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Sample records for control predicts human

  1. Predictive Models of Procedural Human Supervisory Control Behavior

    Science.gov (United States)

    2011-01-01

    821708, Brest , France. Page 139 of 150 Boussemart, Y. and M. L. Cummings (2010). "Predicting Supervisory Control Behavior with Hidden Markov Models...Strategies for Strike Planning. COGIS 2006 - Cognitive Systems with Interactive Sensors, Paris . Burges, C. (1998). "A Tutorial on Support Vector Machines

  2. Optimal control predicts human performance on objects with internal degrees of freedom.

    Directory of Open Access Journals (Sweden)

    Arne J Nagengast

    2009-06-01

    Full Text Available On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.

  3. Applied predictive control

    CERN Document Server

    Sunan, Huang; Heng, Lee Tong

    2002-01-01

    The presence of considerable time delays in the dynamics of many industrial processes, leading to difficult problems in the associated closed-loop control systems, is a well-recognized phenomenon. The performance achievable in conventional feedback control systems can be significantly degraded if an industrial process has a relatively large time delay compared with the dominant time constant. Under these circumstances, advanced predictive control is necessary to improve the performance of the control system significantly. The book is a focused treatment of the subject matter, including the fundamentals and some state-of-the-art developments in the field of predictive control. Three main schemes for advanced predictive control are addressed in this book: • Smith Predictive Control; • Generalised Predictive Control; • a form of predictive control based on Finite Spectrum Assignment. A substantial part of the book addresses application issues in predictive control, providing several interesting case studie...

  4. Predicting Human Cooperation.

    Directory of Open Access Journals (Sweden)

    John J Nay

    Full Text Available The Prisoner's Dilemma has been a subject of extensive research due to its importance in understanding the ever-present tension between individual self-interest and social benefit. A strictly dominant strategy in a Prisoner's Dilemma (defection, when played by both players, is mutually harmful. Repetition of the Prisoner's Dilemma can give rise to cooperation as an equilibrium, but defection is as well, and this ambiguity is difficult to resolve. The numerous behavioral experiments investigating the Prisoner's Dilemma highlight that players often cooperate, but the level of cooperation varies significantly with the specifics of the experimental predicament. We present the first computational model of human behavior in repeated Prisoner's Dilemma games that unifies the diversity of experimental observations in a systematic and quantitatively reliable manner. Our model relies on data we integrated from many experiments, comprising 168,386 individual decisions. The model is composed of two pieces: the first predicts the first-period action using solely the structural game parameters, while the second predicts dynamic actions using both game parameters and history of play. Our model is successful not merely at fitting the data, but in predicting behavior at multiple scales in experimental designs not used for calibration, using only information about the game structure. We demonstrate the power of our approach through a simulation analysis revealing how to best promote human cooperation.

  5. Attention, predictions and expectations, and their violation: attentional control in the human brain

    OpenAIRE

    Vossel, Simone; Geng, Joy J.; Friston, Karl J.

    2014-01-01

    In the complex scenes of everyday life, our brains must select from among many competing inputs for perceptual synthesis—so that only the most relevant are fully processed and irrelevant (distracting) information is suppressed. At the same time, we must remain responsive to salient events outside our current focus of attention—and balancing these two processing modes is a fundamental task our brain constantly needs to solve.This Research Topic examines how attentional control is guided by sen...

  6. Understanding predictability and exploration in human mobility

    DEFF Research Database (Denmark)

    Cuttone, Andrea; Jørgensen, Sune Lehmann; González, Marta C.

    2018-01-01

    Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying...... strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms...... are important factors limiting our ability to predict human mobility....

  7. Generalized Predictive Control and Neural Generalized Predictive Control

    Directory of Open Access Journals (Sweden)

    Sadhana CHIDRAWAR

    2008-12-01

    Full Text Available As Model Predictive Control (MPC relies on the predictive Control using a multilayer feed forward network as the plants linear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. This paper presents a detailed derivation of the Generalized Predictive Control and Neural Generalized Predictive Control with Newton-Raphson as minimization algorithm. Taking three separate systems, performances of the system has been tested. Simulation results show the effect of neural network on Generalized Predictive Control. The performance comparison of this three system configurations has been given in terms of ISE and IAE.

  8. Human motion simulation predictive dynamics

    CERN Document Server

    Abdel-Malek, Karim

    2013-01-01

    Simulate realistic human motion in a virtual world with an optimization-based approach to motion prediction. With this approach, motion is governed by human performance measures, such as speed and energy, which act as objective functions to be optimized. Constraints on joint torques and angles are imposed quite easily. Predicting motion in this way allows one to use avatars to study how and why humans move the way they do, given specific scenarios. It also enables avatars to react to infinitely many scenarios with substantial autonomy. With this approach it is possible to predict dynamic motion without having to integrate equations of motion -- rather than solving equations of motion, this approach solves for a continuous time-dependent curve characterizing joint variables (also called joint profiles) for every degree of freedom. Introduces rigorous mathematical methods for digital human modelling and simulation Focuses on understanding and representing spatial relationships (3D) of biomechanics Develops an i...

  9. A human hemi-cornea model for eye irritation testing: quality control of production, reliability and predictive capacity.

    Science.gov (United States)

    Engelke, M; Zorn-Kruppa, M; Gabel, D; Reisinger, K; Rusche, B; Mewes, K R

    2013-02-01

    We have developed a 3-dimensional human hemi-cornea which comprises an immortalized epithelial cell line and keratocytes embedded in a collagen stroma. In the present study, we have used MTT reduction of the whole tissue to clarify whether the production of this complex 3-D-model is transferable into other laboratories and whether these tissues can be constructed reproducibly. Our results demonstrate the reproducible production of the hemi-cornea model according to standard operation procedures using 15 independent batches of reconstructed hemi-cornea models in two independent laboratories each. Furthermore, the hemi-cornea tissues have been treated with 20 chemicals of different eye-irritating potential under blind conditions to assess the performance and limitations of our test system comparing three different prediction models. The most suitable prediction model revealed an overall in vitro-in vivo concordance of 80% and 70% in the participating laboratories, respectively, and an inter-laboratory concordance of 80%. Sensitivity of the test was 77% and specificity was between 57% and 86% to discriminate classified from non-classified chemicals. We conclude that additional physiologically relevant endpoints in both epithelium and stroma have to be developed for the reliable prediction of all GHS classes of eye irritation in one stand alone test system. Copyright © 2012 Elsevier Ltd. All rights reserved.

  10. Adaptive filtering prediction and control

    CERN Document Server

    Goodwin, Graham C

    2009-01-01

    Preface1. Introduction to Adaptive TechniquesPart 1. Deterministic Systems2. Models for Deterministic Dynamical Systems3. Parameter Estimation for Deterministic Systems4. Deterministic Adaptive Prediction5. Control of Linear Deterministic Systems6. Adaptive Control of Linear Deterministic SystemsPart 2. Stochastic Systems7. Optimal Filtering and Prediction8. Parameter Estimation for Stochastic Dynamic Systems9. Adaptive Filtering and Prediction10. Control of Stochastic Systems11. Adaptive Control of Stochastic SystemsAppendicesA. A Brief Review of Some Results from Systems TheoryB. A Summary o

  11. HUMAN DECISIONS AND MACHINE PREDICTIONS.

    Science.gov (United States)

    Kleinberg, Jon; Lakkaraju, Himabindu; Leskovec, Jure; Ludwig, Jens; Mullainathan, Sendhil

    2018-02-01

    Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; and these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals. JEL Codes: C10 (Econometric and statistical methods and methodology), C55 (Large datasets: Modeling and analysis), K40 (Legal procedure, the legal system, and illegal behavior).

  12. Dynamic Simulation of Human Gait Model With Predictive Capability.

    Science.gov (United States)

    Sun, Jinming; Wu, Shaoli; Voglewede, Philip A

    2018-03-01

    In this paper, it is proposed that the central nervous system (CNS) controls human gait using a predictive control approach in conjunction with classical feedback control instead of exclusive classical feedback control theory that controls based on past error. To validate this proposition, a dynamic model of human gait is developed using a novel predictive approach to investigate the principles of the CNS. The model developed includes two parts: a plant model that represents the dynamics of human gait and a controller that represents the CNS. The plant model is a seven-segment, six-joint model that has nine degrees-of-freedom (DOF). The plant model is validated using data collected from able-bodied human subjects. The proposed controller utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize the control input so that the predicted error is minimal. To decrease the complexity of the model, two joints are controlled using a proportional-derivative (PD) controller. The developed predictive human gait model is validated by simulating able-bodied human gait. The simulation results show that the developed model is able to simulate the kinematic output close to experimental data.

  13. Human spinal motor control

    DEFF Research Database (Denmark)

    Nielsen, Jens Bo

    2016-01-01

    Human studies in the past three decades have provided us with an emerging understanding of how cortical and spinal networks collaborate to ensure the vast repertoire of human behaviors. We differ from other animals in having direct cortical connections to spinal motoneurons, which bypass spinal...... the central motor command by opening or closing sensory feedback pathways. In the future, human studies of spinal motor control, in close collaboration with animal studies on the molecular biology of the spinal cord, will continue to document the neural basis for human behavior. Expected final online...

  14. Predicting mortality from human faces.

    Science.gov (United States)

    Dykiert, Dominika; Bates, Timothy C; Gow, Alan J; Penke, Lars; Starr, John M; Deary, Ian J

    2012-01-01

    To investigate whether and to what extent mortality is predictable from facial photographs of older people. High-quality facial photographs of 292 members of the Lothian Birth Cohort 1921, taken at the age of about 83 years, were rated in terms of apparent age, health, attractiveness, facial symmetry, intelligence, and well-being by 12 young-adult raters. Cox proportional hazards regression was used to study associations between these ratings and mortality during a 7-year follow-up period. All ratings had adequate reliability. Concurrent validity was found for facial symmetry and intelligence (as determined by correlations with actual measures of fluctuating asymmetry in the faces and Raven Standard Progressive Matrices score, respectively), but not for the other traits. Age as rated from facial photographs, adjusted for sex and chronological age, was a significant predictor of mortality (hazard ratio = 1.36, 95% confidence interval = 1.12-1.65) and remained significant even after controlling for concurrent, objectively measured health and cognitive ability, and the other ratings. Health as rated from facial photographs, adjusted for sex and chronological age, significantly predicted mortality (hazard ratio = 0.81, 95% confidence interval = 0.67-0.99) but not after adjusting for rated age or objectively measured health and cognition. Rated attractiveness, symmetry, intelligence, and well-being were not significantly associated with mortality risk. Rated age of the face is a significant predictor of mortality risk among older people, with predictive value over and above that of objective or rated health status and cognitive ability.

  15. Model predictive control using fuzzy decision functions

    NARCIS (Netherlands)

    Kaymak, U.; Costa Sousa, da J.M.

    2001-01-01

    Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the

  16. Intermittent control: a computational theory of human control.

    Science.gov (United States)

    Gawthrop, Peter; Loram, Ian; Lakie, Martin; Gollee, Henrik

    2011-02-01

    The paradigm of continuous control using internal models has advanced understanding of human motor control. However, this paradigm ignores some aspects of human control, including intermittent feedback, serial ballistic control, triggered responses and refractory periods. It is shown that event-driven intermittent control provides a framework to explain the behaviour of the human operator under a wider range of conditions than continuous control. Continuous control is included as a special case, but sampling, system matched hold, an intermittent predictor and an event trigger allow serial open-loop trajectories using intermittent feedback. The implementation here may be described as "continuous observation, intermittent action". Beyond explaining unimodal regulation distributions in common with continuous control, these features naturally explain refractoriness and bimodal stabilisation distributions observed in double stimulus tracking experiments and quiet standing, respectively. Moreover, given that human control systems contain significant time delays, a biological-cybernetic rationale favours intermittent over continuous control: intermittent predictive control is computationally less demanding than continuous predictive control. A standard continuous-time predictive control model of the human operator is used as the underlying design method for an event-driven intermittent controller. It is shown that when event thresholds are small and sampling is regular, the intermittent controller can masquerade as the underlying continuous-time controller and thus, under these conditions, the continuous-time and intermittent controller cannot be distinguished. This explains why the intermittent control hypothesis is consistent with the continuous control hypothesis for certain experimental conditions.

  17. Human activity recognition and prediction

    CERN Document Server

    2016-01-01

    This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques. .

  18. Data-Based Predictive Control with Multirate Prediction Step

    Science.gov (United States)

    Barlow, Jonathan S.

    2010-01-01

    Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.

  19. Notes on human error analysis and prediction

    International Nuclear Information System (INIS)

    Rasmussen, J.

    1978-11-01

    The notes comprise an introductory discussion of the role of human error analysis and prediction in industrial risk analysis. Following this introduction, different classes of human errors and role in industrial systems are mentioned. Problems related to the prediction of human behaviour in reliability and safety analysis are formulated and ''criteria for analyzability'' which must be met by industrial systems so that a systematic analysis can be performed are suggested. The appendices contain illustrative case stories and a review of human error reports for the task of equipment calibration and testing as found in the US Licensee Event Reports. (author)

  20. Estimating Human Predictability From Mobile Sensor Data

    DEFF Research Database (Denmark)

    Jensen, Bjørn Sand; Larsen, Jakob Eg; Jensen, Kristian

    2010-01-01

    Quantification of human behavior is of prime interest in many applications ranging from behavioral science to practical applications like GSM resource planning and context-aware services. As proxies for humans, we apply multiple mobile phone sensors all conveying information about human behavior....... Using a recent, information theoretic approach it is demonstrated that the trajectories of individual sensors are highly predictable given complete knowledge of the infinite past. We suggest using a new approach to time scale selection which demonstrates that participants have even higher predictability...

  1. Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

    NARCIS (Netherlands)

    Tian, X.; Negenborn, R.R.; Van Overloop, P.J.A.T.M.; Mostert, E.

    2014-01-01

    In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for

  2. Intermittently-visual Tracking Experiments Reveal the Roles of Error-correction and Predictive Mechanisms in the Human Visual-motor Control System

    Science.gov (United States)

    Hayashi, Yoshikatsu; Tamura, Yurie; Sase, Kazuya; Sugawara, Ken; Sawada, Yasuji

    Prediction mechanism is necessary for human visual motion to compensate a delay of sensory-motor system. In a previous study, “proactive control” was discussed as one example of predictive function of human beings, in which motion of hands preceded the virtual moving target in visual tracking experiments. To study the roles of the positional-error correction mechanism and the prediction mechanism, we carried out an intermittently-visual tracking experiment where a circular orbit is segmented into the target-visible regions and the target-invisible regions. Main results found in this research were following. A rhythmic component appeared in the tracer velocity when the target velocity was relatively high. The period of the rhythm in the brain obtained from environmental stimuli is shortened more than 10%. The shortening of the period of rhythm in the brain accelerates the hand motion as soon as the visual information is cut-off, and causes the precedence of hand motion to the target motion. Although the precedence of the hand in the blind region is reset by the environmental information when the target enters the visible region, the hand motion precedes the target in average when the predictive mechanism dominates the error-corrective mechanism.

  3. Prediction and Uncertainty in Human Pavlovian to Instrumental Transfer

    Science.gov (United States)

    Trick, Leanne; Hogarth, Lee; Duka, Theodora

    2011-01-01

    Attentional capture and behavioral control by conditioned stimuli have been dissociated in animals. The current study assessed this dissociation in humans. Participants were trained on a Pavlovian schedule in which 3 visual stimuli, A, B, and C, predicted the occurrence of an aversive noise with 90%, 50%, or 10% probability, respectively.…

  4. Human chorionic gonadotrophin regression rate as a predictive factor of postmolar gestational trophoblastic neoplasm in high-risk hydatidiform mole: a case-control study.

    Science.gov (United States)

    Kim, Bo Wook; Cho, Hanbyoul; Kim, Hyunki; Nam, Eun Ji; Kim, Sang Wun; Kim, Sunghoon; Kim, Young Tae; Kim, Jae-Hoon

    2012-01-01

    The aim of this study was early prediction of postmolar gestational trophoblastic neoplasm (GTN) after evacuation of high-risk mole, by comparison of human chorionic gonadotrophin (hCG) regression rates. Fifty patients with a high-risk mole initially and spontaneously regressing after molar evacuation were selected from January 1, 1996 to May 31, 2010 (spontaneous regression group). Fifty patients with a high-risk mole initially and progressing to postmolar GTN after molar evacuation were selected (postmolar GTN group). hCG regression rates represented as hCG/initial hCG were compared between the two groups. The sensitivity and specificity of these rates for prediction of postmolar GTN were assessed using receiver operating characteristic curves. Multivariate analyses of associations between risk factors and postmolar GTN progression were performed. The mean regression rate of hCG between the two groups was compared. hCG regression rates represented as hCG/initial hCG (%) were 0.36% in the spontaneous regression group and 1.45% in the postmolar GTN group in the second week (p=0.003). Prediction of postmolar GTN by hCG regression rate revealed a sensitivity of 48.0% and specificity of 89.5% with a cut-off value of 0.716% and area under the curve (AUC) of 0.759 in the 2nd week (pfactor for postmolar GTN. Crown Copyright © 2011. Published by Elsevier Ireland Ltd. All rights reserved.

  5. Model predictive Controller for Mobile Robot

    OpenAIRE

    Alireza Rezaee

    2017-01-01

    This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is...

  6. Dynamic Algorithm for LQGPC Predictive Control

    DEFF Research Database (Denmark)

    Hangstrup, M.; Ordys, A.W.; Grimble, M.J.

    1998-01-01

    In this paper the optimal control law is derived for a multi-variable state space Linear Quadratic Gaussian Predictive Controller (LQGPC). A dynamic performance index is utilized resulting in an optimal steady state controller. Knowledge of future reference values is incorporated into the control......In this paper the optimal control law is derived for a multi-variable state space Linear Quadratic Gaussian Predictive Controller (LQGPC). A dynamic performance index is utilized resulting in an optimal steady state controller. Knowledge of future reference values is incorporated...... into the controller design and the solution is derived using the method of Lagrange multipliers. It is shown how well-known GPC controller can be obtained as a special case of the LQGPC controller design. The important advantage of using the LQGPC framework for designing predictive, e.g. GPS is that LQGPC enables...

  7. Hybrid Predictive Control for Dynamic Transport Problems

    CERN Document Server

    Núñez, Alfredo A; Cortés, Cristián E

    2013-01-01

    Hybrid Predictive Control for Dynamic Transport Problems develops methods for the design of predictive control strategies for nonlinear-dynamic hybrid discrete-/continuous-variable systems. The methodology is designed for real-time applications, particularly the study of dynamic transport systems. Operational and service policies are considered, as well as cost reduction. The control structure is based on a sound definition of the key variables and their evolution. A flexible objective function able to capture the predictive behaviour of the system variables is described. Coupled with efficient algorithms, mainly drawn from the area of computational intelligence, this is shown to optimize performance indices for real-time applications. The framework of the proposed predictive control methodology is generic and, being able to solve nonlinear mixed-integer optimization problems dynamically, is readily extendable to other industrial processes. The main topics of this book are: ●hybrid predictive control (HPC) ...

  8. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil

    2016-01-01

    For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

  9. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

    Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp

    2008-01-01

    In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optimal...... steady state is established for terminal constraint model predictive control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend...

  10. Wind turbine control and model predictive control for uncertain systems

    DEFF Research Database (Denmark)

    Thomsen, Sven Creutz

    as disturbance models for controller design. The theoretical study deals with Model Predictive Control (MPC). MPC is an optimal control method which is characterized by the use of a receding prediction horizon. MPC has risen in popularity due to its inherent ability to systematically account for time...

  11. Model Predictive Control for Smart Energy Systems

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus

    pumps, heat tanks, electrical vehicle battery charging/discharging, wind farms, power plants). 2.Embed forecasting methodologies for the weather (e.g. temperature, solar radiation), the electricity consumption, and the electricity price in a predictive control system. 3.Develop optimization algorithms....... Chapter 3 introduces Model Predictive Control (MPC) including state estimation, filtering and prediction for linear models. Chapter 4 simulates the models from Chapter 2 with the certainty equivalent MPC from Chapter 3. An economic MPC minimizes the costs of consumption based on real electricity prices...... that determined the flexibility of the units. A predictive control system easily handles constraints, e.g. limitations in power consumption, and predicts the future behavior of a unit by integrating predictions of electricity prices, consumption, and weather variables. The simulations demonstrate the expected...

  12. Human Posture and Movement Prediction based on Musculoskeletal Modeling

    DEFF Research Database (Denmark)

    Farahani, Saeed Davoudabadi

    2014-01-01

    Abstract This thesis explores an optimization-based formulation, so-called inverse-inverse dynamics, for the prediction of human posture and motion dynamics performing various tasks. It is explained how this technique enables us to predict natural kinematic and kinetic patterns for human posture...... and motion using AnyBody Modeling System (AMS). AMS uses inverse dynamics to analyze musculoskeletal systems and is, therefore, limited by its dependency on input kinematics. We propose to alleviate this dependency by assuming that voluntary postures and movement strategies in humans are guided by a desire...... expenditure, joint forces and other physiological properties derived from the detailed musculoskeletal analysis. Several attempts have been made to uncover the principles underlying motion control strategies in the literature. In case of some movements, like human squat jumping, there is almost no doubt...

  13. Predicting and Controlling Complex Networks

    Science.gov (United States)

    2015-06-22

    ubiquitous in nature and fundamental to evolution in ecosystems. However, a significant chal- lenge remains in understanding biodiversity since, by the...networks and control . . . . . . . . . . . . . . . . . . . 7 3.4 Pattern formation, synchronization and outbreak of biodiversity in cyclically...Ni, Y.-C. Lai, and C. Grebogi, “Pattern formation, synchronization and outbreak of biodiversity in cyclically competing games,” Physical Review E 83

  14. Predictive radiosensitivity tests in human lymphocytes

    International Nuclear Information System (INIS)

    Di Giorgio, Marina; Vallerga, Maria B.; Taja, Maria R.; Sardi, M.; Busto, E.; Mairal, L.; Roth, B.; Menendez, P.; Bonomi, M.

    2004-01-01

    Individual radiosensitivity is an inherent characteristic, associated with an abnormally increased reaction to ionising radiation of both the whole body and cells derived from body tissues. Human population is not uniform in its radiation sensitivity. Radiosensitive sub-groups exist, which would suffer an increased incidence of both deterministic and stochastic effects. Clinical studies have suggested that a large part of the spectrum of normal tissue reaction may be due to differences in individual radiosensitivity. The identification of such sub-groups should be relevant for radiation therapy and radiation protection purposes. It is suggested that DNA repair mechanisms are involved. Consequently, the characterization of DNA repair in lymphocytes through cytokinesis blocked micronucleus (MN) and alkaline single-cell microgel electrophoresis (comet) assays could be a suitable approache to evaluate individual radiosensitivity in vitro. The aims of this study were: 1) To assess the in vitro radiosensitivity of peripheral blood lymphocytes from two groups of cancer patients (prospectively and retrospectively studied), using MN and comet assays, in comparison with the clinical radiation reaction and 2) To test the predictive potential of both techniques for the identification of radiosensitivity sub-groups. 38 cancer patients receiving radiation therapy were enrolled in this study. 19 patients were evaluated prior, mid-way and on completion of treatment (prospective group) and 19 patients were evaluated about 6-18 month after radiotherapy (retrospective group). Cytogenetic data from the prospective group were analysed using a mathematical model to evaluate the attenuation of the cytogenetic effect as a function of the time between a single exposure and blood sampling, estimating a cytogenetic recovery factor k. In the retrospective group, blood samples were irradiated in vitro with 0 (control) or 2 Gy and evaluated using MN test. Cytogenetic data were analysed

  15. Model complexity control for hydrologic prediction

    NARCIS (Netherlands)

    Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.

    2008-01-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore

  16. Delta-Domain Predictive Control and Identification for Control

    DEFF Research Database (Denmark)

    Lauritsen, Morten Bach

    1997-01-01

    The present thesis is concerned with different aspects of modelling, control and identification of linear systems. Traditionally, discrete-time sampled-data systems are represented using shift-operator parametrizations. Such parametrizations are not suitable at fast sampling rates. An alternative...... minimum-variance predictor as a special case and to have a well-defined continuous-time limit. By means of this new prediction method a unified framework for discrete-time and continuous-time predictive control algorithms is developed. This contains a continuous-time like discrete-time predictive...... controller which is insensitive to the choice of sampling period and has a well-defined limit in the continuous-time case. Also more conventional discrete-time predictive control methods may be described within the unified approach. The predictive control algorithms are extended to frequency weighted...

  17. Improved fuzzy PID controller design using predictive functional control structure.

    Science.gov (United States)

    Wang, Yuzhong; Jin, Qibing; Zhang, Ridong

    2017-11-01

    In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  18. Intelligent Predictive Control of Nonlienar Processes Using

    DEFF Research Database (Denmark)

    Nørgård, Peter Magnus; Sørensen, Paul Haase; Poulsen, Niels Kjølstad

    1996-01-01

    This paper presents a novel approach to design of generalized predictive controllers (GPC) for nonlinear processes. A neural network is used for modelling the process and a gain-scheduling type of GPC is subsequently designed. The combination of neural network models and predictive control has...... frequently been discussed in the neural network community. This paper proposes an approximate scheme, the approximate predictive control (APC), which facilitates the implementation and gives a substantial reduction in the required amount of computations. The method is based on a technique for extracting...... linear models from a nonlinear neural network and using them in designing the control system. The performance of the controller is demonstrated in a simulation study of a pneumatic servo system...

  19. Cortisol in human milk predicts child BMI.

    Science.gov (United States)

    Hahn-Holbrook, Jennifer; Le, Tran Bao; Chung, Anna; Davis, Elysia Poggi; Glynn, Laura M

    2016-12-01

    Breastfeeding has been linked to lower rates of childhood obesity. Human milk contains cortisol, known to regulate glucose storage and metabolism. The aim of this study was to to test the hypothesis that early exposure to cortisol in human breast milk helps to modulate infant body mass index (BMI) trajectories over the first 2 years of life. Growth curve modeling was used to examine whether infant exposure to cortisol in human milk at 3 months predicted changes in child body mass index percentile (BMIP) at 6, 12, and 24 months of age in 51 breastfeeding mother-child pairs. Infants exposed to higher milk cortisol levels at 3 months were less likely to exhibit BMIP gains over the first 2 years of life, compared with infants exposed to lower milk cortisol. By age 2, infants exposed to higher milk cortisol levels had lower BMIPs than infants exposed to lower milk cortisol. Milk cortisol was a stronger predictor of BMIP change in girls than boys. Cortisol exposure through human milk may help to program metabolic functioning and childhood obesity risk. Further, because infant formula contains only trace amounts of glucocorticoids, these findings suggest that cortisol in milk is a novel biological pathway through which breastfeeding may protect against later obesity. © 2016 The Obesity Society.

  20. Model predictive control for a thermostatic controlled system

    DEFF Research Database (Denmark)

    Shafiei, Seyed Ehsan; Rasmussen, Henrik; Stoustrup, Jakob

    2013-01-01

    This paper proposes a model predictive control scheme to provide temperature set-points to thermostatic controlled cooling units in refrigeration systems. The control problem is formulated as a convex programming problem to minimize the overall operating cost of the system. The foodstuff temperat......This paper proposes a model predictive control scheme to provide temperature set-points to thermostatic controlled cooling units in refrigeration systems. The control problem is formulated as a convex programming problem to minimize the overall operating cost of the system. The foodstuff...

  1. Does genetic diversity predict health in humans?

    Directory of Open Access Journals (Sweden)

    Hanne C Lie

    2009-07-01

    Full Text Available Genetic diversity, especially at genes important for immune functioning within the Major Histocompatibility Complex (MHC, has been associated with fitness-related traits, including disease resistance, in many species. Recently, genetic diversity has been associated with mate preferences in humans. Here we asked whether these preferences are adaptive in terms of obtaining healthier mates. We investigated whether genetic diversity (heterozygosity and standardized mean d(2 at MHC and nonMHC microsatellite loci, predicted health in 153 individuals. Individuals with greater allelic diversity (d(2 at nonMHC loci and at one MHC locus, linked to HLA-DRB1, reported fewer symptoms over a four-month period than individuals with lower d(2. In contrast, there were no associations between MHC or nonMHC heterozygosity and health. NonMHC-d(2 has previously been found to predict male preferences for female faces. Thus, the current findings suggest that nonMHC diversity may play a role in both natural and sexual selection acting on human populations.

  2. Nonparametric predictive inference in statistical process control

    NARCIS (Netherlands)

    Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.

    2000-01-01

    New methods for statistical process control are presented, where the inferences have a nonparametric predictive nature. We consider several problems in process control in terms of uncertainties about future observable random quantities, and we develop inferences for these random quantities hased on

  3. Nonparametric predictive inference in statistical process control

    NARCIS (Netherlands)

    Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.

    2004-01-01

    Statistical process control (SPC) is used to decide when to stop a process as confidence in the quality of the next item(s) is low. Information to specify a parametric model is not always available, and as SPC is of a predictive nature, we present a control chart developed using nonparametric

  4. Human reliability analysis of control room operators

    Energy Technology Data Exchange (ETDEWEB)

    Santos, Isaac J.A.L.; Carvalho, Paulo Victor R.; Grecco, Claudio H.S. [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil)

    2005-07-01

    Human reliability is the probability that a person correctly performs some system required action in a required time period and performs no extraneous action that can degrade the system Human reliability analysis (HRA) is the analysis, prediction and evaluation of work-oriented human performance using some indices as human error likelihood and probability of task accomplishment. Significant progress has been made in the HRA field during the last years, mainly in nuclear area. Some first-generation HRA methods were developed, as THERP (Technique for human error rate prediction). Now, an array of called second-generation methods are emerging as alternatives, for instance ATHEANA (A Technique for human event analysis). The ergonomics approach has as tool the ergonomic work analysis. It focus on the study of operator's activities in physical and mental form, considering at the same time the observed characteristics of operator and the elements of the work environment as they are presented to and perceived by the operators. The aim of this paper is to propose a methodology to analyze the human reliability of the operators of industrial plant control room, using a framework that includes the approach used by ATHEANA, THERP and the work ergonomics analysis. (author)

  5. Tube Model Predictive Control with an Auxiliary Sliding Mode Controller

    Directory of Open Access Journals (Sweden)

    Miodrag Spasic

    2016-07-01

    Full Text Available This paper studies Tube Model Predictive Control (MPC with a Sliding Mode Controller (SMC as an auxiliary controller. It is shown how to calculate the tube widths under SMC control, and thus how much the constraints of the nominal MPC have to be tightened in order to achieve robust stability and constraint fulfillment. The analysis avoids the assumption of infinitely fast switching in the SMC controller.

  6. [Predictive ocular motor control in Parkinson's disease].

    Science.gov (United States)

    Ying, Li; Liu, Zhen-Guo; Chen, Wei; Gan, Jing; Wang, Wen-An

    2008-02-19

    To investigate the changes of predictive ocular motor function in the patients with Parkinson's disease (PD), and to discuss its clinical value. Videonystagmography (VNG) was used to examine 24 patients with idiopathic Parkinson's disease, 15 males and 9 females, aged 61 +/- 6 (50-69), and 24 sex and age-matched healthy control subjects on random ocular saccade (with the target moving at random intervals to random positions) and predictive ocular saccade (with the 1.25-second light target moving 10 degrees right or left from the center). In the random ocular saccade program, the latency of saccade of the PD patients was 284 ms +/- 58 ms, significantly longer than that of the healthy controls (236 ms +/- 37 ms, P = 0.003). In the predictive ocular saccade pattern, the latency of saccades the PD patients was 150 ms +/- 138 ms, significantly longer than that of the healthy controls (59 ms +/- 102 ms, P = 0.002). The appearance rate of predictive saccades (with the latency of saccade <80 ms) in the PD group was 21%, significantly lower than that in the control group (31%, P = 0.003). There is dysfunction of predictive ocular motor control in the PD patients, and the cognitive function may be impaired at the early stage of PD.

  7. General predictive control using the delta operator

    DEFF Research Database (Denmark)

    Jensen, Morten Rostgaard; Poulsen, Niels Kjølstad; Ravn, Ole

    1993-01-01

    This paper deals with two-discrete-time operators, the conventional forward shift-operator and the δ-operator. Both operators are treated in view of construction of suitable solutions to the Diophantine equation for the purpose of prediction. A general step-recursive scheme is presented. Finally...... a general predictive control (GPC) is formulated and applied adaptively to a continuous-time plant...

  8. Differing Air Traffic Controller Responses to Similar Trajectory Prediction Errors

    Science.gov (United States)

    Mercer, Joey; Hunt-Espinosa, Sarah; Bienert, Nancy; Laraway, Sean

    2016-01-01

    A Human-In-The-Loop simulation was conducted in January of 2013 in the Airspace Operations Laboratory at NASA's Ames Research Center. The simulation airspace included two en route sectors feeding the northwest corner of Atlanta's Terminal Radar Approach Control. The focus of this paper is on how uncertainties in the study's trajectory predictions impacted the controllers ability to perform their duties. Of particular interest is how the controllers interacted with the delay information displayed in the meter list and data block while managing the arrival flows. Due to wind forecasts with 30-knot over-predictions and 30-knot under-predictions, delay value computations included errors of similar magnitude, albeit in opposite directions. However, when performing their duties in the presence of these errors, did the controllers issue clearances of similar magnitude, albeit in opposite directions?

  9. Spatiotemporal property and predictability of large-scale human mobility

    Science.gov (United States)

    Zhang, Hai-Tao; Zhu, Tao; Fu, Dongfei; Xu, Bowen; Han, Xiao-Pu; Chen, Duxin

    2018-04-01

    Spatiotemporal characteristics of human mobility emerging from complexity on individual scale have been extensively studied due to the application potential on human behavior prediction and recommendation, and control of epidemic spreading. We collect and investigate a comprehensive data set of human activities on large geographical scales, including both websites browse and mobile towers visit. Numerical results show that the degree of activity decays as a power law, indicating that human behaviors are reminiscent of scale-free random walks known as Lévy flight. More significantly, this study suggests that human activities on large geographical scales have specific non-Markovian characteristics, such as a two-segment power-law distribution of dwelling time and a high possibility for prediction. Furthermore, a scale-free featured mobility model with two essential ingredients, i.e., preferential return and exploration, and a Gaussian distribution assumption on the exploration tendency parameter is proposed, which outperforms existing human mobility models under scenarios of large geographical scales.

  10. Nonlinear predictive control in the LHC accelerator

    CERN Document Server

    Blanco, E; Cristea, S; Casas, J

    2009-01-01

    This paper describes the application of a nonlinear model-based control strategy in a real challenging process. A predictive controller based on a nonlinear model derived from physical relationships, mainly heat and mass balances, has been developed and commissioned in the inner triplet heat exchanger unit (IT-HXTU) of the large hadron collider (LHC) particle accelerator at European Center for Nuclear Research (CERN). The advanced regulation\\ maintains the magnets temperature at about 1.9 K. The development includes a constrained nonlinear state estimator with a receding horizon estimation procedure to improve the regulator predictions.

  11. Regularity and predictability of human mobility in personal space.

    Directory of Open Access Journals (Sweden)

    Daniel Austin

    Full Text Available Fundamental laws governing human mobility have many important applications such as forecasting and controlling epidemics or optimizing transportation systems. These mobility patterns, studied in the context of out of home activity during travel or social interactions with observations recorded from cell phone use or diffusion of money, suggest that in extra-personal space humans follow a high degree of temporal and spatial regularity - most often in the form of time-independent universal scaling laws. Here we show that mobility patterns of older individuals in their home also show a high degree of predictability and regularity, although in a different way than has been reported for out-of-home mobility. Studying a data set of almost 15 million observations from 19 adults spanning up to 5 years of unobtrusive longitudinal home activity monitoring, we find that in-home mobility is not well represented by a universal scaling law, but that significant structure (predictability and regularity is uncovered when explicitly accounting for contextual data in a model of in-home mobility. These results suggest that human mobility in personal space is highly stereotyped, and that monitoring discontinuities in routine room-level mobility patterns may provide an opportunity to predict individual human health and functional status or detect adverse events and trends.

  12. Applying model predictive control to power system frequency control

    OpenAIRE

    Ersdal, AM; Imsland, L; Cecilio, IM; Fabozzi, D; Thornhill, NF

    2013-01-01

    16.07.14 KB Ok to add accepted version to Spiral Model predictive control (MPC) is investigated as a control method which may offer advantages in frequency control of power systems than the control methods applied today, especially in presence of increased renewable energy penetration. The MPC includes constraints on both generation amount and generation rate of change, and it is tested on a one-area system. The proposed MPC is tested against a conventional proportional-integral (PI) cont...

  13. Distributed model predictive control made easy

    CERN Document Server

    Negenborn, Rudy

    2014-01-01

    The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems.   This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...

  14. Model predictive controller design of hydrocracker reactors

    OpenAIRE

    GÖKÇE, Dila

    2011-01-01

    This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...

  15. Distributed Model Predictive Control via Dual Decomposition

    DEFF Research Database (Denmark)

    Biegel, Benjamin; Stoustrup, Jakob; Andersen, Palle

    2014-01-01

    This chapter presents dual decomposition as a means to coordinate a number of subsystems coupled by state and input constraints. Each subsystem is equipped with a local model predictive controller while a centralized entity manages the subsystems via prices associated with the coupling constraints...

  16. Quantized Predictive Control over Erasure Channels

    DEFF Research Database (Denmark)

    E. Quevedo, Daniel; Østergaard, Jan

    2009-01-01

    .i.d. dropouts, the controller transmits data packets containing quantized plant input predictions. These minimize a finite horizon cost function and are provided by an appropriate optimal entropy coded dithered lattice vector quantizer. Within this context, we derive an equivalent noise-shaping model...

  17. Nonlinear model predictive control theory and algorithms

    CERN Document Server

    Grüne, Lars

    2017-01-01

    This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

  18. Fault Tolerant Control Using Gaussian Processes and Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Yang Xiaoke

    2015-03-01

    Full Text Available Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control.

  19. Predictive Mechanisms Are Not Involved the Same Way during Human-Human vs. Human-Machine Interactions: A Review

    Directory of Open Access Journals (Sweden)

    Aïsha Sahaï

    2017-10-01

    Full Text Available Nowadays, interactions with others do not only involve human peers but also automated systems. Many studies suggest that the motor predictive systems that are engaged during action execution are also involved during joint actions with peers and during other human generated action observation. Indeed, the comparator model hypothesis suggests that the comparison between a predicted state and an estimated real state enables motor control, and by a similar functioning, understanding and anticipating observed actions. Such a mechanism allows making predictions about an ongoing action, and is essential to action regulation, especially during joint actions with peers. Interestingly, the same comparison process has been shown to be involved in the construction of an individual's sense of agency, both for self-generated and observed other human generated actions. However, the implication of such predictive mechanisms during interactions with machines is not consensual, probably due to the high heterogeneousness of the automata used in the experimentations, from very simplistic devices to full humanoid robots. The discrepancies that are observed during human/machine interactions could arise from the absence of action/observation matching abilities when interacting with traditional low-level automata. Consistently, the difficulties to build a joint agency with this kind of machines could stem from the same problem. In this context, we aim to review the studies investigating predictive mechanisms during social interactions with humans and with automated artificial systems. We will start by presenting human data that show the involvement of predictions in action control and in the sense of agency during social interactions. Thereafter, we will confront this literature with data from the robotic field. Finally, we will address the upcoming issues in the field of robotics related to automated systems aimed at acting as collaborative agents.

  20. Plant control using embedded predictive models

    International Nuclear Information System (INIS)

    Godbole, S.S.; Gabler, W.E.; Eschbach, S.L.

    1990-01-01

    B and W recently undertook the design of an advanced light water reactor control system. A concept new to nuclear steam system (NSS) control was developed. The concept, which is called the Predictor-Corrector, uses mathematical models of portions of the controlled NSS to calculate, at various levels within the system, demand and control element position signals necessary to satisfy electrical demand. The models give the control system the ability to reduce overcooling and undercooling of the reactor coolant system during transients and upsets. Two types of mathematical models were developed for use in designing and testing the control system. One model was a conventional, comprehensive NSS model that responds to control system outputs and calculates the resultant changes in plant variables that are then used as inputs to the control system. Two other models, embedded in the control system, were less conventional, inverse models. These models accept as inputs plant variables, equipment states, and demand signals and predict plant operating conditions and control element states that will satisfy the demands. This paper reports preliminary results of closed-loop Reactor Coolant (RC) pump trip and normal load reduction testing of the advanced concept. Results of additional transient testing, and of open and closed loop stability analyses will be reported as they are available

  1. Predictive access control for distributed computation

    DEFF Research Database (Denmark)

    Yang, Fan; Hankin, Chris; Nielson, Flemming

    2013-01-01

    We show how to use aspect-oriented programming to separate security and trust issues from the logical design of mobile, distributed systems. The main challenge is how to enforce various types of security policies, in particular predictive access control policies — policies based on the future beh...... behavior of a program. A novel feature of our approach is that we can define policies concerning secondary use of data....

  2. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik

    2016-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....

  3. NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES

    Directory of Open Access Journals (Sweden)

    SILVA R. G.

    1999-01-01

    Full Text Available A new algorithm for model predictive control is presented. The algorithm utilizes a simultaneous solution and optimization strategy to solve the model's differential equations. The equations are discretized by equidistant collocation, and along with the algebraic model equations are included as constraints in a nonlinear programming (NLP problem. This algorithm is compared with the algorithm that uses orthogonal collocation on finite elements. The equidistant collocation algorithm results in simpler equations, providing a decrease in computation time for the control moves. Simulation results are presented and show a satisfactory performance of this algorithm.

  4. Predictability, Force and (Anti-)Resonance in Complex Object Control.

    Science.gov (United States)

    Maurice, Pauline; Hogan, Neville; Sternad, Dagmar

    2018-04-18

    Manipulation of complex objects as in tool use is ubiquitous and has given humans an evolutionary advantage. This study examined the strategies humans choose when manipulating an object with underactuated internal dynamics, such as a cup of coffee. The object's dynamics renders the temporal evolution complex, possibly even chaotic, and difficult to predict. A cart-and-pendulum model, loosely mimicking coffee sloshing in a cup, was implemented in a virtual environment with a haptic interface. Participants rhythmically manipulated the virtual cup containing a rolling ball; they could choose the oscillation frequency, while the amplitude was prescribed. Three hypotheses were tested: 1) humans decrease interaction forces between hand and object; 2) humans increase the predictability of the object dynamics; 3) humans exploit the resonances of the coupled object-hand system. Analysis revealed that humans chose either a high-frequency strategy with anti-phase cup-and-ball movements or a low-frequency strategy with in-phase cup-and-ball movements. Counter Hypothesis 1, they did not decrease interaction force; instead, they increased the predictability of the interaction dynamics, quantified by mutual information, supporting Hypothesis 2. To address Hypothesis 3, frequency analysis of the coupled hand-object system revealed two resonance frequencies separated by an anti-resonance frequency. The low-frequency strategy exploited one resonance, while the high-frequency strategy afforded more choice, consistent with the frequency response of the coupled system; both strategies avoided the anti-resonance. Hence, humans did not prioritize interaction force, but rather strategies that rendered interactions predictable. These findings highlight that physical interactions with complex objects pose control challenges not present in unconstrained movements.

  5. Wind farms production: Control and prediction

    Science.gov (United States)

    El-Fouly, Tarek Hussein Mostafa

    Wind energy resources, unlike dispatchable central station generation, produce power dependable on external irregular source and that is the incident wind speed which does not always blow when electricity is needed. This results in the variability, unpredictability, and uncertainty of wind resources. Therefore, the integration of wind facilities to utility electrical grid presents a major challenge to power system operator. Such integration has significant impact on the optimum power flow, transmission congestion, power quality issues, system stability, load dispatch, and economic analysis. Due to the irregular nature of wind power production, accurate prediction represents the major challenge to power system operators. Therefore, in this thesis two novel models are proposed for wind speed and wind power prediction. One proposed model is dedicated to short-term prediction (one-hour ahead) and the other involves medium term prediction (one-day ahead). The accuracy of the proposed models is revealed by comparing their results with the corresponding values of a reference prediction model referred to as the persistent model. Utility grid operation is not only impacted by the uncertainty of the future production of wind farms, but also by the variability of their current production and how the active and reactive power exchange with the grid is controlled. To address this particular task, a control technique for wind turbines, driven by doubly-fed induction generators (DFIGs), is developed to regulate the terminal voltage by equally sharing the generated/absorbed reactive power between the rotor-side and the gridside converters. To highlight the impact of the new developed technique in reducing the power loss in the generator set, an economic analysis is carried out. Moreover, a new aggregated model for wind farms is proposed that accounts for the irregularity of the incident wind distribution throughout the farm layout. Specifically, this model includes the wake effect

  6. Hierarchical Model Predictive Control for Resource Distribution

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob

    2010-01-01

    units. The approach is inspired by smart-grid electric power production and consumption systems, where the flexibility of a large number of power producing and/or power consuming units can be exploited in a smart-grid solution. The objective is to accommodate the load variation on the grid, arising......This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...... on one hand from varying consumption, on the other hand by natural variations in power production e.g. from wind turbines. The approach presented is based on quadratic optimization and possess the properties of low algorithmic complexity and of scalability. In particular, the proposed design methodology...

  7. Domestic appliances energy optimization with model predictive control

    International Nuclear Information System (INIS)

    Rodrigues, E.M.G.; Godina, R.; Pouresmaeil, E.; Ferreira, J.R.; Catalão, J.P.S.

    2017-01-01

    Highlights: • An alternative power management control for home appliances that require thermal regulation is presented. • A Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat. • Problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. • A modulation scheme of a two-level Model Predictive Control signal as an interface block is presented. • The implementation costs in home appliances with thermal regulation requirements are reduced. - Abstract: A vital element in making a sustainable world is correctly managing the energy in the domestic sector. Thus, this sector evidently stands as a key one for to be addressed in terms of climate change goals. Increasingly, people are aware of electricity savings by turning off the equipment that is not been used, or connect electrical loads just outside the on-peak hours. However, these few efforts are not enough to reduce the global energy consumption, which is increasing. Much of the reduction was due to technological improvements, however with the advancing of the years new types of control arise. Domestic appliances with the purpose of heating and cooling rely on thermostatic regulation technique. The study in this paper is focused on the subject of an alternative power management control for home appliances that require thermal regulation. In this paper a Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat with the aim of minimizing the cooling energy consumption through the minimization of the energy cost while satisfying the adequate temperature range for the human comfort. In addition, the Model Predictive Control problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. For this purpose, the typical consumption of a 24 h period of a summer day was simulated a three-level tariff scheme was used. The new

  8. Optimization control of LNG regasification plant using Model Predictive Control

    Science.gov (United States)

    Wahid, A.; Adicandra, F. F.

    2018-03-01

    Optimization of liquified natural gas (LNG) regasification plant is important to minimize costs, especially operational costs. Therefore, it is important to choose optimum LNG regasification plant design and maintaining the optimum operating conditions through the implementation of model predictive control (MPC). Optimal tuning parameter for MPC such as P (prediction horizon), M (control of the horizon) and T (sampling time) are achieved by using fine-tuning method. The optimal criterion for design is the minimum amount of energy used and for control is integral of square error (ISE). As a result, the optimum design is scheme 2 which is developed by Devold with an energy savings of 40%. To maintain the optimum conditions, required MPC with P, M and T as follows: tank storage pressure: 90, 2, 1; product pressure: 95, 2, 1; temperature vaporizer: 65, 2, 2; and temperature heater: 35, 6, 5, with ISE value at set point tracking respectively 0.99, 1792.78, 34.89 and 7.54, or improvement of control performance respectively 4.6%, 63.5%, 3.1% and 58.2% compared to PI controller performance. The energy savings that MPC controllers can make when there is a disturbance in temperature rise 1°C of sea water is 0.02 MW.

  9. Patterned control of human locomotion

    Science.gov (United States)

    Lacquaniti, Francesco; Ivanenko, Yuri P; Zago, Myrka

    2012-01-01

    There is much experimental evidence for the existence of biomechanical constraints which simplify the problem of control of multi-segment movements. In addition, it has been hypothesized that movements are controlled using a small set of basic temporal components or activation patterns, shared by several different muscles and reflecting global kinematic and kinetic goals. Here we review recent studies on human locomotion showing that muscle activity is accounted for by a combination of few basic patterns, each one timed at a different phase of the gait cycle. Similar patterns are involved in walking and running at different speeds, walking forwards or backwards, and walking under different loading conditions. The corresponding weights of distribution to different muscles may change as a function of the condition, allowing highly flexible control. Biomechanical correlates of each activation pattern have been described, leading to the hypothesis that the co-ordination of limb and body segments arises from the coupling of neural oscillators between each other and with limb mechanical oscillators. Muscle activations need only intervene during limited time epochs to force intrinsic oscillations of the system when energy is lost. PMID:22411012

  10. Patterned control of human locomotion.

    Science.gov (United States)

    Lacquaniti, Francesco; Ivanenko, Yuri P; Zago, Myrka

    2012-05-15

    There is much experimental evidence for the existence of biomechanical constraints which simplify the problem of control of multi-segment movements. In addition, it has been hypothesized that movements are controlled using a small set of basic temporal components or activation patterns, shared by several different muscles and reflecting global kinematic and kinetic goals. Here we review recent studies on human locomotion showing that muscle activity is accounted for by a combination of few basic patterns, each one timed at a different phase of the gait cycle. Similar patterns are involved in walking and running at different speeds, walking forwards or backwards, and walking under different loading conditions. The corresponding weights of distribution to different muscles may change as a function of the condition, allowing highly flexible control. Biomechanical correlates of each activation pattern have been described, leading to the hypothesis that the co-ordination of limb and body segments arises from the coupling of neural oscillators between each other and with limb mechanical oscillators. Muscle activations need only intervene during limited time epochs to force intrinsic oscillations of the system when energy is lost.

  11. Predictive torque and flux control of an induction machine drive ...

    Indian Academy of Sciences (India)

    Finite-state model predictive control; fuzzy decision making; multi-objective optimization; predictive torque control. Abstract. Among the numerous direct torque control techniques, the finite-state predictive torque control (FS-PTC) has emerged as a powerful alternative as it offers the fast dynamic response and the flexibility to ...

  12. Model Predictive Control for Load Frequency Control with Wind Turbines

    Directory of Open Access Journals (Sweden)

    Yi Zhang

    2015-01-01

    Full Text Available Reliable load frequency (LFC control is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-area interconnected power system with wind turbines, this paper presents a distributed model predictive control (DMPC based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The scheme incorporates the two critical nonlinear constraints, for example, the generation rate constraint (GRC and the valve limit, into convex optimization problems. Furthermore, the algorithm reduces the impact on the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and that without the participation of the wind turbines is carried out. Good performance is obtained in the presence of power system nonlinearities due to the governors and turbines constraints and load change disturbances.

