996. Daarom was die doel van hierdie ondersoek om die verskille tussen twee groepe eerstejaarleerders in Bestuurs- en Ekonomiese Wetenskap (aanlyn en konvensionele leerders te bepaal ten opsigte van biografiese en demografiese eienskappe en lokus van beheer. Die populasie het bestaan uit 586 eerstejaarleerders waarvan 185 die Lokus van Beheer Vraelys voltooi het. Die resultate toon dat die twee groepe leerders nie statisties beduidend van mekaar verskil het met betrekking tot lokus van beheer nie. Die bevindinge en implikasies word ook bespreek.

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

    Science.gov (United States)

    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.

  11. Learning, Leading and Letting go of Control

    DEFF Research Database (Denmark)

    Jensen, Annie Aarup; Kjær-Rasmussen, Lone Krogh; Iversen, Ann-Merete

    Learning, leading and letting go of control – Learner Led Approaches in Education Annie Aarup Jensen, Lone Krogh Kjær-Rasmussen, Ann-Merete Iversen and Anni Stavnskær Pedersen Abstract The aim of the paper is to introduce a new term in teaching in Higher Education: Learner Led Approaches...... in Education: LED. The sources of inspiration are many as are the experiences we draw from. Problem-based project work (PBL) being one, various classical teacher centered methods, and last but not least a variety of methods aiming towards developing creativity, innovational skills and entrepreneurship. LED...... is inspired by collaboration between professors from Aalborg University, Cornwall College and University College of Northern Denmark. Moravec (2008) claims that educational systems still operate in 1.0 or perhaps 2.0 mode while the surrounding cultures and societies operate in 3.0 mode. The amount...

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

    Science.gov (United States)

    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…

  13. The control of tonic pain by active relief learning.

    Science.gov (United States)

    Zhang, Suyi; Mano, Hiroaki; Lee, Michael; Yoshida, Wako; Kawato, Mitsuo; Robbins, Trevor W; Seymour, Ben

    2018-02-27

    Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning to reduce the cause of the pain. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system uses an uncertainty ('associability') signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that reduces ongoing pain when learning about potential relief. © 2018, Zhang et al.

  14. The control of tonic pain by active relief learning

    Science.gov (United States)

    Mano, Hiroaki; Lee, Michael; Yoshida, Wako; Kawato, Mitsuo; Robbins, Trevor W

    2018-01-01

    Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning to reduce the cause of the pain. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system uses an uncertainty (‘associability’) signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that reduces ongoing pain when learning about potential relief. PMID:29482716

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

    Science.gov (United States)

    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.

  16. Learning-based identification and iterative learning control of direct-drive robots

    NARCIS (Netherlands)

    Bukkems, B.H.M.; Kostic, D.; Jager, de A.G.; Steinbuch, M.

    2005-01-01

    A combination of model-based and Iterative Learning Control is proposed as a method to achieve high-quality motion control of direct-drive robots in repetitive motion tasks. We include both model-based and learning components in the total control law, as their individual properties influence the

  17. Predictive Variable Gain Iterative Learning Control for PMSM

    Directory of Open Access Journals (Sweden)

    Huimin Xu

    2015-01-01

    Full Text Available A predictive variable gain strategy in iterative learning control (ILC is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking. A scheme based on predictive variable gain iterative learning control for eliminating undesirable vibrations of PMSM system is proposed. The basic idea is that undesirable vibrations of PMSM system are eliminated from two aspects of iterative domain and time domain. The predictive method is utilized to determine the learning gain in the ILC algorithm. Compression mapping principle is used to prove the convergence of the algorithm. Simulation results demonstrate that the predictive variable gain is superior to constant gain and other variable gains.

  18. Learning feedforward controller for a mobile robot vehicle

    NARCIS (Netherlands)

    Starrenburg, J.G.; Starrenburg, J.G.; van Luenen, W.T.C.; van Luenen, W.T.C.; Oelen, W.; Oelen, W.; van Amerongen, J.

    1996-01-01

    This paper describes the design and realisation of an on-line learning posetracking controller for a three-wheeled mobile robot vehicle. The controller consists of two components. The first is a constant-gain feedback component, designed on the basis of a second-order model. The second is a learning

  19. Continuous residual reinforcement learning for traffic signal control optimization

    NARCIS (Netherlands)

    Aslani, Mohammad; Seipel, Stefan; Wiering, Marco

    2018-01-01

    Traffic signal control can be naturally regarded as a reinforcement learning problem. Unfortunately, it is one of the most difficult classes of reinforcement learning problems owing to its large state space. A straightforward approach to address this challenge is to control traffic signals based on

  20. Adaptive learning fuzzy control of a mobile robot

    International Nuclear Information System (INIS)

    Tsukada, Akira; Suzuki, Katsuo; Fujii, Yoshio; Shinohara, Yoshikuni

    1989-11-01

    In this report a problem is studied to construct a fuzzy controller for a mobile robot to move autonomously along a given reference direction curve, for which control rules are generated and acquired through an adaptive learning process. An adaptive learning fuzzy controller has been developed for a mobile robot. Good properties of the controller are shown through the travelling experiments of the mobile robot. (author)

  1. Integrated Programme Control Systems: Lessons Learned

    Energy Technology Data Exchange (ETDEWEB)

    Brown, C. W. [Babcock International Group PLC (formerly UKAEA Ltd) B21 Forss, Thurso, Caithness, Scotland (United Kingdom)

    2013-08-15

    Dounreay was the UK's centre of fast reactor research and development from 1955 until 1994 and is now Scotland's largest nuclear clean up and demolition project. After four decades of research, Dounreay is now a site of construction, demolition and waste management, designed to return the site to as near as practicable to its original condition. Dounreay has a turnover in the region of Pounds 150 million a year and employs approximately 900 people. It subcontracts work to 50 or so companies in the supply chain and this provides employment for a similar number of people. The plan for decommissioning the site anticipates all redundant buildings will be cleared in the short term. The target date to achieve interim end state by 2039 is being reviewed in light of Government funding constraints, and will be subject to change through the NDA led site management competition. In the longer term, controls will be put in place on the use of contaminated land until 2300. In supporting the planning, management and organisational aspects for this complex decommissioning programme an integrated programme controls system has been developed and deployed. This consists of a combination of commercial and bespoke tools integrated to support all aspects of programme management, namely scope, schedule, cost, estimating and risk in order to provide baseline and performance management data based upon the application of earned value management principles. Through system evolution and lessons learned, the main benefits of this approach are management data consistency, rapid communication of live information, and increased granularity of data providing summary and detailed reports which identify performance trends that lead to corrective actions. The challenges of such approach are effective use of the information to realise positive changes, balancing the annual system support and development costs against the business needs, and maximising system performance. (author)

  2. Episodic reinforcement learning control approach for biped walking

    Directory of Open Access Journals (Sweden)

    Katić Duško

    2012-01-01

    Full Text Available This paper presents a hybrid dynamic control approach to the realization of humanoid biped robotic walk, focusing on the policy gradient episodic reinforcement learning with fuzzy evaluative feedback. The proposed structure of controller involves two feedback loops: a conventional computed torque controller and an episodic reinforcement learning controller. The reinforcement learning part includes fuzzy information about Zero-Moment- Point errors. Simulation tests using a medium-size 36-DOF humanoid robot MEXONE were performed to demonstrate the effectiveness of our method.

  3. A fuzzy controller with a robust learning function

    International Nuclear Information System (INIS)

    Tanji, Jun-ichi; Kinoshita, Mitsuo

    1987-01-01

    A self-organizing fuzzy controller is able to use linguistic decision rules of control strategy and has a strong adaptive property by virture of its rule learning function. While a simple linguistic description of the learning algorithm first introduced by Procyk, et al. has much flexibility for applications to a wide range of different processes, its detailed formulation, in particular with control stability and learning process convergence, is not clear. In this paper, we describe the formulation of an analytical basis for a self-organizing fuzzy controller by using a method of model reference adaptive control systems (MRACS) for which stability in the adaptive loop is theoretically proven. A detailed formulation is described regarding performance evaluation and rule modification in the rule learning process of the controller. Furthermore, an improved learning algorithm using adaptive rule is proposed. An adaptive rule gives a modification coefficient for a rule change estimating the effect of disturbance occurrence in performance evaluation. The effect of introducing an adaptive rule to improve the learning convergency is described by using a simple iterative formulation. Simulation tests are presented for an application of the proposed self-organizing fuzzy controller to the pressure control system in a Boiling Water Reactor (BWR) plant. Results with the tests confirm the improved learning algorithm has strong convergent properties, even in a very disturbed environment. (author)

  4. Adaptive Trajectory Tracking Control using Reinforcement Learning for Quadrotor

    Directory of Open Access Journals (Sweden)

    Wenjie Lou

    2016-02-01

    Full Text Available Inaccurate system parameters and unpredicted external disturbances affect the performance of non-linear controllers. In this paper, a new adaptive control algorithm under the reinforcement framework is proposed to stabilize a quadrotor helicopter. Based on a command-filtered non-linear control algorithm, adaptive elements are added and learned by policy-search methods. To predict the inaccurate system parameters, a new kernel-based regression learning method is provided. In addition, Policy learning by Weighting Exploration with the Returns (PoWER and Return Weighted Regression (RWR are utilized to learn the appropriate parameters for adaptive elements in order to cancel the effect of external disturbance. Furthermore, numerical simulations under several conditions are performed, and the ability of adaptive trajectory-tracking control with reinforcement learning are demonstrated.

  5. Investigation of Drive-Reinforcement Learning and Application of Learning to Flight Control

    Science.gov (United States)

    1993-08-01

    WL-TR-93-1153 INVESTIGATION OF DRIVE-REINFORCEMEN% LEARNING AND APPLICATION OF LEARNING TO FLIGHT CONTROL AD-A277 442 WALTER L. BAKER (ED), STEPHEN ...OF LEARNING TO FUIGHT CONTROL PE 62204 ___ ___ ___ ___ __ ___ ___ ___ ___ ___ ___ __ PR 2003 6. AUTHOR(S) TA 05 WALTER L. BAKER (ED), STEPHEN C. ATKINS...34 Computers and Thought, E. A. Freigenbaum and J. Feldman (eds.), Mc- Graw Hill, New York, (1959). [19] Holland, J. H., "Escaping Brittleness: The Possibility

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

    Science.gov (United States)

    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.

  7. Learning, Leading, and Letting Go of Control

    DEFF Research Database (Denmark)

    Iversen, Ann-Merete; Pedersen, Anni Stavnskær; Kjær-Rasmussen, Lone Krogh

    2015-01-01

    The article introduces a new term in higher education: learner-led approaches in education (LED). This does not represent a single approach or dogma to replace existing dogmas, but a way of approaching learning and education that mirrors the complexity of society as it develops. LED is based...... on the assumption that all students have their own unique approach to learning and therefore have the potential to design learning processes that are meaningful for them. This removes focus from the teacher and the teaching to the learner and the learning. It builds on the student’s motivation and experienced...... meaningfulness as a driving force, and hence the term learner led. The methods applied in LED change over time, as different learners and teachers together co-create and design methods and approaches appropriate at that particular time, in that particular context and for that particular student or group...

  8. Measuring strategic control in implicit learning: how and why?

    OpenAIRE

    Norman, Elisabeth

    2015-01-01

    Several methods have been developed for measuring the extent to which implicitly learned knowledge can be applied in a strategic, flexible manner. Examples include generation exclusion tasks in Serial Reaction Time (SRT) learning (Goschke, 1998; Destrebecqz and Cleeremans, 2001) and 2-grammar classification tasks in Artificial Grammar Learning (AGL; Dienes et al., 1995; Norman et al., 2011). Strategic control has traditionally been used as a criterion for determining whether acquired knowledg...

  9. Discrete Learning Control with Application to Hydraulic Actuators

    DEFF Research Database (Denmark)

    Andersen, Torben Ole; Pedersen, Henrik Clemmensen; Hansen, Michael R.

    2015-01-01

    In this paper the robustness of a class of learning control algorithms to state disturbances, output noise, and errors in initial conditions is studied. We present a simple learning algorithm and exhibit, via a concise proof, bounds on the asymptotic trajectory errors for the learned input...... and the corresponding state and output trajectories. Furthermore, these bounds are continuous functions of the bounds on the initial condition errors, state disturbance, and output noise, and the bounds are zero in the absence of these disturbances....

  10. Optimal and Autonomous Control Using Reinforcement Learning: A Survey.

    Science.gov (United States)

    Kiumarsi, Bahare; Vamvoudakis, Kyriakos G; Modares, Hamidreza; Lewis, Frank L

    2018-06-01

    This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal and control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review several applications.

  11. Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

    Science.gov (United States)

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

    2013-12-01

    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. 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. 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. 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.

  12. Locus of control and learning strategies as predictors of academic ...

    African Journals Online (AJOL)

    The aim of the research was to determine the relationships which exist between academic success, learning strategies and locus of control. In order to achieve this aim a small-scale quantitative study, utilising two inventories, was done. The first measuring instrument is the Learning and Study Strategies Inventory, which is ...

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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…

  15. Indicators for successful learning in air traffic control training

    NARCIS (Netherlands)

    Van Meeuwen, Ludo; Brand-Gruwel, Saskia; Van Merriënboer, Jeroen; De Bock, Jeano; Kirschner, Paul A.

    2011-01-01

    Van Meeuwen, L. W., Brand-Gruwel, S., Van Merriënboer, J. J. G., De Bock, J. J. P. R., & Kirschner, P. A. (2010, August). Indicators for successful learning in air traffic control training. Paper presented at the 5th EARLI SIG 14 Learning and Professional Development Conference. Munich, Germany.

  16. Systems control with generalized probabilistic fuzzy-reinforcement learning

    NARCIS (Netherlands)

    Hinojosa, J.; Nefti, S.; Kaymak, U.

    2011-01-01

    Reinforcement learning (RL) is a valuable learning method when the systems require a selection of control actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems and RL, the environment is considered to be

  17. Fuzzy self-learning control for magnetic servo system

    Science.gov (United States)

    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.

  18. Application of parsimonious learning feedforward control to mechatronic systems

    NARCIS (Netherlands)

    de Vries, Theodorus J.A.; Velthuis, W.J.R.; Idema, L.J.

    2001-01-01

    For motion control, learning feedforward controllers (LFFCs) should be applied when accurate process modelling is difficult. When controlling such processes with LFFCs in the form of multidimensional B-spline networks, large network sizes and a poor generalising ability may result, known as the

  19. Traffic light control by multiagent reinforcement learning systems

    NARCIS (Netherlands)

    Bakker, B.; Whiteson, S.; Kester, L.; Groen, F.C.A.; Babuška, R.; Groen, F.C.A.

    2010-01-01

    Traffic light control is one of the main means of controlling road traffic. Improving traffic control is important because it can lead to higher traffic throughput and reduced traffic congestion. This chapter describes multiagent reinforcement learning techniques for automatic optimization of

  20. Traffic Light Control by Multiagent Reinforcement Learning Systems

    NARCIS (Netherlands)

    Bakker, B.; Whiteson, S.; Kester, L.J.H.M.; Groen, F.C.A.

    2010-01-01

    Traffic light control is one of the main means of controlling road traffic. Improving traffic control is important because it can lead to higher traffic throughput and reduced traffic congestion. This chapter describes multiagent reinforcement learning techniques for automatic optimization of

  1. Project-Based Learning in Programmable Logic Controller

    Science.gov (United States)

    Seke, F. R.; Sumilat, J. M.; Kembuan, D. R. E.; Kewas, J. C.; Muchtar, H.; Ibrahim, N.

    2018-02-01

    Project-based learning is a learning method that uses project activities as the core of learning and requires student creativity in completing the project. The aims of this study is to investigate the influence of project-based learning methods on students with a high level of creativity in learning the Programmable Logic Controller (PLC). This study used experimental methods with experimental class and control class consisting of 24 students, with 12 students of high creativity and 12 students of low creativity. The application of project-based learning methods into the PLC courses combined with the level of student creativity enables the students to be directly involved in the work of the PLC project which gives them experience in utilizing PLCs for the benefit of the industry. Therefore, it’s concluded that project-based learning method is one of the superior learning methods to apply on highly creative students to PLC courses. This method can be used as an effort to improve student learning outcomes and student creativity as well as to educate prospective teachers to become reliable educators in theory and practice which will be tasked to create qualified human resources candidates in order to meet future industry needs.

  2. Changing pulse-shape basis for molecular learning control

    International Nuclear Information System (INIS)

    Cardoza, David; Langhojer, Florian; Trallero-Herrero, Carlos; Weinacht, Thomas; Monti, Oliver L.A.

    2004-01-01

    We interpret the results of a molecular fragmentation learning control experiment. We show that in the case of a system where control can be related to the structure of the optimal pulse matching the vibrational dynamics of the molecule, a simple change of pulse-shape basis in which the learning algorithm performs the search can reduce the dimensionality of the search space to one or two degrees of freedom

  3. Learning, attentional control, and action video games.

    Science.gov (United States)

    Green, C S; Bavelier, D

    2012-03-20

    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. Copyright © 2012 Elsevier Ltd. All rights reserved.

  4. Learning, attentional control and action video games

    Science.gov (United States)

    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

  5. Robust Control Methods for On-Line Statistical Learning

    Directory of Open Access Journals (Sweden)

    Capobianco Enrico

    2001-01-01

    Full Text Available The issue of controlling that data processing in an experiment results not affected by the presence of outliers is relevant for statistical control and learning studies. Learning schemes should thus be tested for their capacity of handling outliers in the observed training set so to achieve reliable estimates with respect to the crucial bias and variance aspects. We describe possible ways of endowing neural networks with statistically robust properties by defining feasible error criteria. It is convenient to cast neural nets in state space representations and apply both Kalman filter and stochastic approximation procedures in order to suggest statistically robustified solutions for on-line learning.

  6. Generalized projective synchronization of chaotic systems via adaptive learning control

    International Nuclear Information System (INIS)

    Yun-Ping, Sun; Jun-Min, Li; Hui-Lin, Wang; Jiang-An, Wang

    2010-01-01

    In this paper, a learning control approach is applied to the generalized projective synchronisation (GPS) of different chaotic systems with unknown periodically time-varying parameters. Using the Lyapunov–Krasovskii functional stability theory, a differential-difference mixed parametric learning law and an adaptive learning control law are constructed to make the states of two different chaotic systems asymptotically synchronised. The scheme is successfully applied to the generalized projective synchronisation between the Lorenz system and Chen system. Moreover, numerical simulations results are used to verify the effectiveness of the proposed scheme. (general)

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

    Directory of Open Access Journals (Sweden)

    Lisa Katharina Pendt

    Full Text Available 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.

  8. E-learning: controlling costs and increasing value.

    Science.gov (United States)

    Walsh, Kieran

    2015-04-01

    E-learning now accounts for a substantial proportion of medical education provision. This progress has required significant investment and this investment has in turn come under increasing scrutiny so that the costs of e-learning may be controlled and its returns maximised. There are multiple methods by which the costs of e-learning can be controlled and its returns maximised. This short paper reviews some of those methods that are likely to be most effective and that are likely to save costs without compromising quality. Methods might include accessing free or low-cost resources from elsewhere; create short learning resources that will work on multiple devices; using open source platforms to host content; using in-house faculty to create content; sharing resources between institutions; and promoting resources to ensure high usage. Whatever methods are used to control costs or increase value, it is most important to evaluate the impact of these methods.

  9. Reconfigurable control of a power plant deaerator using learning automata

    International Nuclear Information System (INIS)

    Garcia, H.E.; Ray, A.; Edwards, R.M.

    1991-01-01

    A deaerating feedwater heater, equipped with a water level controller and a pressure controller, has been chosen to investigate the feasibility of a reconfigurable control scheme for power plants by incorporating the concept of learning automata. In this paper simulation results based on a model of the Experimental Breeder Reactor (EBR-II) at the Argonne National Laboratory site in Idaho are presented to demonstrate the efficacy of the reconfigurable control scheme

  10. A Parametric Learning and Identification Based Robust Iterative Learning Control for Time Varying Delay Systems

    Directory of Open Access Journals (Sweden)

    Lun Zhai

    2014-01-01

    Full Text Available A parametric learning based robust iterative learning control (ILC scheme is applied to the time varying delay multiple-input and multiple-output (MIMO linear systems. The convergence conditions are derived by using the H∞ and linear matrix inequality (LMI approaches, and the convergence speed is analyzed as well. A practical identification strategy is applied to optimize the learning laws and to improve the robustness and performance of the control system. Numerical simulations are illustrated to validate the above concepts.

  11. Design of fuzzy learning control systems for steam generator water level control

    International Nuclear Information System (INIS)

    Park, Gee Yong

    1996-02-01

    A fuzzy learning algorithm is developed in order to construct the useful control rules and tune the membership functions in the fuzzy logic controller used for water level control of nuclear steam generator. The fuzzy logic controllers have shown to perform better than conventional controllers for ill-defined or complex processes such as nuclear steam generator. Whereas the fuzzy logic controller does not need a detailed mathematical model of a plant to be controlled, its structure is to be made on the basis of the operator's linguistic information experienced from the plant operations. It is not an easy work and also there is no systematic way to translate the operator's linguistic information into quantitative information. When the linguistic information of operators is incomplete, tuning the parameters of fuzzy controller is to be performed for better control performance. It is the time and effort consuming procedure that controller designer has to tune the structure of fuzzy logic controller for optimal performance. And if the number of control inputs is many and the rule base is constructed in multidimensional space, it is very difficult for a controller designer to tune the fuzzy controller structure. Hence, the difficulty in putting the experimental knowledge into quantitative (or numerical) data and the difficulty in tuning the rules are the major problems in designing fuzzy logic controller. In order to overcome the problems described above, a learning algorithm by gradient descent method is included in the fuzzy control system such that the membership functions are tuned and the necessary rules are created automatically for good control performance. For stable learning in gradient descent method, the optimal range of learning coefficient not to be trapped and not to provide too slow learning speed is investigated. With the optimal range of learning coefficient, the optimal value of learning coefficient is suggested and with this value, the gradient

  12. A new subspace based approach to iterative learning control

    NARCIS (Netherlands)

    Nijsse, G.; Verhaegen, M.; Doelman, N.J.

    2001-01-01

    This paper1 presents an iterative learning control (ILC) procedure based on an inverse model of the plant under control. Our first contribution is that we formulate the inversion procedure as a Kalman smoothing problem: based on a compact state space model of a possibly non-minimum phase system,

  13. A control center design revisited: learning from users’ appropriation

    DEFF Research Database (Denmark)

    Souza da Conceição, Carolina; Cordeiro, Cláudia

    2014-01-01

    This paper aims to present the lessons learned during a control center design project by revisiting another control center from the same company designed two and a half years before by the same project team. In light of the experience with the first project and its analysis, the designers and res...

  14. Density control in ITER: an iterative learning control and robust control approach

    Science.gov (United States)

    Ravensbergen, T.; de Vries, P. C.; Felici, F.; Blanken, T. C.; Nouailletas, R.; Zabeo, L.

    2018-01-01

    Plasma density control for next generation tokamaks, such as ITER, is challenging because of multiple reasons. The response of the usual gas valve actuators in future, larger fusion devices, might be too slow for feedback control. Both pellet fuelling and the use of feedforward-based control may help to solve this problem. Also, tight density limits arise during ramp-up, due to operational limits related to divertor detachment and radiative collapses. As the number of shots available for controller tuning will be limited in ITER, in this paper, iterative learning control (ILC) is proposed to determine optimal feedforward actuator inputs based on tracking errors, obtained in previous shots. This control method can take the actuator and density limits into account and can deal with large actuator delays. However, a purely feedforward-based density control may not be sufficient due to the presence of disturbances and shot-to-shot differences. Therefore, robust control synthesis is used to construct a robustly stabilizing feedback controller. In simulations, it is shown that this combined controller strategy is able to achieve good tracking performance in the presence of shot-to-shot differences, tight constraints, and model mismatches.

  15. Impact on learning of an e-learning module on leukaemia: a randomised controlled trial.

    Science.gov (United States)

    Morgulis, Yuri; Kumar, Rakesh K; Lindeman, Robert; Velan, Gary M

    2012-05-28

    e-learning resources may be beneficial for complex or conceptually difficult topics. Leukaemia is one such topic, yet there are no reports on the efficacy of e-learning for leukaemia. This study compared the learning impact on senior medical students of a purpose-built e-learning module on leukaemia, compared with existing online resources. A randomised controlled trial was performed utilising volunteer senior medical students. Participants were randomly allocated to Study and Control groups. Following a pre-test on leukaemia administered to both groups, the Study group was provided with access to the new e-learning module, while the Control group was directed to existing online resources. A post-test and an evaluation questionnaire were administered to both groups at the end of the trial period. Study and Control groups were equivalent in gender distribution, mean academic ability, pre-test performance and time studying leukaemia during the trial. The Study group performed significantly better than the Control group in the post-test, in which the group to which the students had been allocated was the only significant predictor of performance. The Study group's evaluation of the module was overwhelmingly positive. A targeted e-learning module on leukaemia had a significant effect on learning in this cohort, compared with existing online resources. We believe that the interactivity, dialogic feedback and integration with the curriculum offered by the e-learning module contributed to its impact. This has implications for e-learning design in medicine and other disciplines.

  16. Impact on learning of an e-learning module on leukaemia: a randomised controlled trial

    Directory of Open Access Journals (Sweden)

    Morgulis Yuri

    2012-05-01

    Full Text Available Abstract Background e-learning resources may be beneficial for complex or conceptually difficult topics. Leukaemia is one such topic, yet there are no reports on the efficacy of e-learning for leukaemia. This study compared the learning impact on senior medical students of a purpose-built e-learning module on leukaemia, compared with existing online resources. Methods A randomised controlled trial was performed utilising volunteer senior medical students. Participants were randomly allocated to Study and Control groups. Following a pre-test on leukaemia administered to both groups, the Study group was provided with access to the new e-learning module, while the Control group was directed to existing online resources. A post-test and an evaluation questionnaire were administered to both groups at the end of the trial period. Results Study and Control groups were equivalent in gender distribution, mean academic ability, pre-test performance and time studying leukaemia during the trial. The Study group performed significantly better than the Control group in the post-test, in which the group to which the students had been allocated was the only significant predictor of performance. The Study group’s evaluation of the module was overwhelmingly positive. Conclusions A targeted e-learning module on leukaemia had a significant effect on learning in this cohort, compared with existing online resources. We believe that the interactivity, dialogic feedback and integration with the curriculum offered by the e-learning module contributed to its impact. This has implications for e-learning design in medicine and other disciplines.

  17. Impact on learning of an e-learning module on leukaemia: a randomised controlled trial

    Science.gov (United States)

    2012-01-01

    Background e-learning resources may be beneficial for complex or conceptually difficult topics. Leukaemia is one such topic, yet there are no reports on the efficacy of e-learning for leukaemia. This study compared the learning impact on senior medical students of a purpose-built e-learning module on leukaemia, compared with existing online resources. Methods A randomised controlled trial was performed utilising volunteer senior medical students. Participants were randomly allocated to Study and Control groups. Following a pre-test on leukaemia administered to both groups, the Study group was provided with access to the new e-learning module, while the Control group was directed to existing online resources. A post-test and an evaluation questionnaire were administered to both groups at the end of the trial period. Results Study and Control groups were equivalent in gender distribution, mean academic ability, pre-test performance and time studying leukaemia during the trial. The Study group performed significantly better than the Control group in the post-test, in which the group to which the students had been allocated was the only significant predictor of performance. The Study group’s evaluation of the module was overwhelmingly positive. Conclusions A targeted e-learning module on leukaemia had a significant effect on learning in this cohort, compared with existing online resources. We believe that the interactivity, dialogic feedback and integration with the curriculum offered by the e-learning module contributed to its impact. This has implications for e-learning design in medicine and other disciplines. PMID:22640463

  18. Amygdala subsystems and control of feeding behavior by learned cues.

    Science.gov (United States)

    Petrovich, Gorica D; Gallagher, Michela

    2003-04-01

    A combination of behavioral studies and a neural systems analysis approach has proven fruitful in defining the role of the amygdala complex and associated circuits in fear conditioning. The evidence presented in this chapter suggests that this approach is also informative in the study of other adaptive functions that involve the amygdala. In this chapter we present a novel model to study learning in an appetitive context. Furthermore, we demonstrate that long-recognized connections between the amygdala and the hypothalamus play a crucial role in allowing learning to modulate feeding behavior. In the first part we describe a behavioral model for motivational learning. In this model a cue that acquires motivational properties through pairings with food delivery when an animal is hungry can override satiety and promote eating in sated rats. Next, we present evidence that a specific amygdala subsystem (basolateral area) is responsible for allowing such learned cues to control eating (override satiety and promote eating in sated rats). We also show that basolateral amygdala mediates these actions via connectivity with the lateral hypothalamus. Lastly, we present evidence that the amygdalohypothalamic system is specific for the control of eating by learned motivational cues, as it does not mediate another function that depends on intact basolateral amygdala, namely, the ability of a conditioned cue to support new learning based on its acquired value. Knowledge about neural systems through which food-associated cues specifically control feeding behavior provides a defined model for the study of learning. In addition, this model may be informative for understanding mechanisms of maladaptive aspects of learned control of eating that contribute to eating disorders and more moderate forms of overeating.

  19. Short-term memory, executive control, and children's route learning

    OpenAIRE

    Purser, H. R.; Farran, E. K.; Courbois, Y.; Lemahieu, A.; Mellier, D.; Sockeel, P.; Blades, M.

    2012-01-01

    The aim of this study was to investigate route-learning ability in 67 children aged 5 to 11years 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 visuospatial long-term memory; the route-learning task was conducted using a maze in a virtual environment. In contrast to previous research, correlation...

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

    Science.gov (United States)

    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.

  1. Causal Learning in Gambling Disorder: Beyond the Illusion of Control.

    Science.gov (United States)

    Perales, José C; Navas, Juan F; Ruiz de Lara, Cristian M; Maldonado, Antonio; Catena, Andrés

    2017-06-01

    Causal learning is the ability to progressively incorporate raw information about dependencies between events, or between one's behavior and its outcomes, into beliefs of the causal structure of the world. In spite of the fact that some cognitive biases in gambling disorder can be described as alterations of causal learning involving gambling-relevant cues, behaviors, and outcomes, general causal learning mechanisms in gamblers have not been systematically investigated. In the present study, we compared gambling disorder patients against controls in an instrumental causal learning task. Evidence of illusion of control, namely, overestimation of the relationship between one's behavior and an uncorrelated outcome, showed up only in gamblers with strong current symptoms. Interestingly, this effect was part of a more complex pattern, in which gambling disorder patients manifested a poorer ability to discriminate between null and positive contingencies. Additionally, anomalies were related to gambling severity and current gambling disorder symptoms. Gambling-related biases, as measured by a standard psychometric tool, correlated with performance in the causal learning task, but not in the expected direction. Indeed, performance of gamblers with stronger biases tended to resemble the one of controls, which could imply that anomalies of causal learning processes play a role in gambling disorder, but do not seem to underlie gambling-specific biases, at least in a simple, direct way.

  2. Game-Theoretic Learning in Distributed Control

    KAUST Repository

    Marden, Jason R.; Shamma, Jeff S.

    2018-01-01

    from autonomous vehicles to energy to transportation. One approach to control of such distributed architectures is to view the components as players in a game. In this approach, two design considerations are the components’ incentives and the rules

  3. Reinforcement learning for optimal control of low exergy buildings

    International Nuclear Information System (INIS)

    Yang, Lei; Nagy, Zoltan; Goffin, Philippe; Schlueter, Arno

    2015-01-01

    Highlights: • Implementation of reinforcement learning control for LowEx Building systems. • Learning allows adaptation to local environment without prior knowledge. • Presentation of reinforcement learning control for real-life applications. • Discussion of the applicability for real-life situations. - Abstract: Over a third of the anthropogenic greenhouse gas (GHG) emissions stem from cooling and heating buildings, due to their fossil fuel based operation. Low exergy building systems are a promising approach to reduce energy consumption as well as GHG emissions. They consists of renewable energy technologies, such as PV, PV/T and heat pumps. Since careful tuning of parameters is required, a manual setup may result in sub-optimal operation. A model predictive control approach is unnecessarily complex due to the required model identification. Therefore, in this work we present a reinforcement learning control (RLC) approach. The studied building consists of a PV/T array for solar heat and electricity generation, as well as geothermal heat pumps. We present RLC for the PV/T array, and the full building model. Two methods, Tabular Q-learning and Batch Q-learning with Memory Replay, are implemented with real building settings and actual weather conditions in a Matlab/Simulink framework. The performance is evaluated against standard rule-based control (RBC). We investigated different neural network structures and find that some outperformed RBC already during the learning phase. Overall, every RLC strategy for PV/T outperformed RBC by over 10% after the third year. Likewise, for the full building, RLC outperforms RBC in terms of meeting the heating demand, maintaining the optimal operation temperature and compensating more effectively for ground heat. This allows to reduce engineering costs associated with the setup of these systems, as well as decrease the return-of-invest period, both of which are necessary to create a sustainable, zero-emission building

  4. A neural fuzzy controller learning by fuzzy error propagation

    Science.gov (United States)

    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.

  5. Self-teaching neural network learns difficult reactor control problem

    International Nuclear Information System (INIS)

    Jouse, W.C.

    1989-01-01

    A self-teaching neural network used as an adaptive controller quickly learns to control an unstable reactor configuration. The network models the behavior of a human operator. It is trained by allowing it to operate the reactivity control impulsively. It is punished whenever either the power or fuel temperature stray outside technical limits. Using a simple paradigm, the network constructs an internal representation of the punishment and of the reactor system. The reactor is constrained to small power orbits

  6. An iterative learning controller for nonholonomic mobile robots

    International Nuclear Information System (INIS)

    Oriolo, G.; Panzieri, S.; Ulivi, G.

    1998-01-01

    The authors present an iterative learning controller that applies to nonholonomic mobile robots, as well as other systems that can be put in chained form. The learning algorithm exploits the fact that chained-form. The learning algorithm exploits the fact that chained-form systems are linear under piecewise-constant inputs. The proposed control scheme requires the execution of a small number of experiments to drive the system to the desired state in finite time, with nice convergence and robustness properties with respect to modeling inaccuracies as well as disturbances. To avoid the necessity of exactly reinitializing the system at each iteration, the basic method is modified so as to obtain a cyclic controller, by which the system is cyclically steered through an arbitrary sequence of states. As a case study, a carlike mobile robot is considered. Both simulation and experimental results are reported to show the performance of the method

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

    Science.gov (United States)

    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.

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

    International Nuclear Information System (INIS)

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

    2014-01-01

    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

  9. Recent developments in learning control and system identification for robots and structures

    Science.gov (United States)

    Phan, M.; Juang, J.-N.; Longman, R. W.

    1990-01-01

    This paper reviews recent results in learning control and learning system identification, with particular emphasis on discrete-time formulation, and their relation to adaptive theory. Related continuous-time results are also discussed. Among the topics presented are proportional, derivative, and integral learning controllers, time-domain formulation of discrete learning algorithms. Newly developed techniques are described including the concept of the repetition domain, and the repetition domain formulation of learning control by linear feedback, model reference learning control, indirect learning control with parameter estimation, as well as related basic concepts, recursive and non-recursive methods for learning identification.

  10. Learning control for batch thermal sterilization of canned foods.

    Science.gov (United States)

    Syafiie, S; Tadeo, F; Villafin, M; Alonso, A A

    2011-01-01

    A control technique based on Reinforcement Learning is proposed for the thermal sterilization of canned foods. The proposed controller has the objective of ensuring a given degree of sterilization during Heating (by providing a minimum temperature inside the cans during a given time) and then a smooth Cooling, avoiding sudden pressure variations. For this, three automatic control valves are manipulated by the controller: a valve that regulates the admission of steam during Heating, and a valve that regulate the admission of air, together with a bleeder valve, during Cooling. As dynamical models of this kind of processes are too complex and involve many uncertainties, controllers based on learning are proposed. Thus, based on the control objectives and the constraints on input and output variables, the proposed controllers learn the most adequate control actions by looking up a certain matrix that contains the state-action mapping, starting from a preselected state-action space. This state-action matrix is constantly updated based on the performance obtained with the applied control actions. Experimental results at laboratory scale show the advantages of the proposed technique for this kind of processes. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

  11. Application of a fuzzy control algorithm with improved learning speed to nuclear steam generator level control

    International Nuclear Information System (INIS)

    Park, Gee Yong; Seong, Poong Hyun

    1994-01-01

    In order to reduce the load of tuning works by trial-and-error for obtaining the best control performance of conventional fuzzy control algorithm, a fuzzy control algorithm with learning function is investigated in this work. This fuzzy control algorithm can make its rule base and tune the membership functions automatically by use of learning function which needs the data from the control actions of the plant operator or other controllers. Learning process in fuzzy control algorithm is to find the optimal values of parameters, which consist of the membership functions and the rule base, by gradient descent method. Learning speed of gradient descent is significantly improved in this work with the addition of modified momentum. This control algorithm is applied to the steam generator level control by computer simulations. The simulation results confirm the good performance of this control algorithm for level control and show that the fuzzy learning algorithm has the generalization capability for the relation of inputs and outputs and it also has the excellent capability of disturbance rejection

  12. A new 2-d approach to iterative , learning control system

    International Nuclear Information System (INIS)

    Ashraf, S.; Muhammad, E.; Tasleem, M.

    2004-01-01

    The well known two-dimensional system theory is used to analyze and develop a class of learning control system. In this paper we first explore and test a method given by ZHENG and JAMSHIDI. In that paper all the input samples are treated at once. In comparison our paper presents a scheme in which one sample at a time is treated. The 2- D state-space model of proposed learning control scheme is given. An important consequence of the proposed scheme is that given the right choice of gain matrix and sampling time the system's output can be made to converge to any degree of accuracy. (author)

  13. Learning control of a flight simulator stick

    NARCIS (Netherlands)

    Velthuis, W.J.R.; de Vries, Theodorus J.A.; Vrielink, Koen H.J.; Wierda, G.J.; Borghuis, André

    1998-01-01

    Aimportant part of a flight simulator is its control loading system, which is the part that emulates the behaviour of an aircraft as experienced by the pilot through the stick. Such a system consists of a model of the aircraft that is to be simulated and a stick that is driven by an electric motor.

  14. Instructional control of reinforcement learning: a behavioral and neurocomputational investigation.

    Science.gov (United States)

    Doll, Bradley B; Jacobs, W Jake; Sanfey, Alan G; Frank, Michael J

    2009-11-24

    Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub-optimal outcomes (Hayes, S. (Ed.). 1989. Rule-governed behavior: cognition, contingencies, and instructional control. Plenum Press.). Here we examine the control of behavior through instructions in a reinforcement learning task known to depend on striatal dopaminergic function. Participants selected between probabilistically reinforced stimuli, and were (incorrectly) told that a specific stimulus had the highest (or lowest) reinforcement probability. Despite experience to the contrary, instructions drove choice behavior. We present neural network simulations that capture the interactions between instruction-driven and reinforcement-driven behavior via two potential neural circuits: one in which the striatum is inaccurately trained by instruction representations coming from prefrontal cortex/hippocampus (PFC/HC), and another in which the striatum learns the environmentally based reinforcement contingencies, but is "overridden" at decision output. Both models capture the core behavioral phenomena but, because they differ fundamentally on what is learned, make distinct predictions for subsequent behavioral and neuroimaging experiments. Finally, we attempt to distinguish between the proposed computational mechanisms governing instructed behavior by fitting a series of abstract "Q-learning" and Bayesian models to subject data. The best-fitting model supports one of the neural models, suggesting the existence of a "confirmation bias" in which the PFC/HC system trains the reinforcement system by amplifying outcomes that are consistent with instructions while diminishing inconsistent outcomes.

  15. Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

    International Nuclear Information System (INIS)

    Dong Yun Kim; Poong Hyun Seong; .

    1997-01-01

    In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate gains, which minimize the error of system. The proposed algorithm can reduce the time and effort required for obtaining the fuzzy rules through the intelligent learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller. (author)

  16. Measuring strategic control in implicit learning: how and why?

    Science.gov (United States)

    Norman, Elisabeth

    2015-01-01

    Several methods have been developed for measuring the extent to which implicitly learned knowledge can be applied in a strategic, flexible manner. Examples include generation exclusion tasks in Serial Reaction Time (SRT) learning (Goschke, 1998; Destrebecqz and Cleeremans, 2001) and 2-grammar classification tasks in Artificial Grammar Learning (AGL; Dienes et al., 1995; Norman et al., 2011). Strategic control has traditionally been used as a criterion for determining whether acquired knowledge is conscious or unconscious, or which properties of knowledge are consciously available. In this paper I first summarize existing methods that have been developed for measuring strategic control in the SRT and AGL tasks. I then address some methodological and theoretical questions. Methodological questions concern choice of task, whether the measurement reflects inhibitory control or task switching, and whether or not strategic control should be measured on a trial-by-trial basis. Theoretical questions concern the rationale for including measurement of strategic control, what form of knowledge is strategically controlled, and how strategic control can be combined with subjective awareness measures.

  17. Structural learning in feedforward and feedback control.

    Science.gov (United States)

    Yousif, Nada; Diedrichsen, Jörn

    2012-11-01

    For smooth and efficient motor control, the brain needs to make fast corrections during the movement to resist possible perturbations. It also needs to adapt subsequent movements to improve future performance. It is important that both feedback corrections and feedforward adaptation need to be made based on noisy and often ambiguous sensory data. Therefore, the initial response of the motor system, both for online corrections and adaptive responses, is guided by prior assumptions about the likely structure of perturbations. In the context of correcting and adapting movements perturbed by a force field, we asked whether these priors are hard wired or whether they can be modified through repeated exposure to differently shaped force fields. We found that both feedback corrections to unexpected perturbations and feedforward adaptation to a new force field changed, such that they were appropriate to counteract the type of force field that participants had experienced previously. We then investigated whether these changes were driven by a common mechanism or by two separate mechanisms. Participants experienced force fields that were either temporally consistent, causing sustained adaptation, or temporally inconsistent, causing little overall adaptation. We found that the consistent force fields modified both feedback and feedforward responses. In contrast, the inconsistent force field modified the temporal shape of feedback corrections but not of the feedforward adaptive response. These results indicate that responses to force perturbations can be modified in a structural manner and that these modifications are at least partly dissociable for feedback and feedforward control.

  18. Fixed Point Learning Based Intelligent Traffic Control System

    Science.gov (United States)

    Zongyao, Wang; Cong, Sui; Cheng, Shao

    2017-10-01

    Fixed point learning has become an important tool to analyse large scale distributed system such as urban traffic network. This paper presents a fixed point learning based intelligence traffic network control system. The system applies convergence property of fixed point theorem to optimize the traffic flow density. The intelligence traffic control system achieves maximum road resources usage by averaging traffic flow density among the traffic network. The intelligence traffic network control system is built based on decentralized structure and intelligence cooperation. No central control is needed to manage the system. The proposed system is simple, effective and feasible for practical use. The performance of the system is tested via theoretical proof and simulations. The results demonstrate that the system can effectively solve the traffic congestion problem and increase the vehicles average speed. It also proves that the system is flexible, reliable and feasible for practical use.

  19. The effects of control versus autonomy in hypermedia learning environments

    NARCIS (Netherlands)

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

    2010-01-01

    Gorissen, C. J. J., Kester, L., Brand-Gruwel, S., & Martens, R. L. (2010, 13-16 May). The effects of control versus autonomy in hypermedia learning environments. Poster presented at the 4th International Self-Determination Theory Conference, Ghent, Belgium: Kennisnet.

  20. Optimal Control via Reinforcement Learning with Symbolic Policy Approximation

    NARCIS (Netherlands)

    Kubalìk, Jiřì; Alibekov, Eduard; Babuska, R.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri

    2017-01-01

    Model-based reinforcement learning (RL) algorithms can be used to derive optimal control laws for nonlinear dynamic systems. With continuous-valued state and input variables, RL algorithms have to rely on function approximators to represent the value function and policy mappings. This paper

  1. Instructional control of reinforcement learning: A behavioral and neurocomputational investigation

    NARCIS (Netherlands)

    Doll, B.B.; Jacobs, W.J.; Sanfey, A.G.; Frank, M.J.

    2009-01-01

    Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub-optimal outcomes (Hayes, S (Ed) 1989. Rule-governed behavior:

  2. Controlling Uncertainty Decision Making and Learning in Complex Worlds

    CERN Document Server

    Osman, Magda

    2010-01-01

    Controlling Uncertainty: Decision Making and Learning in Complex Worlds reviews and discusses the most current research relating to the ways we can control the uncertain world around us.: Features reviews and discussions of the most current research in a number of fields relevant to controlling uncertainty, such as psychology, neuroscience, computer science and engineering; Presents a new framework that is designed to integrate a variety of disparate fields of research; Represents the first book of its kind to provide a general overview of work related to understanding control

  3. Multi Car Elevator Control by using Learning Automaton

    Science.gov (United States)

    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.

