Sample records for drive reinforcement neural

  1. Drive reinforcement neural networks for reactor control. Final report

    International Nuclear Information System (INIS)

    Williams, J.G.; Jouse, W.C.


    In view of the loss of the third year funding, the scope of the project goals has been revised. The revision in project scope no longer allows for the detailed modeling of the EBR-11 start-up task that was originally envisaged. The authors are continuing, however, to model the control of the rapid power ascent of the University of Arizona TRIGA reactor using a model-based controller and using a drive reinforcement neural network. These will be combined during the concluding period of the project into a hierarchical control architecture. In addition, the modeling of a PWR feedwater heater has continued, and an autonomous fault-tolerant software architecture for its control has been proposed

  2. Hunger neurons drive feeding through a sustained, positive reinforcement signal. (United States)

    Chen, Yiming; Lin, Yen-Chu; Zimmerman, Christopher A; Essner, Rachel A; Knight, Zachary A


    The neural mechanisms underlying hunger are poorly understood. AgRP neurons are activated by energy deficit and promote voracious food consumption, suggesting these cells may supply the fundamental hunger drive that motivates feeding. However recent in vivo recording experiments revealed that AgRP neurons are inhibited within seconds by the sensory detection of food, raising the question of how these cells can promote feeding at all. Here we resolve this paradox by showing that brief optogenetic stimulation of AgRP neurons before food availability promotes intense appetitive and consummatory behaviors that persist for tens of minutes in the absence of continued AgRP neuron activation. We show that these sustained behavioral responses are mediated by a long-lasting potentiation of the rewarding properties of food and that AgRP neuron activity is positively reinforcing. These findings reveal that hunger neurons drive feeding by transmitting a positive valence signal that triggers a stable transition between behavioral states.

  3. Neural Basis of Reinforcement Learning and Decision Making (United States)

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


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

  4. Neural network based PWM AC chopper fed induction motor drive

    Directory of Open Access Journals (Sweden)

    Venkatesan Jamuna


    Full Text Available In this paper, a new Simulink model for a neural network controlled PWM AC chopper fed single phase induction motor is proposed. Closed loop speed control is achieved using a neural network controller. To maintain a constant fluid flow with a variation in pressure head, drives like fan and pump are operated with closed loop speed control. The need to improve the quality and reliability of the drive circuit has increased because of the growing demand for improving the performance of motor drives. With the increased availability of MOSFET's and IGBT's, PWM converters can be used efficiently in low and medium power applications. From the simulation studies, it is seen that the PWM AC chopper has a better harmonic spectrum and lesser copper loss than the Phase controlled AC chopper. It is observed that the drive system with the proposed model produces better dynamic performance, reduced overshoot and fast transient response. .

  5. The neural basis of the imitation drive. (United States)

    Hanawa, Sugiko; Sugiura, Motoaki; Nozawa, Takayuki; Kotozaki, Yuka; Yomogida, Yukihito; Ihara, Mizuki; Akimoto, Yoritaka; Thyreau, Benjamin; Izumi, Shinichi; Kawashima, Ryuta


    Spontaneous imitation is assumed to underlie the acquisition of important skills by infants, including language and social interaction. In this study, functional magnetic resonance imaging (fMRI) was used to examine the neural basis of 'spontaneously' driven imitation, which has not yet been fully investigated. Healthy participants were presented with movie clips of meaningless bimanual actions and instructed to observe and imitate them during an fMRI scan. The participants were subsequently shown the movie clips again and asked to evaluate the strength of their 'urge to imitate' (Urge) for each action. We searched for cortical areas where the degree of activation positively correlated with Urge scores; significant positive correlations were observed in the right supplementary motor area (SMA) and bilateral midcingulate cortex (MCC) under the imitation condition. These areas were not explained by explicit reasons for imitation or the kinematic characteristics of the actions. Previous studies performed in monkeys and humans have implicated the SMA and MCC/caudal cingulate zone in voluntary actions. This study also confirmed the functional connectivity between Urge and imitation performance using a psychophysiological interaction analysis. Thus, our findings reveal the critical neural components that underlie spontaneous imitation and provide possible reasons why infants imitate spontaneously. © The Author (2015). Published by Oxford University Press.

  6. Neural and Fuzzy Adaptive Control of Induction Motor Drives

    International Nuclear Information System (INIS)

    Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.


    This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller

  7. Optimizing the Flexural Strength of Beams Reinforced with Fiber Reinforced Polymer Bars Using Back-Propagation Neural Networks

    Directory of Open Access Journals (Sweden)

    Bahman O. Taha


    Full Text Available The reinforced concrete with fiber reinforced polymer (FRP bars (carbon, aramid, basalt and glass is used in places where a high ratio of strength to weight is required and corrosion is not acceptable. Behavior of structural members using (FRP bars is hard to be modeled using traditional methods because of the high non-linearity relationship among factors influencing the strength of structural members. Back-propagation neural network is a very effective method for modeling such complicated relationships. In this paper, back-propagation neural network is used for modeling the flexural behavior of beams reinforced with (FRP bars. 101 samples of beams reinforced with fiber bars were collected from literatures. Five important factors are taken in consideration for predicting the strength of beams. Two models of Multilayer Perceptron (MLP are created, first with single-hidden layer and the second with two-hidden layers. The two-hidden layer model showed better accuracy ratio than the single-hidden layer model. Parametric study has been done for two-hidden layer model only. Equations are derived to be used instead of the model and the importance of input factors is determined. Results showed that the neural network is successful in modeling the behavior of concrete beams reinforced with different types of (FRP bars.

  8. Neural mechanisms of negative reinforcement in children and adolescents with autism spectrum disorders


    Damiano, Cara R; Cockrell, Dillon C; Dunlap, Kaitlyn; Hanna, Eleanor K; Miller, Stephanie; Bizzell, Joshua; Kovac, Megan; Turner-Brown, Lauren; Sideris, John; Kinard, Jessica; Dichter, Gabriel S


    Background Previous research has found accumulating evidence for atypical reward processing in autism spectrum disorders (ASD), particularly in the context of social rewards. Yet, this line of research has focused largely on positive social reinforcement, while little is known about the processing of negative reinforcement in individuals with ASD. Methods The present study examined neural responses to social negative reinforcement (a face displaying negative affect) and non-social negative re...

  9. Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving


    Shalev-Shwartz, Shai; Shammah, Shaked; Shashua, Amnon


    Autonomous driving is a multi-agent setting where the host vehicle must apply sophisticated negotiation skills with other road users when overtaking, giving way, merging, taking left and right turns and while pushing ahead in unstructured urban roadways. Since there are many possible scenarios, manually tackling all possible cases will likely yield a too simplistic policy. Moreover, one must balance between unexpected behavior of other drivers/pedestrians and at the same time not to be too de...

  10. Modeling and Speed Control of Induction Motor Drives Using Neural Networks

    Directory of Open Access Journals (Sweden)

    V. Jamuna


    Full Text Available Speed control of induction motor drives using neural networks is presented. The mathematical model of single phase induction motor is developed. A new simulink model for a neural network-controlled bidirectional chopper fed single phase induction motor is proposed. Under normal operation, the true drive parameters are real-time identified and they are converted into the controller parameters through multilayer forward computation by neural networks. Comparative study has been made between the conventional and neural network controllers. It is observed that the neural network controlled drive system has better dynamic performance, reduced overshoot and faster transient response than the conventional controlled system.

  11. Cracking and induced steel stresses of reinforced and prestressed piles during driving

    NARCIS (Netherlands)

    Zorn, N.F.


    The problem of steel stresses during driving of reinforced and prestressed piles in case of concrete failure is analysed in this report using a momentum trap model that includes amplitude and shape of the reflected compressive wave. Special reference is made to the different performance of

  12. Neural systems underlying aversive conditioning in humans with primary and secondary reinforcers

    Directory of Open Access Journals (Sweden)

    Mauricio R Delgado


    Full Text Available Money is a secondary reinforcer commonly used across a range of disciplines in experimental paradigms investigating reward learning and decision-making. The effectiveness of monetary reinforcers during aversive learning and its neural basis, however, remains a topic of debate. Specifically, it is unclear if the initial acquisition of aversive representations of monetary losses depends on similar neural systems as more traditional aversive conditioning that involves primary reinforcers. This study contrasts the efficacy of a biologically defined primary reinforcer (shock and a socially defined secondary reinforcer (money during aversive learning and its associated neural circuitry. During a two-part experiment, participants first played a gambling game where wins and losses were based on performance to gain an experimental bank. Participants were then exposed to two separate aversive conditioning sessions. In one session, a primary reinforcer (mild shock served as an unconditioned stimulus (US and was paired with one of two colored squares, the conditioned stimuli (CS+ and CS-, respectively. In another session, a secondary reinforcer (loss of money served as the US and was paired with one of two different CS. Skin conductance responses were greater for CS+ compared to CS- trials irrespective of type of reinforcer. Neuroimaging results revealed that the striatum, a region typically linked with reward-related processing, was found to be involved in the acquisition of aversive conditioned response irrespective of reinforcer type. In contrast, the amygdala was involved during aversive conditioning with primary reinforcers, as suggested by both an exploratory fMRI analysis and a follow-up case study with a patient with bilateral amygdala damage. Taken together, these results suggest that learning about potential monetary losses may depend on reinforcement learning related systems, rather than on typical structures involved in more biologically based

  13. Neural mechanisms of negative reinforcement in children and adolescents with autism spectrum disorders. (United States)

    Damiano, Cara R; Cockrell, Dillon C; Dunlap, Kaitlyn; Hanna, Eleanor K; Miller, Stephanie; Bizzell, Joshua; Kovac, Megan; Turner-Brown, Lauren; Sideris, John; Kinard, Jessica; Dichter, Gabriel S


    Previous research has found accumulating evidence for atypical reward processing in autism spectrum disorders (ASD), particularly in the context of social rewards. Yet, this line of research has focused largely on positive social reinforcement, while little is known about the processing of negative reinforcement in individuals with ASD. The present study examined neural responses to social negative reinforcement (a face displaying negative affect) and non-social negative reinforcement (monetary loss) in children with ASD relative to typically developing children, using functional magnetic resonance imaging (fMRI). We found that children with ASD demonstrated hypoactivation of the right caudate nucleus while anticipating non-social negative reinforcement and hypoactivation of a network of frontostriatal regions (including the nucleus accumbens, caudate nucleus, and putamen) while anticipating social negative reinforcement. In addition, activation of the right caudate nucleus during non-social negative reinforcement was associated with individual differences in social motivation. These results suggest that atypical responding to negative reinforcement in children with ASD may contribute to social motivational deficits in this population.

  14. Neural prediction errors reveal a risk-sensitive reinforcement-learning process in the human brain. (United States)

    Niv, Yael; Edlund, Jeffrey A; Dayan, Peter; O'Doherty, John P


    Humans and animals are exquisitely, though idiosyncratically, sensitive to risk or variance in the outcomes of their actions. Economic, psychological, and neural aspects of this are well studied when information about risk is provided explicitly. However, we must normally learn about outcomes from experience, through trial and error. Traditional models of such reinforcement learning focus on learning about the mean reward value of cues and ignore higher order moments such as variance. We used fMRI to test whether the neural correlates of human reinforcement learning are sensitive to experienced risk. Our analysis focused on anatomically delineated regions of a priori interest in the nucleus accumbens, where blood oxygenation level-dependent (BOLD) signals have been suggested as correlating with quantities derived from reinforcement learning. We first provide unbiased evidence that the raw BOLD signal in these regions corresponds closely to a reward prediction error. We then derive from this signal the learned values of cues that predict rewards of equal mean but different variance and show that these values are indeed modulated by experienced risk. Moreover, a close neurometric-psychometric coupling exists between the fluctuations of the experience-based evaluations of risky options that we measured neurally and the fluctuations in behavioral risk aversion. This suggests that risk sensitivity is integral to human learning, illuminating economic models of choice, neuroscientific models of affective learning, and the workings of the underlying neural mechanisms.

  15. A Model to Explain the Emergence of Reward Expectancy neurons using Reinforcement Learning and Neural Network


    Shinya, Ishii; Munetaka, Shidara; Katsunari, Shibata


    In an experiment of multi-trial task to obtain a reward, reward expectancy neurons,###which responded only in the non-reward trials that are necessary to advance###toward the reward, have been observed in the anterior cingulate cortex of monkeys.###In this paper, to explain the emergence of the reward expectancy neuron in###terms of reinforcement learning theory, a model that consists of a recurrent neural###network trained based on reinforcement learning is proposed. The analysis of the###hi...