  13. Prediction of Human Eye Fixations using Symmetry

    OpenAIRE

    Kootstra, Gert; Schomaker, Lambert R. B.

    2009-01-01

    Humans are very sensitive to symmetry in visual patterns. Reaction time experiments show that symmetry is detected and recognized very rapidly. This suggests that symmetry is a highly salient feature. Existing computational models of saliency, however, have mainly focused on contrast as a measure of saliency. In this paper, we discuss local symmetry as a measure of saliency. We propose a number of symmetry models and perform an eye-tracking study with human participants viewing photographic i...

  14. Metabolome of human gut microbiome is predictive of host dysbiosis.

    Science.gov (United States)

    Larsen, Peter E; Dai, Yang

    2015-01-01

    Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome's interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome. Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome-host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.

  15. Metabolome of human gut microbiome is predictive of host dysbiosis

    Energy Technology Data Exchange (ETDEWEB)

    Larsen, Peter E.; Dai, Yang

    2015-09-14

    Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.

  16. Human medial frontal cortex activity predicts learning from errors.

    Science.gov (United States)

    Hester, Robert; Barre, Natalie; Murphy, Kevin; Silk, Tim J; Mattingley, Jason B

    2008-08-01

    Learning from errors is a critical feature of human cognition. It underlies our ability to adapt to changing environmental demands and to tune behavior for optimal performance. The posterior medial frontal cortex (pMFC) has been implicated in the evaluation of errors to control behavior, although it has not previously been shown that activity in this region predicts learning from errors. Using functional magnetic resonance imaging, we examined activity in the pMFC during an associative learning task in which participants had to recall the spatial locations of 2-digit targets and were provided with immediate feedback regarding accuracy. Activity within the pMFC was significantly greater for errors that were subsequently corrected than for errors that were repeated. Moreover, pMFC activity during recall errors predicted future responses (correct vs. incorrect), despite a sizeable interval (on average 70 s) between an error and the next presentation of the same recall probe. Activity within the hippocampus also predicted future performance and correlated with error-feedback-related pMFC activity. A relationship between performance expectations and pMFC activity, in the absence of differing reinforcement value for errors, is consistent with the idea that error-related pMFC activity reflects the extent to which an outcome is "worse than expected."

  17. Validation of the mortality prediction equation for damage control ...

    African Journals Online (AJOL)

    , preoperative lowest pH and lowest core body temperature to derive an equation for the purpose of predicting mortality in damage control surgery. It was shown to reliably predict death despite damage control surgery. The equation derivation ...

  18. Data Driven Economic Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Masoud Kheradmandi

    2018-04-01

    Full Text Available This manuscript addresses the problem of data driven model based economic model predictive control (MPC design. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. The economic improvements yielded by the proposed method are illustrated through simulations on a nonlinear chemical process system example.

  19. Prediction of human eye fixations using symmetry

    NARCIS (Netherlands)

    Kootstra, Gert; Schomaker, Lambert

    2009-01-01

    Humans are very sensitive to symmetry in visual patterns. Reaction time experiments show that symmetry is detected and recognized very rapidly. This suggests that symmetry is a highly salient feature. Existing computational models of saliency, however, have mainly focused on contrast as a measure of

  20. Predicting and managing the human element

    International Nuclear Information System (INIS)

    Duffey, Romney B.; Saull, John W.

    2007-01-01

    Humans introduce new technology into homo-technological systems, as new reactors or any new product. The risk of failure is dependent on our experience, either through technical development and design mistakes and errors, or the product does not have a sufficient competitive advantage in the market. We review the learning curves that are now well known and established for both accident event analysis and product price reduction. We present the facts behind the integral contribution of human error to accidents and risk, and provide the theoretical analysis of outcome rates (undesirable event and cost reduction) based on the Learning Hypothesis. The importance of the accumulated experience on the learning rate, and the depth of experience on the distribution of outcomes, is presented. (author)

  1. Proactive Interference in Human Predictive Learning

    OpenAIRE

    Castro, Leyre; Ortega, Nuria; Matute, Helena

    2002-01-01

    The impairment in responding to a secondly trained association because of the prior training of another (i.e., proactive interference) is a well-established effect in human and animal research, and it has been demonstrated in many paradigms. However, learning theories have been concerned with proactive interference only when the competing stimuli have been presented in compound at some moment of the training phase. In this experiment we investigated the possibility of proactive interference b...

  2. What's next? New evidence for prediction in human vision.

    Science.gov (United States)

    Enns, James T; Lleras, Alejandro

    2008-09-01

    Everyday visual experience involves making implicit predictions, as revealed by our surprise when something disturbs our expectations. Many theories of vision have been premised on the central role played by prediction. Yet, implicit prediction in human vision has been difficult to assess in the laboratory, and many results have not distinguished between the indisputably important role of memory and the future-oriented aspect of prediction. Now, a new and unexpected finding - that humans can resume an interrupted visual search much faster than they can start a new search - offers new hope, because the rapid resumption of a search seems to depend on participants forming an implicit prediction of what they will see after the interruption. These findings combined with results of recent neurophysiology studies provide a framework for studying implicit prediction in perception.

  3. Nonconvex model predictive control for commercial refrigeration

    Science.gov (United States)

    Gybel Hovgaard, Tobias; Boyd, Stephen; Larsen, Lars F. S.; Bagterp Jørgensen, John

    2013-08-01

    We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimise the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimisation method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real time. We demonstrate our method on a realistic model, with a full year simulation and 15-minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid.

  4. Movement prediction using accelerometers in a human population

    DEFF Research Database (Denmark)

    Xiao, L.; He, Bing; Koster, A

    2016-01-01

    We introduce statistical methods for predicting the types of human activity at sub-second resolution using triaxial accelerometry data. The major innovation is that we use labeled activity data from some subjects to predict the activity labels of other subjects. To achieve this, we normalize the ...... as those obtained using their own labeled dictionaries. These findings indicate that prediction of activity types for data collected during natural activities of daily living may actually be possible. © 2015, The International Biometric Society...

  5. Predictive Control of Networked Multiagent Systems via Cloud Computing.

    Science.gov (United States)

    Liu, Guo-Ping

    2017-01-18

    This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.

  6. Can human experts predict solubility better than computers?

    Science.gov (United States)

    Boobier, Samuel; Osbourn, Anne; Mitchell, John B O

    2017-12-13

    In this study, we design and carry out a survey, asking human experts to predict the aqueous solubility of druglike organic compounds. We investigate whether these experts, drawn largely from the pharmaceutical industry and academia, can match or exceed the predictive power of algorithms. Alongside this, we implement 10 typical machine learning algorithms on the same dataset. The best algorithm, a variety of neural network known as a multi-layer perceptron, gave an RMSE of 0.985 log S units and an R 2 of 0.706. We would not have predicted the relative success of this particular algorithm in advance. We found that the best individual human predictor generated an almost identical prediction quality with an RMSE of 0.942 log S units and an R 2 of 0.723. The collection of algorithms contained a higher proportion of reasonably good predictors, nine out of ten compared with around half of the humans. We found that, for either humans or algorithms, combining individual predictions into a consensus predictor by taking their median generated excellent predictivity. While our consensus human predictor achieved very slightly better headline figures on various statistical measures, the difference between it and the consensus machine learning predictor was both small and statistically insignificant. We conclude that human experts can predict the aqueous solubility of druglike molecules essentially equally well as machine learning algorithms. We find that, for either humans or algorithms, combining individual predictions into a consensus predictor by taking their median is a powerful way of benefitting from the wisdom of crowds.

  7. Using Human Capital Planning to Predict Future Talent Needs

    Science.gov (United States)

    Ruse, Donald; Jansen, Karen

    2008-01-01

    Human capital planning is an important tool in predicting future talent needs and sustaining organizational excellence over the long term. This article examines the concept of human capital planning and outlines how institutions can use HCP to identify the type and number of talent needed both now and in the future, recognize and prioritize talent…

  8. Predicting Loss-of-Control Boundaries Toward a Piloting Aid

    Science.gov (United States)

    Barlow, Jonathan; Stepanyan, Vahram; Krishnakumar, Kalmanje

    2012-01-01

    This work presents an approach to predicting loss-of-control with the goal of providing the pilot a decision aid focused on maintaining the pilot's control action within predicted loss-of-control boundaries. The predictive architecture combines quantitative loss-of-control boundaries, a data-based predictive control boundary estimation algorithm and an adaptive prediction method to estimate Markov model parameters in real-time. The data-based loss-of-control boundary estimation algorithm estimates the boundary of a safe set of control inputs that will keep the aircraft within the loss-of-control boundaries for a specified time horizon. The adaptive prediction model generates estimates of the system Markov Parameters, which are used by the data-based loss-of-control boundary estimation algorithm. The combined algorithm is applied to a nonlinear generic transport aircraft to illustrate the features of the architecture.

  9. SHERPA: A systematic human error reduction and prediction approach

    International Nuclear Information System (INIS)

    Embrey, D.E.

    1986-01-01

    This paper describes a Systematic Human Error Reduction and Prediction Approach (SHERPA) which is intended to provide guidelines for human error reduction and quantification in a wide range of human-machine systems. The approach utilizes as its basic current cognitive models of human performance. The first module in SHERPA performs task and human error analyses, which identify likely error modes, together with guidelines for the reduction of these errors by training, procedures and equipment redesign. The second module uses a SARAH approach to quantify the probability of occurrence of the errors identified earlier, and provides cost benefit analyses to assist in choosing the appropriate error reduction approaches in the third module

  10. Nonlinear Model Predictive Control for Cooperative Control and Estimation

    Science.gov (United States)

    Ru, Pengkai

    Recent advances in computational power have made it possible to do expensive online computations for control systems. It is becoming more realistic to perform computationally intensive optimization schemes online on systems that are not intrinsically stable and/or have very small time constants. Being one of the most important optimization based control approaches, model predictive control (MPC) has attracted a lot of interest from the research community due to its natural ability to incorporate constraints into its control formulation. Linear MPC has been well researched and its stability can be guaranteed in the majority of its application scenarios. However, one issue that still remains with linear MPC is that it completely ignores the system's inherent nonlinearities thus giving a sub-optimal solution. On the other hand, if achievable, nonlinear MPC, would naturally yield a globally optimal solution and take into account all the innate nonlinear characteristics. While an exact solution to a nonlinear MPC problem remains extremely computationally intensive, if not impossible, one might wonder if there is a middle ground between the two. We tried to strike a balance in this dissertation by employing a state representation technique, namely, the state dependent coefficient (SDC) representation. This new technique would render an improved performance in terms of optimality compared to linear MPC while still keeping the problem tractable. In fact, the computational power required is bounded only by a constant factor of the completely linearized MPC. The purpose of this research is to provide a theoretical framework for the design of a specific kind of nonlinear MPC controller and its extension into a general cooperative scheme. The controller is designed and implemented on quadcopter systems.

  11. Predicting risk and human reliability: a new approach

    International Nuclear Information System (INIS)

    Duffey, R.; Ha, T.-S.

    2009-01-01

    Learning from experience describes human reliability and skill acquisition, and the resulting theory has been validated by comparison against millions of outcome data from multiple industries and technologies worldwide. The resulting predictions were used to benchmark the classic first generation human reliability methods adopted in probabilistic risk assessments. The learning rate, probabilities and response times are also consistent with the existing psychological models for human learning and error correction. The new approach also implies a finite lower bound probability that is not predicted by empirical statistical distributions that ignore the known and fundamental learning effects. (author)

  12. Prediction of Human Activity by Discovering Temporal Sequence Patterns.

    Science.gov (United States)

    Li, Kang; Fu, Yun

    2014-08-01

    Early prediction of ongoing human activity has become more valuable in a large variety of time-critical applications. To build an effective representation for prediction, human activities can be characterized by a complex temporal composition of constituent simple actions and interacting objects. Different from early detection on short-duration simple actions, we propose a novel framework for long -duration complex activity prediction by discovering three key aspects of activity: Causality, Context-cue, and Predictability. The major contributions of our work include: (1) a general framework is proposed to systematically address the problem of complex activity prediction by mining temporal sequence patterns; (2) probabilistic suffix tree (PST) is introduced to model causal relationships between constituent actions, where both large and small order Markov dependencies between action units are captured; (3) the context-cue, especially interactive objects information, is modeled through sequential pattern mining (SPM), where a series of action and object co-occurrence are encoded as a complex symbolic sequence; (4) we also present a predictive accumulative function (PAF) to depict the predictability of each kind of activity. The effectiveness of our approach is evaluated on two experimental scenarios with two data sets for each: action-only prediction and context-aware prediction. Our method achieves superior performance for predicting global activity classes and local action units.

  13. The Control of Behavior: Human and Environmental

    Science.gov (United States)

    Burhoe, Ralph Wendell

    1972-01-01

    Theological perspective on human and environmental behavior, with a view toward man's ultimate concerns or longest range values and the ultimate controls of behavior. Maintains that all human behavior and destiny is ultimately in the hand of a transcendent power which prevails over any human errors.'' (LK)

  14. Human factors challenges for advanced process control

    International Nuclear Information System (INIS)

    Stubler, W.F.; O'Hara, J..M.

    1996-01-01

    New human-system interface technologies provide opportunities for improving operator and plant performance. However, if these technologies are not properly implemented, they may introduce new challenges to performance and safety. This paper reports the results from a survey of human factors considerations that arise in the implementation of advanced human-system interface technologies in process control and other complex systems. General trends were identified for several areas based on a review of technical literature and a combination of interviews and site visits with process control organizations. Human factors considerations are discussed for two of these areas, automation and controls

  15. Allometric scaling for predicting human clearance of bisphenol A

    Energy Technology Data Exchange (ETDEWEB)

    Collet, Séverine H., E-mail: s.collet@envt.fr; Picard-Hagen, Nicole, E-mail: n.hagen-picard@envt.fr; Lacroix, Marlène Z., E-mail: m.lacroix@envt.fr; Puel, Sylvie, E-mail: s.puel@envt.fr; Viguié, Catherine, E-mail: c.viguie@envt.fr; Bousquet-Melou, Alain, E-mail: a.bousquet-Melou@envt.fr; Toutain, Pierre-Louis, E-mail: pltoutain@wanadoo.fr; Gayrard, Véronique, E-mail: v.gayrard@envt.fr

    2015-05-01

    The investigation of interspecies differences in bisphenol A (BPA) pharmacokinetics (PK) may be useful for translating findings from animal studies to humans, identifying major processes involved in BPA clearance mechanisms, and predicting BPA PK parameters in man. For the first time, a large range of species in terms of body weight, from 0.02 kg (mice) to 495 kg (horses) was used to predict BPA clearance in man by an allometric approach. BPA PK was evaluated after intravenous administration of BPA in horses, sheep, pigs, dogs, rats and mice. A non-compartmental analysis was used to estimate plasma clearance and steady state volume of distribution and predict BPA PK parameters in humans from allometric scaling. In all the species investigated, BPA plasma clearance was high and of the same order of magnitude as their respective hepatic blood flow. By an allometric scaling, the human clearance was estimated to be 1.79 L/min (equivalent to 25.6 mL/kg.min) with a 95% prediction interval of 0.36 to 8.83 L/min. Our results support the hypothesis that there are highly efficient and hepatic mechanisms of BPA clearance in man. - Highlights: • Allometric scaling was used to predict BPA pharmacokinetic parameters in humans. • In all species, BPA plasma clearance approached hepatic blood flow. • Human BPA clearance was estimated to be 1.79 L/min.

  16. Allometric scaling for predicting human clearance of bisphenol A

    International Nuclear Information System (INIS)

    Collet, Séverine H.; Picard-Hagen, Nicole; Lacroix, Marlène Z.; Puel, Sylvie; Viguié, Catherine; Bousquet-Melou, Alain; Toutain, Pierre-Louis; Gayrard, Véronique

    2015-01-01

    The investigation of interspecies differences in bisphenol A (BPA) pharmacokinetics (PK) may be useful for translating findings from animal studies to humans, identifying major processes involved in BPA clearance mechanisms, and predicting BPA PK parameters in man. For the first time, a large range of species in terms of body weight, from 0.02 kg (mice) to 495 kg (horses) was used to predict BPA clearance in man by an allometric approach. BPA PK was evaluated after intravenous administration of BPA in horses, sheep, pigs, dogs, rats and mice. A non-compartmental analysis was used to estimate plasma clearance and steady state volume of distribution and predict BPA PK parameters in humans from allometric scaling. In all the species investigated, BPA plasma clearance was high and of the same order of magnitude as their respective hepatic blood flow. By an allometric scaling, the human clearance was estimated to be 1.79 L/min (equivalent to 25.6 mL/kg.min) with a 95% prediction interval of 0.36 to 8.83 L/min. Our results support the hypothesis that there are highly efficient and hepatic mechanisms of BPA clearance in man. - Highlights: • Allometric scaling was used to predict BPA pharmacokinetic parameters in humans. • In all species, BPA plasma clearance approached hepatic blood flow. • Human BPA clearance was estimated to be 1.79 L/min

  17. Human performance interfaces in air traffic control.

    Science.gov (United States)

    Chang, Yu-Hern; Yeh, Chung-Hsing

    2010-01-01

    This paper examines how human performance factors in air traffic control (ATC) affect each other through their mutual interactions. The paper extends the conceptual SHEL model of ergonomics to describe the ATC system as human performance interfaces in which the air traffic controllers interact with other human performance factors including other controllers, software, hardware, environment, and organisation. New research hypotheses about the relationships between human performance interfaces of the system are developed and tested on data collected from air traffic controllers, using structural equation modelling. The research result suggests that organisation influences play a more significant role than individual differences or peer influences on how the controllers interact with the software, hardware, and environment of the ATC system. There are mutual influences between the controller-software, controller-hardware, controller-environment, and controller-organisation interfaces of the ATC system, with the exception of the controller-controller interface. Research findings of this study provide practical insights in managing human performance interfaces of the ATC system in the face of internal or external change, particularly in understanding its possible consequences in relation to the interactions between human performance factors.

  18. Model Predictive Control with Constraints of a Wind Turbine

    DEFF Research Database (Denmark)

    Henriksen, Lars Christian; Poulsen, Niels Kjølstad

    2007-01-01

    Model predictive control of wind turbines offer a more systematic approach of constructing controllers that handle constraints while focusing on the main control objective. In this article several controllers are designed for different wind conditions and appropriate switching conditions ensure a...... an efficient control of the wind turbine over the entire range of wind speeds. Both onshore and floating offshore wind turbines are tested with the controllers.......Model predictive control of wind turbines offer a more systematic approach of constructing controllers that handle constraints while focusing on the main control objective. In this article several controllers are designed for different wind conditions and appropriate switching conditions ensure...

  19. Control system oriented human interface

    International Nuclear Information System (INIS)

    Barale, P.; Jacobson, V.; Kilgore, R.; Rondeau, D.

    1976-11-01

    The on-line control system interface for magnet beam steering and focusing in the Bevalac is described. An Aydin model 5205B display generator was chosen. This display generator will allow the computer to completely rewrite a monitor screen in less than 50 ms and is also capable of controlling a color monitor

  20. Improving Saliency Models by Predicting Human Fixation Patches

    KAUST Repository

    Dubey, Rachit

    2015-04-16

    There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. Our proposed model detects sparse fixation patches with an accuracy of 84 % and eliminates non-fixation patches with an accuracy of 84 % demonstrating that low-level image features can indeed be used to short-list and identify human fixation patches. We then show how these detected fixation patches can be used as saliency priors for popular saliency models, thus, reducing false positives while maintaining true positives. Extensive experimental results show that our proposed approach allows state-of-the-art saliency methods to achieve better prediction performance on benchmark datasets.

  1. Improving Saliency Models by Predicting Human Fixation Patches

    KAUST Repository

    Dubey, Rachit; Dave, Akshat; Ghanem, Bernard

    2015-01-01

    There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. Our proposed model detects sparse fixation patches with an accuracy of 84 % and eliminates non-fixation patches with an accuracy of 84 % demonstrating that low-level image features can indeed be used to short-list and identify human fixation patches. We then show how these detected fixation patches can be used as saliency priors for popular saliency models, thus, reducing false positives while maintaining true positives. Extensive experimental results show that our proposed approach allows state-of-the-art saliency methods to achieve better prediction performance on benchmark datasets.

  2. Power Admission Control with Predictive Thermal Management in Smart Buildings

    DEFF Research Database (Denmark)

    Yao, Jianguo; Costanzo, Giuseppe Tommaso; Zhu, Guchuan

    2015-01-01

    This paper presents a control scheme for thermal management in smart buildings based on predictive power admission control. This approach combines model predictive control with budget-schedulability analysis in order to reduce peak power consumption as well as ensure thermal comfort. First...

  3. Bioinformatic prediction and functional characterization of human KIAA0100 gene

    Directory of Open Access Journals (Sweden)

    He Cui

    2017-02-01

    Full Text Available Our previous study demonstrated that human KIAA0100 gene was a novel acute monocytic leukemia-associated antigen (MLAA gene. But the functional characterization of human KIAA0100 gene has remained unknown to date. Here, firstly, bioinformatic prediction of human KIAA0100 gene was carried out using online softwares; Secondly, Human KIAA0100 gene expression was downregulated by the clustered regularly interspaced short palindromic repeats (CRISPR/CRISPR-associated (Cas 9 system in U937 cells. Cell proliferation and apoptosis were next evaluated in KIAA0100-knockdown U937 cells. The bioinformatic prediction showed that human KIAA0100 gene was located on 17q11.2, and human KIAA0100 protein was located in the secretory pathway. Besides, human KIAA0100 protein contained a signalpeptide, a transmembrane region, three types of secondary structures (alpha helix, extended strand, and random coil , and four domains from mitochondrial protein 27 (FMP27. The observation on functional characterization of human KIAA0100 gene revealed that its downregulation inhibited cell proliferation, and promoted cell apoptosis in U937 cells. To summarize, these results suggest human KIAA0100 gene possibly comes within mitochondrial genome; moreover, it is a novel anti-apoptotic factor related to carcinogenesis or progression in acute monocytic leukemia, and may be a potential target for immunotherapy against acute monocytic leukemia.

  4. 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.

  5. Traffic Predictive Control: Case Study and Evaluation

    Science.gov (United States)

    2017-06-26

    This project developed a quantile regression method for predicting future traffic flow at a signalized intersection by combining both historical and real-time data. The algorithm exploits nonlinear correlations in historical measurements and efficien...

  6. Model Predictive Controller Combined with LQG Controller and Velocity Feedback to Control the Stewart Platform

    DEFF Research Database (Denmark)

    Nadimi, Esmaeil Sharak; Bak, Thomas; Izadi-Zamanabadi, Roozbeh

    2006-01-01

    The main objective of this paper is to investigate the erformance and applicability of two GPC (generalized predictive control) based control methods on a complete benchmark model of the Stewart platform made in MATLAB V6.5. The first method involves an LQG controller (Linear Quadratic Gaussian...

  7. Bayesian geostatistical analysis and prediction of Rhodesian human African trypanosomiasis.

    Directory of Open Access Journals (Sweden)

    Nicola A Wardrop

    2010-12-01

    Full Text Available The persistent spread of Rhodesian human African trypanosomiasis (HAT in Uganda in recent years has increased concerns of a potential overlap with the Gambian form of the disease. Recent research has aimed to increase the evidence base for targeting control measures by focusing on the environmental and climatic factors that control the spatial distribution of the disease.One recent study used simple logistic regression methods to explore the relationship between prevalence of Rhodesian HAT and several social, environmental and climatic variables in two of the most recently affected districts of Uganda, and suggested the disease had spread into the study area due to the movement of infected, untreated livestock. Here we extend this study to account for spatial autocorrelation, incorporate uncertainty in input data and model parameters and undertake predictive mapping for risk of high HAT prevalence in future.Using a spatial analysis in which a generalised linear geostatistical model is used in a Bayesian framework to account explicitly for spatial autocorrelation and incorporate uncertainty in input data and model parameters we are able to demonstrate a more rigorous analytical approach, potentially resulting in more accurate parameter and significance estimates and increased predictive accuracy, thereby allowing an assessment of the validity of the livestock movement hypothesis given more robust parameter estimation and appropriate assessment of covariate effects.Analysis strongly supports the theory that Rhodesian HAT was imported to the study area via the movement of untreated, infected livestock from endemic areas. The confounding effect of health care accessibility on the spatial distribution of Rhodesian HAT and the linkages between the disease's distribution and minimum land surface temperature have also been confirmed via the application of these methods.Predictive mapping indicates an increased risk of high HAT prevalence in the future

  8. Real-time stylistic prediction for whole-body human motions.

    Science.gov (United States)

    Matsubara, Takamitsu; Hyon, Sang-Ho; Morimoto, Jun

    2012-01-01

    The ability to predict human motion is crucial in several contexts such as human tracking by computer vision and the synthesis of human-like computer graphics. Previous work has focused on off-line processes with well-segmented data; however, many applications such as robotics require real-time control with efficient computation. In this paper, we propose a novel approach called real-time stylistic prediction for whole-body human motions to satisfy these requirements. This approach uses a novel generative model to represent a whole-body human motion including rhythmic motion (e.g., walking) and discrete motion (e.g., jumping). The generative model is composed of a low-dimensional state (phase) dynamics and a two-factor observation model, allowing it to capture the diversity of motion styles in humans. A real-time adaptation algorithm was derived to estimate both state variables and style parameter of the model from non-stationary unlabeled sequential observations. Moreover, with a simple modification, the algorithm allows real-time adaptation even from incomplete (partial) observations. Based on the estimated state and style, a future motion sequence can be accurately predicted. In our implementation, it takes less than 15 ms for both adaptation and prediction at each observation. Our real-time stylistic prediction was evaluated for human walking, running, and jumping behaviors. Copyright © 2011 Elsevier Ltd. All rights reserved.

  9. Generic Model Predictive Control Framework for Advanced Driver Assistance Systems

    NARCIS (Netherlands)

    Wang, M.

    2014-01-01

    This thesis deals with a model predictive control framework for control design of Advanced Driver Assistance Systems, where car-following tasks are under control. The framework is applied to design several autonomous and cooperative controllers and to examine the controller properties at the

  10. Multivariate Models for Prediction of Human Skin Sensitization ...

    Science.gov (United States)

    One of the lnteragency Coordinating Committee on the Validation of Alternative Method's (ICCVAM) top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays - the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT) and KeratinoSens TM assay - six physicochemical properties and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches , logistic regression and support vector machine, to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three logistic regression and three support vector machine) with the highest accuracy (92%) used: (1) DPRA, h-CLAT and read-across; (2) DPRA, h-CLAT, read-across and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens and log P. The models performed better at predicting human skin sensitization hazard than the murine

  11. Radiomic analysis in prediction of Human Papilloma Virus status.

    Science.gov (United States)

    Yu, Kaixian; Zhang, Youyi; Yu, Yang; Huang, Chao; Liu, Rongjie; Li, Tengfei; Yang, Liuqing; Morris, Jeffrey S; Baladandayuthapani, Veerabhadran; Zhu, Hongtu

    2017-12-01

    Human Papilloma Virus (HPV) has been associated with oropharyngeal cancer prognosis. Traditionally the HPV status is tested through invasive lab test. Recently, the rapid development of statistical image analysis techniques has enabled precise quantitative analysis of medical images. The quantitative analysis of Computed Tomography (CT) provides a non-invasive way to assess HPV status for oropharynx cancer patients. We designed a statistical radiomics approach analyzing CT images to predict HPV status. Various radiomics features were extracted from CT scans, and analyzed using statistical feature selection and prediction methods. Our approach ranked the highest in the 2016 Medical Image Computing and Computer Assisted Intervention (MICCAI) grand challenge: Oropharynx Cancer (OPC) Radiomics Challenge, Human Papilloma Virus (HPV) Status Prediction. Further analysis on the most relevant radiomic features distinguishing HPV positive and negative subjects suggested that HPV positive patients usually have smaller and simpler tumors.

  12. SCADA system with predictive controller applied to irrigation canals

    OpenAIRE

    Figueiredo, João; Botto, Miguel; Rijo, Manuel

    2013-01-01

    This paper applies a model predictive controller (MPC) to an automatic water canal with sensors and actuators controlled by a network (programmable logic controller), and supervised by a SCADA system (supervisory control and a data acquisition). This canal is composed by a set of distributed sub-systems that control the water level in each canal pool, constrained by discharge gates (control variables) and water off-takes (disturbances). All local controllers are available through an industria...

  13. Biofeedback, voluntary control, and human potential.

    Science.gov (United States)

    Norris, P

    1986-03-01

    This paper examines some of the philosophical and scientific relationships involving self-control, voluntary control, and psychophysiologic self-regulation. The role of biofeedback in mediating conscious and unconscious processes is explored. Demonstrations of superior voluntary control and its relationship to belief, confidence, and expectation are examined. Biofeedback demonstrates the potential of control to oneself, creating confidence in one's ability to establish enhanced and peak performance in athletics, education, and psychophysiologic therapy. Emphasis is placed on the power of images in all human functioning, and in enhancing human potential.

  14. Controlling human oesophagostomiasis in northern Ghana

    NARCIS (Netherlands)

    Ziem, Juventus Benogle

    2006-01-01

    This thesis describes aspects of the epidemiology and attempts to control infection and pathology due to the nematode parasite Oesophagostomum bifurcum . In northern Ghana and Togo O. bifurcum is an important parasite of humans; elsewhere it is predominantly seen as a parasite of non-human primates.

  15. Climate control loads prediction of electric vehicles

    International Nuclear Information System (INIS)

    Zhang, Ziqi; Li, Wanyong; Zhang, Chengquan; Chen, Jiangping

    2017-01-01

    Highlights: • A model of vehicle climate control loads is proposed based on experiments. • Main climate control loads of the modeled vehicle are quantitatively analyzed. • Range reductions of the modeled vehicle under different conditions are simulated. - Abstract: A new model of electric vehicle climate control loads is provided in this paper. The mathematical formulations of the major climate control loads are developed, and the coefficients of the formulations are experimentally determined. Then, the detailed climate control loads are analyzed, and the New European Driving Cycle (NEDC) range reductions due to these loads are calculated under different conditions. It is found that in an electric vehicle, the total climate control loads vary with the vehicle speed, HVAC mode and blower level. The ventilation load is the largest climate control load, followed by the solar radiation load. These two add up to more than 80% of total climate control load in summer. The ventilation load accounts for 70.7–83.9% of total heating load under the winter condition. The climate control loads will cause a 17.2–37.1% reduction of NEDC range in summer, and a 17.1–54.1% reduction in winter, compared to the AC off condition. The heat pump system has an advantage in range extension. A heat pump system with an average heating COP of 1.7 will extend the range by 7.6–21.1% based on the simulation conditions.

  16. Prediction based chaos control via a new neural network

    International Nuclear Information System (INIS)

    Shen Liqun; Wang Mao; Liu Wanyu; Sun Guanghui

    2008-01-01

    In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network

  17. Modeling, robust and distributed model predictive control for freeway networks

    NARCIS (Netherlands)

    Liu, S.

    2016-01-01

    In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of

  18. Reducing prediction uncertainty of weather controlled systems

    NARCIS (Netherlands)

    Doeswijk, T.G.

    2007-01-01

    In closed agricultural systems the weather acts both as a disturbance and as a resource. By using weather forecasts in control strategies the effects of disturbances can be minimized whereas the resources can be utilized. In this situation weather forecast uncertainty and model based control are

  19. Tools for Trustworthy Autonomy: Robust Predictions, Intuitive Control, and Optimized Interaction

    OpenAIRE

    Driggs Campbell, Katherine Rose

    2017-01-01

    In the near future, robotics will impact nearly every aspect of life. Yet for technology to smoothly integrate into society, we need interactive systems to be well modeled and predictable; have robust decision making and control; and be trustworthy to improve cooperation and interaction. To achieve these goals, we propose taking a human-centered approach to ease the transition into human-dominated fields. In this work, our modeling methods and control schemes are validated through user stu...

  20. Model predictive control of a crude oil distillation column

    Directory of Open Access Journals (Sweden)

    Morten Hovd

    1999-04-01

    Full Text Available The project of designing and implementing model based predictive control on the vacuum distillation column at the Nynäshamn Refinery of Nynäs AB is described in this paper. The paper describes in detail the modeling for the model based control, covers the controller implementation, and documents the benefits gained from the model based controller.

  1. The Human Bathtub: Safety and Risk Predictions Including the Dynamic Probability of Operator Errors

    International Nuclear Information System (INIS)

    Duffey, Romney B.; Saull, John W.

    2006-01-01

    Reactor safety and risk are dominated by the potential and major contribution for human error in the design, operation, control, management, regulation and maintenance of the plant, and hence to all accidents. Given the possibility of accidents and errors, now we need to determine the outcome (error) probability, or the chance of failure. Conventionally, reliability engineering is associated with the failure rate of components, or systems, or mechanisms, not of human beings in and interacting with a technological system. The probability of failure requires a prior knowledge of the total number of outcomes, which for any predictive purposes we do not know or have. Analysis of failure rates due to human error and the rate of learning allow a new determination of the dynamic human error rate in technological systems, consistent with and derived from the available world data. The basis for the analysis is the 'learning hypothesis' that humans learn from experience, and consequently the accumulated experience defines the failure rate. A new 'best' equation has been derived for the human error, outcome or failure rate, which allows for calculation and prediction of the probability of human error. We also provide comparisons to the empirical Weibull parameter fitting used in and by conventional reliability engineering and probabilistic safety analysis methods. These new analyses show that arbitrary Weibull fitting parameters and typical empirical hazard function techniques cannot be used to predict the dynamics of human errors and outcomes in the presence of learning. Comparisons of these new insights show agreement with human error data from the world's commercial airlines, the two shuttle failures, and from nuclear plant operator actions and transient control behavior observed in transients in both plants and simulators. The results demonstrate that the human error probability (HEP) is dynamic, and that it may be predicted using the learning hypothesis and the minimum

  2. Model Predictive Control of Wind Turbines

    DEFF Research Database (Denmark)

    Henriksen, Lars Christian

    Wind turbines play a major role in the transformation from a fossil fuel based energy production to a more sustainable production of energy. Total-cost-of-ownership is an important parameter when investors decide in which energy technology they should place their capital. Modern wind turbines...... the need for maintenance of the wind turbine. Either way, better total-cost-of-ownership for wind turbine operators can be achieved by improved control of the wind turbines. Wind turbine control can be improved in two ways, by improving the model on which the controller bases its design or by improving...

  3. Comparison Analysis of Model Predictive Controller with Classical PID Controller For pH Control Process

    Directory of Open Access Journals (Sweden)

    V. Balaji

    2016-12-01

    Full Text Available pH control plays a important role in any chemical plant and process industries. For the past four decades the classical PID controller has been occupied by the industries. Due to the faster computing   technology in the industry demands a tighter advanced control strategy. To fulfill the needs and requirements Model Predictive Control (MPC is the best among all the advanced control algorithms available in the present scenario. The study and analysis has been done for First Order plus Delay Time (FOPDT model controlled by Proportional Integral Derivative (PID and MPC using the Matlab software. This paper explores the capability of the MPC strategy, analyze and compare the control effects with conventional control strategy in pH control. A comparison results between the PID and MPC is plotted using the software. The results clearly show that MPC provide better performance than the classical controller.

  4. Model Predictive Vibration Control Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures

    CERN Document Server

    Takács, Gergely

    2012-01-01

    Real-time model predictive controller (MPC) implementation in active vibration control (AVC) is often rendered difficult by fast sampling speeds and extensive actuator-deformation asymmetry. If the control of lightly damped mechanical structures is assumed, the region of attraction containing the set of allowable initial conditions requires a large prediction horizon, making the already computationally demanding on-line process even more complex. Model Predictive Vibration Control provides insight into the predictive control of lightly damped vibrating structures by exploring computationally efficient algorithms which are capable of low frequency vibration control with guaranteed stability and constraint feasibility. In addition to a theoretical primer on active vibration damping and model predictive control, Model Predictive Vibration Control provides a guide through the necessary steps in understanding the founding ideas of predictive control applied in AVC such as: ·         the implementation of ...

  5. Predictive Control Based upon State Space Models

    Directory of Open Access Journals (Sweden)

    Jens G. Balchen

    1989-04-01

    Full Text Available Repetitive online computation of the control vector by solving the optimal control problem of a non-linear multivariable process with arbitrary performance indices is investigated. Two different methods are considered in the search for an optimal, parameterized control vector: Pontryagin's Maximum Principle and optimization by using the performance index and its gradient directly. Unfortunately, solving this optimization problem has turned out to be a rather time-consuming task which has resulted in a time delay that cannot be accepted when the actual process is exposed to rapidly-varying disturbances. However, an instantaneous feedback strategy operating in parallel with the original control aogorithm was found to be able to cope with this problem.

  6. Stability Constraints for Robust Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Amanda G. S. Ottoni

    2015-01-01

    Full Text Available This paper proposes an approach for the robust stabilization of systems controlled by MPC strategies. Uncertain SISO linear systems with box-bounded parametric uncertainties are considered. The proposed approach delivers some constraints on the control inputs which impose sufficient conditions for the convergence of the system output. These stability constraints can be included in the set of constraints dealt with by existing MPC design strategies, in this way leading to the “robustification” of the MPC.

  7. Multivariate Models for Prediction of Human Skin Sensitization Hazard

    Science.gov (United States)

    Strickland, Judy; Zang, Qingda; Paris, Michael; Lehmann, David M.; Allen, David; Choksi, Neepa; Matheson, Joanna; Jacobs, Abigail; Casey, Warren; Kleinstreuer, Nicole

    2016-01-01

    One of ICCVAM’s top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays—the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT), and KeratinoSens™ assay—six physicochemical properties, and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches, logistic regression (LR) and support vector machine (SVM), to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three LR and three SVM) with the highest accuracy (92%) used: (1) DPRA, h-CLAT, and read-across; (2) DPRA, h-CLAT, read-across, and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens, and log P. The models performed better at predicting human skin sensitization hazard than the murine local lymph node assay (accuracy = 88%), any of the alternative methods alone (accuracy = 63–79%), or test batteries combining data from the individual methods (accuracy = 75%). These results suggest that computational methods are promising tools to effectively identify potential human skin sensitizers without animal testing. PMID:27480324

  8. Model predictive control for spacecraft rendezvous in elliptical orbit

    Science.gov (United States)

    Li, Peng; Zhu, Zheng H.

    2018-05-01

    This paper studies the control of spacecraft rendezvous with attitude stable or spinning targets in an elliptical orbit. The linearized Tschauner-Hempel equation is used to describe the motion of spacecraft and the problem is formulated by model predictive control. The control objective is to maximize control accuracy and smoothness simultaneously to avoid unexpected change or overshoot of trajectory for safe rendezvous. It is achieved by minimizing the weighted summations of control errors and increments. The effects of two sets of horizons (control and predictive horizons) in the model predictive control are examined in terms of fuel consumption, rendezvous time and computational effort. The numerical results show the proposed control strategy is effective.

  9. Human rights and conventionality control in Mexico

    Directory of Open Access Journals (Sweden)

    Azul América Aguiar-Aguilar

    2014-12-01

    Full Text Available The protection of human rights in Mexico has, de jure, suffered an important change in the last years, given a new judicial interpretation delivered by the National Supreme Court of Justice that allows the use of conventionality control, which means, that it allows federal and state judges to verify the conformity of domestic laws with those established in the Inter-American Convention of Human Rights. To what extent domestic actors are protecting human rights using this new legal tool called conventionality control? In this article I explore whom and how is conventionality control being used in Mexico. Using N-Vivo Software I reviewed concluded decisions delivered by intermediate level courts (Collegiate Circuit Courts in three Mexican states. The evidence points that conventionality control is a very useful tool especially to defenders, who appear in sentences claiming compliance with the commitments Mexico has acquired when this country ratified the Convention.

  10. Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

    International Nuclear Information System (INIS)

    Valerio, Luis G.; Arvidson, Kirk B.; Chanderbhan, Ronald F.; Contrera, Joseph F.

    2007-01-01

    Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest is MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200 chemicals

  11. Multiple descriptions for packetized predictive control

    DEFF Research Database (Denmark)

    Østergaard, Jan; Quevedo, Daniel

    2016-01-01

    be reliably reconstructed at the plant side. For the particular case of LTI plant models and i.i.d. channels, we show that the overall system forms a Markov jump linear system. We provide conditions for mean square stability and derive upper bounds on the operational bit rate of the quantizer to guarantee......In this paper, we propose to use multiple descriptions (MDs) to achieve a high degree of robustness towards random packet delays and erasures in networked control systems. In particular, we consider the scenario, where a data-rate limited channel is located between the controller and the plant...

  12. Predicting Tissue-Specific Enhancers in the Human Genome

    Energy Technology Data Exchange (ETDEWEB)

    Pennacchio, Len A.; Loots, Gabriela G.; Nobrega, Marcelo A.; Ovcharenko, Ivan

    2006-07-01

    Determining how transcriptional regulatory signals areencoded in vertebrate genomes is essential for understanding the originsof multi-cellular complexity; yet the genetic code of vertebrate generegulation remains poorly understood. In an attempt to elucidate thiscode, we synergistically combined genome-wide gene expression profiling,vertebrate genome comparisons, and transcription factor binding siteanalysis to define sequence signatures characteristic of candidatetissue-specific enhancers in the human genome. We applied this strategyto microarray-based gene expression profiles from 79 human tissues andidentified 7,187 candidate enhancers that defined their flanking geneexpression, the majority of which were located outside of knownpromoters. We cross-validated this method for its ability to de novopredict tissue-specific gene expression and confirmed its reliability in57 of the 79 available human tissues, with an average precision inenhancer recognition ranging from 32 percent to 63 percent, and asensitivity of 47 percent. We used the sequence signatures identified bythis approach to assign tissue-specific predictions to ~;328,000human-mouse conserved noncoding elements in the human genome. Byoverlapping these genome-wide predictions with a large in vivo dataset ofenhancers validated in transgenic mice, we confirmed our results with a28 percent sensitivity and 50 percent precision. These results indicatethe power of combining complementary genomic datasets as an initialcomputational foray into the global view of tissue-specific generegulation in vertebrates.

  13. Fault Predictive Control of Compact Disk Players

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Wickerhauser, Mladen Victor

    2006-01-01

    Optical disc players such as CD-players have problems playing certain discs with surface faults like scratches and fingerprints. The problem is to be found in the servo controller which positions the optical pick-up, such that the laser beam is focused on the information track. A scheme handling...

  14. Robot trajectory tracking with self-tuning predicted control

    Science.gov (United States)

    Cui, Xianzhong; Shin, Kang G.

    1988-01-01

    A controller that combines self-tuning prediction and control is proposed for robot trajectory tracking. The controller has two feedback loops: one is used to minimize the prediction error, and the other is designed to make the system output track the set point input. Because the velocity and position along the desired trajectory are given and the future output of the system is predictable, a feedforward loop can be designed for robot trajectory tracking with self-tuning predicted control (STPC). Parameters are estimated online to account for the model uncertainty and the time-varying property of the system. The authors describe the principle of STPC, analyze the system performance, and discuss the simplification of the robot dynamic equations. To demonstrate its utility and power, the controller is simulated for a Stanford arm.

  15. Fuzzy model predictive control algorithm applied in nuclear power plant

    International Nuclear Information System (INIS)

    Zuheir, Ahmad

    2006-01-01

    The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)

  16. Model predictive control for wind power gradients

    DEFF Research Database (Denmark)

    Hovgaard, Tobias Gybel; Boyd, Stephen; Jørgensen, John Bagterp

    2015-01-01

    We consider the operation of a wind turbine and a connected local battery or other electrical storage device, taking into account varying wind speed, with the goal of maximizing the total energy generated while respecting limits on the time derivative (gradient) of power delivered to the grid. We...... ranges. The system dynamics are quite non-linear, and the constraints and objectives are not convex functions of the control inputs, so the resulting optimal control problem is difficult to solve globally. In this paper, we show that by a novel change of variables, which focuses on power flows, we can...... wind data and modern wind forecasting methods. The simulation results using real wind data demonstrate the ability to reject the disturbances from fast changes in wind speed, ensuring certain power gradients, with an insignificant loss in energy production....

  17. Model Predictive Control Algorithms for Pen and Pump Insulin Administration

    DEFF Research Database (Denmark)

    Boiroux, Dimitri

    at mealtime, and the case where the insulin sensitivity increases during the night. This thesis consists of a summary report, glucose and insulin proles of the clinical studies and research papers submitted, peer-reviewed and/or published in the period September 2009 - September 2012....... of current closed-loop controllers. In this thesis, we present different control strategies based on Model Predictive Control (MPC) for an artificial pancreas. We use Nonlinear Model Predictive Control (NMPC) in order to determine the optimal insulin and blood glucose profiles. The optimal control problem...

  18. Human Resource Predictive Analytics HRPA For HR Management In Organizations

    Directory of Open Access Journals (Sweden)

    Sujeet N. Mishra

    2015-08-01

    Full Text Available Human resource predictive analytics is an evolving application field of analytics for HRM purposes. The purpose of HRM is measuring employee performance and engagement studying workforce collaboration patterns analyzing employee churn and turnover and modelling employee lifetime value. The motive of applying HRPA is to optimize performances and produce better return on investment for organizations through decision making based on data collection HR metrics and predictive models. The paper is divided into three sections to understand the emergence of HR predictive analytics for HRM. Firstly the paper introduces the concept of HRPA. Secondly the paper discusses three aspects of HRPA a Need b Approach amp Application c Impact. Lastly the paper leads to the conclusion on HRPA.

  19. Distributed Model Predictive Control for Smart Energy Systems

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus Fogtmann; Vandenberghe, Lieven; Poulsen, Niels Kjølstad

    2016-01-01

    Integration of a large number of flexible consumers in a smart grid requires a scalable power balancing strategy. We formulate the control problem as an optimization problem to be solved repeatedly by the aggregator in a model predictive control framework. To solve the large-scale control problem...

  20. Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

    DEFF Research Database (Denmark)

    Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon

    2008-01-01

    This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...

  1. Model predictive control for Z-source power converter

    DEFF Research Database (Denmark)

    Mo, W.; Loh, P.C.; Blaabjerg, Frede

    2011-01-01

    This paper presents Model Predictive Control (MPC) of impedance-source (commonly known as Z-source) power converter. Output voltage control and current control for Z-source inverter are analyzed and simulated. With MPC's ability of multi- system variables regulation, load current and voltage...

  2. Human cervicovaginal fluid biomarkers to predict term and preterm labor

    Science.gov (United States)

    Heng, Yujing J.; Liong, Stella; Permezel, Michael; Rice, Gregory E.; Di Quinzio, Megan K. W.; Georgiou, Harry M.

    2015-01-01

    Preterm birth (PTB; birth before 37 completed weeks of gestation) remains the major cause of neonatal morbidity and mortality. The current generation of biomarkers predictive of PTB have limited utility. In pregnancy, the human cervicovaginal fluid (CVF) proteome is a reflection of the local biochemical milieu and is influenced by the physical changes occurring in the vagina, cervix and adjacent overlying fetal membranes. Term and preterm labor (PTL) share common pathways of cervical ripening, myometrial activation and fetal membranes rupture leading to birth. We therefore hypothesize that CVF biomarkers predictive of labor may be similar in both the term and preterm labor setting. In this review, we summarize some of the existing published literature as well as our team's breadth of work utilizing the CVF for the discovery and validation of putative CVF biomarkers predictive of human labor. Our team established an efficient method for collecting serial CVF samples for optimal 2-dimensional gel electrophoresis resolution and analysis. We first embarked on CVF biomarker discovery for the prediction of spontaneous onset of term labor using 2D-electrophoresis and solution array multiple analyte profiling. 2D-electrophoretic analyses were subsequently performed on CVF samples associated with PTB. Several proteins have been successfully validated and demonstrate that these biomarkers are associated with term and PTL and may be predictive of both term and PTL. In addition, the measurement of these putative biomarkers was found to be robust to the influences of vaginal microflora and/or semen. The future development of a multiple biomarker bed-side test would help improve the prediction of PTB and the clinical management of patients. PMID:26029118

  3. Fourier transform wavefront control with adaptive prediction of the atmosphere.

    Science.gov (United States)

    Poyneer, Lisa A; Macintosh, Bruce A; Véran, Jean-Pierre

    2007-09-01

    Predictive Fourier control is a temporal power spectral density-based adaptive method for adaptive optics that predicts the atmosphere under the assumption of frozen flow. The predictive controller is based on Kalman filtering and a Fourier decomposition of atmospheric turbulence using the Fourier transform reconstructor. It provides a stable way to compensate for arbitrary numbers of atmospheric layers. For each Fourier mode, efficient and accurate algorithms estimate the necessary atmospheric parameters from closed-loop telemetry and determine the predictive filter, adjusting as conditions change. This prediction improves atmospheric rejection, leading to significant improvements in system performance. For a 48x48 actuator system operating at 2 kHz, five-layer prediction for all modes is achievable in under 2x10(9) floating-point operations/s.

  4. Robust predictive control strategy applied for propofol dosing using BIS as a controlled variable during anesthesia

    NARCIS (Netherlands)

    Ionescu, Clara A.; De Keyser, Robin; Torrico, Bismark Claure; De Smet, Tom; Struys, Michel M. R. F.; Normey-Rico, Julio E.

    This paper presents the application of predictive control to drug dosing during anesthesia in patients undergoing surgery. The performance of a generic predictive control strategy in drug dosing control, with a previously reported anesthesia-specific control algorithm, has been evaluated. The

  5. Computational Prediction of Human Salivary Proteins from Blood Circulation and Application to Diagnostic Biomarker Identification

    Science.gov (United States)

    Wang, Jiaxin; Liang, Yanchun; Wang, Yan; Cui, Juan; Liu, Ming; Du, Wei; Xu, Ying

    2013-01-01

    Proteins can move from blood circulation into salivary glands through active transportation, passive diffusion or ultrafiltration, some of which are then released into saliva and hence can potentially serve as biomarkers for diseases if accurately identified. We present a novel computational method for predicting salivary proteins that come from circulation. The basis for the prediction is a set of physiochemical and sequence features we found to be discerning between human proteins known to be movable from circulation to saliva and proteins deemed to be not in saliva. A classifier was trained based on these features using a support-vector machine to predict protein secretion into saliva. The classifier achieved 88.56% average recall and 90.76% average precision in 10-fold cross-validation on the training data, indicating that the selected features are informative. Considering the possibility that our negative training data may not be highly reliable (i.e., proteins predicted to be not in saliva), we have also trained a ranking method, aiming to rank the known salivary proteins from circulation as the highest among the proteins in the general background, based on the same features. This prediction capability can be used to predict potential biomarker proteins for specific human diseases when coupled with the information of differentially expressed proteins in diseased versus healthy control tissues and a prediction capability for blood-secretory proteins. Using such integrated information, we predicted 31 candidate biomarker proteins in saliva for breast cancer. PMID:24324552

  6. Selection of References in Wind Turbine Model Predictive Control Design

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Hovgaard, Tobias

    2015-01-01

    a model predictive controller for a wind turbine. One of the important aspects for a tracking control problem is how to setup the optimal reference tracking problem, as it might be relevant to track, e.g., the three concurrent references: optimal pitch angle, optimal rotational speed, and optimal power......Lowering the cost of energy is one of the major focus areas in the wind turbine industry. Recent research has indicated that wind turbine controllers based on model predictive control methods can be useful in obtaining this objective. A number of design considerations have to be made when designing....... The importance if the individual references differ depending in particular on the wind speed. In this paper we investigate the performance of a reference tracking model predictive controller with two different setups of the used optimal reference signals. The controllers are evaluated using an industrial high...