  4. Splendidly blended: a machine learning set up for CDU control

    Science.gov (United States)

    Utzny, Clemens

    2017-06-01

    As the concepts of machine learning and artificial intelligence continue to grow in importance in the context of internet related applications it is still in its infancy when it comes to process control within the semiconductor industry. Especially the branch of mask manufacturing presents a challenge to the concepts of machine learning since the business process intrinsically induces pronounced product variability on the background of small plate numbers. In this paper we present the architectural set up of a machine learning algorithm which successfully deals with the demands and pitfalls of mask manufacturing. A detailed motivation of this basic set up followed by an analysis of its statistical properties is given. The machine learning set up for mask manufacturing involves two learning steps: an initial step which identifies and classifies the basic global CD patterns of a process. These results form the basis for the extraction of an optimized training set via balanced sampling. A second learning step uses this training set to obtain the local as well as global CD relationships induced by the manufacturing process. Using two production motivated examples we show how this approach is flexible and powerful enough to deal with the exacting demands of mask manufacturing. In one example we show how dedicated covariates can be used in conjunction with increased spatial resolution of the CD map model in order to deal with pathological CD effects at the mask boundary. The other example shows how the model set up enables strategies for dealing tool specific CD signature differences. In this case the balanced sampling enables a process control scheme which allows usage of the full tool park within the specified tight tolerance budget. Overall, this paper shows that the current rapid developments off the machine learning algorithms can be successfully used within the context of semiconductor manufacturing.

  5. [Lessons learned from tobacco control in Spain].

    Science.gov (United States)

    Fernández, Esteve; Villalbí, Joan R; Córdoba, Rodrigo

    2006-01-01

    The growing involvement in Spain by civil society in the demand for tobacco control policies has been notable. The basis for the creation of the National Committee for Tobacco Prevention was established in 2004. At the end of that year, an intensive intervention was aimed at specifying, in law, the regulatory actions in the National Plan for Tobacco Prevention. This would facilitate a qualitative leap, taking advantage of the legal transposition of the European directive on advertising. With broad political consensus, the Law 28/2005 was established regarding sanitary measures for tobacco and the regulation of the sale, supply and consumption of tobacco products. The objective stated in this law is to prevent the initiation of tobacco consumption, especially among youth, guarantee the right of non-smokers to breathe air free from tobacco smoke and make quitting this habit easier for people who wish to do so. The main issues included are the prohibition of tobacco advertising and the limitation of tobacco consumption in common work areas and enclosed public spaces. The new law has replaced the previous rules in Spain, which were some of the most permissive in the European Union in terms of tobacco sales, advertising limitations and restrictions on smoking locations. It is clear that there is still much to be done. At this time, more social support needs to be generated in favor of the new regulations, and an important effort needs to be made to educate the public.

  6. Human-level control through deep reinforcement learning

    Science.gov (United States)

    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.

  7. Human-level control through deep reinforcement learning.

    Science.gov (United States)

    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.

  8. Biomimetic approach to tacit learning based on compound control.

    Science.gov (United States)

    Shimoda, Shingo; Kimura, Hidenori

    2010-02-01

    The remarkable capability of living organisms to adapt to unknown environments is due to learning mechanisms that are totally different from the current artificial machine-learning paradigm. Computational media composed of identical elements that have simple activity rules play a major role in biological control, such as the activities of neurons in brains and the molecular interactions in intracellular control. As a result of integrations of the individual activities of the computational media, new behavioral patterns emerge to adapt to changing environments. We previously implemented this feature of biological controls in a form of machine learning and succeeded to realize bipedal walking without the robot model or trajectory planning. Despite the success of bipedal walking, it was a puzzle as to why the individual activities of the computational media could achieve the global behavior. In this paper, we answer this question by taking a statistical approach that connects the individual activities of computational media to global network behaviors. We show that the individual activities can generate optimized behaviors from a particular global viewpoint, i.e., autonomous rhythm generation and learning of balanced postures, without using global performance indices.

  9. Learning and Control Model of the Arm for Loading

    Science.gov (United States)

    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.

  10. Learning to Learn Online: Using Locus of Control to Help Students Become Successful Online Learners

    Science.gov (United States)

    Lowes, Susan; Lin, Peiyi

    2015-01-01

    In this study, approximately 600 online high school students were asked to take Rotter's locus of control questionnaire and then reflect on the results, with the goal of helping them think about their ability to regulate their learning in this new environment. In addition, it was hoped that the results could provide a diagnostic for teachers who…

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

    Science.gov (United States)

    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…

  12. Machine learning control taming nonlinear dynamics and turbulence

    CERN Document Server

    Duriez, Thomas; Noack, Bernd R

    2017-01-01

    This is the first book on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading r...

  13. Measuring strategic control in implicit learning: How and why?

    Directory of Open Access Journals (Sweden)

    Elisabeth eNorman

    2015-09-01

    Full Text Available Several methods have been developed for measuring the extent to which implicitly learned knowledge can be applied in a strategic, flexible manner. Examples include generation exclusion tasks in SRT learning (Destrebecqz & Cleeremans, 2001; Goschke, 1998 and 2-grammar classification tasks in AGL (Dienes, Altmann, Kwan, & Goode, 1995; Norman, Price, & Jones, 2011. Strategic control has traditionally been used as a criterion for determining whether acquired knowledge is conscious or unconscious, or which properties of knowledge is consciously available. In this paper I first summarize existing methods that have been developed for measuring strategic control in the SRT and AGL tasks. I then address some methodologial and theoretical questions. Methodological questions concern choice of task, whether the measurement reflects inhibitory control or task switching, and whether or not strategic control should be measured on a trial-by-trial basis. Theoretical questions concern the rationale for including measurement of strategic control, what form of knowledge is strategically controlled, and how strategic control can be combined with subjective awareness measures.

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

    Science.gov (United States)

    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-…

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

    Science.gov (United States)

    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.

  16. Learning from authoritarian teachers: Controlling the situation or controlling yourself can sustain motivation

    Directory of Open Access Journals (Sweden)

    Kathryn Everhart Chaffee

    2014-01-01

    Full Text Available Positive psychology encompasses the study of positive outcomes, optimal functioning, and resilience in difficult circumstances. In the context of language learning, positive outcomes include academic engagement, self-determined motivation, persistence in language learning, and eventually becoming a proficient user of the language. These questionnaire studies extend previous research by addressing how these positive outcomes can be achieved even in adverse circumstances. In Study 1, the primary and secondary control scales of interest were validated using 2468 students at a Canadian university. Study 2 examined the capacity of 100 Canadian language learners to adjust themselves to fit in with their environment, termed secondary control, and how it was related to their motivation for and engagement in language learning and their feelings of anxiety speaking in the classroom. Secondary control in the form of adjusting one’s attitude towards language learning challenges through positive reappraisals was positively associated with self-determined motivation, need satisfaction, and engagement. analyses, positive reappraisals were also found to buffer the negative effects of having a controlling instructor on students’ engagement and anxiety. These findings suggest that personal characteristics interact with the learning environment to allow students to function optimally in their language courses even when the teacher is controlling.

  17. Iterative learning control for electrical stimulation and stroke rehabilitation

    CERN Document Server

    Freeman, Chris T; Burridge, Jane H; Hughes, Ann-Marie; Meadmore, Katie L

    2015-01-01

    Iterative learning control (ILC) has its origins in the control of processes that perform a task repetitively with a view to improving accuracy from trial to trial by using information from previous executions of the task. This brief shows how a classic application of this technique – trajectory following in robots – can be extended to neurological rehabilitation after stroke. Regaining upper limb movement is an important step in a return to independence after stroke, but the prognosis for such recovery has remained poor. Rehabilitation robotics provides the opportunity for repetitive task-oriented movement practice reflecting the importance of such intense practice demonstrated by conventional therapeutic research and motor learning theory. Until now this technique has not allowed feedback from one practice repetition to influence the next, also implicated as an important factor in therapy. The authors demonstrate how ILC can be used to adjust external functional electrical stimulation of patients’ mus...

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    Pan, Yongping; Yu, Haoyong

    2017-06-01

    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 trajectories. 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.

  1. Reinforcement learning techniques for controlling resources in power networks

    Science.gov (United States)

    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.

  2. Thalamic Control of Human Attention Driven by Memory and Learning

    OpenAIRE

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

    2014-01-01

    Summary 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 ac...

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

    Science.gov (United States)

    1985-02-01

    TERMS (Continue on retuerse if necessary and identify by block num ber) FIELD YGROUP SUB. GR. Adaptive control, aritificial intelligence , synthetic aetr1...application of Artificial Intelligence methods to Synthetic Aperture Radars (SARs) is investigated. It was shown that the neuron-like Adaptive Learning...wavelength Al SE!RI M RADAR DIVISION REFERENCES 1. Barto, A.G. and R.S. Sutton, Goal Seeking Components for Adaptive Intelligence : An Initial Assessment

  4. Machine Learning Control For Highly Reconfigurable High-Order Systems

    Science.gov (United States)

    2015-01-02

    calibration and applications,” Mechatronics and Embedded Systems and Applications (MESA), 2010 IEEE/ASME International Conference on, IEEE, 2010, pp. 38–43...AFRL-OSR-VA-TR-2015-0012 MACHINE LEARNING CONTROL FOR HIGHLY RECONFIGURABLE HIGH-ORDER SYSTEMS John Valasek TEXAS ENGINEERING EXPERIMENT STATION...DIMENSIONAL RECONFIGURABLE SYSTEMS FA9550-11-1-0302 Period of Performance 1 July 2011 – 29 September 2014 John Valasek Aerospace Engineering

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Learning to push and learning to move: The adaptive control of contact forces

    Directory of Open Access Journals (Sweden)

    Maura eCasadio

    2015-11-01

    Full Text Available 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

  9. Fault Tolerance Automotive Air-Ratio Control Using Extreme Learning Machine Model Predictive Controller

    OpenAIRE

    Pak Kin Wong; Hang Cheong Wong; Chi Man Vong; Tong Meng Iong; Ka In Wong; Xianghui Gao

    2015-01-01

    Effective air-ratio control is desirable to maintain the best engine performance. However, traditional air-ratio control assumes the lambda sensor located at the tail pipe works properly and relies strongly on the air-ratio feedback signal measured by the lambda sensor. When the sensor is warming up during cold start or under failure, the traditional air-ratio control no longer works. To address this issue, this paper utilizes an advanced modelling technique, kernel extreme learning machine (...

  10. Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

    International Nuclear Information System (INIS)

    Kim, Dong Yun

    1997-02-01

    In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate adequate gains, which minimize the error of system. The proposed algorithm can reduce the time and efforts required for obtaining the fuzzy rules through the intelligent learning function. The evolutionary programming algorithm is modified and adopted as the method in order to find the optimal gains which are used as the initial gains of FGS with learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller

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

    Science.gov (United States)

    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

  12. Combining Correlation-Based and Reward-Based Learning in Neural Control for Policy Improvement

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Kolodziejski, Christoph; Wörgötter, Florentin

    2013-01-01

    Classical conditioning (conventionally modeled as correlation-based learning) and operant conditioning (conventionally modeled as reinforcement learning or reward-based learning) have been found in biological systems. Evidence shows that these two mechanisms strongly involve learning about...... associations. Based on these biological findings, we propose a new learning model to achieve successful control policies for artificial systems. This model combines correlation-based learning using input correlation learning (ICO learning) and reward-based learning using continuous actor–critic reinforcement...... learning (RL), thereby working as a dual learner system. The model performance is evaluated by simulations of a cart-pole system as a dynamic motion control problem and a mobile robot system as a goal-directed behavior control problem. Results show that the model can strongly improve pole balancing control...

  13. Hilar GABAergic Interneuron Activity Controls Spatial Learning and Memory Retrieval

    Science.gov (United States)

    Andrews-Zwilling, Yaisa; Gillespie, Anna K.; Kravitz, Alexxai V.; Nelson, Alexandra B.; Devidze, Nino; Lo, Iris; Yoon, Seo Yeon; Bien-Ly, Nga; Ring, Karen; Zwilling, Daniel; Potter, Gregory B.; Rubenstein, John L. R.; Kreitzer, Anatol C.; Huang, Yadong

    2012-01-01

    Background Although extensive research has demonstrated the importance of excitatory granule neurons in the dentate gyrus of the hippocampus in normal learning and memory and in the pathogenesis of amnesia in Alzheimer's disease (AD), the role of hilar GABAergic inhibitory interneurons, which control the granule neuron activity, remains unclear. Methodology and Principal Findings We explored the function of hilar GABAergic interneurons in spatial learning and memory by inhibiting their activity through Cre-dependent viral expression of enhanced halorhodopsin (eNpHR3.0)—a light-driven chloride pump. Hilar GABAergic interneuron-specific expression of eNpHR3.0 was achieved by bilaterally injecting adeno-associated virus containing a double-floxed inverted open-reading frame encoding eNpHR3.0 into the hilus of the dentate gyrus of mice expressing Cre recombinase under the control of an enhancer specific for GABAergic interneurons. In vitro and in vivo illumination with a yellow laser elicited inhibition of hilar GABAergic interneurons and consequent activation of dentate granule neurons, without affecting pyramidal neurons in the CA3 and CA1 regions of the hippocampus. We found that optogenetic inhibition of hilar GABAergic interneuron activity impaired spatial learning and memory retrieval, without affecting memory retention, as determined in the Morris water maze test. Importantly, optogenetic inhibition of hilar GABAergic interneuron activity did not alter short-term working memory, motor coordination, or exploratory activity. Conclusions and Significance Our findings establish a critical role for hilar GABAergic interneuron activity in controlling spatial learning and memory retrieval and provide evidence for the potential contribution of GABAergic interneuron impairment to the pathogenesis of amnesia in AD. PMID:22792368

  14. Hilar GABAergic interneuron activity controls spatial learning and memory retrieval.

    Directory of Open Access Journals (Sweden)

    Yaisa Andrews-Zwilling

    Full Text Available Although extensive research has demonstrated the importance of excitatory granule neurons in the dentate gyrus of the hippocampus in normal learning and memory and in the pathogenesis of amnesia in Alzheimer's disease (AD, the role of hilar GABAergic inhibitory interneurons, which control the granule neuron activity, remains unclear.We explored the function of hilar GABAergic interneurons in spatial learning and memory by inhibiting their activity through Cre-dependent viral expression of enhanced halorhodopsin (eNpHR3.0--a light-driven chloride pump. Hilar GABAergic interneuron-specific expression of eNpHR3.0 was achieved by bilaterally injecting adeno-associated virus containing a double-floxed inverted open-reading frame encoding eNpHR3.0 into the hilus of the dentate gyrus of mice expressing Cre recombinase under the control of an enhancer specific for GABAergic interneurons. In vitro and in vivo illumination with a yellow laser elicited inhibition of hilar GABAergic interneurons and consequent activation of dentate granule neurons, without affecting pyramidal neurons in the CA3 and CA1 regions of the hippocampus. We found that optogenetic inhibition of hilar GABAergic interneuron activity impaired spatial learning and memory retrieval, without affecting memory retention, as determined in the Morris water maze test. Importantly, optogenetic inhibition of hilar GABAergic interneuron activity did not alter short-term working memory, motor coordination, or exploratory activity.Our findings establish a critical role for hilar GABAergic interneuron activity in controlling spatial learning and memory retrieval and provide evidence for the potential contribution of GABAergic interneuron impairment to the pathogenesis of amnesia in AD.

  15. Failure of operant control of vocal learning in budgerigars

    Directory of Open Access Journals (Sweden)

    Yoshimasa Seki

    2018-02-01

    Full Text Available Budgerigars were trained by operant conditioning to produce contact calls immediately after hearing a stimulus contact call. In Experiments 1 and 2, playback stimuli were chosen from two different contact call classes from the bird’s repertoire. Once this task was learned, the birds were then tested with other probe stimulus calls from its repertoire, which differed from the original calls drawn from the two classes. Birds failed to mimic the probe stimuli but instead produced one of the two call classes as in the training sessions, showing that birds learned that each stimulus call served as a discriminative stimulus but not as a vocal template for imitation. In Experiment 3, birds were then trained with stimulus calls falling along a 24-step acoustic gradient which varied between the two sounds representing the two contact call categories. As before, birds obtained a reward when the bird’s vocalization matched that of the stimulus above a criterion level. Since the first step and the last step in the gradient were the birds’ original contact calls, these two patterns were easily matched. Intermediate contact calls in the gradient were much harder for the birds to match. After extensive training, one bird learned to produce contact calls that had only a modest similarity to the intermediate contact calls along the gradient. In spite of remarkable vocal plasticity under natural conditions, operant conditioning methods with budgerigars, even after extensive training and rigorous control of vocal discriminative stimuli, failed to show vocal learning.

  16. Steering the dynamics within reduced space through quantum learning control

    International Nuclear Information System (INIS)

    Kim, Young Sik

    2003-01-01

    In quantum dynamics of many-body systems, to identify the Hamiltonian becomes more difficult very rapidly as the number of degrees of freedom increases. In order to simplify the dynamics and to deduce dynamically relevant Hamiltonian information, it is desirable to control the dynamics to lie within a reduced space. With a judicious choice for the cost functional, the closed loop optimal control experiments can be manipulated efficiently to steer the dynamics to lie within a subspace of the system eigenstates without requiring any prior detailed knowledge about the system Hamiltonian. The procedure is simulated for optimally controlled population transfer experiments in the system of two degrees of freedom. To show the feasibility of steering the dynamics to lie in a specified subspace, the learning algorithms guiding the dynamics are presented along with frequency filtering. The results demonstrate that the optimal control fields derive the system to the desired target state through the desired subspace

  17. A Neuro-Control Design Based on Fuzzy Reinforcement Learning

    DEFF Research Database (Denmark)

    Katebi, S.D.; Blanke, M.

    This paper describes a neuro-control fuzzy critic design procedure based on reinforcement learning. An important component of the proposed intelligent control configuration is the fuzzy credit assignment unit which acts as a critic, and through fuzzy implications provides adjustment mechanisms....... The fuzzy credit assignment unit comprises a fuzzy system with the appropriate fuzzification, knowledge base and defuzzification components. When an external reinforcement signal (a failure signal) is received, sequences of control actions are evaluated and modified by the action applier unit. The desirable...... ones instruct the neuro-control unit to adjust its weights and are simultaneously stored in the memory unit during the training phase. In response to the internal reinforcement signal (set point threshold deviation), the stored information is retrieved by the action applier unit and utilized for re...

  18. A Novel Extreme Learning Control Framework of Unmanned Surface Vehicles.

    Science.gov (United States)

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

    2016-05-01

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

  19. Quantum Ensemble Classification: A Sampling-Based Learning Control Approach.

    Science.gov (United States)

    Chen, Chunlin; Dong, Daoyi; Qi, Bo; Petersen, Ian R; Rabitz, Herschel

    2017-06-01

    Quantum ensemble classification (QEC) has significant applications in discrimination of atoms (or molecules), separation of isotopes, and quantum information extraction. However, quantum mechanics forbids deterministic discrimination among nonorthogonal states. The classification of inhomogeneous quantum ensembles is very challenging, since there exist variations in the parameters characterizing the members within different classes. In this paper, we recast QEC as a supervised quantum learning problem. A systematic classification methodology is presented by using a sampling-based learning control (SLC) approach for quantum discrimination. The classification task is accomplished via simultaneously steering members belonging to different classes to their corresponding target states (e.g., mutually orthogonal states). First, a new discrimination method is proposed for two similar quantum systems. Then, an SLC method is presented for QEC. Numerical results demonstrate the effectiveness of the proposed approach for the binary classification of two-level quantum ensembles and the multiclass classification of multilevel quantum ensembles.

  20. QA lessons learned for parameter control from the WIPP Project

    International Nuclear Information System (INIS)

    Richards, R.R.

    1998-01-01

    This paper provides a summary of lessons learned from experiences on the Waste Isolation Pilot Plant (WJPP) Project in implementation of quality assurance controls surrounding inputs for performance assessment analysis. Since the performance assessment (PA) process is inherent in compliance determination for any waste repository, these lessons-learned are intended to be useful to investigators, analysts, and Quality Assurance (QA) practitioners working on high level waste disposal projects. On the WIPP Project, PA analyses for regulatory-compliance determination utilized several inter-related computer programs (codes) that mathematically modeled phenomena such as radionuclide release, retardation, and transport. The input information for those codes are the parameters that are the subject of this paper. Parameters were maintained in a computer database, which was then queried electronically by the PA codes whenever input was needed as the analyses were run

  1. Controlling changes - lessons learned from waste management facilities

    International Nuclear Information System (INIS)

    Johnson, B.M.; Koplow, A.S.; Stoll, F.E.; Waetje, W.D.

    1995-01-01

    This paper discusses lessons learned about change control at the Waste Reduction Operations Complex (WROC) and Waste Experimental Reduction Facility (WERF) of the Idaho National Engineering Laboratory (INEL). WROC and WERF have developed and implemented change control and an as-built drawing process and have identified structures, systems, and components (SSCS) for configuration management. The operations have also formed an Independent Review Committee to minimize costs and resources associated with changing documents. WROC and WERF perform waste management activities at the INEL. WROC activities include storage, treatment, and disposal of hazardous and mixed waste. WERF provides volume reduction of solid low-level waste through compaction, incineration, and sizing operations. WROC and WERF's efforts aim to improve change control processes that have worked inefficiently in the past

  2. Iterative learning control for multi-agent systems coordination

    CERN Document Server

    Yang, Shiping; Li, Xuefang; Shen, Dong

    2016-01-01

    A timely guide using iterative learning control (ILC) as a solution for multi-agent systems (MAS) challenges, this book showcases recent advances and industrially relevant applications. Readers are first given a comprehensive overview of the intersection between ILC and MAS, then introduced to a range of topics that include both basic and advanced theoretical discussions, rigorous mathematics, engineering practice, and both linear and nonlinear systems. Through systematic discussion of network theory and intelligent control, the authors explore future research possibilities, develop new tools, and provide numerous applications such as power grids, communication and sensor networks, intelligent transportation systems, and formation control. Readers will gain a roadmap of the latest advances in the fields and can use their newfound knowledge to design their own algorithms.

  3. Robust Learning Control Design for Quantum Unitary Transformations.

    Science.gov (United States)

    Wu, Chengzhi; Qi, Bo; Chen, Chunlin; Dong, Daoyi

    2017-12-01

    Robust control design for quantum unitary transformations has been recognized as a fundamental and challenging task in the development of quantum information processing due to unavoidable decoherence or operational errors in the experimental implementation of quantum operations. In this paper, we extend the systematic methodology of sampling-based learning control (SLC) approach with a gradient flow algorithm for the design of robust quantum unitary transformations. The SLC approach first uses a "training" process to find an optimal control strategy robust against certain ranges of uncertainties. Then a number of randomly selected samples are tested and the performance is evaluated according to their average fidelity. The approach is applied to three typical examples of robust quantum transformation problems including robust quantum transformations in a three-level quantum system, in a superconducting quantum circuit, and in a spin chain system. Numerical results demonstrate the effectiveness of the SLC approach and show its potential applications in various implementation of quantum unitary transformations.

  4. Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

    International Nuclear Information System (INIS)

    Kim, Dong Yun; Seong, Poong Hyun

    1996-01-01

    In this study, we proposed a fuzzy gain scheduler with intelligent learning algorithm for a reactor control. In the proposed algorithm, we used the gradient descent method to learn the rule bases of a fuzzy algorithm. These rule bases are learned toward minimizing an objective function, which is called a performance cost function. The objective of fuzzy gain scheduler with intelligent learning algorithm is the generation of adequate gains, which minimize the error of system. The condition of every plant is generally changed as time gose. That is, the initial gains obtained through the analysis of system are no longer suitable for the changed plant. And we need to set new gains, which minimize the error stemmed from changing the condition of a plant. In this paper, we applied this strategy for reactor control of nuclear power plant (NPP), and the results were compared with those of a simple PI controller, which has fixed gains. As a result, it was shown that the proposed algorithm was superior to the simple PI controller

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

    Science.gov (United States)

    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. © 2016 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    Menon, V.

    2018-01-01

    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. PMID:27339012

  7. A Learning Framework for Control-Oriented Modeling of Buildings

    Energy Technology Data Exchange (ETDEWEB)

    Rubio-Herrero, Javier; Chandan, Vikas; Siegel, Charles M.; Vishnu, Abhinav; Vrabie, Draguna L.

    2018-01-18

    Buildings consume a significant amount of energy worldwide. Several building optimization and control use cases require models of energy consumption which are control oriented, have high predictive capability, imposes minimal data pre-processing requirements, and have the ability to be adapted continuously to account for changing conditions as new data becomes available. Data driven modeling techniques, that have been investigated so far, while promising in the context of buildings, have been unable to simultaneously satisfy all the requirements mentioned above. In this context, deep learning techniques such as Recurrent Neural Networks (RNNs) hold promise, empowered by advanced computational capabilities and big data opportunities. In this paper, we propose a deep learning based methodology for the development of control oriented models for building energy management and test in on data from a real building. Results show that the proposed methodology outperforms other data driven modeling techniques significantly. We perform a detailed analysis of the proposed methodology along dimensions such as topology, sensitivity, and downsampling. Lastly, we conclude by envisioning a building analytics suite empowered by the proposed deep framework, that can drive several use cases related to building energy management.

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

    Science.gov (United States)

    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. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  9. Lessons learned on the Ground Test Accelerator control system

    International Nuclear Information System (INIS)

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

    1994-01-01

    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

  10. Learned helplessness and learned prevalence: exploring the causal relations among perceived controllability, reward prevalence, and exploration.

    Science.gov (United States)

    Teodorescu, Kinneret; Erev, Ido

    2014-10-01

    Exposure to uncontrollable outcomes has been found to trigger learned helplessness, a state in which the agent, because of lack of exploration, fails to take advantage of regained control. Although the implications of this phenomenon have been widely studied, its underlying cause remains undetermined. One can learn not to explore because the environment is uncontrollable, because the average reinforcement for exploring is low, or because rewards for exploring are rare. In the current research, we tested a simple experimental paradigm that contrasts the predictions of these three contributors and offers a unified psychological mechanism that underlies the observed phenomena. Our results demonstrate that learned helplessness is not correlated with either the perceived controllability of one's environment or the average reward, which suggests that reward prevalence is a better predictor of exploratory behavior than the other two factors. A simple computational model in which exploration decisions were based on small samples of past experiences captured the empirical phenomena while also providing a cognitive basis for feelings of uncontrollability. © The Author(s) 2014.

  11. Computer Simulation Tests of Feedback Error Learning Controller with IDM and ISM for Functional Electrical Stimulation in Wrist Joint Control

    OpenAIRE

    Watanabe, Takashi; Sugi, Yoshihiro

    2010-01-01

    Feedforward controller would be useful for hybrid Functional Electrical Stimulation (FES) system using powered orthotic devices. In this paper, Feedback Error Learning (FEL) controller for FES (FEL-FES controller) was examined using an inverse statics model (ISM) with an inverse dynamics model (IDM) to realize a feedforward FES controller. For FES application, the ISM was tested in learning off line using training data obtained by PID control of very slow movements. Computer simulation tests ...

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

    Science.gov (United States)

    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…

  13. Contextual control of attentional allocation in human discrimination learning.

    Science.gov (United States)

    Uengoer, Metin; Lachnit, Harald; Lotz, Anja; Koenig, Stephan; Pearce, John M

    2013-01-01

    In 3 human predictive learning experiments, we investigated whether the allocation of attention can come under the control of contextual stimuli. In each experiment, participants initially received a conditional discrimination for which one set of cues was trained as relevant in Context 1 and irrelevant in Context 2, and another set was relevant in Context 2 and irrelevant in Context 1. For Experiments 1 and 2, we observed that a second discrimination based on cues that had previously been trained as relevant in Context 1 during the conditional discrimination was acquired more rapidly in Context 1 than in Context 2. Experiment 3 revealed a similar outcome when new stimuli from the original dimensions were used in the test stage. Our results support the view that the associability of a stimulus can be controlled by the stimuli that accompany it.

  14. Digital control systems training on a distance learning platform

    Directory of Open Access Journals (Sweden)

    Jan PIECHA

    2009-01-01

    Full Text Available The paper deals with new training technologies development based on approach to distance learning website, implemented in the laboratory of a Traffic Engineering study branch at Faculty of Transport. The discussed computing interface allows students complete knowledge of traffic controllers’ architecture and machine language programming fundamentals. These training facilities are available at home; at their remote terminal. The training resources consist of electronic / computer based training; guidebooks and software units. The laboratory provides the students with an interface entering into simulation packages and programming interfaces, supporting the web training facilities. The courseware complexity selection is one of the most difficult factors in intelligent training unit’s development. The dynamically configured application provides the user with his individually set structure of the training resources. The trainee controls the application structure and complexity, from the time he started. For simplifying the training process and studying activities, several unifications were provided. The introduced ideas need various standardisations, simplifying the e-learning units’ development and application control processes [8], [9]. Further training facilities development concerns virtual laboratory environment organisation in laboratories of Transport Faculty.

  15. Self-Learning Power Control in Wireless Sensor Networks.

    Science.gov (United States)

    Chincoli, Michele; Liotta, Antonio

    2018-01-27

    Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay.

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

    Science.gov (United States)

    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.

  17. Learning Control Over Emotion Networks Through Connectivity-Based Neurofeedback.

    Science.gov (United States)

    Koush, Yury; Meskaldji, Djalel-E; Pichon, Swann; Rey, Gwladys; Rieger, Sebastian W; Linden, David E J; Van De Ville, Dimitri; Vuilleumier, Patrik; Scharnowski, Frank

    2017-02-01

    Most mental functions are associated with dynamic interactions within functional brain networks. Thus, training individuals to alter functional brain networks might provide novel and powerful means to improve cognitive performance and emotions. Using a novel connectivity-neurofeedback approach based on functional magnetic resonance imaging (fMRI), we show for the first time that participants can learn to change functional brain networks. Specifically, we taught participants control over a key component of the emotion regulation network, in that they learned to increase top-down connectivity from the dorsomedial prefrontal cortex, which is involved in cognitive control, onto the amygdala, which is involved in emotion processing. After training, participants successfully self-regulated the top-down connectivity between these brain areas even without neurofeedback, and this was associated with concomitant increases in subjective valence ratings of emotional stimuli of the participants. Connectivity-based neurofeedback goes beyond previous neurofeedback approaches, which were limited to training localized activity within a brain region. It allows to noninvasively and nonpharmacologically change interconnected functional brain networks directly, thereby resulting in specific behavioral changes. Our results demonstrate that connectivity-based neurofeedback training of emotion regulation networks enhances emotion regulation capabilities. This approach can potentially lead to powerful therapeutic emotion regulation protocols for neuropsychiatric disorders. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  18. Group performance and group learning at dynamic system control tasks

    International Nuclear Information System (INIS)

    Drewes, Sylvana

    2013-01-01

    Proper management of dynamic systems (e.g. cooling systems of nuclear power plants or production and warehousing) is important to ensure public safety and economic success. So far, research has provided broad evidence for systematic shortcomings in individuals' control performance of dynamic systems. This research aims to investigate whether groups manifest synergy (Larson, 2010) and outperform individuals and if so, what processes lead to these performance advantages. In three experiments - including simulations of a nuclear power plant and a business setting - I compare the control performance of three-person-groups to the average individual performance and to nominal groups (N = 105 groups per experiment). The nominal group condition captures the statistical advantage of aggregated group judgements not due to social interaction. First, results show a superior performance of groups compared to individuals. Second, a meta-analysis across all three experiments shows interaction-based process gains in dynamic control tasks: Interacting groups outperform the average individual performance as well as the nominal group performance. Third, group interaction leads to stable individual improvements of group members that exceed practice effects. In sum, these results provide the first unequivocal evidence for interaction-based performance gains of groups in dynamic control tasks and imply that employers should rely on groups to provide opportunities for individual learning and to foster dynamic system control at its best.

  19. Algebraic and adaptive learning in neural control systems

    Science.gov (United States)

    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.

  20. Computer Simulation Tests of Feedback Error Learning Controller with IDM and ISM for Functional Electrical Stimulation in Wrist Joint Control

    Directory of Open Access Journals (Sweden)

    Takashi Watanabe

    2010-01-01

    Full Text Available Feedforward controller would be useful for hybrid Functional Electrical Stimulation (FES system using powered orthotic devices. In this paper, Feedback Error Learning (FEL controller for FES (FEL-FES controller was examined using an inverse statics model (ISM with an inverse dynamics model (IDM to realize a feedforward FES controller. For FES application, the ISM was tested in learning off line using training data obtained by PID control of very slow movements. Computer simulation tests in controlling wrist joint movements showed that the ISM performed properly in positioning task and that IDM learning was improved by using the ISM showing increase of output power ratio of the feedforward controller. The simple ISM learning method and the FEL-FES controller using the ISM would be useful in controlling the musculoskeletal system that has nonlinear characteristics to electrical stimulation and therefore is expected to be useful in applying to hybrid FES system using powered orthotic device.

  1. Sensorless speed control of switched reluctance motor using brain emotional learning based intelligent controller

    International Nuclear Information System (INIS)

    Dehkordi, Behzad Mirzaeian; Parsapoor, Amir; Moallem, Mehdi; Lucas, Caro

    2011-01-01

    In this paper, a brain emotional learning based intelligent controller (BELBIC) is developed to control the switched reluctance motor (SRM) speed. Like other intelligent controllers, BELBIC is model free and is suitable to control nonlinear systems. Motor parameter changes, operating point changes, measurement noise, open circuit fault in one phase and asymmetric phases in SRM are also simulated to show the robustness and superior performance of BELBIC. To compare the BELBIC performance with other intelligent controllers, Fuzzy Logic Controller (FLC) is developed. System responses with BELBIC and FLC are compared. Furthermore, by eliminating the position sensor, a method is introduced to estimate the rotor position. This method is based on Adaptive Neuro Fuzzy Inference System (ANFIS). The estimator inputs are four phase flux linkages. Suggested rotor position estimator is simulated in different conditions. Simulation results confirm the accurate rotor position estimation in different loads and speeds.

  2. Sensorless speed control of switched reluctance motor using brain emotional learning based intelligent controller

    Energy Technology Data Exchange (ETDEWEB)

    Dehkordi, Behzad Mirzaeian, E-mail: mirzaeian@eng.ui.ac.i [Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Hezar-Jerib St., Postal code 8174673441, Isfahan (Iran, Islamic Republic of); Parsapoor, Amir, E-mail: amirparsapoor@yahoo.co [Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Hezar-Jerib St., Postal code 8174673441, Isfahan (Iran, Islamic Republic of); Moallem, Mehdi, E-mail: moallem@cc.iut.ac.i [Department of Electrical Engineering, Isfahan University of Technology, Isfahan (Iran, Islamic Republic of); Lucas, Caro, E-mail: lucas@ut.ac.i [Centre of Excellence for Control and Intelligent Processing, Electrical and Computer Engineering Faculty, College of Engineering, University of Tehran, Tehran (Iran, Islamic Republic of)

    2011-01-15

    In this paper, a brain emotional learning based intelligent controller (BELBIC) is developed to control the switched reluctance motor (SRM) speed. Like other intelligent controllers, BELBIC is model free and is suitable to control nonlinear systems. Motor parameter changes, operating point changes, measurement noise, open circuit fault in one phase and asymmetric phases in SRM are also simulated to show the robustness and superior performance of BELBIC. To compare the BELBIC performance with other intelligent controllers, Fuzzy Logic Controller (FLC) is developed. System responses with BELBIC and FLC are compared. Furthermore, by eliminating the position sensor, a method is introduced to estimate the rotor position. This method is based on Adaptive Neuro Fuzzy Inference System (ANFIS). The estimator inputs are four phase flux linkages. Suggested rotor position estimator is simulated in different conditions. Simulation results confirm the accurate rotor position estimation in different loads and speeds.

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

    Science.gov (United States)

    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

  4. Which Management Control System principles and aspects are relevant when deploying a learning machine?

    OpenAIRE

    Martin, Johansson; Mikael, Göthager

    2017-01-01

    How shall a business adapt its management control systems when learning machines enter the arena? Will the control system continue to focus on humans aspects and continue to consider a learning machine to be an automation tool as any other historically programmed computer? Learning machines introduces productivity capabilities that achieve very high levels of efficiency and quality. A learning machine can sort through large amounts of data and make conclusions difficult by a human mind. Howev...

  5. Design of intelligent comfort control system with human learning and minimum power control strategies

    International Nuclear Information System (INIS)

    Liang, J.; Du, R.

    2008-01-01

    This paper presents the design of an intelligent comfort control system by combining the human learning and minimum power control strategies for the heating, ventilating and air conditioning (HVAC) system. In the system, the predicted mean vote (PMV) is adopted as the control objective to improve indoor comfort level by considering six comfort related variables, whilst a direct neural network controller is designed to overcome the nonlinear feature of the PMV calculation for better performance. To achieve the highest comfort level for the specific user, a human learning strategy is designed to tune the user's comfort zone, and then, a VAV and minimum power control strategy is proposed to minimize the energy consumption further. In order to validate the system design, a series of computer simulations are performed based on a derived HVAC and thermal space model. The simulation results confirm the design of the intelligent comfort control system. In comparison to the conventional temperature controller, this system can provide a higher comfort level and better system performance, so it has great potential for HVAC applications in the future

  6. Autonomy supported, learner-controlled or system-controlled learning in hypermedia environments and the influence of academic self-regulation style

    NARCIS (Netherlands)

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

    2012-01-01

    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

  7. Autonomy supported, learner-controlled or system-controlled learning in hypermedia environments and the influence of academic self-regulation style

    NARCIS (Netherlands)

    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

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

    Science.gov (United States)

    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…

  9. Contingency learning without awareness: evidence for implicit control.

    Science.gov (United States)

    Schmidt, James R; Crump, Matthew J C; Cheesman, Jim; Besner, Derek

    2007-06-01

    The results of four experiments provide evidence for controlled processing in the absence of awareness. Participants identified the colour of a neutral distracter word. Each of four words (e.g., MOVE) was presented in one of the four colours 75% of the time (Experiments 1 and 4) or 50% of the time (Experiments 2 and 3). Colour identification was faster when the words appeared in the colour they were most often presented in relative to when they appeared in another colour, even for participants who were subjectively unaware of any contingencies between the words and the colours. An analysis of sequence effects showed that participants who were unaware of the relation between distracter words and colours nonetheless controlled the impact of the word on performance depending on the nature of the previous trial. A block analysis of contingency-unaware participants revealed that contingencies were learned rapidly in the first block of trials. Experiment 3 showed that the contingency effect does not depend on the level of awareness, thus ruling out explicit strategy accounts. Finally, Experiment 4 showed that the contingency effect results from behavioural control and not from semantic association or stimulus familiarity. These results thus provide evidence for implicit control.

  10. Fuzzy control in robot-soccer, evolutionary learning in the first layer of control

    Directory of Open Access Journals (Sweden)

    Peter J Thomas

    2003-02-01

    Full Text Available In this paper an evolutionary algorithm is developed to learn a fuzzy knowledge base for the control of a soccer playing micro-robot from any configuration belonging to a grid of initial configurations to hit the ball along the ball to goal line of sight. The knowledge base uses relative co-ordinate system including left and right wheel velocities of the robot. Final path positions allow forward and reverse facing robot to ball and include its physical dimensions.

  11. Learning sequential control in a Neural Blackboard Architecture for in situ concept reasoning

    NARCIS (Netherlands)

    van der Velde, Frank; van der Velde, Frank; Besold, Tarek R.; Lamb, Luis; Serafini, Luciano; Tabor, Whitney

    2016-01-01

    Simulations are presented and discussed of learning sequential control in a Neural Blackboard Architecture (NBA) for in situ concept-based reasoning. Sequential control is learned in a reservoir network, consisting of columns with neural circuits. This allows the reservoir to control the dynamics of

  12. Sensitivity-based self-learning fuzzy logic control for a servo system

    NARCIS (Netherlands)

    Balenovic, M.

    1998-01-01

    Describes an experimental verification of a self-learning fuzzy logic controller (SLFLC). The SLFLC contains a learning algorithm that utilizes a second-order reference model and a sensitivity model related to the fuzzy controller parameters. The effectiveness of the proposed controller has been

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  14. Nuclear Instrumentation and Control Cyber Testbed Considerations - Lessons Learned

    International Nuclear Information System (INIS)

    Jonathan, Peter Grey; Robert, S Anderson; Julio, G Rodriguez; Lee, Cheol Kwon

    2014-01-01

    Identifying and understanding digital instrumentation and control (I and 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. To demonstrate objective evidence that cyber vulnerabilities have been adequately identified and mitigated, a test bed representing a facility's critical nuclear equipment must be replicated. Idaho National Laboratory (INL) has built and operated similar test beds for common critical infrastructure I and C for over 10 years. This experience developing, operating, and maintaining an I and C test bed 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 test bed. The following information will discuss I and C test bed lessons learned and the impact of these experiences to KAERI

  15. Lessons learned from the MIT Tara control and data system

    International Nuclear Information System (INIS)

    Gaudreau, M.P.J.; Sullivan, J.D.; Fredian, T.W.; Irby, J.H.; Karcher, C.A.; Rameriz, R.A.; Sevillano, E.; Stillerman, J.A.; Thomas, P.

    1987-10-01

    The control and data system of the MIT Tara Tandem Mirror has worked successfully throughout the lifetime of the experiment (1983 through 1987). As the Tara project winds down, it is appropriate to summarize the lessons learned from the implementation and operation of the control and data system over the years and in its final form. The control system handled ∼2400 I/0 points in real time throughout the 5 to 10 minute shot cycle while the data system, in near real time, handled ∼1000 signals with a total of 5 to 7 Mbytes of data each shot. The implementation depended upon a consistent approach based on separating physics and engineering functions and on detailed functional diagrams with narrowly defined cross communication. This paper is a comprehensive treatment of the principal successes, residual problems, and dilemmas that arose from the beginning until the final hardware and software implementation. Suggestions for future systems of either similar size or of larger scale such as CIT are made in the conclusion. 11 refs., 1 fig

  16. A Software Control Framework for Learning Coordinated, Multi-Robot Strategies in Open Environments

    National Research Council Canada - National Science Library

    Grupen, Roderic

    2003-01-01

    .... The UMass effort marries high-level process descriptions, discrete event analysis and model checking, learning and stochastic exploration, and a control theoretic substrate to accomplish these goals...

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

    Science.gov (United States)

    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…

  18. A Combination of Machine Learning and Cerebellar Models for the Motor Control and Learning of a Modular Robot

    DEFF Research Database (Denmark)

    Baira Ojeda, Ismael; Tolu, Silvia; Pacheco, Moises

    2017-01-01

    We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist, forming a Unit Learning Machine. The LWPR optimizes the input space...... and learns the internal model of a single robot module to command the robot to follow a desired trajectory with its end-effector. The cerebellar microcircuit refines the LWPR output delivering corrective commands. We contrasted distinct cerebellar circuits including analytical models and spiking models...

  19. Computer simulation of nuclear reactor control by means of heuristic learning controller

    International Nuclear Information System (INIS)

    Bubak, M.; Moscinski, J.

    1976-01-01

    A trial of application of two techniques of Artificial Intelligence: heuristic Programming and Learning Machines Theory for nuclear reactor control is presented. Considering complexity of the mathematical models describing satisfactorily the nuclear reactors, value changes of these models parameters in course of operation, knowledge of some parameters value with too small exactness, there appear diffucluties in the classical approach application for these objects control systems design. The classical approach consists in definition of the permissible control actions set on the base of the set performance index and the object mathematical model. The Artificial Intelligence methods enable construction of the control system, which gets during work an information being a priori inaccessible and uses it for its action change for the control to be the optimum one. Applying these methods we have elaborated the reactor power control system. As the performance index there has been taken the integral of the error square. For the control system there are only accessible: the set power trajectory, the reactor power and the control rod position. The set power trajectory has been divided into time intervals called heuristic intervals. At the beginning of every heuristic interval, on the base of the obtained experience, the control system chooses from the control (heuristic) set the optimum control. The heuristic set it is the set of relations between the control rod rate and the state variables, the set and the obtained power, similar to simplifications applied by nuclear reactors operators. The results obtained for the different control rod rates and different reactor (simulated on the digital computer) show the proper work of the system. (author)

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

    Science.gov (United States)

    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…

  1. Online Adaptation and Over-Trial Learning in Macaque Visuomotor Control

    Science.gov (United States)

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

    2011-01-01

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

  2. Motor control and learning with lower-limb myoelectric control in amputees.

    Science.gov (United States)

    Alcaide-Aguirre, Ramses E; Morgenroth, David C; Ferris, Daniel P

    2013-01-01

    Advances in robotic technology have recently enabled the development of powered lower-limb prosthetic limbs. A major hurdle in developing commercially successful powered prostheses is the control interface. Myoelectric signals are one way for prosthetic users to provide feedforward volitional control of prosthesis mechanics. The goal of this study was to assess motor learning in people with lower-limb amputation using proportional myoelectric control from residual-limb muscles. We examined individuals with transtibial amputation and nondisabled controls performing tracking tasks of a virtual object. We assessed how quickly the individuals with amputation improved their performance and whether years since amputation correlated with performance. At the beginning of training, subjects with amputation performed much worse than control subjects. By the end of a short training period, tracking error did not significantly differ between subjects with amputation and nondisabled subjects. Initial but not final performance correlated significantly with time since amputation. This study demonstrates that although subjects with amputation may initially have poor volitional control of their residual lower-limb muscles, training can substantially improve their volitional control. These findings are encouraging for the future use of proportional myoelectric control of powered lower-limb prostheses.