  16. Neural mechanisms of reinforcement learning in unmedicated patients with major depressive disorder. (United States)

    Rothkirch, Marcus; Tonn, Jonas; Köhler, Stephan; Sterzer, Philipp


    According to current concepts, major depressive disorder is strongly related to dysfunctional neural processing of motivational information, entailing impairments in reinforcement learning. While computational modelling can reveal the precise nature of neural learning signals, it has not been used to study learning-related neural dysfunctions in unmedicated patients with major depressive disorder so far. We thus aimed at comparing the neural coding of reward and punishment prediction errors, representing indicators of neural learning-related processes, between unmedicated patients with major depressive disorder and healthy participants. To this end, a group of unmedicated patients with major depressive disorder (n = 28) and a group of age- and sex-matched healthy control participants (n = 30) completed an instrumental learning task involving monetary gains and losses during functional magnetic resonance imaging. The two groups did not differ in their learning performance. Patients and control participants showed the same level of prediction error-related activity in the ventral striatum and the anterior insula. In contrast, neural coding of reward prediction errors in the medial orbitofrontal cortex was reduced in patients. Moreover, neural reward prediction error signals in the medial orbitofrontal cortex and ventral striatum showed negative correlations with anhedonia severity. Using a standard instrumental learning paradigm we found no evidence for an overall impairment of reinforcement learning in medication-free patients with major depressive disorder. Importantly, however, the attenuated neural coding of reward in the medial orbitofrontal cortex and the relation between anhedonia and reduced reward prediction error-signalling in the medial orbitofrontal cortex and ventral striatum likely reflect an impairment in experiencing pleasure from rewarding events as a key mechanism of anhedonia in major depressive disorder. © The Author (2017). Published by Oxford

  17. Operant conditioning of neural activity in freely behaving monkeys with intracranial reinforcement. (United States)

    Eaton, Ryan W; Libey, Tyler; Fetz, Eberhard E


    Operant conditioning of neural activity has typically been performed under controlled behavioral conditions using food reinforcement. This has limited the duration and behavioral context for neural conditioning. To reward cell activity in unconstrained primates, we sought sites in nucleus accumbens (NAc) whose stimulation reinforced operant responding. In three monkeys, NAc stimulation sustained performance of a manual target-tracking task, with response rates that increased monotonically with increasing NAc stimulation. We recorded activity of single motor cortex neurons and documented their modulation with wrist force. We conditioned increased firing rates with the monkey seated in the training booth and during free behavior in the cage using an autonomous head-fixed recording and stimulating system. Spikes occurring above baseline rates triggered single or multiple electrical pulses to the reinforcement site. Such rate-contingent, unit-triggered stimulation was made available for periods of 1-3 min separated by 3-10 min time-out periods. Feedback was presented as event-triggered clicks both in-cage and in-booth, and visual cues were provided in many in-booth sessions. In-booth conditioning produced increases in single neuron firing probability with intracranial reinforcement in 48 of 58 cells. Reinforced cell activity could rise more than five times that of non-reinforced activity. In-cage conditioning produced significant increases in 21 of 33 sessions. In-cage rate changes peaked later and lasted longer than in-booth changes, but were often comparatively smaller, between 13 and 18% above non-reinforced activity. Thus intracranial stimulation reinforced volitional increases in cortical firing rates during both free behavior and a controlled environment, although changes in the latter were more robust. NEW & NOTEWORTHY Closed-loop brain-computer interfaces (BCI) were used to operantly condition increases in muscle and neural activity in monkeys by delivering

  18. Estimation of Muscle Force Based on Neural Drive in a Hemispheric Stroke Survivor. (United States)

    Dai, Chenyun; Zheng, Yang; Hu, Xiaogang


    Robotic assistant-based therapy holds great promise to improve the functional recovery of stroke survivors. Numerous neural-machine interface techniques have been used to decode the intended movement to control robotic systems for rehabilitation therapies. In this case report, we tested the feasibility of estimating finger extensor muscle forces of a stroke survivor, based on the decoded descending neural drive through population motoneuron discharge timings. Motoneuron discharge events were obtained by decomposing high-density surface electromyogram (sEMG) signals of the finger extensor muscle. The neural drive was extracted from the normalized frequency of the composite discharge of the motoneuron pool. The neural-drive-based estimation was also compared with the classic myoelectric-based estimation. Our results showed that the neural-drive-based approach can better predict the force output, quantified by lower estimation errors and higher correlations with the muscle force, compared with the myoelectric-based estimation. Our findings suggest that the neural-drive-based approach can potentially be used as a more robust interface signal for robotic therapies during the stroke rehabilitation.

  19. Surface electromyographic amplitude does not identify differences in neural drive to synergistic muscles. (United States)

    Martinez-Valdes, Eduardo; Negro, Francesco; Falla, Deborah; De Nunzio, Alessandro Marco; Farina, Dario


    Surface electromyographic (EMG) signal amplitude is typically used to compare the neural drive to muscles. We experimentally investigated this association by studying the motor unit (MU) behavior and action potentials in the vastus medialis (VM) and vastus lateralis (VL) muscles. Eighteen participants performed isometric knee extensions at four target torques [10, 30, 50, and 70% of the maximum torque (MVC)] while high-density EMG signals were recorded from the VM and VL. The absolute EMG amplitude was greater for VM than VL ( P differences in EMG amplitude can be due to both differences in the neural drive and in the size of the MU action potentials, we indirectly inferred the neural drives received by the two muscles by estimating the synaptic inputs received by the corresponding motor neuron pools. For this purpose, we analyzed the increase in discharge rate from recruitment to target torque for motor units matched by recruitment threshold in the two muscles. This analysis indicated that the two muscles received similar levels of neural drive. Nonetheless, the size of the MU action potentials was greater for VM than VL ( P difference explained most of the differences in EMG amplitude between the two muscles (~63% of explained variance). These results indicate that EMG amplitude, even following normalization, does not reflect the neural drive to synergistic muscles. Moreover, absolute EMG amplitude is mainly explained by the size of MU action potentials. NEW & NOTEWORTHY Electromyographic (EMG) amplitude is widely used to compare indirectly the strength of neural drive received by synergistic muscles. However, there are no studies validating this approach with motor unit data. Here, we compared between-muscles differences in surface EMG amplitude and motor unit behavior. The results clarify the limitations of surface EMG to interpret differences in neural drive between muscles.

  20. Extending unified-theory-of-reinforcement neural networks to steady-state operant behavior. (United States)

    Calvin, Olivia L; McDowell, J J


    The unified theory of reinforcement has been used to develop models of behavior over the last 20 years (Donahoe et al., 1993). Previous research has focused on the theory's concordance with the respondent behavior of humans and animals. In this experiment, neural networks were developed from the theory to extend the unified theory of reinforcement to operant behavior on single-alternative variable-interval schedules. This area of operant research was selected because previously developed neural networks could be applied to it without significant alteration. Previous research with humans and animals indicates that the pattern of their steady-state behavior is hyperbolic when plotted against the obtained rate of reinforcement (Herrnstein, 1970). A genetic algorithm was used in the first part of the experiment to determine parameter values for the neural networks, because values that were used in previous research did not result in a hyperbolic pattern of behavior. After finding these parameters, hyperbolic and other similar functions were fitted to the behavior produced by the neural networks. The form of the neural network's behavior was best described by an exponentiated hyperbola (McDowell, 1986; McLean and White, 1983; Wearden, 1981), which was derived from the generalized matching law (Baum, 1974). In post-hoc analyses the addition of a baseline rate of behavior significantly improved the fit of the exponentiated hyperbola and removed systematic residuals. The form of this function was consistent with human and animal behavior, but the estimated parameter values were not. Copyright © 2016 Elsevier B.V. All rights reserved.

  1. Adolescent-specific patterns of behavior and neural activity during social reinforcement learning. (United States)

    Jones, Rebecca M; Somerville, Leah H; Li, Jian; Ruberry, Erika J; Powers, Alisa; Mehta, Natasha; Dyke, Jonathan; Casey, B J


    Humans are sophisticated social beings. Social cues from others are exceptionally salient, particularly during adolescence. Understanding how adolescents interpret and learn from variable social signals can provide insight into the observed shift in social sensitivity during this period. The present study tested 120 participants between the ages of 8 and 25 years on a social reinforcement learning task where the probability of receiving positive social feedback was parametrically manipulated. Seventy-eight of these participants completed the task during fMRI scanning. Modeling trial-by-trial learning, children and adults showed higher positive learning rates than did adolescents, suggesting that adolescents demonstrated less differentiation in their reaction times for peers who provided more positive feedback. Forming expectations about receiving positive social reinforcement correlated with neural activity within the medial prefrontal cortex and ventral striatum across age. Adolescents, unlike children and adults, showed greater insular activity during positive prediction error learning and increased activity in the supplementary motor cortex and the putamen when receiving positive social feedback regardless of the expected outcome, suggesting that peer approval may motivate adolescents toward action. While different amounts of positive social reinforcement enhanced learning in children and adults, all positive social reinforcement equally motivated adolescents. Together, these findings indicate that sensitivity to peer approval during adolescence goes beyond simple reinforcement theory accounts and suggest possible explanations for how peers may motivate adolescent behavior.

  2. Invariant recognition drives neural representations of action sequences.

    Directory of Open Access Journals (Sweden)

    Andrea Tacchetti


    Full Text Available Recognizing the actions of others from visual stimuli is a crucial aspect of human perception that allows individuals to respond to social cues. Humans are able to discriminate between similar actions despite transformations, like changes in viewpoint or actor, that substantially alter the visual appearance of a scene. This ability to generalize across complex transformations is a hallmark of human visual intelligence. Advances in understanding action recognition at the neural level have not always translated into precise accounts of the computational principles underlying what representations of action sequences are constructed by human visual cortex. Here we test the hypothesis that invariant action discrimination might fill this gap. Recently, the study of artificial systems for static object perception has produced models, Convolutional Neural Networks (CNNs, that achieve human level performance in complex discriminative tasks. Within this class, architectures that better support invariant object recognition also produce image representations that better match those implied by human and primate neural data. However, whether these models produce representations of action sequences that support recognition across complex transformations and closely follow neural representations of actions remains unknown. Here we show that spatiotemporal CNNs accurately categorize video stimuli into action classes, and that deliberate model modifications that improve performance on an invariant action recognition task lead to data representations that better match human neural recordings. Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed in the brain. These results broaden the scope of the invariant recognition framework for understanding visual intelligence from perception of inanimate objects and faces in static images to the study of human perception of action sequences.

  3. Damage Level Prediction of Reinforced Concrete Building Based on Earthquake Time History Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Suryanita Reni


    Full Text Available The strong motion earthquake could cause the building damage in case of the building not considered in the earthquake design of the building. The study aims to predict the damage-level of building due to earthquake using Artificial Neural Networks method. The building model is a reinforced concrete building with ten floors and height between floors is 3.6 m. The model building received a load of the earthquake based on nine earthquake time history records. Each time history scaled to 0,5g, 0,75g, and 1,0g. The Artificial Neural Networks are designed in 4 architectural models using the MATLAB program. Model 1 used the displacement, velocity, and acceleration as input and Model 2 used the displacement only as the input. Model 3 used the velocity as input, and Model 4 used the acceleration just as input. The output of the Neural Networks is the damage level of the building with the category of Safe (1, Immediate Occupancy (2, Life Safety (3 or in a condition of Collapse Prevention (4. According to the results, Neural Network models have the prediction rate of the damage level between 85%-95%. Therefore, one of the solutions for analyzing the structural responses and the damage level promptly and efficiently when the earthquake occurred is by using Artificial Neural Network

  4. Higher incentives can impair performance: neural evidence on reinforcement and rationality. (United States)

    Achtziger, Anja; Alós-Ferrer, Carlos; Hügelschäfer, Sabine; Steinhauser, Marco


    Standard economic thinking postulates that increased monetary incentives should increase performance. Human decision makers, however, frequently focus on past performance, a form of reinforcement learning occasionally at odds with rational decision making. We used an incentivized belief-updating task from economics to investigate this conflict through measurements of neural correlates of reward processing. We found that higher incentives fail to improve performance when immediate feedback on decision outcomes is provided. Subsequent analysis of the feedback-related negativity, an early event-related potential following feedback, revealed the mechanism behind this paradoxical effect. As incentives increase, the win/lose feedback becomes more prominent, leading to an increased reliance on reinforcement and more errors. This mechanism is relevant for economic decision making and the debate on performance-based payment. © The Author (2015). Published by Oxford University Press. For Permissions, please email:

  5. Prediction of strain values in reinforcements and concrete of a RC frame using neural networks (United States)

    Vafaei, Mohammadreza; Alih, Sophia C.; Shad, Hossein; Falah, Ali; Halim, Nur Hajarul Falahi Abdul


    The level of strain in structural elements is an important indicator for the presence of damage and its intensity. Considering this fact, often structural health monitoring systems employ strain gauges to measure strains in critical elements. However, because of their sensitivity to the magnetic fields, inadequate long-term durability especially in harsh environments, difficulties in installation on existing structures, and maintenance cost, installation of strain gauges is not always possible for all structural components. Therefore, a reliable method that can accurately estimate strain values in critical structural elements is necessary for damage identification. In this study, a full-scale test was conducted on a planar RC frame to investigate the capability of neural networks for predicting the strain values. Two neural networks each of which having a single hidden layer was trained to relate the measured rotations and vertical displacements of the frame to the strain values measured at different locations of the frame. Results of trained neural networks indicated that they accurately estimated the strain values both in reinforcements and concrete. In addition, the trained neural networks were capable of predicting strains for the unseen input data set.