  7. Moment based model predictive control for systems with additive uncertainty

    NARCIS (Netherlands)

    Saltik, M.B.; Ozkan, L.; Weiland, S.; Ludlage, J.H.A.

    2017-01-01

    In this paper, we present a model predictive control (MPC) strategy based on the moments of the state variables and the cost functional. The statistical properties of the state predictions are calculated through the open loop iteration of dynamics and used in the formulation of MPC cost function. We

  8. Rate-Based Model Predictive Control of Turbofan Engine Clearance

    Science.gov (United States)

    DeCastro, Jonathan A.

    2006-01-01

    An innovative model predictive control strategy is developed for control of nonlinear aircraft propulsion systems and sub-systems. At the heart of the controller is a rate-based linear parameter-varying model that propagates the state derivatives across the prediction horizon, extending prediction fidelity to transient regimes where conventional models begin to lose validity. The new control law is applied to a demanding active clearance control application, where the objectives are to tightly regulate blade tip clearances and also anticipate and avoid detrimental blade-shroud rub occurrences by optimally maintaining a predefined minimum clearance. Simulation results verify that the rate-based controller is capable of satisfying the objectives during realistic flight scenarios where both a conventional Jacobian-based model predictive control law and an unconstrained linear-quadratic optimal controller are incapable of doing so. The controller is evaluated using a variety of different actuators, illustrating the efficacy and versatility of the control approach. It is concluded that the new strategy has promise for this and other nonlinear aerospace applications that place high importance on the attainment of control objectives during transient regimes.

  9. Prediction of heat-illness symptoms with the prediction of human vascular response in hot environment under resting condition.

    Science.gov (United States)

    Aggarwal, Yogender; Karan, Bhuwan Mohan; Das, Barsa Nand; Sinha, Rakesh Kumar

    2008-04-01

    The thermoregulatory control of human skin blood flow is vital to maintain the body heat storage during challenges of thermal homeostasis under heat stress. Whenever thermal homeostasis disturbed, the heat load exceeds heat dissipation capacity, which alters the cutaneous vascular responses along with other body physiological variables. Whole body skin blood flow has been calculated from the forearm blood flow. Present model has been designed using electronics circuit simulator (Multisim 8.0, National Instruments, USA), is to execute a series of predictive equations for early prediction of physiological parameters of young nude subjects during resting condition at various level of dry heat stress under almost still air to avoid causalities associated with hot environmental. The users can execute the model by changing the environmental temperature in degrees C and exposure time in minutes. The model would be able to predict and detect the changes in human vascular responses along with other physiological parameters and from this predicted values heat related-illness symptoms can be inferred.

  10. [Computational prediction of human immunodeficiency resistance to reverse transcriptase inhibitors].

    Science.gov (United States)

    Tarasova, O A; Filimonov, D A; Poroikov, V V

    2017-10-01

    Human immunodeficiency virus (HIV) causes acquired immunodeficiency syndrome (AIDS) and leads to over one million of deaths annually. Highly active antiretroviral treatment (HAART) is a gold standard in the HIV/AIDS therapy. Nucleoside and non-nucleoside inhibitors of HIV reverse transcriptase (RT) are important component of HAART, but their effect depends on the HIV susceptibility/resistance. HIV resistance mainly occurs due to mutations leading to conformational changes in the three-dimensional structure of HIV RT. The aim of our work was to develop and test a computational method for prediction of HIV resistance associated with the mutations in HIV RT. Earlier we have developed a method for prediction of HIV type 1 (HIV-1) resistance; it is based on the usage of position-specific descriptors. These descriptors are generated using the particular amino acid residue and its position; the position of certain residue is determined in a multiple alignment. The training set consisted of more than 1900 sequences of HIV RT from the Stanford HIV Drug Resistance database; for these HIV RT variants experimental data on their resistance to ten inhibitors are presented. Balanced accuracy of prediction varies from 80% to 99% depending on the method of classification (support vector machine, Naive Bayes, random forest, convolutional neural networks) and the drug, resistance to which is obtained. Maximal balanced accuracy was obtained for prediction of resistance to zidovudine, stavudine, didanosine and efavirenz by the random forest classifier. Average accuracy of prediction is 89%.

  11. Model Predictive Control for Integrating Traffic Control Measures

    NARCIS (Netherlands)

    Hegyi, A.

    2004-01-01

    Dynamic traffic control measures, such as ramp metering and dynamic speed limits, can be used to better utilize the available road capacity. Due to the increasing traffic volumes and the increasing number of traffic jams the interaction between the control measures has increased such that local

  12. Objective Model Selection for Identifying the Human Feedforward Response in Manual Control

    OpenAIRE

    Drop, F.M.; Pool, D.M.; van Paassen, M.M.; Mulder, M.; Bülthoff, Heinrich H.

    2017-01-01

    Realistic manual control tasks typically involve predictable target signals and random disturbances. The human controller (HC) is hypothesized to use a feedforward control strategy for target-following, in addition to feedback control for disturbance-rejection. Little is known about human feedforward control, partly because common system identification methods have difficulty in identifying whether, and (if so) how, the HC applies a feedforward strategy. In this paper, an identification proce...

  13. Model Predictive Control of a Wave Energy Converter

    DEFF Research Database (Denmark)

    Andersen, Palle; Pedersen, Tom Søndergård; Nielsen, Kirsten Mølgaard

    2015-01-01

    In this paper reactive control and Model Predictive Control (MPC) for a Wave Energy Converter (WEC) are compared. The analysis is based on a WEC from Wave Star A/S designed as a point absorber. The model predictive controller uses wave models based on the dominating sea states combined with a model...... connecting undisturbed wave sequences to sequences of torque. Losses in the conversion from mechanical to electrical power are taken into account in two ways. Conventional reactive controllers are tuned for each sea state with the assumption that the converter has the same efficiency back and forth. MPC...

  14. A multicontroller structure for teaching and designing predictive control strategies

    International Nuclear Information System (INIS)

    Hodouin, D.; Desbiens, A.

    1999-01-01

    The paper deals with the unification of the existing linear control algorithms in order to facilitate their transfer to the engineering students and to industry's engineers. The resulting control algorithm is the Global Predictive Control (GlobPC), which is now taught at the graduate and continuing education levels. GlobPC is based on an internal model framework where three independent control criteria are minimized: one for tracking, one for regulation and one for feedforward. This structure allows to obtain desired tracking, regulation and feedforward behaviors in an optimal way while keeping them perfectly separated. It also cleanly separates the deterministic and stochastic predictions of the process model output. (author)

  15. Robust Output Model Predictive Control of an Unstable Rijke Tube

    Directory of Open Access Journals (Sweden)

    Fabian Jarmolowitz

    2012-01-01

    Full Text Available This work investigates the active control of an unstable Rijke tube using robust output model predictive control (RMPC. As internal model a polytopic linear system with constraints is assumed to account for uncertainties. For guaranteed stability, a linear state feedback controller is designed using linear matrix inequalities and used within a feedback formulation of the model predictive controller. For state estimation a robust gain-scheduled observer is developed. It is shown that the proposed RMPC ensures robust stability under constraints over the considered operating range.

  16. Predicting human developmental toxicity of pharmaceuticals using human embryonic stem cells and metabolomics

    International Nuclear Information System (INIS)

    West, Paul R.; Weir, April M.; Smith, Alan M.; Donley, Elizabeth L.R.; Cezar, Gabriela G.

    2010-01-01

    Teratogens, substances that may cause fetal abnormalities during development, are responsible for a significant number of birth defects. Animal models used to predict teratogenicity often do not faithfully correlate to human response. Here, we seek to develop a more predictive developmental toxicity model based on an in vitro method that utilizes both human embryonic stem (hES) cells and metabolomics to discover biomarkers of developmental toxicity. We developed a method where hES cells were dosed with several drugs of known teratogenicity then LC-MS analysis was performed to measure changes in abundance levels of small molecules in response to drug dosing. Statistical analysis was employed to select for specific mass features that can provide a prediction of the developmental toxicity of a substance. These molecules can serve as biomarkers of developmental toxicity, leading to better prediction of teratogenicity. In particular, our work shows a correlation between teratogenicity and changes of greater than 10% in the ratio of arginine to asymmetric dimethylarginine levels. In addition, this study resulted in the establishment of a predictive model based on the most informative mass features. This model was subsequently tested for its predictive accuracy in two blinded studies using eight drugs of known teratogenicity, where it correctly predicted the teratogenicity for seven of the eight drugs. Thus, our initial data shows that this platform is a robust alternative to animal and other in vitro models for the prediction of the developmental toxicity of chemicals that may also provide invaluable information about the underlying biochemical pathways.

  17. Prediction of Human Pharmacokinetic Profile After Transdermal Drug Application Using Excised Human Skin.

    Science.gov (United States)

    Yamamoto, Syunsuke; Karashima, Masatoshi; Arai, Yuta; Tohyama, Kimio; Amano, Nobuyuki

    2017-09-01

    Although several mathematical models have been reported for the estimation of human plasma concentration profiles of drug substances after dermal application, the successful cases that can predict human pharmacokinetic profiles are limited. Therefore, the aim of this study is to investigate the prediction of human plasma concentrations after dermal application using in vitro permeation parameters obtained from excised human skin. The in vitro skin permeability of 7 marketed drug products was evaluated. The plasma concentration-time profiles of the drug substances in humans after their dermal application were simulated using compartment models and the clinical pharmacokinetic parameters. The transdermal process was simulated using the in vitro skin permeation rate and lag time assuming a zero-order absorption. These simulated plasma concentration profiles were compared with the clinical data. The result revealed that the steady-state plasma concentration of diclofenac and the maximum concentrations of nicotine, bisoprolol, rivastigmine, and lidocaine after topical application were within 2-fold of the clinical data. Furthermore, the simulated concentration profiles of bisoprolol, nicotine, and rivastigmine reproduced the decrease in absorption due to drug depletion from the formulation. In conclusion, this simple compartment model using in vitro human skin permeation parameters as zero-order absorption predicted the human plasma concentrations accurately. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  18. PLMPC - supervisor predictive control; PLMPC - controle supervisorio preditivo

    Energy Technology Data Exchange (ETDEWEB)

    Ferreira, Amalia Burger Santa Brigida; Matuck, Fuad Jorge [White Martins S.A., Rio de Janeiro, RJ (Brazil)

    2010-07-01

    MPC is the latest and most sophisticated technology for controlling chemical plants with several interactive variables. Since 1984, over 2000 MPC systems have been installed worldwide, mostly at oil refineries and large petrochemical facilities. Praxair was the first company to apply MPC technology to the air separation industry. MPC technology is now Praxair's standard platform for supervisory control of cryogenic air separation plants. Most new Praxair plants are controlled by MPC systems. The Pipeline MPC (PLMPC) drives at least 2 plants, A and B, GO2 production towards optimum targets during the pipeline variations. The purpose of the PLMPC is to optimize gas oxygen (GO2) production according to demand, while ensuring a quickly pipeline response. It is implemented using AspenTech DMCPlus software, which is configured with a model file and a controller configuration file, that executes periodically. (author)

  19. Wind turbine control with constraint handling: a model predictive control approach

    DEFF Research Database (Denmark)

    Henriksen, Lars Christian; Hansen, Morten Hartvig; Poulsen, Niels Kjølstad

    2012-01-01

    on model predictive control, a control method well suited for constraint handling. The performance of the presented controller during an extreme operating gust is compared to that of a proportional-integral controller with integrator anti-windup. Furthermore, the presented controller-s capability...

  20. Hologram QSAR model for the prediction of human oral bioavailability.

    Science.gov (United States)

    Moda, Tiago L; Montanari, Carlos A; Andricopulo, Adriano D

    2007-12-15

    A drug intended for use in humans should have an ideal balance of pharmacokinetics and safety, as well as potency and selectivity. Unfavorable pharmacokinetics can negatively affect the clinical development of many otherwise promising drug candidates. A variety of in silico ADME (absorption, distribution, metabolism, and excretion) models are receiving increased attention due to a better appreciation that pharmacokinetic properties should be considered in early phases of the drug discovery process. Human oral bioavailability is an important pharmacokinetic property, which is directly related to the amount of drug available in the systemic circulation to exert pharmacological and therapeutic effects. In the present work, hologram quantitative structure-activity relationships (HQSAR) were performed on a training set of 250 structurally diverse molecules with known human oral bioavailability. The most significant HQSAR model (q(2)=0.70, r(2)=0.93) was obtained using atoms, bond, connection, and chirality as fragment distinction. The predictive ability of the model was evaluated by an external test set containing 52 molecules not included in the training set, and the predicted values were in good agreement with the experimental values. The HQSAR model should be useful for the design of new drug candidates having increased bioavailability as well as in the process of chemical library design, virtual screening, and high-throughput screening.

  1. Implementation of neural network based non-linear predictive control

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1999-01-01

    This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control...... of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...... on an efficient quasi-Newton algorithm. The performance is demonstrated on a pneumatic servo system....

  2. Model Predictive Control of Buoy Type Wave Energy Converter

    DEFF Research Database (Denmark)

    Soltani, Mohsen; Sichani, Mahdi Teimouri; Mirzaei, Mahmood

    2014-01-01

    by forcing this condition. In the paper the theoretical framework for this principal is shown. The optimal controller requires information of the sea state for infinite horizon which is not applicable. Model Predictive Controllers (MPC) can have finite horizon which crosses out this requirement....... This approach is then taken into account and an MPC controller is designed for a model WEC and implemented on a numerical example. Further, the power outtake of this controller is compared to the optimal controller as an indicator of the performance of the designed controller....

  3. Model Predictive Control of Buoy Type Wave Energy Converter

    DEFF Research Database (Denmark)

    Soltani, Mohsen N.; Sichani, Mahdi T.; Mirzaei, Mahmood

    2014-01-01

    by forcing this condition. In the paper the theoretical framework for this principal is shown. The optimal controller requires information of the sea state for infinite horizon which is not applicable. Model Predictive Controllers (MPC) can have finite horizon which crosses out this requirement....... This approach is then taken into account and an MPC controller is designed for a model wave energy converter and implemented on a numerical example. Further, the power outtake of this controller is compared to the optimal controller as an indicator of the performance of the designed controller....

  4. Morphometric analysis of human embryos to predict developmental competence

    DEFF Research Database (Denmark)

    Ziebe, Søren

    2013-01-01

    pregnancy test, no matter what we choose in the laboratory. Still, both with the increasing complexity of infertile patients treated today and the important focus on reducing multiple pregnancies, it becomes increasingly important to improve our ability to predict the developmental competence of each embryo....... This involves an improved understanding of the basic biology controlling early embryonic development and, over the years, many groups have tried to identify parameters reflecting embryonic competence....

  5. Pre-stimulus thalamic theta power predicts human memory formation.

    Science.gov (United States)

    Sweeney-Reed, Catherine M; Zaehle, Tino; Voges, Jürgen; Schmitt, Friedhelm C; Buentjen, Lars; Kopitzki, Klaus; Richardson-Klavehn, Alan; Hinrichs, Hermann; Heinze, Hans-Jochen; Knight, Robert T; Rugg, Michael D

    2016-09-01

    Pre-stimulus theta (4-8Hz) power in the hippocampus and neocortex predicts whether a memory for a subsequent event will be formed. Anatomical studies reveal thalamus-hippocampal connectivity, and lesion, neuroimaging, and electrophysiological studies show that memory processing involves the dorsomedial (DMTN) and anterior thalamic nuclei (ATN). The small size and deep location of these nuclei have limited real-time study of their activity, however, and it is unknown whether pre-stimulus theta power predictive of successful memory formation is also found in these subcortical structures. We recorded human electrophysiological data from the DMTN and ATN of 7 patients receiving deep brain stimulation for refractory epilepsy. We found that greater pre-stimulus theta power in the right DMTN was associated with successful memory encoding, predicting both behavioral outcome and post-stimulus correlates of successful memory formation. In particular, significant correlations were observed between right DMTN theta power and both frontal theta and right ATN gamma (32-50Hz) phase alignment, and frontal-ATN theta-gamma cross-frequency coupling. We draw the following primary conclusions. Our results provide direct electrophysiological evidence in humans of a role for the DMTN as well as the ATN in memory formation. Furthermore, prediction of subsequent memory performance by pre-stimulus thalamic oscillations provides evidence that post-stimulus differences in thalamic activity that index successful and unsuccessful encoding reflect brain processes specifically underpinning memory formation. Finally, the findings broaden the understanding of brain states that facilitate memory encoding to include subcortical as well as cortical structures. Copyright © 2016 Elsevier Inc. All rights reserved.

  6. Predictive control, with restrictions for the climate of a greenhouse

    International Nuclear Information System (INIS)

    Pinon, Sandra; Pena, Miguel; Kuchen, Benjamin

    2002-01-01

    A proposal for controlling nonlinear systems under constraints is presented. a combination of model predictive control and feedback linearization is used. An alternative that uses extended kalman filter as non-measured variable estimator is applied for performing the constrained optimization. Finally, an observability analysis is done in closed loop in order to demonstrate observer convergence

  7. Neural networks for predictive control of the mechanism of ...

    African Journals Online (AJOL)

    In this paper, we are interested in the study of the control of orientation of a wind turbine like means of optimization of his output/input ratio (efficiency). The approach suggested is based on the neural predictive control which is justified by the randomness of the wind on the one hand, and on the other hand by the capacity of ...

  8. Neural Generalized Predictive Control of a non-linear Process

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1998-01-01

    The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability qu...... detail and discuss the implementation difficulties. The neural generalized predictive controller is tested on a pneumatic servo sys-tem.......The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability...... qualities. The controller is a non-linear version of the well-known generalized predictive controller developed in linear control theory. It involves minimization of a cost function which in the present case has to be done numerically. Therefore, we develop the numerical algorithms necessary in substantial...

  9. Sparsely-Packetized Predictive Control by Orthogonal Matching Pursuit

    DEFF Research Database (Denmark)

    Nagahara, Masaaki; Quevedo, Daniel; Østergaard, Jan

    2012-01-01

    We study packetized predictive control, known to be robust against packet dropouts in networked systems. To obtain sparse packets for rate-limited networks, we design control packets via an ℓ0 optimization, which can be eectively solved by orthogonal matching pursuit. Our formulation ensures...

  10. Predicting worsening asthma control following the common cold

    NARCIS (Netherlands)

    Walter, M. J.; Castro, M.; Kunselman, S. J.; Chinchilli, V. M.; Reno, M.; Ramkumar, T. P.; Avila, P. C.; Boushey, H. A.; Ameredes, B. T.; Bleecker, E. R.; Calhoun, W. J.; Cherniack, R. M.; Craig, T. J.; Denlinger, L. C.; Israel, E.; Fahy, J. V.; Jarjour, N. N.; Kraft, M.; Lazarus, S. C.; Lemanske, R. F.; Martin, R. J.; Peters, S. P.; Ramsdell, J. W.; Sorkness, C. A.; Sutherland, E. R.; Szefler, S. J.; Wasserman, S. I.; Wechsler, M. E.

    2008-01-01

    The asthmatic response to the common cold is highly variable, and early characteristics that predict worsening of asthma control following a cold have not been identified. In this prospective multicentric cohort study of 413 adult subjects with asthma, the mini-Asthma Control Questionnaire

  11. Model Predictive Control for Offset-Free Reference Tracking

    Czech Academy of Sciences Publication Activity Database

    Belda, Květoslav

    2016-01-01

    Roč. 5, č. 1 (2016), s. 8-13 ISSN 1805-3386 Institutional support: RVO:67985556 Keywords : offset-free reference tracking * predictive control * ARX model * state-space model * multi-input multi-output system * robotic system * mechatronic system Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2016/AS/belda-0458355.pdf

  12. Neural Network Predictive Control for Vanadium Redox Flow Battery

    Directory of Open Access Journals (Sweden)

    Hai-Feng Shen

    2013-01-01

    Full Text Available The vanadium redox flow battery (VRB is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.

  13. Modeling, Prediction, and Control of Heating Temperature for Tube Billet

    Directory of Open Access Journals (Sweden)

    Yachun Mao

    2015-01-01

    Full Text Available Annular furnaces have multivariate, nonlinear, large time lag, and cross coupling characteristics. The prediction and control of the exit temperature of a tube billet are important but difficult. We establish a prediction model for the final temperature of a tube billet through OS-ELM-DRPLS method. We address the complex production characteristics, integrate the advantages of PLS and ELM algorithms in establishing linear and nonlinear models, and consider model update and data lag. Based on the proposed model, we design a prediction control algorithm for tube billet temperature. The algorithm is validated using the practical production data of Baosteel Co., Ltd. Results show that the model achieves the precision required in industrial applications. The temperature of the tube billet can be controlled within the required temperature range through compensation control method.

  14. Two stage neural network modelling for robust model predictive control.

    Science.gov (United States)

    Patan, Krzysztof

    2018-01-01

    The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  15. Development of predictive control strategies for building climate control

    OpenAIRE

    NAGPAL, HIMANSHU

    2018-01-01

    APPROVED The rapid growth in energy usage and CO2 emissions has become a critical issue for the whole world. It is noteworthy that buildings are a major contributor to global primary energy consumption. Among building services, use of energy in heating-ventilation-air-conditioning (HVAC) system is particularly significant (about 50\\% of the total building energy consumption). Therefore, the development and implementation of effective control strategies to optimize the operation of HVAC sys...

  16. Predictive control and identification: Applications to steering dynamics

    DEFF Research Database (Denmark)

    Hansen, Anca Daniela

    1996-01-01

    and of the loss function, which defines the optimality of the control. Some guidelines on how to choose the design parameters, depending on the type of process to be controlled and on the required control performance, are presented. A predictive track keeping system for a Mariner Class Vessel is formulated based...... the under- standing of the connection between identification and control, analysed in Chapter 7. Chapter 7 focuses on how to make the on-line identification for predictive control more robust towards unmodelled dynamics. The theory is verified via simulation studies on a Mariner Class Vessel. The effects...... and the need of a prefilter in the estimation are analysed and illustrated. Based on the idea that the control criterion must be dual to the estimation criterion, an iterative optimal prefilter is designed. This seems to be an appealing way to tune the model towards the objective for which the model...

  17. Offset Free Tracking Predictive Control Based on Dynamic PLS Framework

    Directory of Open Access Journals (Sweden)

    Jin Xin

    2017-10-01

    Full Text Available This paper develops an offset free tracking model predictive control based on a dynamic partial least square (PLS framework. First, state space model is used as the inner model of PLS to describe the dynamic system, where subspace identification method is used to identify the inner model. Based on the obtained model, multiple independent model predictive control (MPC controllers are designed. Due to the decoupling character of PLS, these controllers are running separately, which is suitable for distributed control framework. In addition, the increment of inner model output is considered in the cost function of MPC, which involves integral action in the controller. Hence, the offset free tracking performance is guaranteed. The results of an industry background simulation demonstrate the effectiveness of proposed method.

  18. Catalytic cracking models developed for predictive control purposes

    Directory of Open Access Journals (Sweden)

    Dag Ljungqvist

    1993-04-01

    Full Text Available The paper deals with state-space modeling issues in the context of model-predictive control, with application to catalytic cracking. Emphasis is placed on model establishment, verification and online adjustment. Both the Fluid Catalytic Cracking (FCC and the Residual Catalytic Cracking (RCC units are discussed. Catalytic cracking units involve complex interactive processes which are difficult to operate and control in an economically optimal way. The strong nonlinearities of the FCC process mean that the control calculation should be based on a nonlinear model with the relevant constraints included. However, the model can be simple compared to the complexity of the catalytic cracking plant. Model validity is ensured by a robust online model adjustment strategy. Model-predictive control schemes based on linear convolution models have been successfully applied to the supervisory dynamic control of catalytic cracking units, and the control can be further improved by the SSPC scheme.

  19. Nonlinear Predictive Sliding Mode Control for Active Suspension System

    Directory of Open Access Journals (Sweden)

    Dazhuang Wang

    2018-01-01

    Full Text Available An active suspension system is important in meeting the requirements of the ride comfort and handling stability for vehicles. In this work, a nonlinear model of active suspension system and a corresponding nonlinear robust predictive sliding mode control are established for the control problem of active suspension. Firstly, a seven-degree-of-freedom active suspension model is established considering the nonlinear effects of springs and dampers; and secondly, the dynamic model is expanded in the time domain, and the corresponding predictive sliding mode control is established. The uncertainties in the controller are approximated by the fuzzy logic system, and the adaptive controller reduces the approximation error to increase the robustness of the control system. Finally, the simulation results show that the ride comfort and handling stability performance of the active suspension system is better than that of the passive suspension system and the Skyhook active suspension. Thus, the system can obviously improve the shock absorption performance of vehicles.

  20. Fuzzy predictive filtering in nonlinear economic model predictive control for demand response

    DEFF Research Database (Denmark)

    Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.

    2016-01-01

    problem. Moreover, to reduce the computation time and improve the controller's performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy...

  1. Range-Space Predictive Control for Optimal Robot Motion

    Czech Academy of Sciences Publication Activity Database

    Belda, Květoslav; Böhm, Josef

    2008-01-01

    Roč. 1, č. 1 (2008), s. 1-7 ISSN 1998-0140 R&D Projects: GA ČR GP102/06/P275 Institutional research plan: CEZ:AV0Z10750506 Keywords : Accurate manipulation * Industrial robotics * Predictive control * Range-space control Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/historie/belda-0305644.pdf

  2. An adaptive predictive controller and its applications in power stations

    Energy Technology Data Exchange (ETDEWEB)

    Yang Zhiyuan; Lu Huiming; Zhang Xinggao [North China Electric Power University, Beijing (China); Song Chunping [Tsinghua University, Beijing (China). Dept. of Thermal Energy Engineering

    1999-07-01

    Based on the objective function in the form of integration of generalized model error, a globally convergent model reference adaptive predictive control algorithm (MRAPC) containing inertia-time compensators is presented in this paper. MRAPC has been successfully applied to control important thermal process of more than 20 units in many Chinese power stations. In this paper three representative examples are described. Continual operation results for years demonstrate that MRAPC is a successful attempt for the practical applications of adaptive control techniques. (author)

  3. Recent Advances in Explicit Multiparametric Nonlinear Model Predictive Control

    KAUST Repository

    Domínguez, Luis F.

    2011-01-19

    In this paper we present recent advances in multiparametric nonlinear programming (mp-NLP) algorithms for explicit nonlinear model predictive control (mp-NMPC). Three mp-NLP algorithms for NMPC are discussed, based on which novel mp-NMPC controllers are derived. The performance of the explicit controllers are then tested and compared in a simulation example involving the operation of a continuous stirred-tank reactor (CSTR). © 2010 American Chemical Society.

  4. Operating Comfort Prediction Model of Human-Machine Interface Layout for Cabin Based on GEP.

    Science.gov (United States)

    Deng, Li; Wang, Guohua; Chen, Bo

    2015-01-01

    In view of the evaluation and decision-making problem of human-machine interface layout design for cabin, the operating comfort prediction model is proposed based on GEP (Gene Expression Programming), using operating comfort to evaluate layout scheme. Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture. Factor analysis is adopted to decrease the variable dimension; the model's input variables are reduced from 16 joint angles to 4 comfort impact factors, and the output variable is operating comfort score. The Chinese virtual human body model is built by CATIA software, which will be used to simulate and evaluate the operators' operating comfort. With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively. The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency.

  5. Operating Comfort Prediction Model of Human-Machine Interface Layout for Cabin Based on GEP

    Directory of Open Access Journals (Sweden)

    Li Deng

    2015-01-01

    Full Text Available In view of the evaluation and decision-making problem of human-machine interface layout design for cabin, the operating comfort prediction model is proposed based on GEP (Gene Expression Programming, using operating comfort to evaluate layout scheme. Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture. Factor analysis is adopted to decrease the variable dimension; the model’s input variables are reduced from 16 joint angles to 4 comfort impact factors, and the output variable is operating comfort score. The Chinese virtual human body model is built by CATIA software, which will be used to simulate and evaluate the operators’ operating comfort. With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively. The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency.

  6. Model Predictive Control for a Small Scale Unmanned Helicopter

    Directory of Open Access Journals (Sweden)

    Jianfu Du

    2008-11-01

    Full Text Available Kinematical and dynamical equations of a small scale unmanned helicoper are presented in the paper. Based on these equations a model predictive control (MPC method is proposed for controlling the helicopter. This novel method allows the direct accounting for the existing time delays which are used to model the dynamics of actuators and aerodynamics of the main rotor. Also the limits of the actuators are taken into the considerations during the controller design. The proposed control algorithm was verified in real flight experiments where good perfomance was shown in postion control mode.

  7. Supervisory Model Predictive Control of the Heat Integrated Distillation Column

    DEFF Research Database (Denmark)

    Meyer, Kristian; Bisgaard, Thomas; Huusom, Jakob Kjøbsted

    2017-01-01

    This paper benchmarks a centralized control system based on model predictive control for the operation of the heat integrated distillation column (HIDiC) against a fully decentralized control system using the most complete column model currently available in the literature. The centralized control...... system outperforms the decentralized system, because it handles the interactions in the HIDiC process better. The integral absolute error (IAE) is reduced by a factor of 2 and a factor of 4 for control of the top and bottoms compositions, respectively....

  8. Recognition and Prediction of Human Actions for Safe Human-Robot Collaboration

    DEFF Research Database (Denmark)

    Andersen, Rasmus Skovgaard; Bøgh, Simon; Ceballos, Iker

    Collaborative industrial robots are creating new opportunities for collaboration between humans and robots in shared workspaces. In order for such collaboration to be efficient, robots - as well as humans - need to have an understanding of the other's intentions and current ongoing action....... In this work, we propose a method for learning, classifying, and predicting actions taken by a human. Our proposed method is based on the human skeleton model from a Kinect. For demonstration of our approach we chose a typical pick-and-place scenario. Therefore, only arms and upper body are considered......; in total 15 joints. Based on trajectories on these joints, different classes of motion are separated using partitioning around medoids (PAM). Subsequently, SVM is used to train the classes to form a library of human motions. The approach allows run-time detection of when a new motion has been initiated...

  9. Stressor controllability modulates fear extinction in humans

    Science.gov (United States)

    Hartley, Catherine A.; Gorun, Alyson; Reddan, Marianne C.; Ramirez, Franchesca; Phelps, Elizabeth A.

    2014-01-01

    Traumatic events are proposed to play a role in the development of anxiety disorders, however not all individuals exposed to extreme stress experience a pathological increase in fear. Recent studies in animal models suggest that the degree to which one is able to control an aversive experience is a critical factor determining its behavioral consequences. In this study, we examined whether stressor controllability modulates subsequent conditioned fear expression in humans. Participants were randomly assigned to an escapable stressor condition, a yoked inescapable stressor condition, or a control condition involving no stress exposure. One week later, all participants underwent fear conditioning, fear extinction, and a test of extinction retrieval the following day. Participants exposed to inescapable stress showed impaired fear extinction learning and increased fear expression the following day. In contrast, escapable stress improved fear extinction and prevented the spontaneous recovery of fear. Consistent with the bidirectional controllability effects previously reported in animal models, these results suggest that one's degree of control over aversive experiences may be an important factor influencing the development of psychological resilience or vulnerability in humans. PMID:24333646

  10. Nonlinear Economic Model Predictive Control Strategy for Active Smart Buildings

    DEFF Research Database (Denmark)

    Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.

    2016-01-01

    Nowadays, the development of advanced and innovative intelligent control techniques for energy management in buildings is a key issue within the smart grid topic. A nonlinear economic model predictive control (EMPC) scheme, based on the branch-and-bound tree search used as optimization algorithm ...... controller is shown very reliable keeping the comfort levels in the two considered seasons and shifting the load away from peak hours in order to achieve the desired flexible electricity consumption.......Nowadays, the development of advanced and innovative intelligent control techniques for energy management in buildings is a key issue within the smart grid topic. A nonlinear economic model predictive control (EMPC) scheme, based on the branch-and-bound tree search used as optimization algorithm...

  11. Enhanced pid vs model predictive control applied to bldc motor

    Science.gov (United States)

    Gaya, M. S.; Muhammad, Auwal; Aliyu Abdulkadir, Rabiu; Salim, S. N. S.; Madugu, I. S.; Tijjani, Aminu; Aminu Yusuf, Lukman; Dauda Umar, Ibrahim; Khairi, M. T. M.

    2018-01-01

    BrushLess Direct Current (BLDC) motor is a multivariable and highly complex nonlinear system. Variation of internal parameter values with environment or reference signal increases the difficulty in controlling the BLDC effectively. Advanced control strategies (like model predictive control) often have to be integrated to satisfy the control desires. Enhancing or proper tuning of a conventional algorithm results in achieving the desired performance. This paper presents a performance comparison of Enhanced PID and Model Predictive Control (MPC) applied to brushless direct current motor. The simulation results demonstrated that the PSO-PID is slightly better than the PID and MPC in tracking the trajectory of the reference signal. The proposed scheme could be useful algorithms for the system.

  12. Effective and Robust Generalized Predictive Speed Control of Induction Motor

    Directory of Open Access Journals (Sweden)

    Patxi Alkorta

    2013-01-01

    Full Text Available This paper presents and validates a new proposal for effective speed vector control of induction motors based on linear Generalized Predictive Control (GPC law. The presented GPC-PI cascade configuration simplifies the design with regard to GPC-GPC cascade configuration, maintaining the advantages of the predictive control algorithm. The robust stability of the closed loop system is demonstrated by the poles placement method for several typical cases of uncertainties in induction motors. The controller has been tested using several simulations and experiments and has been compared with Proportional Integral Derivative (PID and Sliding Mode (SM control schemes, obtaining outstanding results in speed tracking even in the presence of parameter uncertainties, unknown load disturbance, and measurement noise in the loop signals, suggesting its use in industrial applications.

  13. Modeling and Control of CSTR using Model based Neural Network Predictive Control

    OpenAIRE

    Shrivastava, Piyush

    2012-01-01

    This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some commen...

  14. Finite-Control-Set Model Predictive Control (FCS-MPC) for Islanded Hybrid Microgrids

    OpenAIRE

    Yi, Zhehan; Babqi, Abdulrahman J.; Wang, Yishen; Shi, Di; Etemadi, Amir H.; Wang, Zhiwei; Huang, Bibin

    2018-01-01

    Microgrids consisting of multiple distributed energy resources (DERs) provide a promising solution to integrate renewable energies, e.g., solar photovoltaic (PV) systems. Hybrid AC/DC microgrids leverage the merits of both AC and DC power systems. In this paper, a control strategy for islanded multi-bus hybrid microgrids is proposed based on the Finite-Control-Set Model Predictive Control (FCS-MPC) technologies. The control loops are expedited by predicting the future states and determining t...

  15. A Robust Practical Generalized Predictive Control for BoilerSuper Heater Temperature Control

    OpenAIRE

    Zaki Maki Mohialdeen

    2015-01-01

    A practical method of robust generalized predictive controller (GPC) application is developed using a combination of Ziegler-Nichols type functions relating the GPC controller parameters to a first order with time delay process parameters and a model matching controller. The GPC controller and the model matching controller are used in a master/slave configuration, with the GPC as the master controller and the model matching controller as the slave controlle...

  16. Networked Predictive Control for Nonlinear Systems With Arbitrary Region Quantizers.

    Science.gov (United States)

    Yang, Hongjiu; Xu, Yang; Xia, Yuanqing; Zhang, Jinhui

    2017-04-06

    In this paper, networked predictive control is investigated for planar nonlinear systems with quantization by an extended state observer (ESO). The ESO is used not only to deal with nonlinear terms but also to generate predictive states for dealing with network-induced delays. Two arbitrary region quantizers are applied to take effective values of signals in forward channel and feedback channel, respectively. Based on a "zoom" strategy, sufficient conditions are given to guarantee stabilization of the closed-loop networked control system with quantization. A simulation example is proposed to exhibit advantages and availability of the results.

  17. Prediction-Based Control for Nonlinear Systems with Input Delay

    Directory of Open Access Journals (Sweden)

    I. Estrada-Sánchez

    2017-01-01

    Full Text Available This work has two primary objectives. First, it presents a state prediction strategy for a class of nonlinear Lipschitz systems subject to constant time delay in the input signal. As a result of a suitable change of variable, the state predictor asymptotically provides the value of the state τ units of time ahead. Second, it proposes a solution to the stabilization and trajectory tracking problems for the considered class of systems using predicted states. The predictor-controller convergence is proved by considering a complete Lyapunov functional. The proposed predictor-based controller strategy is evaluated using numerical simulations.

  18. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...... and related to the uncertainty of the impulse response coefficients. The simulations can be used to benchmark l2 MPC against FIR based robust MPC as well as to estimate the maximum performance improvements by robust MPC....

  19. Generalized predictive control in the delta-domain

    DEFF Research Database (Denmark)

    Lauritsen, Morten Bach; Jensen, Morten Rostgaard; Poulsen, Niels Kjølstad

    1995-01-01

    This paper describes new approaches to generalized predictive control formulated in the delta (δ) domain. A new δ-domain version of the continuous-time emulator-based predictor is presented. It produces the optimal estimate in the deterministic case whenever the predictor order is chosen greater...... than or equal to the number of future predicted samples, however a “good” estimate is usually obtained in a much longer range of samples. This is particularly advantageous at fast sampling rates where a “conventional” predictor is bound to become very computationally demanding. Two controllers...

  20. DemQSAR: predicting human volume of distribution and clearance of drugs.

    Science.gov (United States)

    Demir-Kavuk, Ozgur; Bentzien, Jörg; Muegge, Ingo; Knapp, Ernst-Walter

    2011-12-01

    In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure-activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VD(ss)) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VD(ss) and CL is

  1. Predictive Feedback and Feedforward Control for Systems with Unknown Disturbances

    Science.gov (United States)

    Juang, Jer-Nan; Eure, Kenneth W.

    1998-01-01

    Predictive feedback control has been successfully used in the regulation of plate vibrations when no reference signal is available for feedforward control. However, if a reference signal is available it may be used to enhance regulation by incorporating a feedforward path in the feedback controller. Such a controller is known as a hybrid controller. This paper presents the theory and implementation of the hybrid controller for general linear systems, in particular for structural vibration induced by acoustic noise. The generalized predictive control is extended to include a feedforward path in the multi-input multi-output case and implemented on a single-input single-output test plant to achieve plate vibration regulation. There are cases in acoustic-induce vibration where the disturbance signal is not available to be used by the hybrid controller, but a disturbance model is available. In this case the disturbance model may be used in the feedback controller to enhance performance. In practice, however, neither the disturbance signal nor the disturbance model is available. This paper presents the theory of identifying and incorporating the noise model into the feedback controller. Implementations are performed on a test plant and regulation improvements over the case where no noise model is used are demonstrated.

  2. REALIGNED MODEL PREDICTIVE CONTROL OF A PROPYLENE DISTILLATION COLUMN

    Directory of Open Access Journals (Sweden)

    A. I. Hinojosa

    Full Text Available Abstract In the process industry, advanced controllers usually aim at an economic objective, which usually requires closed-loop stability and constraints satisfaction. In this paper, the application of a MPC in the optimization structure of an industrial Propylene/Propane (PP splitter is tested with a controller based on a state space model, which is suitable for heavily disturbed environments. The simulation platform is based on the integration of the commercial dynamic simulator Dynsim® and the rigorous steady-state optimizer ROMeo® with the real-time facilities of Matlab. The predictive controller is the Infinite Horizon Model Predictive Control (IHMPC, based on a state-space model that that does not require the use of a state observer because the non-minimum state is built with the past inputs and outputs. The controller considers the existence of zone control of the outputs and optimizing targets for the inputs. We verify that the controller is efficient to control the propylene distillation system in a disturbed scenario when compared with a conventional controller based on a state observer. The simulation results show a good performance in terms of stability of the controller and rejection of large disturbances in the composition of the feed of the propylene distillation column.

  3. Nonlinear Model Predictive Control with Constraint Satisfactions for a Quadcopter

    Science.gov (United States)

    Wang, Ye; Ramirez-Jaime, Andres; Xu, Feng; Puig, Vicenç

    2017-01-01

    This paper presents a nonlinear model predictive control (NMPC) strategy combined with constraint satisfactions for a quadcopter. The full dynamics of the quadcopter describing the attitude and position are nonlinear, which are quite sensitive to changes of inputs and disturbances. By means of constraint satisfactions, partial nonlinearities and modeling errors of the control-oriented model of full dynamics can be transformed into the inequality constraints. Subsequently, the quadcopter can be controlled by an NMPC controller with the updated constraints generated by constraint satisfactions. Finally, the simulation results applied to a quadcopter simulator are provided to show the effectiveness of the proposed strategy.

  4. Economic Model Predictive Control for Smart Energy Systems

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus

    Model Predictive Control (MPC) can be used to control the energy distribution in a Smart Grid with a high share of stochastic energy production from renewable energy sources like wind. Heat pumps for heating residential buildings can exploit the slow heat dynamics of a building to store heat and ...... and hereby shift the heat pump power consumption to periods with both low electricity prices and a high fraction of green energy in the grid.......Model Predictive Control (MPC) can be used to control the energy distribution in a Smart Grid with a high share of stochastic energy production from renewable energy sources like wind. Heat pumps for heating residential buildings can exploit the slow heat dynamics of a building to store heat...

  5. Multimodel Predictive Control Approach for UAV Formation Flight

    Directory of Open Access Journals (Sweden)

    Chang-jian Ru

    2014-01-01

    Full Text Available Formation flight problem is the most important and interesting problem of multiple UAVs (unmanned aerial vehicles cooperative control. In this paper, a novel approach for UAV formation flight based on multimodel predictive control is designed. Firstly, the state equation of relative motion is obtained and then discretized. By the geometrical method, the characteristic points of state are determined. Afterwards, based on the linearization technique, the standard linear discrete model is obtained at each characteristic state point. Then, weighted model set is proposed using the idea of T-S (Takagi-Sugeno fuzzy control and the predictive control is carried out based on the multimodel method. Finally, to verify the performance of the proposed method, two different simulation scenarios are performed.

  6. Multi-Objective Predictive Balancing Control of Battery Packs Based on Predictive Current

    Directory of Open Access Journals (Sweden)

    Wenbiao Li

    2016-04-01

    Full Text Available Various balancing topology and control methods have been proposed for the inconsistency problem of battery packs. However, these strategies only focus on a single objective, ignore the mutual interaction among various factors and are only based on the external performance of the battery pack inconsistency, such as voltage balancing and state of charge (SOC balancing. To solve these problems, multi-objective predictive balancing control (MOPBC based on predictive current is proposed in this paper, namely, in the driving process of an electric vehicle, using predictive control to predict the battery pack output current the next time. Based on this information, the impact of the battery pack temperature caused by the output current can be obtained. Then, the influence is added to the battery pack balancing control, which makes the present degradation, temperature, and SOC imbalance achieve balance automatically due to the change of the output current the next moment. According to MOPBC, the simulation model of the balancing circuit is built with four cells in Matlab/Simulink. The simulation results show that MOPBC is not only better than the other traditional balancing control strategies but also reduces the energy loss in the balancing process.

  7. Integration of Predictive Display and Aircraft Flight Control System

    Directory of Open Access Journals (Sweden)

    Efremov A.V.

    2017-01-01

    Full Text Available The synthesis of predictive display information and direct lift control system are considered for the path control tracking tasks (in particular landing task. The both solutions are based on pilot-vehicle system analysis and requirements to provide the highest accuracy and lowest pilot workload. The investigation was carried out for cases with and without time delay in aircraft dynamics. The efficiency of the both ways for the flying qualities improvement and their integration is tested by ground based simulation.

  8. Generalized Predictive Control for Non-Stationary Systems

    DEFF Research Database (Denmark)

    Palsson, Olafur Petur; Madsen, Henrik; Søgaard, Henning Tangen

    1994-01-01

    This paper shows how the generalized predictive control (GPC) can be extended to non-stationary (time-varying) systems. If the time-variation is slow, then the classical GPC can be used in context with an adaptive estimation procedure of a time-invariant ARIMAX model. However, in this paper prior...... knowledge concerning the nature of the parameter variations is assumed available. The GPC is based on the assumption that the prediction of the system output can be expressed as a linear combination of present and future controls. Since the Diophantine equation cannot be used due to the time......-variation of the parameters, the optimal prediction is found as the general conditional expectation of the system output. The underlying model is of an ARMAX-type instead of an ARIMAX-type as in the original version of the GPC (Clarke, D. W., C. Mohtadi and P. S. Tuffs (1987). Automatica, 23, 137-148) and almost all later...

  9. Model Predictive Control of Mineral Column Flotation Process

    Directory of Open Access Journals (Sweden)

    Yahui Tian

    2018-06-01

    Full Text Available Column flotation is an efficient method commonly used in the mineral industry to separate useful minerals from ores of low grade and complex mineral composition. Its main purpose is to achieve maximum recovery while ensuring desired product grade. This work addresses a model predictive control design for a mineral column flotation process modeled by a set of nonlinear coupled heterodirectional hyperbolic partial differential equations (PDEs and ordinary differential equations (ODEs, which accounts for the interconnection of well-stirred regions represented by continuous stirred tank reactors (CSTRs and transport systems given by heterodirectional hyperbolic PDEs, with these two regions combined through the PDEs’ boundaries. The model predictive control considers both optimality of the process operations and naturally present input and state/output constraints. For the discrete controller design, spatially varying steady-state profiles are obtained by linearizing the coupled ODE–PDE model, and then the discrete system is obtained by using the Cayley–Tustin time discretization transformation without any spatial discretization and/or without model reduction. The model predictive controller is designed by solving an optimization problem with input and state/output constraints as well as input disturbance to minimize the objective function, which leads to an online-solvable finite constrained quadratic regulator problem. Finally, the controller performance to keep the output at the steady state within the constraint range is demonstrated by simulation studies, and it is concluded that the optimal control scheme presented in this work makes this flotation process more efficient.

  10. Prediction of active control of subsonic centrifugal compressor rotating stall

    Science.gov (United States)

    Lawless, Patrick B.; Fleeter, Sanford

    1993-01-01

    A mathematical model is developed to predict the suppression of rotating stall in a centrifugal compressor with a vaned diffuser. This model is based on the employment of a control vortical waveform generated upstream of the impeller inlet to damp weak potential disturbances that are the early stages of rotating stall. The control system is analyzed by matching the perturbation pressure in the compressor inlet and exit flow fields with a model for the unsteady behavior of the compressor. The model was effective at predicting the stalling behavior of the Purdue Low Speed Centrifugal Compressor for two distinctly different stall patterns. Predictions made for the effect of a controlled inlet vorticity wave on the stability of the compressor show that for minimum control wave magnitudes, on the order of the total inlet disturbance magnitude, significant damping of the instability can be achieved. For control waves of sufficient amplitude, the control phase angle appears to be the most important factor in maintaining a stable condition in the compressor.

  11. Predicting human height by Victorian and genomic methods.

    Science.gov (United States)

    Aulchenko, Yurii S; Struchalin, Maksim V; Belonogova, Nadezhda M; Axenovich, Tatiana I; Weedon, Michael N; Hofman, Albert; Uitterlinden, Andre G; Kayser, Manfred; Oostra, Ben A; van Duijn, Cornelia M; Janssens, A Cecile J W; Borodin, Pavel M

    2009-08-01

    In the Victorian era, Sir Francis Galton showed that 'when dealing with the transmission of stature from parents to children, the average height of the two parents, ... is all we need care to know about them' (1886). One hundred and twenty-two years after Galton's work was published, 54 loci showing strong statistical evidence for association to human height were described, providing us with potential genomic means of human height prediction. In a population-based study of 5748 people, we find that a 54-loci genomic profile explained 4-6% of the sex- and age-adjusted height variance, and had limited ability to discriminate tall/short people, as characterized by the area under the receiver-operating characteristic curve (AUC). In a family-based study of 550 people, with both parents having height measurements, we find that the Galtonian mid-parental prediction method explained 40% of the sex- and age-adjusted height variance, and showed high discriminative accuracy. We have also explored how much variance a genomic profile should explain to reach certain AUC values. For highly heritable traits such as height, we conclude that in applications in which parental phenotypic information is available (eg, medicine), the Victorian Galton's method will long stay unsurpassed, in terms of both discriminative accuracy and costs. For less heritable traits, and in situations in which parental information is not available (eg, forensics), genomic methods may provide an alternative, given that the variants determining an essential proportion of the trait's variation can be identified.

  12. Human short-term spatial memory: precision predicts capacity.

    Science.gov (United States)

    Banta Lavenex, Pamela; Boujon, Valérie; Ndarugendamwo, Angélique; Lavenex, Pierre

    2015-03-01

    Here, we aimed to determine the capacity of human short-term memory for allocentric spatial information in a real-world setting. Young adults were tested on their ability to learn, on a trial-unique basis, and remember over a 1-min interval the location(s) of 1, 3, 5, or 7 illuminating pads, among 23 pads distributed in a 4m×4m arena surrounded by curtains on three sides. Participants had to walk to and touch the pads with their foot to illuminate the goal locations. In contrast to the predictions from classical slot models of working memory capacity limited to a fixed number of items, i.e., Miller's magical number 7 or Cowan's magical number 4, we found that the number of visited locations to find the goals was consistently about 1.6 times the number of goals, whereas the number of correct choices before erring and the number of errorless trials varied with memory load even when memory load was below the hypothetical memory capacity. In contrast to resource models of visual working memory, we found no evidence that memory resources were evenly distributed among unlimited numbers of items to be remembered. Instead, we found that memory for even one individual location was imprecise, and that memory performance for one location could be used to predict memory performance for multiple locations. Our findings are consistent with a theoretical model suggesting that the precision of the memory for individual locations might determine the capacity of human short-term memory for spatial information. Copyright © 2015 Elsevier Inc. All rights reserved.