  3. Information-educational environment with adaptive control of learning process

    Science.gov (United States)

    Modjaev, A. D.; Leonova, N. M.

    2017-01-01

    Recent years, a new scientific branch connected with the activities in social sphere management developing intensively and it is called "Social Cybernetics". In the framework of this scientific branch, theory and methods of management of social sphere are formed. Considerable attention is paid to the management, directly in real time. However, the decision of such management tasks is largely constrained by the lack of or insufficiently deep study of the relevant sections of the theory and methods of management. The article discusses the use of cybernetic principles in solving problems of control in social systems. Applying to educational activities a model of composite interrelated objects representing the behaviour of students at various stages of educational process is introduced. Statistical processing of experimental data obtained during the actual learning process is being done. If you increase the number of features used, additionally taking into account the degree and nature of variability of levels of current progress of students during various types of studies, new properties of students' grouping are discovered. L-clusters were identified, reflecting the behaviour of learners with similar characteristics during lectures. It was established that the characteristics of the clusters contain information about the dynamics of learners' behaviour, allowing them to be used in additional lessons. The ways of solving the problem of adaptive control based on the identified dynamic characteristics of the learners are planned.

  4. Off-policy reinforcement learning for H∞ control design.

    Science.gov (United States)

    Luo, Biao; Wu, Huai-Ning; Huang, Tingwen

    2015-01-01

    The H∞ control design problem is considered for nonlinear systems with unknown internal system model. It is known that the nonlinear H∞ control problem can be transformed into solving the so-called Hamilton-Jacobi-Isaacs (HJI) equation, which is a nonlinear partial differential equation that is generally impossible to be solved analytically. Even worse, model-based approaches cannot be used for approximately solving HJI equation, when the accurate system model is unavailable or costly to obtain in practice. To overcome these difficulties, an off-policy reinforcement leaning (RL) method is introduced to learn the solution of HJI equation from real system data instead of mathematical system model, and its convergence is proved. In the off-policy RL method, the system data can be generated with arbitrary policies rather than the evaluating policy, which is extremely important and promising for practical systems. For implementation purpose, a neural network (NN)-based actor-critic structure is employed and a least-square NN weight update algorithm is derived based on the method of weighted residuals. Finally, the developed NN-based off-policy RL method is tested on a linear F16 aircraft plant, and further applied to a rotational/translational actuator system.

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

    Science.gov (United States)

    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.

  6. Impaired learning of punishments in Parkinson's disease with and without impulse control disorder.

    Science.gov (United States)

    Leplow, Bernd; Sepke, Maria; Schönfeld, Robby; Pohl, Johannes; Oelsner, Henriette; Latzko, Lea; Ebersbach, Georg

    2017-02-01

    To document specific learning mechanisms in patients with Parkinson's disease (PD) with and without impulse control disorder (ICD). Thirty-two PD patients receiving dopamine replacement therapy (DRT) were investigated. Sixteen were diagnosed with ICD (ICD + ) and 16 PD patients matched for levodopa equivalence dosage, and DRT duration and severity of disease did not show impulsive behavior (non-ICD). Short-term learning of inhibitory control was assessed by an experimental procedure which was intended to mimic everyday life. Correct inhibition especially, had to be learned without reward (passive avoidance), and the failure to inhibit a response was punished (punishment learning). Results were compared to 16 healthy controls (HC) matched for age and sex. In ICD + patients within-session learning of non-rewarded inhibition was at chance levels. Whereas healthy controls rapidly developed behavioral inhibition, non-ICD patients were also significantly impaired compared to HC, but gradually developed some degree of control. Both patient groups showed significantly decreased learning if the failure to withhold a response was punished. PD patients receiving DRT show impaired ability to acquire both punishment learning and passive avoidance learning, irrespective of whether or not ICD was developed. In ICD + PD patients, behavioral inhibition is nearly absent. Results demonstrate that by means of subtle learning paradigms it is possible to identify PD-DRT patients who show subtle alterations of punishment learning. This may be a behavioral measure for the identification of PD patients who are prone to develop ICD if DRT is continued.

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

    Science.gov (United States)

    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.

  8. Learning feedback and feedforward control in a mirror-reversed visual environment.

    Science.gov (United States)

    Kasuga, Shoko; Telgen, Sebastian; Ushiba, Junichi; Nozaki, Daichi; Diedrichsen, Jörn

    2015-10-01

    When we learn a novel task, the motor system needs to acquire both feedforward and feedback control. Currently, little is known about how the learning of these two mechanisms relate to each other. In the present study, we tested whether feedforward and feedback control need to be learned separately, or whether they are learned as common mechanism when a new control policy is acquired. Participants were trained to reach to two lateral and one central target in an environment with mirror (left-right)-reversed visual feedback. One group was allowed to make online movement corrections, whereas the other group only received visual information after the end of the movement. Learning of feedforward control was assessed by measuring the accuracy of the initial movement direction to lateral targets. Feedback control was measured in the responses to sudden visual perturbations of the cursor when reaching to the central target. Although feedforward control improved in both groups, it was significantly better when online corrections were not allowed. In contrast, feedback control only adaptively changed in participants who received online feedback and remained unchanged in the group without online corrections. Our findings suggest that when a new control policy is acquired, feedforward and feedback control are learned separately, and that there may be a trade-off in learning between feedback and feedforward controllers. Copyright © 2015 the American Physiological Society.

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

    Science.gov (United States)

    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…

  10. A deep learning / neuroevolution hybrid for visual control

    DEFF Research Database (Denmark)

    Poulsen, Andreas Precht; Thorhauge, Mark; Funch, Mikkel Hvilshj

    2017-01-01

    This paper presents a deep learning / neuroevolution hybrid approach called DLNE, which allows FPS bots to learn to aim & shoot based only on high-dimensional raw pixel input. The deep learning component is responsible for visual recognition and translating raw pixels to compact feature...... representations, while the evolving network takes those features as inputs to infer actions. The results suggest that combining deep learning and neuroevolution in a hybrid approach is a promising research direction that could make complex visual domains directly accessible to networks trained through evolution....

  11. Associative learning and the control of human dietary behavior.

    Science.gov (United States)

    Brunstrom, Jeffrey M

    2007-07-01

    Most of our food likes and disliked are learned. Relevant forms of associative learning have been identified in animals. However, observations of the same associative processes are relatively scarce in humans. The first section of this paper outlines reasons why this might be the case. Emphasis is placed on recent research exploring individual differences and the importance or otherwise of hunger and contingency awareness. The second section briefly considers the effect of learning on meal size, and the author revisits the question of how learned associations might come to influence energy intake in humans.

  12. ISO learning approximates a solution to the inverse-controller problem in an unsupervised behavioral paradigm.

    Science.gov (United States)

    Porr, Bernd; von Ferber, Christian; Wörgötter, Florentin

    2003-04-01

    In "Isotropic Sequence Order Learning" (pp. 831-864 in this issue), we introduced a novel algorithm for temporal sequence learning (ISO learning). Here, we embed this algorithm into a formal nonevaluating (teacher free) environment, which establishes a sensor-motor feedback. The system is initially guided by a fixed reflex reaction, which has the objective disadvantage that it can react only after a disturbance has occurred. ISO learning eliminates this disadvantage by replacing the reflex-loop reactions with earlier anticipatory actions. In this article, we analytically demonstrate that this process can be understood in terms of control theory, showing that the system learns the inverse controller of its own reflex. Thereby, this system is able to learn a simple form of feedforward motor control.

  13. Women with learning disabilities and access to cervical screening: retrospective cohort study using case control methods

    Science.gov (United States)

    Reynolds, Fiona; Stanistreet, Debbi; Elton, Peter

    2008-01-01

    Background Several studies in the UK have suggested that women with learning disabilities may be less likely to receive cervical screening tests and a previous local study in had found that GPs considered screening unnecessary for women with learning disabilities. This study set out to ascertain whether women with learning disabilities are more likely to be ceased from a cervical screening programme than women without; and to examine the reasons given for ceasing women with learning disabilities. It was carried out in Bury, Heywood-and-Middleton and Rochdale. Methods Carried out using retrospective cohort study methods, women with learning disabilities were identified by Read code; and their cervical screening records were compared with the Call-and-Recall records of women without learning disabilities in order to examine their screening histories. Analysis was carried out using case-control methods – 1:2 (women with learning disabilities: women without learning disabilities), calculating odds ratios. Results 267 women's records were compared with the records of 534 women without learning disabilities. Women with learning disabilities had an odds ratio (OR) of 0.48 (Confidence Interval (CI) 0.38 – 0.58; X2: 72.227; p.value learning disabilities. Conclusion The reasons given for ceasing and/or not screening suggest that merely being coded as having a learning disability is not the sole reason for these actions. There are training needs among smear takers regarding appropriate reasons not to screen and providing screening for women with learning disabilities. PMID:18218106

  14. Teaching Self-Control Procedures to Learning Disabled Youths.

    Science.gov (United States)

    Foster, Carol; And Others

    The study involving two learning disabled (LD) seventh graders was designed to develop and evaluate a self instructional booklet that teaches adolescents to change their behaviors with minimal intervention from other individuals. The first part of the study examined whether LD Ss could learn the principles of self monitoring, goal establishment,…

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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…

  17. Are marketing students in control in problem-based learning?

    NARCIS (Netherlands)

    Geitz, Gerry; Joosten-ten Brinke, Desirée; Kirschner, Paul A.

    2018-01-01

    This study investigated to what extent self-efficacy, learning behavior, and performance outcomes relate to each other and how they can be positively influenced by students asking for and seeking feedback within a problem-based learning (PBL) environment in order to meet today’s requirements of

  18. Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks

    Directory of Open Access Journals (Sweden)

    Erdal Kayacan

    2017-01-01

    Full Text Available A learning control strategy is preferred for the control and guidance of a fixed-wing unmanned aerial vehicle to deal with lack of modeling and flight uncertainties. For learning the plant model as well as changing working conditions online, a fuzzy neural network (FNN is used in parallel with a conventional P (proportional controller. Among the learning algorithms in the literature, a derivative-free one, sliding mode control (SMC theory-based learning algorithm, is preferred as it has been proved to be computationally efficient in real-time applications. Its proven robustness and finite time converging nature make the learning algorithm appropriate for controlling an unmanned aerial vehicle as the computational power is always limited in unmanned aerial vehicles (UAVs. The parameter update rules and stability conditions of the learning are derived, and the proof of the stability of the learning algorithm is shown by using a candidate Lyapunov function. Intensive simulations are performed to illustrate the applicability of the proposed controller which includes the tracking of a three-dimensional trajectory by the UAV subject to time-varying wind conditions. The simulation results show the efficiency of the proposed control algorithm, especially in real-time control systems because of its computational efficiency.

  19. Patients with Parkinson's disease learn to control complex systems-an indication for intact implicit cognitive skill learning.

    Science.gov (United States)

    Witt, Karsten; Daniels, Christine; Daniel, Victoria; Schmitt-Eliassen, Julia; Volkmann, Jens; Deuschl, Günther

    2006-01-01

    Implicit memory and learning mechanisms are composed of multiple processes and systems. Previous studies demonstrated a basal ganglia involvement in purely cognitive tasks that form stimulus response habits by reinforcement learning such as implicit classification learning. We will test the basal ganglia influence on two cognitive implicit tasks previously described by Berry and Broadbent, the sugar production task and the personal interaction task. Furthermore, we will investigate the relationship between certain aspects of an executive dysfunction and implicit learning. To this end, we have tested 22 Parkinsonian patients and 22 age-matched controls on two implicit cognitive tasks, in which participants learned to control a complex system. They interacted with the system by choosing an input value and obtaining an output that was related in a complex manner to the input. The objective was to reach and maintain a specific target value across trials (dynamic system learning). The two tasks followed the same underlying complex rule but had different surface appearances. Subsequently, participants performed an executive test battery including the Stroop test, verbal fluency and the Wisconsin card sorting test (WCST). The results demonstrate intact implicit learning in patients, despite an executive dysfunction in the Parkinsonian group. They lead to the conclusion that the basal ganglia system affected in Parkinson's disease does not contribute to the implicit acquisition of a new cognitive skill. Furthermore, the Parkinsonian patients were able to reach a specific goal in an implicit learning context despite impaired goal directed behaviour in the WCST, a classic test of executive functions. These results demonstrate a functional independence of implicit cognitive skill learning and certain aspects of executive functions.

  20. A randomised controlled trial of blended learning to improve the newborn examination skills of medical students.

    Science.gov (United States)

    Stewart, Alice; Inglis, Garry; Jardine, Luke; Koorts, Pieter; Davies, Mark William

    2013-03-01

    To evaluate the hypotheses that a blended learning approach would improve the newborn examination skills of medical students and yield a higher level of satisfaction with learning newborn examination. Undergraduate medical students at a tertiary teaching hospital were individually randomised to receive either a standard neonatology teaching programme (control group), or additional online access to the PENSKE Baby Check Learning Module (blended learning group). The primary outcome was performance of newborn examination on standardised assessment by blinded investigators. The secondary outcomes were performance of all 'essential' items of the examination, and participant satisfaction. The recruitment rate was 88% (71/81). The blended learning group achieved a significantly higher mean score than the control group (p=0.02) for newborn examination. There was no difference for performance of essential items, or satisfaction with learning newborn examination. The blended learning group rated the module highly for effective use of learning time and ability to meet specific learning needs. A blended learning approach resulted in a higher level of performance of newborn examination on standardised assessment. This is consistent with published literature on blended learning and has implications for all neonatal clinicians including junior doctors, midwifes and nurse practitioners.

  1. Research on Open-Closed-Loop Iterative Learning Control with Variable Forgetting Factor of Mobile Robots

    Directory of Open Access Journals (Sweden)

    Hongbin Wang

    2016-01-01

    Full Text Available We propose an iterative learning control algorithm (ILC that is developed using a variable forgetting factor to control a mobile robot. The proposed algorithm can be categorized as an open-closed-loop iterative learning control, which produces control instructions by using both previous and current data. However, introducing a variable forgetting factor can weaken the former control output and its variance in the control law while strengthening the robustness of the iterative learning control. If it is applied to the mobile robot, this will reduce position errors in robot trajectory tracking control effectively. In this work, we show that the proposed algorithm guarantees tracking error bound convergence to a small neighborhood of the origin under the condition of state disturbances, output measurement noises, and fluctuation of system dynamics. By using simulation, we demonstrate that the controller is effective in realizing the prefect tracking.

  2. Formation Learning Control of Multiple Autonomous Underwater Vehicles With Heterogeneous Nonlinear Uncertain Dynamics.

    Science.gov (United States)

    Yuan, Chengzhi; Licht, Stephen; He, Haibo

    2017-09-26

    In this paper, a new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs' heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.

  3. Self-Control of Haptic Assistance for Motor Learning: Influences of Frequency and Opinion of Utility

    Science.gov (United States)

    Williams, Camille K.; Tseung, Victrine; Carnahan, Heather

    2017-01-01

    Studies of self-controlled practice have shown benefits when learners controlled feedback schedule, use of assistive devices and task difficulty, with benefits attributed to information processing and motivational advantages of self-control. Although haptic assistance serves as feedback, aids task performance and modifies task difficulty, researchers have yet to explore whether self-control over haptic assistance could be beneficial for learning. We explored whether self-control of haptic assistance would be beneficial for learning a tracing task. Self-controlled participants selected practice blocks on which they would receive haptic assistance, while participants in a yoked group received haptic assistance on blocks determined by a matched self-controlled participant. We inferred learning from performance on retention tests without haptic assistance. From qualitative analysis of open-ended questions related to rationales for/experiences of the haptic assistance that was chosen/provided, themes emerged regarding participants’ views of the utility of haptic assistance for performance and learning. Results showed that learning was directly impacted by the frequency of haptic assistance for self-controlled participants only and view of haptic assistance. Furthermore, self-controlled participants’ views were significantly associated with their requested haptic assistance frequency. We discuss these findings as further support for the beneficial role of self-controlled practice for motor learning. PMID:29255438

  4. Integrating E-Learning and Classroom Learning for Engineering Quality Control unit - Curtin University Experience

    Directory of Open Access Journals (Sweden)

    Ali M. Darabi Golshani

    2011-08-01

    Full Text Available Engineering employers expect engineering graduates to possess a wide range of skills that goes beyond their technical knowledge. It is vital that graduates have skills which demonstrate that they are responsible for their own development and careers. Some of these skills include; communication abilities, organizational skills, self-promotion, the ability to work as part of a team, be an effective problem solver, be a critical thinker, have good negotiation skills, have the ability to be a leader and being able to network effectively. Department of Civil Engineering at Curtin University of Technology in Perth, Australia offers a Master of Engineering Management degree for Engineers from various disciplines. One of the units taught in this Master degree program is Engineering Quality Control. It was decided to incorporate these non-technical skills in this unit by using an e-learning platform (Blackboard together with an adaptation of the Seven Principles of Good Practice and Dr Meredith Belbin’s team role theory to divide participants into groups. At the end of the unit, most of the participants were showing improvements in their non-technical skills.

  5. Learning Representation and Control in Markov Decision Processes

    Science.gov (United States)

    2013-10-21

    449–456. MIT Press, 2006. [35] D. Koller and N. Friedman. Graphical Models. MIT Press, 2009. [36] J. Zico Kolter and Andrew Y. Ng. Regularization and...ICML ’09, pages 521–528, New York, NY, USA, 2009. ACM. [37] J. Zico Kolter and Andrew Y. Ng. Regularization and feature selection in least-squares...temporal differ- ence learning. In Proceedings of 27 th International Conference on Machine Learning, 2009. [38] J. Zico Z. Kolter . The Fixed Points of Off

  6. Inner-Learning Mechanism Based Control Scheme for Manipulator with Multitasking and Changing Load

    Directory of Open Access Journals (Sweden)

    Fangzheng Xue

    2014-05-01

    Full Text Available With the rapid development of robot technology and its application, manipulators may face complex tasks and dynamic environments in the coming future, which leads to two challenges of control: multitasking and changing load. In this paper, a novel multicontroller strategy is presented to meet such challenges. The presented controller is composed of three parts: subcontrollers, inner-learning mechanism, and switching rules. Each subcontroller is designed with self-learning skills to fit the changing load under a special task. When a new task comes, switching rule reselects the most suitable subcontroller as the working controller to handle current task instead of the older one. Inner-learning mechanism makes the subcontrollers learn from the working controller when load changes so that the switching action causes smaller tracking error than the traditional switch controller. The results of the simulation experiments on two-degree manipulator show the proposed method effect.

  7. GA-based fuzzy reinforcement learning for control of a magnetic bearing system.

    Science.gov (United States)

    Lin, C T; Jou, C P

    2000-01-01

    This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network to guide the learning of the other network (the action network) which determines the outputs (actions) of the TDGAR learning system. The action network can be a normal neural network or a neural fuzzy network. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. The key concept of the TDGAR learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA such that the GA can evaluate the candidate solutions (chromosomes) regularly, even during periods without external feedback from the environment. This enables the GA to proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning since a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problem. The proposed TDGAR learning system has been used to control an active magnetic bearing (AMB) system in practice. A systematic design procedure is developed to achieve successful integration of all the subsystems including magnetic suspension, mechanical structure, and controller training. The results show that the TDGAR learning scheme can successfully find a neural controller or a neural fuzzy controller for a self-designed magnetic bearing system.

  8. Selecting Learning Tasks: Effects of Adaptation and Shared Control on Learning Efficiency and Task Involvement

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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…

  10. Velocity Tracking Control of Wheeled Mobile Robots by Iterative Learning Control

    Directory of Open Access Journals (Sweden)

    Xiaochun Lu

    2016-05-01

    Full Text Available This paper presents an iterative learning control (ILC strategy to resolve the trajectory tracking problem of wheeled mobile robots (WMRs based on dynamic model. In the previous study of WMRs’ trajectory tracking, ILC was usually applied to the kinematical model of WMRs with the assumption that desired velocity can be tracked immediately. However, this assumption cannot be realized in the real world at all. The kinematic and dynamic models of WMRs are deduced in this chapter, and a novel combination of D-type ILC algorithm and dynamic model of WMR with random bounded disturbances are presented. To analyze the convergence of the algorithm, the method of contracting mapping, which shows that the designed controller can make the velocity tracking errors converge to zero completely when the iteration times tend to infinite, is adopted. Simulation results show the effectiveness of D-type ILC in the trajectory tracking problem of WMRs, demonstrating the effectiveness and robustness of the algorithm in the condition of random bounded disturbance. A comparative study conducted between D-type ILC and compound cosine function neural network (NN controller also demonstrates the effectiveness of the ILC strategy.

  11. Self-learning basic life support: A randomised controlled trial on learning conditions.

    Science.gov (United States)

    Pedersen, Tina Heidi; Kasper, Nina; Roman, Hari; Egloff, Mike; Marx, David; Abegglen, Sandra; Greif, Robert

    2018-05-01

    To investigate whether pure self-learning without instructor support, resulted in the same BLS-competencies as facilitator-led learning, when using the same commercially available video BLS teaching kit. First-year medical students were randomised to either BLS self-learning without supervision or facilitator-led BLS-teaching. Both groups used the MiniAnne kit (Laerdal Medical, Stavanger, Norway) in the students' local language. Directly after the teaching and three months later, all participants were tested on their BLS-competencies in a simulated scenario, using the Resusci Anne SkillReporter™ (Laerdal Medical, Stavanger, Norway). The primary outcome was percentage of correct cardiac compressions three months after the teaching. Secondary outcomes were all other BLS parameters recorded by the SkillReporter and parameters from a BLS-competence rating form. 240 students were assessed at baseline and 152 students participated in the 3-month follow-up. For our primary outcome, the percentage of correct compressions, we found a median of 48% (interquartile range (IQR) 10-83) for facilitator-led learning vs. 42% (IQR 14-81) for self-learning (p = 0.770) directly after the teaching. In the 3-month follow-up, the rate of correct compressions dropped to 28% (IQR 6-59) for facilitator-led learning (p = 0.043) and did not change significantly in the self-learning group (47% (IQR 12-78), p = 0.729). Self-learning is not inferior to facilitator-led learning in the short term. Self-learning resulted in a better retention of BLS-skills three months after training compared to facilitator-led training. Copyright © 2018 Elsevier B.V. All rights reserved.

  12. Video Demo: Deep Reinforcement Learning for Coordination in Traffic Light Control

    NARCIS (Netherlands)

    van der Pol, E.; Oliehoek, F.A.; Bosse, T.; Bredeweg, B.

    2016-01-01

    This video demonstration contrasts two approaches to coordination in traffic light control using reinforcement learning: earlier work, based on a deconstruction of the state space into a linear combination of vehicle states, and our own approach based on the Deep Q-learning algorithm.

  13. The Role of Executive Control of Attention and Selective Encoding for Preschoolers' Learning

    Science.gov (United States)

    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…

  14. Effectiveness of Adaptive Assessment versus Learner Control in a Multimedia Learning System

    Science.gov (United States)

    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…

  15. Child predictors of learning to control variables via instruction or self-discovery

    NARCIS (Netherlands)

    Wagensveld, B.; Segers, P.C.J.; Kleemans, M.A.J.; Verhoeven, L.T.W.

    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.

  16. Real time reinforcement learning control of dynamic systems applied to an inverted pendulum

    NARCIS (Netherlands)

    van Luenen, W.T.C.; van Luenen, W.T.C.; Stender, J.; Addis, T.

    1990-01-01

    Describes work started in order to investigate the use of neural networks for application in adaptive or learning control systems. Neural networks have learning capabilities and they can be used to realize non-linear mappings. These are attractive features which could make them useful building

  17. The Effects of Variations in Lesson Control and Practice on Learning from Interactive Video.

    Science.gov (United States)

    Hannafin, Michael J.; Colamaio, MaryAnne E.

    1987-01-01

    Discussion of the effects of variations in lesson control and practice on the learning of facts, procedures, and problem-solving skills during interactive video instruction focuses on a study of graduates and advanced level undergraduates learning cardiopulmonary resuscitation (CPR). Embedded questioning methods and posttests used are described.…

  18. A Plant Control Technology Using Reinforcement Learning Method with Automatic Reward Adjustment

    Science.gov (United States)

    Eguchi, Toru; Sekiai, Takaaki; Yamada, Akihiro; Shimizu, Satoru; Fukai, Masayuki

    A control technology using Reinforcement Learning (RL) and Radial Basis Function (RBF) Network has been developed to reduce environmental load substances exhausted from power and industrial plants. This technology consists of the statistic model using RBF Network, which estimates characteristics of plants with respect to environmental load substances, and RL agent, which learns the control logic for the plants using the statistic model. In this technology, it is necessary to design an appropriate reward function given to the agent immediately according to operation conditions and control goals to control plants flexibly. Therefore, we propose an automatic reward adjusting method of RL for plant control. This method adjusts the reward function automatically using information of the statistic model obtained in its learning process. In the simulations, it is confirmed that the proposed method can adjust the reward function adaptively for several test functions, and executes robust control toward the thermal power plant considering the change of operation conditions and control goals.

  19. Does peer learning or higher levels of e-learning improve learning abilities? A randomized controlled trial.

    Science.gov (United States)

    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.

  20. Does peer learning or higher levels of e-learning improve learning abilities? A randomized controlled trial

    Directory of Open Access Journals (Sweden)

    Bjarne Skjødt Worm

    2013-11-01

    Full Text Available 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.

  1. Does peer learning or higher levels of e-learning improve learning abilities? A randomized controlled trial

    Science.gov (United States)

    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

  2. Action Control, L2 Motivational Self System, and Motivated Learning Behavior in a Foreign Language Learning Context

    Science.gov (United States)

    Khany, Reza; Amiri, Majid

    2018-01-01

    Theoretical developments in second or foreign language motivation research have led to a better understanding of the convoluted nature of motivation in the process of language acquisition. Among these theories, action control theory has recently shown a good deal of explanatory power in second language learning contexts and in the presence of…

  3. Action Control, Motivated Strategies, and Integrative Motivation as Predictors of Language Learning Affect and the Intention to Continue Learning French

    Science.gov (United States)

    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…

  4. Organization of students’ knowledge control in the process of distance learning

    Directory of Open Access Journals (Sweden)

    Miklashevich N. V.

    2016-07-01

    Full Text Available the article observes the main problems of organizing and carrying out the educational diagnosis in distance learning. Studying different approaches to monitoring showed that such control types as routine control and self-control are more efficient and effective. There is a difficulty of carrying out the control in distance learning: the need for accurate identification of the learner's personality. Despite existing technologies and recent developments in this area, the problem of preventing the test results from falsification is not fully resolved. According to the authors, the basic type of routine control when educating distantly remains the student obligatory attendance.

  5. Evolutionary Acquisition of the Global Command and Control System: Lessons Learned

    National Research Council Canada - National Science Library

    Wallis, Johnathan

    1998-01-01

    This paper summarizes a "lessons learned" study that reviews DoD's approach to managing the GCCS program on behalf on the Assistant Secretary of Defense for Command, Control, Communications, and Intelligence (ASD/C3I...

  6. Can Working Memory and Inhibitory Control Predict Second Language Learning in the Classroom?

    Directory of Open Access Journals (Sweden)

    Jared A. Linck

    2015-10-01

    Full Text Available The role of executive functioning in second language (L2 aptitude remains unclear. Whereas some studies report a relationship between working memory (WM and L2 learning, others have argued against this association. Similarly, being bilingual appears to benefit inhibitory control, and individual differences in inhibitory control are related to online L2 processing. The current longitudinal study examines whether these two components of executive functioning predict learning gains in an L2 classroom context using a pretest/posttest design. We assessed 25 university students in language courses, who completed measures of WM and inhibitory control. They also completed a proficiency measure at the beginning and end of a semester and reported their grade point average (GPA. WM was positively related to L2 proficiency and learning, but inhibitory control was not. These results support the notion that WM is an important component of L2 aptitude, particularly for predicting the early stages of L2 classroom learning.

  7. Development of fuzzy algorithm with learning function for nuclear steam generator level control

    International Nuclear Information System (INIS)

    Park, Gee Yong; Seong, Poong Hyun

    1993-01-01

    A fuzzy algorithm with learning function is applied to the steam generator level control of nuclear power plant. This algorithm can make its rule base and membership functions suited for steam generator level control by use of the data obtained from the control actions of a skilled operator or of other controllers (i.e., PID controller). The rule base of fuzzy controller with learning function is divided into two parts. One part of the rule base is provided to level control of steam generator at low power level (0 % - 30 % of full power) and the other to level control at high power level (30 % - 100 % of full power). Response time of steam generator level control at low power range with this rule base is shown to be shorter than that of fuzzy controller with direct inference. (Author)

  8. SNS Superconducting RF cavity modeling-iterative learning control

    International Nuclear Information System (INIS)

    Kwon, S.-I.; Regan, Amy; Wang, Y.-M.

    2002-01-01

    The Spallation Neutron Source (SNS) Superconducting RF (SRF) linear accelerator is operated with a pulsed beam. For the SRF control system to track the repetitive electromagnetic field reference trajectory, both feedback and feedforward controllers have been proposed. The feedback controller is utilized to guarantee the closed loop system stability and the feedforward controller is used to improve the tracking performance for the repetitive reference trajectory and to suppress repetitive disturbances. As the iteration number increases, the feedforward controller decreases the tracking error. Numerical simulations demonstrate that inclusion of the feedforward controller significantly improves the control system performance over its performance with just the feedback controller

  9. SNS Superconducting RF cavity modeling-iterative learning control

    CERN Document Server

    Kwon, S I; Wang, Y M

    2002-01-01

    The Spallation Neutron Source (SNS) Superconducting RF (SRF) linear accelerator is operated with a pulsed beam. For the SRF control system to track the repetitive electromagnetic field reference trajectory, both feedback and feedforward controllers have been proposed. The feedback controller is utilized to guarantee the closed loop system stability and the feedforward controller is used to improve the tracking performance for the repetitive reference trajectory and to suppress repetitive disturbances. As the iteration number increases, the feedforward controller decreases the tracking error. Numerical simulations demonstrate that inclusion of the feedforward controller significantly improves the control system performance over its performance with just the feedback controller.

  10. The Mediating Role of Maladaptive Perfectionism in the Association between Psychological Control and Learned Helplessness

    Science.gov (United States)

    Filippello, Pina; Larcan, Rosalba; Sorrenti, Luana; Buzzai, Caterina; Orecchio, Susanna; Costa, Sebastiano

    2017-01-01

    Despite the extensive research on parental psychological control, no study has explored the relation between parental and teacher psychological control, maladaptive perfectionism and learned helplessness (LH). The purpose of this study was to investigate (1) whether perceived teacher psychological control predicts positively LH, (2) whether…

  11. Locus of Control in Offenders and Alleged Offenders with Learning Disabilities

    Science.gov (United States)

    Goodman, Wendy; Leggett, Janice; Garrett, Tanya

    2007-01-01

    Locus of control can be a useful measure of treatment outcome in offenders from the general population. However, there is little information regarding locus of control and offenders with learning disabilities. Existing measures of locus of control use complex language and abstract ideas that may not be accessible to individuals in this group. A…

  12. Optimization and control of a continuous stirred tank fermenter using learning system

    Energy Technology Data Exchange (ETDEWEB)

    Thibault, J [Dept. of Chemical Engineering, Laval Univ., Quebec City, PQ (Canada); Najim, K [CNRS, URA 192, GRECO SARTA, Ecole Nationale Superieure d' Ingenieurs de Genie Chimique, 31 - Toulouse (France)

    1993-05-01

    A variable structure learning automaton is used as an optimization and control of a continuous stirred tank fermenter. The alogrithm requires no modelling of the process. The use of appropriate learning rules enables to locate the optimum dilution rate in order to maximize an objective cost function. It is shown that a hierarchical structure of automata can adapt to environmental changes and can also modify efficiently the domain of variation of the control variable in order to encompass the optimum value. (orig.)

  13. Stable schizophrenia patients learn equally well as age-matched controls and better than elderly controls in two sensorimotor rotary pursuit tasks

    NARCIS (Netherlands)

    Picker, L.J. De; Cornelis, C.; Hulstijn, W.; Dumont, G.J.H.; Fransen, E.; Timmers, M.; Janssens, L.; Morrens, M.; Sabbe, B.G.C.

    2014-01-01

    Objective: To compare sensorimotor performance and learning in stable schizophrenia patients, healthy age- and sex-matched controls and elderly controls on two variations of the rotary pursuit: circle pursuit (true motor learning) and figure pursuit (motor and sequence learning). Method: In the

  14. The significance of clinical experience on learning outcome from resuscitation training-a randomised controlled study

    DEFF Research Database (Denmark)

    Jensen, Morten Lind; Lippert, Freddy; Hesselfeldt, Rasmus

    2008-01-01

    CONTEXT: The impact of clinical experience on learning outcome from a resuscitation course has not been systematically investigated. AIM: To determine whether half a year of clinical experience before participation in an Advanced Life Support (ALS) course increases the immediate learning outcome...... and retention of learning. MATERIALS AND METHODS: This was a prospective single blinded randomised controlled study of the learning outcome from a standard ALS course on a volunteer sample of the entire cohort of newly graduated doctors from Copenhagen University. The outcome measurement was ALS...... immediately following graduation. RESULTS: Invitation to participate was accepted by 154/240 (64%) graduates and 117/154 (76%) completed the study. There was no difference between the intervention and control groups with regard to the immediate learning outcome. The intervention group had significantly higher...

  15. Reinforcement Learning for Online Control of Evolutionary Algorithms

    NARCIS (Netherlands)

    Eiben, A.; Horvath, Mark; Kowalczyk, Wojtek; Schut, Martijn

    2007-01-01

    The research reported in this paper is concerned with assessing the usefulness of reinforcment learning (RL) for on-line calibration of parameters in evolutionary algorithms (EA). We are running an RL procedure and the EA simultaneously and the RL is changing the EA parameters on-the-fly. We

  16. Simultaneously learning and optimizing using controlled variance pricing

    NARCIS (Netherlands)

    Boer, den A.V.; Zwart, B.

    2014-01-01

    Price experimentation is an important tool for firms to find the optimal selling price of their products. It should be conducted properly, since experimenting with selling prices can be costly. A firm, therefore, needs to find a pricing policy that optimally balances between learning the optimal

  17. Learning state representation for deep actor-critic control

    NARCIS (Netherlands)

    Munk, J.; Kober, J.; Babuska, R.; Bullo, Francesco; Prieur, Christophe; Giua, Alessandro

    2016-01-01

    Deep Neural Networks (DNNs) can be used as function approximators in Reinforcement Learning (RL). One advantage of DNNs is that they can cope with large input dimensions. Instead of relying on feature engineering to lower the input dimension, DNNs can extract the features from raw observations. The

  18. Learning about goals : development of action perception and action control

    NARCIS (Netherlands)

    Verschoor, Stephan Alexander

    2014-01-01

    By using innovative paradigms, the present thesis provides convincing evidence that action-effect learning, and sensorimotor processes in general play a crucial role in the development of action- perception and production in infancy. This finding was further generalized to sequential action.

  19. INTELLIGENT FRACTIONAL ORDER ITERATIVE LEARNING CONTROL USING FEEDBACK LINEARIZATION FOR A SINGLE-LINK ROBOT

    Directory of Open Access Journals (Sweden)

    Iman Ghasemi

    2017-05-01

    Full Text Available In this paper, iterative learning control (ILC is combined with an optimal fractional order derivative (BBO-Da-type ILC and optimal fractional and proportional-derivative (BBO-PDa-type ILC. In the update law of Arimoto's derivative iterative learning control, a first order derivative of tracking error signal is used. In the proposed method, fractional order derivative of the error signal is stated in term of 'sa' where  to update iterative learning control law. Two types of fractional order iterative learning control namely PDa-type ILC and Da-type ILC are gained for different value of a. In order to improve the performance of closed-loop control system, coefficients of both  and  learning law i.e. proportional , derivative  and  are optimized using Biogeography-Based optimization algorithm (BBO. Outcome of the simulation results are compared with those of the conventional fractional order iterative learning control to verify effectiveness of BBO-Da-type ILC and BBO-PDa-type ILC

  20. Consensus-based distributed cooperative learning from closed-loop neural control systems.

    Science.gov (United States)

    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.

  1. Procedural Memory: Computer Learning in Control Subjects and in Parkinson’s Disease Patients

    Directory of Open Access Journals (Sweden)

    C. Thomas-Antérion

    1996-01-01

    Full Text Available We used perceptual motor tasks involving the learning of mouse control by looking at a Macintosh computer screen. We studied 90 control subjects aged between sixteen and seventy-five years. There was a significant time difference between the scales of age but improvement was the same for all subjects. We also studied 24 patients with Parkinson's disease (PD. We observed an influence of age and also of educational levels. The PD patients had difficulties of learning in all tests but they did not show differences in time when compared to the control group in the first learning session (Student's t-test. They learned two or four and a half times less well than the control group. In the first test, they had some difficulty in initiating the procedure and learned eight times less well than the control group. Performances seemed to be heterogeneous: patients with only tremor (seven and patients without treatment (five performed better than others but learned less. Success in procedural tasks for the PD group seemed to depend on the capacity to initiate the response and not on the development of an accurate strategy. Many questions still remain unanswered, and we have to study different kinds of implicit memory tasks to differentiate performance in control and basal ganglia groups.

  2. Developing Learning Tool of Control System Engineering Using Matrix Laboratory Software Oriented on Industrial Needs

    Science.gov (United States)

    Isnur Haryudo, Subuh; Imam Agung, Achmad; Firmansyah, Rifqi

    2018-04-01

    The purpose of this research is to develop learning media of control technique using Matrix Laboratory software with industry requirement approach. Learning media serves as a tool for creating a better and effective teaching and learning situation because it can accelerate the learning process in order to enhance the quality of learning. Control Techniques using Matrix Laboratory software can enlarge the interest and attention of students, with real experience and can grow independent attitude. This research design refers to the use of research and development (R & D) methods that have been modified by multi-disciplinary team-based researchers. This research used Computer based learning method consisting of computer and Matrix Laboratory software which was integrated with props. Matrix Laboratory has the ability to visualize the theory and analysis of the Control System which is an integration of computing, visualization and programming which is easy to use. The result of this instructional media development is to use mathematical equations using Matrix Laboratory software on control system application with DC motor plant and PID (Proportional-Integral-Derivative). Considering that manufacturing in the field of Distributed Control systems (DCSs), Programmable Controllers (PLCs), and Microcontrollers (MCUs) use PID systems in production processes are widely used in industry.

  3. A learning flight control system for the F8-DFBW aircraft. [Digital Fly-By-Wire

    Science.gov (United States)

    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.

  4. Exploring machine-learning-based control plane intrusion detection techniques in software defined optical networks

    Science.gov (United States)

    Zhang, Huibin; Wang, Yuqiao; Chen, Haoran; Zhao, Yongli; Zhang, Jie

    2017-12-01

    In software defined optical networks (SDON), the centralized control plane may encounter numerous intrusion threatens which compromise the security level of provisioned services. In this paper, the issue of control plane security is studied and two machine-learning-based control plane intrusion detection techniques are proposed for SDON with properly selected features such as bandwidth, route length, etc. We validate the feasibility and efficiency of the proposed techniques by simulations. Results show an accuracy of 83% for intrusion detection can be achieved with the proposed machine-learning-based control plane intrusion detection techniques.

  5. Extracting quantum dynamics from genetic learning algorithms through principal control analysis

    International Nuclear Information System (INIS)

    White, J L; Pearson, B J; Bucksbaum, P H

    2004-01-01

    Genetic learning algorithms are widely used to control ultrafast optical pulse shapes for photo-induced quantum control of atoms and molecules. An unresolved issue is how to use the solutions found by these algorithms to learn about the system's quantum dynamics. We propose a simple method based on covariance analysis of the control space, which can reveal the degrees of freedom in the effective control Hamiltonian. We have applied this technique to stimulated Raman scattering in liquid methanol. A simple model of two-mode stimulated Raman scattering is consistent with the results. (letter to the editor)

  6. Interacting Learning Processes during Skill Acquisition: Learning to control with gradually changing system dynamics.

    Science.gov (United States)

    Ludolph, Nicolas; Giese, Martin A; Ilg, Winfried

    2017-10-16

    There is increasing evidence that sensorimotor learning under real-life conditions relies on a composition of several learning processes. Nevertheless, most studies examine learning behaviour in relation to one specific learning mechanism. In this study, we examined the interaction between reward-based skill acquisition and motor adaptation to changes of object dynamics. Thirty healthy subjects, split into two groups, acquired the skill of balancing a pole on a cart in virtual reality. In one group, we gradually increased the gravity, making the task easier in the beginning and more difficult towards the end. In the second group, subjects had to acquire the skill on the maximum, most difficult gravity level. We hypothesized that the gradual increase in gravity during skill acquisition supports learning despite the necessary adjustments to changes in cart-pole dynamics. We found that the gradual group benefits from the slow increment, although overall improvement was interrupted by the changes in gravity and resulting system dynamics, which caused short-term degradations in performance and timing of actions. In conclusion, our results deliver evidence for an interaction of reward-based skill acquisition and motor adaptation processes, which indicates the importance of both processes for the development of optimized skill acquisition schedules.

  7. Understanding Self-Controlled Motor Learning Protocols through the Self-Determination Theory.

    Science.gov (United States)

    Sanli, Elizabeth A; Patterson, Jae T; Bray, Steven R; Lee, Timothy D

    2012-01-01

    The purpose of the present review was to provide a theoretical understanding of the learning advantages underlying a self-controlled practice context through the tenets of the self-determination theory (SDT). Three micro-theories within the macro-theory of SDT (Basic psychological needs theory, Cognitive Evaluation Theory, and Organismic Integration Theory) are used as a framework for examining the current self-controlled motor learning literature. A review of 26 peer-reviewed, empirical studies from the motor learning and medical training literature revealed an important limitation of the self-controlled research in motor learning: that the effects of motivation have been assumed rather than quantified. The SDT offers a basis from which to include measurements of motivation into explanations of changes in behavior. This review suggests that a self-controlled practice context can facilitate such factors as feelings of autonomy and competence of the learner, thereby supporting the psychological needs of the learner, leading to long term changes to behavior. Possible tools for the measurement of motivation and regulation in future studies are discussed. The SDT not only allows for a theoretical reinterpretation of the extant motor learning research supporting self-control as a learning variable, but also can help to better understand and measure the changes occurring between the practice environment and the observed behavioral outcomes.

  8. Understanding self-controlled motor learning protocols through the self determination theory

    Directory of Open Access Journals (Sweden)

    Elizabeth Ann Sanli

    2013-01-01

    Full Text Available The purpose of the present review was to provide a theoretical understanding of the learning advantages underlying a self-controlled practice context through the tenets of the self-determination theory (SDT. Three micro theories within the macro theory of SDT (Basic psychological needs theory, Cognitive Evaluation Theory & Organismic Integration Theory are used as a framework for examining the current self-controlled motor learning literature. A review of 26 peer-reviewed, empirical studies from the motor learning and medical training literature revealed an important limitation of the self-controlled research in motor learning: that the effects of motivation have been assumed rather than quantified. The SDT offers a basis from which to include measurements of motivation into explanations of changes in behavior. This review suggests that a self-controlled practice context can facilitate such factors as feelings of autonomy and competence of the learner, thereby supporting the psychological needs of the learner, leading to long term changes to behavior. Possible tools for the measurement of motivation and regulation in future studies are discussed. The SDT not only allows for a theoretical reinterpretation of the extant motor learning research supporting self-control as a learning variable, but also can help to better understand and measure the changes occurring between the practice environment and the observed behavioral outcomes.