  6. A plausible neural circuit for decision making and its formation based on reinforcement learning. (United States)

    Wei, Hui; Dai, Dawei; Bu, Yijie


    A human's, or lower insects', behavior is dominated by its nervous system. Each stable behavior has its own inner steps and control rules, and is regulated by a neural circuit. Understanding how the brain influences perception, thought, and behavior is a central mandate of neuroscience. The phototactic flight of insects is a widely observed deterministic behavior. Since its movement is not stochastic, the behavior should be dominated by a neural circuit. Based on the basic firing characteristics of biological neurons and the neural circuit's constitution, we designed a plausible neural circuit for this phototactic behavior from logic perspective. The circuit's output layer, which generates a stable spike firing rate to encode flight commands, controls the insect's angular velocity when flying. The firing pattern and connection type of excitatory and inhibitory neurons are considered in this computational model. We simulated the circuit's information processing using a distributed PC array, and used the real-time average firing rate of output neuron clusters to drive a flying behavior simulation. In this paper, we also explored how a correct neural decision circuit is generated from network flow view through a bee's behavior experiment based on the reward and punishment feedback mechanism. The significance of this study: firstly, we designed a neural circuit to achieve the behavioral logic rules by strictly following the electrophysiological characteristics of biological neurons and anatomical facts. Secondly, our circuit's generality permits the design and implementation of behavioral logic rules based on the most general information processing and activity mode of biological neurons. Thirdly, through computer simulation, we achieved new understanding about the cooperative condition upon which multi-neurons achieve some behavioral control. Fourthly, this study aims in understanding the information encoding mechanism and how neural circuits achieve behavior control

  7. Increased respiratory neural drive and work of breathing in exercise-induced laryngeal obstruction. (United States)

    Walsted, Emil S; Faisal, Azmy; Jolley, Caroline J; Swanton, Laura L; Pavitt, Matthew J; Luo, Yuan-Ming; Backer, Vibeke; Polkey, Michael I; Hull, James H


    Exercise-induced laryngeal obstruction (EILO), a phenomenon in which the larynx closes inappropriately during physical activity, is a prevalent cause of exertional dyspnea in young individuals. The physiological ventilatory impact of EILO and its relationship to dyspnea are poorly understood. The objective of this study was to evaluate exercise-related changes in laryngeal aperture on ventilation, pulmonary mechanics, and respiratory neural drive. We prospectively evaluated 12 subjects (6 with EILO and 6 healthy age- and gender-matched controls). Subjects underwent baseline spirometry and a symptom-limited incremental exercise test with simultaneous and synchronized recording of endoscopic video and gastric, esophageal, and transdiaphragmatic pressures, diaphragm electromyography, and respiratory airflow. The EILO and control groups had similar peak work rates and minute ventilation (V̇e) (work rate: 227 ± 35 vs. 237 ± 35 W; V̇e: 103 ± 20 vs. 98 ± 23 l/min; P > 0.05). At submaximal work rates (140-240 W), subjects with EILO demonstrated increased work of breathing ( P respiratory neural drive ( P respiratory mechanics and diaphragm electromyography with endoscopic video, we demonstrate, for the first time, increased work of breathing and respiratory neural drive in association with the development of EILO. Future detailed investigations are now needed to understand the role of upper airway closure in causing exertional dyspnea and exercise limitation. NEW & NOTEWORTHY Exercise-induced laryngeal obstruction is a prevalent cause of exertional dyspnea in young individuals; yet, how laryngeal closure affects breathing is unknown. In this study we synchronized endoscopic video with respiratory physiological measurements, thus providing the first detailed commensurate assessment of respiratory mechanics and neural drive in relation to laryngeal closure. Laryngeal closure was associated with increased work of breathing and respiratory neural drive preceded by an

  8. Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning. (United States)

    Zhu, Lusha; Mathewson, Kyle E; Hsu, Ming


    Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents' beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.

  9. Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network. (United States)

    Liu, Yu-Ting; Lin, Yang-Yin; Wu, Shang-Lin; Chuang, Chun-Hsiang; Lin, Chin-Teng


    This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.

  10. A neural network driving curve generation method for the heavy-haul train

    Directory of Open Access Journals (Sweden)

    Youneng Huang


    Full Text Available The heavy-haul train has a series of characteristics, such as the locomotive traction properties, the longer length of train, and the nonlinear train pipe pressure during train braking. When the train is running on a continuous long and steep downgrade railway line, the safety of the train is ensured by cycle braking, which puts high demands on the driving skills of the driver. In this article, a driving curve generation method for the heavy-haul train based on a neural network is proposed. First, in order to describe the nonlinear characteristics of train braking, the neural network model is constructed and trained by practical driving data. In the neural network model, various nonlinear neurons are interconnected to work for information processing and transmission. The target value of train braking pressure reduction and release time is achieved by modeling the braking process. The equation of train motion is computed to obtain the driving curve. Finally, in four typical operation scenarios, comparing the curve data generated by the method with corresponding practical data of the Shuohuang heavy-haul railway line, the results show that the method is effective.

  11. Correlated evolution of male and female reproductive traits drive a cascading effect of reinforcement in Drosophila yakuba (United States)

    Comeault, Aaron A.; Venkat, Aarti; Matute, Daniel R.


    Selection against maladaptive hybridization can drive the evolution of reproductive isolation in a process called reinforcement. While the importance of reinforcement in evolution has been historically debated, many examples now exist. Despite these examples, we typically lack a detailed understanding of the mechanisms limiting the spread of reinforced phenotypes throughout a species' range. Here we address this issue in the fruit fly Drosophila yakuba, a species that hybridizes with its sister species D. santomea and is undergoing reinforcement in a well-defined hybrid zone on the island of São Tomé. Within this region, female D. yakuba show increased postmating-prezygotic (gametic) isolation towards D. santomea when compared with females from allopatric populations. We use a combination of natural collections, fertility assays, and experimental evolution to understand why reinforced gametic isolation in D. yakuba is confined to this hybrid zone. We show that, among other traits, D. yakuba males from sympatric populations sire fewer progeny than allopatric males when mated to allopatric D. yakuba females. Our results provide a novel example of reinforcement acting on a postmating-prezygotic trait in males, resulting in a cascade of reproductive isolation among conspecific populations. PMID:27440664


    Directory of Open Access Journals (Sweden)

    Toshiya HIROSE, M.S.


    Nowadays, tailormade medical treatment is receiving much attention in the field of medical care. It is also desirable for driving support systems to reflect the driving characteristics of individuals as much as possible, begin monitoring the driver when a driver starts driving and calculates the driver model, and supports them with a model that makes the driver feel quite normal. That is the construction of Tailormade Driving Support Systems (TDSS. This research proposes a concept and a framework of TDSS, and presents a driver model that uses a neural network to build the system. As for the feasibility of this system, the research selects braking as a typical constituent element, and illustrates and reviews the results of experiments and simulations.

  13. On-Line Tracking Controller for Brushless DC Motor Drives Using Artificial Neural Networks (United States)

    Rubaai, Ahmed


    A real-time control architecture is developed for time-varying nonlinear brushless dc motors operating in a high performance drives environment. The developed control architecture possesses the capabilities of simultaneous on-line identification and control. The dynamics of the motor are modeled on-line and controlled using an artificial neural network, as the system runs. The control architecture combines the experience and dependability of adaptive tracking systems with potential and promise of the neural computing technology. The sensitivity of real-time controller to parametric changes that occur during training is investigated. Such changes are usually manifested by rapid changes in the load of the brushless motor drives. This sudden change in the external load is simulated for the sigmoidal and sinusoidal reference tracks. The ability of the neuro-controller to maintain reasonable tracking accuracy in the presence of external noise is also verified for a number of desired reference trajectories.

  14. Estimation of the neural drive to the muscle from surface electromyograms (United States)

    Hofmann, David

    Muscle force is highly correlated with the standard deviation of the surface electromyogram (sEMG) produced by the active muscle. Correctly estimating this quantity of non-stationary sEMG and understanding its relation to neural drive and muscle force is of paramount importance. The single constituents of the sEMG are called motor unit action potentials whose biphasic amplitude can interfere (named amplitude cancellation), potentially affecting the standard deviation (Keenan etal. 2005). However, when certain conditions are met the Campbell-Hardy theorem suggests that amplitude cancellation does not affect the standard deviation. By simulation of the sEMG, we verify the applicability of this theorem to myoelectric signals and investigate deviations from its conditions to obtain a more realistic setting. We find no difference in estimated standard deviation with and without interference, standing in stark contrast to previous results (Keenan etal. 2008, Farina etal. 2010). Furthermore, since the theorem provides us with the functional relationship between standard deviation and neural drive we conclude that complex methods based on high density electrode arrays and blind source separation might not bear substantial advantages for neural drive estimation (Farina and Holobar 2016). Funded by NIH Grant Number 1 R01 EB022872 and NSF Grant Number 1208126.

  15. Analysis of tribological behaviour of zirconia reinforced Al-SiC hybrid composites using statistical and artificial neural network technique (United States)

    Arif, Sajjad; Tanwir Alam, Md; Ansari, Akhter H.; Bilal Naim Shaikh, Mohd; Arif Siddiqui, M.


    The tribological performance of aluminium hybrid composites reinforced with micro SiC (5 wt%) and nano zirconia (0, 3, 6 and 9 wt%) fabricated through powder metallurgy technique were investigated using statistical and artificial neural network (ANN) approach. The influence of zirconia reinforcement, sliding distance and applied load were analyzed with test based on full factorial design of experiments. Analysis of variance (ANOVA) was used to evaluate the percentage contribution of each process parameters on wear loss. ANOVA approach suggested that wear loss be mainly influenced by sliding distance followed by zirconia reinforcement and applied load. Further, a feed forward back propagation neural network was applied on input/output date for predicting and analyzing the wear behaviour of fabricated composite. A very close correlation between experimental and ANN output were achieved by implementing the model. Finally, ANN model was effectively used to find the influence of various control factors on wear behaviour of hybrid composites.

  16. Shear strength estimation of the concrete beams reinforced with FRP; comparison of artificial neural network and equations of regulations

    Directory of Open Access Journals (Sweden)

    Mahmood Akbari


    Full Text Available In recent years, numerous experimental tests were done on the concrete beams reinforced with the fiber-reinforced polymer (FRP. In this way, some equations were proposed to estimate the shear strength of the beams reinforced with FRP. The aim of this study is to explore the feasibility of using a feed-forward artificial neural network (ANN model to predict the ultimate shear strength of the beams strengthened with FRP composites. For this purpose, a database consists of 304 reinforced FRP concrete beams have been collected from the available articles on the analysis of shear behavior of these beams. The inputs to the ANN model consists of the 11 variables including the geometric dimensions of the section, steel reinforcement amount, FRP amount and the properties of the concrete, steel reinforcement and FRP materials while the output variable is the shear strength of the FRP beam. To assess the performance of the ANN model for estimating the shear strength of the reinforced beams, the outputs of the ANN are compared to those of equations of the Iranian code (Publication No. 345 and the American code (ACI 440. The comparisons between the outputs of Iran and American regulations with those of the proposed model indicates that the predictive power of this model is much better than the experimental codes. Specifically, for under study data, mean absolute relative error (MARE criteria is 13%, 34% and 39% for the ANN model, the American and the Iranian codes, respectively.

  17. Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications (United States)

    Ferreria, Paulo Victor R.; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy M.; Bilen, Sven G.; Reinhart, Richard C.; Mortensen, Dale J.


    Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

  18. Synaptic energy drives the information processing mechanisms in spiking neural networks. (United States)

    El Laithy, Karim; Bogdan, Martin


    Flow of energy and free energy minimization underpins almost every aspect of naturally occurring physical mechanisms. Inspired by this fact this work establishes an energy-based framework that spans the multi-scale range of biological neural systems and integrates synaptic dynamic, synchronous spiking activity and neural states into one consistent working paradigm. Following a bottom-up approach, a hypothetical energy function is proposed for dynamic synaptic models based on the theoretical thermodynamic principles and the Hopfield networks. We show that a synapse exposes stable operating points in terms of its excitatory postsynaptic potential as a function of its synaptic strength. We postulate that synapses in a network operating at these stable points can drive this network to an internal state of synchronous firing. The presented analysis is related to the widely investigated temporal coherent activities (cell assemblies) over a certain range of time scales (binding-by-synchrony). This introduces a novel explanation of the observed (poly)synchronous activities within networks regarding the synaptic (coupling) functionality. On a network level the transitions from one firing scheme to the other express discrete sets of neural states. The neural states exist as long as the network sustains the internal synaptic energy.

  19. Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces. (United States)

    Wang, Yiwen; Wang, Fang; Xu, Kai; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang


    Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the trial initial timing. However, the movements in BMI applications can be quite complicated, and the action timing explicitly shows the intention when to move. The rich actions and the corresponding neural states form a large state-action space, imposing generalization difficulty on Q-learning. In this paper, we propose to adopt attention-gated reinforcement learning (AGREL) as a new learning scheme for BMIs to adaptively decode high-dimensional neural activities into seven distinct movements (directional moves, holdings and resting) due to the efficient weight-updating. We apply AGREL on neural data recorded from M1 of a monkey to directly predict a seven-action set in a time sequence to reconstruct the trajectory of a center-out task. Compared to Q-learning techniques, AGREL could improve the target acquisition rate to 90.16% in average with faster convergence and more stability to follow neural activity over multiple days, indicating the potential to achieve better online decoding performance for more complicated BMI tasks.

  20. Dynamic neural networks based on-line identification and control of high performance motor drives (United States)

    Rubaai, Ahmed; Kotaru, Raj


    In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

  1. CAPES: Unsupervised Storage Performance Tuning Using Neural Network-Based Deep Reinforcement Learning

    CERN Multimedia

    CERN. Geneva


    Parameter tuning is an important task of storage performance optimization. Current practice usually involves numerous tweak-benchmark cycles that are slow and costly. To address this issue, we developed CAPES, a model-less deep reinforcement learning-based unsupervised parameter tuning system driven by a deep neural network (DNN). It is designed to nd the optimal values of tunable parameters in computer systems, from a simple client-server system to a large data center, where human tuning can be costly and often cannot achieve optimal performance. CAPES takes periodic measurements of a target computer system’s state, and trains a DNN which uses Q-learning to suggest changes to the system’s current parameter values. CAPES is minimally intrusive, and can be deployed into a production system to collect training data and suggest tuning actions during the system’s daily operation. Evaluation of a prototype on a Lustre system demonstrates an increase in I/O throughput up to 45% at saturation point. About the...