  13. Model Predictive Control of the Hybrid Ventilation for Livestock

    DEFF Research Database (Denmark)

    Wu, Zhuang; Stoustrup, Jakob; Trangbæk, Klaus

    2006-01-01

    In this paper, design and simulation results of Model Predictive Control (MPC) strategy for livestock hybrid ventilation systems and associated indoor climate through variable valve openings and exhaust fans are presented. The design is based on thermal comfort parameters for poultry in barns...

  14. Real-Time Optimization for Economic Model Predictive Control

    DEFF Research Database (Denmark)

    Sokoler, Leo Emil; Edlund, Kristian; Frison, Gianluca

    2012-01-01

    In this paper, we develop an efficient homogeneous and self-dual interior-point method for the linear programs arising in economic model predictive control. To exploit structure in the optimization problems, the algorithm employs a highly specialized Riccati iteration procedure. Simulations show...

  15. Model predictive control of hybrid systems : stability and robustness

    NARCIS (Netherlands)

    Lazar, M.

    2006-01-01

    This thesis considers the stabilization and the robust stabilization of certain classes of hybrid systems using model predictive control. Hybrid systems represent a broad class of dynamical systems in which discrete behavior (usually described by a finite state machine) and continuous behavior

  16. Stochastic Predictive Control of Multi-Microgrid Systems

    DEFF Research Database (Denmark)

    Bazmohammadi, Najmeh; Tahsiri, Ahmadreza; Anvari-Moghaddam, Amjad

    2018-01-01

    This paper presents a stochastic predictive control algorithm for a number of microgrids connected to the same distribution system. Each microgrid includes a variety of distributed resources such as wind turbine, photo voltaic units, energy storage devices and loads. Considering the uncertainty...

  17. When predictions take control: The effect of task predictions on task switching performance

    Directory of Open Access Journals (Sweden)

    Wout eDuthoo

    2012-08-01

    Full Text Available In this paper, we aimed to investigate the role of self-generated predictions in the flexible control of behaviour. Therefore, we ran a task switching experiment in which participants were asked to try to predict the upcoming task in three conditions varying in switch rate (30%, 50% and 70%. Irrespective of their predictions, the colour of the target indicated which task participants had to perform. In line with previous studies (Mayr, 2006; Monsell & Mizon, 2006, the switch cost was attenuated as the switch rate increased. Importantly, a clear task repetition bias was found in all conditions, yet the task repetition prediction rate dropped from 78% over 66% to 49% with increasing switch probability in the three conditions. Irrespective of condition, the switch cost was strongly reduced in expectation of a task alternation compared to the cost of an unexpected task alternation following repetition predictions. Hence, our data suggest that the reduction in the switch cost with increasing switch probability is caused by a diminished expectancy for the task to repeat. Taken together, this paper highlights the importance of predictions in the flexible control of behaviour, and suggests a crucial role for task repetition expectancy in the context-sensitive adjusting of task switching performance.

  18. Body odor quality predicts behavioral attractiveness in humans.

    Science.gov (United States)

    Roberts, S Craig; Kralevich, Alexandra; Ferdenzi, Camille; Saxton, Tamsin K; Jones, Benedict C; DeBruine, Lisa M; Little, Anthony C; Havlicek, Jan

    2011-12-01

    Growing effort is being made to understand how different attractive physical traits co-vary within individuals, partly because this might indicate an underlying index of genetic quality. In humans, attention has focused on potential markers of quality such as facial attractiveness, axillary odor quality, the second-to-fourth digit (2D:4D) ratio and body mass index (BMI). Here we extend this approach to include visually-assessed kinesic cues (nonverbal behavior linked to movement) which are statistically independent of structural physical traits. The utility of such kinesic cues in mate assessment is controversial, particularly during everyday conversational contexts, as they could be unreliable and susceptible to deception. However, we show here that the attractiveness of nonverbal behavior, in 20 male participants, is predicted by perceived quality of their axillary body odor. This finding indicates covariation between two desirable traits in different sensory modalities. Depending on two different rating contexts (either a simple attractiveness rating or a rating for long-term partners by 10 female raters not using hormonal contraception), we also found significant relationships between perceived attractiveness of nonverbal behavior and BMI, and between axillary odor ratings and 2D:4D ratio. Axillary odor pleasantness was the single attribute that consistently predicted attractiveness of nonverbal behavior. Our results demonstrate that nonverbal kinesic cues could reliably reveal mate quality, at least in males, and could corroborate and contribute to mate assessment based on other physical traits.

  19. Comparison of two metrological approaches for the prediction of human haptic perception

    Science.gov (United States)

    Neumann, Annika; Frank, Daniel; Vondenhoff, Thomas; Schmitt, Robert

    2016-06-01

    Haptic perception is regarded as a key component of customer appreciation and acceptance for various products. The prediction of customers’ haptic perception is of interest both during product development and production phases. This paper presents the results of a multivariate analysis between perceived roughness and texture related surface measurements, to examine whether perceived roughness can be accurately predicted using technical measurements. Studies have shown that standardized measurement parameters, such as the roughness coefficients (e.g. Rz or Ra), do not show a one-dimensional linear correlation with the human perception (of roughness). Thus, an alternative measurement method was compared to standard measurements of roughness, in regard to its capability of predicting perceived roughness through technical measurements. To estimate perceived roughness, an experimental study was conducted in which 102 subjects evaluated four sets of 12 different geometrical surface structures regarding their relative perceived roughness. The two different metrological procedures were examined in relation to their capability to predict the perceived roughness of the subjects stated within the study. The standardized measurements of the surface roughness were made using a structured light 3D-scanner. As an alternative method, surface induced vibrations were measured by a finger-like sensor during robot-controlled traverse over a surface. The presented findings provide a better understanding of the predictability of human haptic perception using technical measurements.

  20. Patterns, Entropy, and Predictability of Human Mobility and Life

    Science.gov (United States)

    Qin, Shao-Meng; Verkasalo, Hannu; Mohtaschemi, Mikael; Hartonen, Tuomo; Alava, Mikko

    2012-01-01

    Cellular phones are now offering an ubiquitous means for scientists to observe life: how people act, move and respond to external influences. They can be utilized as measurement devices of individual persons and for groups of people of the social context and the related interactions. The picture of human life that emerges shows complexity, which is manifested in such data in properties of the spatiotemporal tracks of individuals. We extract from smartphone-based data for a set of persons important locations such as “home”, “work” and so forth over fixed length time-slots covering the days in the data-set (see also [1], [2]). This set of typical places is heavy-tailed, a power-law distribution with an exponent close to −1.7. To analyze the regularities and stochastic features present, the days are classified for each person into regular, personal patterns. To this are superimposed fluctuations for each day. This randomness is measured by “life” entropy, computed both before and after finding the clustering so as to subtract the contribution of a number of patterns. The main issue that we then address is how predictable individuals are in their mobility. The patterns and entropy are reflected in the predictability of the mobility of the life both individually and on average. We explore the simple approaches to guess the location from the typical behavior, and of exploiting the transition probabilities with time from location or activity A to B. The patterns allow an enhanced predictability, at least up to a few hours into the future from the current location. Such fixed habits are most clearly visible in the working-day length. PMID:23300542

  1. Reminder cues modulate the renewal effect in human predictive learning

    Directory of Open Access Journals (Sweden)

    Javier Bustamante

    2016-12-01

    Full Text Available Associative learning refers to our ability to learn about regularities in our environment. When a stimulus is repeatedly followed by a specific outcome, we learn to expect the outcome in the presence of the stimulus. We are also able to modify established expectations in the face of disconfirming information (the stimulus is no longer followed by the outcome. Both the change of environmental regularities and the related processes of adaptation are referred to as extinction. However, extinction does not erase the initially acquired expectations. For instance, following successful extinction, the initially learned expectations can recover when there is a context change – a phenomenon called the renewal effect, which is considered as a model for relapse after exposure therapy. Renewal was found to be modulated by reminder cues of acquisition and extinction. However, the mechanisms underlying the effectiveness of reminder cues are not well understood. The aim of the present study was to investigate the impact of reminder cues on renewal in the field of human predictive learning. Experiment I demonstrated that renewal in human predictive learning is modulated by cues related to acquisition or extinction. Initially, participants received pairings of a stimulus and an outcome in one context. These stimulus-outcome pairings were preceded by presentations of a reminder cue (acquisition cue. Then, participants received extinction in a different context in which presentations of the stimulus were no longer followed by the outcome. These extinction trials were preceded by a second reminder cue (extinction cue. During a final phase conducted in a third context, participants showed stronger expectations of the outcome in the presence of the stimulus when testing was accompanied by the acquisition cue compared to the extinction cue. Experiment II tested an explanation of the reminder cue effect in terms of simple cue-outcome associations. Therefore

  2. Economic Model Predictive Control for Spray Drying Plants

    DEFF Research Database (Denmark)

    Petersen, Lars Norbert

    and a complexity reduced control model is used for state estimation and prediction in the controllers. These models facilitate development and comparison of control strategies. We develop two MPC strategies; a linear tracking MPC with a Real-Time Optimization layer (MPC with RTO) and an Economic Nonlinear MPC (E...... horizon, out of which only the first input is applied to the dryer. This procedure is repeated at each sample instant and is solved numerically in real-time. The MPC with RTO tracks a target that optimizes the cost of operation at steady-state. The E-MPC optimizes the cost of operation directly by having...... this objective directly in the controller. The need for the RTO layer is then eliminated. We demonstrate the application of the proposed MPC with RTO to control an industrial GEA MSDTM-1250 spray dryer, which produces approximately 7500 kg/hr of enriched milk powder. Compared to the conventional PI controller...

  3. Adaptive model predictive process control using neural networks

    Science.gov (United States)

    Buescher, K.L.; Baum, C.C.; Jones, R.D.

    1997-08-19

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

  4. Composite control for raymond mill based on model predictive control and disturbance observer

    Directory of Open Access Journals (Sweden)

    Dan Niu

    2016-03-01

    Full Text Available In the raymond mill grinding process, precise control of operating load is vital for the high product quality. However, strong external disturbances, such as variations of ore size and ore hardness, usually cause great performance degradation. It is not easy to control the current of raymond mill constant. Several control strategies have been proposed. However, most of them (such as proportional–integral–derivative and model predictive control reject disturbances just through feedback regulation, which may lead to poor control performance in the presence of strong disturbances. For improving disturbance rejection, a control method based on model predictive control and disturbance observer is put forward in this article. The scheme employs disturbance observer as feedforward compensation and model predictive control controller as feedback regulation. The test results illustrate that compared with model predictive control method, the proposed disturbance observer–model predictive control method can obtain significant superiority in disturbance rejection, such as shorter settling time and smaller peak overshoot under strong disturbances.

  5. Frequency weighted model predictive control of wind turbine

    DEFF Research Database (Denmark)

    Klauco, Martin; Poulsen, Niels Kjølstad; Mirzaei, Mahmood

    2013-01-01

    This work is focused on applying frequency weighted model predictive control (FMPC) on three blade horizontal axis wind turbine (HAWT). A wind turbine is a very complex, non-linear system influenced by a stochastic wind speed variation. The reduced dynamics considered in this work are the rotatio...... predictive controller are presented. Statistical comparison between frequency weighted MPC, standard MPC and baseline PI controller is shown as well.......This work is focused on applying frequency weighted model predictive control (FMPC) on three blade horizontal axis wind turbine (HAWT). A wind turbine is a very complex, non-linear system influenced by a stochastic wind speed variation. The reduced dynamics considered in this work...... are the rotational degree of freedom of the rotor and the tower for-aft movement. The MPC design is based on a receding horizon policy and a linearised model of the wind turbine. Due to the change of dynamics according to wind speed, several linearisation points must be considered and the control design adjusted...

  6. Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

    DEFF Research Database (Denmark)

    Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon

    2008-01-01

    This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...... reference tracking and disturbance rejection in an economically optimal way. The performance function is chosen as a mixture of the `1-norm and a linear weighting to model the economics of the system. Simulations show a significant improvement of the performance of the MPC compared to the current...

  7. MODELLING OF DYNAMIC SPEED LIMITS USING THE MODEL PREDICTIVE CONTROL

    Directory of Open Access Journals (Sweden)

    Andrey Borisovich Nikolaev

    2017-09-01

    Full Text Available The article considers the issues of traffic management using intelligent system “Car-Road” (IVHS, which consist of interacting intelligent vehicles (IV and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering. Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC. The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic.

  8. Thermal Storage Power Balancing with Model Predictive Control

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus; Poulsen, Niels Kjølstad; Madsen, Henrik

    2013-01-01

    The method described in this paper balances power production and consumption with a large number of thermal loads. Linear controllers are used for the loads to track a temperature set point, while Model Predictive Control (MPC) and model estimation of the load behavior are used for coordination....... The total power consumption of all loads is controlled indirectly through a real-time price. The MPC incorporates forecasts of the power production and disturbances that influence the loads, e.g. time-varying weather forecasts, in order to react ahead of time. A simulation scenario demonstrates...

  9. Model Predictive Control of Wind Turbines using Uncertain LIDAR Measurements

    DEFF Research Database (Denmark)

    Mirzaei, Mahmood; Soltani, Mohsen; Poulsen, Niels Kjølstad

    2013-01-01

    , we simplify state prediction for the MPC. Consequently, the control problem of the nonlinear system is simplified into a quadratic programming. We consider uncertainty in the wind propagation time, which is the traveling time of wind from the LIDAR measurement point to the rotor. An algorithm based......The problem of Model predictive control (MPC) of wind turbines using uncertain LIDAR (LIght Detection And Ranging) measurements is considered. A nonlinear dynamical model of the wind turbine is obtained. We linearize the obtained nonlinear model for different operating points, which are determined...... on wind speed estimation and measurements from the LIDAR is devised to find an estimate of the delay and compensate for it before it is used in the controller. Comparisons between the MPC with error compensation, the MPC without error compensation and an MPC with re-linearization at each sample point...

  10. Electric vehicle charge planning using Economic Model Predictive Control

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus; Poulsen, Niels K.; Madsen, Henrik

    2012-01-01

    Economic Model Predictive Control (MPC) is very well suited for controlling smart energy systems since electricity price and demand forecasts are easily integrated in the controller. Electric vehicles (EVs) are expected to play a large role in the future Smart Grid. They are expected to provide...... grid services, both for peak reduction and for ancillary services, by absorbing short term variations in the electricity production. In this paper the Economic MPC minimizes the cost of electricity consumption for a single EV. Simulations show savings of 50–60% of the electricity costs compared...... to uncontrolled charging from load shifting based on driving pattern predictions. The future energy system in Denmark will most likely be based on renewable energy sources e.g. wind and solar power. These green energy sources introduce stochastic fluctuations in the electricity production. Therefore, energy...

  11. Robust Model Predictive Control of a Wind Turbine

    DEFF Research Database (Denmark)

    Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2012-01-01

    In this work the problem of robust model predictive control (robust MPC) of a wind turbine in the full load region is considered. A minimax robust MPC approach is used to tackle the problem. Nonlinear dynamics of the wind turbine are derived by combining blade element momentum (BEM) theory...... of the uncertain system is employed and a norm-bounded uncertainty model is used to formulate a minimax model predictive control. The resulting optimization problem is simplified by semidefinite relaxation and the controller obtained is applied on a full complexity, high fidelity wind turbine model. Finally...... and first principle modeling of the turbine flexible structure. Thereafter the nonlinear model is linearized using Taylor series expansion around system operating points. Operating points are determined by effective wind speed and an extended Kalman filter (EKF) is employed to estimate this. In addition...

  12. Stability analysis of embedded nonlinear predictor neural generalized predictive controller

    Directory of Open Access Journals (Sweden)

    Hesham F. Abdel Ghaffar

    2014-03-01

    Full Text Available Nonlinear Predictor-Neural Generalized Predictive Controller (NGPC is one of the most advanced control techniques that are used with severe nonlinear processes. In this paper, a hybrid solution from NGPC and Internal Model Principle (IMP is implemented to stabilize nonlinear, non-minimum phase, variable dead time processes under high disturbance values over wide range of operation. Also, the superiority of NGPC over linear predictive controllers, like GPC, is proved for severe nonlinear processes over wide range of operation. The necessary conditions required to stabilize NGPC is derived using Lyapunov stability analysis for nonlinear processes. The NGPC stability conditions and improvement in disturbance suppression are verified by both simulation using Duffing’s nonlinear equation and real-time using continuous stirred tank reactor. Up to our knowledge, the paper offers the first hardware embedded Neural GPC which has been utilized to verify NGPC–IMP improvement in realtime.

  13. Economic model predictive control theory, formulations and chemical process applications

    CERN Document Server

    Ellis, Matthew; Christofides, Panagiotis D

    2017-01-01

    This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency. Specifically, the book proposes: Lyapunov-based EMPC methods for nonlinear systems; two-tier EMPC architectures that are highly computationally efficient; and EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics. The proposed methods employ a variety of tools ranging from nonlinear systems analysis, through Lyapunov-based control techniques to nonlinear dynamic optimization. The applicability and performance of the proposed methods are demonstrated through a number of chemical process examples. The book presents state-of-the-art methods for the design of economic model predictive control systems for chemical processes. In addition to being...

  14. Investigation of the effects of human body stability on joint angles’ prediction

    Energy Technology Data Exchange (ETDEWEB)

    Pasha Zanoosi, A. A., E-mail: aliakbar.pasha@yahoo.com, E-mail: aliakbar.pasha@qiau.ac.ir [Islamic Azad University, Faculty of Industrial & Mechanical Engineering, Qazvin Branch (Iran, Islamic Republic of); Naderi, D.; Sadeghi-Mehr, M.; Feri, M. [Bu Ali-Sina University, Mechanical Engineering Department, Faculty of Engineering (Iran, Islamic Republic of); Beheshtiha, A. Sh. [Leibniz Universität Hannover, Institute of Mechanics and Computational Mechanics (Germany); Fallahnejad, K. [Flinders University, Discipline of Mechanical Engineering, School of Computer Science, Engineering and Mathematics (Australia)

    2015-10-15

    Loosing stability control in elderly or paralyzed has motivated researchers to study how a stability control system works and how to determine its state at every time instant. Studying the stability of a human body is not only an important problem from a scientific viewpoint, but also finally leads to new designs of prostheses and orthoses and rehabilitation methods. Computer modeling enables researchers to study and describe the reactions and propose a suitable and optimized motion pattern to strengthen the neuromuscular system and helps a human body maintain its stability. A perturbation as a tilting is exposed to an underfoot plate of a musculoskeletal model of the body to study the stability. The studied model of a human body included four links and three degrees of freedom with eight muscles in the sagittal plane. Lagrangian dynamics was used for deriving equations of motion and muscles were modeled using Hill’s model. Using experimental data of joint trajectories for a human body under tilting perturbation, forward dynamics has been applied to predict joint trajectories and muscle activation. This study investigated the effects of stability on predicting body joints’ motion. A new stability function for a human body, based on the zero moment point, has been employed in a forward dynamics procedure using a direct collocation method. A multi-objective optimization based on genetic algorithm has been proposed to employ stability as a robotic objective function along with muscle stresses as a biological objective function. The obtained results for joints’ motion were compared to experimental data. The results show that, for this type of perturbations, muscle stresses are in conflict with body stability. This means that more body stability requires more stresses in muscles and reverse. Results also show the effects of the stability objective function in better prediction of joint trajectories.

  15. Investigation of the effects of human body stability on joint angles’ prediction

    International Nuclear Information System (INIS)

    Pasha Zanoosi, A. A.; Naderi, D.; Sadeghi-Mehr, M.; Feri, M.; Beheshtiha, A. Sh.; Fallahnejad, K.

    2015-01-01

    Loosing stability control in elderly or paralyzed has motivated researchers to study how a stability control system works and how to determine its state at every time instant. Studying the stability of a human body is not only an important problem from a scientific viewpoint, but also finally leads to new designs of prostheses and orthoses and rehabilitation methods. Computer modeling enables researchers to study and describe the reactions and propose a suitable and optimized motion pattern to strengthen the neuromuscular system and helps a human body maintain its stability. A perturbation as a tilting is exposed to an underfoot plate of a musculoskeletal model of the body to study the stability. The studied model of a human body included four links and three degrees of freedom with eight muscles in the sagittal plane. Lagrangian dynamics was used for deriving equations of motion and muscles were modeled using Hill’s model. Using experimental data of joint trajectories for a human body under tilting perturbation, forward dynamics has been applied to predict joint trajectories and muscle activation. This study investigated the effects of stability on predicting body joints’ motion. A new stability function for a human body, based on the zero moment point, has been employed in a forward dynamics procedure using a direct collocation method. A multi-objective optimization based on genetic algorithm has been proposed to employ stability as a robotic objective function along with muscle stresses as a biological objective function. The obtained results for joints’ motion were compared to experimental data. The results show that, for this type of perturbations, muscle stresses are in conflict with body stability. This means that more body stability requires more stresses in muscles and reverse. Results also show the effects of the stability objective function in better prediction of joint trajectories

  16. Social Bots: Human-Like by Means of Human Control?

    Science.gov (United States)

    Grimme, Christian; Preuss, Mike; Adam, Lena; Trautmann, Heike

    2017-12-01

    Social bots are currently regarded an influential but also somewhat mysterious factor in public discourse and opinion making. They are considered to be capable of massively distributing propaganda in social and online media, and their application is even suspected to be partly responsible for recent election results. Astonishingly, the term social bot is not well defined and different scientific disciplines use divergent definitions. This work starts with a balanced definition attempt, before providing an overview of how social bots actually work (taking the example of Twitter) and what their current technical limitations are. Despite recent research progress in Deep Learning and Big Data, there are many activities bots cannot handle well. We then discuss how bot capabilities can be extended and controlled by integrating humans into the process and reason that this is currently the most promising way to realize meaningful interactions with other humans. This finally leads to the conclusion that hybridization is a challenge for current detection mechanisms and has to be handled with more sophisticated approaches to identify political propaganda distributed with social bots.

  17. One-Step-Ahead Predictive Control for Hydroturbine Governor

    Directory of Open Access Journals (Sweden)

    Zhihuai Xiao

    2015-01-01

    Full Text Available The hydroturbine generator regulating system can be considered as one system synthetically integrating water, machine, and electricity. It is a complex and nonlinear system, and its configuration and parameters are time-dependent. A one-step-ahead predictive control based on on-line trained neural networks (NNs for hydroturbine governor with variation in gate position is described in this paper. The proposed control algorithm consists of a one-step-ahead neuropredictor that tracks the dynamic characteristics of the plant and predicts its output and a neurocontroller to generate the optimal control signal. The weights of two NNs, initially trained off-line, are updated on-line according to the scalar error. The proposed controller can thus track operating conditions in real-time and produce the optimal control signal over the wide operating range. Only the inputs and outputs of the generator are measured and there is no need to determine the other states of the generator. Simulations have been performed with varying operating conditions and different disturbances to compare the performance of the proposed controller with that of a conventional PID controller and validate the feasibility of the proposed approach.

  18. Robust predictive control of a gasoline debutanizer column

    Directory of Open Access Journals (Sweden)

    E. Almeida Neto

    2000-12-01

    Full Text Available This paper studies the application of Model Predictive Control to moderately nonlinear processes. The system used in this work is an industrial gasoline debutanizer column. The paper presents two new formulations of MPC: MMPC (Multi-Model Predictive Controller and RSMPC (Robust Stable MPC. The approach is based on the concepts of Linear Matrix Inequalities (LMI, which have been recently introduced in the MPC field. Model uncertainty is considered by assuming that the true process model belongs to a convex set (polytope of possible plants. The controller has guaranteed stability when a Lyapunov type inequality constraint is included in the MPC problem. In the debutanizer column, several nonlinearities are present in the advanced control level when the manipulated inputs are the reflux flow and the reboiler heat duty. In most cases the controlled outputs are the contents of C5+ (pentane and heavier hydrocarbons in the LPG (Liquefied Petroleum Gas and the gasoline vapor pressure (P VR. In this case the QDMC algorithm which is usually applied to the debutanizer column has a poor performance and stability problems reflected in an oscillatory behavior of the process. The new approach considers several process models representing different operating conditions where linear models are identified. The results presented here show that the multimodel controller is capable of controlling the process in the entire operating window while the conventional MPC has a limited operating range.

  19. Using micro saint to predict performance in a nuclear power plant control room

    International Nuclear Information System (INIS)

    Lawless, M.T.; Laughery, K.R.; Persenky, J.J.

    1995-09-01

    The United States Nuclear Regulatory Commission (NRC) requires a technical basis for regulatory actions. In the area of human factors, one possible technical basis is human performance modeling technology including task network modeling. This study assessed the feasibility and validity of task network modeling to predict the performance of control room crews. Task network models were built that matched the experimental conditions of a study on computerized procedures that was conducted at North Carolina State University. The data from the open-quotes paper proceduresclose quotes conditions were used to calibrate the task network models. Then, the models were manipulated to reflect expected changes when computerized procedures were used. These models' predictions were then compared to the experimental data from the open-quotes computerized conditionsclose quotes of the North Carolina State University study. Analyses indicated that the models predicted some subsets of the data well, but not all. Implications for the use of task network modeling are discussed

  20. Robust stability in predictive control with soft constraints

    DEFF Research Database (Denmark)

    Thomsen, Sven Creutz; Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2010-01-01

    In this paper we take advantage of the primary and dual Youla parameterizations for setting up a soft constrained model predictive control (MPC) scheme for which stability is guaranteed in face of norm-bounded uncertainties. Under special conditions guarantees are also given for hard input...... constraints. In more detail, we parameterize the MPC predictions in terms of the primary Youla parameter and use this parameter as the online optimization variable. The uncertainty is parameterized in terms of the dual Youla parameter. Stability can then be guaranteed through small gain arguments on the loop...

  1. Robust stability in constrained predictive control through the Youla parameterisations

    DEFF Research Database (Denmark)

    Thomsen, Sven Creutz; Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2011-01-01

    In this article we take advantage of the primary and dual Youla parameterisations to set up a soft constrained model predictive control (MPC) scheme. In this framework it is possible to guarantee stability in face of norm-bounded uncertainties. Under special conditions guarantees are also given...... for hard input constraints. In more detail, we parameterise the MPC predictions in terms of the primary Youla parameter and use this parameter as the on-line optimisation variable. The uncertainty is parameterised in terms of the dual Youla parameter. Stability can then be guaranteed through small gain...

  2. High level model predictive control for plug-and-play process control with stability guaranty

    DEFF Research Database (Denmark)

    Michelsen, Axel Gottlieb; Stoustrup, Jakob

    2010-01-01

    In this paper a method for designing a stabilizing high level model predictive controller for a hierarchical plug- and-play process is presented. This is achieved by abstracting the lower layers of the controller structure as low order models with uncertainty and by using a robust model predictive...... controller for generating the references for these. A simulation example, in which the actuators in a process control system are changed, is reported to show the potential of this approach for plug and play process control....

  3. Human Homeostatic Control Matrix in Norm

    Directory of Open Access Journals (Sweden)

    Alexander G. Kruglov

    2016-09-01

    Full Text Available We undertook our research to study and systemize the relationship between hemodynamics and biochemical parameters of arterial and venous blood in healthy people. Hemodynamic and biochemical characteristics were obtained through a probe by using catheterization in various vascular areas (aorta, brain, heart, lungs, and liver. Correlation and factor analyses were conducted to study the relationship between the obtained characteristics of the regional and systemic blood flow. Due to the nature of the correlation analysis, the significant (p<0.05 relation signs (+, 0, - without regard to their power were considered. The obtained results suggested that there are sets of both intra-organ and system regulatory relationships between metabolic and hemodynamic characteristics. The complex of relationships among the studied parameters makes it possible to maintain the homeostatic equilibrium in the body. The psychophysiological control system includes the subsystems we described: 1 the cardiac-hepatic-pulmonary complex having properties of the metabolic and hemodynamic information field providing biological stability of the homeostasis; any significant imbalance of its elements triggers afferent information flows actualizing an afferent synthesis; 2 the mind forming gradient patterns of targeted behavior to eliminate metabolic imbalance, to achieve goals both as coded biological parameters and as the highest forms of behavior, to reach the ultimate goal: parametric, homeostatic equilibrium in the “biosphere” of the human body. By using the results of our research and the complex of dynamic relationships in human homeostasis, we built a homeostatic control matrix (HCM.

  4. Force Control for a Pneumatic Cylinder Using Generalized Predictive Controller Approach

    OpenAIRE

    Mohd Faudzi, Ahmad ’Athif; Mustafa, Nu’man Din; Osman, Khairuddin

    2014-01-01

    Pneumatic cylinder is a well-known device because of its high power to weight ratio, easy use, and environmental safety. Pneumatic cylinder uses air as its power source and converts it to a possible movement such as linear and rotary movement. In order to control the pneumatic cylinder, controller algorithm is needed to control the on-off solenoid valve with encoder and pressure sensor as the feedback inputs. In this paper, generalized predictive controller (GPC) is proposed as the control st...

  5. The application of human engineering in control room of HFETR

    International Nuclear Information System (INIS)

    Yang Shuchun; Shan Songlin

    2003-01-01

    The human-machine system for improving the working environment in the control room of HFETR is described. The reliability of the equipment, instruments and operation by human engineering is increased. The relations between human engineering and lowering human failure in HFETR are also discussed. It is concluded that the further application of human engineering can increase interaction of the human and machine in the control room and provide assurances for the safe and reliable operation of reactor. (authors)

  6. The application of human engineering in control room of HFETR

    Energy Technology Data Exchange (ETDEWEB)

    Shuchun, Yang; Songlin, Shan [Nuclear Power Inst. of China, Chengdu (China)

    2003-07-01

    The human-machine system for improving the working environment in the control room of HFETR is described. The reliability of the equipment, instruments and operation by human engineering is increased. The relations between human engineering and lowering human failure in HFETR are also discussed. It is concluded that the further application of human engineering can increase interaction of the human and machine in the control room and provide assurances for the safe and reliable operation of reactor. (authors)

  7. Model predictive control of room temperature with disturbance compensation

    Science.gov (United States)

    Kurilla, Jozef; Hubinský, Peter

    2017-08-01

    This paper deals with temperature control of multivariable system of office building. The system is simplified to several single input-single output systems by decoupling their mutual linkages, which are separately controlled by regulator based on generalized model predictive control. Main part of this paper focuses on the accuracy of the office temperature with respect to occupancy profile and effect of disturbance. Shifting of desired temperature and changing of weighting coefficients are used to achieve the desired accuracy of regulation. The final structure of regulation joins advantages of distributed computing power and possibility to use network communication between individual controllers to consider the constraints. The advantage of using decoupled MPC controllers compared to conventional PID regulators is demonstrated in a simulation study.

  8. Nonlinear Dynamic Inversion Baseline Control Law: Architecture and Performance Predictions

    Science.gov (United States)

    Miller, Christopher J.

    2011-01-01

    A model reference dynamic inversion control law has been developed to provide a baseline control law for research into adaptive elements and other advanced flight control law components. This controller has been implemented and tested in a hardware-in-the-loop simulation; the simulation results show excellent handling qualities throughout the limited flight envelope. A simple angular momentum formulation was chosen because it can be included in the stability proofs for many basic adaptive theories, such as model reference adaptive control. Many design choices and implementation details reflect the requirements placed on the system by the nonlinear flight environment and the desire to keep the system as basic as possible to simplify the addition of the adaptive elements. Those design choices are explained, along with their predicted impact on the handling qualities.

  9. Low trait self-control predicts self-handicapping.

    Science.gov (United States)

    Uysal, Ahmet; Knee, C Raymond

    2012-02-01

    Past research has shown that self-handicapping stems from uncertainty about one's ability and self-presentational concerns. The present studies suggest that low dispositional self-control is also associated with self-handicapping. In 3 studies (N = 289), the association between self-control and self-handicapping was tested. Self-control was operationalized as trait self-control, whereas self-handicapping was operationalized as trait self-handicapping in Study 1 (N = 160), self-reported self-handicapping in Study 2 (N = 74), and behavioral self-handicapping in Study 3 (N = 55). In all 3 studies, hierarchical regression analyses revealed that low self-control predicts self-handicapping, independent of self-esteem, self-doubt, social desirability, and gender. © 2012 The Authors. Journal of Personality © 2012, Wiley Periodicals, Inc.

  10. Health-aware Model Predictive Control of Pasteurization Plant

    Science.gov (United States)

    Karimi Pour, Fatemeh; Puig, Vicenç; Ocampo-Martinez, Carlos

    2017-01-01

    In order to optimize the trade-off between components life and energy consumption, the integration of a system health management and control modules is required. This paper proposes the integration of model predictive control (MPC) with a fatigue estimation approach that minimizes the damage of the components of a pasteurization plant. The fatigue estimation is assessed with the rainflow counting algorithm. Using data from this algorithm, a simplified model that characterizes the health of the system is developed and integrated with MPC. The MPC controller objective is modified by adding an extra criterion that takes into account the accumulated damage. But, a steady-state offset is created by adding this extra criterion. Finally, by including an integral action in the MPC controller, the steady-state error for regulation purpose is eliminated. The proposed control scheme is validated in simulation using a simulator of a utility-scale pasteurization plant.

  11. Structure, function, and control of the human musculoskeletal network.

    Directory of Open Access Journals (Sweden)

    Andrew C Murphy

    2018-01-01

    Full Text Available The human body is a complex organism, the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musculoskeletal interconnection facilitates stability, voluntary movement, and robustness to injury. However, a fundamental understanding of this network and its control by neural systems has remained elusive. Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure, predicted function, and neural control of the musculoskeletal system. We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points. We demonstrated that, using this simplified model, a muscle's role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury. Finally, we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex. This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury, inspiring future advances in clinical treatments.

  12. A LIDAR-assisted model predictive controller added on a traditional wind turbine controller

    DEFF Research Database (Denmark)

    Mirzaei, Mahmood; Hansen, Morten Hartvig

    2016-01-01

    control and opens the market of retrofitting existing wind turbines with the new technology. In this paper, we suggest a model predictive controller (MPC) that is added to the basic gain scheduled PI controller of a WT to enhance the performance of the closed loop system using LIDAR measurements...

  13. Identification of the feedforward component in manual control with predictable target signals.

    Science.gov (United States)

    Drop, Frank M; Pool, Daan M; Damveld, Herman J; van Paassen, Marinus M; Mulder, Max

    2013-12-01

    In the manual control of a dynamic system, the human controller (HC) often follows a visible and predictable reference path. Compared with a purely feedback control strategy, performance can be improved by making use of this knowledge of the reference. The operator could effectively introduce feedforward control in conjunction with a feedback path to compensate for errors, as hypothesized in literature. However, feedforward behavior has never been identified from experimental data, nor have the hypothesized models been validated. This paper investigates human control behavior in pursuit tracking of a predictable reference signal while being perturbed by a quasi-random multisine disturbance signal. An experiment was done in which the relative strength of the target and disturbance signals were systematically varied. The anticipated changes in control behavior were studied by means of an ARX model analysis and by fitting three parametric HC models: two different feedback models and a combined feedforward and feedback model. The ARX analysis shows that the experiment participants employed control action on both the error and the target signal. The control action on the target was similar to the inverse of the system dynamics. Model fits show that this behavior can be modeled best by the combined feedforward and feedback model.

  14. Model Predictive Control of Integrated Gasification Combined Cycle Power Plants

    Energy Technology Data Exchange (ETDEWEB)

    B. Wayne Bequette; Priyadarshi Mahapatra

    2010-08-31

    The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques.

  15. Fault Tolerant Flight Control Using Sliding Modes and Subspace Identification-Based Predictive Control

    KAUST Repository

    Siddiqui, Bilal A.; El-Ferik, Sami; Abdelkader, Mohamed

    2016-01-01

    In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.

  16. Fault Tolerant Flight Control Using Sliding Modes and Subspace Identification-Based Predictive Control

    KAUST Repository

    Siddiqui, Bilal A.

    2016-07-26

    In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.

  17. Prediction of molecular mimicry candidates in human pathogenic bacteria.

    Science.gov (United States)

    Doxey, Andrew C; McConkey, Brendan J

    2013-08-15

    Molecular mimicry of host proteins is a common strategy adopted by bacterial pathogens to interfere with and exploit host processes. Despite the availability of pathogen genomes, few studies have attempted to predict virulence-associated mimicry relationships directly from genomic sequences. Here, we analyzed the proteomes of 62 pathogenic and 66 non-pathogenic bacterial species, and screened for the top pathogen-specific or pathogen-enriched sequence similarities to human proteins. The screen identified approximately 100 potential mimicry relationships including well-characterized examples among the top-scoring hits (e.g., RalF, internalin, yopH, and others), with about 1/3 of predicted relationships supported by existing literature. Examination of homology to virulence factors, statistically enriched functions, and comparison with literature indicated that the detected mimics target key host structures (e.g., extracellular matrix, ECM) and pathways (e.g., cell adhesion, lipid metabolism, and immune signaling). The top-scoring and most widespread mimicry pattern detected among pathogens consisted of elevated sequence similarities to ECM proteins including collagens and leucine-rich repeat proteins. Unexpectedly, analysis of the pathogen counterparts of these proteins revealed that they have evolved independently in different species of bacterial pathogens from separate repeat amplifications. Thus, our analysis provides evidence for two classes of mimics: complex proteins such as enzymes that have been acquired by eukaryote-to-pathogen horizontal transfer, and simpler repeat proteins that have independently evolved to mimic the host ECM. Ultimately, computational detection of pathogen-specific and pathogen-enriched similarities to host proteins provides insights into potentially novel mimicry-mediated virulence mechanisms of pathogenic bacteria.

  18. Model predictive control of a wind turbine modelled in Simpack

    International Nuclear Information System (INIS)

    Jassmann, U; Matzke, D; Reiter, M; Abel, D; Berroth, J; Schelenz, R; Jacobs, G

    2014-01-01

    Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine

  19. Model predictive control of a wind turbine modelled in Simpack

    Science.gov (United States)

    Jassmann, U.; Berroth, J.; Matzke, D.; Schelenz, R.; Reiter, M.; Jacobs, G.; Abel, D.

    2014-06-01

    Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine to

  20. An empirical study on the basic human error probabilities for NPP advanced main control room operation using soft control

    International Nuclear Information System (INIS)

    Jang, Inseok; Kim, Ar Ryum; Harbi, Mohamed Ali Salem Al; Lee, Seung Jun; Kang, Hyun Gook; Seong, Poong Hyun

    2013-01-01

    Highlights: ► The operation environment of MCRs in NPPs has changed by adopting new HSIs. ► The operation action in NPP Advanced MCRs is performed by soft control. ► Different basic human error probabilities (BHEPs) should be considered. ► BHEPs in a soft control operation environment are investigated empirically. ► This work will be helpful to verify if soft control has positive or negative effects. -- Abstract: By adopting new human–system interfaces that are based on computer-based technologies, the operation environment of main control rooms (MCRs) in nuclear power plants (NPPs) has changed. The MCRs that include these digital and computer technologies, such as large display panels, computerized procedures, soft controls, and so on, are called Advanced MCRs. Among the many features in Advanced MCRs, soft controls are an important feature because the operation action in NPP Advanced MCRs is performed by soft control. Using soft controls such as mouse control, touch screens, and so on, operators can select a specific screen, then choose the controller, and finally manipulate the devices. However, because of the different interfaces between soft control and hardwired conventional type control, different basic human error probabilities (BHEPs) should be considered in the Human Reliability Analysis (HRA) for advanced MCRs. Although there are many HRA methods to assess human reliabilities, such as Technique for Human Error Rate Prediction (THERP), Accident Sequence Evaluation Program (ASEP), Human Error Assessment and Reduction Technique (HEART), Human Event Repository and Analysis (HERA), Nuclear Computerized Library for Assessing Reactor Reliability (NUCLARR), Cognitive Reliability and Error Analysis Method (CREAM), and so on, these methods have been applied to conventional MCRs, and they do not consider the new features of advance MCRs such as soft controls. As a result, there is an insufficient database for assessing human reliabilities in advanced

  1. Characterizing and predicting submovements during human three-dimensional arm reaches.

    Directory of Open Access Journals (Sweden)

    James Y Liao

    Full Text Available We have demonstrated that 3D target-oriented human arm reaches can be represented as linear combinations of discrete submovements, where the submovements are a set of minimum-jerk basis functions for the reaches. We have also demonstrated the ability of deterministic feed-forward Artificial Neural Networks (ANNs to predict the parameters of the submovements. ANNs were trained using kinematic data obtained experimentally from five human participants making target-directed movements that were decomposed offline into minimum-jerk submovements using an optimization algorithm. Under cross-validation, the ANNs were able to accurately predict the parameters (initiation-time, amplitude, and duration of the individual submovements. We also demonstrated that the ANNs can together form a closed-loop model of human reaching capable of predicting 3D trajectories with VAF >95.9% and RMSE ≤4.32 cm relative to the actual recorded trajectories. This closed-loop model is a step towards a practical arm trajectory generator based on submovements, and should be useful for the development of future arm prosthetic devices that are controlled by brain computer interfaces or other user interfaces.

  2. Computationally efficient model predictive control algorithms a neural network approach

    CERN Document Server

    Ławryńczuk, Maciej

    2014-01-01

    This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: ·         A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. ·         Implementation details of the MPC algorithms for feedforward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. ·         The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). ·         The MPC algorithms with neural approximation with no on-line linearization. ·         The MPC algorithms with guaranteed stability and robustness. ·         Cooperation between the MPC algorithms and set-point optimization. Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require d...

  3. Distributed predictive control of spiral wave in cardiac excitable media

    International Nuclear Information System (INIS)

    Zheng-Ning, Gan; Xin-Ming, Cheng

    2010-01-01

    In this paper, we propose the distributed predictive control strategies of spiral wave in cardiac excitable media. The modified FitzHugh–Nagumo model was used to express the cardiac excitable media approximately. Based on the control-Lyapunov theory, we obtained the distributed control equation, which consists of a positive control-Lyapunov function and a positive cost function. Using the equation, we investigate two kinds of robust control strategies: the time-dependent distributed control strategy and the space-time dependent distributed control strategy. The feasibility of the strategies was demonstrated via an illustrative example, in which the spiral wave was prevented to occur, and the possibility for inducing ventricular fibrillation was eliminated. The strategies are helpful in designing various cardiac devices. Since the second strategy is more efficient and robust than the first one, and the response time in the second strategy is far less than that in the first one, the former is suitable for the quick-response control systems. In addition, our spatiotemporal control strategies, especially the second strategy, can be applied to other cardiac models, even to other reaction-diffusion systems. (general)

  4. Water hammer prediction and control: the Green's function method

    Science.gov (United States)

    Xuan, Li-Jun; Mao, Feng; Wu, Jie-Zhi

    2012-04-01

    By Green's function method we show that the water hammer (WH) can be analytically predicted for both laminar and turbulent flows (for the latter, with an eddy viscosity depending solely on the space coordinates), and thus its hazardous effect can be rationally controlled and minimized. To this end, we generalize a laminar water hammer equation of Wang et al. (J. Hydrodynamics, B2, 51, 1995) to include arbitrary initial condition and variable viscosity, and obtain its solution by Green's function method. The predicted characteristic WH behaviors by the solutions are in excellent agreement with both direct numerical simulation of the original governing equations and, by adjusting the eddy viscosity coefficient, experimentally measured turbulent flow data. Optimal WH control principle is thereby constructed and demonstrated.

  5. Finite element predictions of active buckling control of stiffened panels

    Science.gov (United States)

    Thompson, Danniella M.; Griffin, O. H., Jr.

    1993-04-01

    Materials systems and structures that can respond 'intelligently' to their environment are currently being proposed and investigated. A series of finite element analyses was performed to investigate the potential for active buckling control of two different stiffened panels by embedded shape memory alloy (SMA) rods. Changes in the predicted buckling load increased with the magnitude of the actuation level for a given structural concept. Increasing the number of actuators for a given concept yielded greater predicted increases in buckling load. Considerable control authority was generated with a small number of actuators, with greater authority demonstrated for those structural concepts where the activated SMA rods could develop greater forces and moments on the structure. Relatively simple and inexpensive analyses were performed with standard finite elements to determine such information, indicating the viability of these types of models for design purposes.

  6. 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.

  7. Interpreting Disruption Prediction Models to Improve Plasma Control

    Science.gov (United States)

    Parsons, Matthew

    2017-10-01

    In order for the tokamak to be a feasible design for a fusion reactor, it is necessary to minimize damage to the machine caused by plasma disruptions. Accurately predicting disruptions is a critical capability for triggering any mitigative actions, and a modest amount of attention has been given to efforts that employ machine learning techniques to make these predictions. By monitoring diagnostic signals during a discharge, such predictive models look for signs that the plasma is about to disrupt. Typically these predictive models are interpreted simply to give a `yes' or `no' response as to whether a disruption is approaching. However, it is possible to extract further information from these models to indicate which input signals are more strongly correlated with the plasma approaching a disruption. If highly accurate predictive models can be developed, this information could be used in plasma control schemes to make better decisions about disruption avoidance. This work was supported by a Grant from the 2016-2017 Fulbright U.S. Student Program, administered by the Franco-American Fulbright Commission in France.

  8. Stochastic Model Predictive Control with Applications in Smart Energy Systems

    DEFF Research Database (Denmark)

    Sokoler, Leo Emil; Edlund, Kristian; Mølbak, Tommy

    2012-01-01

    to cover more than 50% of the total consumption by 2050. Energy systems based on significant amounts of renewable energy sources are subject to uncertainties. To accommodate the need for model predictive control (MPC) of such systems, the effect of the stochastic effects on the constraints must...... study, we consider a system consisting of fuel-fired thermal power plants, wind farms and electric vehicles....

  9. Predictability of solute transport in diffusion-controlled hydrogeologic regimes

    International Nuclear Information System (INIS)

    Gillham, R.W.; Cherry, J.A.

    1983-01-01

    Hydrogeologic regimes that are favourable for the subsurface management of low-level radioactive wastes must have transport properties that will limit the migration velocity of contaminants to some acceptably low value. Of equal importance, for the purpose of impact assessment and licensing, is the need to be able to predict, with a reasonable degree of certainty and over long time periods, what the migration velocity of the various contaminants of interest will be. This paper presents arguments to show that in addition to having favourable velocity characteristics, transport in saturated, diffusion-controlled hydrogeologic regimes is considerably more predictable than in the most common alternatives. The classical transport models for unsaturated, saturated-advection-controlled and saturated-diffusion-controlled environments are compared, with particular consideration being given to the difficulties associated with the characterization of the respective transport parameters. Results are presented which show that the diffusion of non-reactive solutes and solutes that react according to a constant partitioning ratio (K/sub d/) are highly predictable under laboratory conditions and that the diffusion coefficients for the reactive solutes can be determined with a reasonable degree of accuracy from independent measurements of bulk density, porosity, distribution coefficient and tortuosity. Field evidence is presented which shows that the distribution of environmental isotopes and chloride in thick clayey deposits is consistent with a diffusion-type transport process in these media. These results are particularly important in that they not only demonstrate the occurrence of diffusion-controlled hydrogeologic regimes, but they also demonstrate the predictability of the migration characteristics over very long time periods

  10. 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

  11. Cognition and procedure representational requirements for predictive human performance models

    Science.gov (United States)

    Corker, K.

    1992-01-01

    Models and modeling environments for human performance are becoming significant contributors to early system design and analysis procedures. Issues of levels of automation, physical environment, informational environment, and manning requirements are being addressed by such man/machine analysis systems. The research reported here investigates the close interaction between models of human cognition and models that described procedural performance. We describe a methodology for the decomposition of aircrew procedures that supports interaction with models of cognition on the basis of procedures observed; that serves to identify cockpit/avionics information sources and crew information requirements; and that provides the structure to support methods for function allocation among crew and aiding systems. Our approach is to develop an object-oriented, modular, executable software representation of the aircrew, the aircraft, and the procedures necessary to satisfy flight-phase goals. We then encode in a time-based language, taxonomies of the conceptual, relational, and procedural constraints among the cockpit avionics and control system and the aircrew. We have designed and implemented a goals/procedures hierarchic representation sufficient to describe procedural flow in the cockpit. We then execute the procedural representation in simulation software and calculate the values of the flight instruments, aircraft state variables and crew resources using the constraints available from the relationship taxonomies. The system provides a flexible, extensible, manipulative and executable representation of aircrew and procedures that is generally applicable to crew/procedure task-analysis. The representation supports developed methods of intent inference, and is extensible to include issues of information requirements and functional allocation. We are attempting to link the procedural representation to models of cognitive functions to establish several intent inference methods

  12. Predicting human activities in sequences of actions in RGB-D videos

    Science.gov (United States)

    Jardim, David; Nunes, Luís.; Dias, Miguel

    2017-03-01

    In our daily activities we perform prediction or anticipation when interacting with other humans or with objects. Prediction of human activity made by computers has several potential applications: surveillance systems, human computer interfaces, sports video analysis, human-robot-collaboration, games and health-care. We propose a system capable of recognizing and predicting human actions using supervised classifiers trained with automatically labeled data evaluated in our human activity RGB-D dataset (recorded with a Kinect sensor) and using only the position of the main skeleton joints to extract features. Using conditional random fields (CRFs) to model the sequential nature of actions in a sequence has been used before, but where other approaches try to predict an outcome or anticipate ahead in time (seconds), we try to predict what will be the next action of a subject. Our results show an activity prediction accuracy of 89.9% using an automatically labeled dataset.

  13. Empirical modelling to predict the refractive index of human blood

    Science.gov (United States)

    Yahya, M.; Saghir, M. Z.

    2016-02-01

    Optical techniques used for the measurement of the optical properties of blood are of great interest in clinical diagnostics. Blood analysis is a routine procedure used in medical diagnostics to confirm a patient’s condition. Measuring the optical properties of blood is difficult due to the non-homogenous nature of the blood itself. In addition, there is a lot of variation in the refractive indices reported in the literature. These are the reasons that motivated the researchers to develop a mathematical model that can be used to predict the refractive index of human blood as a function of concentration, temperature and wavelength. The experimental measurements were conducted on mimicking phantom hemoglobin samples using the Abbemat Refractometer. The results analysis revealed a linear relationship between the refractive index and concentration as well as temperature, and a non-linear relationship between refractive index and wavelength. These results are in agreement with those found in the literature. In addition, a new formula was developed based on empirical modelling which suggests that temperature and wavelength coefficients be added to the Barer formula. The verification of this correlation confirmed its ability to determine refractive index and/or blood hematocrit values with appropriate clinical accuracy.