  9. The Study of Reinforcement Learning for Traffic Self-Adaptive Control under Multiagent Markov Game Environment

    Directory of Open Access Journals (Sweden)

    Lun-Hui Xu

    2013-01-01

    Full Text Available Urban traffic self-adaptive control problem is dynamic and uncertain, so the states of traffic environment are hard to be observed. Efficient agent which controls a single intersection can be discovered automatically via multiagent reinforcement learning. However, in the majority of the previous works on this approach, each agent needed perfect observed information when interacting with the environment and learned individually with less efficient coordination. This study casts traffic self-adaptive control as a multiagent Markov game problem. The design employs traffic signal control agent (TSCA for each signalized intersection that coordinates with neighboring TSCAs. A mathematical model for TSCAs’ interaction is built based on nonzero-sum markov game which has been applied to let TSCAs learn how to cooperate. A multiagent Markov game reinforcement learning approach is constructed on the basis of single-agent Q-learning. This method lets each TSCA learn to update its Q-values under the joint actions and imperfect information. The convergence of the proposed algorithm is analyzed theoretically. The simulation results show that the proposed method is convergent and effective in realistic traffic self-adaptive control setting.

  10. An open-closed-loop iterative learning control approach for nonlinear switched systems with application to freeway traffic control

    Science.gov (United States)

    Sun, Shu-Ting; Li, Xiao-Dong; Zhong, Ren-Xin

    2017-10-01

    For nonlinear switched discrete-time systems with input constraints, this paper presents an open-closed-loop iterative learning control (ILC) approach, which includes a feedforward ILC part and a feedback control part. Under a given switching rule, the mathematical induction is used to prove the convergence of ILC tracking error in each subsystem. It is demonstrated that the convergence of ILC tracking error is dependent on the feedforward control gain, but the feedback control can speed up the convergence process of ILC by a suitable selection of feedback control gain. A switched freeway traffic system is used to illustrate the effectiveness of the proposed ILC law.

  11. Prefrontal control of cerebellum-dependent associative motor learning.

    Science.gov (United States)

    Chen, Hao; Yang, Li; Xu, Yan; Wu, Guang-yan; Yao, Juan; Zhang, Jun; Zhu, Zhi-ru; Hu, Zhi-an; Sui, Jian-feng; Hu, Bo

    2014-02-01

    Behavioral studies have demonstrated that both medial prefrontal cortex (mPFC) and cerebellum play critical roles in trace eyeblink conditioning. However, little is known regarding the mechanism by which the two brain regions interact. By use of electrical stimulation of the caudal mPFC as a conditioned stimulus, we show evidence that persistent outputs from the mPFC to cerebellum are necessary and sufficient for the acquisition and expression of a trace conditioned response (CR)-like response. Specifically, the persistent outputs of caudal mPFC are relayed to the cerebellum via the rostral part of lateral pontine nuclei. Moreover, interfering with persistent activity by blockade of the muscarinic Ach receptor in the caudal mPFC impairs the expression of learned trace CRs. These results suggest an important way for the caudal mPFC to interact with the cerebellum during associative motor learning.

  12. Language experience differentiates prefrontal and subcortical activation of the cognitive control network in novel word learning.

    Science.gov (United States)

    Bradley, Kailyn A L; King, Kelly E; Hernandez, Arturo E

    2013-02-15

    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 2h 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. Copyright © 2012 Elsevier Inc. All rights reserved.

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

    International Nuclear Information System (INIS)

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

    2015-01-01

    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. (paper)

  14. Neural signatures of second language learning and control.

    Science.gov (United States)

    Bartolotti, James; Bradley, Kailyn; Hernandez, Arturo E; Marian, Viorica

    2017-04-01

    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. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Export control training - Experience and pedagogical lessons learned

    International Nuclear Information System (INIS)

    Heine, P.

    2013-01-01

    This series of slides draws a picture of the training offerings in export control within the framework of the International Nonproliferation Export Control Program. These courses are organized around 3 topics: licensing, enterprise outreach and enforcement. There are about 10 courses, a brief content of their curricula is given. The goal of these courses is not to make participants into export control experts or trade analysts, but enable them to properly take export controls into account. (A.C.)

  16. Associative visual learning by tethered bees in a controlled visual environment.

    Science.gov (United States)

    Buatois, Alexis; Pichot, Cécile; Schultheiss, Patrick; Sandoz, Jean-Christophe; Lazzari, Claudio R; Chittka, Lars; Avarguès-Weber, Aurore; Giurfa, Martin

    2017-10-10

    Free-flying honeybees exhibit remarkable cognitive capacities but the neural underpinnings of these capacities cannot be studied in flying insects. Conversely, immobilized bees are accessible to neurobiological investigation but display poor visual learning. To overcome this limitation, we aimed at establishing a controlled visual environment in which tethered bees walking on a spherical treadmill learn to discriminate visual stimuli video projected in front of them. Freely flying bees trained to walk into a miniature Y-maze displaying these stimuli in a dark environment learned the visual discrimination efficiently when one of them (CS+) was paired with sucrose and the other with quinine solution (CS-). Adapting this discrimination to the treadmill paradigm with a tethered, walking bee was successful as bees exhibited robust discrimination and preferred the CS+ to the CS- after training. As learning was better in the maze, movement freedom, active vision and behavioral context might be important for visual learning. The nature of the punishment associated with the CS- also affects learning as quinine and distilled water enhanced the proportion of learners. Thus, visual learning is amenable to a controlled environment in which tethered bees learn visual stimuli, a result that is important for future neurobiological studies in virtual reality.

  17. Automatic learning rate adjustment for self-supervising autonomous robot control

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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. Copyright © 2014. Published by Elsevier B.V.

  19. Visual memory and learning in extremely low-birth-weight/extremely preterm adolescents compared with controls: a geographic study.

    Science.gov (United States)

    Molloy, Carly S; Wilson-Ching, Michelle; Doyle, Lex W; Anderson, Vicki A; Anderson, Peter J

    2014-04-01

    Contemporary data on visual memory and learning in survivors born extremely preterm (EP; Visual learning and memory data were available for 221 (74.2%) EP/ELBW subjects and 159 (60.7%) controls. EP/ELBW adolescents exhibited significantly poorer performance across visual memory and learning variables compared with controls. Visual learning and delayed visual memory were particularly problematic and remained so after controlling for visual-motor integration and visual perception and excluding adolescents with neurosensory disability, and/or IQ visual memory and learning outcomes compared with controls, which cannot be entirely explained by poor visual perceptual or visual constructional skills or intellectual impairment.

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

    Science.gov (United States)

    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.

  1. Online learning versus blended learning of clinical supervisee skills with pre-registration nursing students: A randomised controlled trial.

    Science.gov (United States)

    McCutcheon, Karen; O'Halloran, Peter; Lohan, Maria

    2018-06-01

    The World Health Organisation amongst others recognises the need for the introduction of clinical supervision education in health professional education as a central strategy for improving patient safety and patient care. Online and blended learning methods are growing exponentially in use in higher education and the systematic evaluation of these methods will aid understanding of how best to teach clinical supervision. The purpose of this study was to test whether undergraduate nursing students who received clinical supervisee skills training via a blended learning approach would score higher in terms of motivation and attitudes towards clinical supervision, knowledge of clinical supervision and satisfaction of learning method, when compared to those students who received an online only teaching approach. A post-test-only randomised controlled trial. Participants were a total of 122 pre-registration nurses enrolled at one United Kingdom university, randomly assigned to the online learning control group (n = 60) or the blended learning intervention group (n = 62). The blended learning intervention group participated in a face-to-face tutorial and the online clinical supervisee skills training app. The online learning control group participated in an online discussion forum and the same online clinical supervisee skills training app. The outcome measures were motivation and attitudes using the modified Manchester Clinical Supervision Scale, knowledge using a 10 point Multiple Choice Questionnaire and satisfaction using a university training evaluation tool. Statistical analysis was performed using independent t-tests to compare the differences between the means of the control group and the intervention group. Thematic analysis was used to analyse responses to open-ended questions. All three of our study hypotheses were confirmed. Participants who received clinical supervisee skills training via a blended learning approach scored higher in terms of motivation

  2. Iterative learning control with sampled-data feedback for robot manipulators

    Directory of Open Access Journals (Sweden)

    Delchev Kamen

    2014-09-01

    Full Text Available This paper deals with the improvement of the stability of sampled-data (SD feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more, while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached

  3. Emotional learning based intelligent controller for a PWR nuclear reactor core during load following operation

    International Nuclear Information System (INIS)

    Khorramabadi, Sima Seidi; Boroushaki, Mehrdad; Lucas, Caro

    2008-01-01

    The design and evaluation of a novel approach to reactor core power control based on emotional learning is described. The controller includes a neuro-fuzzy system with power 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 critic's stress is reduced. Simulation results show that the controller has good convergence and performance robustness characteristics over a wide range of operational parameters

  4. Women with learning disabilities and access to cervical screening: retrospective cohort study using case control methods

    Directory of Open Access Journals (Sweden)

    Stanistreet Debbi

    2008-01-01

    Full Text Available Abstract Background Several studies in the UK have suggested that women with learning disabilities may be less likely to receive cervical screening tests and a previous local study in had found that GPs considered screening unnecessary for women with learning disabilities. This study set out to ascertain whether women with learning disabilities are more likely to be ceased from a cervical screening programme than women without; and to examine the reasons given for ceasing women with learning disabilities. It was carried out in Bury, Heywood-and-Middleton and Rochdale. Methods Carried out using retrospective cohort study methods, women with learning disabilities were identified by Read code; and their cervical screening records were compared with the Call-and-Recall records of women without learning disabilities in order to examine their screening histories. Analysis was carried out using case-control methods – 1:2 (women with learning disabilities: women without learning disabilities, calculating odds ratios. Results 267 women's records were compared with the records of 534 women without learning disabilities. Women with learning disabilities had an odds ratio (OR of 0.48 (Confidence Interval (CI 0.38 – 0.58; X2: 72.227; p.value X2: 24.236; p.value X2: 286.341; p.value Conclusion The reasons given for ceasing and/or not screening suggest that merely being coded as having a learning disability is not the sole reason for these actions. There are training needs among smear takers regarding appropriate reasons not to screen and providing screening for women with learning disabilities.

  5. Pilot exemption of the controlled area from regulatory control at NPP A1 -lessons learned

    International Nuclear Information System (INIS)

    Slaninka, A.; Listjak, M.; Slavik, O.; Rau, L.

    2014-01-01

    The contribution includes the lessons learned within frame of the radiological characterisation of surface ground layer in the NPP A1 site of area approximately 60 m2 (9 x 7 m) that was a part of Controlled area. Aim of the characterisation was a demonstration that the area fulfils the requirements to exemption from Controlled area for purpose of decommissioning activities carry out within frame of II. stage of NPP A1 decommissioning project.The requirements on free release of materials into the environment were applied (e.g. 300 Bq/kg for single 137 Cs). Radiological characterisation was performed by two independent methods; in situ scintillation gamma spectrometry and systematic sampling in regular grid followed by gamma spectrometry analyses in accredited laboratory of VUJE, Inc. (S-219). This improved the quality of monitoring and at the same time it enabled the inter-comparison of results obtained by both mentioned independent methods.Characterised ground area was partitioned to smaller sub-areas of 4 m2. At ground layer of 20 cm it means approximately 1000 kg of ground (in compliance with requirements on reference area at even activity distribution according to government regulation No 345/2006) The results of measurements showed that under appropriate conditions (sufficiently low radiation background, on interfering external sources) also the designed in situ method is effective and reliable tool for contaminated ground layer identification. In addition the in situ method is more effective in terms of time and cost consumption on unit of monitored area. (authors)

  6. Using Feedback Error Learning for Control of Electro Hydraulic Servo System by Laguerre

    Directory of Open Access Journals (Sweden)

    Amir Reza Zare Bidaki

    2014-01-01

    Full Text Available In this paper, a new Laguerre controller is proposed to control the electro hydraulic servo system. The proposed controller uses feedback error learning method and leads to significantly improve performance in terms of settling time and amplitude of control signal rather than other controllers. All derived results are validated by simulation of nonlinear mathematical model of the system. The simulation results show the advantages of the proposed method for improved control in terms of both settling time and amplitude of control signal.

  7. Theories and control models and motor learning: clinical applications in neuro-rehabilitation.

    Science.gov (United States)

    Cano-de-la-Cuerda, R; Molero-Sánchez, A; Carratalá-Tejada, M; Alguacil-Diego, I M; Molina-Rueda, F; Miangolarra-Page, J C; Torricelli, D

    2015-01-01

    In recent decades there has been a special interest in theories that could explain the regulation of motor control, and their applications. These theories are often based on models of brain function, philosophically reflecting different criteria on how movement is controlled by the brain, each being emphasised in different neural components of the movement. The concept of motor learning, regarded as the set of internal processes associated with practice and experience that produce relatively permanent changes in the ability to produce motor activities through a specific skill, is also relevant in the context of neuroscience. Thus, both motor control and learning are seen as key fields of study for health professionals in the field of neuro-rehabilitation. The major theories of motor control are described, which include, motor programming theory, systems theory, the theory of dynamic action, and the theory of parallel distributed processing, as well as the factors that influence motor learning and its applications in neuro-rehabilitation. At present there is no consensus on which theory or model defines the regulations to explain motor control. Theories of motor learning should be the basis for motor rehabilitation. The new research should apply the knowledge generated in the fields of control and motor learning in neuro-rehabilitation. Copyright © 2011 Sociedad Española de Neurología. Published by Elsevier Espana. All rights reserved.

  8. Automation and Control Learning Environment with Mixed Reality Remote Experiments Architecture

    Directory of Open Access Journals (Sweden)

    Frederico M. Schaf

    2007-05-01

    Full Text Available This work aims to the use of remotely web-based experiments to improve the learning process of automation and control systems theory courses. An architecture combining virtual learning environments, remote experiments, students guide and experiments analysis is proposed based on a wide state of art study. The validation of the architecture uses state of art technologies and new simple developed programs to implement the case studies presented. All implementations presented use an internet accessible virtual learning environment providing educational resources, guides and learning material to create a distance learning course associated with the remote mixed reality experiment. This work is part of the RExNet consortium, supported by the European Alfa project.

  9. Optimal critic learning for robot control in time-varying environments.

    Science.gov (United States)

    Wang, Chen; Li, Yanan; Ge, Shuzhi Sam; Lee, Tong Heng

    2015-10-01

    In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.

  10. Articulatory Control in Childhood Apraxia of Speech in a Novel Word-Learning Task

    Science.gov (United States)

    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…

  11. An Experience Sampling Study of Learning, Affect, and the Demands Control Support Model

    Science.gov (United States)

    Daniels, Kevin; Boocock, Grahame; Glover, Jane; Hartley, Ruth; Holland, Julie

    2009-01-01

    The demands control support model (R. A. Karasek & T. Theorell, 1990) indicates that job control and social support enable workers to engage in problem solving. In turn, problem solving is thought to influence learning and well-being (e.g., anxious affect, activated pleasant affect). Two samples (N = 78, N = 106) provided data up to 4 times per…

  12. Iterative Learning Control design for uncertain and time-windowed systems

    NARCIS (Netherlands)

    Wijdeven, van de J.J.M.

    2008-01-01

    Iterative Learning Control (ILC) is a control strategy capable of dramatically increasing the performance of systems that perform batch repetitive tasks. This performance improvement is achieved by iteratively updating the command signal, using measured error data from previous trials, i.e., by

  13. Controlled Experiment Replication in Evaluation of E-Learning System's Educational Influence

    Science.gov (United States)

    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…

  14. Analysis of learning curves in the on-the-job training of air traffic controllers

    NARCIS (Netherlands)

    Oprins, E.A.P.B.; Bruggraaff, E.; Roe, R.

    2011-01-01

    This chapter describes a competence-based assessment system, called CBAS, for air traffic control (ATC) simulator and on-the-job training (OJT), developed at Air Traffic Control The Netherlands (LVNL). In contrast with simulator training, learning processes in OJT are difficult to assess, because

  15. Implementing Motivational Features in Reactive Blended Learning: Application to an Introductory Control Engineering Course

    Science.gov (United States)

    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…

  16. Lessons Learned from the Node 1 Temperature and Humidity Control Subsystem Design

    Science.gov (United States)

    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

  17. Practical iterative learning control with frequency domain design and sampled data implementation

    CERN Document Server

    Wang, Danwei; Zhang, Bin

    2014-01-01

    This book is on the iterative learning control (ILC) with focus on the design and implementation. We approach the ILC design based on the frequency domain analysis and address the ILC implementation based on the sampled data methods. This is the first book of ILC from frequency domain and sampled data methodologies. The frequency domain design methods offer ILC users insights to the convergence performance which is of practical benefits. This book presents a comprehensive framework with various methodologies to ensure the learnable bandwidth in the ILC system to be set with a balance between learning performance and learning stability. The sampled data implementation ensures effective execution of ILC in practical dynamic systems. The presented sampled data ILC methods also ensure the balance of performance and stability of learning process. Furthermore, the presented theories and methodologies are tested with an ILC controlled robotic system. The experimental results show that the machines can work in much h...

  18. Adaptive critic learning techniques for engine torque and air-fuel ratio control.

    Science.gov (United States)

    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.

  19. Microeconomics of yield learning and process control in semiconductor manufacturing

    Science.gov (United States)

    Monahan, Kevin M.

    2003-06-01

    Simple microeconomic models that directly link yield learning to profitability in semiconductor manufacturing have been rare or non-existent. In this work, we review such a model and provide links to inspection capability and cost. Using a small number of input parameters, we explain current yield management practices in 200mm factories. The model is then used to extrapolate requirements for 300mm factories, including the impact of technology transitions to 130nm design rules and below. We show that the dramatic increase in value per wafer at the 300mm transition becomes a driver for increasing metrology and inspection capability and sampling. These analyses correlate well wtih actual factory data and often identify millions of dollars in potential cost savings. We demonstrate this using the example of grating-based overlay metrology for the 65nm node.

  20. How does a specific learning and memory system in the mammalian brain gain control of behavior?

    Science.gov (United States)

    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

  1. Schedule-controlled learning and memory in a regulatory context

    Science.gov (United States)

    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...

  2. Alignment Condition-Based Robust Adaptive Iterative Learning Control of Uncertain Robot System

    Directory of Open Access Journals (Sweden)

    Guofeng Tong

    2014-04-01

    Full Text Available This paper proposes an adaptive iterative learning control strategy integrated with saturation-based robust control for uncertain robot system in presence of modelling uncertainties, unknown parameter, and external disturbance under alignment condition. An important merit is that it achieves adaptive switching of gain matrix both in conventional PD-type feedforward control and robust adaptive control in the iteration domain simultaneously. The analysis of convergence of proposed control law is based on Lyapunov's direct method under alignment initial condition. Simulation results demonstrate the faster learning rate and better robust performance with proposed algorithm by comparing with other existing robust controllers. The actual experiment on three-DOF robot manipulator shows its better practical effectiveness.

  3. Learning-based controller for biotechnology processing, and method of using

    Science.gov (United States)

    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.

  4. Self-Control of Task Difficulty During Early Practice Promotes Motor Skill Learning.

    Science.gov (United States)

    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.

  5. Lessons Learned and Flight Results from the F15 Intelligent Flight Control System Project

    Science.gov (United States)

    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

  6. Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning.

    Science.gov (United States)

    Pilarski, Patrick M; Dawson, Michael R; Degris, Thomas; Fahimi, Farbod; Carey, Jason P; Sutton, Richard S

    2011-01-01

    As a contribution toward the goal of adaptable, intelligent artificial limbs, this work introduces a continuous actor-critic reinforcement learning method for optimizing the control of multi-function myoelectric devices. Using a simulated upper-arm robotic prosthesis, we demonstrate how it is possible to derive successful limb controllers from myoelectric data using only a sparse human-delivered training signal, without requiring detailed knowledge about the task domain. This reinforcement-based machine learning framework is well suited for use by both patients and clinical staff, and may be easily adapted to different application domains and the needs of individual amputees. To our knowledge, this is the first my-oelectric control approach that facilitates the online learning of new amputee-specific motions based only on a one-dimensional (scalar) feedback signal provided by the user of the prosthesis. © 2011 IEEE

  7. THE DYNAMIC MODEL FOR CONTROL OF STUDENT’S LEARNING INDIVIDUAL TRAJECTORY

    Directory of Open Access Journals (Sweden)

    A. A. Mitsel

    2015-01-01

    Full Text Available In connection with the transition of the educational system to a competence-oriented approach, the problem of learning outcomes assessment and creating an individual learning trajectory of a student has become relevant. Its solution requires the application of modern information technologies. The third generation of Federal state educational standards of higher professional education (FSES HPE defines the requirements for the results of Mastering the basic educational programs (BEP. According to FSES HPE up to 50% of subjects have a variable character, i.e. depend on the choice of a student. It significantly influences on the results of developing various competencies. The problem of forming student’s learning trajectory is analyzed in general and the choice of an individual direction was studied in details. Various methods, models and algorithms of the student’s individual learning trajectory formation were described. The analysis of the model of educational process organization in terms of individual approach makes it possible to develop a decision support system (DSS. DSS is a set of interrelated programs and data used for analysis of situation, development of alternative solutions and selection of the most acceptable alternative. DSSs are often used when building individual learning path, because this task can be considered as a discrete multi-criteria problem, creating a significant burden on the decision maker. A new method of controlling the learning trajectory has been developed. The article discusses problem statement and solution of determining student’s optimal individual educational trajectory as a dynamic model of learning trajectory control, which uses score assessment to construct a sequence of studied subjects. A new model of management learning trajectory is based on dynamic models for tracking the reference trajectory. The task can be converted to an equivalent model of linear programming, for which a reliable solution

  8. Understanding well-being and learning of Nigerian nurses: a job demand control support model approach.

    Science.gov (United States)

    van Doorn, Yvonne; van Ruysseveldt, Joris; van Dam, Karen; Mistiaen, Wilhelm; Nikolova, Irina

    2016-10-01

    This study investigated whether Nigerian nurses' emotional exhaustion and active learning were predicted by job demands, control and social support. Limited research has been conducted concerning nurses' work stress in developing countries, such as Nigeria. Accordingly, it is not clear whether work interventions for improving nurses' well-being in these countries can be based on work stress models that are developed in Western countries, such as the job demand control support model, as well as on empirical findings of job demand control support research. Nurses from Nurses Across the Borders Nigeria were invited to complete an online questionnaire containing validated scales; 210 questionnaires were fully completed and analysed. Multiple regression analysis was used to test the hypotheses. Emotional exhaustion was higher for nurses who experienced high demands and low supervisor support. Active learning occurred when nurses worked under conditions of high control and high supervisor support. The findings suggest that the job demand control support model is applicable in a Nigerian nursing situation; the model indicates which occupational stressors contribute to poor well-being in Nigerian nurses and which work characteristics may boost nurses' active learning. Job (re)design interventions can enhance nurses' well-being and learning by guarding nurses' job demands, and stimulating job control and supervisor support. © 2016 John Wiley & Sons Ltd.

  9. Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

    Directory of Open Access Journals (Sweden)

    Bin Xu

    2017-01-01

    Full Text Available This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.

  10. Methodological vs. strategic control in artificial grammar learning: A commentary on Norman, Price and Jones (2011).

    Science.gov (United States)

    Jiménez, Luis

    2011-12-01

    Norman et al. (2011) reported that participants exposed in succession to two artificial grammars could be able to learn implicitly about them, and could apply their knowledge strategically to select which string corresponds to one of these two grammars. In this commentary, I identify an artifact that could account for the learning obtained not only in this study, but also in some previous studies using the same procedures. I claim that more methodological control is needed before jumping to conclusions on the kind of strategic control that could be achieved unconsciously. Copyright © 2011 Elsevier Inc. All rights reserved.

  11. Space Stirling Cryocooler Contamination Lessons Learned and Recommended Control Procedures

    Science.gov (United States)

    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.

  12. Iterative Learning Control of Hysteresis in Piezoelectric Actuators

    Directory of Open Access Journals (Sweden)

    Guilin Zhang

    2014-01-01

    input in hysteretic systems. In the analysis, the Prandtl-Ishlinskii model is utilized to capture the nonlinear behavior in piezoelectric actuators. Finally, we apply the control algorithm to an experimental piezoelectric actuator and conclude that the tracking error is reduced to 0.15% of the total displacement, which is approximately the noise level of the sensor measurement.

  13. Multiagent reinforcement learning for urban traffic control using coordination graphs

    NARCIS (Netherlands)

    Kuyer, L.; Whiteson, S.; Bakker, B.; Vlassis, N.

    2008-01-01

    Since traffic jams are ubiquitous in the modern world, optimizing the behavior of traffic lights for efficient traffic flow is a critically important goal. Though most current traffic lights use simple heuristic protocols, more efficient controllers can be discovered automatically via multiagent

  14. Schoenfeld's problem solving theory in a student controlled learning environment

    NARCIS (Netherlands)

    Harskamp, E.; Suhre, C.

    2007-01-01

    This paper evaluates the effectiveness of a student controlled computer program for high school mathematics based on instruction principles derived from Schoenfeld's theory of problem solving. The computer program allows students to choose problems and to make use of hints during different episodes

  15. Engineering and malaria control: learning from the past 100 years

    DEFF Research Database (Denmark)

    Konradsen, Flemming; van der Hoek, Wim; Amerasinghe, Felix P

    2004-01-01

    Traditionally, engineering and environment-based interventions have contributed to the prevention of malaria in Asia. However, with the introduction of DDT and other potent insecticides, chemical control became the dominating strategy. The renewed interest in environmental-management-based approa......Traditionally, engineering and environment-based interventions have contributed to the prevention of malaria in Asia. However, with the introduction of DDT and other potent insecticides, chemical control became the dominating strategy. The renewed interest in environmental......-management-based approaches for the control of malaria vectors follows the rapid development of resistance by mosquitoes to the widely used insecticides, the increasing cost of developing new chemicals, logistical constraints involved in the implementation of residual-spraying programs and the environmental concerns linked...... cases are discussed in the wider context of environment-based approaches for the control of malaria vectors, including current relevance. Clearly, some of the interventions piloted and implemented early in the last century still have relevance today but generally in a very site-specific manner...

  16. Online learning algorithms : For passivity-based and distributed control

    NARCIS (Netherlands)

    Nageshrao, S.P.

    2016-01-01

    Over the last couple of decades the demand for high precision and enhanced performance of physical systems has been steadily increasing. This demand often results in miniaturization and complex design, thus increasing the need for complex nonlinear control methods. Some of the state of the art

  17. Application of machine learning and expert systems to Statistical Process Control (SPC) chart interpretation

    Science.gov (United States)

    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.

  18. Mental health first aid training by e-learning: a randomized controlled trial.

    Science.gov (United States)

    Jorm, Anthony F; Kitchener, Betty A; Fischer, Julie-Anne; Cvetkovski, Stefan

    2010-12-01

    Mental Health First Aid training is a course for the public that teaches how to give initial help to a person developing a mental health problem or in a mental health crisis. The present study evaluated the effects of Mental Health First Aid training delivered by e-learning on knowledge about mental disorders, stigmatizing attitudes and helping behaviour. A randomized controlled trial was carried out with 262 members of the Australian public. Participants were randomly assigned to complete an e-learning CD, read a Mental Health First Aid manual or be in a waiting list control group. The effects of the interventions were evaluated using online questionnaires pre- and post-training and at 6-months follow up. The questionnaires covered mental health knowledge, stigmatizing attitudes, confidence in providing help to others, actions taken to implement mental health first aid and participant mental health. Both e-learning and the printed manual increased aspects of knowledge, reduced stigma and increased confidence compared to waiting list. E-learning also improved first aid actions taken more than waiting list, and was superior to the printed manual in reducing stigma and disability due to mental ill health. Mental Health First Aid information received by either e-learning or printed manual had positive effects, but e-learning was better at reducing stigma.

  19. Cerebellar supervised learning revisited: biophysical modeling and degrees-of-freedom control.

    Science.gov (United States)

    Kawato, Mitsuo; Kuroda, Shinya; Schweighofer, Nicolas

    2011-10-01

    The biophysical models of spike-timing-dependent plasticity have explored dynamics with molecular basis for such computational concepts as coincidence detection, synaptic eligibility trace, and Hebbian learning. They overall support different learning algorithms in different brain areas, especially supervised learning in the cerebellum. Because a single spine is physically very small, chemical reactions at it are essentially stochastic, and thus sensitivity-longevity dilemma exists in the synaptic memory. Here, the cascade of excitable and bistable dynamics is proposed to overcome this difficulty. All kinds of learning algorithms in different brain regions confront with difficult generalization problems. For resolution of this issue, the control of the degrees-of-freedom can be realized by changing synchronicity of neural firing. Especially, for cerebellar supervised learning, the triangle closed-loop circuit consisting of Purkinje cells, the inferior olive nucleus, and the cerebellar nucleus is proposed as a circuit to optimally control synchronous firing and degrees-of-freedom in learning. Copyright © 2011 Elsevier Ltd. All rights reserved.

  20. Robust Monotonically Convergent Iterative Learning Control for Discrete-Time Systems via Generalized KYP Lemma

    Directory of Open Access Journals (Sweden)

    Jian Ding

    2014-01-01

    Full Text Available This paper addresses the problem of P-type iterative learning control for a class of multiple-input multiple-output linear discrete-time systems, whose aim is to develop robust monotonically convergent control law design over a finite frequency range. It is shown that the 2 D iterative learning control processes can be taken as 1 D state space model regardless of relative degree. With the generalized Kalman-Yakubovich-Popov lemma applied, it is feasible to describe the monotonically convergent conditions with the help of linear matrix inequality technique and to develop formulas for the control gain matrices design. An extension to robust control law design against systems with structured and polytopic-type uncertainties is also considered. Two numerical examples are provided to validate the feasibility and effectiveness of the proposed method.

  1. A statistical learning strategy for closed-loop control of fluid flows

    Science.gov (United States)

    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.

  2. A neural learning classifier system with self-adaptive constructivism for mobile robot control.

    Science.gov (United States)

    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.

  3. Effects of Mobile Augmented Reality Learning Compared to Textbook Learning on Medical Students: Randomized Controlled Pilot Study

    Science.gov (United States)

    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

  4. Effects of mobile augmented reality learning compared to textbook learning on medical students: randomized controlled pilot study.

    Science.gov (United States)

    Albrecht, Urs-Vito; Folta-Schoofs, Kristian; Behrends, Marianne; von Jan, Ute

    2013-08-20

    By adding new levels of experience, mobile Augmented Reality (mAR) can significantly increase the attractiveness of mobile learning applications in medical education. 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). 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. 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 selectivity of RPB=0.2. For m

  5. Wordless intervention for epilepsy in learning disabilities (WIELD): study protocol for a randomized controlled feasibility trial.

    Science.gov (United States)

    Durand, Marie-Anne; Gates, Bob; Parkes, Georgina; Zia, Asif; Friedli, Karin; Barton, Garry; Ring, Howard; Oostendorp, Linda; Wellsted, David

    2014-11-20

    Epilepsy is the most common neurological problem that affects people with learning disabilities. The high seizure frequency, resistance to treatments, associated skills deficit and co-morbidities make the management of epilepsy particularly challenging for people with learning disabilities. The Books Beyond Words booklet for epilepsy uses images to help people with learning disabilities manage their condition and improve quality of life. Our aim is to conduct a randomized controlled feasibility trial exploring key methodological, design and acceptability issues, in order to subsequently undertake a large-scale randomized controlled trial of the Books Beyond Words booklet for epilepsy. We will use a two-arm, single-centre randomized controlled feasibility design, over a 20-month period, across five epilepsy clinics in Hertfordshire, United Kingdom. We will recruit 40 eligible adults with learning disabilities and a confirmed diagnosis of epilepsy and will randomize them to use either the Books Beyond Words booklet plus usual care (intervention group) or to receive routine information and services (control group). We will collect quantitative data about the number of eligible participants, number of recruited participants, demographic data, discontinuation rates, variability of the primary outcome measure (quality of life: Epilepsy and Learning Disabilities Quality of Life scale), seizure severity, seizure control, intervention's patterns of use, use of other epilepsy-related information, resource use and the EQ-5D-5L health questionnaire. We will also gather qualitative data about the feasibility and acceptability of the study procedures and the Books Beyond Words booklet. Ethical approval for this study was granted on 28 April 2014, by the Wales Research Ethics Committee 5. Recruitment began on 1 July 2014. The outcomes of this feasibility study will be used to inform the design and methodology of a definitive study, adequately powered to determine the impact of

  6. Orphan sources: Consequences, regaining control and learning the lessons

    International Nuclear Information System (INIS)

    Croft, J.R.

    2001-01-01

    The safety and security record of technologies that use radiation sources is adequate in most cases, but on occasions there has been a lack of appropriate controls or circumvention of those that exist, leading to radiological accidents. Particular concern rise those radiation sources that have become orphans i.e. sources that were never subject to regulatory control, or were abandoned, lost or misplaced, stolen, or removed without authorisation. These sources are likely to be found in the public domain; examples include sources that used in radiotherapy units which have been unintentionally sold as scrap metal and melted thereafter, or which have been found by unsuspecting individuals or stolen, causing serious radiation exposure of people and contamination of the human habitat

  7. The relationship between mood state and perceived control in contingency learning: effects of individualist and collectivist values

    OpenAIRE

    Msetfi, Rachel M.; Kornbrot, Diana E.; Matute, Helena; Murphy, Robin A.

    2015-01-01

    Perceived control in contingency learning is linked to psychological wellbeing with low levels of perceived control thought to be a cause or consequence of depression and high levels of control considered to be the hallmark of mental healthiness. However, it is not clear whether this is a universal phenomenon or whether the value that people ascribe to control influences these relationships. Here we hypothesize that values affect learning about control contingencies and influence the relation...

  8. The relationship between mood state and perceived control in contingency learning: Effects of individualist and collectivist values

    OpenAIRE

    Rachel M. Msetfi; Rachel M. Msetfi; Diana eKornbrot; Helena eMatute; Robin A. Murphy

    2015-01-01

    Perceived control in contingency learning is linked to psychological wellbeing with low levels of perceived control thought to be a cause or consequence of depression and high levels of control considered to be the hallmark of mental healthiness. However, it is not clear whether this is a universal phenomenon or whether the value that people ascribe to control influences these relationships. Here we hypothesize that values affect learning about control contingencies and influence the relation...

  9. Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning.

    Science.gov (United States)

    Ross, Mindy K; Yoon, Jinsung; van der Schaar, Auke; van der Schaar, Mihaela

    2018-01-01

    Pediatric asthma has variable underlying inflammation and symptom control. Approaches to addressing this heterogeneity, such as clustering methods to find phenotypes and predict outcomes, have been investigated. However, clustering based on the relationship between treatment and clinical outcome has not been performed, and machine learning approaches for long-term outcome prediction in pediatric asthma have not been studied in depth. Our objectives were to use our novel machine learning algorithm, predictor pursuit (PP), to discover pediatric asthma phenotypes on the basis of asthma control in response to controller medications, to predict longitudinal asthma control among children with asthma, and to identify features associated with asthma control within each discovered pediatric phenotype. We applied PP to the Childhood Asthma Management Program study data (n = 1,019) to discover phenotypes on the basis of asthma control between assigned controller therapy groups (budesonide vs. nedocromil). We confirmed PP's ability to discover phenotypes using the Asthma Clinical Research Network/Childhood Asthma Research and Education network data. We next predicted children's asthma control over time and compared PP's performance with that of traditional prediction methods. Last, we identified clinical features most correlated with asthma control in the discovered phenotypes. Four phenotypes were discovered in both datasets: allergic not obese (A + /O - ), obese not allergic (A - /O + ), allergic and obese (A + /O + ), and not allergic not obese (A - /O - ). Of the children with well-controlled asthma in the Childhood Asthma Management Program dataset, we found more nonobese children treated with budesonide than with nedocromil (P = 0.015) and more obese children treated with nedocromil than with budesonide (P = 0.008). Within the obese group, more A + /O + children's asthma was well controlled with nedocromil than with budesonide (P = 0.022) or with placebo

  10. Pediatric emergency medicine asynchronous e-learning: a multicenter randomized controlled Solomon four-group study.

    Science.gov (United States)

    Chang, Todd P; Pham, Phung K; Sobolewski, Brad; Doughty, Cara B; Jamal, Nazreen; Kwan, Karen Y; Little, Kim; Brenkert, Timothy E; Mathison, David J

    2014-08-01

    Asynchronous e-learning allows for targeted teaching, particularly advantageous when bedside and didactic education is insufficient. An asynchronous e-learning curriculum has not been studied across multiple centers in the context of a clinical rotation. We hypothesize that an asynchronous e-learning curriculum during the pediatric emergency medicine (EM) rotation improves medical knowledge among residents and students across multiple participating centers. Trainees on pediatric EM rotations at four large pediatric centers from 2012 to 2013 were randomized in a Solomon four-group design. The experimental arms received an asynchronous e-learning curriculum consisting of nine Web-based, interactive, peer-reviewed Flash/HTML5 modules. Postrotation testing and in-training examination (ITE) scores quantified improvements in knowledge. A 2 × 2 analysis of covariance (ANCOVA) tested interaction and main effects, and Pearson's correlation tested associations between module usage, scores, and ITE scores. A total of 256 of 458 participants completed all study elements; 104 had access to asynchronous e-learning modules, and 152 were controls who used the current education standards. No pretest sensitization was found (p = 0.75). Use of asynchronous e-learning modules was associated with an improvement in posttest scores (p effect (partial η(2) = 0.19). Posttest scores correlated with ITE scores (r(2) = 0.14, p e-learning is an effective educational tool to improve knowledge in a clinical rotation. Web-based asynchronous e-learning is a promising modality to standardize education among multiple institutions with common curricula, particularly in clinical rotations where scheduling difficulties, seasonality, and variable experiences limit in-hospital learning. © 2014 by the Society for Academic Emergency Medicine.

  11. Magnetic induction of hyperthermia by a modified self-learning fuzzy temperature controller

    Science.gov (United States)

    Wang, Wei-Cheng; Tai, Cheng-Chi

    2017-07-01

    The aim of this study involved developing a temperature controller for magnetic induction hyperthermia (MIH). A closed-loop controller was applied to track a reference model to guarantee a desired temperature response. The MIH system generated an alternating magnetic field to heat a high magnetic permeability material. This wireless induction heating had few side effects when it was extensively applied to cancer treatment. The effects of hyperthermia strongly depend on the precise control of temperature. However, during the treatment process, the control performance is degraded due to severe perturbations and parameter variations. In this study, a modified self-learning fuzzy logic controller (SLFLC) with a gain tuning mechanism was implemented to obtain high control performance in a wide range of treatment situations. This implementation was performed by appropriately altering the output scaling factor of a fuzzy inverse model to adjust the control rules. In this study, the proposed SLFLC was compared to the classical self-tuning fuzzy logic controller and fuzzy model reference learning control. Additionally, the proposed SLFLC was verified by conducting in vitro experiments with porcine liver. The experimental results indicated that the proposed controller showed greater robustness and excellent adaptability with respect to the temperature control of the MIH system.

  12. A model reference and sensitivity model-based self-learning fuzzy logic controller as a solution for control of nonlinear servo systems

    NARCIS (Netherlands)

    Kovacic, Z.; Bogdan, S.; Balenovic, M.

    1999-01-01

    In this paper, the design, simulation and experimental verification of a self-learning fuzzy logic controller (SLFLC) suitable for the control of nonlinear servo systems are described. The SLFLC contains a learning algorithm that utilizes a second-order reference model and a sensitivity model

  13. A Mock Randomized Controlled Trial With Audience Response Technology for Teaching and Learning Epidemiology.

    Science.gov (United States)

    Baker, Philip R A; Francis, Daniel P; Cathcart, Abby

    2017-04-01

    The study's objective was to apply and assess an active learning approach to epidemiology and critical appraisal. Active learning comprised a mock, randomized controlled trial (RCT) conducted with learners in 3 countries. The mock trial consisted of blindly eating red Smarties candy (intervention) compared to yellow Smarties (control) to determine whether red Smarties increase happiness. Audience response devices were employed with the 3-fold purposes to produce outcome data for analysis of the effects of red Smarties, identify baseline and subsequent changes in participant's knowledge and confidence in understanding of RCTs, and assess the teaching approach. Of those attending, 82% (117 of 143 learners) participated in the trial component. Participating in the mock trial was a positive experience, and the use of the technology aided learning. The trial produced data that learners analyzed in "real time" during the class. The mock RCT is a fun and engaging approach to teaching RCTs and helping students to develop skills in critical appraisal.

  14. Non-Hebbian learning implementation in light-controlled resistive memory devices.

    Science.gov (United States)

    Ungureanu, Mariana; Stoliar, Pablo; Llopis, Roger; Casanova, Fèlix; Hueso, Luis E

    2012-01-01

    Non-Hebbian learning is often encountered in different bio-organisms. In these processes, the strength of a synapse connecting two neurons is controlled not only by the signals exchanged between the neurons, but also by an additional factor external to the synaptic structure. Here we show the implementation of non-Hebbian learning in a single solid-state resistive memory device. The output of our device is controlled not only by the applied voltages, but also by the illumination conditions under which it operates. We demonstrate that our metal/oxide/semiconductor device learns more efficiently at higher applied voltages but also when light, an external parameter, is present during the information writing steps. Conversely, memory erasing is more efficiently at higher applied voltages and in the dark. Translating neuronal activity into simple solid-state devices could provide a deeper understanding of complex brain processes and give insight into non-binary computing possibilities.

  15. Quasilinear Extreme Learning Machine Model Based Internal Model Control for Nonlinear Process

    Directory of Open Access Journals (Sweden)

    Dazi Li

    2015-01-01

    Full Text Available A new strategy for internal model control (IMC is proposed using a regression algorithm of quasilinear model with extreme learning machine (QL-ELM. Aimed at the chemical process with nonlinearity, the learning process of the internal model and inverse model is derived. The proposed QL-ELM is constructed as a linear ARX model with a complicated nonlinear coefficient. It shows some good approximation ability and fast convergence. The complicated coefficients are separated into two parts. The linear part is determined by recursive least square (RLS, while the nonlinear part is identified through extreme learning machine. The parameters of linear part and the output weights of ELM are estimated iteratively. The proposed internal model control is applied to CSTR process. The effectiveness and accuracy of the proposed method are extensively verified through numerical results.

  16. SVC control enhancement applying self-learning fuzzy algorithm for islanded microgrid

    Directory of Open Access Journals (Sweden)

    Hossam Gabbar

    2016-03-01

    Full Text Available Maintaining voltage stability, within acceptable levels, for islanded Microgrids (MGs is a challenge due to limited exchange power between generation and loads. This paper proposes an algorithm to enhance the dynamic performance of islanded MGs in presence of load disturbance using Static VAR Compensator (SVC with Fuzzy Model Reference Learning Controller (FMRLC. The proposed algorithm compensates MG nonlinearity via fuzzy membership functions and inference mechanism imbedded in both controller and inverse model. Hence, MG keeps the desired performance as required at any operating condition. Furthermore, the self-learning capability of the proposed control algorithm compensates for grid parameter’s variation even with inadequate information about load dynamics. A reference model was designed to reject bus voltage disturbance with achievable performance by the proposed fuzzy controller. Three simulations scenarios have been presented to investigate effectiveness of proposed control algorithm in improving steady-state and transient performance of islanded MGs. The first scenario conducted without SVC, second conducted with SVC using PID controller and third conducted using FMRLC algorithm. A comparison for results shows ability of proposed control algorithm to enhance disturbance rejection due to learning process.

  17. A controllable sensor management algorithm capable of learning

    Science.gov (United States)

    Osadciw, Lisa A.; Veeramacheneni, Kalyan K.

    2005-03-01

    Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network"s operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.

  18. Adaptive and Energy Efficient Walking in a Hexapod Robot under Neuromechanical Control and Sensorimotor Learning

    DEFF Research Database (Denmark)

    Xiong, Xiaofeng; Wörgötter, Florentin; Manoonpong, Poramate

    2016-01-01

    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...... 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...

  19. Implementation Challenges for Multivariable Control: What You Did Not Learn in School

    Science.gov (United States)

    Garg, Sanjay

    2008-01-01

    Multivariable control allows controller designs that can provide decoupled command tracking and robust performance in the presence of modeling uncertainties. Although the last two decades have seen extensive development of multivariable control theory and example applications to complex systems in software/hardware simulations, there are no production flying systems aircraft or spacecraft, that use multivariable control. This is because of the tremendous challenges associated with implementation of such multivariable control designs. Unfortunately, the curriculum in schools does not provide sufficient time to be able to provide an exposure to the students in such implementation challenges. The objective of this paper is to share the lessons learned by a practitioner of multivariable control in the process of applying some of the modern control theory to the Integrated Flight Propulsion Control (IFPC) design for an advanced Short Take-Off Vertical Landing (STOVL) aircraft simulation.