  2. Classification of amyotrophic lateral sclerosis disease based on convolutional neural network and reinforcement sample learning algorithm. (United States)

    Sengur, Abdulkadir; Akbulut, Yaman; Guo, Yanhui; Bajaj, Varun


    Electromyogram (EMG) signals contain useful information of the neuromuscular diseases like amyotrophic lateral sclerosis (ALS). ALS is a well-known brain disease, which can progressively degenerate the motor neurons. In this paper, we propose a deep learning based method for efficient classification of ALS and normal EMG signals. Spectrogram, continuous wavelet transform (CWT), and smoothed pseudo Wigner-Ville distribution (SPWVD) have been employed for time-frequency (T-F) representation of EMG signals. A convolutional neural network is employed to classify these features. In it, Two convolution layers, two pooling layer, a fully connected layer and a lost function layer is considered in CNN architecture. The CNN architecture is trained with the reinforcement sample learning strategy. The efficiency of the proposed implementation is tested on publicly available EMG dataset. The dataset contains 89 ALS and 133 normal EMG signals with 24 kHz sampling frequency. Experimental results show 96.80% accuracy. The obtained results are also compared with other methods, which show the superiority of the proposed method.

  3. Evaluation of axial pile bearing capacity based on pile driving analyzer (PDA) test using Neural Network (United States)

    Maizir, H.; Suryanita, R.


    A few decades, many methods have been developed to predict and evaluate the bearing capacity of driven piles. The problem of the predicting and assessing the bearing capacity of the pile is very complicated and not yet established, different soil testing and evaluation produce a widely different solution. However, the most important thing is to determine methods used to predict and evaluate the bearing capacity of the pile to the required degree of accuracy and consistency value. Accurate prediction and evaluation of axial bearing capacity depend on some variables, such as the type of soil, diameter, and length of pile, etc. The aims of the study of Artificial Neural Networks (ANNs) are utilized to obtain more accurate and consistent axial bearing capacity of a driven pile. ANNs can be described as mapping an input to the target output data. The method using the ANN model developed to predict and evaluate the axial bearing capacity of the pile based on the pile driving analyzer (PDA) test data for more than 200 selected data. The results of the predictions obtained by the ANN model and the PDA test were then compared. This research as the neural network models give a right prediction and evaluation of the axial bearing capacity of piles using neural networks.

  4. Could LC-NE-Dependent Adjustment of Neural Gain Drive Functional Brain Network Reorganization?

    Directory of Open Access Journals (Sweden)

    Carole Guedj


    Full Text Available The locus coeruleus-norepinephrine (LC-NE system is thought to act at synaptic, cellular, microcircuit, and network levels to facilitate cognitive functions through at least two different processes, not mutually exclusive. Accordingly, as a reset signal, the LC-NE system could trigger brain network reorganizations in response to salient information in the environment and/or adjust the neural gain within its target regions to optimize behavioral responses. Here, we provide evidence of the co-occurrence of these two mechanisms at the whole-brain level, in resting-state conditions following a pharmacological stimulation of the LC-NE system. We propose that these two mechanisms are interdependent such that the LC-NE-dependent adjustment of the neural gain inferred from the clustering coefficient could drive functional brain network reorganizations through coherence in the gamma rhythm. Via the temporal dynamic of gamma-range band-limited power, the release of NE could adjust the neural gain, promoting interactions only within the neuronal populations whose amplitude envelopes are correlated, thus making it possible to reorganize neuronal ensembles, functional networks, and ultimately, behavioral responses. Thus, our proposal offers a unified framework integrating the putative influence of the LC-NE system on both local- and long-range adjustments of brain dynamics underlying behavioral flexibility.

  5. Optimisation of hybrid high-modulus/high-strength carbon fiber reinforced plastic composite drive


    Montagnier, Olivier; Hochard, Christian


    International audience; This study deals with the optimisation of hybrid composite drive shafts operating at subcritical or supercritical speeds, using a genetic algorithm. A formulation for the flexural vibrations of a composite drive shaft mounted on viscoelastic supports including shear effects is developed. In particular, an analytic stability criterion is developed to ensure the integrity of the system in the supercritical regime. Then it is shown that the torsional strength can be compu...

  6. Systems and processes within Halliburton Canada to reinforce safe driving behaviors

    Energy Technology Data Exchange (ETDEWEB)

    Karowich, P.; Mallett, C. [Halliburton Energy Processing Canada, Calgary, AB (Canada)


    An overview of Halliburton Canada's driver training program was presented. Vehicle incident statistics for the year 2000 were provided. Various causes of poor driving were examined, including over driving road conditions, fatigue and complacency. A speeding model was presented, with details of activators, driver behavior and potential consequences. It was noted that direction alone is not sufficient to change behavior. Different factors contributing to fatigue included overworking, pressure, diet and exercise issues. It was suggested that initial safety awareness and carefulness is often short-lived because of a natural learning process called drift. Elements of the Halliburton training program were reviewed. The skid car system is used by the organization, as well as collision avoidance techniques. All employees are trained and classroom discussions and commentaries are provided. Pre-hire driving evaluations with a third-party assessor are conducted, with emphasis on past driving experience. Fatigue management skills are also taught, with a focus on the daily cycles that bodies go through that cause fatigue and triggers that can stimulate alertness. Interactive assessments must be passed to complete the course. Journey management techniques are used as well as traffic safety awareness. Transportation Tuesday is a communication tool that teaches employees about reckless driving and how to avoid incidents. It was concluded that the Halliburton program is designed to improve safety performance at all levels of the organization by changing the way individuals think about safety, as well as understanding why a person drives at risk before exploring possible solutions. Vehicle incident statistics from before and after the program was implemented were presented, along with near miss reports. tabs, figs.

  7. Drive Control Scheme of Electric Power Assisted Wheelchair Based on Neural Network Learning of Human Wheelchair Operation Characteristics (United States)

    Tanohata, Naoki; Seki, Hirokazu

    This paper describes a novel drive control scheme of electric power assisted wheelchairs based on neural network learning of human wheelchair operation characteristics. “Electric power assisted wheelchair” which enhances the drive force of the operator by employing electric motors is expected to be widely used as a mobility support system for elderly and disabled people. However, some handicapped people with paralysis of the muscles of one side of the body cannot maneuver the wheelchair as desired because of the difference in the right and left input force. Therefore, this study proposes a neural network learning system of such human wheelchair operation characteristics and a drive control scheme with variable distribution and assistance ratios. Some driving experiments will be performed to confirm the effectiveness of the proposed control system.

  8. Safety Assessment for Electrical Motor Drive System Based on SOM Neural Network

    Directory of Open Access Journals (Sweden)

    Linghui Meng


    Full Text Available With the development of the urban rail train, safety and reliability have become more and more important. In this paper, the fault degree and health degree of the system are put forward based on the analysis of electric motor drive system’s control principle. With the self-organizing neural network’s advantage of competitive learning and unsupervised clustering, the system’s health clustering and safety identification are worked out. With the switch devices’ faults data obtained from the dSPACE simulation platform, the health assessment algorithm is verified. And the results show that the algorithm can achieve the system’s fault diagnosis and health assessment, which has a point in the health assessment and maintenance for the train.

  9. Investigation of Drive-Reinforcement Learning and Application of Learning to Flight Control (United States)


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

  10. On-road magnetic emissions prediction of electric cars in terms of driving dynamics using neural networks

    NARCIS (Netherlands)

    Wefky, Ahmed M.; Espinosa, Felipe; Leferink, Frank Bernardus Johannes; Gardel, Alfredo; Vogt-Ardatjew, R.A.


    This paper presents a novel artificial neural network (ANN) model estimating vehicle-level radiated magnetic emissions of an electric car as a function of the corresponding driving pattern. Real world electromagnetic interference (EMI) experiments have been realized in a semi-anechoic chamber using

  11. Design, Fabrication and Testing of Carbon Fiber Reinforced Epoxy Drive Shaft for All Terrain Vehicle using Filament Winding

    Directory of Open Access Journals (Sweden)

    Yeshwant Nayak Suhas


    Full Text Available Filament winding is a composite material fabrication technique that is used to manufacture concentric hollow components. In this study Carbon/Epoxy composite drive shafts were fabricated using filament winding process with a fiber orientation of [852/±452/252]s. Carbon in the form of multifilament fibers of Tairyfil TC-33 having 3000 filaments/strand was used as reinforcement with low viscosity epoxy resin as the matrix material. The driveshaft is designed to be used in SAE Baja All Terrain Vehicle (ATV that makes use of a fully floating axle in its rear wheel drive system. The torsional strength of the shaft was tested and compared to that of an OEM steel shaft that was previously used in the ATV. Results show that the composite shaft had 8.5% higher torsional strength in comparison to the OEM steel shaft and was also lighter by 60%. Scanning electron microscopy (SEM micrographs were studied to investigate the probable failure mechanism. Delamination, matrix agglomeration, fiber pull-out and matrix cracking were the prominent failure mechanisms identified.

  12. Neural activity in ventral medial prefrontal cortex is modulated more before approach than avoidance during reinforced and extinction trial blocks. (United States)

    Gentry, Ronny N; Roesch, Matthew R


    Ventromedial prefrontal cortex (vmPFC) is thought to provide regulatory control over Pavlovian fear responses and has recently been implicated in appetitive approach behavior, but much less is known about its role in contexts where appetitive and aversive outcomes can be obtained and avoided, respectively. To address this issue, we recorded from single neurons in vmPFC while male rats performed our combined approach and avoidance task under reinforced and non-reinforced (extinction) conditions. Surprisingly, we found that cues predicting reward modulated cell firing in vmPFC more often and more robustly than cues preceding avoidable shock; additionally, firing of vmPFC neurons was both response (press or no-press) and outcome (reinforced or extinction) selective. These results suggest a complex role for vmPFC in regulating behavior and supports its role in appetitive contexts during both reinforced and non-reinforced conditions. SIGNIFICANCE STATEMENT Selecting context-appropriate behaviors to gain reward or avoid punishment is critical for survival. While the role of ventromedial prefrontal cortex (vmPFC) in mediating fear responses is well-established, vmPFC has also been implicated in the regulation of reward-guided approach and extinction. Many studies have used indirect methods and simple behavioral procedures to study vmPFC, which leaves the literature incomplete. We recorded vmFPC neural activity during a complex cue-driven combined approach and avoidance task and during extinction. Surprisingly, we found very little vmPFC modulation to cues predicting avoidable shock, while cues predicting reward approach robustly modulated vmPFC firing in a response- and outcome-selective manner. This suggests a more complex role for vmPFC than current theories suggest, specifically regarding context-specific behavioral optimization. Copyright © 2018 the authors.

  13. Neural Crest-Derived Mesenchymal Cells Require Wnt Signaling for Their Development and Drive Invagination of the Telencephalic Midline (United States)

    Choe, Youngshik; Zarbalis, Konstantinos S.; Pleasure, Samuel J.


    Embryonic neural crest cells contribute to the development of the craniofacial mesenchyme, forebrain meninges and perivascular cells. In this study, we investigated the function of ß-catenin signaling in neural crest cells abutting the dorsal forebrain during development. In the absence of ß-catenin signaling, neural crest cells failed to expand in the interhemispheric region and produced ectopic smooth muscle cells instead of generating dermal and calvarial mesenchyme. In contrast, constitutive expression of stabilized ß-catenin in neural crest cells increased the number of mesenchymal lineage precursors suggesting that ß-catenin signaling is necessary for the expansion of neural crest-derived mesenchymal cells. Interestingly, the loss of neural crest-derived mesenchymal stem cells (MSCs) leads to failure of telencephalic midline invagination and causes ventricular system defects. This study shows that ß-catenin signaling is required for the switch of neural crest cells to MSCs and mediates the expansion of MSCs to drive the formation of mesenchymal structures of the head. Furthermore, loss of these structures causes striking defects in forebrain morphogenesis. PMID:24516524

  14. Neural crest-derived mesenchymal cells require Wnt signaling for their development and drive invagination of the telencephalic midline.

    Directory of Open Access Journals (Sweden)

    Youngshik Choe

    Full Text Available Embryonic neural crest cells contribute to the development of the craniofacial mesenchyme, forebrain meninges and perivascular cells. In this study, we investigated the function of ß-catenin signaling in neural crest cells abutting the dorsal forebrain during development. In the absence of ß-catenin signaling, neural crest cells failed to expand in the interhemispheric region and produced ectopic smooth muscle cells instead of generating dermal and calvarial mesenchyme. In contrast, constitutive expression of stabilized ß-catenin in neural crest cells increased the number of mesenchymal lineage precursors suggesting that ß-catenin signaling is necessary for the expansion of neural crest-derived mesenchymal cells. Interestingly, the loss of neural crest-derived mesenchymal stem cells (MSCs leads to failure of telencephalic midline invagination and causes ventricular system defects. This study shows that ß-catenin signaling is required for the switch of neural crest cells to MSCs and mediates the expansion of MSCs to drive the formation of mesenchymal structures of the head. Furthermore, loss of these structures causes striking defects in forebrain morphogenesis.

  15. Buffering social influence: neural correlates of response inhibition predict driving safety in the presence of a peer. (United States)

    Cascio, Christopher N; Carp, Joshua; O'Donnell, Matthew Brook; Tinney, Francis J; Bingham, C Raymond; Shope, Jean T; Ouimet, Marie Claude; Pradhan, Anuj K; Simons-Morton, Bruce G; Falk, Emily B


    Adolescence is a period characterized by increased sensitivity to social cues, as well as increased risk-taking in the presence of peers. For example, automobile crashes are the leading cause of death for adolescents, and driving with peers increases the risk of a fatal crash. Growing evidence points to an interaction between neural systems implicated in cognitive control and social and emotional context in predicting adolescent risk. We tested such a relationship in recently licensed teen drivers. Participants completed an fMRI session in which neural activity was measured during a response inhibition task, followed by a separate driving simulator session 1 week later. Participants drove alone and with a peer who was randomly assigned to express risk-promoting or risk-averse social norms. The experimentally manipulated social context during the simulated drive moderated the relationship between individual differences in neural activity in the hypothesized cognitive control network (right inferior frontal gyrus, BG) and risk-taking in the driving context a week later. Increased activity in the response inhibition network was not associated with risk-taking in the presence of a risky peer but was significantly predictive of safer driving in the presence of a cautious peer, above and beyond self-reported susceptibility to peer pressure. Individual differences in recruitment of the response inhibition network may allow those with stronger inhibitory control to override risky tendencies when in the presence of cautious peers. This relationship between social context and individual differences in brain function expands our understanding of neural systems involved in top-down cognitive control during adolescent development.