  14. Empirical modelling to predict the refractive index of human blood

    International Nuclear Information System (INIS)

    Yahya, M; Saghir, M Z

    2016-01-01

    Optical techniques used for the measurement of the optical properties of blood are of great interest in clinical diagnostics. Blood analysis is a routine procedure used in medical diagnostics to confirm a patient’s condition. Measuring the optical properties of blood is difficult due to the non-homogenous nature of the blood itself. In addition, there is a lot of variation in the refractive indices reported in the literature. These are the reasons that motivated the researchers to develop a mathematical model that can be used to predict the refractive index of human blood as a function of concentration, temperature and wavelength. The experimental measurements were conducted on mimicking phantom hemoglobin samples using the Abbemat Refractometer. The results analysis revealed a linear relationship between the refractive index and concentration as well as temperature, and a non-linear relationship between refractive index and wavelength. These results are in agreement with those found in the literature. In addition, a new formula was developed based on empirical modelling which suggests that temperature and wavelength coefficients be added to the Barer formula. The verification of this correlation confirmed its ability to determine refractive index and/or blood hematocrit values with appropriate clinical accuracy. (paper)

  15. Time scaling internal state predictive control of a solar plant

    Energy Technology Data Exchange (ETDEWEB)

    Silva, R.N. [DEE-FCT/UNL, Caparica (Portugal); Rato, L.M. [INESC-ID/University, Evora (Portugal); Lemos, J.M. [INESC-ID/IST, Lisboa (Portugal)

    2003-12-01

    The control of a distributed collector solar field is addressed in this work, exploiting the plant's transport characteristic. The plant is modeled by a hyperbolic type partial differential equation (PDE) where the transport speed is the manipulated flow, i.e. the controller output. The model has an external distributed source, which is the solar radiation captured along the collector, approximated to depend only of time. From the solution of the PDE, a linear discrete state space model is obtained by using time-scaling and the redefinition of the control input. This method allows overcoming the dependency of the time constants with the operating point. A model-based predictive adaptive controller is derived with the internal temperature distribution estimated with a state observer. Experimental results at the solar power plant are presented, illustrating the advantages of the approach under consideration. (author)

  16. Data-Driven Predictive Direct Load Control of Refrigeration Systems

    DEFF Research Database (Denmark)

    Shafiei, Seyed Ehsan; Knudsen, Torben; Wisniewski, Rafal

    2015-01-01

    A predictive control using subspace identification is applied for the smart grid integration of refrigeration systems under a direct load control scheme. A realistic demand response scenario based on regulation of the electrical power consumption is considered. A receding horizon optimal control...... is proposed to fulfil two important objectives: to secure high coefficient of performance and to participate in power consumption management. Moreover, a new method for design of input signals for system identification is put forward. The control method is fully data driven without an explicit use of model...... against real data. The performance improvement results in a 22% reduction in the energy consumption. A comparative simulation is accomplished showing the superiority of the method over the existing approaches in terms of the load following performance....

  17. Predictive Smart Grid Control with Exact Aggregated Power Constraints

    DEFF Research Database (Denmark)

    Trangbæk, K; Petersen, Mette Højgaard; Bendtsen, Jan Dimon

    2012-01-01

    of autonomous consumers. The control system is tasked with balancing electric power production and consumption within the smart grid, and makes active use of the flexibility of a large number of power producing and/or power consuming units. The load variations on the grid arise on one hand from varying......This chapter deals with hierarchical model predictive control (MPC) of smart grid systems. The design consists of a high-level MPC controller, a second level of so-called aggregators,which reduces the computational and communication related load on the high-level control, and a lower level...... consumption, and on the other hand from natural variations in power production from e.g. wind turbines. The consumers represent energy-consuming units such as heat pumps, car batteries etc. These units obviously have limits on how much power and energy they can consume at any given time, which impose...

  18. Model predictive control approach for a CPAP-device

    Directory of Open Access Journals (Sweden)

    Scheel Mathias

    2017-09-01

    Full Text Available The obstructive sleep apnoea syndrome (OSAS is characterized by a collapse of the upper respiratory tract, resulting in a reduction of the blood oxygen- and an increase of the carbon dioxide (CO2 - concentration, which causes repeated sleep disruptions. The gold standard to treat the OSAS is the continuous positive airway pressure (CPAP therapy. The continuous pressure keeps the upper airway open and prevents the collapse of the upper respiratory tract and the pharynx. Most of the available CPAP-devices cannot maintain the pressure reference [1]. In this work a model predictive control approach is provided. This control approach has the possibility to include the patient’s breathing effort into the calculation of the control variable. Therefore a patient-individualized control strategy can be developed.

  19. Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems

    DEFF Research Database (Denmark)

    Morstyn, Thomas; Hredzak, Branislav; Aguilera, Ricardo P.

    2018-01-01

    , and converter current constraints to be addressed. In addition, nonlinear variations in the charge and discharge efficiencies of lithium ion batteries are analyzed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical......This brief proposes a new convex model predictive control (MPC) strategy for dynamic optimal power flow between battery energy storage (ES) systems distributed in an ac microgrid. The proposed control strategy uses a new problem formulation, based on a linear $d$ – $q$ reference frame voltage...... feeder, with distributed battery ES systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on nonconvex optimization, while reducing the required computation time by a factor of 1000, making it suitable for a real...

  20. Control System Design for Cylindrical Tank Process Using Neural Model Predictive Control Technique

    Directory of Open Access Journals (Sweden)

    M. Sridevi

    2010-10-01

    Full Text Available Chemical manufacturing and process industry requires innovative technologies for process identification. This paper deals with model identification and control of cylindrical process. Model identification of the process was done using ARMAX technique. A neural model predictive controller was designed for the identified model. The performance of the controllers was evaluated using MATLAB software. The performance of NMPC controller was compared with Smith Predictor controller and IMC controller based on rise time, settling time, overshoot and ISE and it was found that the NMPC controller is better suited for this process.

  1. Dynamics and control of quadcopter using linear model predictive control approach

    Science.gov (United States)

    Islam, M.; Okasha, M.; Idres, M. M.

    2017-12-01

    This paper investigates the dynamics and control of a quadcopter using the Model Predictive Control (MPC) approach. The dynamic model is of high fidelity and nonlinear, with six degrees of freedom that include disturbances and model uncertainties. The control approach is developed based on MPC to track different reference trajectories ranging from simple ones such as circular to complex helical trajectories. In this control technique, a linearized model is derived and the receding horizon method is applied to generate the optimal control sequence. Although MPC is computer expensive, it is highly effective to deal with the different types of nonlinearities and constraints such as actuators’ saturation and model uncertainties. The MPC parameters (control and prediction horizons) are selected by trial-and-error approach. Several simulation scenarios are performed to examine and evaluate the performance of the proposed control approach using MATLAB and Simulink environment. Simulation results show that this control approach is highly effective to track a given reference trajectory.

  2. Controlled test for predictive power of Lyapunov exponents: their inability to predict epileptic seizures.

    Science.gov (United States)

    Lai, Ying-Cheng; Harrison, Mary Ann F; Frei, Mark G; Osorio, Ivan

    2004-09-01

    Lyapunov exponents are a set of fundamental dynamical invariants characterizing a system's sensitive dependence on initial conditions. For more than a decade, it has been claimed that the exponents computed from electroencephalogram (EEG) or electrocorticogram (ECoG) signals can be used for prediction of epileptic seizures minutes or even tens of minutes in advance. The purpose of this paper is to examine the predictive power of Lyapunov exponents. Three approaches are employed. (1) We present qualitative arguments suggesting that the Lyapunov exponents generally are not useful for seizure prediction. (2) We construct a two-dimensional, nonstationary chaotic map with a parameter slowly varying in a range containing a crisis, and test whether this critical event can be predicted by monitoring the evolution of finite-time Lyapunov exponents. This can thus be regarded as a "control test" for the claimed predictive power of the exponents for seizure. We find that two major obstacles arise in this application: statistical fluctuations of the Lyapunov exponents due to finite time computation and noise from the time series. We show that increasing the amount of data in a moving window will not improve the exponents' detective power for characteristic system changes, and that the presence of small noise can ruin completely the predictive power of the exponents. (3) We report negative results obtained from ECoG signals recorded from patients with epilepsy. All these indicate firmly that, the use of Lyapunov exponents for seizure prediction is practically impossible as the brain dynamical system generating the ECoG signals is more complicated than low-dimensional chaotic systems, and is noisy. Copyright 2004 American Institute of Physics

  3. Predictive models of glucose control: roles for glucose-sensing neurones

    Science.gov (United States)

    Kosse, C.; Gonzalez, A.; Burdakov, D.

    2018-01-01

    stimulate (from liver, into muscle) are balanced. Estimating nutrient challenges from indirect sensory cues may become more difficult when the cues become complex and variable (e.g. like human foods today). Consequent errors of predictive glucose control may contribute to obesity and diabetes. PMID:25131833

  4. Predictive models of glucose control: roles for glucose-sensing neurones.

    Science.gov (United States)

    Kosse, C; Gonzalez, A; Burdakov, D

    2015-01-01

    (from liver, into muscle) are balanced. Estimating nutrient challenges from indirect sensory cues may become more difficult when the cues become complex and variable (e.g. like human foods today). Consequent errors of predictive glucose control may contribute to obesity and diabetes. © 2014 The Authors. Acta Physiologica published by John Wiley & Sons Ltd on behalf of Scandinavian Physiological Society.

  5. Model Predictive Control for Connected Hybrid Electric Vehicles

    Directory of Open Access Journals (Sweden)

    Kaijiang Yu

    2015-01-01

    Full Text Available This paper presents a new model predictive control system for connected hybrid electric vehicles to improve fuel economy. The new features of this study are as follows. First, the battery charge and discharge profile and the driving velocity profile are simultaneously optimized. One is energy management for HEV for Pbatt; the other is for the energy consumption minimizing problem of acc control of two vehicles. Second, a system for connected hybrid electric vehicles has been developed considering varying drag coefficients and the road gradients. Third, the fuel model of a typical hybrid electric vehicle is developed using the maps of the engine efficiency characteristics. Fourth, simulations and analysis (under different parameters, i.e., road conditions, vehicle state of charge, etc. are conducted to verify the effectiveness of the method to achieve higher fuel efficiency. The model predictive control problem is solved using numerical computation method: continuation and generalized minimum residual method. Computer simulation results reveal improvements in fuel economy using the proposed control method.

  6. IMPORTANCE OF KINETIC MEASURES IN TRAJECTORY PREDICTION WITH OPTIMAL CONTROL

    Directory of Open Access Journals (Sweden)

    Ömer GÜNDOĞDU

    2001-02-01

    Full Text Available A two-dimensional sagittally symmetric human-body model was established to simulate an optimal trajectory for manual material handling tasks. Nonlinear control techniques and genetic algorithms were utilized in the optimizations to explore optimal lifting patterns. The simulation results were then compared with the experimental data. Since the kinetic measures such as joint reactions and moments are vital parameters in injury determination, the importance of comparing kinetic measures rather than kinematical ones was emphasized.

  7. A Traffic Prediction Algorithm for Street Lighting Control Efficiency

    Directory of Open Access Journals (Sweden)

    POPA Valentin

    2013-01-01

    Full Text Available This paper presents the development of a traffic prediction algorithm that can be integrated in a street lighting monitoring and control system. The prediction algorithm must enable the reduction of energy costs and improve energy efficiency by decreasing the light intensity depending on the traffic level. The algorithm analyses and processes the information received at the command center based on the traffic level at different moments. The data is collected by means of the Doppler vehicle detection sensors integrated within the system. Thus, two methods are used for the implementation of the algorithm: a neural network and a k-NN (k-Nearest Neighbor prediction algorithm. For 500 training cycles, the mean square error of the neural network is 9.766 and for 500.000 training cycles the error amounts to 0.877. In case of the k-NN algorithm the error increases from 8.24 for k=5 to 12.27 for a number of 50 neighbors. In terms of a root means square error parameter, the use of a neural network ensures the highest performance level and can be integrated in a street lighting control system.

  8. Model predictive control of wind energy conversion systems

    CERN Document Server

    Yaramasu, Venkata Narasimha R

    2017-01-01

    The authors provide a comprehensive analysis on the model predictive control of power converters employed in a wide variety of variable-speed wind energy conversion systems (WECS). The contents of this book includes an overview of wind energy system configurations, power converters for variable-speed WECS, digital control techniques, MPC, modeling of power converters and wind generators for MPC design. Other topics include the mapping of continuous-time models to discrete-time models by various exact, approximate, and quasi-exact discretization methods, modeling and control of wind turbine grid-side two-level and multilevel voltage source converters. The authors also focus on the MPC of several power converter configurations for full variable-speed permanent magnet synchronous generator based WECS, squirrel-cage induction generator based WECS, and semi-variable-speed doubly fed induction generator based WECS.

  9. Modeling Smart Energy Systems for Model Predictive Control

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus; Poulsen, Niels Kjølstad; Madsen, Henrik

    2012-01-01

    as it is produced requires a very exible and controllable power consumption. Examples of controllable electric loads are heat pumps in buildings and Electric Vehicles (EVs) that are expected to play a large role in the future danish energy system. These units in a smart energy system can potentially oer exibility...... on a time scale ranging from seconds to several days by moving power consumption, exploiting thermal inertia or battery storage capacity, respectively. Using advanced control algorithms these systems are able to reduce their own electricity costs by planning ahead and moving consumption to periods...... future price should also be available in order for the individual units to plan ahead in the most feasible way. This is necessary since Economic MPCs do not respond to the absolute cost of electricity, but to variations of the price over the prediction horizon. Economic MPC is ideal for price responsive...

  10. Structural Acoustic Prediction and Interior Noise Control Technology

    Science.gov (United States)

    Mathur, G. P.; Chin, C. L.; Simpson, M. A.; Lee, J. T.; Palumbo, Daniel L. (Technical Monitor)

    2001-01-01

    This report documents the results of Task 14, "Structural Acoustic Prediction and Interior Noise Control Technology". The task was to evaluate the performance of tuned foam elements (termed Smart Foam) both analytically and experimentally. Results taken from a three-dimensional finite element model of an active, tuned foam element are presented. Measurements of sound absorption and sound transmission loss were taken using the model. These results agree well with published data. Experimental performance data were taken in Boeing's Interior Noise Test Facility where 12 smart foam elements were applied to a 757 sidewall. Several configurations were tested. Noise reductions of 5-10 dB were achieved over the 200-800 Hz bandwidth of the controller. Accelerometers mounted on the panel provided a good reference for the controller. Configurations with far-field error microphones outperformed near-field cases.

  11. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...

  12. Model Predictive Voltage Control of Wind Power Plants

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei

    2018-01-01

    the efficacy of the proposed WFVC, two case scenarios were designed: the wind farm is under normal operating conditions and the internal wind power fluctuation is considered; and besides internal power fluctuation, the impact of the external grid on the wind farm is considered.......This chapter proposes an autonomous wind farm voltage controller (WFVC) based on model predictive control (MPC). It also introduces the analytical expressions for the voltage sensitivity to tap positions of a transformer. The chapter then describes the discrete models for the wind turbine...... generators (WTGs) and static var compensators (SVCs)/static var generators (SVGs). Next, it describes the implementation of the on‐load tap changing (OLTC) in the MPC. Furthermore, the chapter examines the cost function as well as the constraints of the MPC‐based WFVC for both control modes. In order to test...

  13. Explicit Nonlinear Model Predictive Control Theory and Applications

    CERN Document Server

    Grancharova, Alexandra

    2012-01-01

    Nonlinear Model Predictive Control (NMPC) has become the accepted methodology to solve complex control problems related to process industries. The main motivation behind explicit NMPC is that an explicit state feedback law avoids the need for executing a numerical optimization algorithm in real time. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation and the possibility to design embedded control systems with low software and hardware complexity. This book considers the multi-parametric Nonlinear Programming (mp-NLP) approaches to explicit approximate NMPC of constrained nonlinear systems, developed by the authors, as well as their applications to various NMPC problem formulations and several case studies. The following types of nonlinear systems are considered, resulting in different NMPC problem formulations: Ø  Nonlinear systems described by first-principles models and nonlinear systems described by black-box models; �...

  14. Study of Model Predictive Control for Path-Following Autonomous Ground Vehicle Control under Crosswind Effect

    Directory of Open Access Journals (Sweden)

    Fitri Yakub

    2016-01-01

    Full Text Available We present a comparative study of model predictive control approaches of two-wheel steering, four-wheel steering, and a combination of two-wheel steering with direct yaw moment control manoeuvres for path-following control in autonomous car vehicle dynamics systems. Single-track mode, based on a linearized vehicle and tire model, is used. Based on a given trajectory, we drove the vehicle at low and high forward speeds and on low and high road friction surfaces for a double-lane change scenario in order to follow the desired trajectory as close as possible while rejecting the effects of wind gusts. We compared the controller based on both simple and complex bicycle models without and with the roll vehicle dynamics for different types of model predictive control manoeuvres. The simulation result showed that the model predictive control gave a better performance in terms of robustness for both forward speeds and road surface variation in autonomous path-following control. It also demonstrated that model predictive control is useful to maintain vehicle stability along the desired path and has an ability to eliminate the crosswind effect.

  15. Comparison of H-infinity control and generalized predictive control for a laser scanner system

    DEFF Research Database (Denmark)

    Ordys, A.W.; Stoustrup, Jakob; Smillie, I.

    2000-01-01

    This paper describes tests performed on a laser scanner system to assess the feasibility of H-infinity control and generalized predictive control design techniques in achieving a required performance in a trajectory folowing problem. The two methods are compared with respect to achieved scan times...

  16. The human brain maintains contradictory and redundant auditory sensory predictions.

    Directory of Open Access Journals (Sweden)

    Marika Pieszek

    Full Text Available Computational and experimental research has revealed that auditory sensory predictions are derived from regularities of the current environment by using internal generative models. However, so far, what has not been addressed is how the auditory system handles situations giving rise to redundant or even contradictory predictions derived from different sources of information. To this end, we measured error signals in the event-related brain potentials (ERPs in response to violations of auditory predictions. Sounds could be predicted on the basis of overall probability, i.e., one sound was presented frequently and another sound rarely. Furthermore, each sound was predicted by an informative visual cue. Participants' task was to use the cue and to discriminate the two sounds as fast as possible. Violations of the probability based prediction (i.e., a rare sound as well as violations of the visual-auditory prediction (i.e., an incongruent sound elicited error signals in the ERPs (Mismatch Negativity [MMN] and Incongruency Response [IR]. Particular error signals were observed even in case the overall probability and the visual symbol predicted different sounds. That is, the auditory system concurrently maintains and tests contradictory predictions. Moreover, if the same sound was predicted, we observed an additive error signal (scalp potential and primary current density equaling the sum of the specific error signals. Thus, the auditory system maintains and tolerates functionally independently represented redundant and contradictory predictions. We argue that the auditory system exploits all currently active regularities in order to optimally prepare for future events.

  17. Enhanced Voltage Control of VSC-HVDC Connected Offshore Wind Farms Based on Model Predictive Control

    DEFF Research Database (Denmark)

    Guo, Yifei; Gao, Houlei; Wu, Qiuwei

    2018-01-01

    This paper proposes an enhanced voltage control strategy (EVCS) based on model predictive control (MPC) for voltage source converter based high voltage direct current (VSCHVDC) connected offshore wind farms (OWFs). In the proposed MPC based EVCS, all wind turbine generators (WTGs) as well...... as the wind farm side VSC are optimally coordinated to keep voltages within the feasible range and reduce system power losses. Considering the high ratio of the OWF collector system, the effects of active power outputs of WTGs on voltage control are also taken into consideration. The predictive model of VSC...

  18. Online prediction and control in nonlinear stochastic systems

    DEFF Research Database (Denmark)

    Nielsen, Torben Skov

    2002-01-01

    speed and the relationship between (primarily) wind speed and wind power (the power curve). In paper G the model parameters are estimated using a RLS algorithm and any systematic time-variation of the model parameters is disregarded. Two di erent parameterizations of the power curve is considered...... are estimated using the algorithm proposed in paper C. The power curve and the diurnal variation of wind speed is estimated separately using the local polynomial regression procedure described in paper A . In paper J the parameters of the prediction model is assumed to be smooth functions of wind direction (and......The present thesis consists of a summary report and ten research papers. The subject of the thesis is on-line prediction and control of non-linear and non-stationary systems based on stochastic modelling. The thesis consists of three parts where the rst part deals with on-line estimation in linear...

  19. Output Feedback Stabilization with Nonlinear Predictive Control: Asymptotic properties

    Directory of Open Access Journals (Sweden)

    Lars Imsland

    2003-07-01

    Full Text Available State space based nonlinear model predictive control (NM PC needs the state for the prediction of the system behaviour. Unfortunately, for most applications, not all states are directly measurable. To recover the unmeasured states, typically a stable state observer is used. However, this implies that the stability of the closed-loop should be examined carefully, since no general nonlinear separation principle exists. Recently semi-global practical stability results for output feedback NMPC using a high-gain observer for state estimation have been established. One drawback of this result is that (in general the observer gain must be increased, if the desired set the state should converge to is made smaller. We show that under slightly stronger assumptions, not only practical stability, but also convergence of the system states and observer error to the origin for a sufficiently large but bounded observer gain can be achieved.

  20. Profile control simulations and experiments on TCV : A controller test environment and results using a model-based predictive controller

    NARCIS (Netherlands)

    Maljaars, E.; Felici, F.; Blanken, T.C.; Galperti, C.; Sauter, O.; de Baar, M.R.; Carpanese, F.; Goodman, T.P.; Kim, D.; Kim, S.H.; Kong, M.G.; Mavkov, B.; Merle, A.; Moret, J.M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A.A.; Vu, N.M.T.

    2017-01-01

    The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety

  1. Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

    NARCIS (Netherlands)

    Maljaars, B.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J.; Nouailletas, R.; Scheffer, M.; Teplukhina, A.; Vu, T.

    2017-01-01

    The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety

  2. Dinucleotide controlled null models for comparative RNA gene prediction

    Directory of Open Access Journals (Sweden)

    Gesell Tanja

    2008-05-01

    Full Text Available Abstract Background Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. Results We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. Conclusion SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require

  3. Dinucleotide controlled null models for comparative RNA gene prediction.

    Science.gov (United States)

    Gesell, Tanja; Washietl, Stefan

    2008-05-27

    Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered. SISSIz

  4. Development of a statistical method for predicting human driver decisions.

    Science.gov (United States)

    2015-09-01

    As autonomous vehicles enter the fleet, there will be a long period when these vehicles will have to interact with : human drivers. One of the challenges for autonomous vehicles is that human drivers do not communicate their : decisions well. However...

  5. Bridging the gap between the linear and nonlinear predictive control: Adaptations fo refficient building climate control

    Czech Academy of Sciences Publication Activity Database

    Pčolka, M.; Žáčeková, E.; Robinett, R.; Čelikovský, Sergej; Šebek, M.

    2016-01-01

    Roč. 53, č. 1 (2016), s. 124-138 ISSN 0967-0661 R&D Projects: GA ČR GA13-20433S Institutional support: RVO:67985556 Keywords : Model predictive control * Identification for control * Building climatecontrol Subject RIV: BC - Control Systems Theory Impact factor: 2.602, year: 2016 http://library.utia.cas.cz/separaty/2016/TR/celikovsky-0460306.pdf

  6. Translational Modeling in Schizophrenia: Predicting Human Dopamine D2 Receptor Occupancy.

    Science.gov (United States)

    Johnson, Martin; Kozielska, Magdalena; Pilla Reddy, Venkatesh; Vermeulen, An; Barton, Hugh A; Grimwood, Sarah; de Greef, Rik; Groothuis, Geny M M; Danhof, Meindert; Proost, Johannes H

    2016-04-01

    To assess the ability of a previously developed hybrid physiology-based pharmacokinetic-pharmacodynamic (PBPKPD) model in rats to predict the dopamine D2 receptor occupancy (D2RO) in human striatum following administration of antipsychotic drugs. A hybrid PBPKPD model, previously developed using information on plasma concentrations, brain exposure and D2RO in rats, was used as the basis for the prediction of D2RO in human. The rat pharmacokinetic and brain physiology parameters were substituted with human population pharmacokinetic parameters and human physiological information. To predict the passive transport across the human blood-brain barrier, apparent permeability values were scaled based on rat and human brain endothelial surface area. Active efflux clearance in brain was scaled from rat to human using both human brain endothelial surface area and MDR1 expression. Binding constants at the D2 receptor were scaled based on the differences between in vitro and in vivo systems of the same species. The predictive power of this physiology-based approach was determined by comparing the D2RO predictions with the observed human D2RO of six antipsychotics at clinically relevant doses. Predicted human D2RO was in good agreement with clinically observed D2RO for five antipsychotics. Models using in vitro information predicted human D2RO well for most of the compounds evaluated in this analysis. However, human D2RO was under-predicted for haloperidol. The rat hybrid PBPKPD model structure, integrated with in vitro information and human pharmacokinetic and physiological information, constitutes a scientific basis to predict the time course of D2RO in man.

  7. Reproduction (II): Human Control of Reproductive Processes

    Science.gov (United States)

    Jost, Alfred

    1970-01-01

    Describes methods of intervening in reproduction of animals and humans (artificial insemination, contraception, ovular and blastodisc transplants, pre selection of sex, cloning) and discusses the social implications of their use with humans. (AL)

  8. Local Model Predictive Control for T-S Fuzzy Systems.

    Science.gov (United States)

    Lee, Donghwan; Hu, Jianghai

    2017-09-01

    In this paper, a new linear matrix inequality-based model predictive control (MPC) problem is studied for discrete-time nonlinear systems described as Takagi-Sugeno fuzzy systems. A recent local stability approach is applied to improve the performance of the proposed MPC scheme. At each time k , an optimal state-feedback gain that minimizes an objective function is obtained by solving a semidefinite programming problem. The local stability analysis, the estimation of the domain of attraction, and feasibility of the proposed MPC are proved. Examples are given to demonstrate the advantages of the suggested MPC over existing approaches.

  9. Predictive control of thermal state of blast furnace

    Science.gov (United States)

    Barbasova, T. A.; Filimonova, A. A.

    2018-05-01

    The work describes the structure of the model for predictive control of the thermal state of a blast furnace. The proposed model contains the following input parameters: coke rate; theoretical combustion temperature, comprising: natural gas consumption, blasting temperature, humidity, oxygen, blast furnace cooling water; blast furnace gas utilization rate. The output parameter is the cast iron temperature. The results for determining the cast iron temperature were obtained following the identification using the Hammerstein-Wiener model. The result of solving the cast iron temperature stabilization problem was provided for the calculated values of process parameters of the target area of the respective blast furnace operation mode.

  10. Offset-Free Direct Power Control of DFIG Under Continuous-Time Model Predictive Control

    DEFF Research Database (Denmark)

    Errouissi, Rachid; Al-Durra, Ahmed; Muyeen, S.M.

    2017-01-01

    This paper presents a robust continuous-time model predictive direct power control for doubly fed induction generator (DFIG). The proposed approach uses Taylor series expansion to predict the stator current in the synchronous reference frame over a finite time horizon. The predicted stator current...... is directly used to compute the required rotor voltage in order to minimize the difference between the actual stator currents and their references over the predictive time. However, as the proposed strategy is sensitive to parameter variations and external disturbances, a disturbance observer is embedded...... into the control loop to remove the steady-state error of the stator current. It turns out that the steady-state and the transient performances can be identified by simple design parameters. In this paper, the reference of the stator current is directly calculated from the desired stator active and reactive powers...

  11. Effects of controlled element dynamics on human feedforward behavior in ramp-tracking tasks.

    Science.gov (United States)

    Laurense, Vincent A; Pool, Daan M; Damveld, Herman J; van Paassen, Marinus René M; Mulder, Max

    2015-02-01

    In real-life manual control tasks, human controllers are often required to follow a visible and predictable reference signal, enabling them to use feedforward control actions in conjunction with feedback actions that compensate for errors. Little is known about human control behavior in these situations. This paper investigates how humans adapt their feedforward control dynamics to the controlled element dynamics in a combined ramp-tracking and disturbance-rejection task. A human-in-the-loop experiment is performed with a pursuit display and vehicle-like controlled elements, ranging from a single integrator through second-order systems with a break frequency at either 3, 2, or 1 rad/s, to a double integrator. Because the potential benefits of feedforward control increase with steeper ramp segments in the target signal, three steepness levels are tested to investigate their possible effect on feedforward control with the various controlled elements. Analyses with four novel models of the operator, fitted to time-domain data, reveal feedforward control for all tested controlled elements and both (nonzero) tested levels of ramp steepness. For the range of controlled element dynamics investigated, it is found that humans adapt to these dynamics in their feedforward response, with a close to perfect inversion of the controlled element dynamics. No significant effects of ramp steepness on the feedforward model parameters are found.

  12. Predictive Function Control for Communication-Based Train Control (CBTC Systems

    Directory of Open Access Journals (Sweden)

    Bing Bu

    2013-01-01

    Full Text Available In Communication-Based Train Control (CBTC systems, random transmission delays and packet drops are inevitable in the wireless networks, which could result in unnecessary traction, brakes or even emergency brakes of trains, losses of line capacity and passenger dissatisfaction. This paper applies predictive function control technology with a mixed H2/∞ control approach to improve the control performances. The controller is in the state feedback form and satisfies the requirement of quadratic input and state constraints. A linear matrix inequality (LMI approach is developed to solve the control problem. The proposed method attenuates disturbances by incorporating H2/∞ into the control scheme. The control command from the automatic train operation (ATO is included in the reward function to optimize the train's running profile. The influence of transmission delays and packet drops is alleviated through improving the performances of the controller. Simulation results show that the method is effective to improve the performances and robustness of CBTC systems.

  13. Predictive IP controller for robust position control of linear servo system.

    Science.gov (United States)

    Lu, Shaowu; Zhou, Fengxing; Ma, Yajie; Tang, Xiaoqi

    2016-07-01

    Position control is a typical application of linear servo system. In this paper, to reduce the system overshoot, an integral plus proportional (IP) controller is used in the position control implementation. To further improve the control performance, a gain-tuning IP controller based on a generalized predictive control (GPC) law is proposed. Firstly, to represent the dynamics of the position loop, a second-order linear model is used and its model parameters are estimated on-line by using a recursive least squares method. Secondly, based on the GPC law, an optimal control sequence is obtained by using receding horizon, then directly supplies the IP controller with the corresponding control parameters in the real operations. Finally, simulation and experimental results are presented to show the efficiency of proposed scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  14. Basic considerations in predicting error probabilities in human task performance

    International Nuclear Information System (INIS)

    Fleishman, E.A.; Buffardi, L.C.; Allen, J.A.; Gaskins, R.C. III

    1990-04-01

    It is well established that human error plays a major role in the malfunctioning of complex systems. This report takes a broad look at the study of human error and addresses the conceptual, methodological, and measurement issues involved in defining and describing errors in complex systems. In addition, a review of existing sources of human reliability data and approaches to human performance data base development is presented. Alternative task taxonomies, which are promising for establishing the comparability on nuclear and non-nuclear tasks, are also identified. Based on such taxonomic schemes, various data base prototypes for generalizing human error rates across settings are proposed. 60 refs., 3 figs., 7 tabs

  15. A new approach to predict human intestinal absorption using porcine intestinal tissue and biorelevant matrices

    NARCIS (Netherlands)

    Westerhout, J.; Steeg, E. van de; Grossouw, D.; Zeijdner, E.E.; Krul, C.A.M.; Verwei, M.; Wortelboer, H.M.

    2014-01-01

    A reliable prediction of the oral bioavailability in humans is crucial and of high interest for pharmaceutical and food industry. The predictive value of currently used in silico methods, in vitro cell lines, ex vivo intestinal tissue and/or in vivo animal studies for human intestinal absorption,

  16. Genome-Wide Prediction and Analysis of 3D-Domain Swapped Proteins in the Human Genome from Sequence Information.

    Science.gov (United States)

    Upadhyay, Atul Kumar; Sowdhamini, Ramanathan

    2016-01-01

    3D-domain swapping is one of the mechanisms of protein oligomerization and the proteins exhibiting this phenomenon have many biological functions. These proteins, which undergo domain swapping, have acquired much attention owing to their involvement in human diseases, such as conformational diseases, amyloidosis, serpinopathies, proteionopathies etc. Early realisation of proteins in the whole human genome that retain tendency to domain swap will enable many aspects of disease control management. Predictive models were developed by using machine learning approaches with an average accuracy of 78% (85.6% of sensitivity, 87.5% of specificity and an MCC value of 0.72) to predict putative domain swapping in protein sequences. These models were applied to many complete genomes with special emphasis on the human genome. Nearly 44% of the protein sequences in the human genome were predicted positive for domain swapping. Enrichment analysis was performed on the positively predicted sequences from human genome for their domain distribution, disease association and functional importance based on Gene Ontology (GO). Enrichment analysis was also performed to infer a better understanding of the functional importance of these sequences. Finally, we developed hinge region prediction, in the given putative domain swapped sequence, by using important physicochemical properties of amino acids.

  17. Predicting, monitoring and controlling geomechanical effects of CO2 injection

    International Nuclear Information System (INIS)

    Streit, J.E.; Siggins, A.F.

    2005-01-01

    A key objective of geological carbon dioxide (CO 2 ) storage in porous rock is long-term subsurface containment of CO 2 . Fault stability and maximum sustainable pore-fluid pressures should be estimated in geomechanical studies in order to avoid damage to reservoir seals and fault seals of storage sites during CO 2 injection. Such analyses rely on predicting the evolution of effective stresses in rocks and faults during CO 2 injection. However, geomechanical analyses frequently do not incorporate poroelastic behaviour of reservoir rock, as relevant poroelastic properties are rarely known. The knowledge of rock poroelastic properties would allow the use of seismic methods for the accurate measurement of the effective stress evolution during CO 2 injection. This paper discussed key geomechanical effects of CO 2 injection into porous rock, and in particular, focused on the effects that the poroelasticity of reservoir rocks and pore pressure/stress coupling have on effective stresses. Relevant geophysical monitoring techniques were also suggested. The paper also outlined how these techniques could be applied to measure stress changes related to poroelastic rock behaviour during CO 2 injection and to test the predictions of sustainable changes in effective stress in CO 2 storage sites. It was concluded that a combination of predictive geomechanical techniques and application of geophysical monitoring techniques is a valid new concept for controlling and monitoring the geomechanical effects of CO 2 storage. 36 refs., 5 figs

  18. Demand Management Based on Model Predictive Control Techniques

    Directory of Open Access Journals (Sweden)

    Yasser A. Davizón

    2014-01-01

    Full Text Available Demand management (DM is the process that helps companies to sell the right product to the right customer, at the right time, and for the right price. Therefore the challenge for any company is to determine how much to sell, at what price, and to which market segment while maximizing its profits. DM also helps managers efficiently allocate undifferentiated units of capacity to the available demand with the goal of maximizing revenue. This paper introduces control system approach to demand management with dynamic pricing (DP using the model predictive control (MPC technique. In addition, we present a proper dynamical system analogy based on active suspension and a stability analysis is provided via the Lyapunov direct method.

  19. Predictive Solar-Integrated Commercial Building Load Control

    Energy Technology Data Exchange (ETDEWEB)

    Glasgow, Nathan [EdgePower Inc., Aspen, CO (United States)

    2017-01-31

    This report is the final technical report for the Department of Energy SunShot award number EE0007180 to EdgePower Inc., for the project entitled “Predictive Solar-Integrated Commercial Building Load Control.” The goal of this project was to successfully prove that the integration of solar forecasting and building load control can reduce demand charge costs for commercial building owners with solar PV. This proof of concept Tier 0 project demonstrated its value through a pilot project at a commercial building. This final report contains a summary of the work completed through he duration of the project. Clean Power Research was a sub-recipient on the award.

  20. Fast core prediction simulator for load follow control

    International Nuclear Information System (INIS)

    Yim, Man Sung; Lee, Sang Hoon; Lee, Un Chul

    1990-01-01

    An operator-assisting system for the reactor core control under power changing operating condition was developed. The system is consisted of core simulator routine and Xenon and Iodine initial condition generation routine. The initial condition generation routine, without exactly knowing the core status, is capable of providing accurate number densities and axial offset conditions of Xenon and Iodine after several hours of predictor- corrector calculations using the plant instrumentation signals of power level and power axial offset. The core simulator routine, even with the two node core model, gives equivalently accurate results as the one-dimensional model for the core behaviour simulation under power changing condition and can provide proper control strategies for load follow operation. The core simulator can also be used by the operator to develop remedial actions to restore the distorted power distribution by using its prediction capability

  1. New technologies in predicting, preventing and controlling emerging infectious diseases.

    Science.gov (United States)

    Christaki, Eirini

    2015-01-01

    Surveillance of emerging infectious diseases is vital for the early identification of public health threats. Emergence of novel infections is linked to human factors such as population density, travel and trade and ecological factors like climate change and agricultural practices. A wealth of new technologies is becoming increasingly available for the rapid molecular identification of pathogens but also for the more accurate monitoring of infectious disease activity. Web-based surveillance tools and epidemic intelligence methods, used by all major public health institutions, are intended to facilitate risk assessment and timely outbreak detection. In this review, we present new methods for regional and global infectious disease surveillance and advances in epidemic modeling aimed to predict and prevent future infectious diseases threats.

  2. Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome.

    Directory of Open Access Journals (Sweden)

    Huiying Zhao

    Full Text Available As more and more protein sequences are uncovered from increasingly inexpensive sequencing techniques, an urgent task is to find their functions. This work presents a highly reliable computational technique for predicting DNA-binding function at the level of protein-DNA complex structures, rather than low-resolution two-state prediction of DNA-binding as most existing techniques do. The method first predicts protein-DNA complex structure by utilizing the template-based structure prediction technique HHblits, followed by binding affinity prediction based on a knowledge-based energy function (Distance-scaled finite ideal-gas reference state for protein-DNA interactions. A leave-one-out cross validation of the method based on 179 DNA-binding and 3797 non-binding protein domains achieves a Matthews correlation coefficient (MCC of 0.77 with high precision (94% and high sensitivity (65%. We further found 51% sensitivity for 82 newly determined structures of DNA-binding proteins and 56% sensitivity for the human proteome. In addition, the method provides a reasonably accurate prediction of DNA-binding residues in proteins based on predicted DNA-binding complex structures. Its application to human proteome leads to more than 300 novel DNA-binding proteins; some of these predicted structures were validated by known structures of homologous proteins in APO forms. The method [SPOT-Seq (DNA] is available as an on-line server at http://sparks-lab.org.

  3. Force Control for a Pneumatic Cylinder Using Generalized Predictive Controller Approach

    Directory of Open Access Journals (Sweden)

    Ahmad ’Athif Mohd Faudzi

    2014-01-01

    Full Text Available Pneumatic cylinder is a well-known device because of its high power to weight ratio, easy use, and environmental safety. Pneumatic cylinder uses air as its power source and converts it to a possible movement such as linear and rotary movement. In order to control the pneumatic cylinder, controller algorithm is needed to control the on-off solenoid valve with encoder and pressure sensor as the feedback inputs. In this paper, generalized predictive controller (GPC is proposed as the control strategy for the pneumatic cylinder force control. To validate and compare the performance, proportional-integral (PI controller is also presented. Both controllers algorithms GPC and PI are developed using existing linear model of the cylinder from previous research. Results are presented in simulation and experimental approach using MATLAB-Simulink as the platform. The results show that the GPC is capable of fast response with low steady state error and percentage overshoot compared to PI.

  4. Linear-quadratic model predictions for tumor control probability

    International Nuclear Information System (INIS)

    Yaes, R.J.

    1987-01-01

    Sigmoid dose-response curves for tumor control are calculated from the linear-quadratic model parameters α and Β, obtained from human epidermoid carcinoma cell lines, and are much steeper than the clinical dose-response curves for head and neck cancers. One possible explanation is the presence of small radiation-resistant clones arising from mutations in an initially homogeneous tumor. Using the mutation theory of Delbruck and Luria and of Goldie and Coldman, the authors discuss the implications of such radiation-resistant clones for clinical radiation therapy

  5. Constrained Fuzzy Predictive Control Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Oussama Ait Sahed

    2015-01-01

    Full Text Available A fuzzy predictive controller using particle swarm optimization (PSO approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.

  6. Study on Noise Prediction Model and Control Schemes for Substation

    Science.gov (United States)

    Gao, Yang; Liu, Songtao

    2014-01-01

    With the government's emphasis on environmental issues of power transmission and transformation project, noise pollution has become a prominent problem now. The noise from the working transformer, reactor, and other electrical equipment in the substation will bring negative effect to the ambient environment. This paper focuses on using acoustic software for the simulation and calculation method to control substation noise. According to the characteristics of the substation noise and the techniques of noise reduction, a substation's acoustic field model was established with the SoundPLAN software to predict the scope of substation noise. On this basis, 4 reasonable noise control schemes were advanced to provide some helpful references for noise control during the new substation's design and construction process. And the feasibility and application effect of these control schemes can be verified by using the method of simulation modeling. The simulation results show that the substation always has the problem of excessive noise at boundary under the conventional measures. The excess noise can be efficiently reduced by taking the corresponding noise reduction methods. PMID:24672356

  7. Prediction models and control algorithms for predictive applications of setback temperature in cooling systems

    International Nuclear Information System (INIS)

    Moon, Jin Woo; Yoon, Younju; Jeon, Young-Hoon; Kim, Sooyoung

    2017-01-01

    Highlights: • Initial ANN model was developed for predicting the time to the setback temperature. • Initial model was optimized for producing accurate output. • Optimized model proved its prediction accuracy. • ANN-based algorithms were developed and tested their performance. • ANN-based algorithms presented superior thermal comfort or energy efficiency. - Abstract: In this study, a temperature control algorithm was developed to apply a setback temperature predictively for the cooling system of a residential building during occupied periods by residents. An artificial neural network (ANN) model was developed to determine the required time for increasing the current indoor temperature to the setback temperature. This study involved three phases: development of the initial ANN-based prediction model, optimization and testing of the initial model, and development and testing of three control algorithms. The development and performance testing of the model and algorithm were conducted using TRNSYS and MATLAB. Through the development and optimization process, the final ANN model employed indoor temperature and the temperature difference between the current and target setback temperature as two input neurons. The optimal number of hidden layers, number of neurons, learning rate, and moment were determined to be 4, 9, 0.6, and 0.9, respectively. The tangent–sigmoid and pure-linear transfer function was used in the hidden and output neurons, respectively. The ANN model used 100 training data sets with sliding-window method for data management. Levenberg-Marquart training method was employed for model training. The optimized model had a prediction accuracy of 0.9097 root mean square errors when compared with the simulated results. Employing the ANN model, ANN-based algorithms maintained indoor temperatures better within target ranges. Compared to the conventional algorithm, the ANN-based algorithms reduced the duration of time, in which the indoor temperature

  8. Dorsolateral Prefrontal Cortex GABA Concentration in Humans Predicts Working Memory Load Processing Capacity.

    Science.gov (United States)

    Yoon, Jong H; Grandelis, Anthony; Maddock, Richard J

    2016-11-16

    The discovery of neural mechanisms of working memory (WM) would significantly enhance our understanding of complex human behaviors and guide treatment development for WM-related impairments found in neuropsychiatric conditions and aging. Although the dorsolateral prefrontal cortex (DLPFC) has long been considered critical for WM, we still know little about the neural elements and pathways within the DLPFC that support WM in humans. In this study, we tested whether an individual's DLPFC gamma-aminobutryic acid (GABA) content predicts individual differences in WM task performance using a novel behavioral approach. Twenty-three healthy adults completed a task that measured the unique contribution of major WM components (memory load, maintenance, and distraction resistance) to performance. This was done to address the possibility that components have differing GABA dependencies and the failure to parse WM into components would lead to missing true associations with GABA. The subjects then had their DLPFC GABA content measured by single-voxel proton magnetic spectroscopy. We found that individuals with lower DLPFC GABA showed greater performance degradation with higher load, accounting for 31% of variance, p (corrected) = 0.015. This relationship was component, neurochemical, and brain region specific. DLPFC GABA content did not predict performance sensitivity to other components tested; DLPFC glutamate + glutamine and visual cortical GABA content did not predict load sensitivity. These results confirm the involvement of DLPFC GABA in WM load processing in humans and implicate factors controlling DLPFC GABA content in the neural mechanisms of WM and its impairments. This study demonstrated for the first time that the amount of gamma-aminobutryic acid (GABA), the major inhibitory neurotransmitter of the brain, in an individual's prefrontal cortex predicts working memory (WM) task performance. Given that WM is required for many of the most characteristic cognitive and

  9. Distributed Model Predictive Control for Active Power Control of Wind Farm

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei; Rasmussen, Claus Nygaard

    2014-01-01

    This paper presents the active power control of a wind farm using the Distributed Model Predictive Controller (D- MPC) via dual decomposition. Different from the conventional centralized wind farm control, multiple objectives such as power reference tracking performance and wind turbine load can...... be considered to achieve a trade-off between them. Additionally, D- MPC is based on communication among the subsystems. Through the interaction among the neighboring subsystems, the global optimization could be achieved, which significantly reduces the computation burden. It is suitable for the modern large......-scale wind farm control....

  10. Model Predictive Control-based gait pattern generation for wearable exoskeletons.

    Science.gov (United States)

    Wang, Letian; van Asseldonk, Edwin H F; van der Kooij, Herman

    2011-01-01

    This paper introduces a new method for controlling wearable exoskeletons that do not need predefined joint trajectories. Instead, it only needs basic gait descriptors such as step length, swing duration, and walking speed. End point Model Predictive Control (MPC) is used to generate the online joint trajectories based on these gait parameters. Real-time ability and control performance of the method during the swing phase of gait cycle is studied in this paper. Experiments are performed by helping a human subject swing his leg with different patterns in the LOPES gait trainer. Results show that the method is able to assist subjects to make steps with different step length and step duration without predefined joint trajectories and is fast enough for real-time implementation. Future study of the method will focus on controlling the exoskeletons in the entire gait cycle. © 2011 IEEE

  11. CONTROL PREDICTIVO DE UN ROBOT TIPO SCARA PREDICTIVE CONTROL OF A SCARA ROBOT

    Directory of Open Access Journals (Sweden)

    Oscar Andrés Vivas Albán

    2006-08-01

    Full Text Available Este artículo presenta una aplicación eficiente de un control por modelo de referencia sobre un robot de tipo SCARA. El control estudiado es un control predictivo funcional, el que hace uso de un modelo dinámico simplificado del robot. Los ensayos simulados se realizan sobre un robot de cuatro grados de libertad, tipo SCARA. Con el fin de comparar diferentes estrategias de control, se diseña un controlador clásico tipo PID y dos controladores basados en el modelo de referencia. En este último caso el sistema se linealiza y se desacoplada por realimentación, lo que transforma el sistema a controlar en un simple par de integradores. Al sistema lineal y desacoplado resultante se le aplica el control por par calculado y el control predictivo funcional. Los tres controladores estudiados se simulan sobre el robot SCARA con valores numéricos reales. Las pruebas permiten valorar las respuestas de estos controladores en seguimiento de trayectoria, rechazo de perturbaciones y presencia de errores en el modelado con consignas complejas similares a las utilizadas en procesos de fabricación.This paper describes an efficient approach for model based control, applied on a SCARA robot. The studied control is the predictive functional control which uses a simplified dynamical model of the robot. The simulated tests are made on a SCARA type robot, with four DOF. To compare several control strategies, a classical PID control and two model based controllers are designed. In the last case, the model is first linearized and decoupled by feedback, transforming the system into a double set of integrators. Computed torque control and predictive functional control are applied to the linear and decoupled system. The three studied controllers are simulated on the SCARA robot with real numerical values. Tracking performance, disturbance rejection and model robot mismatch are enlightened, using complex machining tasks trajectories and error presence in the modelling

  12. Seasonal temperature prediction skill over Southern Africa and human health

    CSIR Research Space (South Africa)

    Lazenby, MJ

    2014-10-01

    Full Text Available An assessment of probabilistic prediction skill of seasonal temperature extremes over Southern Africa is presented. Verification results are presented for six run-on seasons; September to November, October to December, November to January, December...

  13. Combined Active and Reactive Power Control of Wind Farms based on Model Predictive Control

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei; Wang, Jianhui

    2017-01-01

    This paper proposes a combined wind farm controller based on Model Predictive Control (MPC). Compared with the conventional decoupled active and reactive power control, the proposed control scheme considers the significant impact of active power on voltage variations due to the low X=R ratio...... of wind farm collector systems. The voltage control is improved. Besides, by coordination of active and reactive power, the Var capacity is optimized to prevent potential failures due to Var shortage, especially when the wind farm operates close to its full load. An analytical method is used to calculate...... the sensitivity coefficients to improve the computation efficiency and overcome the convergence problem. Two control modes are designed for both normal and emergency conditions. A wind farm with 20 wind turbines was used to verify the proposed combined control scheme....

  14. Inlet Flow Control and Prediction Technologies for Embedded Propulsion Systems

    Science.gov (United States)

    McMillan, Michelle L.; Mackie, Scott A.; Gissen, Abe; Vukasinovic, Bojan; Lakebrink, Matthew T.; Glezer, Ari; Mani, Mori; Mace, James L.

    2011-01-01

    Fail-safe, hybrid, flow control (HFC) is a promising technology for meeting high-speed cruise efficiency, low-noise signature, and reduced fuel-burn goals for future, Hybrid-Wing-Body (HWB) aircraft with embedded engines. This report details the development of HFC technology that enables improved inlet performance in HWB vehicles with highly integrated inlets and embedded engines without adversely affecting vehicle performance. In addition, new test techniques for evaluating Boundary-Layer-Ingesting (BLI)-inlet flow-control technologies developed and demonstrated through this program are documented, including the ability to generate a BLI-like inlet-entrance flow in a direct-connect, wind-tunnel facility, as well as, the use of D-optimal, statistically designed experiments to optimize test efficiency and enable interpretation of results. Validated improvements in numerical analysis tools and methods accomplished through this program are also documented, including Reynolds-Averaged Navier-Stokes CFD simulations of steady-state flow physics for baseline, BLI-inlet diffuser flow, as well as, that created by flow-control devices. Finally, numerical methods were employed in a ground-breaking attempt to directly simulate dynamic distortion. The advances in inlet technologies and prediction tools will help to meet and exceed "N+2" project goals for future HWB aircraft.