  20. Measuring and Modelling Delays in Robot Manipulators for Temporally Precise Control using Machine Learning

    DEFF Research Database (Denmark)

    Andersen, Thomas Timm; Amor, Heni Ben; Andersen, Nils Axel

    2015-01-01

    and separate. In this paper, we present a data-driven methodology for separating and modelling inherent delays during robot control. We show how both actuation and response delays can be modelled using modern machine learning methods. The resulting models can be used to predict the delays as well...

  1. An e-Learning System with MR for Experiments Involving Circuit Construction to Control a Robot

    Science.gov (United States)

    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…

  2. Preparing Content-Rich Learning Environments with VPython and Excel, Controlled by Visual Basic for Applications

    Science.gov (United States)

    Prayaga, Chandra

    2008-01-01

    A simple interface between VPython and Microsoft (MS) Office products such as Word and Excel, controlled by Visual Basic for Applications, is described. The interface allows the preparation of content-rich, interactive learning environments by taking advantage of the three-dimensional (3D) visualization capabilities of VPython and the GUI…

  3. Learner-Controlled Scaffolding Linked to Open-Ended Problems in a Digital Learning Environment

    Science.gov (United States)

    Edson, Alden Jack

    2017-01-01

    This exploratory study reports on how students activated learner-controlled scaffolding and navigated through sequences of connected problems in a digital learning environment. A design experiment was completed to (re)design, iteratively develop, test, and evaluate a digital version of an instructional unit focusing on binomial distributions and…

  4. Beyond the Personal Learning Environment: Attachment and Control in the Classroom of the Future

    Science.gov (United States)

    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…

  5. Social Learning, Social Control, and Strain Theories: A Formalization of Micro-level Criminological Theories

    OpenAIRE

    Proctor, Kristopher Ryan

    2010-01-01

    This dissertation proposes theoretical formalization as a way of enhancing theory development within criminology. Differential association, social learning, social control, and general strain theories are formalized in order to identify assumptions of human nature, key theoretical concepts, theoretical knowledge claims, and scope conditions. The resulting formalization allows greater comparability between theories in terms of explanatory power, and additionally provides insights into integrat...

  6. Instructional Control of Cognitive Load in the Design of Complex Learning Environments

    NARCIS (Netherlands)

    Kester, Liesbeth; Paas, Fred; Van Merriënboer, Jeroen

    2010-01-01

    Kester, L., Paas, F., & Van Merriënboer, J. J. G. (2010). Instructional control of cognitive load in the design of complex learning environments. In J. L. Plass, R. Moreno, & Roland Brünken (Eds.), Cognitive Load Theory (pp. 109-130). New York: Cambridge University Press.

  7. Studies in Motor Behavior: 75 Years of Research in Motor Development, Learning, and Control

    Science.gov (United States)

    Ulrich, Beverly D.; Reeve, T. Gilmour

    2005-01-01

    Research focused on human motor development, learning, and control has been a prominent feature in the Research Quarterly for Exercise and Sport (RQES) since it was first published in 1930. The purpose of this article is to provide an overview of the papers in the RQES that demonstrate the journal's contributions to the study of motor development,…

  8. Learning Control: Sense-Making, CNC Machines, and Changes in Vocational Training for Industrial Work

    Science.gov (United States)

    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…

  9. Environmental test chamber for the support of learning and teaching in intelligent control

    OpenAIRE

    Taylor, C. James

    2004-01-01

    The paper describes the utility of a low cost, 1 m2 by 2 m forced ventilation, micro-climate test chamber, for the support of research and teaching in mechatronics. Initially developed for the evaluation of a new ventilation rate controller, the fully instrumented chamber now provides numerous learning opportunities and individual projects for both undergraduate and postgraduate research students.

  10. Self-directed learning skills in air-traffic control; A cued retrospective reporting study

    NARCIS (Netherlands)

    Van Meeuwen, Ludo; Brand-Gruwel, Saskia; Van Merriënboer, Jeroen; Kirschner, Paul A.; De Bock, Jeano

    2011-01-01

    Van Meeuwen, L. W., Brand-Gruwel, S., Van Merriënboer, J. J. G., Kirschner, P. A., & De Bock, J. J. P. R. (2010, May). Self-directed learning skills in air-traffic control; A cued retrospective reporting study. Presented at the Scandinavian Workshop on Applied Eye-tracking. Lund, Sweden.

  11. Self-Controlled Practice Enhances Motor Learning in Introverts and Extroverts

    Science.gov (United States)

    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…

  12. Self-directed learning skills in air-traffic control training; An eye-tracking approach

    NARCIS (Netherlands)

    Van Meeuwen, Ludo; Brand-Gruwel, Saskia; Van Merriënboer, Jeroen; Bock, Jeano; Kirschner, Paul A.

    2011-01-01

    Van Meeuwen, L. W., Brand-Gruwel, S., De Bock, J. J. P. R., Kirschner, P. A., & Van Merriënboer, J. J. G. (2010, September). Self-directed Learning Skills in Air-traffic Control Training; An Eye-tracking Approach. Paper presented at the European Association for Aviation Psychology, Budapest.

  13. Knowledge-based errors in anesthesia: a paired, controlled trial of learning and retention.

    Science.gov (United States)

    Goldhaber-Fiebert, Sara N; Goldhaber-Fiebert, Jeremy D; Rosow, Carl E

    2009-01-01

    Optimizing patient safety by improving the training of physicians is a major challenge of medical education. In this pilot study, we hypothesized that a brief lecture, targeted to rare but potentially dangerous situations, could improve anesthesia practitioners' knowledge levels with significant retention of learning at six months. In this paired controlled trial, anesthesia residents and attending physicians at Massachusetts General Hospital took the same 14-question multiple choice examination three times: at baseline, immediately after a brief lecture, and six months later. The lecture covered material on seven "intervention" questions; the remaining seven were "control" questions. The authors measured immediate knowledge acquisition, defined as the change in percentage of correct answers on intervention questions between baseline and post-lecture, and measured learning retention as the difference between baseline and six months. Both measurements were corrected for change in performance on control questions. Fifty of the 89 subjects completed all three examinations. The post-lecture increase in percentage of questions answered correctly, adjusted for control, was 22.2% [95% confidence interval (CI) 16.0-28.4%; P learning at six months. Exposing residents or other practitioners to this type of inexpensive teaching intervention may help them to avoid preventable uncommon errors that are rooted in unfamiliarity with the situation or the equipment. The methods used for this study may also be applied to compare the effect of various other teaching modalities while, at the same time, preserving participant anonymity and making adjustments for ongoing learning.

  14. A Joint Learning Activity in Process Control and Distance Collaboration between Future Engineers and Technicians

    Science.gov (United States)

    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…

  15. Design strategy for optimal iterative learning control applied on a deep drawing process

    DEFF Research Database (Denmark)

    Endelt, Benny Ørtoft

    2017-01-01

    Metal forming processes in general can be characterised as repetitive processes; this work will take advantage of this characteristic by developing an algorithm or control system which transfers process information from part to part, reducing the impact of repetitive uncertainties, e.g. a gradual...... changes in the material properties. The process is highly non-linear and the system plant is modelled using a non-linear finite element and the gain factors for the iterative learning controller is identified solving a non-linear optimal control problem. The optimal control problem is formulated as a non...

  16. An Investigation into the Academic Success of Prospective Teachers in Terms of Learning Strategies, Learning Styles and the Locus of Control

    Science.gov (United States)

    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…

  17. Lessons learned in process control at the Halden Reactor Project

    International Nuclear Information System (INIS)

    Kennedy, W.G.

    1989-12-01

    This report provides a list of those findings particularly relevant to regulatory authorities that can be derived from the research and development activities in computerized process control conducted at the Halden Reactor Project. The report was prepared by a staff member of the US Nuclear Regulatory Commission working at Halden. It identifies those results that may be of use to regulatory organizations in three main areas: as support for new requirements, as part of regulatory evaluations of the acceptability of new methods and techniques, and in exploratory research and development of new approaches to improve operator performance. More than 200 findings arranged in nine major categories are presented. The findings were culled from Halden Reactor Project documents, which are listed in the report

  18. Learning and strain among newcomers: a three-wave study on the effects of job demands and job control.

    Science.gov (United States)

    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.

  19. Examining Change in Metacognitive Knowledge and Metacognitive Control During Motor Learning: What Can be Learned by Combining Methodological Approaches?

    Directory of Open Access Journals (Sweden)

    Claire Sangster Jokić

    2014-04-01

    Full Text Available Growing recognition of the importance of understanding metacognitive behaviour as it occurs in everyday learning situations has prompted an expansion of the methodological approaches used to examine metacognition. This becomes especially pertinent when examining the process of metacognitive change, where 'on-line' observational approaches able to capture metacognitive performance as it occurs during socially-mediated learning are being increasingly applied. This study applied a mixed methods approach to examine children's metacognitive performance as it was exhibited during participation in an intervention program aimed at addressing motor performance difficulties. Participants in the study were ten 7-9 year old children with developmental coordination disorder (DCD, a condition characterized by poor motor coordination and difficulty acquiring motor-based tasks. All participants engaged in a 10-session program in which children were taught to use a problem-solving strategy for addressing motor performance difficulties. To examine children's metacognitive performance, sessions were video-taped and subsequently analysed using a quantitative observational coding method and an in-depth qualitative review of therapist-child interactions. This allowed for a fine-grained analysis of children's demonstration of metacognitive knowledge and control and how such performance evolved over the course of the program. Of particular interest was the finding that while children were often able to express task-specific knowledge, they failed to apply this knowledge during practice. Conversely, children were often able to demonstrate performance-based evidence for metacognitive control but were not able to make conscious reports of such skill following practice. This finding is consistent with models of metacognitive development which suggest that the emergence of performance-based metacognitive skills precede the ability for the conscious expression of

  20. Sustainability in a state comprehensive cancer control coalition: lessons learned.

    Science.gov (United States)

    Desmond, Renee A; Chapman, Kathryn; Graf, Gavin; Stanfield, Bret; Waterbor, John W

    2014-03-01

    The Alabama Comprehensive Cancer Control Coalition (ACCCC) has developed an integrated and coordinated approach to reducing cancer incidence, morbidity, and mortality, and to improving the quality of life for cancer survivors, their families, and their caregivers. The ACCCC is currently in a maintenance phase and a formal plan for sustainability of the coalition was needed to keep the members engaged and productive. A training session in coalition sustainability conducted in 2013 identified the following elements as essential to success: (1) increased marketing of the coalition by simplifying its mission; (2) improved networking including flexibility in coalition meeting location and attendance; (3) increased membership satisfaction through transformational leadership; (4) revision of the working structure of committees and improved accountability; and (5) enhancement of partner satisfaction with coalition activities designed to recruit and retain new partners. A self-administered membership satisfaction survey was given to assess coalition mission, meeting logistics, organization, capacity building, and coalition goals. Results indicated that the subcategories of communication, mission, and meeting logistics were rated satisfied to very satisfied on a five-point scale. Although the ACCCC had clearly written goals, improvement could be made in leadership participation and new member orientation could be improved. Most members rated their parent organization as highly involved with the ACCCC and many offered suggestions on capacity building. Results of the sustainability training have clarified the ACCCC's plans to ensure coalition viability and improve strategies to inform stakeholders of the benefits of participation in the coalition.

  1. Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems

    Directory of Open Access Journals (Sweden)

    Elmar eRückert

    2013-10-01

    Full Text Available A salient feature of human motor skill learning is the ability to exploitsimilarities across related tasks.In biological motor control, it has been hypothesized that muscle synergies,coherent activations of groups of muscles, allow for exploiting shared knowledge.Recent studies have shown that a rich set of complex motor skills can be generated bya combination of a small number of muscle synergies.In robotics, dynamic movement primitives are commonlyused for motor skill learning. This machine learning approach implements a stable attractor systemthat facilitates learning and it can be used in high-dimensional continuous spaces. However, it does not allow for reusing shared knowledge, i.e. for each task an individual set of parameters has to be learned.We propose a novel movement primitive representationthat employs parametrized basis functions, which combines the benefits of muscle synergiesand dynamic movement primitives. For each task asuperposition of synergies modulates a stable attractor system.This approach leads to a compact representation of multiple motor skills andat the same time enables efficient learning in high-dimensional continuous systems.The movement representation supports discrete and rhythmic movements andin particular includes the dynamic movement primitive approach as a special case.We demonstrate the feasibility of the movement representation in three multi-task learning simulated scenarios.First, the characteristics of the proposed representation are illustrated in a point-mass task.Second, in complex humanoid walking experiments,multiple walking patterns with different step heights are learned robustly and efficiently.Finally, in a multi-directional reaching task simulated with a musculoskeletal modelof the human arm, we show how the proposed movement primitives can be used tolearn appropriate muscle excitation patterns and to generalize effectively to new reaching skills.

  2. Food2Learn: Randomized control trial investigating influence of krill oil supplementation on learning, cognition, and behaviour in healthy adolescents. Design presentation

    NARCIS (Netherlands)

    Van der Wurff, Inge; Von Schacky, Clemens; Berge, Kjetil; Kirschner, Paul A.; De Groot, Renate

    2014-01-01

    Food2Learn is a double blind randomized controlled trial which looks at the influence of Krill oil (rich in LCPUFA) on the cognitive performance, academic performance and mental well-being of student of lower vocational schools.

  3. Learning of Temporal and Spatial Movement Aspects: A Comparison of Four Types of Haptic Control and Concurrent Visual Feedback.

    Science.gov (United States)

    Rauter, Georg; Sigrist, Roland; Riener, Robert; Wolf, Peter

    2015-01-01

    In literature, the effectiveness of haptics for motor learning is controversially discussed. Haptics is believed to be effective for motor learning in general; however, different types of haptic control enhance different movement aspects. Thus, in dependence on the movement aspects of interest, one type of haptic control may be effective whereas another one is not. Therefore, in the current work, it was investigated if and how different types of haptic controllers affect learning of spatial and temporal movement aspects. In particular, haptic controllers that enforce active participation of the participants were expected to improve spatial aspects. Only haptic controllers that provide feedback about the task's velocity profile were expected to improve temporal aspects. In a study on learning a complex trunk-arm rowing task, the effect of training with four different types of haptic control was investigated: position control, path control, adaptive path control, and reactive path control. A fifth group (control) trained with visual concurrent augmented feedback. As hypothesized, the position controller was most effective for learning of temporal movement aspects, while the path controller was most effective in teaching spatial movement aspects of the rowing task. Visual feedback was also effective for learning temporal and spatial movement aspects.

  4. Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

    Science.gov (United States)

    Williams-Hayes, Peggy S.

    2004-01-01

    The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

  5. Repeated Strains, Social Control, Social Learning, and Delinquency: Testing an Integrated Model of General Strain Theory in China

    Science.gov (United States)

    Bao, Wan-Ning; Haas, Ain; Chen, Xiaojin; Pi, Yijun

    2014-01-01

    In Agnew's general strain theory, repeated strains can generate crime and delinquency by reducing social control and fostering social learning of crime. Using a sample of 615 middle-and high-school students in China, this study examines how social control and social learning variables mediate the effect of repeated strains in school and at home on…

  6. Manifold traversing as a model for learning control of autonomous robots

    Science.gov (United States)

    Szakaly, Zoltan F.; Schenker, Paul S.

    1992-01-01

    This paper describes a recipe for the construction of control systems that support complex machines such as multi-limbed/multi-fingered robots. The robot has to execute a task under varying environmental conditions and it has to react reasonably when previously unknown conditions are encountered. Its behavior should be learned and/or trained as opposed to being programmed. The paper describes one possible method for organizing the data that the robot has learned by various means. This framework can accept useful operator input even if it does not fully specify what to do, and can combine knowledge from autonomous, operator assisted and programmed experiences.

  7. The proportion valid effect in covert orienting: strategic control or implicit learning?

    Science.gov (United States)

    Risko, Evan F; Stolz, Jennifer A

    2010-03-01

    It is well known that the difference in performance between valid and invalid trials in the covert orienting paradigm (i.e., the cueing effect) increases as the proportion of valid trials increases. This proportion valid effect is widely assumed to reflect "strategic" control over the distribution of attention. In the present experiments we determine if this effect results from an explicit strategy or implicit learning by probing participant's awareness of the proportion of valid trials. Results support the idea that the proportion valid effect in the covert orienting paradigm reflects implicit learning not an explicit strategy.

  8. Mobile-Based Video Learning Outcomes in Clinical Nursing Skill Education: A Randomized Controlled Trial.

    Science.gov (United States)

    Lee, Nam-Ju; Chae, Sun-Mi; Kim, Haejin; Lee, Ji-Hye; Min, Hyojin Jennifer; Park, Da-Eun

    2016-01-01

    Mobile devices are a regular part of daily life among the younger generations. Thus, now is the time to apply mobile device use to nursing education. The purpose of this study was to identify the effects of a mobile-based video clip on learning motivation, competence, and class satisfaction in nursing students using a randomized controlled trial with a pretest and posttest design. A total of 71 nursing students participated in this study: 36 in the intervention group and 35 in the control group. A video clip of how to perform a urinary catheterization was developed, and the intervention group was able to download it to their own mobile devices for unlimited viewing throughout 1 week. All of the students participated in a practice laboratory to learn urinary catheterization and were blindly tested for their performance skills after participation in the laboratory. The intervention group showed significantly higher levels of learning motivation and class satisfaction than did the control. Of the fundamental nursing competencies, the intervention group was more confident in practicing catheterization than their counterparts. Our findings suggest that video clips using mobile devices are useful tools that educate student nurses on relevant clinical skills and improve learning outcomes.

  9. Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps

    Science.gov (United States)

    Kamimura, Ryotaro

    2014-01-01

    We attempt to demonstrate the effectiveness of multiple points of view toward neural networks. By restricting ourselves to two points of view of a neuron, we propose a new type of information-theoretic method called “cooperation-controlled learning.” In this method, individual and collective neurons are distinguished from one another, and we suppose that the characteristics of individual and collective neurons are different. To implement individual and collective neurons, we prepare two networks, namely, cooperative and uncooperative networks. The roles of these networks and the roles of individual and collective neurons are controlled by the cooperation parameter. As the parameter is increased, the role of cooperative networks becomes more important in learning, and the characteristics of collective neurons become more dominant. On the other hand, when the parameter is small, individual neurons play a more important role. We applied the method to the automobile and housing data from the machine learning database and examined whether explicit class boundaries could be obtained. Experimental results showed that cooperation-controlled learning, in particular taking into account information on input units, could be used to produce clearer class structure than conventional self-organizing maps. PMID:25309950

  10. Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps

    Directory of Open Access Journals (Sweden)

    Ryotaro Kamimura

    2014-01-01

    Full Text Available We attempt to demonstrate the effectiveness of multiple points of view toward neural networks. By restricting ourselves to two points of view of a neuron, we propose a new type of information-theoretic method called “cooperation-controlled learning.” In this method, individual and collective neurons are distinguished from one another, and we suppose that the characteristics of individual and collective neurons are different. To implement individual and collective neurons, we prepare two networks, namely, cooperative and uncooperative networks. The roles of these networks and the roles of individual and collective neurons are controlled by the cooperation parameter. As the parameter is increased, the role of cooperative networks becomes more important in learning, and the characteristics of collective neurons become more dominant. On the other hand, when the parameter is small, individual neurons play a more important role. We applied the method to the automobile and housing data from the machine learning database and examined whether explicit class boundaries could be obtained. Experimental results showed that cooperation-controlled learning, in particular taking into account information on input units, could be used to produce clearer class structure than conventional self-organizing maps.

  11. The barriers and motivators to learning infection control in clinical placements: interviews with midwifery students.

    Science.gov (United States)

    Ward, Deborah J

    2013-05-01

    To investigate the barriers to and motivators for learning infection prevention and control as identified by midwifery students. Semi-structured interviews were undertaken with 15 undergraduate midwifery students within one large university. Data were analysed using Framework Analysis. Barriers to good clinical practice were identified by students which were concordant with previous literature related to reasons for non-compliance with infection control precautions. Issues such as competing demands specific to midwifery were also identified. Factors which act as barriers to learning good practice in placements included conflicting information and practices from different staff and placement areas and staff attitudes towards students who tried to comply with precautions. Motivators to good practice included the perceived vulnerability of infants to infection, the role modelling of good practice to new mothers and the monitoring of practice. This study demonstrated that midwifery students perceive barriers and motivators to learning infection prevention and control in their clinical placements. Many of the barriers identified are related to the attitudes and practices of qualified staff. Some of the motivators are related specifically to midwifery practice. Midwives need to be aware of the effects of what is observed in practice on midwifery students and how their practices and attitudes can influence learning both positively and negatively. As healthcare-associated infection and poor compliance with precautions are a global problem, this research should be of benefit to midwives and midwifery educators worldwide in terms of addressing barriers and ensuring better clinical education. Copyright © 2012 Elsevier Ltd. All rights reserved.

  12. Enhancing current density profile control in tokamak experiments using iterative learning control

    NARCIS (Netherlands)

    Felici, F.A.A.; Oomen, T.A.E.

    2015-01-01

    Tokamaks are toroidal devices to create and confine high-temperature plasmas, and are presently at the forefront of nuclear fusion research. Many parameters in a tokamak are feedback controlled, but some quantities that are either difficult to measure or difficult to control are still controlled by

  13. Synergetic motor control paradigm for optimizing energy efficiency of multijoint reaching via tacit learning

    Science.gov (United States)

    Hayashibe, Mitsuhiro; Shimoda, Shingo

    2014-01-01

    A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works.Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system.In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics.We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques.We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions.Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is significantly minimized down to 12% for no load at hand, 16% for a 0.5 kg load condition.The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach

  14. Synergetic motor control paradigm for optimizing energy efficiency of multijoint reaching via tacit learning.

    Science.gov (United States)

    Hayashibe, Mitsuhiro; Shimoda, Shingo

    2014-01-01

    A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works.Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system.In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics.We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques.We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions.Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is significantly minimized down to 12% for no load at hand, 16% for a 0.5 kg load condition.The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach.

  15. Pathological gamblers are more vulnerable to the illusion of control in a standard associative learning task

    Directory of Open Access Journals (Sweden)

    Cristina eOrgaz

    2013-06-01

    Full Text Available An illusion of control is said to occur when a person believes that he or she controls an outcome that is uncontrollable. Pathological gambling has often been related to an illusion of control, but the assessment of the illusion has generally used introspective methods in domain-specific (i.e., gambling situations. The illusion of control of pathological gamblers, however, could be a more general problem, affecting other aspects of their daily life. Thus, we tested them using a standard associative learning task which is known to produce illusions of control in most people under certain conditions. The results showed that the illusion was significantly stronger in pathological gamblers than in a control undiagnosed sample. This suggests (a that the experimental tasks used in basic associative learning research could be used to detect illusions of control in gamblers in a more indirect way, as compared to introspective and domain-specific questionnaires; and (b, that in addition to gambling-specific problems, pathological gamblers may have a higher-than-normal illusion of control in their daily life.

  16. Goal selection versus process control while learning to use a brain-computer interface

    Science.gov (United States)

    Royer, Audrey S.; Rose, Minn L.; He, Bin

    2011-06-01

    A brain-computer interface (BCI) can be used to accomplish a task without requiring motor output. Two major control strategies used by BCIs during task completion are process control and goal selection. In process control, the user exerts continuous control and independently executes the given task. In goal selection, the user communicates their goal to the BCI and then receives assistance executing the task. A previous study has shown that goal selection is more accurate and faster in use. An unanswered question is, which control strategy is easier to learn? This study directly compares goal selection and process control while learning to use a sensorimotor rhythm-based BCI. Twenty young healthy human subjects were randomly assigned either to a goal selection or a process control-based paradigm for eight sessions. At the end of the study, the best user from each paradigm completed two additional sessions using all paradigms randomly mixed. The results of this study were that goal selection required a shorter training period for increased speed, accuracy, and information transfer over process control. These results held for the best subjects as well as in the general subject population. The demonstrated characteristics of goal selection make it a promising option to increase the utility of BCIs intended for both disabled and able-bodied users.

  17. The active learning hypothesis of the job-demand-control model: an experimental examination.

    Science.gov (United States)

    Häusser, Jan Alexander; Schulz-Hardt, Stefan; Mojzisch, Andreas

    2014-01-01

    The active learning hypothesis of the job-demand-control model [Karasek, R. A. 1979. "Job Demands, Job Decision Latitude, and Mental Strain: Implications for Job Redesign." Administration Science Quarterly 24: 285-307] proposes positive effects of high job demands and high job control on performance. We conducted a 2 (demands: high vs. low) × 2 (control: high vs. low) experimental office workplace simulation to examine this hypothesis. Since performance during a work simulation is confounded by the boundaries of the demands and control manipulations (e.g. time limits), we used a post-test, in which participants continued working at their task, but without any manipulation of demands and control. This post-test allowed for examining active learning (transfer) effects in an unconfounded fashion. Our results revealed that high demands had a positive effect on quantitative performance, without affecting task accuracy. In contrast, high control resulted in a speed-accuracy tradeoff, that is participants in the high control conditions worked slower but with greater accuracy than participants in the low control conditions.

  18. A Combination of Machine Learning and Cerebellar-like Neural Networks for the Motor Control and Motor Learning of the Fable Modular Robot

    DEFF Research Database (Denmark)

    Baira Ojeda, Ismael; Tolu, Silvia; Pacheco, Moises

    2017-01-01

    We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist, in the form of a Unit Learning Machine. The LWPR algorithm optimizes...... the input space and learns the internal model of a single robot module to command the robot to follow a desired trajectory with its end-effector. The cerebellar-like microcircuit refines the LWPR output delivering corrective commands. We contrasted distinct cerebellar-like circuits including analytical...

  19. Habit learning and the genetics of the dopamine D3 receptor: evidence from patients with schizophrenia and healthy controls.

    Science.gov (United States)

    Kéri, Szabolcs; Juhász, Anna; Rimanóczy, Agnes; Szekeres, György; Kelemen, Oguz; Cimmer, Csongor; Szendi, István; Benedek, György; Janka, Zoltán

    2005-06-01

    In this study, the authors investigated the relationship between the Ser9Gly (SG) polymorphism of the dopamine D3 receptor (DRD3) and striatal habit learning in healthy controls and patients with schizophrenia. Participants were given the weather prediction task, during which probabilistic cue-response associations were learned for tarot cards and weather outcomes (rain or sunshine). In both healthy controls and patients with schizophrenia, participants with Ser9Ser (SS) genotype did not learn during the early phase of the task (1-50 trials), whereas participants with SG genotype did so. During the late phase of the task (51-100 trials), both participants with SS and SG genotype exhibited significant learning. Learning rate was normal in patients with schizophrenia. These results suggest that the DRD3 variant containing glycine is associated with more efficient striatal habit learning in healthy controls and patients with schizophrenia. (c) 2005 APA, all rights reserved.

  20. Visual working memory gives up attentional control early in learning: ruling out interhemispheric cancellation.

    Science.gov (United States)

    Reinhart, Robert M G; Carlisle, Nancy B; Woodman, Geoffrey F

    2014-08-01

    Current research suggests that we can watch visual working memory surrender the control of attention early in the process of learning to search for a specific object. This inference is based on the observation that the contralateral delay activity (CDA) rapidly decreases in amplitude across trials when subjects search for the same target object. Here, we tested the alternative explanation that the role of visual working memory does not actually decline across learning, but instead lateralized representations accumulate in both hemispheres across trials and wash out the lateralized CDA. We show that the decline in CDA amplitude occurred even when the target objects were consistently lateralized to a single visual hemifield. Our findings demonstrate that reductions in the amplitude of the CDA during learning are not simply due to the dilution of the CDA from interhemispheric cancellation. Copyright © 2014 Society for Psychophysiological Research.

  1. Representation and Integration: Combining Robot Control, High-Level Planning, and Action Learning

    DEFF Research Database (Denmark)

    Petrick, Ronald; Kraft, Dirk; Mourao, Kira

    We describe an approach to integrated robot control, high-level planning, and action effect learning that attempts to overcome the representational difficulties that exist between these diverse areas. Our approach combines ideas from robot vision, knowledgelevel planning, and connectionist machine......-level action specifications, suitable for planning, from a robot’s interactions with the world. We present a detailed overview of our approach and show how it supports the learning of certain aspects of a high-level lepresentation from low-level world state information....... learning, and focuses on the representational needs of these components.We also make use of a simple representational unit called an instantiated state transition fragment (ISTF) and a related structure called an object-action complex (OAC). The goal of this work is a general approach for inducing high...

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

    Science.gov (United States)

    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.

  3. Online and Compositional Learning of Controllers with Application to Floor Heating

    DEFF Research Database (Denmark)

    Larsen, Kim Guldstrand; Mikučionis, Marius; Muniz, Marco

    2016-01-01

    possible input temperatures and an arbitrary time horizon, we propose an on-line synthesis methodology, where we periodically compute the controller only for the near future based on the current sensor readings. This computation is itself done by employing machine learning in order to avoid enumeration...... of continuous variables (e.g. temperature readings in the different rooms) and even after digitization, the state-space remains huge and cannot be fully explored. We suggest a general and scalable methodology for controller synthesis for such systems. Instead of off-line synthesis of a controller for all...

  4. Transformations to diagonal bases in closed-loop quantum learning control experiments

    International Nuclear Information System (INIS)

    Cardoza, David; Trallero-Herrero, Carlos; Langhojer, Florian; Rabitz, Herschel; Weinacht, Thomas

    2005-01-01

    This paper discusses transformations between bases used in closed-loop learning control experiments. The goal is to transform to a basis in which the number of control parameters is minimized and in which the parameters act independently. We demonstrate a simple procedure for testing whether a unitary linear transformation (i.e., a rotation amongst the control variables) is sufficient to reduce the search problem to a set of globally independent variables. This concept is demonstrated with closed-loop molecular fragmentation experiments utilizing shaped, ultrafast laser pulses

  5. Sixth Graders Benefit from Educational Software when Learning about Fractions: A Controlled Classroom study

    Directory of Open Access Journals (Sweden)

    Susanne Scharnagl

    2014-01-01

    Full Text Available This study analyses the effectiveness of an educational web-based software package for teaching mathematics in schools. In all, 864 sixth graders and their teachers took part in the controlled study. Students learned the addition and subtraction of fractions with (intervention group; n = 469 or without (control group; n = 395 the support of the educational software. Compared to the controls, students who used the software showed better results in the post-test. Gains were dose dependent and particularly marked in high-ability students and students with lower scores of math anxiety.

  6. Chaos Synchronization Using Adaptive Dynamic Neural Network Controller with Variable Learning Rates

    Directory of Open Access Journals (Sweden)

    Chih-Hong Kao

    2011-01-01

    Full Text Available This paper addresses the synchronization of chaotic gyros with unknown parameters and external disturbance via an adaptive dynamic neural network control (ADNNC system. The proposed ADNNC system is composed of a neural controller and a smooth compensator. The neural controller uses a dynamic RBF (DRBF network to online approximate an ideal controller. The DRBF network can create new hidden neurons online if the input data falls outside the hidden layer and prune the insignificant hidden neurons online if the hidden neuron is inappropriate. The smooth compensator is designed to compensate for the approximation error between the neural controller and the ideal controller. Moreover, the variable learning rates of the parameter adaptation laws are derived based on a discrete-type Lyapunov function to speed up the convergence rate of the tracking error. Finally, the simulation results which verified the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized using the proposed ADNNC scheme.

  7. The Roles of Feedback and Feedforward as Humans Learn to Control Unknown Dynamic Systems.

    Science.gov (United States)

    Zhang, Xingye; Wang, Shaoqian; Hoagg, Jesse B; Seigler, T Michael

    2018-02-01

    We present results from an experiment in which human subjects interact with an unknown dynamic system 40 times during a two-week period. During each interaction, subjects are asked to perform a command-following (i.e., pursuit tracking) task. Each subject's performance at that task improves from the first trial to the last trial. For each trial, we use subsystem identification to estimate each subject's feedforward (or anticipatory) control, feedback (or reactive) control, and feedback time delay. Over the 40 trials, the magnitudes of the identified feedback controllers and the identified feedback time delays do not change significantly. In contrast, the identified feedforward controllers do change significantly. By the last trial, the average identified feedforward controller approximates the inverse of the dynamic system. This observation provides evidence that a fundamental component of human learning is updating the anticipatory control until it models the inverse dynamics.

  8. Remote-online case-based learning: A comparison of remote-online and face-to-face, case-based learning - a randomized controlled trial.

    Science.gov (United States)

    Nicklen, Peter; Keating, Jenny L; Paynter, Sophie; Storr, Michael; Maloney, Stephen

    2016-01-01

    Case-based learning (CBL) is an educational approach where students work in small, collaborative groups to solve problems. Computer assisted learning (CAL) is the implementation of computer technology in education. The purpose of this study was to compare the effects of a remote-online CBL (RO-CBL) with traditional face-to-face CBL on learning the outcomes of undergraduate physiotherapy students. Participants were randomized to either the control (face-to-face CBL) or to the CAL intervention (RO-CBL). The entire 3rd year physiotherapy cohort (n = 41) at Monash University, Victoria, Australia, were invited to participate in the randomized controlled trial. Outcomes included a postintervention multiple-choice test evaluating the knowledge gained from the CBL, a self-assessment of learning based on examinable learning objectives and student satisfaction with the CBL. In addition, a focus group was conducted investigating perceptions and responses to the online format. Thirty-eight students (control n = 19, intervention n = 19) participated in two CBL sessions and completed the outcome assessments. CBL median scores for the postintervention multiple-choice test were comparable (Wilcoxon rank sum P = 0.61) (median/10 [range] intervention group: 9 [8-10] control group: 10 [7-10]). Of the 15 examinable learning objectives, eight were significantly in favor of the control group, suggesting a greater perceived depth of learning. Eighty-four percent of students (16/19) disagreed with the statement "I enjoyed the method of CBL delivery." Key themes identified from the focus group included risks associated with the implementation of, challenges of communicating in, and flexibility offered, by web-based programs. RO-CBL appears to provide students with a comparable learning experience to traditional CBL. Procedural and infrastructure factors need to be addressed in future studies to counter student dissatisfaction and decreased perceived depth of learning.

  9. Effects of Anodal Transcranial Direct Current Stimulation on Visually Guided Learning of Grip Force Control

    Directory of Open Access Journals (Sweden)

    Tamas Minarik

    2015-03-01

    Full Text Available Anodal transcranial Direct Current Stimulation (tDCS has been shown to be an effective non-invasive brain stimulation method for improving cognitive and motor functioning in patients with neurological deficits. tDCS over motor cortex (M1, for instance, facilitates motor learning in stroke patients. However, the literature on anodal tDCS effects on motor learning in healthy participants is inconclusive, and the effects of tDCS on visuo-motor integration are not well understood. In the present study we examined whether tDCS over the contralateral motor cortex enhances learning of grip-force output in a visually guided feedback task in young and neurologically healthy volunteers. Twenty minutes of 1 mA anodal tDCS were applied over the primary motor cortex (M1 contralateral to the dominant (right hand, during the first half of a 40 min power-grip task. This task required the control of a visual signal by modulating the strength of the power-grip for six seconds per trial. Each participant completed a two-session sham-controlled crossover protocol. The stimulation conditions were counterbalanced across participants and the sessions were one week apart. Performance measures comprised time-on-target and target-deviation, and were calculated for the periods of stimulation (or sham and during the afterphase respectively. Statistical analyses revealed significant performance improvements over the stimulation and the afterphase, but this learning effect was not modulated by tDCS condition. This suggests that the form of visuomotor learning taking place in the present task was not sensitive to neurostimulation. These null effects, together with similar reports for other types of motor tasks, lead to the proposition that tDCS facilitation of motor learning might be restricted to cases or situations where the motor system is challenged, such as motor deficits, advanced age, or very high task demand.

  10. Development of Computer-Aided Learning Programs on Nuclear Nonproliferation and Control

    International Nuclear Information System (INIS)

    Kim, Hyun Chul

    2011-01-01

    The fulfillment of international norms for nuclear nonproliferation is indispensable to the promotion of nuclear energy. The education and training for personnel and mangers related to the nuclear material are one of crucial factors to avoid unintended non-compliance to international norms. Korea Institute of Nuclear Nonproliferation and Control (KINAC) has been providing education and training on nuclear control as its legal duty. One of the legally mandatory educations is 'nuclear control education' performed since 2006 for the observation of the international norms on nuclear nonproliferation and the spread of the nuclear control culture. The other is 'physical protection education' performed since 2010 for maintaining the national physical protection regime effectively and the spread of the nuclear security culture. The 2010 Nuclear Security Summit was held in Washington, DC to enhance international cooperation to prevent nuclear terrorism. During the Summit, the South Korea was chosen to host the second Nuclear Summit in 2012. South Korean President announced that South Korea would share its expertise and support the Summit's mission by setting up an international education and training center on nuclear security in 2014. KINAC is making a full effort to set up the center successfully. An important function of the center is education and training in the subjects of nuclear nonproliferation, nuclear safeguards, nuclear security, and nuclear export/import control. With increasing importance of education and training education on nuclear nonproliferation and control, KINAC has been developing computer-aided learning programs on nuclear nonproliferation and control to overcome the weaknesses in classroom educations. This paper shows two learning programs. One is an e-learning system on the nuclear nonproliferation and control and the other is a virtual reality program for training nuclear material accountancy inspection of light water reactor power plants

  11. Development of Computer-Aided Learning Programs on Nuclear Nonproliferation and Control

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Hyun Chul [Korea Institute of Nuclear Nonproliferation and Control, Daejeon (Korea, Republic of)

    2011-10-15

    The fulfillment of international norms for nuclear nonproliferation is indispensable to the promotion of nuclear energy. The education and training for personnel and mangers related to the nuclear material are one of crucial factors to avoid unintended non-compliance to international norms. Korea Institute of Nuclear Nonproliferation and Control (KINAC) has been providing education and training on nuclear control as its legal duty. One of the legally mandatory educations is 'nuclear control education' performed since 2006 for the observation of the international norms on nuclear nonproliferation and the spread of the nuclear control culture. The other is 'physical protection education' performed since 2010 for maintaining the national physical protection regime effectively and the spread of the nuclear security culture. The 2010 Nuclear Security Summit was held in Washington, DC to enhance international cooperation to prevent nuclear terrorism. During the Summit, the South Korea was chosen to host the second Nuclear Summit in 2012. South Korean President announced that South Korea would share its expertise and support the Summit's mission by setting up an international education and training center on nuclear security in 2014. KINAC is making a full effort to set up the center successfully. An important function of the center is education and training in the subjects of nuclear nonproliferation, nuclear safeguards, nuclear security, and nuclear export/import control. With increasing importance of education and training education on nuclear nonproliferation and control, KINAC has been developing computer-aided learning programs on nuclear nonproliferation and control to overcome the weaknesses in classroom educations. This paper shows two learning programs. One is an e-learning system on the nuclear nonproliferation and control and the other is a virtual reality program for training nuclear material accountancy inspection of light water

  12. Oncology E-Learning for Undergraduate. A Prospective Randomized Controlled Trial.

    Science.gov (United States)

    da Costa Vieira, René Aloisio; Lopes, Ana Helena; Sarri, Almir José; Benedetti, Zuleica Caulada; de Oliveira, Cleyton Zanardo

    2017-06-01

    The e-learning education is a promising method, but there are few prospective randomized publications in oncology. The purpose of this study was to assess the level of retention of information in oncology from undergraduate students of physiotherapy. A prospective, controlled, randomized, crossover study, 72 undergraduate students of physiotherapy, from the second to fourth years, were randomized to perform a course of physiotherapy in oncology (PHO) using traditional classroom or e-learning. Students were offered the same content of the subject. The teacher in the traditional classroom model and the e-learning students used the Articulate® software. The course tackled the main issues related to PHO, and it was divided into six modules, 18 lessons, evaluated by 126 questions. A diagnosis evaluation was performed previous to the course and after every module. The sample consisted of 67 students, allocated in groups A (n = 35) and B (n = 32), and the distribution was homogeneous between the groups. Evaluating the correct answers, we observed a limited score in the pre-test (average grade 44.6 %), which has significant (p e-learning, a fact that encourages the use of e-learning in oncology. REBECU1111-1142-1963.

  13. Stable schizophrenia patients learn equally well as age-matched controls and better than elderly controls in two sensorimotor Rotary Pursuit tasks

    Directory of Open Access Journals (Sweden)

    Livia J. De Picker

    2014-11-01

    Full Text Available Objective: To compare sensorimotor performance and learning in stable schizophrenia patients, healthy age- and sex-matched controls and elderly controls on two variations of the Rotary Pursuit: Circle Pursuit (true motor learning and Figure Pursuit (motor and sequence learning.Method: In the Circle Pursuit a target circle, rotating with increasing speed along a predictable circular path on the computer screen, must be followed by a cursor controlled by a pen on a writing tablet. In the eight-trial Figure Pursuit, subjects learn to draw a complex figure by pursuing the target circle that moves along an invisible trajectory between and around several goals. Tasks were administered thrice (day 1, day 2, day 7 to 30 patients with stable schizophrenia (S, 30 healthy age- and sex-matched controls (C and 30 elderly participants (>65y; E and recorded with a digitizing tablet and pressure-sensitive pen. The outcome measure accuracy (% of time that cursor is within the target was used to assess performance.Results: We observed significant group differences in accuracy, both in Circle and Figure Pursuit tasks (Elearning effects were found in each group. Learning curves were similar in Circle Pursuit but differed between groups in Figure Pursuit. When corrected for group differences in starting level, the learning gains over the three sessions of schizophrenia patients and age-matched controls were equal and both were larger than those of the elderly controls. Conclusion: Despite the reduced sensorimotor performance that was found in the schizophrenia patients their sensorimotor learning seems to be preserved. The relevance of this finding for the evaluation of procedural learning in schizophrenia is discussed. The better performance and learning rate of the patients compared to the elderly controls was unexpected and deserves further study.

  14. Friend or Foe? Flipped Classroom for Undergraduate Electrocardiogram Learning: a Randomized Controlled Study.

    Science.gov (United States)

    Rui, Zeng; Lian-Rui, Xiang; Rong-Zheng, Yue; Jing, Zeng; Xue-Hong, Wan; Chuan, Zuo

    2017-03-07

    Interpreting an electrocardiogram (ECG) is not only one of the most important parts of clinical diagnostics but also one of the most difficult topics to teach and learn. In order to enable medical students to master ECG interpretation skills in a limited teaching period, the flipped teaching method has been recommended by previous research to improve teaching effect on undergraduate ECG learning. A randomized controlled trial for ECG learning was conducted, involving 181 junior-year medical undergraduates using a flipped classroom as an experimental intervention, compared with Lecture-Based Learning (LBL) as a control group. All participants took an examination one week after the intervention by analysing 20 ECGs from actual clinical cases and submitting their ECG reports. A self-administered questionnaire was also used to evaluate the students' attitudes, total learning time, and conditions under each teaching method. The students in the experimental group scored significantly higher than the control group (8.72 ± 1.01 vs 8.03 ± 1.01, t = 4.549, P = 0.000) on ECG interpretation. The vast majority of the students in the flipped classroom group held positive attitudes toward the flipped classroom method and also supported LBL. There was no significant difference (4.07 ± 0.96 vs 4.16 ± 0.89, Z = - 0.948, P = 0.343) between the groups. Prior to class, the students in the flipped class group devoted significantly more time than those in the control group (42.33 ± 22.19 vs 30.55 ± 10.15, t = 4.586, P = 0.000), whereas after class, the time spent by the two groups were not significantly different (56.50 ± 46.80 vs 54.62 ± 31.77, t = 0.317, P = 0.752). Flipped classroom teaching can improve medical students' interest in learning and their self-learning abilities. It is an effective teaching model that needs to be further studied and promoted.

  15. Self-control over combined video feedback and modeling facilitates motor learning.

    Science.gov (United States)

    Post, Phillip G; Aiken, Christopher A; Laughlin, David D; Fairbrother, Jeffrey T

    2016-06-01

    Allowing learners to control the video presentation of knowledge of performance (KP) or an expert model during practice has been shown to facilitate motor learning (Aiken, Fairbrother, & Post, 2012; Wulf, Raupach, & Pfeiffer, 2005). Split-screen replay features now allow for the simultaneous presentation of these modes of instructional support. It is uncertain, however, if such a combination incorporated into a self-control protocol would yield similar benefits seen in earlier self-control studies. Therefore, the purpose of the present study was to examine the effects of self-controlled split-screen replay on the learning of a golf chip shot. Participants completed 60 practice trials, three administrations of the Intrinsic Motivation Inventory, and a questionnaire on day one. Retention and transfer tests and a final motivation inventory were completed on day two. Results revealed significantly higher form and accuracy scores for the self-control group during transfer. The self-control group also had significantly higher scores on the perceived competence subscale, reported requesting feedback mostly after perceived poor trials, and recalled a greater number of critical task features compared to the yoked group. The findings for the performance measures were consistent with previous self-control research. Copyright © 2016 Elsevier B.V. All rights reserved.