  16. Neural correlates of reinforcement learning and social preferences in competitive bidding. (United States)

    van den Bos, Wouter; Talwar, Arjun; McClure, Samuel M


    In competitive social environments, people often deviate from what rational choice theory prescribes, resulting in losses or suboptimal monetary gains. We investigate how competition affects learning and decision-making in a common value auction task. During the experiment, groups of five human participants were simultaneously scanned using MRI while playing the auction task. We first demonstrate that bidding is well characterized by reinforcement learning with biased reward representations dependent on social preferences. Indicative of reinforcement learning, we found that estimated trial-by-trial prediction errors correlated with activity in the striatum and ventromedial prefrontal cortex. Additionally, we found that individual differences in social preferences were related to activity in the temporal-parietal junction and anterior insula. Connectivity analyses suggest that monetary and social value signals are integrated in the ventromedial prefrontal cortex and striatum. Based on these results, we argue for a novel mechanistic account for the integration of reinforcement history and social preferences in competitive decision-making.

  17. Phasic dopamine as a prediction error of intrinsic and extrinsic reinforcements driving both action acquisition and reward maximization: a simulated robotic study. (United States)

    Mirolli, Marco; Santucci, Vieri G; Baldassarre, Gianluca


    An important issue of recent neuroscientific research is to understand the functional role of the phasic release of dopamine in the striatum, and in particular its relation to reinforcement learning. The literature is split between two alternative hypotheses: one considers phasic dopamine as a reward prediction error similar to the computational TD-error, whose function is to guide an animal to maximize future rewards; the other holds that phasic dopamine is a sensory prediction error signal that lets the animal discover and acquire novel actions. In this paper we propose an original hypothesis that integrates these two contrasting positions: according to our view phasic dopamine represents a TD-like reinforcement prediction error learning signal determined by both unexpected changes in the environment (temporary, intrinsic reinforcements) and biological rewards (permanent, extrinsic reinforcements). Accordingly, dopamine plays the functional role of driving both the discovery and acquisition of novel actions and the maximization of future rewards. To validate our hypothesis we perform a series of experiments with a simulated robotic system that has to learn different skills in order to get rewards. We compare different versions of the system in which we vary the composition of the learning signal. The results show that only the system reinforced by both extrinsic and intrinsic reinforcements is able to reach high performance in sufficiently complex conditions. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. Physiological mechanisms of dyspnea during exercise with external thoracic restriction: Role of increased neural respiratory drive (United States)

    Mendonca, Cassandra T.; Schaeffer, Michele R.; Riley, Patrick


    We tested the hypothesis that neuromechanical uncoupling of the respiratory system forms the mechanistic basis of dyspnea during exercise in the setting of “abnormal” restrictive constraints on ventilation (VE). To this end, we examined the effect of chest wall strapping (CWS) sufficient to mimic a “mild” restrictive lung deficit on the interrelationships between VE, breathing pattern, dynamic operating lung volumes, esophageal electrode-balloon catheter-derived measures of the diaphragm electromyogram (EMGdi) and the transdiaphragmatic pressure time product (PTPdi), and sensory intensity and unpleasantness ratings of dyspnea during exercise. Twenty healthy men aged 25.7 ± 1.1 years (means ± SE) completed symptom-limited incremental cycle exercise tests under two randomized conditions: unrestricted control and CWS to reduce vital capacity (VC) by 21.6 ± 0.5%. Compared with control, exercise with CWS was associated with 1) an exaggerated EMGdi and PTPdi response; 2) no change in the relationship between EMGdi and each of tidal volume (expressed as a percentage of VC), inspiratory reserve volume, and PTPdi, thus indicating relative preservation of neuromechanical coupling; 3) increased sensory intensity and unpleasantness ratings of dyspnea; and 4) no change in the relationship between increasing EMGdi and each of the intensity and unpleasantness of dyspnea. In conclusion, the increased intensity and unpleasantness of dyspnea during exercise with CWS could not be readily explained by increased neuromechanical uncoupling but likely reflected the awareness of increased neural respiratory drive (EMGdi) needed to achieve any given VE during exercise in the setting of “abnormal” restrictive constraints on tidal volume expansion. PMID:24356524

  19. Adolescent-specific patterns of behavior and neural activity during social reinforcement learning


    Jones, Rebecca M.; Somerville, Leah H.; Li, Jian; Ruberry, Erika J.; Powers, Alisa; Mehta, Natasha; Dyke, Jonathan; Casey, BJ


    Humans are sophisticated social beings. Social cues from others are exceptionally salient, particularly during adolescence. Understanding how adolescents interpret and learn from variable social signals can provide insight into the observed shift in social sensitivity during this period. The current study tested 120 participants between the ages of 8 and 25 years on a social reinforcement learning task where the probability of receiving positive social feedback was parametrically manipulated....

  20. Habituation of reinforcer effectiveness


    David R Lloyd; David R Lloyd; Douglas J Medina; Larry W Hawk; Whitney D Fosco; Jerry B Richards


    In this paper we propose an integrative model of habituation of reinforcer effectiveness (HRE) that links behavioral and neural based explanations of reinforcement. We argue that habituation of reinforcer effectiveness (HRE) is a fundamental property of reinforcing stimuli. Most reinforcement models implicitly suggest that the effectiveness of a reinforcer is stable across repeated presentations. In contrast, an HRE approach predicts decreased effectiveness due to repeated presentation. We ar...

  1. Convolutional Neural Network-Based Classification of Driver's Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors. (United States)

    Lee, Kwan Woo; Yoon, Hyo Sik; Song, Jong Min; Park, Kang Ryoung


    Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver's body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver's emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver's face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods.

  2. Blunted perception of neural respiratory drive and breathlessness in patients with cystic fibrosis

    Directory of Open Access Journals (Sweden)

    Charles C. Reilly


    Full Text Available The electromyogram recorded from the diaphragm (EMGdi and parasternal intercostal muscle using surface electrodes (sEMGpara provides a measure of neural respiratory drive (NRD, the magnitude of which reflects lung disease severity in stable cystic fibrosis. The aim of this study was to explore perception of NRD and breathlessness in both healthy individuals and patients with cystic fibrosis. Given chronic respiratory loading and increased NRD in cystic fibrosis, often in the absence of breathlessness at rest, we hypothesised that patients with cystic fibrosis would be able to tolerate higher levels of NRD for a given level of breathlessness compared to healthy individuals during exercise. 15 cystic fibrosis patients (mean forced expiratory volume in 1 s (FEV1 53.5% predicted and 15 age-matched, healthy controls were studied. Spirometry was measured in all subjects and lung volumes measured in the cystic fibrosis patients. EMGdi and sEMGpara were recorded at rest and during incremental cycle exercise to exhaustion and expressed as a percentage of maximum (% max obtained from maximum respiratory manoeuvres. Borg breathlessness scores were recorded at rest and during each minute of exercise. EMGdi % max and sEMGpara % max and associated Borg breathlessness scores differed significantly between healthy subjects and cystic fibrosis patients at rest and during exercise. The relationship between EMGdi % max and sEMGpara % max and Borg score was shifted to the right in the cystic fibrosis patients, such that at comparable levels of EMGdi % max and sEMGpara % max the cystic fibrosis patients reported significantly lower Borg breathlessness scores compared to the healthy individuals. At Borg score 1 (clinically significant increase in breathlessness from baseline corresponding levels of EMGdi % max (20.2±12% versus 32.15±15%, p=0.02 and sEMGpara % max (18.9±8% versus 29.2±15%, p=0.04 were lower in the healthy individuals compared to the cystic

  3. The Racer’s Brain – How Domain Expertise is Reflected in the Neural Substrates of Driving

    Directory of Open Access Journals (Sweden)

    Otto eLappi


    Full Text Available A fundamental question in human brain plasticity is how sensory, motor, and cognitive functions adapt in the process of skill acquisition extended over a period of many years. Recently, there has emerged a growing interest in cognitive neuroscience on studying the functional and structural differences in the brains of elite athletes. Elite performance in sports, music or the arts, allows us to observe sensorimotor and cognitive performance at the limits of human capability. In this mini-review we look at driving expertise. The emerging brain imaging literature on the neural substrates of real and simulated driving is reviewed (for the first time, and used as the context for interpreting recent findings on the differences between racing drivers and non-athlete controls. Also the cognitive psychology and cognitive neuroscience of expertise are discussed.

  4. Model-Based Fault Diagnosis in Electric Drive Inverters Using Artificial Neural Network

    National Research Council Canada - National Science Library

    Masrur, Abul; Chen, ZhiHang; Zhang, Baifang; Jia, Hongbin; Murphey, Yi-Lu


    .... A normal model and various faulted models of the inverter-motor combination were developed, and voltages and current signals were generated from those models to train an artificial neural network for fault diagnosis...

  5. Reinforcement Learning of Linking and Tracing Contours in Recurrent Neural Networks (United States)

    Brosch, Tobias; Neumann, Heiko; Roelfsema, Pieter R.


    The processing of a visual stimulus can be subdivided into a number of stages. Upon stimulus presentation there is an early phase of feedforward processing where the visual information is propagated from lower to higher visual areas for the extraction of basic and complex stimulus features. This is followed by a later phase where horizontal connections within areas and feedback connections from higher areas back to lower areas come into play. In this later phase, image elements that are behaviorally relevant are grouped by Gestalt grouping rules and are labeled in the cortex with enhanced neuronal activity (object-based attention in psychology). Recent neurophysiological studies revealed that reward-based learning influences these recurrent grouping processes, but it is not well understood how rewards train recurrent circuits for perceptual organization. This paper examines the mechanisms for reward-based learning of new grouping rules. We derive a learning rule that can explain how rewards influence the information flow through feedforward, horizontal and feedback connections. We illustrate the efficiency with two tasks that have been used to study the neuronal correlates of perceptual organization in early visual cortex. The first task is called contour-integration and demands the integration of collinear contour elements into an elongated curve. We show how reward-based learning causes an enhancement of the representation of the to-be-grouped elements at early levels of a recurrent neural network, just as is observed in the visual cortex of monkeys. The second task is curve-tracing where the aim is to determine the endpoint of an elongated curve composed of connected image elements. If trained with the new learning rule, neural networks learn to propagate enhanced activity over the curve, in accordance with neurophysiological data. We close the paper with a number of model predictions that can be tested in future neurophysiological and computational studies

  6. Enhancing neural activity to drive respiratory plasticity following cervical spinal cord injury (United States)

    Hormigo, Kristiina M.; Zholudeva, Lyandysha V.; Spruance, Victoria M.; Marchenko, Vitaliy; Cote, Marie-Pascale; Vinit, Stephane; Giszter, Simon; Bezdudnaya, Tatiana; Lane, Michael A.


    Cervical spinal cord injury (SCI) results in permanent life-altering sensorimotor deficits, among which impaired breathing is one of the most devastating and life-threatening. While clinical and experimental research has revealed that some spontaneous respiratory improvement (functional plasticity) can occur post-SCI, the extent of the recovery is limited and significant deficits persist. Thus, increasing effort is being made to develop therapies that harness and enhance this neuroplastic potential to optimize long-term recovery of breathing in injured individuals. One strategy with demonstrated therapeutic potential is the use of treatments that increase neural and muscular activity (e.g. locomotor training, neural and muscular stimulation) and promote plasticity. With a focus on respiratory function post-SCI, this review will discuss advances in the use of neural interfacing strategies and activity-based treatments, and highlights some recent results from our own research. PMID:27582085

  7. A Mechanistic Neural Field Theory of How Anesthesia Suppresses Consciousness: Synaptic Drive Dynamics, Bifurcations, Attractors, and Partial State Equipartitioning. (United States)

    Hou, Saing Paul; Haddad, Wassim M; Meskin, Nader; Bailey, James M


    With the advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in understanding the molecular properties of anesthetic agents. However, there has been little focus on how the molecular properties of anesthetic agents lead to the observed macroscopic property that defines the anesthetic state, that is, lack of responsiveness to noxious stimuli. In this paper, we use dynamical system theory to develop a mechanistic mean field model for neural activity to study the abrupt transition from consciousness to unconsciousness as the concentration of the anesthetic agent increases. The proposed synaptic drive firing-rate model predicts the conscious-unconscious transition as the applied anesthetic concentration increases, where excitatory neural activity is characterized by a Poincaré-Andronov-Hopf bifurcation with the awake state transitioning to a stable limit cycle and then subsequently to an asymptotically stable unconscious equilibrium state. Furthermore, we address the more general question of synchronization and partial state equipartitioning of neural activity without mean field assumptions. This is done by focusing on a postulated subset of inhibitory neurons that are not themselves connected to other inhibitory neurons. Finally, several numerical experiments are presented to illustrate the different aspects of the proposed theory.

  8. Effects of reinforcement-blocking doses of pimozide on neural systems driven by rewarding stimulation of the MFB: a /sup 14/C-2-deoxyglucose analysis

    Energy Technology Data Exchange (ETDEWEB)

    Gomita, Y.; Gallistel, C.R.