  15. Outdoor flocking of quadcopter drones with decentralized model predictive control.

    Science.gov (United States)

    Yuan, Quan; Zhan, Jingyuan; Li, Xiang

    2017-11-01

    In this paper, we present a multi-drone system featured with a decentralized model predictive control (DMPC) flocking algorithm. The drones gather localized information from neighbors and update their velocities using the DMPC flocking algorithm. In the multi-drone system, data packages are transmitted through XBee ® wireless modules in broadcast mode, yielding such an anonymous and decentralized system where all the calculations and controls are completed on an onboard minicomputer of each drone. Each drone is a double-layered agent system with the coordination layer running multi-drone flocking algorithms and the flight control layer navigating the drone, and the final formation of the flock relies on both the communication range and the desired inter-drone distance. We give both numerical simulations and field tests with a flock of five drones, showing that the DMPC flocking algorithm performs well on the presented multi-drone system in both the convergence rate and the ability of tracking a desired path. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  16. Humans with chimpanzee-like major histocompatibility complex-specificities control HIV-1 infection

    DEFF Research Database (Denmark)

    Hoof, Ilka; Kesmir, Can; Lund, Ole

    2008-01-01

    and the progression rate to AIDS. Chimpanzees control HIV-1 viral replication and develop a chronic infection without progressing to AIDS. A similar course of disease is observed in human long-term non-progressors. Objective: To investigate if long-term non-progressors and chimpanzees have functional similarities...... in their MHC class I repertoire. Methods: We compared the specificity of groups of human MHC molecules associated with different levels of viremia in HIV-1 infected individuals with those of chimpanzee. Results and conclusion: We demonstrate that human MHC with control of HIV-1 viral load share binding motifs...... with chimpanzee MHC. Moreover, we find that chimpanzee and human MHC associated with low viral load are predicted to elicit broader Gag-specific immune responses than human MHC associated with high viral load, thus supporting earlier findings that Gag-specific immune responses are essential for HIV-1 control....

  17. Human factors methods for nuclear control room design. Volume I. Human factors enhancement of existing nuclear control rooms. Final report

    International Nuclear Information System (INIS)

    Seminara, J.L.; Seidenstein, S.; Eckert, S.K.; Smith, D.L.

    1979-11-01

    Human factors engineering is an interdisciplinary specialty concerned with influencing the design of equipment systems, facilities, and operational environments to promote safe, efficient, and reliable operator performance. Human factors approaches were applied in the design of representative nuclear power plant control panels. First, methods for upgrading existing operational control panels were examined. Then, based on detailed human factors analyses of operator information and control requirements, designs of reactor, feedwater, and turbine-generator control panels were developed to improve the operator-control board interface, thereby reducing the potential for operator errors. In addition to examining present-generation concepts, human factors aspects of advanced systems and of hybrid combinations of advanced and conventional designs were investigated. Special attention was given to warning system designs. Also, a survey was conducted among control board designers to (1) develop an overview of design practices in the industry, and (2) establish appropriate measures leading to a more systematic concern for human factors in control board design

  18. Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

    Science.gov (United States)

    Maljaars, E.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J. M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A. A.; Vu, N. M. T.; The EUROfusion MST1-team; The TCV-team

    2017-12-01

    The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety factor profile (q-profile) and kinetic plasma parameters such as the plasma beta. This demands to establish reliable profile control routines in presently operational tokamaks. We present a model predictive profile controller that controls the q-profile and plasma beta using power requests to two clusters of gyrotrons and the plasma current request. The performance of the controller is analyzed in both simulation and TCV L-mode discharges where successful tracking of the estimated inverse q-profile as well as plasma beta is demonstrated under uncertain plasma conditions and the presence of disturbances. The controller exploits the knowledge of the time-varying actuator limits in the actuator input calculation itself such that fast transitions between targets are achieved without overshoot. A software environment is employed to prepare and test this and three other profile controllers in parallel in simulations and experiments on TCV. This set of tools includes the rapid plasma transport simulator RAPTOR and various algorithms to reconstruct the plasma equilibrium and plasma profiles by merging the available measurements with model-based predictions. In this work the estimated q-profile is merely based on RAPTOR model predictions due to the absence of internal current density measurements in TCV. These results encourage to further exploit model predictive profile control in experiments on TCV and other (future) tokamaks.

  19. Functional corticostriatal connection topographies predict goal directed behaviour in humans

    NARCIS (Netherlands)

    Marquand, A.F.; Haak, K.V.; Beckmann, C.F.

    2017-01-01

    Anatomical tracing studies in non-human primates have suggested that corticostriatal connectivity is topographically organized: nearby locations in striatum are connected with nearby locations in cortex. The topographic organization of corticostriatal connectivity is thought to underpin many

  20. Robust Model Predictive Control Schemes for Tracking Setpoints

    Directory of Open Access Journals (Sweden)

    Vu Trieu Minh

    2010-01-01

    Full Text Available This paper briefly reviews the development of nontracking robust model predictive control (RMPC schemes for uncertain systems using linear matrix inequalities (LMIs subject to input saturated and softened state constraints. Then we develop two new tracking setpoint RMPC schemes with common Lyapunov function and with zero terminal equality subject to input saturated and softened state constraints. The novel tracking setpoint RMPC schemes are able to stabilize uncertain systems once the output setpoints lead to the violation of the state constraints. The state violation can be regulated by changing the value of the weighting factor. A brief comparative simulation study of the two tracking setpoint RMPC schemes is done via simple examples to demonstrate the ability of the softened state constraint schemes. Finally, some features of future research from this study are discussed.

  1. Numerical Prediction of Hydromechanical Behaviour of Controllable Pitch Propeller

    Directory of Open Access Journals (Sweden)

    Saman Tarbiat

    2014-01-01

    Full Text Available The research described in this paper was carried out to predict hydrodynamic and frictional forces of controllable pitch propeller (CPP that bring about fretting problems in a blade bearing. The governing equations are Reynolds-averaged Navier-Stokes (RANS and are solved by OpenFOAM solver for hydrodynamic forces behind the ship’s wake. Frictional forces are calculated by practical mechanical formulae. Different advance velocities with constant rotational speed for blades are used to achieve hydrodynamic coefficients in open water and the wake behind the propeller. Results are compared at four different pitches. Detailed numerical results of 3D modelling of the propeller, hydrodynamic characteristics, and probability of the fretting motion in the propeller are presented. Results show that the probability of the fretting movement is related to the pitch.

  2. Prediction-based association control scheme in dense femtocell networks

    Science.gov (United States)

    Pham, Ngoc-Thai; Huynh, Thong; Hwang, Won-Joo; You, Ilsun; Choo, Kim-Kwang Raymond

    2017-01-01

    The deployment of large number of femtocell base stations allows us to extend the coverage and efficiently utilize resources in a low cost manner. However, the small cell size of femtocell networks can result in frequent handovers to the mobile user, and consequently throughput degradation. Thus, in this paper, we propose predictive association control schemes to improve the system’s effective throughput. Our design focuses on reducing handover frequency without impacting on throughput. The proposed schemes determine handover decisions that contribute most to the network throughput and are proper for distributed implementations. The simulation results show significant gains compared with existing methods in terms of handover frequency and network throughput perspective. PMID:28328992

  3. The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

    Directory of Open Access Journals (Sweden)

    César Hernández-Hernández

    2017-06-01

    Full Text Available Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation.

  4. Comparison of predictability for human pharmacokinetics parameters among monkeys, rats, and chimeric mice with humanised liver.

    Science.gov (United States)

    Miyamoto, Maki; Iwasaki, Shinji; Chisaki, Ikumi; Nakagawa, Sayaka; Amano, Nobuyuki; Hirabayashi, Hideki

    2017-12-01

    1. The aim of the present study was to evaluate the usefulness of chimeric mice with humanised liver (PXB mice) for the prediction of clearance (CL t ) and volume of distribution at steady state (Vd ss ), in comparison with monkeys, which have been reported as a reliable model for human pharmacokinetics (PK) prediction, and with rats, as a conventional PK model. 2. CL t and Vd ss values in PXB mice, monkeys and rats were determined following intravenous administration of 30 compounds known to be mainly eliminated in humans via the hepatic metabolism by various drug-metabolising enzymes. Using single-species allometric scaling, human CL t and Vd ss values were predicted from the three animal models. 3. Predicted CL t values from PXB mice exhibited the highest predictability: 25 for PXB mice, 21 for monkeys and 14 for rats were predicted within a three-fold range of actual values among 30 compounds. For predicted human Vd ss values, the number of compounds falling within a three-fold range was 23 for PXB mice, 24 for monkeys, and 16 for rats among 29 compounds. PXB mice indicated a higher predictability for CL t and Vd ss values than the other animal models. 4. These results demonstrate the utility of PXB mice in predicting human PK parameters.

  5. SCHEME (Soft Control Human error Evaluation MEthod) for advanced MCR HRA

    International Nuclear Information System (INIS)

    Jang, Inseok; Jung, Wondea; Seong, Poong Hyun

    2015-01-01

    The Technique for Human Error Rate Prediction (THERP), Korean Human Reliability Analysis (K-HRA), Human Error Assessment and Reduction Technique (HEART), A Technique for Human Event Analysis (ATHEANA), Cognitive Reliability and Error Analysis Method (CREAM), and Simplified Plant Analysis Risk Human Reliability Assessment (SPAR-H) in relation to NPP maintenance and operation. Most of these methods were developed considering the conventional type of Main Control Rooms (MCRs). They are still used for HRA in advanced MCRs even though the operating environment of advanced MCRs in NPPs has been considerably changed by the adoption of new human-system interfaces such as computer-based soft controls. Among the many features in advanced MCRs, soft controls are an important feature because the operation action in NPP advanced MCRs is performed by soft controls. Consequently, those conventional methods may not sufficiently consider the features of soft control execution human errors. To this end, a new framework of a HRA method for evaluating soft control execution human error is suggested by performing the soft control task analysis and the literature reviews regarding widely accepted human error taxonomies. In this study, the framework of a HRA method for evaluating soft control execution human error in advanced MCRs is developed. First, the factors which HRA method in advanced MCRs should encompass are derived based on the literature review, and soft control task analysis. Based on the derived factors, execution HRA framework in advanced MCRs is developed mainly focusing on the features of soft control. Moreover, since most current HRA database deal with operation in conventional type of MCRs and are not explicitly designed to deal with digital HSI, HRA database are developed under lab scale simulation

  6. Current status of prediction of drug disposition and toxicity in humans using chimeric mice with humanized liver.

    Science.gov (United States)

    Kitamura, Shigeyuki; Sugihara, Kazumi

    2014-01-01

    1. Human-chimeric mice with humanized liver have been constructed by transplantation of human hepatocytes into several types of mice having genetic modifications that injure endogenous liver cells. Here, we focus on liver urokinase-type plasminogen activator-transgenic severe combined immunodeficiency (uPA/SCID) mice, which are the most widely used human-chimeric mice. Studies so far indicate that drug metabolism, drug transport, pharmacological effects and toxicological action in these mice are broadly similar to those in humans. 2. Expression of various drug-metabolizing enzymes is known to be different between humans and rodents. However, the expression pattern of cytochrome P450, aldehyde oxidase and phase II enzymes in the liver of human-chimeric mice resembles that in humans, not that in the host mice. 3. Metabolism of various drugs, including S-warfarin, zaleplon, ibuprofen, naproxen, coumarin, troglitazone and midazolam, in human-chimeric mice is mediated by human drug-metabolizing enzymes, not by host mouse enzymes, and thus resembles that in humans. 4. Pharmacological and toxicological effects of various drugs in human-chimeric mice are also similar to those in humans. 5. The current consensus is that chimeric mice with humanized liver are useful to predict drug metabolism catalyzed by cytochrome P450, aldehyde oxidase and phase II enzymes in humans in vivo and in vitro. Some remaining issues are discussed in this review.

  7. HUMAN FACTORS GUIDANCE FOR CONTROL ROOM EVALUATION

    International Nuclear Information System (INIS)

    OHARA, J.; BROWN, W.; STUBLER, W.; HIGGINS, J.; WACHTEL, J.; PERSENSKY, J.J.

    2000-01-01

    The Human-System Interface Design Review Guideline (NUREG-0700, Revision 1) was developed by the US Nuclear Regulatory Commission (NRC) to provide human factors guidance as a basis for the review of advanced human-system interface technologies. The guidance consists of three components: design review procedures, human factors engineering guidelines, and a software application to provide design review support called the ''Design Review Guideline.'' Since it was published in June 1996, Rev. 1 to NUREG-0700 has been used successfully by NRC staff, contractors and nuclear industry organizations, as well as by interested organizations outside the nuclear industry. The NRC has committed to the periodic update and improvement of the guidance to ensure that it remains a state-of-the-art design evaluation tool in the face of emerging and rapidly changing technology. This paper addresses the current research to update of NUREG-0700 based on the substantial work that has taken place since the publication of Revision 1

  8. Modeling the Temporal Nature of Human Behavior for Demographics Prediction

    DEFF Research Database (Denmark)

    Felbo, Bjarke; Sundsøy, Pål; Pentland, Alex

    2017-01-01

    Mobile phone metadata is increasingly used for humanitarian purposes in developing countries as traditional data is scarce. Basic demographic information is however often absent from mobile phone datasets, limiting the operational impact of the datasets. For these reasons, there has been a growing...... interest in predicting demographic information from mobile phone metadata. Previous work focused on creating increasingly advanced features to be modeled with standard machine learning algorithms. We here instead model the raw mobile phone metadata directly using deep learning, exploiting the temporal...... on both age and gender prediction using only the temporal modality in mobile metadata. We finally validate our method on low activity users and evaluate the modeling assumptions....

  9. Steering Angle Control of Car for Dubins Path-tracking Using Model Predictive Control

    Science.gov (United States)

    Kusuma Rahma Putri, Dian; Subchan; Asfihani, Tahiyatul

    2018-03-01

    Car as one of transportation is inseparable from technological developments. About ten years, there are a lot of research and development on lane keeping system(LKS) which is a system that automaticaly controls the steering to keep the vehicle especially car always on track. This system can be developed for unmanned cars. Unmanned system car requires navigation, guidance and control which is able to direct the vehicle to move toward the desired path. The guidance system is represented by using Dubins-Path that will be controlled by using Model Predictive Control. The control objective is to keep the car’s movement that represented by dinamic lateral motion model so car can move according to the path appropriately. The simulation control on the four types of trajectories that generate the value for steering angle and steering angle changes are at the specified interval.

  10. Spatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans.

    Science.gov (United States)

    Fouragnan, Elsa; Queirazza, Filippo; Retzler, Chris; Mullinger, Karen J; Philiastides, Marios G

    2017-07-06

    Reward learning depends on accurate reward associations with potential choices. These associations can be attained with reinforcement learning mechanisms using a reward prediction error (RPE) signal (the difference between actual and expected rewards) for updating future reward expectations. Despite an extensive body of literature on the influence of RPE on learning, little has been done to investigate the potentially separate contributions of RPE valence (positive or negative) and surprise (absolute degree of deviation from expectations). Here, we coupled single-trial electroencephalography with simultaneously acquired fMRI, during a probabilistic reversal-learning task, to offer evidence of temporally overlapping but largely distinct spatial representations of RPE valence and surprise. Electrophysiological variability in RPE valence correlated with activity in regions of the human reward network promoting approach or avoidance learning. Electrophysiological variability in RPE surprise correlated primarily with activity in regions of the human attentional network controlling the speed of learning. Crucially, despite the largely separate spatial extend of these representations our EEG-informed fMRI approach uniquely revealed a linear superposition of the two RPE components in a smaller network encompassing visuo-mnemonic and reward areas. Activity in this network was further predictive of stimulus value updating indicating a comparable contribution of both signals to reward learning.

  11. Attention and predictions: control of spatial attention beyond the endogenous-exogenous dichotomy

    Directory of Open Access Journals (Sweden)

    Emiliano eMacaluso

    2013-10-01

    Full Text Available The mechanisms of attention control have been extensively studied with a variety of methodologies in animals and in humans. Human studies using non-invasive imaging techniques highlighted a remarkable difference between the pattern of responses in dorsal fronto-parietal regions vs. ventral fronto-parietal regions, primarily lateralized to the right hemisphere. Initially, this distinction at the neuro-physiological level has been related to the distinction between cognitive processes associated with strategic/endogenous vs. stimulus-driven/exogenous of attention control. Nonetheless, quite soon it has become evident that, in almost any situation, attention control entails a complex combination of factors related to both the current sensory input and endogenous aspects associated with the experimental context. Here, we review several of these aspects first discussing the joint contribution of endogenous and stimulus-driven factors during spatial orienting in complex environments and, then, turning to the role of expectations and predictions in spatial re-orienting. We emphasize that strategic factors play a pivotal role for the activation of the ventral system during stimulus-driven control, and that the dorsal system makes use of stimulus-driven signals for top-down control. We conclude that both the dorsal and the ventral fronto-parietal networks integrate endogenous and exogenous signals during spatial attention control and that future investigations should manipulate both these factors concurrently, so as to reveal to full extent of these interactions.

  12. Attention and predictions: control of spatial attention beyond the endogenous-exogenous dichotomy

    Science.gov (United States)

    Macaluso, Emiliano; Doricchi, Fabrizio

    2013-01-01

    The mechanisms of attention control have been extensively studied with a variety of methodologies in animals and in humans. Human studies using non-invasive imaging techniques highlighted a remarkable difference between the pattern of responses in dorsal fronto-parietal regions vs. ventral fronto-parietal (vFP) regions, primarily lateralized to the right hemisphere. Initially, this distinction at the neuro-physiological level has been related to the distinction between cognitive processes associated with strategic/endogenous vs. stimulus-driven/exogenous of attention control. Nonetheless, quite soon it has become evident that, in almost any situation, attention control entails a complex combination of factors related to both the current sensory input and endogenous aspects associated with the experimental context. Here, we review several of these aspects first discussing the joint contribution of endogenous and stimulus-driven factors during spatial orienting in complex environments and, then, turning to the role of expectations and predictions in spatial re-orienting. We emphasize that strategic factors play a pivotal role for the activation of the ventral system during stimulus-driven control, and that the dorsal system makes use of stimulus-driven signals for top-down control. We conclude that both the dorsal and the vFP networks integrate endogenous and exogenous signals during spatial attention control and that future investigations should manipulate both these factors concurrently, so as to reveal to full extent of these interactions. PMID:24155707

  13. Attention and predictions: control of spatial attention beyond the endogenous-exogenous dichotomy.

    Science.gov (United States)

    Macaluso, Emiliano; Doricchi, Fabrizio

    2013-01-01

    The mechanisms of attention control have been extensively studied with a variety of methodologies in animals and in humans. Human studies using non-invasive imaging techniques highlighted a remarkable difference between the pattern of responses in dorsal fronto-parietal regions vs. ventral fronto-parietal (vFP) regions, primarily lateralized to the right hemisphere. Initially, this distinction at the neuro-physiological level has been related to the distinction between cognitive processes associated with strategic/endogenous vs. stimulus-driven/exogenous of attention control. Nonetheless, quite soon it has become evident that, in almost any situation, attention control entails a complex combination of factors related to both the current sensory input and endogenous aspects associated with the experimental context. Here, we review several of these aspects first discussing the joint contribution of endogenous and stimulus-driven factors during spatial orienting in complex environments and, then, turning to the role of expectations and predictions in spatial re-orienting. We emphasize that strategic factors play a pivotal role for the activation of the ventral system during stimulus-driven control, and that the dorsal system makes use of stimulus-driven signals for top-down control. We conclude that both the dorsal and the vFP networks integrate endogenous and exogenous signals during spatial attention control and that future investigations should manipulate both these factors concurrently, so as to reveal to full extent of these interactions.

  14. Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

    Science.gov (United States)

    Spiliopoulou, Athina; Nagy, Reka; Bermingham, Mairead L.; Huffman, Jennifer E.; Hayward, Caroline; Vitart, Veronique; Rudan, Igor; Campbell, Harry; Wright, Alan F.; Wilson, James F.; Pong-Wong, Ricardo; Agakov, Felix; Navarro, Pau; Haley, Chris S.

    2015-01-01

    We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge. PMID:25918167

  15. Coordinated Voltage Control of a Wind Farm based on Model Predictive Control

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai

    2016-01-01

    This paper presents an autonomous wind farm voltage controller based on Model Predictive Control (MPC). The reactive power compensation and voltage regulation devices of the wind farm include Static Var Compensators (SVCs), Static Var Generators (SVGs), Wind Turbine Generators (WTGs) and On...... are calculated based on an analytical method to improve the computation efficiency and overcome the convergence problem. Two control modes are designed for both voltage violated and normal operation conditions. A wind farm with 20 wind turbines was used to conduct case studies to verify the proposed coordinated...

  16. Efficient prediction of human protein-protein interactions at a global scale.

    Science.gov (United States)

    Schoenrock, Andrew; Samanfar, Bahram; Pitre, Sylvain; Hooshyar, Mohsen; Jin, Ke; Phillips, Charles A; Wang, Hui; Phanse, Sadhna; Omidi, Katayoun; Gui, Yuan; Alamgir, Md; Wong, Alex; Barrenäs, Fredrik; Babu, Mohan; Benson, Mikael; Langston, Michael A; Green, James R; Dehne, Frank; Golshani, Ashkan

    2014-12-10

    Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

  17. Finite Control Set Model Predictive Control for Multiple Distributed Generators Microgrids

    Science.gov (United States)

    Babqi, Abdulrahman Jamal

    This dissertation proposes two control strategies for AC microgrids that consist of multiple distributed generators (DGs). The control strategies are valid for both grid-connected and islanded modes of operation. In general, microgrid can operate as a stand-alone system (i.e., islanded mode) or while it is connected to the utility grid (i.e., grid connected mode). To enhance the performance of a micrgorid, a sophisticated control scheme should be employed. The control strategies of microgrids can be divided into primary and secondary controls. The primary control regulates the output active and reactive powers of each DG in grid-connected mode as well as the output voltage and frequency of each DG in islanded mode. The secondary control is responsible for regulating the microgrid voltage and frequency in the islanded mode. Moreover, it provides power sharing schemes among the DGs. In other words, the secondary control specifies the set points (i.e. reference values) for the primary controllers. In this dissertation, Finite Control Set Model Predictive Control (FCS-MPC) was proposed for controlling microgrids. FCS-MPC was used as the primary controller to regulate the output power of each DG (in the grid-connected mode) or the voltage of the point of DG coupling (in the islanded mode of operation). In the grid-connected mode, Direct Power Model Predictive Control (DPMPC) was implemented to manage the power flow between each DG and the utility grid. In the islanded mode, Voltage Model Predictive Control (VMPC), as the primary control, and droop control, as the secondary control, were employed to control the output voltage of each DG and system frequency. The controller was equipped with a supplementary current limiting technique in order to limit the output current of each DG in abnormal incidents. The control approach also enabled smooth transition between the two modes. The performance of the control strategy was investigated and verified using PSCAD/EMTDC software

  18. Uniquely Human Self-Control Begins at School Age

    Science.gov (United States)

    Herrmann, Esther; Misch, Antonia; Hernandez-Lloreda, Victoria; Tomasello, Michael

    2015-01-01

    Human beings have remarkable skills of self-control, but the evolutionary origins of these skills are unknown. Here we compare children at 3 and 6 years of age with one of humans' two nearest relatives, chimpanzees, on a battery of reactivity and self-control tasks. Three-year-old children and chimpanzees were very similar in their abilities to…

  19. Human Splice-Site Prediction with Deep Neural Networks.

    Science.gov (United States)

    Naito, Tatsuhiko

    2018-04-18

    Accurate splice-site prediction is essential to delineate gene structures from sequence data. Several computational techniques have been applied to create a system to predict canonical splice sites. For classification tasks, deep neural networks (DNNs) have achieved record-breaking results and often outperformed other supervised learning techniques. In this study, a new method of splice-site prediction using DNNs was proposed. The proposed system receives an input sequence data and returns an answer as to whether it is splice site. The length of input is 140 nucleotides, with the consensus sequence (i.e., "GT" and "AG" for the donor and acceptor sites, respectively) in the middle. Each input sequence model is applied to the pretrained DNN model that determines the probability that an input is a splice site. The model consists of convolutional layers and bidirectional long short-term memory network layers. The pretraining and validation were conducted using the data set tested in previously reported methods. The performance evaluation results showed that the proposed method can outperform the previous methods. In addition, the pattern learned by the DNNs was visualized as position frequency matrices (PFMs). Some of PFMs were very similar to the consensus sequence. The trained DNN model and the brief source code for the prediction system are uploaded. Further improvement will be achieved following the further development of DNNs.

  20. predicted peak expiratory flow in human and the clinical implication ...

    African Journals Online (AJOL)

    DR. AMINU

    predicted PEF varied widely across formulae and choice of a particular formula may alter guideline- base care. This work has therefore accepted a recently published population-base equation proposed as the reference standard for future asthma guidelines. Keywords: Peak expiratory flow, Asthma, Practice guidelines, ...

  1. Predicting and Supplying Human Resource Requirements for the Future.

    Science.gov (United States)

    Blake, Larry J.

    After asserting that public institutions should not provide training for nonexistent jobs, this paper reviews problems associated with the accurate prediction of future manpower needs. The paper reviews the processes currently used to project labor force needs and notes the difficulty of accurately forecasting labor market "surprises,"…

  2. Prediction of human protein function according to Gene Ontology categories

    DEFF Research Database (Denmark)

    Jensen, Lars Juhl; Gupta, Ramneek; Stærfeldt, Hans Henrik

    2003-01-01

    developed a method for prediction of protein function for a subset of classes from the Gene Ontology classification scheme. This subset includes several pharmaceutically interesting categories-transcription factors, receptors, ion channels, stress and immune response proteins, hormones and growth factors...

  3. Prediction of community prevalence of human onchocerciasis in the Amazonian onchocerciasis focus: Bayesian approach.

    Science.gov (United States)

    Carabin, Hélène; Escalona, Marisela; Marshall, Clare; Vivas-Martínez, Sarai; Botto, Carlos; Joseph, Lawrence; Basáñez, María-Gloria

    2003-01-01

    To develop a Bayesian hierarchical model for human onchocerciasis with which to explore the factors that influence prevalence of microfilariae in the Amazonian focus of onchocerciasis and predict the probability of any community being at least mesoendemic (>20% prevalence of microfilariae), and thus in need of priority ivermectin treatment. Models were developed with data from 732 individuals aged > or =15 years who lived in 29 Yanomami communities along four rivers of the south Venezuelan Orinoco basin. The models' abilities to predict prevalences of microfilariae in communities were compared. The deviance information criterion, Bayesian P-values, and residual values were used to select the best model with an approximate cross-validation procedure. A three-level model that acknowledged clustering of infection within communities performed best, with host age and sex included at the individual level, a river-dependent altitude effect at the community level, and additional clustering of communities along rivers. This model correctly classified 25/29 (86%) villages with respect to their need for priority ivermectin treatment. Bayesian methods are a flexible and useful approach for public health research and control planning. Our model acknowledges the clustering of infection within communities, allows investigation of links between individual- or community-specific characteristics and infection, incorporates additional uncertainty due to missing covariate data, and informs policy decisions by predicting the probability that a new community is at least mesoendemic.

  4. In silico prediction of xenobiotic metabolism in humans

    Energy Technology Data Exchange (ETDEWEB)

    Mu, Fangping [Los Alamos National Laboratory

    2009-01-01

    Xenobiotic metabolism in humans is catalyzed by a few enzymes with broad substrate specificities, which provide the overall broad chemical specificity for nearly all xenobiotics that humans encounter. Xenobiotic metabolism are classified into functional group biotransformations. Based on bona fide reactions and negative examples for each reaction class, support vector machine (SVM) classifiers are built. The input to SVM is a set of atomic and molecular features to define the electrostatic, steric, energetic, geometrical and topological environment of the atoms in the reaction center under the molecule. Results show that the overall sensitivity and specificity of classifiers is around 87%.

  5. Controller Strategies for Automation Tool Use under Varying Levels of Trajectory Prediction Uncertainty

    Science.gov (United States)

    Morey, Susan; Prevot, Thomas; Mercer, Joey; Martin, Lynne; Bienert, Nancy; Cabrall, Christopher; Hunt, Sarah; Homola, Jeffrey; Kraut, Joshua

    2013-01-01

    A human-in-the-loop simulation was conducted to examine the effects of varying levels of trajectory prediction uncertainty on air traffic controller workload and performance, as well as how strategies and the use of decision support tools change in response. This paper focuses on the strategies employed by two controllers from separate teams who worked in parallel but independently under identical conditions (airspace, arrival traffic, tools) with the goal of ensuring schedule conformance and safe separation for a dense arrival flow in en route airspace. Despite differences in strategy and methods, both controllers achieved high levels of schedule conformance and safe separation. Overall, results show that trajectory uncertainties introduced by wind and aircraft performance prediction errors do not affect the controllers' ability to manage traffic. Controller strategies were fairly robust to changes in error, though strategies were affected by the amount of delay to absorb (scheduled time of arrival minus estimated time of arrival). Using the results and observations, this paper proposes an ability to dynamically customize the display of information including delay time based on observed error to better accommodate different strategies and objectives.

  6. Human-vehicle embodiment when predictability is violated.

    Science.gov (United States)

    Tanida, Koji; Paolini, Marco; Silveira, Sarita

    2017-09-01

    Embodiment in human-vehicle interaction is higher for perceived safety than for perceived risk. When operational anticipations are violated, experiencing a vehicle as body-extension is negatively correlated with operational effort as indicated by neural activation in the motor system. © 2017 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

  7. Prospective versus predictive control in timing of hitting a falling ball.

    Science.gov (United States)

    Katsumata, Hiromu; Russell, Daniel M

    2012-02-01

    Debate exists as to whether humans use prospective or predictive control to intercept an object falling under gravity (Baurès et al. in Vis Res 47:2982-2991, 2007; Zago et al. in Vis Res 48:1532-1538, 2008). Prospective control involves using continuous information to regulate action. τ, the ratio of the size of the gap to the rate of gap closure, has been proposed as the information used in guiding interceptive actions prospectively (Lee in Ecol Psychol 10:221-250, 1998). This form of control is expected to generate movement modulation, where variability decreases over the course of an action based upon more accurate timing information. In contrast, predictive control assumes that a pre-programmed movement is triggered at an appropriate criterion timing variable. For a falling object it is commonly argued that an internal model of gravitational acceleration is used to predict the motion of the object and determine movement initiation. This form of control predicts fixed duration movements initiated at consistent time-to-contact (TTC), either across conditions (constant criterion operational timing) or within conditions (variable criterion operational timing). The current study sought to test predictive and prospective control hypotheses by disrupting continuous visual information of a falling ball and examining consistency in movement initiation and duration, and evidence for movement modulation. Participants (n = 12) batted a ball dropped from three different heights (1, 1.3 and 1.5 m), under both full-vision and partial occlusion conditions. In the occlusion condition, only the initial ball drop and the final 200 ms of ball flight to the interception point could be observed. The initiation of the swing did not occur at a consistent TTC, τ, or any other timing variable across drop heights, in contrast with previous research. However, movement onset was not impacted by occluding the ball flight for 280-380 ms. This finding indicates that humans did not

  8. Quality by control: Towards model predictive control of mammalian cell culture bioprocesses.

    Science.gov (United States)

    Sommeregger, Wolfgang; Sissolak, Bernhard; Kandra, Kulwant; von Stosch, Moritz; Mayer, Martin; Striedner, Gerald

    2017-07-01

    The industrial production of complex biopharmaceuticals using recombinant mammalian cell lines is still mainly built on a quality by testing approach, which is represented by fixed process conditions and extensive testing of the end-product. In 2004 the FDA launched the process analytical technology initiative, aiming to guide the industry towards advanced process monitoring and better understanding of how critical process parameters affect the critical quality attributes. Implementation of process analytical technology into the bio-production process enables moving from the quality by testing to a more flexible quality by design approach. The application of advanced sensor systems in combination with mathematical modelling techniques offers enhanced process understanding, allows on-line prediction of critical quality attributes and subsequently real-time product quality control. In this review opportunities and unsolved issues on the road to a successful quality by design and dynamic control implementation are discussed. A major focus is directed on the preconditions for the application of model predictive control for mammalian cell culture bioprocesses. Design of experiments providing information about the process dynamics upon parameter change, dynamic process models, on-line process state predictions and powerful software environments seem to be a prerequisite for quality by control realization. © 2017 The Authors. Biotechnology Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  9. Expanded prediction equations of human sweat loss and water needs.

    Science.gov (United States)

    Gonzalez, R R; Cheuvront, S N; Montain, S J; Goodman, D A; Blanchard, L A; Berglund, L G; Sawka, M N

    2009-08-01

    The Institute of Medicine expressed a need for improved sweating rate (msw) prediction models that calculate hourly and daily water needs based on metabolic rate, clothing, and environment. More than 25 years ago, the original Shapiro prediction equation (OSE) was formulated as msw (g.m(-2).h(-1))=27.9.Ereq.(Emax)(-0.455), where Ereq is required evaporative heat loss and Emax is maximum evaporative power of the environment; OSE was developed for a limited set of environments, exposures times, and clothing systems. Recent evidence shows that OSE often overpredicts fluid needs. Our study developed a corrected OSE and a new msw prediction equation by using independent data sets from a wide range of environmental conditions, metabolic rates (rest to losses were carefully measured in 101 volunteers (80 males and 21 females; >500 observations) by using a variety of metabolic rates over a range of environmental conditions (ambient temperature, 15-46 degrees C; water vapor pressure, 0.27-4.45 kPa; wind speed, 0.4-2.5 m/s), clothing, and equipment combinations and durations (2-8 h). Data are expressed as grams per square meter per hour and were analyzed using fuzzy piecewise regression. OSE overpredicted sweating rates (Pdata (21 males and 9 females; >200 observations). OSEC and PW were more accurate predictors of sweating rate (58 and 65% more accurate, Perror (standard error estimate<100 g.m(-2).h(-1)) for conditions both within and outside the original OSE domain of validity. The new equations provide for more accurate sweat predictions over a broader range of conditions with applications to public health, military, occupational, and sports medicine settings.

  10. Human Core Temperature Prediction for Heat-Injury Prevention

    Science.gov (United States)

    2015-05-01

    Rio de Janeiro State University, Rio de Janeiro , Brazil, and the B.B.A. degree in business administration from Rio ... de Janeiro Federal Univer- sity, Rio de Janeiro , in 1980 and 1985, respectively, and the M.S.E. and Ph.D. degrees in nuclear engi- neering from the...predictions [13]. Importantly, we found that because AR models only de - pend on the frequency of the underlying time-series data, and because the

  11. Electric Vehicle Longitudinal Stability Control Based on a New Multimachine Nonlinear Model Predictive Direct Torque Control

    Directory of Open Access Journals (Sweden)

    M’hamed Sekour

    2017-01-01

    Full Text Available In order to improve the driving performance and the stability of electric vehicles (EVs, a new multimachine robust control, which realizes the acceleration slip regulation (ASR and antilock braking system (ABS functions, based on nonlinear model predictive (NMP direct torque control (DTC, is proposed for four permanent magnet synchronous in-wheel motors. The in-wheel motor provides more possibilities of wheel control. One of its advantages is that it has low response time and almost instantaneous torque generation. Moreover, it can be independently controlled, enhancing the limits of vehicular control. For an EV equipped with four in-wheel electric motors, an advanced control may be envisaged. Taking advantage of the fast and accurate torque of in-wheel electric motors which is directly transmitted to the wheels, a new approach for longitudinal control realized by ASR and ABS is presented in this paper. In order to achieve a high-performance torque control for EVs, the NMP-DTC strategy is proposed. It uses the fuzzy logic control technique that determines online the accurate values of the weighting factors and generates the optimal switching states that optimize the EV drives’ decision. The simulation results built in Matlab/Simulink indicate that the EV can achieve high-performance vehicle longitudinal stability control.

  12. Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

    Science.gov (United States)

    Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

    1997-01-01

    One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

  13. Human Gut Microbiota Predicts Susceptibility to Vibrio cholerae Infection.

    Science.gov (United States)

    Midani, Firas S; Weil, Ana A; Chowdhury, Fahima; Begum, Yasmin A; Khan, Ashraful I; Debela, Meti D; Durand, Heather K; Reese, Aspen T; Nimmagadda, Sai N; Silverman, Justin D; Ellis, Crystal N; Ryan, Edward T; Calderwood, Stephen B; Harris, Jason B; Qadri, Firdausi; David, Lawrence A; LaRocque, Regina C

    2018-04-12

    Cholera is a public health problem worldwide and the risk factors for infection are only partially understood. We prospectively studied household contacts of cholera patients to compare those who were infected with those who were not. We constructed predictive machine learning models of susceptibility using baseline gut microbiota data. We identified bacterial taxa associated with susceptibility to Vibrio cholerae infection and tested these taxa for interactions with V. cholerae in vitro. We found that machine learning models based on gut microbiota predicted V. cholerae infection as well as models based on known clinical and epidemiological risk factors. A 'predictive gut microbiota' of roughly 100 bacterial taxa discriminated between contacts who developed infection and those who did not. Susceptibility to cholera was associated with depleted levels of microbes from the phylum Bacteroidetes. By contrast, a microbe associated with cholera by our modeling framework, Paracoccus aminovorans, promoted the in vitro growth of V. cholerae. Gut microbiota structure, clinical outcome, and age were also linked. These findings support the hypothesis that abnormal gut microbial communities are a host factor related to V. cholerae susceptibility.

  14. A human capital predictive model for agent performance in contact centres

    Directory of Open Access Journals (Sweden)

    Chris Jacobs

    2011-10-01

    Research purpose: The primary focus of this article was to develop a theoretically derived human capital predictive model for agent performance in contact centres and Business Process Outsourcing (BPO based on a review of current empirical research literature. Motivation for the study: The study was motivated by the need for a human capital predictive model that can predict agent and overall business performance. Research design: A nonempirical (theoretical research paradigm was adopted for this study and more specifically a theory or model-building approach was followed. A systematic review of published empirical research articles (for the period 2000–2009 in scholarly search portals was performed. Main findings: Eight building blocks of the human capital predictive model for agent performance in contact centres were identified. Forty-two of the human capital contact centre related articles are detailed in this study. Key empirical findings suggest that person– environment fit, job demands-resources, human resources management practices, engagement, agent well-being, agent competence; turnover intention; and agent performance are related to contact centre performance. Practical/managerial implications: The human capital predictive model serves as an operational management model that has performance implications for agents and ultimately influences the contact centre’s overall business performance. Contribution/value-add: This research can contribute to the fields of human resource management (HRM, human capital and performance management within the contact centre and BPO environment.

  15. Human error mode identification for NPP main control room operations using soft controls

    International Nuclear Information System (INIS)

    Lee, Seung Jun; Kim, Jaewhan; Jang, Seung-Cheol

    2011-01-01

    The operation environment of main control rooms (MCRs) in modern nuclear power plants (NPPs) has considerably changed over the years. Advanced MCRs, which have been designed by adapting digital and computer technologies, have simpler interfaces using large display panels, computerized displays, soft controls, computerized procedure systems, and so on. The actions for the NPP operations are performed using soft controls in advanced MCRs. Soft controls have different features from conventional controls. Operators need to navigate the screens to find indicators and controls and manipulate controls using a mouse, touch screens, and so on. Due to these different interfaces, different human errors should be considered in the human reliability analysis (HRA) for advanced MCRs. In this work, human errors that could occur during operation executions using soft controls were analyzed. This work classified the human errors in soft controls into six types, and the reasons that affect the occurrence of the human errors were also analyzed. (author)

  16. A predictive control framework for torque-based steering assistance to improve safety in highway driving

    Science.gov (United States)

    Ercan, Ziya; Carvalho, Ashwin; Tseng, H. Eric; Gökaşan, Metin; Borrelli, Francesco

    2018-05-01

    Haptic shared control framework opens up new perspectives on the design and implementation of the driver steering assistance systems which provide torque feedback to the driver in order to improve safety. While designing such a system, it is important to account for the human-machine interactions since the driver feels the feedback torque through the hand wheel. The controller should consider the driver's impact on the steering dynamics to achieve a better performance in terms of driver's acceptance and comfort. In this paper we present a predictive control framework which uses a model of driver-in-the-loop steering dynamics to optimise the torque intervention with respect to the driver's neuromuscular response. We first validate the system in simulations to compare the performance of the controller in nominal and model mismatch cases. Then we implement the controller in a test vehicle and perform experiments with a human driver. The results show the effectiveness of the proposed system in avoiding hazardous situations under different driver behaviours.

  17. Predictive powertrain control using powertrain history and GPS data

    Science.gov (United States)

    Weslati, Feisel; Krupadanam, Ashish A

    2015-03-03

    A method and powertrain apparatus that predicts a route of travel for a vehicle and uses historical powertrain loads and speeds for the predicted route of travel to optimize at least one powertrain operation for the vehicle.

  18. Building Predictive Human Performance Models of Skill Acquisition in a Data Entry Task

    National Research Council Canada - National Science Library

    Fu, Wai-Tat; Gonzalez, Cleotilde; Healy, Alice F; Kole, James A; Bourne, Jr., Lyle E

    2006-01-01

    .... Since data entry is a central component in most human-machine interaction, a predictive model of performance will provide useful information that informs interface design and effectiveness of training...

  19. Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives.

    Science.gov (United States)

    Nath, Abhigyan; Kumari, Priyanka; Chaube, Radha

    2018-01-01

    Identification of drug targets and drug target interactions are important steps in the drug-discovery pipeline. Successful computational prediction methods can reduce the cost and time demanded by the experimental methods. Knowledge of putative drug targets and their interactions can be very useful for drug repurposing. Supervised machine learning methods have been very useful in drug target prediction and in prediction of drug target interactions. Here, we describe the details for developing prediction models using supervised learning techniques for human drug target prediction and their interactions.

  20. Regional decadal predictions of coupled climate-human systems

    Science.gov (United States)

    Curchitser, E. N.; Lawrence, P.; Felder, F.; Large, W.; Bacmeister, J. T.; Andrews, C.; Kopp, R. E.

    2016-12-01

    We present results from a project to develop a framework for investigating the interactions between human activity and the climate system using state-of-the-art multi-scale, climate and economic models. The model is applied to the highly industrialized and urbanized coastal region of the northeast US with an emphasis on New Jersey. The framework is developed around the NCAR Community Earth System Model (CESM). The CESM model capabilities are augmented with enhanced resolution of the atmosphere (25 km), land surface (I km) and ocean models (7 km) in our region of interest. To the climate model, we couple human activity models for the utility sector and a 300-equation econometric model with sectorial details of an input-output model for the New Jersey economy. We will present results to date showing the potential impact of climate change on electricity markets on its consequences on economic activity in the region.

  1. Human demography and reserve size predict wildlife extinction in West Africa.

    OpenAIRE

    Brashares, J S; Arcese, P; Sam, M K

    2001-01-01

    Species-area models have become the primary tool used to predict baseline extinction rates for species in isolated habitats, and have influenced conservation and land-use planning worldwide. In particular, these models have been used to predict extinction rates following the loss or fragmentation of natural habitats in the absence of direct human influence on species persistence. Thus, where direct human influences, such as hunting, put added pressure on species in remnant habitat patches, we...

  2. Ventromedial Frontal Cortex Is Critical for Guiding Attention to Reward-Predictive Visual Features in Humans.

    Science.gov (United States)

    Vaidya, Avinash R; Fellows, Lesley K

    2015-09-16

    Adaptively interacting with our environment requires extracting information that will allow us to successfully predict reward. This can be a challenge, particularly when there are many candidate cues, and when rewards are probabilistic. Recent work has demonstrated that visual attention is allocated to stimulus features that have been associated with reward on previous trials. The ventromedial frontal lobe (VMF) has been implicated in learning in dynamic environments of this kind, but the mechanism by which this region influences this process is not clear. Here, we hypothesized that the VMF plays a critical role in guiding attention to reward-predictive stimulus features based on feedback. We tested the effects of VMF damage in human subjects on a visual search task in which subjects were primed to attend to task-irrelevant colors associated with different levels of reward, incidental to the search task. Consistent with previous work, we found that distractors had a greater influence on reaction time when they appeared in colors associated with high reward in the previous trial compared with colors associated with low reward in healthy control subjects and patients with prefrontal damage sparing the VMF. However, this reward modulation of attentional priming was absent in patients with VMF damage. Thus, an intact VMF is necessary for directing attention based on experience with cue-reward associations. We suggest that this region plays a role in selecting reward-predictive cues to facilitate future learning. There has been a swell of interest recently in the ventromedial frontal cortex (VMF), a brain region critical to associative learning. However, the underlying mechanism by which this region guides learning is not well understood. Here, we tested the effects of damage to this region in humans on a task in which rewards were linked incidentally to visual features, resulting in trial-by-trial attentional priming. Controls and subjects with prefrontal damage

  3. Are animal models predictive for human postmortem muscle protein degradation?

    Science.gov (United States)

    Ehrenfellner, Bianca; Zissler, Angela; Steinbacher, Peter; Monticelli, Fabio C; Pittner, Stefan

    2017-11-01

    A most precise determination of the postmortem interval (PMI) is a crucial aspect in forensic casework. Although there are diverse approaches available to date, the high heterogeneity of cases together with the respective postmortal changes often limit the validity and sufficiency of many methods. Recently, a novel approach for time since death estimation by the analysis of postmortal changes of muscle proteins was proposed. It is however necessary to improve the reliability and accuracy, especially by analysis of possible influencing factors on protein degradation. This is ideally investigated on standardized animal models that, however, require legitimization by a comparison of human and animal tissue, and in this specific case of protein degradation profiles. Only if protein degradation events occur in comparable fashion within different species, respective findings can sufficiently be transferred from the animal model to application in humans. Therefor samples from two frequently used animal models (mouse and pig), as well as forensic cases with representative protein profiles of highly differing PMIs were analyzed. Despite physical and physiological differences between species, western blot analysis revealed similar patterns in most of the investigated proteins. Even most degradation events occurred in comparable fashion. In some other aspects, however, human and animal profiles depicted distinct differences. The results of this experimental series clearly indicate the huge importance of comparative studies, whenever animal models are considered. Although animal models could be shown to reflect the basic principles of protein degradation processes in humans, we also gained insight in the difficulties and limitations of the applicability of the developed methodology in different mammalian species regarding protein specificity and methodic functionality.

  4. How to support action prediction: Evidence from human coordination tasks

    DEFF Research Database (Denmark)

    Vesper, Cordula

    2014-01-01

    When two or more people perform actions together such as shaking hands, playing ensemble music or carrying an object together, they often naturally adjust the spatial and temporal parameters of their movements to facilitate smooth task performance. This paper reviews recent findings from experime......”) might be a useful approach also for robotic systems to assist human users, thereby reducing cognitive load and flexibly supporting the acquisition of new skills....

  5. Human reliability: an evaluation of its understanding and prediction

    International Nuclear Information System (INIS)

    Joksimovich, V.

    1987-01-01

    This paper presents a viewpoint on the state-of-the-art in human reliability. The bases for this viewpoint are, by and large, research projects conducted by the NUS for the Electric Power Research Institute (EPRI) primarily with the objective of further enhancing the credibility of PRA methodology. The presentation is divided into the following key sections: Background and Overview, Methodology and Data Base with emphasis on the simulator data base

  6. Human as the chief controller in the complex system

    International Nuclear Information System (INIS)

    Jung, Yeonsub

    2012-01-01

    Due to accuracy of measurement and improvement of control logic, human beings are freed from time consuming and repeated task. When there are situations where the control logic cannot calculate the next state of system, human beings interrupt the system and steer the system manually. The most scope of human factors is focused on this interruption, and economists are concern how to present information cognitively and reliably. Fukushima nuclear accident has considered the role of human beings again. Human beings are forced to do something without proper knowledge, procedure, and process information. Thus post Fukushima actions should include how for human beings to be trained and how to get real time information. Finally because safety culture can determine behaviors of human beings, the method to cultivate safety culture should be considered

  7. Predicting the Motions and Forces of Wearable Robotic Systems Using Optimal Control

    Directory of Open Access Journals (Sweden)

    Matthew Millard

    2017-08-01

    Full Text Available Wearable robotic systems are being developed to prevent injury to the low back. Designing a wearable robotic system is challenging because it is difficult to predict how the exoskeleton will affect the movement of the wearer. To aid the design of exoskeletons, we formulate and numerically solve an optimal control problem (OCP to predict the movements and forces of a person as they lift a 15 kg box from the ground both without (human-only OCP and with (with-exo OCP the aid of an exoskeleton. We model the human body as a sagittal-plane multibody system that is actuated by agonist and antagonist pairs of muscle torque generators (MTGs at each joint. Using the literature as a guide, we have derived a set of MTGs that capture the active torque–angle, passive torque–angle, and torque–velocity characteristics of the flexor and extensor groups surrounding the hip, knee, ankle, lumbar spine, shoulder, elbow, and wrist. Uniquely, these MTGs are continuous to the second derivative and so are compatible with gradient-based optimization. The exoskeleton is modeled as a rigid-body mechanism that is actuated by a motor at the hip and the lumbar spine and is coupled to the wearer through kinematic constraints. We evaluate our results by comparing our predictions with experimental recordings of a human subject. Our results indicate that the predicted peak lumbar-flexion angles and extension torques of the human-only OCP are within the range reported in the literature. The results of the with-exo OCP indicate that the exoskeleton motors should provide relatively little support during the descent to the box but apply a substantial amount of support during the ascent phase. The support provided by the lumbar motor is similar in shape to the net moment generated at the L5/S1 joint by the body; however, the support of the hip motor is more complex because it is coupled to the passive forces that are being generated by the hip extensors of the human subject

  8. Autonomous formation flight of helicopters: Model predictive control approach

    Science.gov (United States)

    Chung, Hoam

    Formation flight is the primary movement technique for teams of helicopters. However, the potential for accidents is greatly increased when helicopter teams are required to fly in tight formations and under harsh conditions. This dissertation proposes that the automation of helicopter formations is a realistic solution capable of alleviating risks. Helicopter formation flight operations in battlefield situations are highly dynamic and dangerous, and, therefore, we maintain that both a high-level formation management system and a distributed coordinated control algorithm should be implemented to help ensure safe formations. The starting point for safe autonomous formation flights is to design a distributed control law attenuating external disturbances coming into a formation, so that each vehicle can safely maintain sufficient clearance between it and all other vehicles. While conventional methods are limited to homogeneous formations, our decentralized model predictive control (MPC) approach allows for heterogeneity in a formation. In order to avoid the conservative nature inherent in distributed MPC algorithms, we begin by designing a stable MPC for individual vehicles, and then introducing carefully designed inter-agent coupling terms in a performance index. Thus the proposed algorithm works in a decentralized manner, and can be applied to the problem of helicopter formations comprised of heterogenous vehicles. Individual vehicles in a team may be confronted by various emerging situations that will require the capability for in-flight reconfiguration. We propose the concept of a formation manager to manage separation, join, and synchronization of flight course changes. The formation manager accepts an operator's commands, information from neighboring vehicles, and its own vehicle states. Inside the formation manager, there are multiple modes and complex mode switchings represented as a finite state machine (FSM). Based on the current mode and collected

  9. Predicting human age using regional morphometry and inter-regional morphological similarity

    Science.gov (United States)

    Wang, Xun-Heng; Li, Lihua

    2016-03-01

    The goal of this study is predicting human age using neuro-metrics derived from structural MRI, as well as investigating the relationships between age and predictive neuro-metrics. To this end, a cohort of healthy subjects were recruited from 1000 Functional Connectomes Project. The ages of the participations were ranging from 7 to 83 (36.17+/-20.46). The structural MRI for each subject was preprocessed using FreeSurfer, resulting in regional cortical thickness, mean curvature, regional volume and regional surface area for 148 anatomical parcellations. The individual age was predicted from the combination of regional and inter-regional neuro-metrics. The prediction accuracy is r = 0.835, p Pearson correlation coefficient between predicted ages and actual ages. Moreover, the LASSO linear regression also found certain predictive features, most of which were inter-regional features. The turning-point of the developmental trajectories in human brain was around 40 years old based on regional cortical thickness. In conclusion, structural MRI could be potential biomarkers for the aging in human brain. The human age could be successfully predicted from the combination of regional morphometry and inter-regional morphological similarity. The inter-regional measures could be beneficial to investigating human brain connectome.