  16. Learning effects of dynamic postural control by auditory biofeedback versus visual biofeedback training.

    Science.gov (United States)

    Hasegawa, Naoya; Takeda, Kenta; Sakuma, Moe; Mani, Hiroki; Maejima, Hiroshi; Asaka, Tadayoshi

    2017-10-01

    Augmented sensory biofeedback (BF) for postural control is widely used to improve postural stability. However, the effective sensory information in BF systems of motor learning for postural control is still unknown. The purpose of this study was to investigate the learning effects of visual versus auditory BF training in dynamic postural control. Eighteen healthy young adults were randomly divided into two groups (visual BF and auditory BF). In test sessions, participants were asked to bring the real-time center of pressure (COP) in line with a hidden target by body sway in the sagittal plane. The target moved in seven cycles of sine curves at 0.23Hz in the vertical direction on a monitor. In training sessions, the visual and auditory BF groups were required to change the magnitude of a visual circle and a sound, respectively, according to the distance between the COP and target in order to reach the target. The perceptual magnitudes of visual and auditory BF were equalized according to Stevens' power law. At the retention test, the auditory but not visual BF group demonstrated decreased postural performance errors in both the spatial and temporal parameters under the no-feedback condition. These findings suggest that visual BF increases the dependence on visual information to control postural performance, while auditory BF may enhance the integration of the proprioceptive sensory system, which contributes to motor learning without BF. These results suggest that auditory BF training improves motor learning of dynamic postural control. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Biomechanical Reconstruction Using the Tacit Learning System: Intuitive Control of Prosthetic Hand Rotation.

    Science.gov (United States)

    Oyama, Shintaro; Shimoda, Shingo; Alnajjar, Fady S K; Iwatsuki, Katsuyuki; Hoshiyama, Minoru; Tanaka, Hirotaka; Hirata, Hitoshi

    2016-01-01

    Background: For mechanically reconstructing human biomechanical function, intuitive proportional control, and robustness to unexpected situations are required. Particularly, creating a functional hand prosthesis is a typical challenge in the reconstruction of lost biomechanical function. Nevertheless, currently available control algorithms are in the development phase. The most advanced algorithms for controlling multifunctional prosthesis are machine learning and pattern recognition of myoelectric signals. Despite the increase in computational speed, these methods cannot avoid the requirement of user consciousness and classified separation errors. "Tacit Learning System" is a simple but novel adaptive control strategy that can self-adapt its posture to environment changes. We introduced the strategy in the prosthesis rotation control to achieve compensatory reduction, as well as evaluated the system and its effects on the user. Methods: We conducted a non-randomized study involving eight prosthesis users to perform a bar relocation task with/without Tacit Learning System support. Hand piece and body motions were recorded continuously with goniometers, videos, and a motion-capture system. Findings: Reduction in the participants' upper extremity rotatory compensation motion was monitored during the relocation task in all participants. The estimated profile of total body energy consumption improved in five out of six participants. Interpretation: Our system rapidly accomplished nearly natural motion without unexpected errors. The Tacit Learning System not only adapts human motions but also enhances the human ability to adapt to the system quickly, while the system amplifies compensation generated by the residual limb. The concept can be extended to various situations for reconstructing lost functions that can be compensated.

  18. Machine learning and predictive data analytics enabling metrology and process control in IC fabrication

    Science.gov (United States)

    Rana, Narender; Zhang, Yunlin; Wall, Donald; Dirahoui, Bachir; Bailey, Todd C.

    2015-03-01

    Integrate circuit (IC) technology is going through multiple changes in terms of patterning techniques (multiple patterning, EUV and DSA), device architectures (FinFET, nanowire, graphene) and patterning scale (few nanometers). These changes require tight controls on processes and measurements to achieve the required device performance, and challenge the metrology and process control in terms of capability and quality. Multivariate data with complex nonlinear trends and correlations generally cannot be described well by mathematical or parametric models but can be relatively easily learned by computing machines and used to predict or extrapolate. This paper introduces the predictive metrology approach which has been applied to three different applications. Machine learning and predictive analytics have been leveraged to accurately predict dimensions of EUV resist patterns down to 18 nm half pitch leveraging resist shrinkage patterns. These patterns could not be directly and accurately measured due to metrology tool limitations. Machine learning has also been applied to predict the electrical performance early in the process pipeline for deep trench capacitance and metal line resistance. As the wafer goes through various processes its associated cost multiplies. It may take days to weeks to get the electrical performance readout. Predicting the electrical performance early on can be very valuable in enabling timely actionable decision such as rework, scrap, feedforward, feedback predicted information or information derived from prediction to improve or monitor processes. This paper provides a general overview of machine learning and advanced analytics application in the advanced semiconductor development and manufacturing.

  19. An H(∞) control approach to robust learning of feedforward neural networks.

    Science.gov (United States)

    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. Copyright © 2011 Elsevier Ltd. All rights reserved.

  20. Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems.

    Science.gov (United States)

    Liu, Yan-Jun; Tang, Li; Tong, Shaocheng; Chen, C L Philip; Li, Dong-Juan

    2015-01-01

    Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: 1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and 2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.

  1. Composite Intelligent Learning Control of Strict-Feedback Systems With Disturbance.

    Science.gov (United States)

    Xu, Bin; Sun, Fuchun

    2018-02-01

    This paper addresses the dynamic surface control of uncertain nonlinear systems on the basis of composite intelligent learning and disturbance observer in presence of unknown system nonlinearity and time-varying disturbance. The serial-parallel estimation model with intelligent approximation and disturbance estimation is built to obtain the prediction error and in this way the composite law for weights updating is constructed. The nonlinear disturbance observer is developed using intelligent approximation information while the disturbance estimation is guaranteed to converge to a bounded compact set. The highlight is that different from previous work directly toward asymptotic stability, the transparency of the intelligent approximation and disturbance estimation is included in the control scheme. The uniformly ultimate boundedness stability is analyzed via Lyapunov method. Through simulation verification, the composite intelligent learning with disturbance observer can efficiently estimate the effect caused by system nonlinearity and disturbance while the proposed approach obtains better performance with higher accuracy.

  2. Experimental Learning of Digital Power Controller for Photovoltaic Module Using Proteus VSM

    Directory of Open Access Journals (Sweden)

    Abhijit V. Padgavhankar

    2014-01-01

    Full Text Available The electric power supplied by photovoltaic module depends on light intensity and temperature. It is necessary to control the operating point to draw the maximum power of photovoltaic module. This paper presents the design and implementation of digital power converters using Proteus software. Its aim is to enhance student’s learning for virtual system modeling and to simulate in software for PIC microcontroller along with the hardware design. The buck and boost converters are designed to interface with the renewable energy source that is PV module. PIC microcontroller is used as a digital controller, which senses the PV electric signal for maximum power using sensors and output voltage of the dc-dc converter and according to that switching pulse is generated for the switching of MOSFET. The implementation of proposed system is based on learning platform of Proteus virtual system modeling (VSM and the experimental results are presented.

  3. Ideal gender identity related to parent images and locus of control: Jungian and social learning perspectives.

    Science.gov (United States)

    Shimoda, Hiroko; Keskinen, Soili

    2004-06-01

    In this research, we wanted to clarify how gender images are different or invariant and related to parents, attributes, and the attitude of controlling life (locus of control) in two cultural contexts, Japan and Finland. For this purpose, students' ideal gender images, consisting of ideal mother, female, father and male images, and parents' similarity to the four ideal gender images were studied in 135 Japanese and 119 Finnish university students. Major findings were (a) Japanese students' ideal gender images were more stereotypic than those of Finnish students; (b) students' ideal mother image and parents' similarity to the ideal mother image were related only to their sex, which supports Jung's theory; (c) students socially learned other ideal gender images, but these did not fit with expectation from social learning theory; (d) Japanese students' mothers are models or examples of gender images, but Finnish male students did not seem to base their ideal gender images on their parents. Implication of measures was discussed.

  4. Reinforcement learning solution for HJB equation arising in constrained optimal control problem.

    Science.gov (United States)

    Luo, Biao; Wu, Huai-Ning; Huang, Tingwen; Liu, Derong

    2015-11-01

    The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE and the optimal control policy from real system data. One important feature of the off-policy RL is that its policy evaluation can be realized with data generated by other behavior policies, not necessarily the target policy, which solves the insufficient exploration problem. The convergence of the off-policy RL is proved by demonstrating its equivalence to the successive approximation approach. Its implementation procedure is based on the actor-critic neural networks structure, where the function approximation is conducted with linearly independent basis functions. Subsequently, the convergence of the implementation procedure with function approximation is also proved. Finally, its effectiveness is verified through computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. Switched Two-Level H∞ and Robust Fuzzy Learning Control of an Overhead Crane

    Directory of Open Access Journals (Sweden)

    Kao-Ting Hung

    2013-01-01

    Full Text Available Overhead cranes are typical dynamic systems which can be modeled as a combination of a nominal linear part and a highly nonlinear part. For such kind of systems, we propose a control scheme that deals with each part separately, yet ensures global Lyapunov stability. The former part is readily controllable by the H∞ PDC techniques, and the latter part is compensated by fuzzy mixture of affine constants, leaving the remaining unmodeled dynamics or modeling error under robust learning control using the Nelder-Mead simplex algorithm. Comparison with the adaptive fuzzy control method is given via simulation studies, and the validity of the proposed control scheme is demonstrated by experiments on a prototype crane system.

  6. Robust iterative learning contouring controller with disturbance observer for machine tool feed drives.

    Science.gov (United States)

    Simba, Kenneth Renny; Bui, Ba Dinh; Msukwa, Mathew Renny; Uchiyama, Naoki

    2018-04-01

    In feed drive systems, particularly machine tools, a contour error is more significant than the individual axial tracking errors from the view point of enhancing precision in manufacturing and production systems. The contour error must be within the permissible tolerance of given products. In machining complex or sharp-corner products, large contour errors occur mainly owing to discontinuous trajectories and the existence of nonlinear uncertainties. Therefore, it is indispensable to design robust controllers that can enhance the tracking ability of feed drive systems. In this study, an iterative learning contouring controller consisting of a classical Proportional-Derivative (PD) controller and disturbance observer is proposed. The proposed controller was evaluated experimentally by using a typical sharp-corner trajectory, and its performance was compared with that of conventional controllers. The results revealed that the maximum contour error can be reduced by about 37% on average. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  7. The relationship between strategic control and conscious structural knowledge in artificial grammar learning.

    Science.gov (United States)

    Norman, Elisabeth; Scott, Ryan B; Price, Mark C; Dienes, Zoltan

    2016-05-01

    We address Jacoby's (1991) proposal that strategic control over knowledge requires conscious awareness of that knowledge. In a two-grammar artificial grammar learning experiment all participants were trained on two grammars, consisting of a regularity in letter sequences, while two other dimensions (colours and fonts) varied randomly. Strategic control was measured as the ability to selectively apply the grammars during classification. For each classification, participants also made a combined judgement of (a) decision strategy and (b) relevant stimulus dimension. Strategic control was found for all types of decision strategy, including trials where participants claimed to lack conscious structural knowledge. However, strong evidence of strategic control only occurred when participants knew or guessed that the letter dimension was relevant, suggesting that strategic control might be associated with - or even causally requires - global awareness of the nature of the rules even though it does not require detailed knowledge of their content. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  8. Impact of a Differential Learning Approach on Practical Exam Performance: A Controlled Study in a Preclinical Dental Course.

    Science.gov (United States)

    Pabel, Sven-Olav; Pabel, Anne-Kathrin; Schmickler, Jan; Schulz, Xenia; Wiegand, Annette

    2017-09-01

    The aim of this study was to evaluate if differential learning in a preclinical dental course impacted the performance of dental students in a practical exam (preparation of a gold partial crown) immediately after the training session and 20 weeks later compared to conventional learning. This controlled study was performed in a preclinical course in operative dentistry at a dental school in Germany. Third-year students were trained in preparing gold partial crowns by using either the conventional learning (n=41) or the differential learning approach (n=32). The differential learning approach consisted of 20 movement exercises with a continuous change of movement execution during the learning session, while the conventional learning approach was mainly based on repetition, a methodological series of exercises, and correction of preparations during the training phase. Practical exams were performed immediately after the training session (T1) and 20 weeks later (T2, retention test). Preparations were rated by four independent and blinded examiners. At T1, no significant difference between the performance (exam passed) of the two groups was detected (conventional learning: 54.3%, differential learning: 68.0%). At T2, significantly more students passed the exam when trained by the differential learning approach (68.8%) than by the conventional learning approach (18.9%). Interrater reliability was moderate (Kappa: 0.57, T1) or substantial (Kappa: 0.67, T2), respectively. These results suggest that a differential learning approach can increase the manual skills of dental students.

  9. Lessons learned in digital upgrade projects digital control system implementation at US nuclear power stations

    International Nuclear Information System (INIS)

    Kelley, S.; Bolian, T. W.

    2006-01-01

    AREVA NP has gained significant experience during the past five years in digital upgrades at operating nuclear power stations in the US. Plants are seeking modernization with digital technology to address obsolescence, spare parts availability, vendor support, increasing age-related failures and diminished reliability. New systems offer improved reliability and functionality, and decreased maintenance requirements. Significant lessons learned have been identified relating to the areas of licensing, equipment qualification, software quality assurance and other topics specific to digital controls. Digital control systems have been installed in non safety-related control applications at many utilities within the last 15 years. There have also been a few replacements of small safety-related systems with digital technology. Digital control systems are proving to be reliable, accurate, and easy to maintain. Digital technology is gaining acceptance and momentum with both utilities and regulatory agencies based upon the successes of these installations. Also, new plants are being designed with integrated digital control systems. To support plant life extension and address obsolescence of critical components, utilities are beginning to install digital technology for primary safety-system replacement. AREVA NP analyzed operating experience and lessons learned from its own digital upgrade projects as well as industry-wide experience to identify key issues that should be considered when implementing digital controls in nuclear power stations

  10. Implementing blended learning in emergency airway management training: a randomized controlled trial.

    Science.gov (United States)

    Kho, Madeleine Huei Tze; Chew, Keng Sheng; Azhar, Muhaimin Noor; Hamzah, Mohd Lotfi; Chuah, Kee Man; Bustam, Aida; Chan, Hiang Chuan

    2018-01-15

    While emergency airway management training is conventionally conducted via face-to-face learning (F2FL) workshops, there are inherent cost, time, place and manpower limitations in running such workshops. Blended learning (BL) refers to the systematic integration of online and face-to-face learning aimed to facilitate complex thinking skills and flexible participation at a reduced financial, time and manpower cost. This study was conducted to evaluate its effectiveness in emergency airway management training. A single-center prospective randomised controlled trial involving 30 doctors from Sarawak General Hospital, Malaysia was conducted from September 2016 to February 2017 to compare the effectiveness of BL versus F2FL for emergency airway management training. Participants in the BL arm were given a period of 12 days to go through the online materials in a learning management system while those in the F2FL arm attended a-day of face-to-face lectures (8 h). Participants from both arms then attended a day of hands-on session consisting of simulation skills training with airway manikins. Pre- and post-tests in knowledge and practical skills were administered. E-learning experience and the perception towards BL among participants in the BL arm were also assessed. Significant improvements in post-test scores as compared to pre-test scores were noted for participants in both BL and F2FL arms for knowledge, practical, and total scores. The degree of increment between the BL group and the F2FL arms for all categories were not significantly different (total scores: 35 marks, inter-quartile range (IQR) 15.0 - 41.0 vs. 31 marks, IQR 24.0 - 41.0, p = 0.690; theory scores: 18 marks, IQR 9 - 24 vs. 19 marks, IQR 15 - 20, p = 0.992; practical scores: 11 marks, IQR 5 -18 vs. 10 marks, IQR 9 - 20, p = 0.461 respectively). The overall perception towards BL was positive. Blended learning is as effective as face-to-face learning for emergency airway management training

  11. Request Stream Control for the Access to Broadband Multimedia Educational Resources in the Distance Learning System

    Directory of Open Access Journals (Sweden)

    Irina Pavlovna Bolodurina

    2013-10-01

    Full Text Available This article presents a model of queuing system for broadband multimedia educational resources, as well as a model of access to a hybrid cloud system storage. These models are used to enhance the efficiency of computing resources in a distance learning system. An additional OpenStack control module has been developed to achieve the distribution of request streams and balance the load between cloud nodes.

  12. Advanced motion control for next-generation precision mechatronics: Challenges for control, identification, and learning

    NARCIS (Netherlands)

    Oomen, Tom

    2017-01-01

    Manufacturing equipment and scientific instruments, including wafer scanners, printers, microscopes, and medical imaging scanners, require accurate and fast motions. Increasing requirements necessitate enhanced control performance. The aim of this paper is to identify several challenges for advanced

  13. Event-Triggered Distributed Control of Nonlinear Interconnected Systems Using Online Reinforcement Learning With Exploration.

    Science.gov (United States)

    Narayanan, Vignesh; Jagannathan, Sarangapani

    2017-09-07

    In this paper, a distributed control scheme for an interconnected system composed of uncertain input affine nonlinear subsystems with event triggered state feedback is presented by using a novel hybrid learning scheme-based approximate dynamic programming with online exploration. First, an approximate solution to the Hamilton-Jacobi-Bellman equation is generated with event sampled neural network (NN) approximation and subsequently, a near optimal control policy for each subsystem is derived. Artificial NNs are utilized as function approximators to develop a suite of identifiers and learn the dynamics of each subsystem. The NN weight tuning rules for the identifier and event-triggering condition are derived using Lyapunov stability theory. Taking into account, the effects of NN approximation of system dynamics and boot-strapping, a novel NN weight update is presented to approximate the optimal value function. Finally, a novel strategy to incorporate exploration in online control framework, using identifiers, is introduced to reduce the overall cost at the expense of additional computations during the initial online learning phase. System states and the NN weight estimation errors are regulated and local uniformly ultimately bounded results are achieved. The analytical results are substantiated using simulation studies.

  14. The effect of learner's control of self-observation strategies on learning of front crawl.

    Science.gov (United States)

    Marques, Priscila Garcia; Corrêa, Umberto Cesar

    2016-02-01

    This study investigated the effect of learner's control of self-observation strategies on motor skill learning. For this purpose, beginner and intermediate learner swimmers practised the front crawl. Seventy college students took part in this experiment. They comprised 40 novice learners, both male (n=19) and female (n=21), with an average age of 20.7 years (±0.44), and 30 intermediate learners, both male (n=17) and female (n=13), with an average age of 21.1 years (±0.86). The design involved a pretest (one day), four acquisition sessions (four days), and a retention test (one day). They were divided into three groups: (1) choice, which could choose to watch a video with their best or overall performance during practise; (2) yoked, which were paired to those of the choice group; and (3) control (did not watch any video). The measures included the performance of front crawl and self-efficacy. The results showed that: (1) beginners who chose a type of observation strategy had superior motor skill learning; (2) for intermediate learners, self-observation promoted better motor learning, regardless of the control of choices; (3) self-observation improved self-efficacy beliefs. Copyright © 2016 Elsevier B.V. All rights reserved.

  15. Learning-based traffic signal control algorithms with neighborhood information sharing: An application for sustainable mobility

    Energy Technology Data Exchange (ETDEWEB)

    Aziz, H. M. Abdul [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Zhu, Feng [Purdue University, West Lafayette, IN (United States). Lyles School of Civil Engineering; Ukkusuri, Satish V. [Purdue University, West Lafayette, IN (United States). Lyles School of Civil Engineering

    2017-10-04

    Here, this research applies R-Markov Average Reward Technique based reinforcement learning (RL) algorithm, namely RMART, for vehicular signal control problem leveraging information sharing among signal controllers in connected vehicle environment. We implemented the algorithm in a network of 18 signalized intersections and compare the performance of RMART with fixed, adaptive, and variants of the RL schemes. Results show significant improvement in system performance for RMART algorithm with information sharing over both traditional fixed signal timing plans and real time adaptive control schemes. Additionally, the comparison with reinforcement learning algorithms including Q learning and SARSA indicate that RMART performs better at higher congestion levels. Further, a multi-reward structure is proposed that dynamically adjusts the reward function with varying congestion states at the intersection. Finally, the results from test networks show significant reduction in emissions (CO, CO2, NOx, VOC, PM10) when RL algorithms are implemented compared to fixed signal timings and adaptive schemes.

  16. Online learning control using adaptive critic designs with sparse kernel machines.

    Science.gov (United States)

    Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

    2013-05-01

    In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

  17. Efficacy of the LiSN & Learn auditory training software: randomized blinded controlled study

    Directory of Open Access Journals (Sweden)

    Sharon Cameron

    2012-09-01

    Full Text Available Children with a spatial processing disorder (SPD require a more favorable signal-to-noise ratio in the classroom because they have difficulty perceiving sound source location cues. Previous research has shown that a novel training program - LiSN & Learn - employing spatialized sound, overcomes this deficit. Here we investigate whether improvements in spatial processing ability are specific to the LiSN & Learn training program. Participants were ten children (aged between 6;0 [years;months] and 9;9 with normal peripheral hearing who were diagnosed as having SPD using the Listening in Spatialized Noise - Sentences test (LiSN-S. In a blinded controlled study, the participants were randomly allocated to train with either the LiSN & Learn or another auditory training program - Earobics - for approximately 15 min per day for twelve weeks. There was a significant improvement post-training on the conditions of the LiSN-S that evaluate spatial processing ability for the LiSN & Learn group (P=0.03 to 0.0008, η 2=0.75 to 0.95, n=5, but not for the Earobics group (P=0.5 to 0.7, η 2=0.1 to 0.04, n=5. Results from questionnaires completed by the participants and their parents and teachers revealed improvements in real-world listening performance post-training were greater in the LiSN & Learn group than the Earobics group. LiSN & Learn training improved binaural processing ability in children with SPD, enhancing their ability to understand speech in noise. Exposure to non-spatialized auditory training does not produce similar outcomes, emphasizing the importance of deficit-specific remediation.

  18. Efficacy of the LiSN & Learn Auditory Training Software: randomized blinded controlled study

    Directory of Open Access Journals (Sweden)

    Sharon Cameron

    2012-01-01

    Full Text Available Background: Children with a spatial processing disorder (SPD require a more favorable signal-to-noise ratio in the classroom because they have difficulty perceiving sound source location cues. Previous research has shown that a novel training program - LiSN & Learn - employing spatialized sound, overcomes this deficit. Here we investigate whether improvements in spatial processing ability are specific to the LiSN & Learn training program. Materials and methods: Participants were ten children (aged between 6;0 [years;months] and 9;9 with normal peripheral hearing who were diagnosed as having SPD using the Listening in Spatialized Noise – Sentences Test (LISN-S. In a blinded controlled study, the participants were randomly allocated to train with either the LiSN & Learn or another auditory training program – Earobics - for approximately 15 minutes per day for twelve weeks. Results: There was a significant improvement post-training on the conditions of the LiSN-S that evaluate spatial processing ability for the LiSN & Learn group (p=0.03 to 0.0008, η2=0.75 to 0.95, n=5, but not for the Earobics group (p=0.5 to 0.7, η2=0.1 to 0.04, n=5. Results from questionnaires completed by the participants and their parents and teachers revealed improvements in real-world listening performance post-training were greater in the LiSN & Learn group than the Earobics group. Conclusions: LiSN & Learn training improved binaural processing ability in children with SPD, enhancing their ability to understand speech in noise. Exposure to non-spatialized auditory training does not produce similar outcomes, emphasizing the importance of deficit-specific remediation.

  19. Assessing the Effectiveness of Case-Based Collaborative Learning via Randomized Controlled Trial.

    Science.gov (United States)

    Krupat, Edward; Richards, Jeremy B; Sullivan, Amy M; Fleenor, Thomas J; Schwartzstein, Richard M

    2016-05-01

    Case-based collaborative learning (CBCL) is a novel small-group approach that borrows from team-based learning principles and incorporates elements of problem-based learning (PBL) and case-based learning. CBCL includes a preclass readiness assurance process and case-based in-class activities in which students respond to focused, open-ended questions individually, discuss their answers in groups of 4, and then reach consensus in larger groups of 16. This study introduces CBCL and assesses its effectiveness in one course at Harvard Medical School. In a 2013 randomized controlled trial, 64 medical and dental student volunteers were assigned randomly to one of four 8-person PBL tutorial groups (control; n = 32) or one of two 16-person CBCL tutorial groups (experimental condition; n = 32) as part of a required first-year physiology course. Outcomes for the PBL and CBCL groups were compared using final exam scores, student responses to a postcourse survey, and behavioral coding of portions of video-recorded class sessions. Overall, the course final exam scores for CBCL and PBL students were not significantly different. However, CBCL students whose mean exam performance in prior courses was below the participant median scored significantly higher than their PBL counterparts on the physiology course final exam. The most common adjectives students used to describe CBCL were "engaging," "fun," and "thought-provoking." Coding of observed behaviors indicated that individual affect was significantly higher in the CBCL groups than in the PBL groups. CBCL is a viable, engaging, active learning method. It may particularly benefit students with lower academic performance.

  20. A Scalable Neuro-inspired Robot Controller Integrating a Machine Learning Algorithm and a Spiking Cerebellar-like Network

    DEFF Research Database (Denmark)

    Baira Ojeda, Ismael; Tolu, Silvia; Lund, Henrik Hautop

    2017-01-01

    Combining Fable robot, a modular robot, with a neuroinspired controller, we present the proof of principle of a system that can scale to several neurally controlled compliant modules. The motor control and learning of a robot module are carried out by a Unit Learning Machine (ULM) that embeds...... the Locally Weighted Projection Regression algorithm (LWPR) and a spiking cerebellar-like microcircuit. The LWPR guarantees both an optimized representation of the input space and the learning of the dynamic internal model (IM) of the robot. However, the cerebellar-like sub-circuit integrates LWPR input...

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

    Science.gov (United States)

    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.

  2. Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network

    Directory of Open Access Journals (Sweden)

    Houli Duan

    2010-01-01

    Full Text Available We propose a new multiobjective control algorithm based on reinforcement learning for urban traffic signal control, named multi-RL. A multiagent structure is used to describe the traffic system. A vehicular ad hoc network is used for the data exchange among agents. A reinforcement learning algorithm is applied to predict the overall value of the optimization objective given vehicles' states. The policy which minimizes the cumulative value of the optimization objective is regarded as the optimal one. In order to make the method adaptive to various traffic conditions, we also introduce a multiobjective control scheme in which the optimization objective is selected adaptively to real-time traffic states. The optimization objectives include the vehicle stops, the average waiting time, and the maximum queue length of the next intersection. In addition, we also accommodate a priority control to the buses and the emergency vehicles through our model. The simulation results indicated that our algorithm could perform more efficiently than traditional traffic light control methods.

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

    Science.gov (United States)

    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.

  4. Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration

    Science.gov (United States)

    Arenas, Ana M.; Sun, Tingxiao

    2018-01-01

    Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able to provide anticipatory control, since they interface directly with muscle signals, but typically require expert placement of sensors on muscle bodies. We present an implementation of an adaptive sEMG-based exoskeleton controller that learns a mapping between muscle activation and the desired system state during interaction with a user, generating a personalized sEMG feature classifier to allow for anticipatory control. This system is robust to novice placement of sEMG sensors, as well as subdermal muscle shifts. We validate this method with 18 subjects using a thumb exoskeleton to complete a book-placement task. This learning-from-demonstration system for exoskeleton control allows for very short training times, as well as the potential for improvement in intent recognition over time, and adaptation to physiological changes in the user, such as those due to fatigue. PMID:29401754

  5. Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration

    Directory of Open Access Journals (Sweden)

    Ho Chit Siu

    2018-02-01

    Full Text Available Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG, but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able to provide anticipatory control, since they interface directly with muscle signals, but typically require expert placement of sensors on muscle bodies. We present an implementation of an adaptive sEMG-based exoskeleton controller that learns a mapping between muscle activation and the desired system state during interaction with a user, generating a personalized sEMG feature classifier to allow for anticipatory control. This system is robust to novice placement of sEMG sensors, as well as subdermal muscle shifts. We validate this method with 18 subjects using a thumb exoskeleton to complete a book-placement task. This learning-from-demonstration system for exoskeleton control allows for very short training times, as well as the potential for improvement in intent recognition over time, and adaptation to physiological changes in the user, such as those due to fatigue.

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

    Science.gov (United States)

    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.

  7. Flyback CCM inverter for AC module applications: iterative learning control and convergence analysis

    Science.gov (United States)

    Lee, Sung-Ho; Kim, Minsung

    2017-12-01

    This paper presents an iterative learning controller (ILC) for an interleaved flyback inverter operating in continuous conduction mode (CCM). The flyback CCM inverter features small output ripple current, high efficiency, and low cost, and hence it is well suited for photovoltaic power applications. However, it exhibits the non-minimum phase behaviour, because its transfer function from control duty to output current has the right-half-plane (RHP) zero. Moreover, the flyback CCM inverter suffers from the time-varying grid voltage disturbance. Thus, conventional control scheme results in inaccurate output tracking. To overcome these problems, the ILC is first developed and applied to the flyback inverter operating in CCM. The ILC makes use of both predictive and current learning terms which help the system output to converge to the reference trajectory. We take into account the nonlinear averaged model and use it to construct the proposed controller. It is proven that the system output globally converges to the reference trajectory in the absence of state disturbances, output noises, or initial state errors. Numerical simulations are performed to validate the proposed control scheme, and experiments using 400-W AC module prototype are carried out to demonstrate its practical feasibility.

  8. Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.

    Science.gov (United States)

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

    2012-01-01

    Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.

  9. Discrete-time online learning control for a class of unknown nonaffine nonlinear systems using reinforcement learning.

    Science.gov (United States)

    Yang, Xiong; Liu, Derong; Wang, Ding; Wei, Qinglai

    2014-07-01

    In this paper, a reinforcement-learning-based direct adaptive control is developed to deliver a desired tracking performance for a class of discrete-time (DT) nonlinear systems with unknown bounded disturbances. We investigate multi-input-multi-output unknown nonaffine nonlinear DT systems and employ two neural networks (NNs). By using Implicit Function Theorem, an action NN is used to generate the control signal and it is also designed to cancel the nonlinearity of unknown DT systems, for purpose of utilizing feedback linearization methods. On the other hand, a critic NN is applied to estimate the cost function, which satisfies the recursive equations derived from heuristic dynamic programming. The weights of both the action NN and the critic NN are directly updated online instead of offline training. By utilizing Lyapunov's direct method, the closed-loop tracking errors and the NN estimated weights are demonstrated to be uniformly ultimately bounded. Two numerical examples are provided to show the effectiveness of the present approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. Case-based learning and simulation: useful tools to enhance nurses' education? Nonrandomized controlled trial.

    Science.gov (United States)

    Raurell-Torredà, Marta; Olivet-Pujol, Josep; Romero-Collado, Àngel; Malagon-Aguilera, Maria Carmen; Patiño-Masó, Josefina; Baltasar-Bagué, Alícia

    2015-01-01

    To compare skills acquired by undergraduate nursing students enrolled in a medical-surgical course. To compare skills demonstrated by students with no previous clinical practice (undergraduates) and nurses with clinical experience enrolled in continuing professional education (CPE). In a nonrandomized clinical trial, 101 undergraduates enrolled in the "Adult Patients 1" course were assigned to the traditional lecture and discussion (n = 66) or lecture and discussion plus case-based learning (n = 35) arm of the study; 59 CPE nurses constituted a comparison group to assess the effects of previous clinical experience on learning outcomes. Scores on an objective structured clinical examination (OSCE), using a human patient simulator and cases validated by the National League for Nursing, were compared for the undergraduate control and intervention groups, and for CPE nurses (Student's t test). Controls scored lower than the intervention group on patient assessment (6.3 ± 2.3 vs 7.5 ± 1.4, p = .04, mean difference, -1.2 [95% confidence interval (CI) -2.4 to -0.03]) but the intervention group did not differ from CPE nurses (7.5 ± 1.4 vs 8.8 ± 1.5, p = .06, mean difference, -1.3 [95% CI -2.6 to 0.04]). The CPE nurses committed more "rules-based errors" than did undergraduates, specifically patient identifications (77.2% vs 55%, p = .7) and checking allergies before administering medication (68.2% vs 60%, p = .1). The intervention group developed better patient assessment skills than the control group. Case-based learning helps to standardize the process, which can contribute to quality and consistency in practice: It is essential to correctly identify a problem in order to treat it. Clinical experience of CPE nurses was not associated with better adherence to safety protocols. Case-based learning improves the patient assessment skills of undergraduate nursing students, thereby preparing them for clinical practice. © 2014 Sigma Theta Tau International.

  11. Degraded expression of learned feedforward control in movements released by startle.

    Science.gov (United States)

    Wright, Zachary A; Carlsen, Anthony N; MacKinnon, Colum D; Patton, James L

    2015-08-01

    Recent work has shown that preplanned motor programs can be rapidly released via fast conducting pathways using a startling acoustic stimulus. Our question was whether the startle-elicited response might also release a recently learned internal model, which draws on experience to predict and compensate for expected perturbations in a feedforward manner. Our initial investigation using adaptation to robotically produced forces showed some evidence of this, but the results were potentially confounded by co-contraction caused by startle. In this study, we eliminated this confound by asking subjects to make reaching movements in the presence of a visual distortion. Results show that a startle stimulus (1) decreased performance of the recently learned task and (2) reduced after-effect magnitude. Since the recall of learned control was reduced, but not eliminated during startle trials, we suggest that multiple neural centers (cortical and subcortical) are involved in such learning and adaptation. These findings have implications for motor training in areas such as piloting, teleoperation, sports, and rehabilitation.

  12. Deep learning and model predictive control for self-tuning mode-locked lasers

    Science.gov (United States)

    Baumeister, Thomas; Brunton, Steven L.; Nathan Kutz, J.

    2018-03-01

    Self-tuning optical systems are of growing importance in technological applications such as mode-locked fiber lasers. Such self-tuning paradigms require {\\em intelligent} algorithms capable of inferring approximate models of the underlying physics and discovering appropriate control laws in order to maintain robust performance for a given objective. In this work, we demonstrate the first integration of a {\\em deep learning} (DL) architecture with {\\em model predictive control} (MPC) in order to self-tune a mode-locked fiber laser. Not only can our DL-MPC algorithmic architecture approximate the unknown fiber birefringence, it also builds a dynamical model of the laser and appropriate control law for maintaining robust, high-energy pulses despite a stochastically drifting birefringence. We demonstrate the effectiveness of this method on a fiber laser which is mode-locked by nonlinear polarization rotation. The method advocated can be broadly applied to a variety of optical systems that require robust controllers.

  13. Effects of errorless skill learning in people with mild-to-moderate or severe dementia: a randomized controlled pilot study.

    NARCIS (Netherlands)

    Kessels, R.P.C.; Hensken, L.M.

    2009-01-01

    This pilot study examines whether learning without errors is advantageous compared to trial-and-error learning in people with dementia using a procedural task and a randomized case-control design. A sample of 60 people was recruited, consisting of 20 patients with severe dementia, 20 patients with

  14. Effects of errorless skill learning in people with mild-to-moderate or severe dementia: A randomized controlled pilot study

    NARCIS (Netherlands)

    Kessels, R.P.C.; Olde Hensken, L.M.G.

    2009-01-01

    This pilot study examines whether learning without errors is advantageous compared to trial-and-error learning in people with dementia using a procedural task and a randomized case-control design. A sample of 60 people was recruited, consisting of 20 patients with severe dementia, 20 patients with

  15. LOCUS OF CONTROL AND LEARNED HELPLESSNESS PHENOMENON IN PATIENTS WITH CHRONIC INTERNAL DISEASES

    Directory of Open Access Journals (Sweden)

    Grekhov R.A.

    2016-04-01

    Full Text Available The article discloses the concept of locus of control (or the level of subjective control, the phenomenon of learned helplessness in the framework of psychosomatic medicine, and their impact on the efficacy of treatment process. The data on the impact of these factors on the daily living and emotional state of patients, their interpersonal and social relationships, the reasons for the formation of learned helplessness are listened. The alternative psychophysiological treatment methods for emotional and behavioral disorders in psychosomatic diseases, in particular the effectiveness of biofeedback therapy in different types of physical pathology, which opens up the possibility of the patient to implement self-regulation mechanisms are presented. Biofeedback is a practically single psychophysiological evidence-based method of alternative medicine and it is regarded as a branch of behavioral therapy, which aims not only to the regulation of psychophysiological state, but also to shift the external locus of control to the inside. During the application of biofeedback, developed “functional system of self-regulation” form its perfect result. Biofeedback is the process of achieving a greater patient’s awareness of many physiological functions of his body, primarily with the use of tools that provide him with information on his activities, in order to obtain the possibility to manage the systems of his body by his own discretion. The probable mechanism of therapeutic action is the cognitive effect of biofeedback experiences, learning skills of self-control which patients had never happened before. The faith of the patient in his ability to control the symptoms of the disease is considered as a critical value, not a degree of measurable physiological changes.

  16. Internet-Based Assessment of Oncology Health Care Professional Learning Style and Optimization of Materials for Web-Based Learning: Controlled Trial With Concealed Allocation.

    Science.gov (United States)

    Micheel, Christine M; Anderson, Ingrid A; Lee, Patricia; Chen, Sheau-Chiann; Justiss, Katy; Giuse, Nunzia B; Ye, Fei; Kusnoor, Sheila V; Levy, Mia A

    2017-07-25

    Precision medicine has resulted in increasing complexity in the treatment of cancer. Web-based educational materials can help address the needs of oncology health care professionals seeking to understand up-to-date treatment strategies. This study aimed to assess learning styles of oncology health care professionals and to determine whether learning style-tailored educational materials lead to enhanced learning. In all, 21,465 oncology health care professionals were invited by email to participate in the fully automated, parallel group study. Enrollment and follow-up occurred between July 13 and September 7, 2015. Self-enrolled participants took a learning style survey and were assigned to the intervention or control arm using concealed alternating allocation. Participants in the intervention group viewed educational materials consistent with their preferences for learning (reading, listening, and/or watching); participants in the control group viewed educational materials typical of the My Cancer Genome website. Educational materials covered the topic of treatment of metastatic estrogen receptor-positive (ER+) breast cancer using cyclin-dependent kinases 4/6 (CDK4/6) inhibitors. Participant knowledge was assessed immediately before (pretest), immediately after (posttest), and 2 weeks after (follow-up test) review of the educational materials. Study statisticians were blinded to group assignment. A total of 751 participants enrolled in the study. Of these, 367 (48.9%) were allocated to the intervention arm and 384 (51.1%) were allocated to the control arm. Of those allocated to the intervention arm, 256 (69.8%) completed all assessments. Of those allocated to the control arm, 296 (77.1%) completed all assessments. An additional 12 participants were deemed ineligible and one withdrew. Of the 552 participants, 438 (79.3%) self-identified as multimodal learners. The intervention arm showed greater improvement in posttest score compared to the control group (0.4 points

  17. Improving the Critic Learning for Event-Based Nonlinear $H_{\\infty }$ Control Design.

    Science.gov (United States)

    Wang, Ding; He, Haibo; Liu, Derong

    2017-10-01

    In this paper, we aim at improving the critic learning criterion to cope with the event-based nonlinear H ∞ state feedback control design. First of all, the H ∞ control problem is regarded as a two-player zero-sum game and the adaptive critic mechanism is used to achieve the minimax optimization under event-based environment. Then, based on an improved updating rule, the event-based optimal control law and the time-based worst-case disturbance law are obtained approximately by training a single critic neural network. The initial stabilizing control is no longer required during the implementation process of the new algorithm. Next, the closed-loop system is formulated as an impulsive model and its stability issue is handled by incorporating the improved learning criterion. The infamous Zeno behavior of the present event-based design is also avoided through theoretical analysis on the lower bound of the minimal intersample time. Finally, the applications to an aircraft dynamics and a robot arm plant are carried out to verify the efficient performance of the present novel design method.

  18. QFT Based Robust Positioning Control of the PMSM Using Automatic Loop Shaping with Teaching Learning Optimization

    Directory of Open Access Journals (Sweden)

    Nitish Katal

    2016-01-01

    Full Text Available Automation of the robust control system synthesis for uncertain systems is of great practical interest. In this paper, the loop shaping step for synthesizing quantitative feedback theory (QFT based controller for a two-phase permanent magnet stepper motor (PMSM has been automated using teaching learning-based optimization (TLBO algorithm. The QFT controller design problem has been posed as an optimization problem and TLBO algorithm has been used to minimize the proposed cost function. This facilitates designing low-order fixed-structure controller, eliminates the need of manual loop shaping step on the Nichols charts, and prevents the overdesign of the controller. A performance comparison of the designed controller has been made with the classical PID tuning method of Ziegler-Nichols and QFT controller tuned using other optimization algorithms. The simulation results show that the designed QFT controller using TLBO offers robust stability, disturbance rejection, and proper reference tracking over a range of PMSM’s parametric uncertainties as compared to the classical design techniques.

  19. The design of instructional tools affects secondary school students' learning of cardiopulmonary resuscitation (CPR) in reciprocal peer learning: a randomized controlled trial.

    Science.gov (United States)

    Iserbyt, Peter; Byra, Mark

    2013-11-01

    Research investigating design effects of instructional tools for learning Basic Life Support (BLS) is almost non-existent. To demonstrate the design of instructional tools matter. The effect of spatial contiguity, a design principle stating that people learn more deeply when words and corresponding pictures are placed close (i.e., integrated) rather than far from each other on a page was investigated on task cards for learning Cardiopulmonary Resuscitation (CPR) during reciprocal peer learning. A randomized controlled trial. A total of 111 students (mean age: 13 years) constituting six intact classes learned BLS through reciprocal learning with task cards. Task cards combine a picture of the skill with written instructions about how to perform it. In each class, students were randomly assigned to the experimental group or the control. In the control, written instructions were placed under the picture on the task cards. In the experimental group, written instructions were placed close to the corresponding part of the picture on the task cards reflecting application of the spatial contiguity principle. One-way analysis of variance found significantly better performances in the experimental group for ventilation volumes (P=.03, ηp2=.10) and flow rates (P=.02, ηp2=.10). For chest compression depth, compression frequency, compressions with correct hand placement, and duty cycles no significant differences were found. This study shows that the design of instructional tools (i.e., task cards) affects student learning. Research-based design of learning tools can enhance BLS and CPR education. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  20. Team Leader Structuring for Team Effectiveness and Team Learning in Command-and-Control Teams

    Science.gov (United States)

    van der Haar, Selma; Koeslag-Kreunen, Mieke; Euwe, Eline; Segers, Mien

    2017-01-01

    Due to their crucial and highly consequential task, it is of utmost importance to understand the levers leading to effectiveness of multidisciplinary emergency management command-and-control (EMCC) teams. We argue that the formal EMCC team leader needs to initiate structure in the team meetings to support organizing the work as well as facilitate team learning, especially the team learning process of constructive conflict. In a sample of 17 EMCC teams performing a realistic EMCC exercise, including one or two team meetings (28 in sum), we coded the team leader’s verbal structuring behaviors (1,704 events), rated constructive conflict by external experts, and rated team effectiveness by field experts. Results show that leaders of effective teams use structuring behaviors more often (except asking procedural questions) but decreasingly over time. They support constructive conflict by clarifying and by making summaries that conclude in a command or decision in a decreasing frequency over time. PMID:28490856

  1. Team Leader Structuring for Team Effectiveness and Team Learning in Command-and-Control Teams.

    Science.gov (United States)

    van der Haar, Selma; Koeslag-Kreunen, Mieke; Euwe, Eline; Segers, Mien

    2017-04-01

    Due to their crucial and highly consequential task, it is of utmost importance to understand the levers leading to effectiveness of multidisciplinary emergency management command-and-control (EMCC) teams. We argue that the formal EMCC team leader needs to initiate structure in the team meetings to support organizing the work as well as facilitate team learning, especially the team learning process of constructive conflict. In a sample of 17 EMCC teams performing a realistic EMCC exercise, including one or two team meetings (28 in sum), we coded the team leader's verbal structuring behaviors (1,704 events), rated constructive conflict by external experts, and rated team effectiveness by field experts. Results show that leaders of effective teams use structuring behaviors more often (except asking procedural questions) but decreasingly over time. They support constructive conflict by clarifying and by making summaries that conclude in a command or decision in a decreasing frequency over time.