    An analysis by means of /sup 14/C-2-deoxyglucose autoradiography of the neural systems unilaterally activated by the reinforcing stimulation used in the two accompanying papers revealed strong and reliable effects in the nucleus of the diagonal band of Broca, in the medial forebrain bundle (MFB) and/or the fornix throughout the diencephalon, and in the part of the anterior ventral tegmentum where the dopaminergic projection to the lateral habenula originates. The terminal fields of the dopaminergic forebrain projections were not affected, but there was bilateral suppression of lateral habenular activity. A second experiment found that the same systems are still activated by (automatically administered) reinforcing stimulation in rats treated with reinforcement blocking doses of pimozide. The only clear effect of pimozide was to reverse the bilateral suppressive effect of the stimulation on lateral habenular activity. Animals treated with pimozide show greatly elevated activity in the lateral habenula, whether or not they receive reinforcing stimulation. The results suggest that pimozide's effect on reinforcement is mediated by the circuitry interconnecting the lateral habenula with the nucleus of the diagonal band of Broca and/or the anterior ventral tegmentum.

  9. Neural Network Control-Based Drive Design of Servomotor and Its Application to Automatic Guided Vehicle

    Directory of Open Access Journals (Sweden)

    Ming-Shyan Wang


    Full Text Available An automatic guided vehicle (AGV is extensively used for productions in a flexible manufacture system with high efficiency and high flexibility. A servomotor-based AGV is designed and implemented in this paper. In order to steer the AGV to go along a predefined path with corner or arc, the conventional proportional-integral-derivative (PID control is used in the system. However, it is difficult to tune PID gains at various conditions. As a result, the neural network (NN control is considered to assist the PID control for gain tuning. The experimental results are first provided to verify the correctness of the neural network plus PID control for 400 W-motor control system. Secondly, the AGV includes two sets of the designed motor systems and CAN BUS transmission so that it can move along the straight line and curve paths shown in the taped videos.

  10. Construction of multi-agent mobile robots control system in the problem of persecution with using a modified reinforcement learning method based on neural networks (United States)

    Patkin, M. L.; Rogachev, G. N.


    A method for constructing a multi-agent control system for mobile robots based on training with reinforcement using deep neural networks is considered. Synthesis of the management system is proposed to be carried out with reinforcement training and the modified Actor-Critic method, in which the Actor module is divided into Action Actor and Communication Actor in order to simultaneously manage mobile robots and communicate with partners. Communication is carried out by sending partners at each step a vector of real numbers that are added to the observation vector and affect the behaviour. Functions of Actors and Critic are approximated by deep neural networks. The Critics value function is trained by using the TD-error method and the Actor’s function by using DDPG. The Communication Actor’s neural network is trained through gradients received from partner agents. An environment in which a cooperative multi-agent interaction is present was developed, computer simulation of the application of this method in the control problem of two robots pursuing two goals was carried out.

  11. New Smith Internal Model Control of Two-Motor Drive System Based on Neural Network Generalized Inverse

    Directory of Open Access Journals (Sweden)

    Guohai Liu


    Full Text Available Multimotor drive system is widely applied in industrial control system. Considering the characteristics of multi-input multioutput, nonlinear, strong-coupling, and time-varying delay in two-motor drive systems, this paper proposes a new Smith internal model (SIM control method, which is based on neural network generalized inverse (NNGI. This control strategy adopts the NNGI system to settle the decoupling issue and utilizes the SIM control structure to solve the delay problem. The NNGI method can decouple the original system into several composite pseudolinear subsystems and also complete the pole-zero allocation of subsystems. Furthermore, based on the precise model of pseudolinear system, the proposed SIM control structure is used to compensate the network delay and enhance the interference resisting the ability of the whole system. Both simulation and experimental results are given, verifying that the proposed control strategy can effectively solve the decoupling problem and exhibits the strong robustness to load impact disturbance at various operations.

  12. Hedgehog regulates Norrie disease protein to drive neural progenitor self-renewal. (United States)

    McNeill, Brian; Mazerolle, Chantal; Bassett, Erin A; Mears, Alan J; Ringuette, Randy; Lagali, Pamela; Picketts, David J; Paes, Kim; Rice, Dennis; Wallace, Valerie A


    Norrie disease (ND) is a congenital disorder characterized by retinal hypovascularization and cognitive delay. ND has been linked to mutations in 'Norrie Disease Protein' (Ndp), which encodes the secreted protein Norrin. Norrin functions as a secreted angiogenic factor, although its role in neural development has not been assessed. Here, we show that Ndp expression is initiated in retinal progenitors in response to Hedgehog (Hh) signaling, which induces Gli2 binding to the Ndp promoter. Using a combination of genetic epistasis and acute RNAi-knockdown approaches, we show that Ndp is required downstream of Hh activation to induce retinal progenitor proliferation in the retina. Strikingly, Ndp regulates the rate of cell-cycle re-entry and not cell-cycle kinetics, thereby uncoupling the self-renewal and cell-cycle progression functions of Hh. Taken together, we have uncovered a cell autonomous function for Ndp in retinal progenitor proliferation that is independent of its function in the retinal vasculature, which could explain the neural defects associated with ND.

  13. Reinforcement Learning Approach to Generate Goal-directed Locomotion of a Snake-Like Robot with Screw-Drive Units

    DEFF Research Database (Denmark)

    Chatterjee, Sromona; Nachstedt, Timo; Tamosiunaite, Minija


    Abstract—In this paper we apply a policy improvement algorithm called Policy Improvement using Path Integrals (PI2) to generate goal-directed locomotion of a complex snake-like robot with screw-drive units. PI2 is numerically simple and has an ability to deal with high dimensional systems. Here...

  14. Multi-mode energy management strategy for fuel cell electric vehicles based on driving pattern identification using learning vector quantization neural network algorithm (United States)

    Song, Ke; Li, Feiqiang; Hu, Xiao; He, Lin; Niu, Wenxu; Lu, Sihao; Zhang, Tong


    The development of fuel cell electric vehicles can to a certain extent alleviate worldwide energy and environmental issues. While a single energy management strategy cannot meet the complex road conditions of an actual vehicle, this article proposes a multi-mode energy management strategy for electric vehicles with a fuel cell range extender based on driving condition recognition technology, which contains a patterns recognizer and a multi-mode energy management controller. This paper introduces a learning vector quantization (LVQ) neural network to design the driving patterns recognizer according to a vehicle's driving information. This multi-mode strategy can automatically switch to the genetic algorithm optimized thermostat strategy under specific driving conditions in the light of the differences in condition recognition results. Simulation experiments were carried out based on the model's validity verification using a dynamometer test bench. Simulation results show that the proposed strategy can obtain better economic performance than the single-mode thermostat strategy under dynamic driving conditions.

  15. Adapting without reinforcement. (United States)

    Kheifets, Aaron; Gallistel, C Randy


    Our data rule out a broad class of behavioral models in which behavioral change is guided by differential reinforcement. To demonstrate this, we showed that the number of reinforcers missed before the subject shifted its behavior was not sufficient to drive behavioral change. What's more, many subjects shifted their behavior to a more optimal strategy even when they had not yet missed a single reinforcer. Naturally, differential reinforcement cannot be said to drive a process that shifts to accommodate to new conditions so adeptly that it doesn't miss a single reinforcer: it would have no input on which to base this shift.

  16. Heavy-duty mentor : Inthinc's trucker-coaching software reinforces best-practices driving

    Energy Technology Data Exchange (ETDEWEB)

    Menzies, J.


    This article described Inthinc's tiwi and waySmart driver mentoring systems and how they are being put to use in the Canadian oil patch. The automated monitoring system for truck drivers generates audible alerts for unsafe practices, including verbal warnings. The use of the technology in the United States resulted in substantial improvements in seat-belt use, speeding violations, aggressive driving behaviours, and crash rates. The resulting safety improvements result in cost savings that justify the cost of the system. The system can also monitor and reduce vehicle idle-time and electronically log driver hours of service, including changes from vehicle to vehicle, which provides particularly useful data for the oil industry. Accelerometers are used to detect aggressive driving maneuvers, and the system can be customized with different degrees of leeway. The system is designed to be corrective rather than punitive. Drivers are given time to adjust their behaviour. Only repeated failure results in notification to the designated supervisor. In the future, the system will be able to prevent calling and texting from cell phones while the vehicle is in motion and provide instruction for walk-around, pre-trip truck inspections. The mentoring capability is the key distinction system, as the focus is on changing the behaviour of the driver in the vehicle. 1 fig.

  17. Wind Turbine Driving a PM Synchronous Generator Using Novel Recurrent Chebyshev Neural Network Control with the Ideal Learning Rate

    Directory of Open Access Journals (Sweden)

    Chih-Hong Lin


    Full Text Available A permanent magnet (PM synchronous generator system driven by wind turbine (WT, connected with smart grid via AC-DC converter and DC-AC converter, are controlled by the novel recurrent Chebyshev neural network (NN and amended particle swarm optimization (PSO to regulate output power and output voltage in two power converters in this study. Because a PM synchronous generator system driven by WT is an unknown non-linear and time-varying dynamic system, the on-line training novel recurrent Chebyshev NN control system is developed to regulate DC voltage of the AC-DC converter and AC voltage of the DC-AC converter connected with smart grid. Furthermore, the variable learning rate of the novel recurrent Chebyshev NN is regulated according to discrete-type Lyapunov function for improving the control performance and enhancing convergent speed. Finally, some experimental results are shown to verify the effectiveness of the proposed control method for a WT driving a PM synchronous generator system in smart grid.

  18. Artificial neural network and regression modelling to study the effect of reinforcement and deformation on volumetric wear of red mud nano particle reinforced aluminium matrix composites synthesized by stir casting

    Directory of Open Access Journals (Sweden)

    Gampala Satyanarayana


    Full Text Available Artificial neural network (ANN approach was used for the prediction of effect of reinforcement and deformation on volumetric wear of red mud nano particle reinforced aluminium matrix composites synthesized by stir casting. Red mud obtained from alumina processing industry was milled in a high energy ball mill and the particle size was reduced to 40 nm in 30 h. Sliding wear characteristics of the composites were evaluated on pin on disc wear tester at different loads of 10 N, 20 N and 30 N and sliding speeds of 200, 400, and 600 RPM. The wear rate of the composite was decreased with increase in weight fraction of red mud up to 10% and beyond that the wear rate was increased. The interfacial area between the matrix and the reinforcement increases with increase in red mud volume fraction, leading to increase in strength and wear resistance. Mathematical regression model and ANN model have been developed to predict theoretical wear rate of the composite and observed that ANN predictions have excellent agreement with measured values than other models. Thus, the prediction of wear rate of the nano composites using artificial neural network before actual manufacture will considerably saves the project time, effort and cost. Resumen: Se utilizó el método de red neuronal artificial (RNA para predecir el efecto del refuerzo y la deformación sobre el desgaste volumétrico de los materiales compuestos de matriz de aluminio reforzada con nanopartículas de barro rojo sintetizados por agitación. El barro rojo obtenido de la industria de procesamiento de alúmina se molió en un molino de bolas de alta energía y el tamaño de la partícula se redujo a 40 nm en 30 h. Las características de desgaste de los materiales compuestos se evaluaron en los probadores pin-on-disk de desgaste en diferentes cargas de 10N, 20N y 30N, y velocidades de deslizamiento de 200, 400 y 600 rpm. El índice de desgaste del material compuesto se redujo con el aumento en

  19. Convolutional Neural Network-Based Classification of Driver’s Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors

    Directory of Open Access Journals (Sweden)

    Kwan Woo Lee


    Full Text Available Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG or electrocardiogram (ECG sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver’s body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver’s emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN-based method of detecting emotion to identify aggressive driving using input images of the driver’s face, obtained using near-infrared (NIR light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed driving. Our proposed method demonstrates better performance than existing methods.

  20. Habituation of reinforcer effectiveness

    Directory of Open Access Journals (Sweden)

    David R Lloyd


    Full Text Available In this paper we propose an integrative model of habituation of reinforcer effectiveness (HRE that links behavioral and neural based explanations of reinforcement. We argue that habituation of reinforcer effectiveness (HRE is a fundamental property of reinforcing stimuli. Most reinforcement models implicitly suggest that the effectiveness of a reinforcer is stable across repeated presentations. In contrast, an HRE approach predicts decreased effectiveness due to repeated presentation. We argue that repeated presentation of reinforcing stimuli decreases their effectiveness and that these decreases are described by the behavioral characteristics of habituation (McSweeney and Murphy, 2009;Rankin et al., 2009. We describe a neural model that postulates a positive association between dopamine neurotransmission and HRE. We present evidence that stimulant drugs, which artificially increase dopamine neurotransmission, disrupt (slow normally occurring HRE and also provide evidence that stimulant drugs have differential effects on operant responding maintained by reinforcers with rapid vs. slow HRE rates. We hypothesize that abnormal HRE due to genetic and/or environmental factors may underlie some behavioral disorders. For example, recent research indicates that slow-HRE is predictive of obesity. In contrast ADHD may reflect ‘accelerated-HRE’. Consideration of HRE is important for the development of effective reinforcement based treatments. Finally, we point out that most of the reinforcing stimuli that regulate daily behavior are non-consumable environmental/social reinforcers which have rapid-HRE. The almost exclusive use of consumable reinforcers with slow-HRE in pre-clinical studies with animals may have caused the importance of HRE to be overlooked. Further study of reinforcing stimuli with rapid-HRE is needed in order to understand how habituation and reinforcement interact and regulate behavior.