  10. Modeling Human Error Mechanism for Soft Control in Advanced Control Rooms (ACRs)

    International Nuclear Information System (INIS)

    Aljneibi, Hanan Salah Ali; Ha, Jun Su; Kang, Seongkeun; Seong, Poong Hyun

    2015-01-01

    To achieve the switch from conventional analog-based design to digital design in ACRs, a large number of manual operating controls and switches have to be replaced by a few common multi-function devices which is called soft control system. The soft controls in APR-1400 ACRs are classified into safety-grade and non-safety-grade soft controls; each was designed using different and independent input devices in ACRs. The operations using soft controls require operators to perform new tasks which were not necessary in conventional controls such as navigating computerized displays to monitor plant information and control devices. These kinds of computerized displays and soft controls may make operations more convenient but they might cause new types of human error. In this study the human error mechanism during the soft controls is studied and modeled to be used for analysis and enhancement of human performance (or human errors) during NPP operation. The developed model would contribute to a lot of applications to improve human performance (or reduce human errors), HMI designs, and operators' training program in ACRs. The developed model of human error mechanism for the soft control is based on assumptions that a human operator has certain amount of capacity in cognitive resources and if resources required by operating tasks are greater than resources invested by the operator, human error (or poor human performance) is likely to occur (especially in 'slip'); good HMI (Human-machine Interface) design decreases the required resources; operator's skillfulness decreases the required resources; and high vigilance increases the invested resources. In this study the human error mechanism during the soft controls is studied and modeled to be used for analysis and enhancement of human performance (or reduction of human errors) during NPP operation

  11. Modeling Human Error Mechanism for Soft Control in Advanced Control Rooms (ACRs)

    Energy Technology Data Exchange (ETDEWEB)

    Aljneibi, Hanan Salah Ali [Khalifa Univ., Abu Dhabi (United Arab Emirates); Ha, Jun Su; Kang, Seongkeun; Seong, Poong Hyun [KAIST, Daejeon (Korea, Republic of)

    2015-10-15

    To achieve the switch from conventional analog-based design to digital design in ACRs, a large number of manual operating controls and switches have to be replaced by a few common multi-function devices which is called soft control system. The soft controls in APR-1400 ACRs are classified into safety-grade and non-safety-grade soft controls; each was designed using different and independent input devices in ACRs. The operations using soft controls require operators to perform new tasks which were not necessary in conventional controls such as navigating computerized displays to monitor plant information and control devices. These kinds of computerized displays and soft controls may make operations more convenient but they might cause new types of human error. In this study the human error mechanism during the soft controls is studied and modeled to be used for analysis and enhancement of human performance (or human errors) during NPP operation. The developed model would contribute to a lot of applications to improve human performance (or reduce human errors), HMI designs, and operators' training program in ACRs. The developed model of human error mechanism for the soft control is based on assumptions that a human operator has certain amount of capacity in cognitive resources and if resources required by operating tasks are greater than resources invested by the operator, human error (or poor human performance) is likely to occur (especially in 'slip'); good HMI (Human-machine Interface) design decreases the required resources; operator's skillfulness decreases the required resources; and high vigilance increases the invested resources. In this study the human error mechanism during the soft controls is studied and modeled to be used for analysis and enhancement of human performance (or reduction of human errors) during NPP operation.

  12. Human Information Processing and Supervisory Control.

    Science.gov (United States)

    1980-05-01

    errors (that is of the output of the human operator). There is growing evidence (Senders, personal communication; Norman , personal communication...relates to the relative tendency to depend on sensory information or to be more analytic and independent. Norman (personal communication) has referred...decision process model. Ergonomics, 12, 543-557. Senders, J., Elkid, J., Grignetti, M., & Smallwood , R. 1966. An investigation of the visual sampling

  13. Hybrid model predictive control applied to switching control of burner load for a compact marine boiler design

    DEFF Research Database (Denmark)

    Solberg, Brian; Andersen, Palle; Maciejowski, Jan

    2008-01-01

    This paper discusses the application of hybrid model predictive control to control switching between different burner modes in a novel compact marine boiler design. A further purpose of the present work is to point out problems with finite horizon model predictive control applied to systems for w...

  14. Using human genetics to predict the effects and side-effects of drugs

    DEFF Research Database (Denmark)

    Stender, Stefan; Tybjærg-Hansen, Anne

    2016-01-01

    PURPOSE OF REVIEW: 'Genetic proxies' are increasingly being used to predict the effects of drugs. We present an up-to-date overview of the use of human genetics to predict effects and adverse effects of lipid-targeting drugs. RECENT FINDINGS: LDL cholesterol lowering variants in HMG-Coenzyme A re...

  15. Computational Model-Based Prediction of Human Episodic Memory Performance Based on Eye Movements

    Science.gov (United States)

    Sato, Naoyuki; Yamaguchi, Yoko

    Subjects' episodic memory performance is not simply reflected by eye movements. We use a ‘theta phase coding’ model of the hippocampus to predict subjects' memory performance from their eye movements. Results demonstrate the ability of the model to predict subjects' memory performance. These studies provide a novel approach to computational modeling in the human-machine interface.

  16. An estimator-based distributed voltage-predictive control strategy for ac islanded microgrids

    DEFF Research Database (Denmark)

    Wang, Yanbo; Chen, Zhe; Wang, Xiongfei

    2015-01-01

    This paper presents an estimator-based voltage predictive control strategy for AC islanded microgrids, which is able to perform voltage control without any communication facilities. The proposed control strategy is composed of a network voltage estimator and a voltage predictive controller for each...... and has a good capability to reject uncertain perturbations of islanded microgrids....

  17. Predicting Genes Involved in Human Cancer Using Network Contextual Information

    Directory of Open Access Journals (Sweden)

    Rahmani Hossein

    2012-03-01

    Full Text Available Protein-Protein Interaction (PPI networks have been widely used for the task of predicting proteins involved in cancer. Previous research has shown that functional information about the protein for which a prediction is made, proximity to specific other proteins in the PPI network, as well as local network structure are informative features in this respect. In this work, we introduce two new types of input features, reflecting additional information: (1 Functional Context: the functions of proteins interacting with the target protein (rather than the protein itself; and (2 Structural Context: the relative position of the target protein with respect to specific other proteins selected according to a novel ANOVA (analysis of variance based measure. We also introduce a selection strategy to pinpoint the most informative features. Results show that the proposed feature types and feature selection strategy yield informative features. A standard machine learning method (Naive Bayes that uses the features proposed here outperforms the current state-of-the-art methods by more than 5% with respect to F-measure. In addition, manual inspection confirms the biological relevance of the top-ranked features.

  18. A robust model predictive control strategy for improving the control performance of air-conditioning systems

    International Nuclear Information System (INIS)

    Huang Gongsheng; Wang Shengwei; Xu Xinhua

    2009-01-01

    This paper presents a robust model predictive control strategy for improving the supply air temperature control of air-handling units by dealing with the associated uncertainties and constraints directly. This strategy uses a first-order plus time-delay model with uncertain time-delay and system gain to describe air-conditioning process of an air-handling unit usually operating at various weather conditions. The uncertainties of the time-delay and system gain, which imply the nonlinearities and the variable dynamic characteristics, are formulated using an uncertainty polytope. Based on this uncertainty formulation, an offline LMI-based robust model predictive control algorithm is employed to design a robust controller for air-handling units which can guarantee a good robustness subject to uncertainties and constraints. The proposed robust strategy is evaluated in a dynamic simulation environment of a variable air volume air-conditioning system in various operation conditions by comparing with a conventional PI control strategy. The robustness analysis of both strategies under different weather conditions is also presented.

  19. Automatic Power Control for Daily Load-following Operation using Model Predictive Control Method

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Keuk Jong; Kim, Han Gon [KH, Daejeon (Korea, Republic of)

    2009-10-15

    Under the circumstances that nuclear power occupies more than 50%, nuclear power plants are required to be operated on load-following operation in order to make the effective management of electric grid system and enhanced responsiveness to rapid changes in power demand. Conventional reactors such as the OPR1000 and APR1400 have a regulating system that controls the average temperature of the reactor core relation to the reference temperature. This conventional method has the advantages of proven technology and ease of implementation. However, this method is unsuitable for controlling the axial power shape, particularly the load following operation. Accordingly, this paper reports on the development of a model predictive control method which is able to control the reactor power and the axial shape index. The purpose of this study is to analyze the behavior of nuclear reactor power and the axial power shape by using a model predictive control method when the power is increased and decreased for a daily load following operation. The study confirms that deviations in the axial shape index (ASI) are within the operating limit.

  20. Visual servo control for a human-following robot

    CSIR Research Space (South Africa)

    Burke, Michael G

    2011-03-01

    Full Text Available This thesis presents work completed on the design of control and vision components for use in a monocular vision-based human-following robot. The use of vision in a controller feedback loop is referred to as vision-based or visual servo control...

  1. Prediction Governors for Input-Affine Nonlinear Systems and Application to Automatic Driving Control

    Directory of Open Access Journals (Sweden)

    Yuki Minami

    2018-04-01

    Full Text Available In recent years, automatic driving control has attracted attention. To achieve a satisfactory driving control performance, the prediction accuracy of the traveling route is important. If a highly accurate prediction method can be used, an accurate traveling route can be obtained. Despite the considerable efforts that have been invested in improving prediction methods, prediction errors do occur in general. Thus, a method to minimize the influence of prediction errors on automatic driving control systems is required. This need motivated us to focus on the design of a mechanism for shaping prediction signals, which is called a prediction governor. In this study, we first extended our previous study to the input-affine nonlinear system case. Then, we analytically derived a solution to an optimal design problem of prediction governors. Finally, we applied the solution to an automatic driving control system, and demonstrated its usefulness through a numerical example and an experiment using a radio controlled car.

  2. Human factors evaluation of the engineering test reactor control room

    International Nuclear Information System (INIS)

    Banks, W.W.; Boone, M.P.

    1981-03-01

    The Reactor and Process Control Rooms at the Engineering Test Reactor were evaluated by a team of human factors engineers using available human factors design criteria. During the evaluation, ETR, equipment and facilities were compared with MIL-STD-1472-B, Human Engineering design Criteria for Military Systems. The focus of recommendations centered on: (a) displays and controls; placing displays and controls in functional groups; (b) establishing a consistent color coding (in compliance with a standard if possible); (c) systematizing annunciator alarms and reducing their number; (d) organizing equipment in functional groups; and (e) modifying labeling and lines of demarcation

  3. Statistical Models for Predicting Threat Detection From Human Behavior

    Science.gov (United States)

    Kelley, Timothy; Amon, Mary J.; Bertenthal, Bennett I.

    2018-01-01

    Users must regularly distinguish between secure and insecure cyber platforms in order to preserve their privacy and safety. Mouse tracking is an accessible, high-resolution measure that can be leveraged to understand the dynamics of perception, categorization, and decision-making in threat detection. Researchers have begun to utilize measures like mouse tracking in cyber security research, including in the study of risky online behavior. However, it remains an empirical question to what extent real-time information about user behavior is predictive of user outcomes and demonstrates added value compared to traditional self-report questionnaires. Participants navigated through six simulated websites, which resembled either secure “non-spoof” or insecure “spoof” versions of popular websites. Websites also varied in terms of authentication level (i.e., extended validation, standard validation, or partial encryption). Spoof websites had modified Uniform Resource Locator (URL) and authentication level. Participants chose to “login” to or “back” out of each website based on perceived website security. Mouse tracking information was recorded throughout the task, along with task performance. After completing the website identification task, participants completed a questionnaire assessing their security knowledge and degree of familiarity with the websites simulated during the experiment. Despite being primed to the possibility of website phishing attacks, participants generally showed a bias for logging in to websites versus backing out of potentially dangerous sites. Along these lines, participant ability to identify spoof websites was around the level of chance. Hierarchical Bayesian logistic models were used to compare the accuracy of two-factor (i.e., website security and encryption level), survey-based (i.e., security knowledge and website familiarity), and real-time measures (i.e., mouse tracking) in predicting risky online behavior during phishing

  4. Statistical Models for Predicting Threat Detection From Human Behavior

    Directory of Open Access Journals (Sweden)

    Timothy Kelley

    2018-04-01

    Full Text Available Users must regularly distinguish between secure and insecure cyber platforms in order to preserve their privacy and safety. Mouse tracking is an accessible, high-resolution measure that can be leveraged to understand the dynamics of perception, categorization, and decision-making in threat detection. Researchers have begun to utilize measures like mouse tracking in cyber security research, including in the study of risky online behavior. However, it remains an empirical question to what extent real-time information about user behavior is predictive of user outcomes and demonstrates added value compared to traditional self-report questionnaires. Participants navigated through six simulated websites, which resembled either secure “non-spoof” or insecure “spoof” versions of popular websites. Websites also varied in terms of authentication level (i.e., extended validation, standard validation, or partial encryption. Spoof websites had modified Uniform Resource Locator (URL and authentication level. Participants chose to “login” to or “back” out of each website based on perceived website security. Mouse tracking information was recorded throughout the task, along with task performance. After completing the website identification task, participants completed a questionnaire assessing their security knowledge and degree of familiarity with the websites simulated during the experiment. Despite being primed to the possibility of website phishing attacks, participants generally showed a bias for logging in to websites versus backing out of potentially dangerous sites. Along these lines, participant ability to identify spoof websites was around the level of chance. Hierarchical Bayesian logistic models were used to compare the accuracy of two-factor (i.e., website security and encryption level, survey-based (i.e., security knowledge and website familiarity, and real-time measures (i.e., mouse tracking in predicting risky online behavior

  5. Statistical Models for Predicting Threat Detection From Human Behavior.

    Science.gov (United States)

    Kelley, Timothy; Amon, Mary J; Bertenthal, Bennett I

    2018-01-01

    Users must regularly distinguish between secure and insecure cyber platforms in order to preserve their privacy and safety. Mouse tracking is an accessible, high-resolution measure that can be leveraged to understand the dynamics of perception, categorization, and decision-making in threat detection. Researchers have begun to utilize measures like mouse tracking in cyber security research, including in the study of risky online behavior. However, it remains an empirical question to what extent real-time information about user behavior is predictive of user outcomes and demonstrates added value compared to traditional self-report questionnaires. Participants navigated through six simulated websites, which resembled either secure "non-spoof" or insecure "spoof" versions of popular websites. Websites also varied in terms of authentication level (i.e., extended validation, standard validation, or partial encryption). Spoof websites had modified Uniform Resource Locator (URL) and authentication level. Participants chose to "login" to or "back" out of each website based on perceived website security. Mouse tracking information was recorded throughout the task, along with task performance. After completing the website identification task, participants completed a questionnaire assessing their security knowledge and degree of familiarity with the websites simulated during the experiment. Despite being primed to the possibility of website phishing attacks, participants generally showed a bias for logging in to websites versus backing out of potentially dangerous sites. Along these lines, participant ability to identify spoof websites was around the level of chance. Hierarchical Bayesian logistic models were used to compare the accuracy of two-factor (i.e., website security and encryption level), survey-based (i.e., security knowledge and website familiarity), and real-time measures (i.e., mouse tracking) in predicting risky online behavior during phishing attacks

  6. 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.

  7. Recent advances using rodent models for predicting human allergenicity

    International Nuclear Information System (INIS)

    Knippels, Leon M.J.; Penninks, Andre H.

    2005-01-01

    The potential allergenicity of newly introduced proteins in genetically engineered foods has become an important safety evaluation issue. However, to evaluate the potential allergenicity and the potency of new proteins in our food, there are still no widely accepted and reliable test systems. The best-known allergy assessment proposal for foods derived from genetically engineered plants was the careful stepwise process presented in the so-called ILSI/IFBC decision tree. A revision of this decision tree strategy was proposed by a FAO/WHO expert consultation. As prediction of the sensitizing potential of the novel introduced protein based on animal testing was considered to be very important, animal models were introduced as one of the new test items, despite the fact that non of the currently studied models has been widely accepted and validated yet. In this paper, recent results are summarized of promising models developed in rat and mouse

  8. Plant Modeling for Human Supervisory Control

    DEFF Research Database (Denmark)

    Lind, Morten

    1999-01-01

    This paper provides an overview of multilevel flow modelling (MFM) and its application for design of displays for the supervisory control of industrial plant. The problem of designing the inforrrzatian content of sacpervisory displays is discussed and plant representations like MFM using levels...

  9. High performance predictive current control of a three phase VSI: An ...

    Indian Academy of Sciences (India)

    ... current control of a three phase VSI: An experimental assessment ... Voltage source inverter; two level inverter; predictive current control; weighting factor ... Conventionally, for reference current tracking control in a two level VSI, the objective ...

  10. How to support action prediction: Evidence from human coordination tasks

    DEFF Research Database (Denmark)

    Vesper, Cordula

    2014-01-01

    When two or more people perform actions together such as shaking hands, playing ensemble music or carrying an object together, they often naturally adjust the spatial and temporal parameters of their movements to facilitate smooth task performance. This paper reviews recent findings from experime......When two or more people perform actions together such as shaking hands, playing ensemble music or carrying an object together, they often naturally adjust the spatial and temporal parameters of their movements to facilitate smooth task performance. This paper reviews recent findings from......”) might be a useful approach also for robotic systems to assist human users, thereby reducing cognitive load and flexibly supporting the acquisition of new skills....

  11. Performance and robustness of hybrid model predictive control for controllable dampers in building models

    Science.gov (United States)

    Johnson, Erik A.; Elhaddad, Wael M.; Wojtkiewicz, Steven F.

    2016-04-01

    A variety of strategies have been developed over the past few decades to determine controllable damping device forces to mitigate the response of structures and mechanical systems to natural hazards and other excitations. These "smart" damping devices produce forces through passive means but have properties that can be controlled in real time, based on sensor measurements of response across the structure, to dramatically reduce structural motion by exploiting more than the local "information" that is available to purely passive devices. A common strategy is to design optimal damping forces using active control approaches and then try to reproduce those forces with the smart damper. However, these design forces, for some structures and performance objectives, may achieve high performance by selectively adding energy, which cannot be replicated by a controllable damping device, causing the smart damper performance to fall far short of what an active system would provide. The authors have recently demonstrated that a model predictive control strategy using hybrid system models, which utilize both continuous and binary states (the latter to capture the switching behavior between dissipative and non-dissipative forces), can provide reductions in structural response on the order of 50% relative to the conventional clipped-optimal design strategy. This paper explores the robustness of this newly proposed control strategy through evaluating controllable damper performance when the structure model differs from the nominal one used to design the damping strategy. Results from the application to a two-degree-of-freedom structure model confirms the robustness of the proposed strategy.

  12. Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor

    DEFF Research Database (Denmark)

    de los Campos, Gustavo; Vazquez, Ana I; Fernando, Rohan

    2013-01-01

    Despite important advances from Genome Wide Association Studies (GWAS), for most complex human traits and diseases, a sizable proportion of genetic variance remains unexplained and prediction accuracy (PA) is usually low. Evidence suggests that PA can be improved using Whole-Genome Regression (WGR......) models where phenotypes are regressed on hundreds of thousands of variants simultaneously. The Genomic Best Linear Unbiased Prediction G-BLUP, a ridge-regression type method) is a commonly used WGR method and has shown good predictive performance when applied to plant and animal breeding populations....... However, breeding and human populations differ greatly in a number of factors that can affect the predictive performance of G-BLUP. Using theory, simulations, and real data analysis, we study the erformance of G-BLUP when applied to data from related and unrelated human subjects. Under perfect linkage...

  13. Human Papilloma Virus Vaccination for Control of Cervical Cancer ...

    African Journals Online (AJOL)

    Human Papilloma Virus Vaccination for Control of Cervical Cancer: A ... Primary HPV prevention may be the key to reducing incidence and burden of cervical cancer ... Other resources included locally-published articles and additional internet ...

  14. Taking account of human factors in control-room design

    International Nuclear Information System (INIS)

    Dien, Y.; Montmayeul, R.

    1995-07-01

    Since the Three Mile Island accident two ways for improving the Human-Machine Interface have mainly been followed: the development of computerized operator aids in existing control-rooms and the design of advanced control-rooms. Insufficient attention paid to human factors in the design of operator aids has generally led to these aids being neglected or unused by their potential users. While for the design of advanced control-rooms efforts have been made for dealing with human factors in more extensive way. Based upon this experience, a general method for taking account of human factors in a control-room design has been devised and is described in this paper. (author)

  15. Consolidated Human Activity Database (CHAD) for use in human exposure and health studies and predictive models

    Science.gov (United States)

    EPA scientists have compiled detailed data on human behavior from 22 separate exposure and time-use studies into CHAD. The database includes more than 54,000 individual study days of detailed human behavior.

  16. Mechanics of human voice production and control.

    Science.gov (United States)

    Zhang, Zhaoyan

    2016-10-01

    As the primary means of communication, voice plays an important role in daily life. Voice also conveys personal information such as social status, personal traits, and the emotional state of the speaker. Mechanically, voice production involves complex fluid-structure interaction within the glottis and its control by laryngeal muscle activation. An important goal of voice research is to establish a causal theory linking voice physiology and biomechanics to how speakers use and control voice to communicate meaning and personal information. Establishing such a causal theory has important implications for clinical voice management, voice training, and many speech technology applications. This paper provides a review of voice physiology and biomechanics, the physics of vocal fold vibration and sound production, and laryngeal muscular control of the fundamental frequency of voice, vocal intensity, and voice quality. Current efforts to develop mechanical and computational models of voice production are also critically reviewed. Finally, issues and future challenges in developing a causal theory of voice production and perception are discussed.

  17. Human factors methods for nuclear control room design. Volume 2. Human factors survey of control room design practices

    International Nuclear Information System (INIS)

    Seminara, J.L.; Parsons, S.O.

    1979-11-01

    An earlier review of the control rooms of operating nuclear power plants identified many design problems having potential for degrading operator performance. As a result, the formal application of human factors principles was found to be needed. This report demonstrates the use of human factors in the design of power plant control rooms. The approaches shown in the report can be applied to operating power plants, as well as to those in the design stage. This study documents human factors techniques required to provide a sustained concern for the man-machine interface from control room concept definition to system implementation

  18. Safety Metrics for Human-Computer Controlled Systems

    Science.gov (United States)

    Leveson, Nancy G; Hatanaka, Iwao

    2000-01-01

    The rapid growth of computer technology and innovation has played a significant role in the rise of computer automation of human tasks in modem production systems across all industries. Although the rationale for automation has been to eliminate "human error" or to relieve humans from manual repetitive tasks, various computer-related hazards and accidents have emerged as a direct result of increased system complexity attributed to computer automation. The risk assessment techniques utilized for electromechanical systems are not suitable for today's software-intensive systems or complex human-computer controlled systems.This thesis will propose a new systemic model-based framework for analyzing risk in safety-critical systems where both computers and humans are controlling safety-critical functions. A new systems accident model will be developed based upon modem systems theory and human cognitive processes to better characterize system accidents, the role of human operators, and the influence of software in its direct control of significant system functions Better risk assessments will then be achievable through the application of this new framework to complex human-computer controlled systems.

  19. Modular control of limb movements during human locomotion

    NARCIS (Netherlands)

    Ivanenko, Yuri P; Cappellini, Germana; Dominici, Nadia; Poppele, Richard E; Lacquaniti, Francesco

    2007-01-01

    The idea that the CNS may control complex interactions by modular decomposition has received considerable attention. We explored this idea for human locomotion by examining limb kinematics. The coordination of limb segments during human locomotion has been shown to follow a planar law for walking at

  20. Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods.

    Science.gov (United States)

    Notaro, Marco; Schubach, Max; Robinson, Peter N; Valentini, Giorgio

    2017-10-12

    The prediction of human gene-abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this context the Human Phenotype Ontology (HPO) provides a standard categorization of the abnormalities associated with human diseases. While the problem of the prediction of gene-disease associations has been widely investigated, the related problem of gene-phenotypic feature (i.e., HPO term) associations has been largely overlooked, even if for most human genes no HPO term associations are known and despite the increasing application of the HPO to relevant medical problems. Moreover most of the methods proposed in literature are not able to capture the hierarchical relationships between HPO terms, thus resulting in inconsistent and relatively inaccurate predictions. We present two hierarchical ensemble methods that we formally prove to provide biologically consistent predictions according to the hierarchical structure of the HPO. The modular structure of the proposed methods, that consists in a "flat" learning first step and a hierarchical combination of the predictions in the second step, allows the predictions of virtually any flat learning method to be enhanced. The experimental results show that hierarchical ensemble methods are able to predict novel associations between genes and abnormal phenotypes with results that are competitive with state-of-the-art algorithms and with a significant reduction of the computational complexity. Hierarchical ensembles are efficient computational methods that guarantee biologically meaningful predictions that obey the true path rule, and can be used as a tool to improve and make consistent the HPO terms predictions starting from virtually any flat learning method. The implementation of the proposed methods is available as an R package from the CRAN repository.

  1. DRD4 genotype predicts longevity in mouse and human.

    Science.gov (United States)

    Grady, Deborah L; Thanos, Panayotis K; Corrada, Maria M; Barnett, Jeffrey C; Ciobanu, Valentina; Shustarovich, Diana; Napoli, Anthony; Moyzis, Alexandra G; Grandy, David; Rubinstein, Marcelo; Wang, Gene-Jack; Kawas, Claudia H; Chen, Chuansheng; Dong, Qi; Wang, Eric; Volkow, Nora D; Moyzis, Robert K

    2013-01-02

    Longevity is influenced by genetic and environmental factors. The brain's dopamine system may be particularly relevant, since it modulates traits (e.g., sensitivity to reward, incentive motivation, sustained effort) that impact behavioral responses to the environment. In particular, the dopamine D4 receptor (DRD4) has been shown to moderate the impact of environments on behavior and health. We tested the hypothesis that the DRD4 gene influences longevity and that its impact is mediated through environmental effects. Surviving participants of a 30-year-old population-based health survey (N = 310; age range, 90-109 years; the 90+ Study) were genotyped/resequenced at the DRD4 gene and compared with a European ancestry-matched younger population (N = 2902; age range, 7-45 years). We found that the oldest-old population had a 66% increase in individuals carrying the DRD4 7R allele relative to the younger sample (p = 3.5 × 10(-9)), and that this genotype was strongly correlated with increased levels of physical activity. Consistent with these results, DRD4 knock-out mice, when compared with wild-type and heterozygous mice, displayed a 7-9.7% decrease in lifespan, reduced spontaneous locomotor activity, and no lifespan increase when reared in an enriched environment. These results support the hypothesis that DRD4 gene variants contribute to longevity in humans and in mice, and suggest that this effect is mediated by shaping behavioral responses to the environment.

  2. A role of the human thalamus in predicting the perceptual consequences of eye movements.

    Science.gov (United States)

    Ostendorf, Florian; Liebermann, Daniela; Ploner, Christoph J

    2013-01-01

    Internal monitoring of oculomotor commands may help to anticipate and keep track of changes in perceptual input imposed by our eye movements. Neurophysiological studies in non-human primates identified corollary discharge (CD) signals of oculomotor commands that are conveyed via thalamus to frontal cortices. We tested whether disruption of these monitoring pathways on the thalamic level impairs the perceptual matching of visual input before and after an eye movement in human subjects. Fourteen patients with focal thalamic stroke and 20 healthy control subjects performed a task requiring a perceptual judgment across eye movements. Subjects reported the apparent displacement of a target cue that jumped unpredictably in sync with a saccadic eye movement. In a critical condition of this task, six patients exhibited clearly asymmetric perceptual performance for rightward vs. leftward saccade direction. Furthermore, perceptual judgments in seven patients systematically depended on oculomotor targeting errors, with self-generated targeting errors erroneously attributed to external stimulus jumps. Voxel-based lesion-symptom mapping identified an area in right central thalamus as critical for the perceptual matching of visual space across eye movements. Our findings suggest that trans-thalamic CD transmission decisively contributes to a correct prediction of the perceptual consequences of oculomotor actions.

  3. A role of the human thalamus in predicting the perceptual consequences of eye movements

    Directory of Open Access Journals (Sweden)

    Florian eOstendorf

    2013-04-01

    Full Text Available Internal monitoring of oculomotor commands may help to anticipate and keep track of changes in perceptual input imposed by our eye movements. Neurophysiological studies in non-human primates identified corollary discharge signals of oculomotor commands that are conveyed via thalamus to frontal cortices. We tested whether disruption of these monitoring pathways on the thalamic level impairs the perceptual matching of visual input before and after an eye movement in human subjects. Fourteen patients with focal thalamic stroke and twenty healthy control subjects performed a task requiring a perceptual judgment across eye movements. Subjects reported the apparent displacement of a target cue that jumped unpredictably in sync with a saccadic eye movement. In a critical condition of this task, six patients exhibited clearly asymmetric perceptual performance for rightward versus leftward saccade direction. Furthermore, perceptual judgments in seven patients systematically depended on oculomotor targeting errors, with self-generated targeting errors erroneously attributed to external stimulus jumps. Voxel-based lesion-symptom mapping identified an area in right central thalamus as critical for the perceptual matching of visual space across eye movements. Our findings suggest that trans-thalamic corollary discharge transmission decisively contributes to a correct prediction of the perceptual consequences of oculomotor actions.

  4. Hippocampal Volume Reduction in Humans Predicts Impaired Allocentric Spatial Memory in Virtual-Reality Navigation.

    Science.gov (United States)

    Guderian, Sebastian; Dzieciol, Anna M; Gadian, David G; Jentschke, Sebastian; Doeller, Christian F; Burgess, Neil; Mishkin, Mortimer; Vargha-Khadem, Faraneh

    2015-10-21

    The extent to which navigational spatial memory depends on hippocampal integrity in humans is not well documented. We investigated allocentric spatial recall using a virtual environment in a group of patients with severe hippocampal damage (SHD), a group of patients with "moderate" hippocampal damage (MHD), and a normal control group. Through four learning blocks with feedback, participants learned the target locations of four different objects in a circular arena. Distal cues were present throughout the experiment to provide orientation. A circular boundary as well as an intra-arena landmark provided spatial reference frames. During a subsequent test phase, recall of all four objects was tested with only the boundary or the landmark being present. Patients with SHD were impaired in both phases of this task. Across groups, performance on both types of spatial recall was highly correlated with memory quotient (MQ), but not with intelligence quotient (IQ), age, or sex. However, both measures of spatial recall separated experimental groups beyond what would be expected based on MQ, a widely used measure of general memory function. Boundary-based and landmark-based spatial recall were both strongly related to bilateral hippocampal volumes, but not to volumes of the thalamus, putamen, pallidum, nucleus accumbens, or caudate nucleus. The results show that boundary-based and landmark-based allocentric spatial recall are similarly impaired in patients with SHD, that both types of recall are impaired beyond that predicted by MQ, and that recall deficits are best explained by a reduction in bilateral hippocampal volumes. In humans, bilateral hippocampal atrophy can lead to profound impairments in episodic memory. Across species, perhaps the most well-established contribution of the hippocampus to memory is not to episodic memory generally but to allocentric spatial memory. However, the extent to which navigational spatial memory depends on hippocampal integrity in humans is

  5. Analysis of explicit model predictive control for path-following control

    Science.gov (United States)

    2018-01-01

    In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can reduce this computational burden using a multi-parametric quadratic programming technique(mp-QP). The control objective is to derive an optimal front steering wheel angle at each sampling time so that autonomous vehicles travel along desired paths, including straight, circular, and clothoid parts, at high entry speeds. In terms of the design of the proposed controller, a method of choosing weighting matrices in an optimization problem and the range of horizons for path-following control are described through simulations. For the verification of the proposed controller, simulation results obtained using other control methods such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed, and CarSim, which reflects the features of a vehicle more realistically than MATLAB/Simulink, is used for reliable demonstration. PMID:29534080

  6. Analysis of explicit model predictive control for path-following control.

    Science.gov (United States)

    Lee, Junho; Chang, Hyuk-Jun

    2018-01-01

    In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can reduce this computational burden using a multi-parametric quadratic programming technique(mp-QP). The control objective is to derive an optimal front steering wheel angle at each sampling time so that autonomous vehicles travel along desired paths, including straight, circular, and clothoid parts, at high entry speeds. In terms of the design of the proposed controller, a method of choosing weighting matrices in an optimization problem and the range of horizons for path-following control are described through simulations. For the verification of the proposed controller, simulation results obtained using other control methods such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed, and CarSim, which reflects the features of a vehicle more realistically than MATLAB/Simulink, is used for reliable demonstration.

  7. Unifying Viral Genetics and Human Transportation Data to Predict the Global Transmission Dynamics of Human Influenza H3N2

    NARCIS (Netherlands)

    P. Lemey (Philippe); A. Rambaut (Andrew); T. Bedford (Trevor); R. Faria (Rui); F. Bielejec (Filip); G. Baele (Guy); C.A. Russell (Colin); D.J. Smith (Derek James); O. Pybus (Oliver); K. Brockmann; M.A. Suchard (Marc)

    2014-01-01

    textabstractInformation on global human movement patterns is central to spatial epidemiological models used to predict the behavior of influenza and other infectious diseases. Yet it remains difficult to test which modes of dispersal drive pathogen spread at various geographic scales using standard

  8. Statistical physics of human beings in games: Controlled experiments

    International Nuclear Information System (INIS)

    Liang Yuan; Huang Ji-Ping

    2014-01-01

    It is important to know whether the laws or phenomena in statistical physics for natural systems with non-adaptive agents still hold for social human systems with adaptive agents, because this implies whether it is possible to study or understand social human systems by using statistical physics originating from natural systems. For this purpose, we review the role of human adaptability in four kinds of specific human behaviors, namely, normal behavior, herd behavior, contrarian behavior, and hedge behavior. The approach is based on controlled experiments in the framework of market-directed resource-allocation games. The role of the controlled experiments could be at least two-fold: adopting the real human decision-making process so that the system under consideration could reflect the performance of genuine human beings; making it possible to obtain macroscopic physical properties of a human system by tuning a particular factor of the system, thus directly revealing cause and effect. As a result, both computer simulations and theoretical analyses help to show a few counterparts of some laws or phenomena in statistical physics for social human systems: two-phase phenomena or phase transitions, entropy-related phenomena, and a non-equilibrium steady state. This review highlights the role of human adaptability in these counterparts, and makes it possible to study or understand some particular social human systems by means of statistical physics coming from natural systems. (topical review - statistical physics and complex systems)

  9. Statistical physics of human beings in games: Controlled experiments

    Science.gov (United States)

    Liang, Yuan; Huang, Ji-Ping

    2014-07-01

    It is important to know whether the laws or phenomena in statistical physics for natural systems with non-adaptive agents still hold for social human systems with adaptive agents, because this implies whether it is possible to study or understand social human systems by using statistical physics originating from natural systems. For this purpose, we review the role of human adaptability in four kinds of specific human behaviors, namely, normal behavior, herd behavior, contrarian behavior, and hedge behavior. The approach is based on controlled experiments in the framework of market-directed resource-allocation games. The role of the controlled experiments could be at least two-fold: adopting the real human decision-making process so that the system under consideration could reflect the performance of genuine human beings; making it possible to obtain macroscopic physical properties of a human system by tuning a particular factor of the system, thus directly revealing cause and effect. As a result, both computer simulations and theoretical analyses help to show a few counterparts of some laws or phenomena in statistical physics for social human systems: two-phase phenomena or phase transitions, entropy-related phenomena, and a non-equilibrium steady state. This review highlights the role of human adaptability in these counterparts, and makes it possible to study or understand some particular social human systems by means of statistical physics coming from natural systems.

  10. Early human pregnancy serum cytokine levels predict autoimmunity in offspring.

    Science.gov (United States)

    Lindehammer, Sabina Resic; Björck, Sara; Lynch, Kristian; Brundin, Charlotte; Marsal, Karel; Agardh, Daniel; Fex, Malin

    2011-09-01

    It is generally believed that pregnancy is mediated by a Th2 response, which includes cytokines that promote placental growth and are involved in inducing tolerance to the foetus. If the balance between Th1/and Th2-mediated cytokines is disrupted, systemic and local changes could predispose the foetus to future disease. Therefore, a shift in the Th1/Th2 balance during pregnancy, possibly caused by underlying environmental factors, could be associated with post-partum autoimmune disease in the offspring. Based on this presumption, we used celiac disease as a model to investigate whether autoimmunity is triggered in the foetus during early pregnancy, observed as changes in the mother's cytokine profile. Ten cytokines were measured by electro-chemi-luminescent multiplex ELISA in serum samples obtained from mothers during early pregnancy. Cases included women with children who had developed verified celiac disease before the age of 5, who were compared with other women as matched controls. We observed that 7 out of 10 cytokine levels were significantly increased in our case mothers when compared to controls. Five of these belonged to what is generally known as a Th1-mediated response (TNFα, IFNγ, IL-2, IL-1β and IL-12) and two were Th2 cytokines (IL-13 and IL-10). However, the IL-10 cytokine is known to have features from both arms of the immune system. These results were confirmed in a logistic regression model where five out of the initial seven cytokines remained. This study suggests that increase in Th1 serum cytokines may be associated with celiac disease in offspring.

  11. Predictive control of irrigation canals – robust design and real-time implementation

    NARCIS (Netherlands)

    Aguilar, José V.; Langarita, Pedro; Rodellar, José; Linares, Lorenzo; Horváth, K.

    2016-01-01

    Predictive control is one of the most commonly used control methods in a variety of application areas, including hydraulic processes such as water distribution canals for irrigation. This article presents the design and application of predictive control for the water discharge entering into an

  12. Efficient predictive model-based and fuzzy control for green urban mobility

    NARCIS (Netherlands)

    Jamshidnejad, A.

    2017-01-01

    In this thesis, we develop efficient predictive model-based control approaches, including model-predictive control (MPC) andmodel-based fuzzy control, for application in urban traffic networks with the aim of reducing a combination of the total time spent by the vehicles within the network and the

  13. Predictive control strategies for an indirect matrix converter operating at fixed switching frequency

    DEFF Research Database (Denmark)

    Rivera, M.; Nasir, U.; Tarisciotti, L.

    2017-01-01

    The classic model predictive control presents a variable switching frequency which could produce high ripple in the controlled waveforms or resonances in the input filter of the matrix converter, affecting the performance of the system. This paper presents two model predictive control strategies...

  14. Buried waste integrated demonstration human engineered control station. Final report

    Energy Technology Data Exchange (ETDEWEB)

    1994-09-01

    This document describes the Human Engineered Control Station (HECS) project activities including the conceptual designs. The purpose of the HECS is to enhance the effectiveness and efficiency of remote retrieval by providing an integrated remote control station. The HECS integrates human capabilities, limitations, and expectations into the design to reduce the potential for human error, provides an easy system to learn and operate, provides an increased productivity, and reduces the ultimate investment in training. The overall HECS consists of the technology interface stations, supporting engineering aids, platform (trailer), communications network (broadband system), and collision avoidance system.

  15. Buried waste integrated demonstration human engineered control station. Final report

    International Nuclear Information System (INIS)

    1994-09-01

    This document describes the Human Engineered Control Station (HECS) project activities including the conceptual designs. The purpose of the HECS is to enhance the effectiveness and efficiency of remote retrieval by providing an integrated remote control station. The HECS integrates human capabilities, limitations, and expectations into the design to reduce the potential for human error, provides an easy system to learn and operate, provides an increased productivity, and reduces the ultimate investment in training. The overall HECS consists of the technology interface stations, supporting engineering aids, platform (trailer), communications network (broadband system), and collision avoidance system

  16. The application of cognitive models to the evaluation and prediction of human reliability

    International Nuclear Information System (INIS)

    Embrey, D.E.; Reason, J.T.

    1986-01-01

    The first section of the paper provides a brief overview of a number of important principles relevant to human reliability modeling that have emerged from cognitive models, and presents a synthesis of these approaches in the form of a Generic Error Modeling System (GEMS). The next section illustrates the application of GEMS to some well known nuclear power plant (NPP) incidents in which human error was a major contributor. The way in which design recommendations can emerge from analyses of this type is illustrated. The third section describes the use of cognitive models in the classification of human errors for prediction and data collection purposes. The final section addresses the predictive modeling of human error as part of human reliability assessment in Probabilistic Risk Assessment

  17. Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment.

    Science.gov (United States)

    Zhan, Yu; Luo, Yuzhou; Deng, Xunfei; Grieneisen, Michael L; Zhang, Minghua; Di, Baofeng

    2018-02-01

    In China, ozone pollution shows an increasing trend and becomes the primary air pollutant in warm seasons. Leveraging the air quality monitoring network, a random forest model is developed to predict the daily maximum 8-h average ozone concentrations ([O 3 ] MDA8 ) across China in 2015 for human exposure assessment. This model captures the observed spatiotemporal variations of [O 3 ] MDA8 by using the data of meteorology, elevation, and recent-year emission inventories (cross-validation R 2  = 0.69 and RMSE = 26 μg/m 3 ). Compared with chemical transport models that require a plenty of variables and expensive computation, the random forest model shows comparable or higher predictive performance based on only a handful of readily-available variables at much lower computational cost. The nationwide population-weighted [O 3 ] MDA8 is predicted to be 84 ± 23 μg/m 3 annually, with the highest seasonal mean in the summer (103 ± 8 μg/m 3 ). The summer [O 3 ] MDA8 is predicted to be the highest in North China (125 ± 17 μg/m 3 ). Approximately 58% of the population lives in areas with more than 100 nonattainment days ([O 3 ] MDA8 >100 μg/m 3 ), and 12% of the population are exposed to [O 3 ] MDA8 >160 μg/m 3 (WHO Interim Target 1) for more than 30 days. As the most populous zones in China, the Beijing-Tianjin Metro, Yangtze River Delta, Pearl River Delta, and Sichuan Basin are predicted to be at 154, 141, 124, and 98 nonattainment days, respectively. Effective controls of O 3 pollution are urgently needed for the highly-populated zones, especially the Beijing-Tianjin Metro with seasonal [O 3 ] MDA8 of 140 ± 29 μg/m 3 in summer. To the best of the authors' knowledge, this study is the first statistical modeling work of ambient O 3 for China at the national level. This timely and extensively validated [O 3 ] MDA8 dataset is valuable for refining epidemiological analyses on O 3 pollution in China. Copyright © 2017 Elsevier Ltd. All rights

  18. Chimeric mice transplanted with human hepatocytes as a model for prediction of human drug metabolism and pharmacokinetics.

    Science.gov (United States)

    Sanoh, Seigo; Ohta, Shigeru

    2014-03-01

    Preclinical studies in animal models are used routinely during drug development, but species differences of pharmacokinetics (PK) between animals and humans have to be taken into account in interpreting the results. Human hepatocytes are also widely used to examine metabolic activities mediated by cytochrome P450 (P450) and other enzymes, but such in vitro metabolic studies also have limitations. Recently, chimeric mice with humanized liver (h-chimeric mice), generated by transplantation of human donor hepatocytes, have been developed as a model for the prediction of metabolism and PK in humans, using both in vitro and in vivo approaches. The expression of human-specific metabolic enzymes and metabolic activities was confirmed in humanized liver of h-chimeric mice with high replacement ratios, and several reports indicate that the profiles of P450 and non-P450 metabolism in these mice adequately reflect those in humans. Further, the combined use of h-chimeric mice and r-chimeric mice, in which endogenous hepatocytes are replaced with rat hepatocytes, is a promising approach for evaluation of species differences in drug metabolism. Recent work has shown that data obtained in h-chimeric mice enable the semi-quantitative prediction of not only metabolites, but also PK parameters, such as hepatic clearance, of drug candidates in humans, although some limitations remain because of differences in the metabolic activities, hepatic blood flow and liver structure between humans and mice. In addition, fresh h-hepatocytes can be isolated reproducibly from h-chimeric mice for metabolic studies. Copyright © 2013 John Wiley & Sons, Ltd.

  19. Robust Model Predictive Control of Networked Control Systems under Input Constraints and Packet Dropouts

    Directory of Open Access Journals (Sweden)

    Deyin Yao

    2014-01-01

    Full Text Available This paper deals with the problem of robust model predictive control (RMPC for a class of linear time-varying systems with constraints and data losses. We take the polytopic uncertainties into account to describe the uncertain systems. First, we design a robust state observer by using the linear matrix inequality (LMI constraints so that the original system state can be tracked. Second, the MPC gain is calculated by minimizing the upper bound of infinite horizon robust performance objective in terms of linear matrix inequality conditions. The method of robust MPC and state observer design is illustrated by a numerical example.

  20. Including model uncertainty in the model predictive control with output feedback

    Directory of Open Access Journals (Sweden)

    Rodrigues M.A.

    2002-01-01

    Full Text Available This paper addresses the development of an efficient numerical output feedback robust model predictive controller for open-loop stable systems. Stability of the closed loop is guaranteed by using an infinite horizon predictive controller and a stable state observer. The performance and the computational burden of this approach are compared to a robust predictive controller from the literature. The case used for this study is based on an industrial gasoline debutanizer column.

  1. Human factors survey of advanced instrumentation and controls

    International Nuclear Information System (INIS)

    Carter, R.J.

    1989-01-01

    A survey oriented towards identifying the human factors issues in regard to the use of advanced instrumentation and controls (I ampersand C) in the nuclear industry was conducted. A number of United States (US) and Canadian nuclear vendors and utilities were participants in the survey. Human factors items, subsumed under the categories of computer-generated displays (CGD), controls, organizational support, training, and related topics, were discussed. The survey found the industry to be concerned about the human factors issues related to the implementation of advanced I ampersand C. Fifteen potential human factors problems were identified. They include: the need for an advanced I ampersand C guideline equivalent to NUREG-0700; a role change in the control room from operator to supervisor; information overload; adequacy of existing training technology for advanced I ampersand C; and operator acceptance and trust. 11 refs., 1 tab

  2. Human machine interface for research reactor instrumentation and control system

    International Nuclear Information System (INIS)

    Mohd Sabri Minhat; Mohd Idris Taib; Izhar Abu Hussin; Zareen Khan Abdul Jalil Khan; Nurfarhana Ayuni Joha

    2010-01-01

    Most present design of Human Machine Interface for Research Reactor Instrumentation and Control System is modular-based, comprise of several cabinets such as Reactor Protection System, Control Console, Information Console as well as Communication Console. The safety, engineering and human factor will be concerned for the design. Redundancy and separation of signal and power supply are the main factor for safety consideration. The design of Operator Interface absolutely takes consideration of human and environmental factors. Physical parameters, experiences, trainability and long-established habit patterns are very important for user interface, instead of the Aesthetic and Operator-Interface Geometry. Physical design for New Instrumentation and Control System of RTP are proposed base on the state-of- the-art Human Machine Interface design. (author)

  3. Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human.

    Science.gov (United States)

    Wu, Chengchao; Yao, Shixin; Li, Xinghao; Chen, Chujia; Hu, Xuehai

    2017-02-16

    DNA methylation plays a significant role in transcriptional regulation by repressing activity. Change of the DNA methylation level is an important factor affecting the expression of target genes and downstream phenotypes. Because current experimental technologies can only assay a small proportion of CpG sites in the human genome, it is urgent to develop reliable computational models for predicting genome-wide DNA methylation. Here, we proposed a novel algorithm that accurately extracted sequence complexity features (seven features) and developed a support-vector-machine-based prediction model with integration of the reported DNA composition features (trinucleotide frequency and GC content, 65 features) by utilizing the methylation profiles of embryonic stem cells in human. The prediction results from 22 human chromosomes with size-varied windows showed that the 600-bp window achieved the best average accuracy of 94.7%. Moreover, comparisons with two existing methods further showed the superiority of our model, and cross-species predictions on mouse data also demonstrated that our model has certain generalization ability. Finally, a statistical test of the experimental data and the predicted data on functional regions annotated by ChromHMM found that six out of 10 regions were consistent, which implies reliable prediction of unassayed CpG sites. Accordingly, we believe that our novel model will be useful and reliable in predicting DNA methylation.

  4. Wheel slip control with torque blending using linear and nonlinear model predictive control

    Science.gov (United States)

    Basrah, M. Sofian; Siampis, Efstathios; Velenis, Efstathios; Cao, Dongpu; Longo, Stefano

    2017-11-01

    Modern hybrid electric vehicles employ electric braking to recuperate energy during deceleration. However, currently anti-lock braking system (ABS) functionality is delivered solely by friction brakes. Hence regenerative braking is typically deactivated at a low deceleration threshold in case high slip develops at the wheels and ABS activation is required. If blending of friction and electric braking can be achieved during ABS events, there would be no need to impose conservative thresholds for deactivation of regenerative braking and the recuperation capacity of the vehicle would increase significantly. In addition, electric actuators are typically significantly faster responding and would deliver better control of wheel slip than friction brakes. In this work we present a control strategy for ABS on a fully electric vehicle with each wheel independently driven by an electric machine and friction brake independently applied at each wheel. In particular we develop linear and nonlinear model predictive control strategies for optimal performance and enforcement of critical control and state constraints. The capability for real-time implementation of these controllers is assessed and their performance is validated in high fidelity simulation.

  5. Control of Warm Compression Stations Using Model Predictive Control: Simulation and Experimental Results

    Science.gov (United States)

    Bonne, F.; Alamir, M.; Bonnay, P.