  2. Self-controlled learning: The importance of protecting perceptions of competence

    Directory of Open Access Journals (Sweden)

    Suzete eChiviacowsky

    2012-11-01

    Full Text Available Recent studies examining the role of self-controlled feedback have shown that learners ask for feedback after what they believe was a good rather than poor trial. Also, trials on which participants request feedback are often more accurate than those without feedback. The present study examined whether manipulating participants’ perception of good performance would have differential effects on learning. All participants practiced a coincident-anticipation timing task with a self-controlled feedback schedule during practice. Specifically, they were able to ask for feedback after 3 trials in each of 3 10-trial practice blocks. While one group (Self-30 was told that an error of 30 ms or less would be considered good performance, another group (Self-4 was informed that an error of 4 ms or less would be considered a good trial. A third, self-control group (Self did not receive any information about what constituted good performance. The results showed that participants of all groups asked for feedback primarily after relatively good trials. At the end of practice, both the Self-30 and Self groups demonstrated greater perceived competence and self-efficacy than the Self-4 group. The Self-30 and Self groups also performed with greater accuracy and less variability on retention and transfer tests (non-dominant hand one day later. The present findings indicated that the typical learning benefits of self-controlled practice can be thwarted by depriving learners of the opportunity of experiencing competence through good performance. They add to the accumulating evidence of motivational influences on motor learning.

  3. Biomechanical procedure to assess sleep restriction on motor control and learning.

    Science.gov (United States)

    Umemura, G S; Noriega, C L; Soares, D F; Forner-Cordero, A

    2017-07-01

    The analysis of sleep quality during long periods and its impact on motor control and learning performance are crucial aspects for human health. The aim of this study is to analyze effects of chronic sleep restriction on motor performance. It is intended to establish motor control indicators in sleep quality analysis. A wearable actigraphy that records accelerometry, ambient light, and body temperature was used to monitor the sleep habits of 12 healthy subjects for two weeks before performing motor control and learning tests. The day of the motor test, the subjects filled two questionnaires about the quality of sleep (Pittsburgh Sleep Quality Index - PSQI) and sleepiness (Epworth Sleepiness Scale - ESS). Afterwards they performed a coincident timing task that consisted of hitting a virtual target falling on the screen with the hand. An elbow flexion in the horizontal plane had to be performed on the correct time to reach the real target on a table at the same time as the virtual target on the screen. The subjects performed three sets of acquisition and transfer blocks of the coincident timing task. The subjects were clustered in two groups based on the PSQI and ESS scores. Actigraphy and motor control parameters (L5, correct responses, time variance) were compared between groups and experimental sets. The group with better sleep parameters did show a constant performance across blocks of task acquisition while the bad sleeper group improved from the first to the second acquisition block. Despite of this improvement, their performance is not better than the one of the good sleepers group. Although the number of subjects is low and it should be increased, these results indicate that the subjects with better sleep converged rapidly to a high level of performance, while the worse sleepers needed more trials to learn the task and their performance was not superior to the other group.

  4. From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control.

    Science.gov (United States)

    Grossberg, Stephen

    2015-09-24

    This article provides an overview of neural models of synaptic learning and memory whose expression in adaptive behavior depends critically on the circuits and systems in which the synapses are embedded. It reviews Adaptive Resonance Theory, or ART, models that use excitatory matching and match-based learning to achieve fast category learning and whose learned memories are dynamically stabilized by top-down expectations, attentional focusing, and memory search. ART clarifies mechanistic relationships between consciousness, learning, expectation, attention, resonance, and synchrony. ART models are embedded in ARTSCAN architectures that unify processes of invariant object category learning, recognition, spatial and object attention, predictive remapping, and eye movement search, and that clarify how conscious object vision and recognition may fail during perceptual crowding and parietal neglect. The generality of learned categories depends upon a vigilance process that is regulated by acetylcholine via the nucleus basalis. Vigilance can get stuck at too high or too low values, thereby causing learning problems in autism and medial temporal amnesia. Similar synaptic learning laws support qualitatively different behaviors: Invariant object category learning in the inferotemporal cortex; learning of grid cells and place cells in the entorhinal and hippocampal cortices during spatial navigation; and learning of time cells in the entorhinal-hippocampal system during adaptively timed conditioning, including trace conditioning. Spatial and temporal processes through the medial and lateral entorhinal-hippocampal system seem to be carried out with homologous circuit designs. Variations of a shared laminar neocortical circuit design have modeled 3D vision, speech perception, and cognitive working memory and learning. A complementary kind of inhibitory matching and mismatch learning controls movement. This article is part of a Special Issue entitled SI: Brain and Memory

  5. Learning basic life support (BLS) with tablet PCs in reciprocal learning at school: are videos superior to pictures? A randomized controlled trial.

    Science.gov (United States)

    Iserbyt, Peter; Charlier, Nathalie; Mols, Liesbet

    2014-06-01

    It is often assumed that animations (i.e., videos) will lead to higher learning compared to static media (i.e., pictures) because they provide a more realistic demonstration of the learning task. To investigate whether learning basic life support (BLS) and cardiopulmonary resuscitation (CPR) from video produce higher learning outcomes compared to pictures in reciprocal learning. A randomized controlled trial. A total of 128 students (mean age: 17 years) constituting eight intact classes from a secondary school learned BLS in reciprocal roles of doer and helper with tablet PCs. Student pairs in each class were randomized over a Picture and a Video group. In the Picture group, students learned BLS by means of pictures combined with written instructions. In the Video group, BLS was learned through videos with on-screen instructions. Informational equivalence was assured since instructions in both groups comprised exactly the same words. BLS assessment occurred unannounced, three weeks following intervention. Analysis of variance demonstrated no significant differences in chest compression depths between the Picture group (M=42 mm, 95% CI=40-45) and the Video group (M=39 mm, 95% CI=36-42). In the Picture group significantly higher percentages of chest compressions with correct hand placement were achieved (M=67%, CI=58-77) compared to the Video group (M=53%, CI=43-63), P=.03, η(p)(2)=.03. No other significant differences were found. Results do not support the assumption that videos are superior to pictures for learning BLS and CPR in reciprocal learning. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  6. Maladaptive Schemas and Affective Control in Students with Learning Disability: Benefits of Mindfulness-Based Cognitive Therapy

    OpenAIRE

    Nasrollah Vaisi; Mohammad Rostami; Zohreh Zangooei; Mohammad-Ali Khaksar-Beldachi

    2015-01-01

    Objectives: This study intended to examine the effectiveness of mindfulness-based cognitive therapy on moderating maladaptive schemas and affective control in students suffering from learning disabilities. Methods: This experimental research was conducted using pretest-posttest and a control group. The population included all the female students who  were studying in the Koohdasht's middle schools (academic year: 2012-2013). The sample included 40 female students suffering from learn...

  7. The Relationship of Assertiveness and Locus of Control with Learning Styles of the Physical Education and Sports School Students

    OpenAIRE

    SUCAN, Serdar; TURAN, Mehmet Behzat; PEPE, Osman; KARAOĞLU, Barış; DOĞAN, Doğan

    2016-01-01

    The present study have been conducted to investigate Physical Education and Sports School students’ learning style preferences in terms of assertiveness and locus of control to see relationship between and learning style.For this purpose “Kolb’s Learning Style Inventory” translated in Turkish by Askar and Akkoyunlu (1993), “Rathus Assertiveness Scale” which was developed by Rathus A.S. (1973) and “Rotter’s Locus of Control Scale” (1966) which was developed into Turkish by Dağ (1990) was admin...

  8. The information system of learning quality control in higher education institutions: achievements and problems of European universities

    Directory of Open Access Journals (Sweden)

    Orekhova Elena

    2016-01-01

    Full Text Available The article deals with the main trends in the development of the system of learning quality control connected with the European integration of higher education and the democratization of education. The authors analyze the state of information systems of learning quality control existing in European higher education and identify their strong and weak points. The authors show that in the learning process universities actively use innovative analytic methods as well as modern means of collecting, storing and transferring information that ensure the successful management of such a complex object as the university of the 21st century.

  9. The relationship between mood state and perceived control in contingency learning: effects of individualist and collectivist values.

    Science.gov (United States)

    Msetfi, Rachel M; Kornbrot, Diana E; Matute, Helena; Murphy, Robin A

    2015-01-01

    Perceived control in contingency learning is linked to psychological wellbeing with low levels of perceived control thought to be a cause or consequence of depression and high levels of control considered to be the hallmark of mental healthiness. However, it is not clear whether this is a universal phenomenon or whether the value that people ascribe to control influences these relationships. Here we hypothesize that values affect learning about control contingencies and influence the relationship between perceived control and symptoms of mood disorders. We tested these hypotheses with European university samples who were categorized as endorsing (or not) values relevant to control-individualist and collectivist values. Three online experimental contingency learning studies (N 1 = 127, N 2 = 324, N 3 = 272) were carried out. Evidence suggested that individualist values influenced basic learning processes via an effect on learning about the context in which events took place. Participants who endorsed individualist values made control judgments that were more in line with an elemental associative learning model, whilst those who were ambivalent about individualist values made judgments that were more consistent with a configural process. High levels of perceived control and individualist values were directly associated with increased euphoric symptoms of bipolar disorder, and such values completely mediated the relation between perceived control and symptoms. The effect of low perceived control on depression was moderated by collectivist values. Anxiety created by dissonance between values and task may be a catalyst for developing mood symptoms. Conclusions are that values play a significant intermediary role in the relation between perceived control and symptoms of mood disturbance.

  10. The relationship between mood state and perceived control in contingency learning: Effects of individualist and collectivist values

    Directory of Open Access Journals (Sweden)

    Rachel M. Msetfi

    2015-09-01

    Full Text Available Perceived control in contingency learning is linked to psychological wellbeing with low levels of perceived control thought to be a cause or consequence of depression and high levels of control considered to be the hallmark of mental healthiness. However, it is not clear whether this is a universal phenomenon or whether the value that people ascribe to control influences these relationships. Here we hypothesize that values affect learning about control contingencies and influence the relationship between perceived control and symptoms of mood disorders. We tested these hypotheses with European university samples who were categorized as endorsing (or not values relevant to control - individualist and collectivist values. Three online experimental contingency learning studies (N1 = 127, N2 = 324, N3 = 272 were carried out. Evidence suggested that individualist values influenced basic learning processes via an effect on learning about the context in which events took place. Participants who endorsed individualist values made control judgments that were more in line with an elemental associative learning model, whilst those who were ambivalent about individualist values made judgments that were more consistent with a configural process. High levels of perceived control and individualist values were directly associated with increased euphoric symptoms of bipolar disorder, and such values completely mediated the relation between perceived control and symptoms. The effect of low perceived control on depression was moderated by collectivist values. Anxiety created by dissonance between values and task may be a catalyst for developing mood symptoms. Conclusions are that values play a significant intermediary role in the relation between perceived control and symptoms of mood disturbance.

  11. Evaluation of an online interactive Diabetes Needs Assessment Tool (DNAT versus online self-directed learning: a randomised controlled trial

    Directory of Open Access Journals (Sweden)

    Kellner Thomas

    2011-06-01

    Full Text Available Abstract Background Methods for the dissemination, understanding and implementation of clinical guidelines need to be examined for their effectiveness to help doctors integrate guidelines into practice. The objective of this randomised controlled trial was to evaluate the effectiveness of an interactive online Diabetes Needs Assessment Tool (DNAT (which constructs an e-learning curriculum based on individually identified knowledge gaps, compared with self-directed e-learning of diabetes guidelines. Methods Health professionals were randomised to a 4-month learning period and either given access to diabetes learning modules alone (control group or DNAT plus learning modules (intervention group. Participants completed knowledge tests before and after learning (primary outcome, and surveys to assess the acceptability of the learning and changes to clinical practice (secondary outcomes. Results Sixty four percent (677/1054 of participants completed both knowledge tests. The proportion of nurses (5.4% was too small for meaningful analysis so they were excluded. For the 650 doctors completing both tests, mean (SD knowledge scores increased from 47.4% (12.6 to 66.8% (11.5 [intervention group (n = 321, 64%] and 47.3% (12.9 to 67.8% (10.8 [control group (n = 329, 66%], (ANCOVA p = 0.186. Both groups were satisfied with the usability and usefulness of the learning materials. Seventy seven percent (218/284 of the intervention group reported combining the DNAT with the recommended reading materials was "very useful"/"useful". The majority in both groups (184/287, 64.1% intervention group and 206/299, 68.9% control group [95% CI for the difference (-2.8 to 12.4] reported integrating the learning into their clinical practice. Conclusions Both groups experienced a similar and significant improvement in knowledge. The learning materials were acceptable and participants incorporated the acquired knowledge into practice. Trial registration ISRCTN: ISRCTN67215088

  12. Self-controlled feedback facilitates motor learning in both high and low activity individuals.

    Science.gov (United States)

    Fairbrother, Jeffrey T; Laughlin, David D; Nguyen, Timothy V

    2012-01-01

    The purpose of this study was to determine if high and low activity individuals differed in terms of the effects of self-controlled feedback on the performance and learning of a movement skill. The task consisted of a blindfolded beanbag toss using the non-preferred arm. Participants were pre-screened according to their physical activity level using the International Physical Activity Questionnaire. An equal number of high activity (HA) and low activity (LA) participants were assigned to self-control (SC) and yoked (YK) feedback conditions, creating four groups: Self-Control-High Activity; Self-Control-Low Activity; Yoked-High Activity; and Yoked-Low Activity. SC condition participants were provided feedback whenever they requested it, while YK condition participants received feedback according to a schedule created by their SC counterpart. Results indicated that the SC condition was more accurate than the YK condition during acquisition and transfer phases, and the HA condition was more accurate than the LA condition during all phases of the experiment. A post-training questionnaire indicated that participants in the SC condition asked for feedback mostly after what they perceived to be "good" trials; those in the YK condition indicated that they would have preferred to receive feedback after "good" trials. This study provided further support for the advantages of self-controlled feedback when learning motor skills, additionally showing benefits for both active and less active individuals. The results suggested that the provision of self-controlled feedback to less active learners may be a potential avenue to teaching motor skills necessary to engage in greater amounts of physical activity.

  13. Self-controlled feedback facilitates motor learning in both high and low activity individuals

    Directory of Open Access Journals (Sweden)

    Jeffrey T. Fairbrother

    2012-08-01

    Full Text Available The purpose of this study was to determine if high and low activity individuals differed in terms of the effects of self-controlled feedback on the performance and learning of a movement skill. The task consisted of a blindfolded beanbag toss using the non-preferred arm. Participants were pre-screened according to their physical activity level using the International Physical Activity Questionnaire. An equal number of high activity (HA and low activity (LA participants were assigned to self-control (SC and yoked (YK feedback conditions, creating four groups: Self-Control High Activity (SC-HA; Self-Control Low Activity (SC-LA; Yoked High Activity (YK-HA; and Yoked Low Activity (YK-LA. SC condition participants were provided feedback whenever they requested it, while YK condition participants received feedback according to a schedule created by their SC counterpart. Results indicated that the SC condition was more accurate than the YK condition during acquisition and transfer phases, and the HA condition was more accurate than the LA condition during all phases of the experiment. A post-training questionnaire indicated that participants in the SC condition asked for feedback mostly after what they perceived to be good trials; those in the YK condition indicated that they would have preferred to receive feedback after good trials. This study provided further support for the advantages of self-controlled feedback when learning motor skills, additionally showing benefits for both active and less active individuals. The results suggested that the provision of self-controlled feedback to less active learners may be a potential avenue to teaching motor skills necessary to engage in greater amounts of physical activity.

  14. The roles of the olivocerebellar pathway in motor learning and motor control. A consensus paper

    Science.gov (United States)

    Lang, Eric J.; Apps, Richard; Bengtsson, Fredrik; Cerminara, Nadia L.; De Zeeuw, Chris I.; Ebner, Timothy J.; Heck, Detlef H.; Jaeger, Dieter; Jörntell, Henrik; Kawato, Mitsuo; Otis, Thomas S.; Ozyildirim, Ozgecan; Popa, Laurentiu S.; Reeves, Alexander M.B.; Schweighofer, Nicolas; Sugihara, Izumi; Xiao, Jianqiang

    2016-01-01

    For many decades the predominant view in the cerebellar field has been that the olivocerebellar system's primary function is to induce plasticity in the cerebellar cortex, specifically, at the parallel fiber-Purkinje cell synapse. However, it has also long been proposed that the olivocerebellar system participates directly in motor control by helping to shape ongoing motor commands being issued by the cerebellum. Evidence consistent with both hypotheses exists; however, they are often investigated as mutually exclusive alternatives. In contrast, here we take the perspective that the olivocerebellar system can contribute to both the motor learning and motor control functions of the cerebellum, and might also play a role in development. We then consider the potential problems and benefits of its having multiple functions. Moreover, we discuss how its distinctive characteristics (e.g., low firing rates, synchronization, variable complex spike waveform) make it more or less suitable for one or the other of these functions, and why its having a dual role makes sense from an evolutionary perspective. We did not attempt to reach a consensus on the specific role(s) the olivocerebellar system plays in different types of movements, as that will ultimately be determined experimentally; however, collectively, the various contributions highlight the flexibility of the olivocerebellar system, and thereby suggest it has the potential to act in both the motor learning and motor control functions of the cerebellum. PMID:27193702

  15. Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

    Directory of Open Access Journals (Sweden)

    Min Wang

    2017-01-01

    Full Text Available A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF neural network (NN approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation (PE condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.

  16. Enhanced Quality Control in Pharmaceutical Applications by Combining Raman Spectroscopy and Machine Learning Techniques

    Science.gov (United States)

    Martinez, J. C.; Guzmán-Sepúlveda, J. R.; Bolañoz Evia, G. R.; Córdova, T.; Guzmán-Cabrera, R.

    2018-06-01

    In this work, we applied machine learning techniques to Raman spectra for the characterization and classification of manufactured pharmaceutical products. Our measurements were taken with commercial equipment, for accurate assessment of variations with respect to one calibrated control sample. Unlike the typical use of Raman spectroscopy in pharmaceutical applications, in our approach the principal components of the Raman spectrum are used concurrently as attributes in machine learning algorithms. This permits an efficient comparison and classification of the spectra measured from the samples under study. This also allows for accurate quality control as all relevant spectral components are considered simultaneously. We demonstrate our approach with respect to the specific case of acetaminophen, which is one of the most widely used analgesics in the market. In the experiments, commercial samples from thirteen different laboratories were analyzed and compared against a control sample. The raw data were analyzed based on an arithmetic difference between the nominal active substance and the measured values in each commercial sample. The principal component analysis was applied to the data for quantitative verification (i.e., without considering the actual concentration of the active substance) of the difference in the calibrated sample. Our results show that by following this approach adulterations in pharmaceutical compositions can be clearly identified and accurately quantified.

  17. Application of Machine Learning in Postural Control Kinematics for the Diagnosis of Alzheimer’s Disease

    Directory of Open Access Journals (Sweden)

    Luís Costa

    2016-01-01

    Full Text Available The use of wearable devices to study gait and postural control is a growing field on neurodegenerative disorders such as Alzheimer’s disease (AD. In this paper, we investigate if machine-learning classifiers offer the discriminative power for the diagnosis of AD based on postural control kinematics. We compared Support Vector Machines (SVMs, Multiple Layer Perceptrons (MLPs, Radial Basis Function Neural Networks (RBNs, and Deep Belief Networks (DBNs on 72 participants (36 AD patients and 36 healthy subjects exposed to seven increasingly difficult postural tasks. The decisional space was composed of 18 kinematic variables (adjusted for age, education, height, and weight, with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA score, top ranked in an error incremental analysis. Classification results were based on threefold cross validation of 50 independent and randomized runs sets: training (50%, test (40%, and validation (10%. Having a decisional space relying solely on postural kinematics, accuracy of AD diagnosis ranged from 71.7 to 86.1%. Adding the MoCA variable, the accuracy ranged between 91 and 96.6%. MLP classifier achieved top performance in both decisional spaces. Having comprehended the interdynamic interaction between postural stability and cognitive performance, our results endorse machine-learning models as a useful tool for computer-aided diagnosis of AD based on postural control kinematics.

  18. Why self-controlled feedback enhances motor learning: Answers from electroencephalography and indices of motivation.

    Science.gov (United States)

    Grand, Kirk F; Bruzi, Alessandro T; Dyke, Ford B; Godwin, Maurice M; Leiker, Amber M; Thompson, Andrew G; Buchanan, Taylor L; Miller, Matthew W

    2015-10-01

    It was tested whether learners who choose when to receive augmented feedback while practicing a motor skill exhibit enhanced augmented feedback processing and intrinsic motivation, along with superior learning, relative to learners who do not control their feedback. Accordingly, participants were assigned to either self-control (Self) or yoked groups and asked to practice a non-dominant arm beanbag toss. Self participants received augmented feedback at their discretion, whereas Yoked participants were given feedback schedules matched to Self counterparts. Participants' visual feedback was occluded, and when they received augmented feedback, their processing of it was indexed with the electroencephalography-derived feedback-related negativity (FRN). Participants self-reported intrinsic motivation via the Intrinsic Motivation Inventory (IMI) after practice, and completed a retention and transfer test the next day to index learning. Results partially support the hypothesis. Specifically, Self participants reported higher IMI scores, exhibited larger FRNs, and demonstrated better accuracy on the transfer test, but not on the retention test, nor did they exhibit greater consistency on the retention or transfer tests. Additionally, post-hoc multiple regression analysis indicated FRN amplitude predicted transfer test accuracy (accounting for IMI score). Results suggest self-controlled feedback schedules enhance feedback processing, which enhances the transfer of a newly acquired motor skill. Copyright © 2015 Elsevier B.V. All rights reserved.

  19. Altitude control in honeybees: joint vision-based learning and guidance.

    Science.gov (United States)

    Portelli, Geoffrey; Serres, Julien R; Ruffier, Franck

    2017-08-23

    Studies on insects' visual guidance systems have shed little light on how learning contributes to insects' altitude control system. In this study, honeybees were trained to fly along a double-roofed tunnel after entering it near either the ceiling or the floor of the tunnel. The honeybees trained to hug the ceiling therefore encountered a sudden change in the tunnel configuration midways: i.e. a "dorsal ditch". Thus, the trained honeybees met a sudden increase in the distance to the ceiling, corresponding to a sudden strong change in the visual cues available in their dorsal field of view. Honeybees reacted by rising quickly and hugging the new, higher ceiling, keeping a similar forward speed, distance to the ceiling and dorsal optic flow to those observed during the training step; whereas bees trained to follow the floor kept on following the floor regardless of the change in the ceiling height. When trained honeybees entered the tunnel via the other entry (the lower or upper entry) to that used during the training step, they quickly changed their altitude and hugged the surface they had previously learned to follow. These findings clearly show that trained honeybees control their altitude based on visual cues memorized during training. The memorized visual cues generated by the surfaces followed form a complex optic flow pattern: trained honeybees may attempt to match the visual cues they perceive with this memorized optic flow pattern by controlling their altitude.

  20. Wordless intervention for people with epilepsy and learning disabilities (WIELD): a randomised controlled feasibility trial

    Science.gov (United States)

    Mengoni, Silvana E; Gates, Bob; Parkes, Georgina; Wellsted, David; Barton, Garry; Ring, Howard; Khoo, Mary Ellen; Monji-Patel, Deela; Friedli, Karin; Zia, Asif; Irvine, Lisa; Durand, Marie-Anne

    2016-01-01

    Objective To investigate the feasibility of a full-scale randomised controlled trial of a picture booklet to improve quality of life for people with epilepsy and learning disabilities. Trial design A randomised controlled feasibility trial. Randomisation was not blinded and was conducted using a centralised secure database and a blocked 1:1 allocation ratio. Setting Epilepsy clinics in 1 English National Health Service (NHS) Trust. Participants Patients with learning disabilities and epilepsy who had: a seizure within the past 12 months, meaningful communication and a carer with sufficient proficiency in English. Intervention Participants in the intervention group used a picture booklet with a trained researcher, and a carer present. These participants kept the booklet, and were asked to use it at least twice more over 20 weeks. The control group received treatment as usual, and were provided with a booklet at the end of the study. Outcome measures 7 feasibility criteria were used relating to recruitment, data collection, attrition, potential effect on epilepsy-related quality of life (Epilepsy and Learning Disabilities Quality of Life Scale, ELDQOL) at 4-week, 12-week and 20-week follow-ups, feasibility of methodology, acceptability of the intervention and potential to calculate cost-effectiveness. Outcome The recruitment rate of eligible patients was 34% and the target of 40 participants was reached. There was minimal missing data and attrition. An intention-to-treat analysis was performed; data from the outcome measures suggest a benefit from the intervention on the ELDQOL behaviour and mood subscales at 4 and 20 weeks follow-up. The booklet and study methods were positively received, and no adverse events were reported. There was a positive indication of the potential for a cost-effectiveness analysis. Conclusions All feasibility criteria were fully or partially met, therefore confirming feasibility of a definitive trial. Trial registration number ISRCTN

  1. Control of zoonoses in emergency situations: lessons learned during recent outbreaks (gaps and weaknesses of current zoonoses control programmes

    Directory of Open Access Journals (Sweden)

    Darem Tabbaa

    2008-12-01

    Full Text Available In emergency situations, domestic animals and wildlife are, like people, exposed to infectious diseases and environmental contaminants in the air, soil, water and food. They can suffer from acute and/or chronic diseases from such exposure. Often animals serve as disease reservoirs or early warning systems for the community in regard to the spread of zoonotic diseases. Over 100 years of experience have shown that animal and human health are closely related. During the past few years, emergent disease episodes have increased; nearly all have involved zoonotic agents. As there is no way to predict when or where the next important new zoonotic pathogen will emerge or what its ultimate importance might be, investigation at the first sign of emergence of a new zoonotic disease is particularly important. Today, in many emerging situations, different activities involving zoonotic disease control are at risk because of failed investigative infrastructures or financial constraints. Considering that zoonotic diseases have their own characteristics, their prevention and control require unique strategies, based more on fundamental and applied research than on traditional approaches. Such strategies require cooperation and coordination between animal and public health sectors and the involvement of other disciplines and experts such as epidemiologists, entomologists, environmentalists and climatologists. Lessons learned from the avian influenza pandemic threat, the Crimean-Congo haemorrhagic fever and rabies outbreaks are presented and the gaps and weakness of current control programmes are discussed.

  2. The relationship between mood state and perceived control in contingency learning: effects of individualist and collectivist values

    Science.gov (United States)

    Msetfi, Rachel M.; Kornbrot, Diana E.; Matute, Helena; Murphy, Robin A.

    2015-01-01

    Perceived control in contingency learning is linked to psychological wellbeing with low levels of perceived control thought to be a cause or consequence of depression and high levels of control considered to be the hallmark of mental healthiness. However, it is not clear whether this is a universal phenomenon or whether the value that people ascribe to control influences these relationships. Here we hypothesize that values affect learning about control contingencies and influence the relationship between perceived control and symptoms of mood disorders. We tested these hypotheses with European university samples who were categorized as endorsing (or not) values relevant to control—individualist and collectivist values. Three online experimental contingency learning studies (N1 = 127, N2 = 324, N3 = 272) were carried out. Evidence suggested that individualist values influenced basic learning processes via an effect on learning about the context in which events took place. Participants who endorsed individualist values made control judgments that were more in line with an elemental associative learning model, whilst those who were ambivalent about individualist values made judgments that were more consistent with a configural process. High levels of perceived control and individualist values were directly associated with increased euphoric symptoms of bipolar disorder, and such values completely mediated the relation between perceived control and symptoms. The effect of low perceived control on depression was moderated by collectivist values. Anxiety created by dissonance between values and task may be a catalyst for developing mood symptoms. Conclusions are that values play a significant intermediary role in the relation between perceived control and symptoms of mood disturbance. PMID:26483707

  3. Effects of Locus of Control on Behavioral Intention and Learning Performance of Energy Knowledge in Game-Based Learning

    Science.gov (United States)

    Yang, Jie Chi; Lin, Yi Lung; Liu, Yi-Chun

    2017-01-01

    Game-based learning has been gradually adopted in energy education as an effective learning tool because digital games have the potential to increase energy literacy and encourage behavior change. However, not every learner can benefit from this support. There is a need to examine how human factors affect learners' reactions to digital games for…

  4. The Analysis of Interactivity in a Teaching and Learning Sequence of Rugby: The Transfer of Control and Learning Responsibility

    Science.gov (United States)

    Llobet-Martí, Bernat; López-Ros, Víctor; Vila, Ignasi

    2018-01-01

    Background: The social constructivist perspective emphasises that learning is a process of self-construction of knowledge in a social context. Game-centred approaches, such as teaching games for understanding, have been used in accordance with this perspective. The process of transferring learning responsibility takes place when the learner is…

  5. Scheduled power tracking control of the wind-storage hybrid system based on the reinforcement learning theory

    Science.gov (United States)

    Li, Ze

    2017-09-01

    In allusion to the intermittency and uncertainty of the wind electricity, energy storage and wind generator are combined into a hybrid system to improve the controllability of the output power. A scheduled power tracking control method is proposed based on the reinforcement learning theory and Q-learning algorithm. In this method, the state space of the environment is formed with two key factors, i.e. the state of charge of the energy storage and the difference value between the actual wind power and scheduled power, the feasible action is the output power of the energy storage, and the corresponding immediate rewarding function is designed to reflect the rationality of the control action. By interacting with the environment and learning from the immediate reward, the optimal control strategy is gradually formed. After that, it could be applied to the scheduled power tracking control of the hybrid system. Finally, the rationality and validity of the method are verified through simulation examples.

  6. Nonparametric Online Learning Control for Soft Continuum Robot: An Enabling Technique for Effective Endoscopic Navigation

    Science.gov (United States)

    Lee, Kit-Hang; Fu, Denny K.C.; Leong, Martin C.W.; Chow, Marco; Fu, Hing-Choi; Althoefer, Kaspar; Sze, Kam Yim; Yeung, Chung-Kwong

    2017-01-01

    Abstract Bioinspired robotic structures comprising soft actuation units have attracted increasing research interest. Taking advantage of its inherent compliance, soft robots can assure safe interaction with external environments, provided that precise and effective manipulation could be achieved. Endoscopy is a typical application. However, previous model-based control approaches often require simplified geometric assumptions on the soft manipulator, but which could be very inaccurate in the presence of unmodeled external interaction forces. In this study, we propose a generic control framework based on nonparametric and online, as well as local, training to learn the inverse model directly, without prior knowledge of the robot's structural parameters. Detailed experimental evaluation was conducted on a soft robot prototype with control redundancy, performing trajectory tracking in dynamically constrained environments. Advanced element formulation of finite element analysis is employed to initialize the control policy, hence eliminating the need for random exploration in the robot's workspace. The proposed control framework enabled a soft fluid-driven continuum robot to follow a 3D trajectory precisely, even under dynamic external disturbance. Such enhanced control accuracy and adaptability would facilitate effective endoscopic navigation in complex and changing environments. PMID:29251567

  7. Control of a reactive batch distillation process using an iterative learning technique

    International Nuclear Information System (INIS)

    Ahn, Hyunsoo; Lee, Kwang Soon; Kim, Mansuk; Lee, Juhyun

    2014-01-01

    Quadratic criterion-based iterative learning control (QILC) was applied to a numerical reactive batch distillation process, in which methacrylic anhydride (MAN) is produced through the reaction of methacrylic acid with acetic anhydride. The role of distillation is to shift the equilibrium conversion toward the direction of the product by removing acetic acid (AcH), a by-product of the reaction. Two temperatures at both ends of the column were controlled by individual control loops. A nonlinear PID controller manipulating the reflux ratio was employed to regulate the top temperature at the boiling point of AcH. A constrained QILC was used for the tracking of the reactor temperature. A time-varying reference trajectory for the reactor temperature that satisfies the target conversion and purity of MAN was obtained through repeated simulations and confirmation experiments in the pilot plant. The QILC achieved satisfactory tracking in several batch runs with gentle control movements, while the PID control as a substitute of the QILC in a comparative study exhibited unacceptable performance

  8. Decision tree-based learning to predict patient controlled analgesia consumption and readjustment

    Directory of Open Access Journals (Sweden)

    Hu Yuh-Jyh

    2012-11-01

    Full Text Available Abstract Background Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA, which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment. Methods The sample in this study included 1099 patients. Every patient was described by 280 attributes, including the class attribute. In addition to commonly studied demographic and physiological factors, this study emphasizes attributes related to PCA. We used decision tree-based learning algorithms to predict analgesic consumption and PCA control readjustment based on the first few hours of PCA medications. We also developed a nearest neighbor-based data cleaning method to alleviate the class-imbalance problem in PCA setting readjustment prediction. Results The prediction accuracies of total analgesic consumption (continuous dose and PCA dose and PCA analgesic requirement (PCA dose only by an ensemble of decision trees were 80.9% and 73.1%, respectively. Decision tree-based learning outperformed Artificial Neural Network, Support Vector Machine, Random Forest, Rotation Forest, and Naïve Bayesian classifiers in analgesic consumption prediction. The proposed data cleaning method improved the performance of every learning method in this study of PCA setting readjustment prediction. Comparative analysis identified the informative attributes from the data mining models and compared them with the correlates of analgesic requirement reported in previous works. Conclusion This study presents a real-world application of data mining to anesthesiology. Unlike previous research, this study considers a wider variety of predictive factors, including PCA

  9. The Prevalence of Neuromyths in Community College: Examining Community College Students' Beliefs in Learning Styles and Impacts on Perceived Academic Locus of Control

    Science.gov (United States)

    Palis, Leila Ann

    2016-01-01

    It was not known if and to what extent there was a relationship between the degree to which community college students believed that learning was enhanced when teachers tailored instruction to individual learning styles and student perceived academic locus of control (PAC). Learning styles theory and locus of control theory formed the theoretical…

  10. Statistical Learning and Adaptive Decision-Making Underlie Human Response Time Variability in Inhibitory Control

    Directory of Open Access Journals (Sweden)

    Ning eMa

    2015-08-01

    Full Text Available Response time (RT is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task, in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop, and stop-signal onset time, SSD (stop-signal delay, with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop and SSD. The human behavioral data (n=20 bear out this prediction, showing P(stop and SSD both to be significant, independent predictors of RT, with P(stop being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

  11. "Learning" Can Improve the Blood Glucose Control Performance for Type 1 Diabetes Mellitus.

    Science.gov (United States)

    Wang, Youqing; Zhang, Jinping; Zeng, Fanmao; Wang, Na; Chen, Xiaoping; Zhang, Bo; Zhao, Dong; Yang, Wenying; Cobelli, Claudio

    2017-01-01

    A learning-type artificial pancreas has been proposed to exploit the repetitive nature in the blood glucose dynamics. We clinically evaluated the efficacy of the learning-type artificial pancreas. We conducted a pilot clinical study in 10 participants of mean age 36.1 years (standard deviation [SD] 12.7; range 16-58) with type 1 diabetes. Each trial was conducted for eight consecutive mornings. The first two mornings were open-loop to obtain the individualized parameters. Then, the following six mornings were closed-loop, during which a learning-type model predictive control algorithm was employed to calculate the insulin infusion rate. To evaluate the algorithm's robustness, each participant took exercise or consumed alcohol on the fourth or sixth closed-loop day and the order was determined randomly. The primary outcome was the percentage of time spent in the target glucose range of 3.9-8.0 mmol/L between 0900 and 1200 h. The percentage of time with glucose spent in target range was significantly improved from 51.6% on day 1 to 71.6% on day 3 (mean difference between groups 17.9%, confidence interval [95% CI] 3.6-32.1; P = 0.020). There were no hypoglycemic episodes developed on day 3 compared with two episodes on day 1. There was no difference in the percentage of time with glucose spent in target range between exercise day versus day 5 and alcohol day versus day 5. The learning-type artificial pancreas system achieved good glycemic regulation and provided increased effectiveness over time. It showed a satisfactory performance even when the blood glucose was challenged by exercise or alcohol.

  12. E-Learning Model in Chronic Kidney Disease Management: a Controlled Clinical Trial.

    Science.gov (United States)

    Barahimi, Hamid; Zolfaghari, Mitra; Abolhassani, Farid; Rahimi Foroushani, Abass; Mohammadi, Aeen; Rajaee, Farahnaz

    2017-07-01

    Chronic kidney disease (CKD) is a challenging health problem. The present study examined impact of self-care education through e-learning on improving kidney function among individuals with CKD. The studied population consisted of CKD patients receiving care at 10 centers for treating noncommunicable diseases in Tehran. Three centers were randomly selected and 39 patients with a glomerular filtration rate (GFR) less than 60 mL/min/1.73 m2, minimum education of grade 9, minimum of 2 years of referrals, and computer literacy of the individual or a first-degree relative were included in the study, while 92 patients were assigned into the control group. Changes in GFR were compared after 6 months following an e-learning program for the patients in the intervention group. The mean change in GFR was 7.5 ± 8.9 mL/min/1.73 m2 for the intervention group after the e-learning intervention, while this was -2.3 ± 8.5 mL/min/1.73 m2. The two groups were also significantly different in terms of age, marital status, education level, mean arterial pressure, and serum high-density lipoprotein level, and therefore, multivariable comparison of GFR was made incorporating these factor into the analysis and showed a significant improvement of GFR in the intervention group. According to the results of this study, effects of the e-learning educational intervention on improvement in kidney function and CKD treatment were established.

  13. Statistical learning and adaptive decision-making underlie human response time variability in inhibitory control.

    Science.gov (United States)

    Ma, Ning; Yu, Angela J

    2015-01-01

    Response time (RT) is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task (SST), in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop), and stop-signal onset time, SSD (stop-signal delay), with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop) and SSD. The human behavioral data (n = 20) bear out this prediction, showing P(stop) and SSD both to be significant, independent predictors of RT, with P(stop) being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

  14. Development of Remote Monitoring and a Control System Based on PLC and WebAccess for Learning Mechatronics

    OpenAIRE

    Wen-Jye Shyr; Te-Jen Su; Chia-Ming Lin

    2013-01-01

    This study develops a novel method for learning mechatronics using remote monitoring and control, based on a programmable logic controller (PLC) and WebAccess. A mechatronics module, a Web‐CAM and a PLC were integrated with WebAccess software to organize a remote laboratory. The proposed system enables users to access the Internet for remote monitoring and control of the mechatronics module via a web browser, thereby enhancing work flexibility by enabling personnel to control mechatronics equ...

  15. Evaluation of eLearning for the teaching of undergraduate ophthalmology at medical school: a randomised controlled crossover study.

    Science.gov (United States)

    Petrarca, Caroline A; Warner, Julia; Simpson, Andrew; Petrarca, Robert; Douiri, Abdel; Byrne, David; Jackson, Timothy L

    2018-05-25

    To compare ophthalmology teaching delivered by eLearning with traditional lectures, in terms of undergraduate performance and satisfaction. Randomised controlled crossover study at King's College London Medical School with 245 third year medical students. The ophthalmology syllabus was divided into ten topics. Five topics were randomised to be taught by traditional lectures and five by electronic learning (eLearning). For the second rotation of students the topics were crossed over, so that those topics taught by traditional lectures were taught by eLearning and vice versa. At the end of each rotation the students sat an optional online mock examination containing 100 questions (ten on each topic). Students' examination performance was compared between the two teaching methods. Student satisfaction was assessed using an online satisfaction survey. Outcome measures were the mean percentage of correct answers across all ten topics, student satisfaction and self-assessed knowledge. The mean examination score for questions taught by eLearning was 58% (95% CI, 55.7-59.6), versus 55% (95% CI 53.1-56.8) for traditional lectures (P = 0.047). Across all topics students were more satisfied with eLearning than traditional lectures, with 87% (95% CI 84.5-88.4) rating eLearning as 'excellent' or 'good' versus 65% (95% CI 62.0-67.4) for lectures (p eLearning compared to traditional lectures, with 166 (69.7%) rating eLearning 'much better' or 'better,' 61 (25.6%) 'neutral' and 11 (4.6%) 'worse' or 'much worse.' Student satisfaction and examination performance are both enhanced by ophthalmology eLearning. Similar eLearning modules may be suitable for other specialties and postgraduate learning.

  16. Design e-learning with flipped learning model to improve layout understanding the concepts basic of the loop control structure

    Science.gov (United States)

    Handayani, D. P.; Sutarno, H.; Wihardi, Y.

    2018-05-01

    This study aimed in design and build e-learning with classroom flipped model to improve the concept of understanding of SMK students on the basic programming subject. Research and development obtained research data from survey questionnaire given to students of SMK class X RPL in SMK Negeri 2 Bandung and interviews to RPL productive teacher. Data also obtained from questionnaire of expert validation and students' assessment from e-learning with flipped classroom models. Data also obtained from multiple-choice test to measure improvements in conceptual understanding. The results of this research are: 1) Developed e- learning with flipped classroom model considered good and worthy of use by the average value of the percentage of 86,3% by media experts, and 85,5% by subjects matter experts, then students gave judgment is very good on e-learning either flipped classroom model with a percentage of 79,15% votes. 2) e-learning with classroom flipped models show an increase in the average value of pre-test before using e-learning 26.67 compared to the average value post-test after using e- learning at 63.37 and strengthened by the calculation of the index gains seen Increased understanding of students 'concepts by 50% with moderate criteria indicating that students' understanding is improving.

  17. The feeling of doing across levels of analysis: The effects of perceived control on learning

    Directory of Open Access Journals (Sweden)

    Ljubica Chatman

    2011-12-01

    Full Text Available A person's sense of control was initially conceptualized in psychology as either a trait (Rotter, 1966, an attribution style (Weiner, 1979 or self-efficacy belief (Bandura, 1989a. More recent work in social cognition focuses on the process of inferring one's own causality and how the feeling of doing comes about. This investigation centers on a cue based process as leading to the experience of agency. These cues include vision, proprioception, social cues, and action relevant thought (Wegner & Sparrow, 2004. Since the advent of Functional Magnetic Resonance Imaging (fMRI, progress has been made in understanding the neural substrates implicated when one's infers own causality (for review see David, Newen, & Vogeley, 2008. An analysis of the different approaches to studying human agency, reveals their contributions with each level of analysis adding to and refining our understanding of perceived control and its effect on learning.

  18. A Reinforcement Learning Approach to Call Admission Control in HAPS Communication System

    Directory of Open Access Journals (Sweden)

    Ni Shu Yan

    2017-01-01

    Full Text Available The large changing of link capacity and number of users caused by the movement of both platform and users in communication system based on high altitude platform station (HAPS will resulting in high dropping rate of handover and reduce resource utilization. In order to solve these problems, this paper proposes an adaptive call admission control strategy based on reinforcement learning approach. The goal of this strategy is to maximize long-term gains of system, with the introduction of cross-layer interaction and the service downgraded. In order to access different traffics adaptively, the access utility of handover traffics and new call traffics is designed in different state of communication system. Numerical simulation result shows that the proposed call admission control strategy can enhance bandwidth resource utilization and the performances of handover traffics.

  19. Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.

    Science.gov (United States)

    Gao, Weinan; Jiang, Zhong-Ping; Weinan Gao; Zhong-Ping Jiang; Gao, Weinan; Jiang, Zhong-Ping

    2018-06-01

    This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.

  20. Off-Policy Reinforcement Learning: Optimal Operational Control for Two-Time-Scale Industrial Processes.

    Science.gov (United States)

    Li, Jinna; Kiumarsi, Bahare; Chai, Tianyou; Lewis, Frank L; Fan, Jialu

    2017-12-01

    Industrial flow lines are composed of unit processes operating on a fast time scale and performance measurements known as operational indices measured at a slower time scale. This paper presents a model-free optimal solution to a class of two time-scale industrial processes using off-policy reinforcement learning (RL). First, the lower-layer unit process control loop with a fast sampling period and the upper-layer operational index dynamics at a slow time scale are modeled. Second, a general optimal operational control problem is formulated to optimally prescribe the set-points for the unit industrial process. Then, a zero-sum game off-policy RL algorithm is developed to find the optimal set-points by using data measured in real-time. Finally, a simulation experiment is employed for an industrial flotation process to show the effectiveness of the proposed method.

  1. Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

    Science.gov (United States)

    Yang, Xiong; Liu, Derong; Wang, Ding

    2014-03-01

    In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

  2. Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors.

    Science.gov (United States)

    Wei, Qinglai; Li, Benkai; Song, Ruizhuo

    2018-04-01

    In this paper, a generalized policy iteration (GPI) algorithm with approximation errors is developed for solving infinite horizon optimal control problems for nonlinear systems. The developed stable GPI algorithm provides a general structure of discrete-time iterative adaptive dynamic programming algorithms, by which most of the discrete-time reinforcement learning algorithms can be described using the GPI structure. It is for the first time that approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The admissibility of the approximate iterative control law can be guaranteed if the approximation errors satisfy the admissibility criteria. The convergence of the developed algorithm is established, which shows that the iterative value function is convergent to a finite neighborhood of the optimal performance index function, if the approximate errors satisfy the convergence criterion. Finally, numerical examples and comparisons are presented.