  1. Capacitance estimation algorithm based on DC-link voltage harmonics using artificial neural network in three-phase motor drive systems

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Davari, Pooya; Wang, Huai


    to industry. In this digest, a condition monitoring methodology that estimates the capacitance value of the dc-link capacitor in a three phase Front-End diode bridge motor drive is proposed. The proposed software methodology is based on Artificial Neural Network (ANN) algorithm. The harmonics of the dc......-link voltage are used as training data to the Artificial Neural Network. Fast Fourier Transform (FFT) of the dc-link voltage is analysed in order to study the impact of capacitance variation on the harmonics order. Laboratory experiments are conducted to validate the proposed methodology and the error analysis......In modern design of power electronic converters, reliability of dc-link capacitors is one of the critical considered aspects. The industrial field have been attracted to the monitoring of their health condition and the estimation of their ageing process status. However, the existing condition...

  2. Habituation of reinforcer effectiveness. (United States)

    Lloyd, David R; Medina, Douglas J; Hawk, Larry W; Fosco, Whitney D; Richards, Jerry B


    In this paper we propose an integrative model of habituation of reinforcer effectiveness (HRE) that links behavioral- and neural-based explanations of reinforcement. We argue that HRE is a fundamental property of reinforcing stimuli. Most reinforcement models implicitly suggest that the effectiveness of a reinforcer is stable across repeated presentations. In contrast, an HRE approach predicts decreased effectiveness due to repeated presentation. We argue that repeated presentation of reinforcing stimuli decreases their effectiveness and that these decreases are described by the behavioral characteristics of habituation (McSweeney and Murphy, 2009; Rankin etal., 2009). We describe a neural model that postulates a positive association between dopamine neurotransmission and HRE. We present evidence that stimulant drugs, which artificially increase dopamine neurotransmission, disrupt (slow) normally occurring HRE and also provide evidence that stimulant drugs have differential effects on operant responding maintained by reinforcers with rapid vs. slow HRE rates. We hypothesize that abnormal HRE due to genetic and/or environmental factors may underlie some behavioral disorders. For example, recent research indicates that slow-HRE is predictive of obesity. In contrast ADHD may reflect "accelerated-HRE." Consideration of HRE is important for the development of effective reinforcement-based treatments. Finally, we point out that most of the reinforcing stimuli that regulate daily behavior are non-consumable environmental/social reinforcers which have rapid-HRE. The almost exclusive use of consumable reinforcers with slow-HRE in pre-clinical studies with animals may have caused the importance of HRE to be overlooked. Further study of reinforcing stimuli with rapid-HRE is needed in order to understand how habituation and reinforcement interact and regulate behavior.

  3. The role of attention in the tinnitus decompensation: reinforcement of a large-scale neural decompensation measure. (United States)

    Low, Yin Fen; Trenado, Carlos; Delb, Wolfgang; Corona-Strauss, Farah I; Strauss, Daniel J


    Large-scale neural correlates of the tinnitus decompensation have been identified by using wavelet phase stability criteria of single sweep sequences of auditory late responses (ALRs). The suggested measure provided an objective quantification of the tinnitus decompensation and allowed for a reliable discrimination between a group of compensated and decompensated tinnitus patients. By interpreting our results with an oscillatory tinnitus model, our synchronization stability measure of ALRs can be linked to the focus of attention on the tinnitus signal. In the following study, we examined in detail the correlates of this attentional mechanism in healthy subjects. The results support our previous findings of the phase synchronization stability measure that reflected neural correlates of the fixation of attention to the tinnitus signal. In this case, enabling the differentiation between the attended and unattended conditions. It is concluded that the wavelet phase synchronization stability of ALRs single sweeps can be used as objective tinnitus decompensation measure and can be interpreted in the framework of the Jastreboff tinnitus model and adaptive resonance theory. Our studies confirm that the synchronization stability in ALR sequences is linked to attention. This measure is not only able to serve as objective quantification of the tinnitus decompensation, but also can be applied in all online and real time neurofeedback therapeutic approach where a direct stimulus locked attention monitoring is compulsory as if it based on a single sweeps processing.

  4. Inactivity-induced respiratory plasticity: Protecting the drive to breathe in disorders that reduce respiratory neural activity☆ (United States)

    Strey, K.A.; Baertsch, N.A.; Baker-Herman, T.L.


    Multiple forms of plasticity are activated following reduced respiratory neural activity. For example, in ventilated rats, a central neural apnea elicits a rebound increase in phrenic and hypoglossal burst amplitude upon resumption of respiratory neural activity, forms of plasticity called inactivity-induced phrenic and hypoglossal motor facilitation (iPMF and iHMF), respectively. Here, we provide a conceptual framework for plasticity following reduced respiratory neural activity to guide future investigations. We review mechanisms giving rise to iPMF and iHMF, present new data suggesting that inactivity-induced plasticity is observed in inspiratory intercostals (iIMF) and point out gaps in our knowledge. We then survey conditions relevant to human health characterized by reduced respiratory neural activity and discuss evidence that inactivity-induced plasticity is elicited during these conditions. Understanding the physiological impact and circumstances in which inactivity-induced respiratory plasticity is elicited may yield novel insights into the treatment of disorders characterized by reductions in respiratory neural activity. PMID:23816599

  5. Vicarious Reinforcement In Rhesus Macaques (Macaca mulatta

    Directory of Open Access Journals (Sweden)

    Steve W. C. Chang


    Full Text Available What happens to others profoundly influences our own behavior. Such other-regarding outcomes can drive observational learning, as well as motivate cooperation, charity, empathy, and even spite. Vicarious reinforcement may serve as one of the critical mechanisms mediating the influence of other-regarding outcomes on behavior and decision-making in groups. Here we show that rhesus macaques spontaneously derive vicarious reinforcement from observing rewards given to another monkey, and that this reinforcement can motivate them to subsequently deliver or withhold rewards from the other animal. We exploited Pavlovian and instrumental conditioning to associate rewards to self (M1 and/or rewards to another monkey (M2 with visual cues. M1s made more errors in the instrumental trials when cues predicted reward to M2 compared to when cues predicted reward to M1, but made even more errors when cues predicted reward to no one. In subsequent preference tests between pairs of conditioned cues, M1s preferred cues paired with reward to M2 over cues paired with reward to no one. By contrast, M1s preferred cues paired with reward to self over cues paired with reward to both monkeys simultaneously. Rates of attention to M2 strongly predicted the strength and valence of vicarious reinforcement. These patterns of behavior, which were absent in nonsocial control trials, are consistent with vicarious reinforcement based upon sensitivity to observed, or counterfactual, outcomes with respect to another individual. Vicarious reward may play a critical role in shaping cooperation and competition, as well as motivating observational learning and group coordination in rhesus macaques, much as it does in humans. We propose that vicarious reinforcement signals mediate these behaviors via homologous neural circuits involved in reinforcement learning and decision-making.

  6. Vicarious reinforcement in rhesus macaques (macaca mulatta). (United States)

    Chang, Steve W C; Winecoff, Amy A; Platt, Michael L


    What happens to others profoundly influences our own behavior. Such other-regarding outcomes can drive observational learning, as well as motivate cooperation, charity, empathy, and even spite. Vicarious reinforcement may serve as one of the critical mechanisms mediating the influence of other-regarding outcomes on behavior and decision-making in groups. Here we show that rhesus macaques spontaneously derive vicarious reinforcement from observing rewards given to another monkey, and that this reinforcement can motivate them to subsequently deliver or withhold rewards from the other animal. We exploited Pavlovian and instrumental conditioning to associate rewards to self (M1) and/or rewards to another monkey (M2) with visual cues. M1s made more errors in the instrumental trials when cues predicted reward to M2 compared to when cues predicted reward to M1, but made even more errors when cues predicted reward to no one. In subsequent preference tests between pairs of conditioned cues, M1s preferred cues paired with reward to M2 over cues paired with reward to no one. By contrast, M1s preferred cues paired with reward to self over cues paired with reward to both monkeys simultaneously. Rates of attention to M2 strongly predicted the strength and valence of vicarious reinforcement. These patterns of behavior, which were absent in non-social control trials, are consistent with vicarious reinforcement based upon sensitivity to observed, or counterfactual, outcomes with respect to another individual. Vicarious reward may play a critical role in shaping cooperation and competition, as well as motivating observational learning and group coordination in rhesus macaques, much as it does in humans. We propose that vicarious reinforcement signals mediate these behaviors via homologous neural circuits involved in reinforcement learning and decision-making.

  7. OptoZIF Drive: a 3D printed implant and assembly tool package for neural recording and optical stimulation in freely moving mice (United States)

    Freedman, David S.; Schroeder, Joseph B.; Telian, Gregory I.; Zhang, Zhengyang; Sunil, Smrithi; Ritt, Jason T.


    Objective. Behavioral neuroscience studies in freely moving rodents require small, light-weight implants to facilitate neural recording and stimulation. Our goal was to develop an integrated package of 3D printed parts and assembly aids for labs to rapidly fabricate, with minimal training, an implant that combines individually positionable microelectrodes, an optical fiber, zero insertion force (ZIF-clip) headstage connection, and secondary recording electrodes, e.g. for electromyography (EMG). Approach. Starting from previous implant designs that position recording electrodes using a control screw, we developed an implant where the main drive body, protective shell, and non-metal components of the microdrives are 3D printed in parallel. We compared alternative shapes and orientations of circuit boards for electrode connection to the headstage, in terms of their size, weight, and ease of wire insertion. We iteratively refined assembly methods, and integrated additional assembly aids into the 3D printed casing. Main results. We demonstrate the effectiveness of the OptoZIF Drive by performing real time optogenetic feedback in behaving mice. A novel feature of the OptoZIF Drive is its vertical circuit board, which facilities direct ZIF-clip connection. This feature requires angled insertion of an optical fiber that still can exit the drive from the center of a ring of recording electrodes. We designed an innovative 2-part protective shell that can be installed during the implant surgery to facilitate making additional connections to the circuit board. We use this feature to show that facial EMG in mice can be used as a control signal to lock stimulation to the animal’s motion, with stable EMG signal over several months. To decrease assembly time, reduce assembly errors, and improve repeatability, we fabricate assembly aids including a drive holder, a drill guide, an implant fixture for microelectode ‘pinning’, and a gold plating fixture. Significance. The

  8. Integration of silicon-based neural probes and micro-drive arrays for chronic recording of large populations of neurons in behaving animals. (United States)

    Michon, Frédéric; Aarts, Arno; Holzhammer, Tobias; Ruther, Patrick; Borghs, Gustaaf; McNaughton, Bruce; Kloosterman, Fabian


    Understanding how neuronal assemblies underlie cognitive function is a fundamental question in system neuroscience. It poses the technical challenge to monitor the activity of populations of neurons, potentially widely separated, in relation to behaviour. In this paper, we present a new system which aims at simultaneously recording from a large population of neurons from multiple separated brain regions in freely behaving animals. The concept of the new device is to combine the benefits of two existing electrophysiological techniques, i.e. the flexibility and modularity of micro-drive arrays and the high sampling ability of electrode-dense silicon probes. Newly engineered long bendable silicon probes were integrated into a micro-drive array. The resulting device can carry up to 16 independently movable silicon probes, each carrying 16 recording sites. Populations of neurons were recorded simultaneously in multiple cortical and/or hippocampal sites in two freely behaving implanted rats. Current approaches to monitor neuronal activity either allow to flexibly record from multiple widely separated brain regions (micro-drive arrays) but with a limited sampling density or to provide denser sampling at the expense of a flexible placement in multiple brain regions (neural probes). By combining these two approaches and their benefits, we present an alternative solution for flexible and simultaneous recordings from widely distributed populations of neurons in freely behaving rats.

  9. Projections from the posterolateral olfactory amygdala to the ventral striatum: neural basis for reinforcing properties of chemical stimuli

    Directory of Open Access Journals (Sweden)

    Lanuza Enrique


    Full Text Available Abstract Background Vertebrates sense chemical stimuli through the olfactory receptor neurons whose axons project to the main olfactory bulb. The main projections of the olfactory bulb are directed to the olfactory cortex and olfactory amygdala (the anterior and posterolateral cortical amygdalae. The posterolateral cortical amygdaloid nucleus mainly projects to other amygdaloid nuclei; other seemingly minor outputs are directed to the ventral striatum, in particular to the olfactory tubercle and the islands of Calleja. Results Although the olfactory projections have been previously described in the literature, injection of dextran-amines into the rat main olfactory bulb was performed with the aim of delimiting the olfactory tubercle and posterolateral cortical amygdaloid nucleus in our own material. Injection of dextran-amines into the posterolateral cortical amygdaloid nucleus of rats resulted in anterograde labeling in the ventral striatum, in particular in the core of the nucleus accumbens, and in the medial olfactory tubercle including some islands of Calleja and the cell bridges across the ventral pallidum. Injections of Fluoro-Gold into the ventral striatum were performed to allow retrograde confirmation of these projections. Conclusion The present results extend previous descriptions of the posterolateral cortical amygdaloid nucleus efferent projections, which are mainly directed to the core of the nucleus accumbens and the medial olfactory tubercle. Our data indicate that the projection to the core of the nucleus accumbens arises from layer III; the projection to the olfactory tubercle arises from layer II and is much more robust than previously thought. This latter projection is directed to the medial olfactory tubercle including the corresponding islands of Calleja, an area recently described as critical node for the neural circuit of addiction to some stimulant drugs of abuse.