    2017-02-01

    This paper deals with multivariable constrained model predictive control for Warm Compression Stations (WCS). WCSs are subject to numerous constraints (limits on pressures, actuators) that need to be satisfied using appropriate algorithms. The strategy is to replace all the PID loops controlling the WCS with an optimally designed model-based multivariable loop. This new strategy leads to high stability and fast disturbance rejection such as those induced by a turbine or a compressor stop, a key-aspect in the case of large scale cryogenic refrigeration. The proposed control scheme can be used to achieve precise control of pressures in normal operation or to avoid reaching stopping criteria (such as excessive pressures) under high disturbances (such as a pulsed heat load expected to take place in future fusion reactors, expected in the cryogenic cooling systems of the International Thermonuclear Experimental Reactor ITER or the Japan Torus-60 Super Advanced fusion experiment JT-60SA). The paper details the simulator used to validate this new control scheme and the associated simulation results on the SBTs WCS. This work is partially supported through the French National Research Agency (ANR), task agreement ANR-13-SEED-0005.

  6. Economic Model Predictive Control for Building Climate Control in a Smart Grid

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus; Poulsen, Niels Kjølstad; Madsen, Henrik

    2012-01-01

    Model Predictive Control (MPC) can be used to control a system of energy producers and consumers in a Smart Grid. In this paper, we use heat pumps for heating residential buildings with a floor heating system. We use the thermal capacity of the building to shift the electricity consumptions...... to periods with low energy prices. In this way the heating system of the house becomes a flexible power consumer in the Smart Grid. This scenario is relevant for systems with a significant share of stochastic energy producers, e.g. wind turbines, where the ability to shift power consumption according...... and electricity price. Simulation studies demonstrate the capabilities of the proposed model and algorithm. Compared to traditional operation of heat pumps with constant electricity prices, the optimized operating strategy saves 25-33% of the electricity cost....

  7. A simplified model for predicting malaria entomologic inoculation rates based on entomologic and parasitologic parameters relevant to control.

    Science.gov (United States)

    Killeen, G F; McKenzie, F E; Foy, B D; Schieffelin, C; Billingsley, P F; Beier, J C

    2000-05-01

    Malaria transmission intensity is modeled from the starting perspective of individual vector mosquitoes and is expressed directly as the entomologic inoculation rate (EIR). The potential of individual mosquitoes to transmit malaria during their lifetime is presented graphically as a function of their feeding cycle length and survival, human biting preferences, and the parasite sporogonic incubation period. The EIR is then calculated as the product of 1) the potential of individual vectors to transmit malaria during their lifetime, 2) vector emergence rate relative to human population size, and 3) the infectiousness of the human population to vectors. Thus, impacts on more than one of these parameters will amplify each other's effects. The EIRs transmitted by the dominant vector species at four malaria-endemic sites from Papua New Guinea, Tanzania, and Nigeria were predicted using field measurements of these characteristics together with human biting rate and human reservoir infectiousness. This model predicted EIRs (+/- SD) that are 1.13 +/- 0.37 (range = 0.84-1.59) times those measured in the field. For these four sites, mosquito emergence rate and lifetime transmission potential were more important determinants of the EIR than human reservoir infectiousness. This model and the input parameters from the four sites allow the potential impacts of various control measures on malaria transmission intensity to be tested under a range of endemic conditions. The model has potential applications for the development and implementation of transmission control measures and for public health education.

  8. Control room design and human engineering in power plants

    International Nuclear Information System (INIS)

    Herbst, L.; Hinz, W.

    1982-01-01

    The concept for modern plant control rooms is primary influenced by: The automation of protection, binary control and closed loop control functions; organization employing functional areas; computer based information processing; human engineered design. Automation reduces the human work load. Employment of functional areas permits optimization of operational sequences. Computer based information processing makes it possible to output information in accordance with operating requirements. Design based on human engineering principles assures the quality of the interaction between the operator and the equipment. The degree to which these conceptional features play a role in design of power plant control rooms depends on the unit rating, the mode of operation and on the requirements respecting safety and availability of the plant. (orig.)

  9. Prediction of human population responses to toxic compounds by a collaborative competition.

    Science.gov (United States)

    Eduati, Federica; Mangravite, Lara M; Wang, Tao; Tang, Hao; Bare, J Christopher; Huang, Ruili; Norman, Thea; Kellen, Mike; Menden, Michael P; Yang, Jichen; Zhan, Xiaowei; Zhong, Rui; Xiao, Guanghua; Xia, Menghang; Abdo, Nour; Kosyk, Oksana; Friend, Stephen; Dearry, Allen; Simeonov, Anton; Tice, Raymond R; Rusyn, Ivan; Wright, Fred A; Stolovitzky, Gustavo; Xie, Yang; Saez-Rodriguez, Julio

    2015-09-01

    The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.

  10. A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait.

    Science.gov (United States)

    Torricelli, Diego; Cortés, Camilo; Lete, Nerea; Bertelsen, Álvaro; Gonzalez-Vargas, Jose E; Del-Ama, Antonio J; Dimbwadyo, Iris; Moreno, Juan C; Florez, Julian; Pons, Jose L

    2018-01-01

    The relative motion between human and exoskeleton is a crucial factor that has remarkable consequences on the efficiency, reliability and safety of human-robot interaction. Unfortunately, its quantitative assessment has been largely overlooked in the literature. Here, we present a methodology that allows predicting the motion of the human joints from the knowledge of the angular motion of the exoskeleton frame. Our method combines a subject-specific skeletal model with a kinematic model of a lower limb exoskeleton (H2, Technaid), imposing specific kinematic constraints between them. To calibrate the model and validate its ability to predict the relative motion in a subject-specific way, we performed experiments on seven healthy subjects during treadmill walking tasks. We demonstrate a prediction accuracy lower than 3.5° globally, and around 1.5° at the hip level, which represent an improvement up to 66% compared to the traditional approach assuming no relative motion between the user and the exoskeleton.

  11. Time-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases.

    Science.gov (United States)

    Peterson, A Townsend; Martínez-Campos, Carmen; Nakazawa, Yoshinori; Martínez-Meyer, Enrique

    2005-09-01

    Numerous human diseases-malaria, dengue, yellow fever and leishmaniasis, to name a few-are transmitted by insect vectors with brief life cycles and biting activity that varies in both space and time. Although the general geographic distributions of these epidemiologically important species are known, the spatiotemporal variation in their emergence and activity remains poorly understood. We used ecological niche modeling via a genetic algorithm to produce time-specific predictive models of monthly distributions of Aedes aegypti in Mexico in 1995. Significant predictions of monthly mosquito activity and distributions indicate that predicting spatiotemporal dynamics of disease vector species is feasible; significant coincidence with human cases of dengue indicate that these dynamics probably translate directly into transmission of dengue virus to humans. This approach provides new potential for optimizing use of resources for disease prevention and remediation via automated forecasting of disease transmission risk.

  12. Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

    Science.gov (United States)

    Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

    2003-01-01

    Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

  13. PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources.

    Science.gov (United States)

    Kahanda, Indika; Funk, Christopher; Verspoor, Karin; Ben-Hur, Asa

    2015-01-01

    The human phenotype ontology (HPO) was recently developed as a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. At present, only a small fraction of human protein coding genes have HPO annotations. But, researchers believe that a large portion of currently unannotated genes are related to disease phenotypes. Therefore, it is important to predict gene-HPO term associations using accurate computational methods. In this work we demonstrate the performance advantage of the structured SVM approach which was shown to be highly effective for Gene Ontology term prediction in comparison to several baseline methods. Furthermore, we highlight a collection of informative data sources suitable for the problem of predicting gene-HPO associations, including large scale literature mining data.

  14. Image-Based Visual Servoing for Manipulation Via Predictive Control – A Survey of Some Results

    Directory of Open Access Journals (Sweden)

    Corneliu Lazăr

    2016-09-01

    Full Text Available In this paper, a review of predictive control algorithms developed by the authors for visual servoing of robots in manipulation applications is presented. Using these algorithms, a control predictive framework was created for image-based visual servoing (IBVS systems. Firstly, considering the point features, in the year 2008 we introduced an internal model predictor based on the interaction matrix. Secondly, distinctly from the set-point trajectory, we introduced in 2011 the reference trajectory using the concept from predictive control. Finally, minimizing a sum of squares of predicted errors, the optimal input trajectory was obtained. The new concept of predictive control for IBVS systems was employed to develop a cascade structure for motion control of robot arms. Simulation results obtained with a simulator for predictive IBVS systems are also presented.

  15. Explicit model predictive control applications in power systems: an AGC study for an isolated industrial system

    DEFF Research Database (Denmark)

    Jiang, Hao; Lin, Jin; Song, Yonghua

    2016-01-01

    Model predictive control (MPC), that can consider system constraints, is one of the most advanced control technology used nowadays. In power systems, MPC is applied in a way that an optimal control sequence is given every step by an online MPC controller. The main drawback is that the control law...

  16. Explicit Generalized Predictive Control of Speed and Position of PMSM Drives

    Czech Academy of Sciences Publication Activity Database

    Belda, Květoslav; Vošmik, D.

    2016-01-01

    Roč. 63, č. 6 (2016), s. 3889-3896 ISSN 0278-0046 Institutional support: RVO:67985556 Keywords : current limitation * field weakening * motion control * permanent magnet synchronous motors * position control * predictive control * speed control Subject RIV: BC - Control Systems Theory Impact factor: 7.168, year: 2016 http://library.utia.cas.cz/separaty/2016/AS/belda-0457259.pdf

  17. Prediction of forces and moments for flight vehicle control effectors. Part 1: Validation of methods for predicting hypersonic vehicle controls forces and moments

    Science.gov (United States)

    Maughmer, Mark D.; Ozoroski, L.; Ozoroski, T.; Straussfogel, D.

    1990-01-01

    Many types of hypersonic aircraft configurations are currently being studied for feasibility of future development. Since the control of the hypersonic configurations throughout the speed range has a major impact on acceptable designs, it must be considered in the conceptual design stage. The ability of the aerodynamic analysis methods contained in an industry standard conceptual design system, APAS II, to estimate the forces and moments generated through control surface deflections from low subsonic to high hypersonic speeds is considered. Predicted control forces and moments generated by various control effectors are compared with previously published wind tunnel and flight test data for three configurations: the North American X-15, the Space Shuttle Orbiter, and a hypersonic research airplane concept. Qualitative summaries of the results are given for each longitudinal force and moment and each control derivative in the various speed ranges. Results show that all predictions of longitudinal stability and control derivatives are acceptable for use at the conceptual design stage. Results for most lateral/directional control derivatives are acceptable for conceptual design purposes; however, predictions at supersonic Mach numbers for the change in yawing moment due to aileron deflection and the change in rolling moment due to rudder deflection are found to be unacceptable. Including shielding effects in the analysis is shown to have little effect on lift and pitching moment predictions while improving drag predictions.

  18. A heuristic model for computational prediction of human branch point sequence.

    Science.gov (United States)

    Wen, Jia; Wang, Jue; Zhang, Qing; Guo, Dianjing

    2017-10-24

    Pre-mRNA splicing is the removal of introns from precursor mRNAs (pre-mRNAs) and the concurrent ligation of the flanking exons to generate mature mRNA. This process is catalyzed by the spliceosome, where the splicing factor 1 (SF1) specifically recognizes the seven-nucleotide branch point sequence (BPS) and the U2 snRNP later displaces the SF1 and binds to the BPS. In mammals, the degeneracy of BPS motifs together with the lack of a large set of experimentally verified BPSs complicates the task of BPS prediction in silico. In this paper, we develop a simple and yet efficient heuristic model for human BPS prediction based on a novel scoring scheme, which quantifies the splicing strength of putative BPSs. The candidate BPS is restricted exclusively within a defined BPS search region to avoid the influences of other elements in the intron and therefore the prediction accuracy is improved. Moreover, using two types of relative frequencies for human BPS prediction, we demonstrate our model outperformed other current implementations on experimentally verified human introns. We propose that the binding energy contributes to the molecular recognition involved in human pre-mRNA splicing. In addition, a genome-wide human BPS prediction is carried out. The characteristics of predicted BPSs are in accordance with experimentally verified human BPSs, and branch site positions relative to the 3'ss and the 5'end of the shortened AGEZ are consistent with the results of published papers. Meanwhile, a webserver for BPS predictor is freely available at http://biocomputer.bio.cuhk.edu.hk/BPS .

  19. Human performance: An essential element in materials control and accountability

    International Nuclear Information System (INIS)

    Haber, S.B.; Allentuck, J.

    1996-01-01

    The importance of the role of human performance in the successful and effective operation of many activities throughout many industries has been well documented. Most closely related to the materials control and accountability area is the work in human factors that has been ongoing in the U.S. nuclear industry since the Three Mile Island Nuclear Power Plant accident in 1979. Research related to the role of human reliability, human-system interface, and organization and management influences has been and is still being conducted to identify ways to enhance the safe and effective operation of nuclear facilities. This paper will discuss these human performance areas and how they relate to the materials control and accountability area. Particular attention will be focussed on the notion of open-quotes safety cultureclose quotes and how it can be defined and measured for understanding the values and attitudes held by individuals working in the materials control area. It is widely believed that the culture of an organization, which reflects the expectations and values of the management of an organization, is a key element to the operation of that organization. The human performance element is one which has not received a great deal of consideration in the materials control and accountability area and yet it will be demonstrated that it is an essential component to ensure the success of safeguards activities

  20. Predicting timing performance of advanced mechatronics control systems

    NARCIS (Netherlands)

    Voeten, J.P.M.; Hendriks, T.; Theelen, B.D.; Schuddemat, J.; Tabingh Suermondt, W.; Gemei, J.; Kotterink, C.; Huet, van J.; Eichler, G.; Kuepper, A.; Schau, V.; Fouchal, H.; Unger, H.

    2011-01-01

    Embedded control is a key product technology differentiator for many high-tech industries, including ASML. The strong increase in complexity of embedded control systems, combined with the occurrence of late changes in control requirements, results in many timing performance problems showing up only

  1. Objective Model Selection for Identifying the Human Feedforward Response in Manual Control.

    Science.gov (United States)

    Drop, Frank M; Pool, Daan M; van Paassen, Marinus Rene M; Mulder, Max; Bulthoff, Heinrich H

    2018-01-01

    Realistic manual control tasks typically involve predictable target signals and random disturbances. The human controller (HC) is hypothesized to use a feedforward control strategy for target-following, in addition to feedback control for disturbance-rejection. Little is known about human feedforward control, partly because common system identification methods have difficulty in identifying whether, and (if so) how, the HC applies a feedforward strategy. In this paper, an identification procedure is presented that aims at an objective model selection for identifying the human feedforward response, using linear time-invariant autoregressive with exogenous input models. A new model selection criterion is proposed to decide on the model order (number of parameters) and the presence of feedforward in addition to feedback. For a range of typical control tasks, it is shown by means of Monte Carlo computer simulations that the classical Bayesian information criterion (BIC) leads to selecting models that contain a feedforward path from data generated by a pure feedback model: "false-positive" feedforward detection. To eliminate these false-positives, the modified BIC includes an additional penalty on model complexity. The appropriate weighting is found through computer simulations with a hypothesized HC model prior to performing a tracking experiment. Experimental human-in-the-loop data will be considered in future work. With appropriate weighting, the method correctly identifies the HC dynamics in a wide range of control tasks, without false-positive results.

  2. Human error prediction and countermeasures based on CREAM in spent nuclear fuel (SNF) transportation

    International Nuclear Information System (INIS)

    Kim, Jae San

    2007-02-01

    Since the 1980s, in order to secure the storage capacity of spent nuclear fuel (SNF) at NPPs, SNF assemblies have been transported on-site from one unit to another unit nearby. However in the future the amount of the spent fuel will approach capacity in the areas used, and some of these SNFs will have to be transported to an off-site spent fuel repository. Most SNF materials used at NPPs will be transported by general cargo ships from abroad, and these SNFs will be stored in an interim storage facility. In the process of transporting SNF, human interactions will involve inspecting and preparing the cask and spent fuel, loading the cask onto the vehicle or ship, transferring the cask as well as storage or monitoring the cask. The transportation of SNF involves a number of activities that depend on reliable human performance. In the case of the transport of a cask, human errors may include spent fuel bundle misidentification or cask transport accidents among others. Reviews of accident events when transporting the Radioactive Material (RAM) throughout the world indicate that human error is the major causes for more than 65% of significant events. For the safety of SNF transportation, it is very important to predict human error and to deduce a method that minimizes the human error. This study examines the human factor effects on the safety of transporting spent nuclear fuel (SNF). It predicts and identifies the possible human errors in the SNF transport process (loading, transfer and storage of the SNF). After evaluating the human error mode in each transport process, countermeasures to minimize the human error are deduced. The human errors in SNF transportation were analyzed using Hollnagel's Cognitive Reliability and Error Analysis Method (CREAM). After determining the important factors for each process, countermeasures to minimize human error are provided in three parts: System design, Operational environment, and Human ability

  3. Comparing human-Salmonella with plant-Salmonella protein-protein interaction predictions

    Directory of Open Access Journals (Sweden)

    Sylvia eSchleker

    2015-01-01

    Full Text Available Salmonellosis is the most frequent food-borne disease world-wide and can be transmitted to humans by a variety of routes, especially via animal and plant products. Salmonella bacteria are believed to use not only animal and human but also plant hosts despite their evolutionary distance. This raises the question if Salmonella employs similar mechanisms in infection of these diverse hosts. Given that most of our understanding comes from its interaction with human hosts, we investigate here to what degree knowledge of Salmonella-human interactions can be transferred to the Salmonella-plant system. Reviewed are recent publications on analysis and prediction of Salmonella-host interactomes. Putative protein-protein interactions (PPIs between Salmonella and its human and Arabidopsis hosts were retrieved utilizing purely interolog-based approaches in which predictions were inferred based on available sequence and domain information of known PPIs, and machine learning approaches that integrate a larger set of useful information from different sources. Transfer learning is an especially suitable machine learning technique to predict plant host targets from the knowledge of human host targets. A comparison of the prediction results with transcriptomic data shows a clear overlap between the host proteins predicted to be targeted by PPIs and their gene ontology enrichment in both host species and regulation of gene expression. In particular, the cellular processes Salmonella interferes with in plants and humans are catabolic processes. The details of how these processes are targeted, however, are quite different between the two organisms, as expected based on their evolutionary and habitat differences. Possible implications of this observation on evolution of host-pathogen communication are discussed.

  4. Model Predictive Control for an Industrial SAG Mill

    DEFF Research Database (Denmark)

    Ohan, Valeriu; Steinke, Florian; Metzger, Michael

    2012-01-01

    identication. When applied to MIMO systems we call this controller a MIMO-ARX based MPC. We use an industrial Semi-Autogenous Grinding (SAG) mill to illustrate the performance of this controller. SAG mills are the primary units in a grinding chain and also the most power consuming units. Therefore, improved...... control of SAG mills has the potential to signicantly improve eciency and reduce the specic energy consumption for mineral processes. Grinding circuits involving SAG mills are multivariate processes. Commissioning of a control system based on a classical single-loop controllers with logic is time...

  5. Robust human body model injury prediction in simulated side impact crashes.

    Science.gov (United States)

    Golman, Adam J; Danelson, Kerry A; Stitzel, Joel D

    2016-01-01

    This study developed a parametric methodology to robustly predict occupant injuries sustained in real-world crashes using a finite element (FE) human body model (HBM). One hundred and twenty near-side impact motor vehicle crashes were simulated over a range of parameters using a Toyota RAV4 (bullet vehicle), Ford Taurus (struck vehicle) FE models and a validated human body model (HBM) Total HUman Model for Safety (THUMS). Three bullet vehicle crash parameters (speed, location and angle) and two occupant parameters (seat position and age) were varied using a Latin hypercube design of Experiments. Four injury metrics (head injury criterion, half deflection, thoracic trauma index and pelvic force) were used to calculate injury risk. Rib fracture prediction and lung strain metrics were also analysed. As hypothesized, bullet speed had the greatest effect on each injury measure. Injury risk was reduced when bullet location was further from the B-pillar or when the bullet angle was more oblique. Age had strong correlation to rib fractures frequency and lung strain severity. The injuries from a real-world crash were predicted using two different methods by (1) subsampling the injury predictors from the 12 best crush profile matching simulations and (2) using regression models. Both injury prediction methods successfully predicted the case occupant's low risk for pelvic injury, high risk for thoracic injury, rib fractures and high lung strains with tight confidence intervals. This parametric methodology was successfully used to explore crash parameter interactions and to robustly predict real-world injuries.

  6. Design of a new PID controller using predictive functional control optimization for chamber pressure in a coke furnace.

    Science.gov (United States)

    Zhang, Jianming

    2017-03-01

    An improved proportional-integral-derivative (PID) controller based on predictive functional control (PFC) is proposed and tested on the chamber pressure in an industrial coke furnace. The proposed design is motivated by the fact that PID controllers for industrial processes with time delay may not achieve the desired control performance because of the unavoidable model/plant mismatches, while model predictive control (MPC) is suitable for such situations. In this paper, PID control and PFC algorithm are combined to form a new PID controller that has the basic characteristic of PFC algorithm and at the same time, the simple structure of traditional PID controller. The proposed controller was tested in terms of set-point tracking and disturbance rejection, where the obtained results showed that the proposed controller had the better ensemble performance compared with traditional PID controllers. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  7. Yeast prions and human prion-like proteins: sequence features and prediction methods.

    Science.gov (United States)

    Cascarina, Sean M; Ross, Eric D

    2014-06-01

    Prions are self-propagating infectious protein isoforms. A growing number of prions have been identified in yeast, each resulting from the conversion of soluble proteins into an insoluble amyloid form. These yeast prions have served as a powerful model system for studying the causes and consequences of prion aggregation. Remarkably, a number of human proteins containing prion-like domains, defined as domains with compositional similarity to yeast prion domains, have recently been linked to various human degenerative diseases, including amyotrophic lateral sclerosis. This suggests that the lessons learned from yeast prions may help in understanding these human diseases. In this review, we examine what has been learned about the amino acid sequence basis for prion aggregation in yeast, and how this information has been used to develop methods to predict aggregation propensity. We then discuss how this information is being applied to understand human disease, and the challenges involved in applying yeast prediction methods to higher organisms.

  8. Validation of a predictive model for smart control of electrical energy storage

    NARCIS (Netherlands)

    Homan, Bart; van Leeuwen, Richard Pieter; Smit, Gerardus Johannes Maria; Zhu, Lei; de Wit, Jan B.

    2016-01-01

    The purpose of this paper is to investigate the applicability of a relatively simple model which is based on energy conservation for model predictions as part of smart control of thermal and electric storage. The paper reviews commonly used predictive models. Model predictions of charging and

  9. Mechanisms of Intentional Binding and Sensory Attenuation: The Role of Temporal Prediction, Temporal Control, Identity Prediction, and Motor Prediction

    Science.gov (United States)

    Hughes, Gethin; Desantis, Andrea; Waszak, Florian

    2013-01-01

    Sensory processing of action effects has been shown to differ from that of externally triggered stimuli, with respect both to the perceived timing of their occurrence (intentional binding) and to their intensity (sensory attenuation). These phenomena are normally attributed to forward action models, such that when action prediction is consistent…

  10. The Effects of Control of Resources on Magnitudes of Sex Differences in Human Mate Preferences

    Directory of Open Access Journals (Sweden)

    Fhionna Moore

    2010-10-01

    Full Text Available We tested the hypothesis that magnitudes of sex differences in human mate preferences would be inversely related to control of resources. Specifically, we predicted that the ideal partner age, maximum and minimum partner ages tolerated and preferences for “physical attractiveness” over “good financial prospects” of female participants would approach parity with that of men with increasing control of resources. In a sample of 3770 participants recruited via an online survey, the magnitudes of sex differences in age preferences increased with resource control whereas the sex difference in preferences for “physical attractiveness” over “good financial prospects” disappeared when resource control was high. Results are inconsistent, and are discussed in the context of adaptive tradeoff and biosocial models of sex differences in human mate preferences.

  11. A Multiple Agent Model of Human Performance in Automated Air Traffic Control and Flight Management Operations

    Science.gov (United States)

    Corker, Kevin; Pisanich, Gregory; Condon, Gregory W. (Technical Monitor)

    1995-01-01

    A predictive model of human operator performance (flight crew and air traffic control (ATC)) has been developed and applied in order to evaluate the impact of automation developments in flight management and air traffic control. The model is used to predict the performance of a two person flight crew and the ATC operators generating and responding to clearances aided by the Center TRACON Automation System (CTAS). The purpose of the modeling is to support evaluation and design of automated aids for flight management and airspace management and to predict required changes in procedure both air and ground in response to advancing automation in both domains. Additional information is contained in the original extended abstract.

  12. Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity

    Directory of Open Access Journals (Sweden)

    Elisa Passini

    2017-09-01

    Full Text Available Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC50/Hill coefficient. Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca2+-transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs. Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca2+/late Na+ currents and Na+/Ca2+-exchanger, reduced Na+/K+-pump are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density

  13. Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity.

    Science.gov (United States)

    Passini, Elisa; Britton, Oliver J; Lu, Hua Rong; Rohrbacher, Jutta; Hermans, An N; Gallacher, David J; Greig, Robert J H; Bueno-Orovio, Alfonso; Rodriguez, Blanca

    2017-01-01

    Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC 50 /Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca 2+ -transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca 2+ /late Na + currents and Na + /Ca 2+ -exchanger, reduced Na + /K + -pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density

  14. Control room human engineering influences on operator performance

    International Nuclear Information System (INIS)

    Finlayson, F.C.

    1977-01-01

    Three general groups of factors influence operator performance in fulfilling their responsibilities in the control room: (1) control room and control system design, informational data displays (operator inputs) as well as control board design (for operator output); (2) operator characteristics, including those skills, mental, physical, and emotional qualities which are functions of operator selection, training, and motivation; (3) job performance guides, the prescribed operating procedures for normal and emergency operations. This paper presents some of the major results of an evaluation of the effect of human engineering on operator performance in the control room. Primary attention is given to discussion of control room and control system design influence on the operator. Brief observations on the influences of operator characteristics and job performance guides (operating procedures) on performance in the control room are also given. Under the objectives of the study, special emphasis was placed on the evaluation of the control room-operator relationships for severe emergency conditions in the power plant. Consequently, this presentation is restricted largely to material related to emergency conditions in the control room, though it is recognized that human engineering of control systems is of equal (or greater) importance for many other aspects of plant operation

  15. Human Factors Engineering Aspects of Modifications in Control Room Modernization

    Energy Technology Data Exchange (ETDEWEB)

    Hugo, Jacques [Idaho National Lab. (INL), Idaho Falls, ID (United States); Clefton, Gordon [Idaho National Lab. (INL), Idaho Falls, ID (United States); Joe, Jeffrey [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2017-06-01

    This report describes the basic aspects of control room modernization projects in the U.S. nuclear industry and the need for supplementary guidance on the integration of human factors considerations into the licensing and regulatory aspects of digital upgrades. The report pays specific attention to the integration of principles described in NUREG-0711 (Human Factors Engineering Program Review Model) and how supplementary guidance can help to raise general awareness in the industry regarding the complexities of control room modernization projects created by many interdependent regulations, standards and guidelines. The report also describes how human factors engineering principles and methods provided by various resources and international standards can help in navigating through the process of licensing digital upgrades. In particular, the integration of human factors engineering guidance and requirements into the process of licensing digital upgrades can help reduce uncertainty related to development of technical bases for digital upgrades that will avoid the introduction of new failure modes.

  16. Human Engineering of Space Vehicle Displays and Controls

    Science.gov (United States)

    Whitmore, Mihriban; Holden, Kritina L.; Boyer, Jennifer; Stephens, John-Paul; Ezer, Neta; Sandor, Aniko

    2010-01-01

    Proper attention to the integration of the human needs in the vehicle displays and controls design process creates a safe and productive environment for crew. Although this integration is critical for all phases of flight, for crew interfaces that are used during dynamic phases (e.g., ascent and entry), the integration is particularly important because of demanding environmental conditions. This panel addresses the process of how human engineering involvement ensures that human-system integration occurs early in the design and development process and continues throughout the lifecycle of a vehicle. This process includes the development of requirements and quantitative metrics to measure design success, research on fundamental design questions, human-in-the-loop evaluations, and iterative design. Processes and results from research on displays and controls; the creation and validation of usability, workload, and consistency metrics; and the design and evaluation of crew interfaces for NASA's Crew Exploration Vehicle are used as case studies.

  17. Everyday robotic action: Lessons from human action control

    Directory of Open Access Journals (Sweden)

    Roy eDe Kleijn

    2014-03-01

    Full Text Available Robots are increasingly capable of performing everyday human activities such as cooking, cleaning, and doing the laundry. This requires the real-time planning and execution of complex, temporally-extended sequential actions under high degrees of uncertainty, which provides many challenges to traditional approaches to robot action control. We argue that important lessons in this respect can be learned from research on human action control. We provide a brief overview of available psychological insights into this issue and focus on four principles that we think could be particularly beneficial for robot control: the integration of symbolic and subsymbolic planning of action sequences, the integration of feedforward and feedback control, the clustering of complex actions into subcomponents, and the contextualization of action-control structures through goal representations.

  18. Human factors measurement for future air traffic control systems.

    Science.gov (United States)

    Langan-Fox, Janice; Sankey, Michael J; Canty, James M

    2009-10-01

    This article provides a critical review of research pertaining to the measurement of human factors (HF) issues in current and future air traffic control (ATC). Growing worldwide air traffic demands call for a radical departure from current ATC systems. Future systems will have a fundamental impact on the roles and responsibilities of ATC officers (ATCOs). Valid and reliable methods of assessing HF issues associated with these changes, such as a potential increase (or decrease) in workload, are of utmost importance for advancing theory and for designing systems, procedures, and training. We outline major aviation changes and how these relate to five key HF issues in ATC. Measures are outlined, compared, and evaluated and are followed by guidelines for assessing these issues in the ATC domain. Recommendations for future research are presented. A review of the literature suggests that situational awareness and workload have been widely researched and assessed using a variety of measures, but researchers have neglected the areas of trust, stress, and boredom. We make recommendations for use of particular measures and the construction of new measures. It is predicted that, given the changing role of ATCOs and profound future airspace requirements and configurations, issues of stress, trust, and boredom will become more significant. Researchers should develop and/or refine existing measures of all five key HF issues to assess their impact on ATCO performance. Furthermore, these issues should be considered in a holistic manner. The current article provides an evaluation of research and measures used in HF research on ATC that will aid research and ATC measurement.

  19. Hierarchical learning induces two simultaneous, but separable, prediction errors in human basal ganglia.

    Science.gov (United States)

    Diuk, Carlos; Tsai, Karin; Wallis, Jonathan; Botvinick, Matthew; Niv, Yael

    2013-03-27

    Studies suggest that dopaminergic neurons report a unitary, global reward prediction error signal. However, learning in complex real-life tasks, in particular tasks that show hierarchical structure, requires multiple prediction errors that may coincide in time. We used functional neuroimaging to measure prediction error signals in humans performing such a hierarchical task involving simultaneous, uncorrelated prediction errors. Analysis of signals in a priori anatomical regions of interest in the ventral striatum and the ventral tegmental area indeed evidenced two simultaneous, but separable, prediction error signals corresponding to the two levels of hierarchy in the task. This result suggests that suitably designed tasks may reveal a more intricate pattern of firing in dopaminergic neurons. Moreover, the need for downstream separation of these signals implies possible limitations on the number of different task levels that we can learn about simultaneously.

  20. Optogenetic control of human neurons in organotypic brain cultures

    DEFF Research Database (Denmark)

    Andersson, My; Avaliani, Natalia; Svensson, Andreas

    2016-01-01

    Optogenetics is one of the most powerful tools in neuroscience, allowing for selective control of specific neuronal populations in the brain of experimental animals, including mammals. We report, for the first time, the application of optogenetic tools to human brain tissue providing a proof......-of-concept for the use of optogenetics in neuromodulation of human cortical and hippocampal neurons as a possible tool to explore network mechanisms and develop future therapeutic strategies....

  1. Sensing human hand motions for controlling dexterous robots

    Science.gov (United States)

    Marcus, Beth A.; Churchill, Philip J.; Little, Arthur D.

    1988-01-01

    The Dexterous Hand Master (DHM) system is designed to control dexterous robot hands such as the UTAH/MIT and Stanford/JPL hands. It is the first commercially available device which makes it possible to accurately and confortably track the complex motion of the human finger joints. The DHM is adaptable to a wide variety of human hand sizes and shapes, throughout their full range of motion.

  2. Human amygdala engagement moderated by early life stress exposure is a biobehavioral target for predicting recovery on antidepressants.

    Science.gov (United States)

    Goldstein-Piekarski, Andrea N; Korgaonkar, Mayuresh S; Green, Erin; Suppes, Trisha; Schatzberg, Alan F; Hastie, Trevor; Nemeroff, Charles B; Williams, Leanne M

    2016-10-18

    Amygdala circuitry and early life stress (ELS) are both strongly and independently implicated in the neurobiology of depression. Importantly, animal models have revealed that the contribution of ELS to the development and maintenance of depression is likely a consequence of structural and physiological changes in amygdala circuitry in response to stress hormones. Despite these mechanistic foundations, amygdala engagement and ELS have not been investigated as biobehavioral targets for predicting functional remission in translational human studies of depression. Addressing this question, we integrated human neuroimaging and measurement of ELS within a controlled trial of antidepressant outcomes. Here we demonstrate that the interaction between amygdala activation engaged by emotional stimuli and ELS predicts functional remission on antidepressants with a greater than 80% cross-validated accuracy. Our model suggests that in depressed people with high ELS, the likelihood of remission is highest with greater amygdala reactivity to socially rewarding stimuli, whereas for those with low-ELS exposure, remission is associated with lower amygdala reactivity to both rewarding and threat-related stimuli. This full model predicted functional remission over and above the contribution of demographics, symptom severity, ELS, and amygdala reactivity alone. These findings identify a human target for elucidating the mechanisms of antidepressant functional remission and offer a target for developing novel therapeutics. The results also offer a proof-of-concept for using neuroimaging as a target for guiding neuroscience-informed intervention decisions at the level of the individual person.

  3. Prediction of three-dimensional arm trajectories based on ECoG signals recorded from human sensorimotor cortex.

    Directory of Open Access Journals (Sweden)

    Yasuhiko Nakanishi

    Full Text Available Brain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neurorehabilitation in the hopes of providing a means to restore lost motor function. Electrocorticography (ECoG has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes. Although several studies have already succeeded in the inference of computer cursor trajectories and finger flexions using human ECoG signals, precise three-dimensional (3D trajectory reconstruction for a human limb from ECoG has not yet been achieved. In this study, we predicted 3D arm trajectories in time series from ECoG signals in humans using a novel preprocessing method and a sparse linear regression. Average Pearson's correlation coefficients and normalized root-mean-square errors between predicted and actual trajectories were 0.44~0.73 and 0.18~0.42, respectively, confirming the feasibility of predicting 3D arm trajectories from ECoG. We foresee this method contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

  4. PID and predictive control of electrical drives and power converters using MATLAB/Simulink

    CERN Document Server

    Wang, Liuping; Yoo, Dae; Gan, Lu; Ng, Ki

    2015-01-01

    A timely introduction to current research on PID and predictive control by one of the leading authors on the subject PID and Predictive Control of Electric Drives and Power Supplies using MATLAB/Simulink examines the classical control system strategies, such as PID control, feed-forward control and cascade control, which are widely used in current practice.  The authors share their experiences in actual design and implementation of the control systems on laboratory test-beds, taking the reader from the fundamentals through to more sophisticated design and analysis.    The book contains secti

  5. Association with humans and seasonality interact to reverse predictions for animal space use.

    Science.gov (United States)

    Laver, Peter N; Alexander, Kathleen A

    2018-01-01

    Variation in animal space use reflects fitness trade-offs associated with ecological constraints. Associated theories such as the metabolic theory of ecology and the resource dispersion hypothesis generate predictions about what drives variation in animal space use. But, metabolic theory is usually tested in macro-ecological studies and is seldom invoked explicitly in within-species studies. Full evaluation of the resource dispersion hypothesis requires testing in more species. Neither have been evaluated in the context of anthropogenic landscape change. In this study, we used data for banded mongooses ( Mungos mungo ) in northeastern Botswana, along a gradient of association with humans, to test for effects of space use drivers predicted by these theories. We used Bayesian parameter estimation and inference from linear models to test for seasonal differences in space use metrics and to model seasonal effects of space use drivers. Results suggest that space use is strongly associated with variation in the level of overlap that mongoose groups have with humans. Seasonality influences this association, reversing seasonal space use predictions historically-accepted by ecologists. We found support for predictions of the metabolic theory when moderated by seasonality, by association with humans and by their interaction. Space use of mongooses living in association with humans was more concentrated in the dry season than the wet season, when historically-accepted ecological theory predicted more dispersed space use. Resource richness factors such as building density were associated with space use only during the dry season. We found negligible support for predictions of the resource dispersion hypothesis in general or for metabolic theory where seasonality and association with humans were not included. For mongooses living in association with humans, space use was not associated with patch dispersion or group size over both seasons. In our study, living in association

  6. INTEGRATED ROBOT-HUMAN CONTROL IN MINING OPERATIONS

    Energy Technology Data Exchange (ETDEWEB)

    George Danko

    2005-04-01

    This report contains a detailed description of the work conducted in the first year of the project on Integrated Robot-Human Control in Mining Operations at University of Nevada, Reno. This project combines human operator control with robotic control concepts to create a hybrid control architecture, in which the strengths of each control method are combined to increase machine efficiency and reduce operator fatigue. The kinematics reconfiguration type differential control of the excavator implemented with a variety of ''software machine kinematics'' is the key feature of the project. This software re-configured excavator is more desirable to execute a given digging task. The human operator retains the master control of the main motion parameters, while the computer coordinates the repetitive movement patterns of the machine links. These repetitive movements may be selected from a pre-defined family of trajectories with different transformations. The operator can make adjustments to this pattern in real time, as needed, to accommodate rapidly-changing environmental conditions. A Bobcat{reg_sign} 435 excavator was retrofitted with electro-hydraulic control valve elements. The modular electronic control was tested and the basic valve characteristics were measured for each valve at the Robotics Laboratory at UNR. Position sensors were added to the individual joint control actuators, and the sensors were calibrated. An electronic central control system consisting of a portable computer, converters and electronic driver components was interfaced to the electro-hydraulic valves and position sensors. The machine is operational with or without the computer control system depending on whether the computer interface is on or off. In preparation for emulated mining tasks tests, typical, repetitive tool trajectories during surface mining operations were recorded at the Newmont Mining Corporation's ''Lone Tree'' mine in Nevada.

  7. A Building Model Framework for a Genetic Algorithm Multi-objective Model Predictive Control

    DEFF Research Database (Denmark)

    Arendt, Krzysztof; Ionesi, Ana; Jradi, Muhyiddine

    2016-01-01

    Model Predictive Control (MPC) of building systems is a promising approach to optimize building energy performance. In contrast to traditional control strategies which are reactive in nature, MPC optimizes the utilization of resources based on the predicted effects. It has been shown that energy ...

  8. An outlook on robust model predictive control algorithms : Reflections on performance and computational aspects

    NARCIS (Netherlands)

    Saltik, M.B.; Özkan, L.; Ludlage, J.H.A.; Weiland, S.; Van den Hof, P.M.J.

    2018-01-01

    In this paper, we discuss the model predictive control algorithms that are tailored for uncertain systems. Robustness notions with respect to both deterministic (or set based) and stochastic uncertainties are discussed and contributions are reviewed in the model predictive control literature. We

  9. Just-In-Time predictive control for a two-wheeled robot

    OpenAIRE

    Nakpong, Nuttapun; Yamamoto, Shigeru

    2012-01-01

    In this paper, we introduce the use of Just-In-Time predictive control to enhance the stability of a two-wheeled robot. Just-In-Time predictive control uses a database which includes a huge amounts of input-output data of the two-wheeled robot and predicts its future movements based on a Just-In-Time algorithm. © 2012 IEEE.

  10. [Prediction model of human-caused fire occurrence in the boreal forest of northern China].

    Science.gov (United States)

    Guo, Fu-tao; Su, Zhang-wen; Wang, Guang-yu; Wang, Qiang; Sun, Long; Yang, Ting-ting

    2015-07-01

    The Chinese boreal forest is an important forest resource in China. However, it has been suffering serious disturbances of forest fires, which were caused equally by natural disasters (e.g., lightning) and human activities. The literature on human-caused fires indicates that climate, topography, vegetation, and human infrastructure are significant factors that impact the occurrence and spread of human-caused fires. But the studies on human-caused fires in the boreal forest of northern China are limited and less comprehensive. This paper applied the spatial analysis tools in ArcGIS 10.0 and Logistic regression model to investigate the driving factors of human-caused fires. Our data included the geographic coordinates of human-caused fires, climate factors during year 1974-2009, topographic information, and forest map. The results indicated that distance to railway (x1) and average relative humidity (x2) significantly impacted the occurrence of human-caused fire in the study area. The logistic model for predicting the fire occurrence probability was formulated as P= 1/[11+e-(3.026-0.00011x1-0.047x2)] with an accuracy rate of 80%. The above model was used to predict the monthly fire occurrence during the fire season of 2015 based on the HADCM2 future weather data. The prediction results showed that the high risk of human-caused fire occurrence concentrated in the months of April, May, June and August, while April and May had higher risk of fire occurrence than other months. According to the spatial distribution of possibility of fire occurrence, the high fire risk zones were mainly in the west and southwest of Tahe, where the major railways were located.

  11. Application of Predictive Control in District Heating Systems

    DEFF Research Database (Denmark)

    Palsson, Olafur Petur; Madsen, Henrik; Søgaard, Henning Tangen

    1993-01-01

    In district heating systems, and in particular if the heat production cakes place at a combined heat and power (CHP) plant, a reasonable control strategy is to keep the supply temperature from the district heating plant as low as possible. However, the control is subject to some restrictions, for...

  12. Robust, Optimal, Predictive, and Integrated Road Traffic Control : Research proposal

    NARCIS (Netherlands)

    Van de Weg, G.S.; Hegyi, A.; Hoogendoorn, S.P.

    2014-01-01

    The development of control strategies for traffic lights, ramp metering installations, and variable speed limits to improve the throughput of road traffic networks can contribute to a more efficient use of road networks. In this project, a hierarchical controller will be developed for the

  13. Modeling, Behaviour Prediction, and Control – Tripartite Essentials ...

    African Journals Online (AJOL)

    Computers have today permeated every sphere of human endeavour and structural engineering is no exception. Multiply-hyperstatic structures which relatively recently took teams of structural engineers several months to analyse can now be elegantly analysed in a matter of seconds using computers equipped with readily ...

  14. Nonlinear model predictive control for chemical looping process

    Science.gov (United States)

    Joshi, Abhinaya; Lei, Hao; Lou, Xinsheng

    2017-08-22

    A control system for optimizing a chemical looping ("CL") plant includes a reduced order mathematical model ("ROM") that is designed by eliminating mathematical terms that have minimal effect on the outcome. A non-linear optimizer provides various inputs to the ROM and monitors the outputs to determine the optimum inputs that are then provided to the CL plant. An estimator estimates the values of various internal state variables of the CL plant. The system has one structure adapted to control a CL plant that only provides pressure measurements in the CL loops A and B, a second structure adapted to a CL plant that provides pressure measurements and solid levels in both loops A, and B, and a third structure adapted to control a CL plant that provides full information on internal state variables. A final structure provides a neural network NMPC controller to control operation of loops A and B.

  15. Single and 30 fraction tumor control doses correlate in xenografted tumor models: implications for predictive assays

    International Nuclear Information System (INIS)

    Gerweck, Leo E.; Dubois, Willum; Baumann, Michael; Suit, Herman D.

    1995-01-01

    Purpose/Objective: In a previous publication we reported that laboratory assays of tumor clonogen number, in combination with intrinsic radiosensitivity measured in-vitro, accurately predicted the rank-order of single fraction 50% tumor control doses, in six rodent and xenografted human tumors. In these studies, tumor hypoxia influenced the absolute value of the tumor control doses across tumor types, but not their rank-order. In the present study we hypothesize that determinants of the single fraction tumor control dose, may also strongly influence the fractionaled tumor control doses, and that knowledge of tumor clonogen number and their sensitivity to fractionated irradiation, may be useful for predicting the relative sensitivity of tumors treated by conventional fractionated irradiation. Methods/Materials: Five tumors of human origin were used for these studies. Special care was taken to ensure that all tumor control dose assays were performed over the same time frame, i.e., in-vitro cells of a similar passage were used to initiate tumor sources which were expanded and used in the 3rd or 4th generation. Thirty fraction tumor control doses were performed in air breathing mice, under normal blood flow conditions (two fractions/day). The results of these studies have been previously published. For studies under uniformly (clamp) hypoxic conditions, tumors arising from the same transplantation were randomized into single or fractionated dose protocols. For estimation of the fractionated TCD50 under hypoxic conditions, tumors were exposed to six 5.4 Gy fractions (∼ 2 Gy equivalent under air), followed by graded 'top-up' dose irradiation for determination of the TCD50; the time interval between doses was 6-9 hours. The single dose equivalent of the six 5.4 Gy doses was used to calculate an extrapolated 30 fraction hypoxic TCD50. Results: Fractionation substantially increased the dose required for tumor control in 4 of the 5 tumors investigated. For these 4 tumors

  16. Predicted congestions never occur. On the gap between transport modeling and human behavior

    Directory of Open Access Journals (Sweden)

    Harald FREY

    2011-01-01

    Full Text Available This paper presents an introduction to meso-scale transport modeling and issues of human behaviour in transport systems. Along with other examples of the human ability to learn in transport systems we look at the comparison of real life data and the prediction of modeling tools for the closure of Vienna’s inner ring road during the 2008 European Football Championship (EURO 2008. Some light is shed on the scientific question, whether currently used modeling tools are able to adequately reproduce the real-life behaviour of human beings in the transport system and should be used for transport policy decision making.

  17. Comparison of predicted with observed biokinetics of inhaled plutonium nitrate and gadolinium oxide in humans

    International Nuclear Information System (INIS)

    Hodgson, A.; Shutt, A.L.; Etherington, G.; Hodgson, S.A.; Rance, E.; Stradling, G.N.; Youngman, M.J.; Ziesenis, A.; Kreyling, W.G.

    2003-01-01

    The absorption kinetics to blood of plutonium and gadolinium after inhalation as nitrate and oxide in humans and animals has been studied. For each material, values describing the time dependence of absorption were derived from the studies in animals and used with the ICRP human respiratory tract model to predict lung retention and cumulative amounts to blood for the volunteers inhaling the same materials. Comparison with the observed behaviour in the volunteers suggests that absorption of plutonium and gadolinium is reasonably species independent, and that data obtained from animal studies can be used to assess their biokinetic behaviour in humans. (author)

  18. Improvement of Disease Prediction and Modeling through the Use of Meteorological Ensembles: Human Plague in Uganda

    Science.gov (United States)

    Moore, Sean M.; Monaghan, Andrew; Griffith, Kevin S.; Apangu, Titus; Mead, Paul S.; Eisen, Rebecca J.

    2012-01-01

    Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens. Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in targeting limited prevention and control resources and may ultimately reduce the burden of diseases. Paradoxically, localities where such models could yield the greatest benefits, such as tropical regions where morbidity and mortality caused by vector-borne diseases is greatest, often lack high-quality in situ local meteorological data. Satellite- and model-based gridded climate datasets can be used to approximate local meteorological conditions in data-sparse regions, however their accuracy varies. Here we investigate how the selection of a particular dataset can influence the outcomes of disease forecasting models. Our model system focuses on plague (Yersinia pestis infection) in the West Nile region of Uganda. The majority of recent human cases have been reported from East Africa and Madagascar, where meteorological observations are sparse and topography yields complex weather patterns. Using an ensemble of meteorological datasets and model-averaging techniques we find that the number of suspected cases in the West Nile region was negatively associated with dry season rainfall (December-February) and positively with rainfall prior to the plague season. We demonstrate that ensembles of available meteorological datasets can be used to quantify climatic uncertainty and minimize its impacts on infectious disease models. These methods are particularly valuable in regions with sparse observational networks and high morbidity and mortality from vector-borne diseases. PMID:23024750

  19. Improvement of disease prediction and modeling through the use of meteorological ensembles: human plague in Uganda.

    Directory of Open Access Journals (Sweden)

    Sean M Moore

    Full Text Available Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens. Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in targeting limited prevention and control resources and may ultimately reduce the burden of diseases. Paradoxically, localities where such models could yield the greatest benefits, such as tropical regions where morbidity and mortality caused by vector-borne diseases is greatest, often lack high-quality in situ local meteorological data. Satellite- and model-based gridded climate datasets can be used to approximate local meteorological conditions in data-sparse regions, however their accuracy varies. Here we investigate how the selection of a particular dataset can influence the outcomes of disease forecasting models. Our model system focuses on plague (Yersinia pestis infection in the West Nile region of Uganda. The majority of recent human cases have been reported from East Africa and Madagascar, where meteorological observations are sparse and topography yields complex weather patterns. Using an ensemble of meteorological datasets and model-averaging techniques we find that the number of suspected cases in the West Nile region was negatively associated with dry season rainfall (December-February and positively with rainfall prior to the plague season. We demonstrate that ensembles of available meteorological datasets can be used to quantify climatic uncertainty and minimize its impacts on infectious disease models. These methods are particularly valuable in regions with sparse observational networks and high morbidity and mortality from vector-borne diseases.

  20. Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking.

    Science.gov (United States)

    Markowitz, Jared; Herr, Hugh

    2016-05-01

    Humans employ a high degree of redundancy in joint actuation, with different combinations of muscle and tendon action providing the same net joint torque. Both the resolution of these redundancies and the energetics of such systems depend on the dynamic properties of muscles and tendons, particularly their force-length relations. Current walking models that use stock parameters when simulating muscle-tendon dynamics tend to significantly overestimate metabolic consumption, perhaps because they do not adequately consider the role of elasticity. As an alternative, we posit that the muscle-tendon morphology of the human leg has evolved to maximize the metabolic efficiency of walking at self-selected speed. We use a data-driven approach to evaluate this hypothesis, utilizing kinematic, kinetic, electromyographic (EMG), and metabolic data taken from five participants walking at self-selected speed. The kinematic and kinetic data are used to estimate muscle-tendon lengths, muscle moment arms, and joint moments while the EMG data are used to estimate muscle activations. For each subject we perform an optimization using prescribed skeletal kinematics, varying the parameters that govern the force-length curve of each tendon as well as the strength and optimal fiber length of each muscle while seeking to simultaneously minimize metabolic cost and maximize agreement with the estimated joint moments. We find that the metabolic cost of transport (MCOT) values of our participants may be correctly matched (on average 0.36±0.02 predicted, 0.35±0.02 measured) with acceptable joint torque fidelity through application of a single constraint to the muscle metabolic budget. The associated optimal muscle-tendon parameter sets allow us to estimate the forces and states of individual muscles, resolving redundancies in joint actuation and lending insight into the potential roles and control objectives of the muscles of the leg throughout the gait cycle.