  3. Learning

    Directory of Open Access Journals (Sweden)

    Mohsen Laabidi

    2014-01-01

    Full Text Available Nowadays learning technologies transformed educational systems with impressive progress of Information and Communication Technologies (ICT. Furthermore, when these technologies are available, affordable and accessible, they represent more than a transformation for people with disabilities. They represent real opportunities with access to an inclusive education and help to overcome the obstacles they met in classical educational systems. In this paper, we will cover basic concepts of e-accessibility, universal design and assistive technologies, with a special focus on accessible e-learning systems. Then, we will present recent research works conducted in our research Laboratory LaTICE toward the development of an accessible online learning environment for persons with disabilities from the design and specification step to the implementation. We will present, in particular, the accessible version “MoodleAcc+” of the well known e-learning platform Moodle as well as new elaborated generic models and a range of tools for authoring and evaluating accessible educational content.

  4. Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy.

    Science.gov (United States)

    Liu, Hui; Li, Yingzi; Zhang, Yingxu; Chen, Yifu; Song, Zihang; Wang, Zhenyu; Zhang, Suoxin; Qian, Jianqiang

    2018-01-01

    Proportional-integral-derivative (PID) parameters play a vital role in the imaging process of an atomic force microscope (AFM). Traditional parameter tuning methods require a lot of manpower and it is difficult to set PID parameters in unattended working environments. In this manuscript, an intelligent tuning method of PID parameters based on iterative learning control is proposed to self-adjust PID parameters of the AFM according to the sample topography. This method gets enough information about the output signals of PID controller and tracking error, which will be used to calculate the proper PID parameters, by repeated line scanning until convergence before normal scanning to learn the topography. Subsequently, the appropriate PID parameters are obtained by fitting method and then applied to the normal scanning process. The feasibility of the method is demonstrated by the convergence analysis. Simulations and experimental results indicate that the proposed method can intelligently tune PID parameters of the AFM for imaging different topographies and thus achieve good tracking performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Fast-Spiking Interneurons Supply Feedforward Control of Bursting, Calcium, and Plasticity for Efficient Learning.

    Science.gov (United States)

    Owen, Scott F; Berke, Joshua D; Kreitzer, Anatol C

    2018-02-08

    Fast-spiking interneurons (FSIs) are a prominent class of forebrain GABAergic cells implicated in two seemingly independent network functions: gain control and network plasticity. Little is known, however, about how these roles interact. Here, we use a combination of cell-type-specific ablation, optogenetics, electrophysiology, imaging, and behavior to describe a unified mechanism by which striatal FSIs control burst firing, calcium influx, and synaptic plasticity in neighboring medium spiny projection neurons (MSNs). In vivo silencing of FSIs increased bursting, calcium transients, and AMPA/NMDA ratios in MSNs. In a motor sequence task, FSI silencing increased the frequency of calcium transients but reduced the specificity with which transients aligned to individual task events. Consistent with this, ablation of FSIs disrupted the acquisition of striatum-dependent egocentric learning strategies. Together, our data support a model in which feedforward inhibition from FSIs temporally restricts MSN bursting and calcium-dependent synaptic plasticity to facilitate striatum-dependent sequence learning. Copyright © 2018 Elsevier Inc. All rights reserved.

  6. Tip off the HAT- Epigenetic control of learning and memory by Drosophila Tip60.

    Science.gov (United States)

    Xu, Songjun; Elefant, Felice

    2015-01-01

    Disruption of epigenetic gene control mechanisms involving histone acetylation in the brain causes cognitive impairment, a debilitating hallmark of most neurodegenerative disorders. Histone acetylation regulates cognitive gene expression via chromatin packaging control in neurons. Unfortunately, the histone acetyltransferases (HATs) that generate such neural epigenetic signatures and their mechanisms of action remain unclear. Our recent findings provide insight into this question by demonstrating that Tip60 HAT action is critical for morphology and function of the mushroom body (MB), the learning and memory center in the Drosophila brain. We show that Tip60 is robustly produced in MB Kenyon cells and extending axonal lobes and that targeted MB Tip60 HAT loss results in axonal outgrowth disruption. Functional consequences of loss and gain of Tip60 HAT levels in the MB are evidenced by defects in memory. Tip60 ChIP-Seq analysis reveals enrichment for genes that function in cognitive processes and accordingly, key genes representing these pathways are misregulated in the Tip60 HAT mutant fly brain. Remarkably, increasing levels of Tip60 in the MB rescues learning and memory deficits resulting from Alzheimer's disease associated amyloid precursor protein (APP) induced neurodegeneration. Our studies highlight the potential of HAT activators as a therapeutic option for cognitive disorders.

  7. HUBUNGAN ANTARA SELF REGULATED LEARNING DAN LOCUS OF CONTROL INTERNAL DENGAN KEMATANGAN VOKASIONAL SISWA SMK

    Directory of Open Access Journals (Sweden)

    Lativa Hartiningtyas

    2016-06-01

    Penelitian ini dilaksanakan pada SMK TKJ (Teknik Komputer dan Jaringan se-Kabupaten Tulungagung. Tujuan dari penelitian ini adalah mengetahui hubungan antara self regulated learning dan locus of control internal sebagai variabel bebas (independent dengan kematangan vokasional sebagai variabel terikat (dependent pada siswa SMK. Populasi penelitian ini adalah seluruh siswa kelas XI Paket Keahlian TKJ di Kabupaten Tulungagung yang berjumlah 337 siswa. Teknik sampling yang digunakan dalam penelitian ini adalah proportionate random sampling. Jumlah sampel pada penelitian ini adalah 182 siswa. Penelitian ini menggunakan pendekatan kuantitatif dengan rancangan penelitian deskriptif korelasional. Instrumen yang digunakan berupa angket self regulated learning, locus of control internal, dan kematangan vokasional yang menggunakan skala Likert dengan 4 skala. Analisis data pada penelitian ini menggunakan korelasi product moment untuk mengetahui hubungan antara variabel bebas dengan variabel terikat secara parsial dan analisis regresi ganda untuk mengetahui hubungan secara simultan antara variabel bebas dan variabel terikat. Berdasarkan analisis data dapat diketahui koefisien korelasi antara X1 dengan Y adalah sebesar 0,511 dan koefisien korelasi antara variabel X2 dengan Y adalah sebesar 0,576.

  8. Impacts of autonomy-supportive versus controlling instructional language on motor learning.

    Science.gov (United States)

    Hooyman, Andrew; Wulf, Gabriele; Lewthwaite, Rebecca

    2014-08-01

    The authors examined the influence of autonomy-supportive (ASL), controlling (CL), and neutral instructional language (NL) on motor skill learning (cricket bowling action). Prior to and several times during the practice phase, participants watched the same video demonstration of the bowling action but with different voice-over instructions. The instructions were designed to provide the same technical information but to vary in terms of the degree of choice performers would perceive when executing the task. In addition to measurements of throwing accuracy (i.e., deviation from the target), perceived choice, self-efficacy, and positive and negative affect were assessed at the end of the practice phase and after a retention test without demonstrations and instructions on Day 2. ASL resulted in perceptions of greater choice, higher self-efficacy, and more positive affect during practice than CL, and enhanced learning as demonstrated by retention test performance. Thus, granting learners autonomy appeared to endow them with confidence in their ability, diminished needs for control of negative emotional responses, and created more positive affect, which may help consolidate motor memories. Copyright © 2014 Elsevier B.V. All rights reserved.

  9. Joint Machine Learning and Game Theory for Rate Control in High Efficiency Video Coding.

    Science.gov (United States)

    Gao, Wei; Kwong, Sam; Jia, Yuheng

    2017-08-25

    In this paper, a joint machine learning and game theory modeling (MLGT) framework is proposed for inter frame coding tree unit (CTU) level bit allocation and rate control (RC) optimization in High Efficiency Video Coding (HEVC). First, a support vector machine (SVM) based multi-classification scheme is proposed to improve the prediction accuracy of CTU-level Rate-Distortion (R-D) model. The legacy "chicken-and-egg" dilemma in video coding is proposed to be overcome by the learning-based R-D model. Second, a mixed R-D model based cooperative bargaining game theory is proposed for bit allocation optimization, where the convexity of the mixed R-D model based utility function is proved, and Nash bargaining solution (NBS) is achieved by the proposed iterative solution search method. The minimum utility is adjusted by the reference coding distortion and frame-level Quantization parameter (QP) change. Lastly, intra frame QP and inter frame adaptive bit ratios are adjusted to make inter frames have more bit resources to maintain smooth quality and bit consumption in the bargaining game optimization. Experimental results demonstrate that the proposed MLGT based RC method can achieve much better R-D performances, quality smoothness, bit rate accuracy, buffer control results and subjective visual quality than the other state-of-the-art one-pass RC methods, and the achieved R-D performances are very close to the performance limits from the FixedQP method.

  10. Aircraft Control Using Engine Thrust: A History of Learning TOC Real-Time

    Science.gov (United States)

    Cole, Jennifer H.; Batteas, Frank; Fullerton, Gordon

    2006-01-01

    A history of learning the operation of Throttles Only Control (TOC) to control an aircraft in real time using engine thrust is shown. The topics include: 1) Past TOC Accidents/Incidents; 2) 1972: DC-10 American Airlines; 3) May 1974: USAF B-52H; 4) April 1975: USAF C-5A; 5) April 1975: USAF C-5A; 6) 1981: USAF B-52G; 7) August 1985: JAL 123 B-747; 8) JAL 123 Survivor Story; 9) JAL 123 Investigation Findings; 10) July 1989: UAL 232 DC-10; 11) UAL 232 DC-10; 12) Eastwind 517 B-737; 13) November 2003: DHL A-300; 14) Historically, TOC has saved lives; 15) Automated Throttles-Only Control; 16) PCA Project; 17) Propulsion-Controlled Aircraft; 18) MD-11 PCA System and Flight Test Envelope; 19) MD-11 Simulation, PCA ILS-Soupled Landing Dispersion; 20) Throttles-Only Pitch and Roll Control Power; 21) PCA in Commercial Fleet; 22) Fall 2005: PCAR Project; 23) PCAR Background - TOC; and 24) PCAR Background - TOC.

  11. Early stage second-language learning improves executive control: evidence from ERP.

    Science.gov (United States)

    Sullivan, Margot D; Janus, Monika; Moreno, Sylvain; Astheimer, Lori; Bialystok, Ellen

    2014-12-01

    A growing body of research has reported a bilingual advantage in performance on executive control tasks, but it is not known at what point in emerging bilingualism these advantages first appear. The present study investigated the effect of early stage second-language training on executive control. Monolingual English-speaking students were tested on a go-nogo task, sentence judgment task, and verbal fluency, before and after 6 months of Spanish instruction. The training group (n = 25) consisted of students enrolled in introductory Spanish and the control group (n = 30) consisted of students enrolled in introductory Psychology. After training, the Spanish group showed larger P3 amplitude on the go-nogo task and smaller P600 amplitude on the judgment task, indicating enhanced performance, with no changes for the control group and no differences between groups on behavioral measures. Results are discussed in terms of neural changes underlying executive control after brief second-language learning. Copyright © 2014 Elsevier Inc. All rights reserved.

  12. Bio-inspired adaptive feedback error learning architecture for motor control.

    Science.gov (United States)

    Tolu, Silvia; Vanegas, Mauricio; Luque, Niceto R; Garrido, Jesús A; Ros, Eduardo

    2012-10-01

    This study proposes an adaptive control architecture based on an accurate regression method called Locally Weighted Projection Regression (LWPR) and on a bio-inspired module, such as a cerebellar-like engine. This hybrid architecture takes full advantage of the machine learning module (LWPR kernel) to abstract an optimized representation of the sensorimotor space while the cerebellar component integrates this to generate corrective terms in the framework of a control task. Furthermore, we illustrate how the use of a simple adaptive error feedback term allows to use the proposed architecture even in the absence of an accurate analytic reference model. The presented approach achieves an accurate control with low gain corrective terms (for compliant control schemes). We evaluate the contribution of the different components of the proposed scheme comparing the obtained performance with alternative approaches. Then, we show that the presented architecture can be used for accurate manipulation of different objects when their physical properties are not directly known by the controller. We evaluate how the scheme scales for simulated plants of high Degrees of Freedom (7-DOFs).

  13. Using multivariate machine learning methods and structural MRI to classify childhood onset schizophrenia and healthy controls

    Directory of Open Access Journals (Sweden)

    Deanna eGreenstein

    2012-06-01

    Full Text Available Introduction: Multivariate machine learning methods can be used to classify groups of schizophrenia patients and controls using structural magnetic resonance imaging (MRI. However, machine learning methods to date have not been extended beyond classification and contemporaneously applied in a meaningful way to clinical measures. We hypothesized that brain measures would classify groups, and that increased likelihood of being classified as a patient using regional brain measures would be positively related to illness severity, developmental delays and genetic risk. Methods: Using 74 anatomic brain MRI sub regions and Random Forest, we classified 98 COS patients and 99 age, sex, and ethnicity-matched healthy controls. We also used Random Forest to determine the likelihood of being classified as a schizophrenia patient based on MRI measures. We then explored relationships between brain-based probability of illness and symptoms, premorbid development, and presence of copy number variation associated with schizophrenia. Results: Brain regions jointly classified COS and control groups with 73.7% accuracy. Greater brain-based probability of illness was associated with worse functioning (p= 0.0004 and fewer developmental delays (p=0.02. Presence of copy number variation (CNV was associated with lower probability of being classified as schizophrenia (p=0.001. The regions that were most important in classifying groups included left temporal lobes, bilateral dorsolateral prefrontal regions, and left medial parietal lobes. Conclusions: Schizophrenia and control groups can be well classified using Random Forest and anatomic brain measures, and brain-based probability of illness has a positive relationship with illness severity and a negative relationship with developmental delays/problems and CNV-based risk.

  14. Feedback error learning controller for functional electrical stimulation assistance in a hybrid robotic system for reaching rehabilitation

    Directory of Open Access Journals (Sweden)

    Francisco Resquín

    2016-07-01

    Full Text Available Hybrid robotic systems represent a novel research field, where functional electrical stimulation (FES is combined with a robotic device for rehabilitation of motor impairment. Under this approach, the design of robust FES controllers still remains an open challenge. In this work, we aimed at developing a learning FES controller to assist in the performance of reaching movements in a simple hybrid robotic system setting. We implemented a Feedback Error Learning (FEL control strategy consisting of a feedback PID controller and a feedforward controller based on a neural network. A passive exoskeleton complemented the FES controller by compensating the effects of gravity. We carried out experiments with healthy subjects to validate the performance of the system. Results show that the FEL control strategy is able to adjust the FES intensity to track the desired trajectory accurately without the need of a previous mathematical model.

  15. Learning and strain among newcomers: a three-wave study on the effects of Job Demands and Job Control

    NARCIS (Netherlands)

    Taris, T.W.; Feij, J.A.

    2004-01-01

    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

  16. Mediating Parent Learning to Promote Social Communication for Toddlers with Autism: Effects from a Randomized Controlled Trial

    Science.gov (United States)

    Schertz, Hannah H.; Odom, Samuel L.; Baggett, Kathleen M.; Sideris, John H.

    2018-01-01

    A randomized controlled trial was conducted to evaluate effects of the Joint Attention Mediated Learning (JAML) intervention. Toddlers with autism spectrum disorders (ASD) aged 16-30 months (n = 144) were randomized to intervention and community control conditions. Parents, who participated in 32 weekly home-based sessions, followed a mediated…

  17. Not that Different in Theory: Discussing the Control-Value Theory of Emotions in Online Learning Environments

    Science.gov (United States)

    Daniels, Lia M.; Stupnisky, Robert H.

    2012-01-01

    This commentary investigates the extent to which the control-value theory of emotions (Pekrun, 2006) is applicable in online learning environments. Four empirical studies in this special issue of "The Internet and Higher Education" explicitly used the control-value theory as their theoretical framework and several others have components of the…

  18. Cooperative learning neural network output feedback control of uncertain nonlinear multi-agent systems under directed topologies

    Science.gov (United States)

    Wang, W.; Wang, D.; Peng, Z. H.

    2017-09-01

    Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.

  19. The Control Room Upgrade in Oskarshamn 2 Modernization Project Lesson Learned from Ongoing Human Factor design

    International Nuclear Information System (INIS)

    Thomas, Gunnarsson; Magnus, Eliasson

    2011-01-01

    Due to recent changes in Swedish commercial nuclear safety system requirements, OKG decided to make the changes required by the new safety requirements, apply for a 30-year license extension, and to concurrently make changes for a major power uprate; this project is called the Plant Life Extension project (PLEX). It was decided, in addition to several plant modifications, to re build the old control room to a new modern screen-based control room located in the same space as the old one, and with the same number of operators. This paper explains the approach taken when modernizing the control room as a part of the Oskarshamn 2 Modernization project PLEX, the results, and the lessons learned from this ongoing work. The combination of changes results in a modernization project that is expected to increase output power by approximately 50 MWe through increased efficiency and to result in an increase in thermal power from 1800 MWt to 2300 MWt (28%) and electrical power from 620 MWe to 840 MWe due to the power uprate. The license to operate OKG2 expires in 2012 The PLEX project is one of the most ambitious nuclear power plant modernization projects ever implemented, world-wide. The application of human factors engineering (HFE) and control room and HSI design is a complex challenge. The original main control room from 1975 in Oskarshamn 2, was quite compact and provided a fairly good overview of the process. New requirements for enhanced safety and other design changes in the process systems and instrumentation led to a step-wise installation of new information and control equipment in the control room. Since the control room was quite limited in space, the control room grew larger, and the new equipment was installed farther away from the operator workplaces into an adjacent control room. This was even the case for the new safety systems. These systems were functioning well separately as such, but in some cases their interfaces were inconsistent, leading to increased

  20. Differential effects of controllable stress exposure on subsequent extinction learning in adult rats

    Directory of Open Access Journals (Sweden)

    Osnat eHadad-Ophir

    2016-01-01

    Full Text Available Deficits in fear extinction are thought to be related to various anxiety disorders. While failure to extinguish conditioned fear may result in pathological anxiety levels, the ability to quickly and efficiently attenuate learned fear through extinction processes can be extremely beneficial for the individual. One of the factors that may affect the efficiency of the extinction process is prior experience of stressful situations. In the current study, we examined whether exposure to controllable stress, which is suggested to induce stress resilience, can affect subsequent fear extinction. Here, following prolonged two-way shuttle (TWS avoidance training and a validation of acquired stress controllability, adult rats underwent either cued or contextual fear-conditioning (FC, followed by an extinction session. We further evaluated long lasting alterations of GABAergic targets in the medial pre-frontal cortex (mPFC, as these were implicated in FC and extinction and stress controllability. In cued, but not in contextual fear extinction, within-session extinction was enhanced following controllable stress compared to a control group. Interestingly, impaired extinction recall was detected in both extinction types following the stress procedure. Additionally, stress controllability-dependent alterations in GABAergic markers expression in infralimbic (IL, but not prelimbic (PL cortex, were detected. These alterations are proposed to be related to the within-session effect, but not the recall impairment. The results emphasize the contribution of prior experience on coping with subsequent stressful experiences. Moreover, the results emphasize that exposure to controllable stress does not generally facilitate future stress coping as previously claimed, but its effects are dependent on specific features of the events taking place.

  1. Effects of arousal on cognitive control: empirical tests of the conflict-modulated Hebbian-learning hypothesis.

    Science.gov (United States)

    Brown, Stephen B R E; van Steenbergen, Henk; Kedar, Tomer; Nieuwenhuis, Sander

    2014-01-01

    An increasing number of empirical phenomena that were previously interpreted as a result of cognitive control, turn out to reflect (in part) simple associative-learning effects. A prime example is the proportion congruency effect, the finding that interference effects (such as the Stroop effect) decrease as the proportion of incongruent stimuli increases. While this was previously regarded as strong evidence for a global conflict monitoring-cognitive control loop, recent evidence has shown that the proportion congruency effect is largely item-specific and hence must be due to associative learning. The goal of our research was to test a recent hypothesis about the mechanism underlying such associative-learning effects, the conflict-modulated Hebbian-learning hypothesis, which proposes that the effect of conflict on associative learning is mediated by phasic arousal responses. In Experiment 1, we examined in detail the relationship between the item-specific proportion congruency effect and an autonomic measure of phasic arousal: task-evoked pupillary responses. In Experiment 2, we used a task-irrelevant phasic arousal manipulation and examined the effect on item-specific learning of incongruent stimulus-response associations. The results provide little evidence for the conflict-modulated Hebbian-learning hypothesis, which requires additional empirical support to remain tenable.

  2. Effects of arousal on cognitive control: Empirical tests of the conflict-modulated Hebbian-learning hypothesis

    Directory of Open Access Journals (Sweden)

    Stephen B.R.E. Brown

    2014-01-01

    Full Text Available An increasing number of empirical phenomena that were previously interpreted as a result of cognitive control, turn out to reflect (in part simple associative-learning effects. A prime example is the proportion congruency effect, the finding that interference effects (such as the Stroop effect decrease as the proportion of incongruent stimuli increases. While this was previously regarded as strong evidence for a global conflict monitoring-cognitive control loop, recent evidence has shown that the proportion congruency effect is largely item-specific and hence must be due to associative learning. The goal of our research was to test a recent hypothesis about the mechanism underlying such associative-learning effects, the conflict-modulated Hebbian-learning hypothesis, which proposes that the effect of conflict on associative learning is mediated by phasic arousal responses. In Experiment 1, we examined in detail the relationship between the item-specific proportion congruency effect and an autonomic measure of phasic arousal: task-evoked pupillary responses. In Experiment 2, we used a task-irrelevant phasic arousal manipulation and examined the effect on item-specific learning of incongruent stimulus-response associations. The results provide little evidence for the conflict-modulated Hebbian-learning hypothesis, which requires additional empirical support to remain tenable.

  3. Altered brain activation in a reversal learning task unmasks adaptive changes in cognitive control in writer's cramp.

    Science.gov (United States)

    Zeuner, Kirsten E; Knutzen, Arne; Granert, Oliver; Sablowsky, Simone; Götz, Julia; Wolff, Stephan; Jansen, Olav; Dressler, Dirk; Schneider, Susanne A; Klein, Christine; Deuschl, Günther; van Eimeren, Thilo; Witt, Karsten

    2016-01-01

    Previous receptor binding studies suggest dopamine function is altered in the basal ganglia circuitry in task-specific dystonia, a condition characterized by contraction of agonist and antagonist muscles while performing specific tasks. Dopamine plays a role in reward-based learning. Using fMRI, this study compared 31 right-handed writer's cramp patients to 35 controls in reward-based learning of a probabilistic reversal-learning task. All subjects chose between two stimuli and indicated their response with their left or right index finger. One stimulus response was rewarded 80%, the other 20%. After contingencies reversal, the second stimulus response was rewarded in 80%. We further linked the DRD2/ANKK1-TaqIa polymorphism, which is associated with 30% reduction of the striatal dopamine receptor density with reward-based learning and assumed impaired reversal learning in A + subjects. Feedback learning in patients was normal. Blood-oxygen level dependent (BOLD) signal in controls increased with negative feedback in the insula, rostral cingulate cortex, middle frontal gyrus and parietal cortex (pFWE based learning. The dACC is connected with the basal ganglia-thalamo-loop modulated by dopaminergic signaling. This finding suggests disturbed integration of reinforcement history in decision making and implicate that the reward system might contribute to the pathogenesis in writer's cramp.

  4. Self-controlled learning benefits: exploring contributions of self-efficacy and intrinsic motivation via path analysis.

    Science.gov (United States)

    Ste-Marie, Diane M; Carter, Michael J; Law, Barbi; Vertes, Kelly; Smith, Victoria

    2016-09-01

    Research has shown learning advantages for self-controlled practice contexts relative to yoked (i.e., experimenter-imposed) contexts; yet, explanations for this phenomenon remain relatively untested. We examined, via path analysis, whether self-efficacy and intrinsic motivation are important constructs for explaining self-controlled learning benefits. The path model was created using theory-based and empirically supported relationships to examine causal links between these psychological constructs and physical performance. We hypothesised that self-efficacy and intrinsic motivation would have greater predictive power for learning under self-controlled compared to yoked conditions. Participants learned double-mini trampoline progressions, and measures of physical performance, self-efficacy and intrinsic motivation were collected over two practice days and a delayed retention day. The self-controlled group (M = 2.04, SD = .98) completed significantly more skill progressions in retention than their yoked counterparts (M = 1.3, SD = .65). The path model displayed adequate fit, and similar significant path coefficients were found for both groups wherein each variable was predominantly predicted by its preceding time point (e.g., self-efficacy time 1 predicts self-efficacy time 2). Interestingly, the model was not moderated by group; thus, failing to support the hypothesis that self-efficacy and intrinsic motivation have greater predictive power for learning under self-controlled relative to yoked conditions.

  5. The more you learn, the less you store : Memory-controlled incremental SVM for visual place recognition

    OpenAIRE

    Pronobis, Andrzej; Jie, Luo; Caputo, Barbara

    2010-01-01

    The capability to learn from experience is a key property for autonomous cognitive systems working in realistic settings. To this end, this paper presents an SVM-based algorithm, capable of learning model representations incrementally while keeping under control memory requirements. We combine an incremental extension of SVMs [43] with a method reducing the number of support vectors needed to build the decision function without any loss in performance [15] introducing a parameter which permit...

  6. The Effects of Locus of Control on University Students' Mobile Learning Adoption

    Science.gov (United States)

    Hsia, Jung-Wen

    2016-01-01

    Since mobile devices have become cheaper, easily accessible, powerful, and popular and the cost of wireless access has declined gradually, mobile learning (m-learning) has begun to spread rapidly. To further improve the effectiveness and efficiency of m-learning for university students, it is critical to understand whether they use m-learning.…

  7. The regulatory control over radiation sources: the Brazilian experience and some lessons learned from industrial applications

    International Nuclear Information System (INIS)

    Costa, E.L.C.; Gomes, J.D.R.L.; Gomes, R.S.; Costa, M.L.L.; Thomé, Z.D.; Instituto Militar de Engenharia

    2017-01-01

    This study gives an overview of the activities of the National Commission of Nuclear Energy (CNEN), as the Brazilian nuclear regulatory authority. These activities are described, especially those related to management of orphan sources and radioactive material in scrap metal considering the actions already put into place by CNEN during the licensing and controlling of radioactive sources in the industry and other facilities. In Brazil, there is not yet an effective system for controlling the scrap metal and recycling industry, thus a coordinated approach to achieve a harmonized and effective response with the involvement of third parties is needed, especially the metal industries and ores facilities. These practices call for stringent regulatory control, in order to reduce the occurrence of orphan sources, and consequently, radioactive material appearing in scrap metal. Some challenges of managing the national radiation sources register systems will be discussed, in order to cover effectively all the radiation source history (in a 'from the cradle to the grave' basis), and the dynamic maintenance and update of these data. The main industrial applications considered in this work are those dealing with constant movement of sources all over the country, with geographical issues to be considered in the managing and controlling actions, such as gammagraphy and well-logging. This study aims to identify and promote good practices to prevent inadvertent diversion of radioactive material, taking into account existing international recommendations and some lessons learned in national level. (author)

  8. The regulatory control over radiation sources: the Brazilian experience and some lessons learned from industrial applications

    Energy Technology Data Exchange (ETDEWEB)

    Costa, E.L.C.; Gomes, J.D.R.L.; Gomes, R.S.; Costa, M.L.L.; Thomé, Z.D., E-mail: evaldo@cnen.gov.br, E-mail: jlopes@cnen.gov.br, E-mail: rogeriog@cnen.gov.br, E-mail: mara@cnen.gov.br, E-mail: zielithome@gmail.com [Comissao Nacional de Energia Nuclear (CNEN), Rio de Janeiro, RJ (Brazil). Diretoria de Radioproteção e Segurança Nuclear; Instituto Militar de Engenharia (IME), Rio de Janeiro, RJ (Brazil). Seção de Engenharia Nuclear

    2017-11-01

    This study gives an overview of the activities of the National Commission of Nuclear Energy (CNEN), as the Brazilian nuclear regulatory authority. These activities are described, especially those related to management of orphan sources and radioactive material in scrap metal considering the actions already put into place by CNEN during the licensing and controlling of radioactive sources in the industry and other facilities. In Brazil, there is not yet an effective system for controlling the scrap metal and recycling industry, thus a coordinated approach to achieve a harmonized and effective response with the involvement of third parties is needed, especially the metal industries and ores facilities. These practices call for stringent regulatory control, in order to reduce the occurrence of orphan sources, and consequently, radioactive material appearing in scrap metal. Some challenges of managing the national radiation sources register systems will be discussed, in order to cover effectively all the radiation source history (in a 'from the cradle to the grave' basis), and the dynamic maintenance and update of these data. The main industrial applications considered in this work are those dealing with constant movement of sources all over the country, with geographical issues to be considered in the managing and controlling actions, such as gammagraphy and well-logging. This study aims to identify and promote good practices to prevent inadvertent diversion of radioactive material, taking into account existing international recommendations and some lessons learned in national level. (author)

  9. Reinforcement learning controller design for affine nonlinear discrete-time systems using online approximators.

    Science.gov (United States)

    Yang, Qinmin; Jagannathan, Sarangapani

    2012-04-01

    In this paper, reinforcement learning state- and output-feedback-based adaptive critic controller designs are proposed by using the online approximators (OLAs) for a general multi-input and multioutput affine unknown nonlinear discretetime systems in the presence of bounded disturbances. The proposed controller design has two entities, an action network that is designed to produce optimal signal and a critic network that evaluates the performance of the action network. The critic estimates the cost-to-go function which is tuned online using recursive equations derived from heuristic dynamic programming. Here, neural networks (NNs) are used both for the action and critic whereas any OLAs, such as radial basis functions, splines, fuzzy logic, etc., can be utilized. For the output-feedback counterpart, an additional NN is designated as the observer to estimate the unavailable system states, and thus, separation principle is not required. The NN weight tuning laws for the controller schemes are also derived while ensuring uniform ultimate boundedness of the closed-loop system using Lyapunov theory. Finally, the effectiveness of the two controllers is tested in simulation on a pendulum balancing system and a two-link robotic arm system.

  10. Stable Myoelectric Control of a Hand Prosthesis using Non-Linear Incremental Learning

    Directory of Open Access Journals (Sweden)

    Arjan eGijsberts

    2014-02-01

    Full Text Available Stable myoelectric control of hand prostheses remains an open problem. The only successful human-machine interface is surface electromyography, typically allowing control of a few degrees of freedom. Machine learning techniques may have the potential to remove these limitations, but their performance is thus far inadequate: myoelectric signals change over time under the influence of various factors, deteriorating control performance. It is therefore necessary, in the standard approach, to regularly retrain a new model from scratch.We hereby propose a non-linear incremental learning method in which occasional updates with a modest amount of novel training data allow continual adaptation to the changes in the signals. In particular, Incremental Ridge Regression and an approximation of the Gaussian Kernel known as Random Fourier Features are combined to predict finger forces from myoelectric signals, both finger-by-finger and grouped in grasping patterns.We show that the approach is effective and practically applicable to this problem by first analyzing its performance while predicting single-finger forces. Surface electromyography and finger forces were collected from 10 intact subjects during four sessions spread over two different days; the results of the analysis show that small incremental updates are indeed effective to maintain a stable level of performance.Subsequently, we employed the same method on-line to teleoperate a humanoid robotic arm equipped with a state-of-the-art commercial prosthetic hand. The subject could reliably grasp, carry and release everyday-life objects, enforcing stable grasping irrespective of the signal changes, hand/arm movements and wrist pronation and supination.

  11. [A study on comparison of learning effects between a board game and a lecture about infection control].

    Science.gov (United States)

    Kawamura, Hitomi; Kishimoto, Keiko; Matsuda, Toshiyuki; Fukushima, Noriko

    2014-01-01

      In order to provide an opportunity for community pharmacists to actively learn about infection control, this study created learning materials through a board game format and verified characteristics of learning by determining and comparing evaluation according to viewpoint and motivational effects between a lecture and the game. To create the board game, we collected cases of infection from 30 community pharmacists. The game was created using collected and created case studies, and we held a workshop on infection control. Participants were assigned to a lecture (n=32) or game group (n=27) and completed a questionnaire before and after the workshop. The questionnaire included the evaluation according to viewpoint based on the ministry's curriculum guidelines and the motivational effect of Keller's ARCS motivation model. In the evaluation according to viewpoint, the lecture group scores were significantly higher on "knowledge and understanding" than the game group scores. In the comparison of the motivational effects, the game group was significantly higher in three out of the four items of the ARCS motivation model, "Attention", "Relevance", and "Satisfaction". These results indicate that learning through the game aroused the curiosity of the learners, increased the learning outcome, and maintained certain levels of motivation. In addition, the evaluation according to viewpoint showed that the lecture group understood the key concepts and knowledge regarding infection control, whereas there was a possibility that the game group required additional motivational factors for learning and maintaining motivation level.

  12. Application Of Reinforcement Learning In Heading Control Of A Fixed Wing UAV Using X-Plane Platform

    Directory of Open Access Journals (Sweden)

    Kimathi

    2017-02-01

    Full Text Available Heading control of an Unmanned Aerial Vehicle UAV is a vital operation of an autopilot system. It is executed by employing a design of control algorithms that control its direction and navigation. Most commonly available autopilots exploit Proportional-Integral-Derivative PID based heading controllers. In this paper we propose an online adaptive reinforcement learning heading controller. The autopilot heading controller will be designed in MatlabSimulink for controlling a UAV in X-Plane test platform. Through this platform the performance of the controller is shown using real time simulations. The performance of this controller is compared to that of a PID controller. The results show that the proposed method performs better than a well tuned PID controller.

  13. A controlled study of team-based learning for undergraduate clinical neurology education

    Directory of Open Access Journals (Sweden)

    Umapathi Thirugnanam

    2011-10-01

    Full Text Available Abstract Background Team-based learning (TBL, a new active learning method, has not been reported for neurology education. We aimed to determine if TBL was more effective than passive learning (PL in improving knowledge outcomes in two key neurology topics - neurological localization and neurological emergencies. Methods We conducted a modified crossover study during a nine-week internal medicine posting involving 49 third-year medical undergraduates, using TBL as the active intervention, compared against self-reading as a PL control, for teaching the two topics. Primary outcome was the mean percentage change in test scores immediately after (post-test 1 and 48 hours after TBL (post-test 2, compared to a baseline pre-test. Student engagement was the secondary outcome. Results Mean percentage change in scores was greater in the TBL versus the PL group in post-test 1 (8.8% vs 4.3%, p = 0.023 and post-test 2 (11.4% vs 3.4%, p = 0.001. After adjustment for gender and second year examination grades, mean percentage change in scores remained greater in the TBL versus the PL group for post-test 1 (10.3% vs 5.8%, mean difference 4.5%,95% CI 0.7 - 8.3%, p = 0.021 and post-test 2 (13.0% vs 4.9%, mean difference 8.1%,95% CI 3.7 - 12.5%, p = 0.001, indicating further score improvement 48 hours post-TBL. Academically weaker students, identified by poorer examination grades, showed a greater increase in scores with TBL versus strong students (p Conclusions Compared to PL, TBL showed greater improvement in knowledge scores, with continued improvement up to 48 hours later. This effect is larger in academically weaker students. TBL is an effective method for improving knowledge in neurological localization and neurological emergencies in undergraduates.

  14. Epigenetic control of learning and memory in Drosophila by Tip60 HAT action.

    Science.gov (United States)

    Xu, Songjun; Wilf, Rona; Menon, Trisha; Panikker, Priyalakshmi; Sarthi, Jessica; Elefant, Felice

    2014-12-01

    Disruption of epigenetic gene control mechanisms in the brain causes significant cognitive impairment that is a debilitating hallmark of most neurodegenerative disorders, including Alzheimer's disease (AD). Histone acetylation is one of the best characterized of these epigenetic mechanisms that is critical for regulating learning- and memory- associated gene expression profiles, yet the specific histone acetyltransferases (HATs) that mediate these effects have yet to be fully characterized. Here, we investigate an epigenetic role for the HAT Tip60 in learning and memory formation using the Drosophila CNS mushroom body (MB) as a well-characterized cognition model. We show that Tip60 is endogenously expressed in the Kenyon cells, the intrinsic neurons of the MB, and in the MB axonal lobes. Targeted loss of Tip60 HAT activity in the MB causes thinner and shorter axonal lobes while increasing Tip60 HAT levels cause no morphological defects. Functional consequences of both loss and gain of Tip60 HAT levels in the MB are evidenced by defects in immediate-recall memory. Our ChIP-Seq analysis reveals that Tip60 target genes are enriched for functions in cognitive processes, and, accordingly, key genes representing these pathways are misregulated in the Tip60 HAT mutant fly brain. Remarkably, we find that both learning and immediate-recall memory deficits that occur under AD-associated, amyloid precursor protein (APP)-induced neurodegenerative conditions can be effectively rescued by increasing Tip60 HAT levels specifically in the MB. Together, our findings uncover an epigenetic transcriptional regulatory role for Tip60 in cognitive function and highlight the potential of HAT activators as a therapeutic option for neurodegenerative disorders. Copyright © 2014 by the Genetics Society of America.

  15. Wordless intervention for people with epilepsy and learning disabilities (WIELD): a randomised controlled feasibility trial.

    Science.gov (United States)

    Mengoni, Silvana E; Gates, Bob; Parkes, Georgina; Wellsted, David; Barton, Garry; Ring, Howard; Khoo, Mary Ellen; Monji-Patel, Deela; Friedli, Karin; Zia, Asif; Irvine, Lisa; Durand, Marie-Anne

    2016-11-10

    To investigate the feasibility of a full-scale randomised controlled trial of a picture booklet to improve quality of life for people with epilepsy and learning disabilities. A randomised controlled feasibility trial. Randomisation was not blinded and was conducted using a centralised secure database and a blocked 1:1 allocation ratio. Epilepsy clinics in 1 English National Health Service (NHS) Trust. Patients with learning disabilities and epilepsy who had: a seizure within the past 12 months, meaningful communication and a carer with sufficient proficiency in English. Participants in the intervention group used a picture booklet with a trained researcher, and a carer present. These participants kept the booklet, and were asked to use it at least twice more over 20 weeks. The control group received treatment as usual, and were provided with a booklet at the end of the study. 7 feasibility criteria were used relating to recruitment, data collection, attrition, potential effect on epilepsy-related quality of life (Epilepsy and Learning Disabilities Quality of Life Scale, ELDQOL) at 4-week, 12-week and 20-week follow-ups, feasibility of methodology, acceptability of the intervention and potential to calculate cost-effectiveness. The recruitment rate of eligible patients was 34% and the target of 40 participants was reached. There was minimal missing data and attrition. An intention-to-treat analysis was performed; data from the outcome measures suggest a benefit from the intervention on the ELDQOL behaviour and mood subscales at 4 and 20 weeks follow-up. The booklet and study methods were positively received, and no adverse events were reported. There was a positive indication of the potential for a cost-effectiveness analysis. All feasibility criteria were fully or partially met, therefore confirming feasibility of a definitive trial. ISRCTN80067039. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence

  16. Controlling the chaotic discrete-Hénon system using a feedforward neural network with an adaptive learning rate

    OpenAIRE

    GÖKCE, Kürşad; UYAROĞLU, Yılmaz

    2013-01-01

    This paper proposes a feedforward neural network-based control scheme to control the chaotic trajectories of a discrete-Hénon map in order to stay within an acceptable distance from the stable fixed point. An adaptive learning back propagation algorithm with online training is employed to improve the effectiveness of the proposed method. The simulation study carried in the discrete-Hénon system verifies the validity of the proposed control system.

  17. Effects of mobile phone-based app learning compared to computer-based web learning on nursing students: pilot randomized controlled trial.

    Science.gov (United States)

    Lee, Myung Kyung

    2015-04-01

    This study aimed to determine the effect of mobile-based discussion versus computer-based discussion on self-directed learning readiness, academic motivation, learner-interface interaction, and flow state. This randomized controlled trial was conducted at one university. Eighty-six nursing students who were able to use a computer, had home Internet access, and used a mobile phone were recruited. Participants were randomly assigned to either the mobile phone app-based discussion group (n = 45) or a computer web-based discussion group (n = 41). The effect was measured at before and after an online discussion via self-reported surveys that addressed academic motivation, self-directed learning readiness, time distortion, learner-learner interaction, learner-interface interaction, and flow state. The change in extrinsic motivation on identified regulation in the academic motivation (p = 0.011) as well as independence and ability to use basic study (p = 0.047) and positive orientation to the future in self-directed learning readiness (p = 0.021) from pre-intervention to post-intervention was significantly more positive in the mobile phone app-based group compared to the computer web-based discussion group. Interaction between learner and interface (p = 0.002), having clear goals (p = 0.012), and giving and receiving unambiguous feedback (p = 0.049) in flow state was significantly higher in the mobile phone app-based discussion group than it was in the computer web-based discussion group at post-test. The mobile phone might offer more valuable learning opportunities for discussion teaching and learning methods in terms of self-directed learning readiness, academic motivation, learner-interface interaction, and the flow state of the learning process compared to the computer.

  18. When Project Commitment Leads to Learning from Failure: The Roles of Perceived Shame and Personal Control

    Directory of Open Access Journals (Sweden)

    Wenzhou Wang

    2018-02-01

    Full Text Available Facing a remarkably changing world, researchers have gradually shifted emphasis from successful experiences to failures. In the current study, we build a model to explore the relationship between project commitment and learning from failure, and test how emotion (i.e., perceived shame after failure and cognition (i.e., attribution for failure affect this process. After randomly selecting 400 firms from the list of high-tech firms reported by the Beijing Municipal Science and Technology Commission, we use a two-wave investigation of the employees, and the final sample consists of 140 teams from 58 companies in the technology industry in mainland China. The results provide evidence for the positive role of personal control attribution in the relationship between project commitment and learning from failure. However, in contrast to previous studies, perceived shame, as the negative emotion after failed events, could bring desirable outcomes during this process. Based on the results, we further expand a model to explain the behavioral responses after failure, and the implications of our findings for research and practice are discussed.The failures and reverses which await men - and one after another sadden the brow of youth - add a dignity to the prospect of human life, which no Arcadian success would do.—Henry David Thoreau

  19. When Project Commitment Leads to Learning from Failure: The Roles of Perceived Shame and Personal Control

    Science.gov (United States)

    Wang, Wenzhou; Wang, Bin; Yang, Ke; Yang, Chong; Yuan, Wenlong; Song, Shanghao

    2018-01-01

    Facing a remarkably changing world, researchers have gradually shifted emphasis from successful experiences to failures. In the current study, we build a model to explore the relationship between project commitment and learning from failure, and test how emotion (i.e., perceived shame after failure) and cognition (i.e., attribution for failure) affect this process. After randomly selecting 400 firms from the list of high-tech firms reported by the Beijing Municipal Science and Technology Commission, we use a two-wave investigation of the employees, and the final sample consists of 140 teams from 58 companies in the technology industry in mainland China. The results provide evidence for the positive role of personal control attribution in the relationship between project commitment and learning from failure. However, in contrast to previous studies, perceived shame, as the negative emotion after failed events, could bring desirable outcomes during this process. Based on the results, we further expand a model to explain the behavioral responses after failure, and the implications of our findings for research and practice are discussed. The failures and reverses which await men - and one after another sadden the brow of youth - add a dignity to the prospect of human life, which no Arcadian success would do. —Henry David Thoreau PMID:29467699

  20. When Project Commitment Leads to Learning from Failure: The Roles of Perceived Shame and Personal Control.

    Science.gov (United States)

    Wang, Wenzhou; Wang, Bin; Yang, Ke; Yang, Chong; Yuan, Wenlong; Song, Shanghao

    2018-01-01

    Facing a remarkably changing world, researchers have gradually shifted emphasis from successful experiences to failures. In the current study, we build a model to explore the relationship between project commitment and learning from failure, and test how emotion (i.e., perceived shame after failure) and cognition (i.e., attribution for failure) affect this process. After randomly selecting 400 firms from the list of high-tech firms reported by the Beijing Municipal Science and Technology Commission, we use a two-wave investigation of the employees, and the final sample consists of 140 teams from 58 companies in the technology industry in mainland China. The results provide evidence for the positive role of personal control attribution in the relationship between project commitment and learning from failure. However, in contrast to previous studies, perceived shame, as the negative emotion after failed events, could bring desirable outcomes during this process. Based on the results, we further expand a model to explain the behavioral responses after failure, and the implications of our findings for research and practice are discussed. The failures and reverses which await men - and one after another sadden the brow of youth - add a dignity to the prospect of human life, which no Arcadian success would do. -Henry David Thoreau.