  10. Complexity and competition in appetitive and aversive neural circuits

    Directory of Open Access Journals (Sweden)

    Crista L. Barberini


    Full Text Available Decision-making often involves using sensory cues to predict possible rewarding or punishing reinforcement outcomes before selecting a course of action. Recent work has revealed complexity in how the brain learns to predict rewards and punishments. Analysis of neural signaling during and after learning in the amygdala and orbitofrontal cortex, two brain areas that process appetitive and aversive stimuli, reveals a dynamic relationship between appetitive and aversive circuits. Specifically, the relationship between signaling in appetitive and aversive circuits in these areas shifts as a function of learning. Furthermore, although appetitive and aversive circuits may often drive opposite behaviors – approaching or avoiding reinforcement depending upon its valence – these circuits can also drive similar behaviors, such as enhanced arousal or attention; these processes also may influence choice behavior. These data highlight the formidable challenges ahead in dissecting how appetitive and aversive neural circuits interact to produce a complex and nuanced range of behaviors.

  11. Sensorless FOC Performance Improved with On-Line Speed and Rotor Resistance Estimator Based on an Artificial Neural Network for an Induction Motor Drive

    Directory of Open Access Journals (Sweden)

    Jose. M. Gutierrez-Villalobos


    Full Text Available Three-phase induction motor drive requires high accuracy in high performance processes in industrial applications. Field oriented control, which is one of the most employed control schemes for induction motors, bases its function on the electrical parameter estimation coming from the motor. These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes. The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime. Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor.

  12. Reinforcement principles for addiction medicine; from recreational drug use to psychiatric disorder. (United States)

    Edwards, Scott


    The transition from recreational drug use to addiction can be conceptualized as a pathological timeline whereby the psychological mechanisms responsible for disordered drug use evolve from positive reinforcement to favor elements of negative reinforcement. Abused substances (ranging from alcohol to psychostimulants) are initially ingested at regular occasions according to their positive reinforcing properties. Importantly, repeated exposure to rewarding substances sets off a chain of secondary reinforcing events, whereby cues and contexts associated with drug use may themselves become reinforcing and thereby contribute to the continued use and possible abuse of the substance(s) of choice. Indeed, the powerful reinforcing efficacy of certain drugs may eclipse that of competing social rewards (such as career and family) and lead to an aberrant narrowing of behavioral repertoire. In certain vulnerable individuals, escalation of drug use over time is thought to drive specific molecular neuroadaptations that foster the development of addiction. Research has identified neurobiological elements of altered reinforcement following excessive drug use that comprise within-circuit and between-circuit neuroadaptations, both of which contribute to addiction. Central to this process is the eventual potentiation of negative reinforcement mechanisms that may represent the final definitive criterion locking vulnerable individuals into a persistent state of addiction. Targeting the neural substrates of reinforcement likely represents our best chances for therapeutic intervention for this devastating disease. © 2016 Elsevier B.V. All rights reserved.

  13. Reinforcing Saccadic Amplitude Variability (United States)

    Paeye, Celine; Madelain, Laurent


    Saccadic endpoint variability is often viewed as the outcome of neural noise occurring during sensorimotor processing. However, part of this variability might result from operant learning. We tested this hypothesis by reinforcing dispersions of saccadic amplitude distributions, while maintaining constant their medians. In a first experiment we…

  14. Rational and Mechanistic Perspectives on Reinforcement Learning (United States)

    Chater, Nick


    This special issue describes important recent developments in applying reinforcement learning models to capture neural and cognitive function. But reinforcement learning, as a theoretical framework, can apply at two very different levels of description: "mechanistic" and "rational." Reinforcement learning is often viewed in mechanistic terms--as…

  15. Altered cingulo-striatal function underlies reward drive deficits in schizophrenia. (United States)

    Park, Il Ho; Chun, Ji Won; Park, Hae-Jeong; Koo, Min-Seong; Park, Sunyoung; Kim, Seok-Hyeong; Kim, Jae-Jin


    Amotivation in schizophrenia is assumed to involve dysfunctional dopaminergic signaling of reward prediction or anticipation. It is unclear, however, whether the translation of neural representation of reward value to behavioral drive is affected in schizophrenia. In order to examine how abnormal neural processing of response valuation and initiation affects incentive motivation in schizophrenia, we conducted functional MRI using a deterministic reinforcement learning task with variable intervals of contingency reversals in 20 clinically stable patients with schizophrenia and 20 healthy controls. Behaviorally, the advantage of positive over negative reinforcer in reinforcement-related responsiveness was not observed in patients. Patients showed altered response valuation and initiation-related striatal activity and deficient rostro-ventral anterior cingulate cortex activation during reward approach initiation. Among these neural abnormalities, rostro-ventral anterior cingulate cortex activation was correlated with positive reinforcement-related responsiveness in controls and social anhedonia and social amotivation subdomain scores in patients. Our findings indicate that the central role of the anterior cingulate cortex is in translating action value into driving force of action, and underscore the role of the cingulo-striatal network in amotivation in schizophrenia. Copyright © 2014 Elsevier B.V. All rights reserved.

  16. An intrinsically irreversible, neural-network-like approach to the Schrödinger equation and some results of application to drive nuclear synthesis research work

    Energy Technology Data Exchange (ETDEWEB)

    Abundo, Ugo [Neural Calculus Lab, J. Von Neumann Foundation, v.Clelia 15, 00181 Rome, Italy Opensharelab, Open Power Association, v.Genzano 95, 00179 Rome (Italy)


    An analogy is drawn among the irreversible evolution of a neural-network-based A.I., an information field associated to spacetime configurations and the behaviour of entities described by the Schrödinger equation.

  17. Drive Stands (United States)

    Federal Laboratory Consortium — The Electrical Systems Laboratory (ESL)houses numerous electrically driven drive stands. A drive stand consists of an electric motor driving a gearbox and a mounting...

  18. Dementia & Driving (United States)

    ... have to give up driving. Many people associate driving with self-reliance and freedom; the loss of driving privileges ... familiar roads and avoid long distances. Avoid heavy traffic and heavily traveled roads. Avoid driving at night and in bad weather. Reduce the ...

  19. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L


    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  20. Study on driving control behavior for lane change maneuver. Analysis of expert driver using neural network system; Shasen henkoji no driver sosa tokusei. Neural network system ni yoru jukuren driver no kaiseki

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Z; Okayama, T; Katayama, T [Japan Automobile Research Institute Inc., Tsukuba (Japan); Kageyama, I [Nihon University, Tokyo (Japan)


    In order to study driver steering control behavior for vehicle, a driver model for single-lane change maneuver is constructed by a neural network system concerned with the man-machine-environment system. And, using sensitivity analysis, it is found that the model represent the driver control behavior, and the relation between the driver control behavior and vehicle responses. The sensitivity analysis is also examined by applying to the 2nd order predictive driver model. The validity of the sensitivity analysis is confirmed. 5 refs., 8 figs.

  1. Spatio-temporal Model of Endogenous ROS and Raft-Dependent WNT/Beta-Catenin Signaling Driving Cell Fate Commitment in Human Neural Progenitor Cells (United States)

    Haack, Fiete; Lemcke, Heiko; Ewald, Roland; Rharass, Tareck; Uhrmacher, Adelinde M.


    Canonical WNT/β-catenin signaling is a central pathway in embryonic development, but it is also connected to a number of cancers and developmental disorders. Here we apply a combined in-vitro and in-silico approach to investigate the spatio-temporal regulation of WNT/β-catenin signaling during the early neural differentiation process of human neural progenitors cells (hNPCs), which form a new prospect for replacement therapies in the context of neurodegenerative diseases. Experimental measurements indicate a second signal mechanism, in addition to canonical WNT signaling, being involved in the regulation of nuclear β-catenin levels during the cell fate commitment phase of neural differentiation. We find that the biphasic activation of β-catenin signaling observed experimentally can only be explained through a model that combines Reactive Oxygen Species (ROS) and raft dependent WNT/β-catenin signaling. Accordingly after initiation of differentiation endogenous ROS activates DVL in a redox-dependent manner leading to a transient activation of down-stream β-catenin signaling, followed by continuous auto/paracrine WNT signaling, which crucially depends on lipid rafts. Our simulation studies further illustrate the elaborate spatio-temporal regulation of DVL, which, depending on its concentration and localization, may either act as direct inducer of the transient ROS/β-catenin signal or as amplifier during continuous auto-/parcrine WNT/β-catenin signaling. In addition we provide the first stochastic computational model of WNT/β-catenin signaling that combines membrane-related and intracellular processes, including lipid rafts/receptor dynamics as well as WNT- and ROS-dependent β-catenin activation. The model’s predictive ability is demonstrated under a wide range of varying conditions for in-vitro and in-silico reference data sets. Our in-silico approach is realized in a multi-level rule-based language, that facilitates the extension and modification of the


    DEFF Research Database (Denmark)


    A composite panel having front and back faces, the panel comprising facing reinforcement, backing reinforcement and matrix material binding to the facing and backing reinforcements, the facing and backing reinforcements each independently comprising one or more reinforcing sheets, the facing rein...... by matrix material, the facing and backing reinforcements being interconnected to resist out-of-plane relative movement. The reinforced composite panel is useful as a barrier element for shielding structures, equipment and personnel from blast and/or ballistic impact damage....

  3. Distracted driving (United States)

    ... including maps) The Dangers of Talking on the Phone While Driving You are four times more likely to get ... of reach. If you are caught using a phone while driving, you may risk a ticket or fine. Most ...

  4. Distracted Driving (United States)

    ... and increased awareness of distracted driving using radio advertisements, news stories, and similar media. After the projects ... available at . Distracted Driving Enforcement – TV Ads (Paid). For re-tagging, go to: www. ...

  5. Electric drives

    CERN Document Server

    Boldea, Ion


    ENERGY CONVERSION IN ELECTRIC DRIVESElectric Drives: A DefinitionApplication Range of Electric DrivesEnergy Savings Pay Off RapidlyGlobal Energy Savings Through PEC DrivesMotor/Mechanical Load MatchMotion/Time Profile MatchLoad Dynamics and StabilityMultiquadrant OperationPerformance IndexesProblemsELECTRIC MOTORS FOR DRIVESElectric Drives: A Typical ConfigurationElectric Motors for DrivesDC Brush MotorsConventional AC MotorsPower Electronic Converter Dependent MotorsEnergy Conversion in Electric Motors/GeneratorsPOWER ELECTRONIC CONVERTERS (PECs) FOR DRIVESPower Electronic Switches (PESs)The

  6. Flexural reinforced concrete member with FRP reinforcement


    Putzolu, Mariana


    One of the most problematic point in construction is the durability of the concrete especially related to corrosion of the steel reinforcement. Due to this problem the construction sector, introduced the use of Fiber Reinforced Polymer, the main fibers used in construction are Glass, Carbon and Aramid. In this study, the author aim to analyse the flexural behaviour of concrete beams reinforced with FRP. This aim is achieved by the analysis of specimens reinforced with GFRP bars, with theoreti...

  7. Glaucoma and Driving: On-Road Driving Characteristics (United States)

    Wood, Joanne M.; Black, Alex A.; Mallon, Kerry; Thomas, Ravi; Owsley, Cynthia


    Purpose To comprehensively investigate the types of driving errors and locations that are most problematic for older drivers with glaucoma compared to those without glaucoma using a standardized on-road assessment. Methods Participants included 75 drivers with glaucoma (mean = 73.2±6.0 years) with mild to moderate field loss (better-eye MD = -1.21 dB; worse-eye MD = -7.75 dB) and 70 age-matched controls without glaucoma (mean = 72.6 ± 5.0 years). On-road driving performance was assessed in a dual-brake vehicle by an occupational therapist using a standardized scoring system which assessed the types of driving errors and the locations where they were made and the number of critical errors that required an instructor intervention. Driving safety was rated on a 10-point scale. Self-reported driving ability and difficulties were recorded using the Driving Habits Questionnaire. Results Drivers with glaucoma were rated as significantly less safe, made more driving errors, and had almost double the rate of critical errors than those without glaucoma. Driving errors involved lane positioning and planning/approach, and were significantly more likely to occur at traffic lights and yield/give-way intersections. There were few between group differences in self-reported driving ability. Conclusions Older drivers with glaucoma with even mild to moderate field loss exhibit impairments in driving ability, particularly during complex driving situations that involve tactical problems with lane-position, planning ahead and observation. These results, together with the fact that these drivers self-report their driving to be relatively good, reinforce the need for evidence-based on-road assessments for evaluating driving fitness. PMID:27472221

  8. Glaucoma and Driving: On-Road Driving Characteristics.

    Directory of Open Access Journals (Sweden)

    Joanne M Wood

    Full Text Available To comprehensively investigate the types of driving errors and locations that are most problematic for older drivers with glaucoma compared to those without glaucoma using a standardized on-road assessment.Participants included 75 drivers with glaucoma (mean = 73.2±6.0 years with mild to moderate field loss (better-eye MD = -1.21 dB; worse-eye MD = -7.75 dB and 70 age-matched controls without glaucoma (mean = 72.6 ± 5.0 years. On-road driving performance was assessed in a dual-brake vehicle by an occupational therapist using a standardized scoring system which assessed the types of driving errors and the locations where they were made and the number of critical errors that required an instructor intervention. Driving safety was rated on a 10-point scale. Self-reported driving ability and difficulties were recorded using the Driving Habits Questionnaire.Drivers with glaucoma were rated as significantly less safe, made more driving errors, and had almost double the rate of critical errors than those without glaucoma. Driving errors involved lane positioning and planning/approach, and were significantly more likely to occur at traffic lights and yield/give-way intersections. There were few between group differences in self-reported driving ability.Older drivers with glaucoma with even mild to moderate field loss exhibit impairments in driving ability, particularly during complex driving situations that involve tactical problems with lane-position, planning ahead and observation. These results, together with the fact that these drivers self-report their driving to be relatively good, reinforce the need for evidence-based on-road assessments for evaluating driving fitness.

  9. Glaucoma and Driving: On-Road Driving Characteristics. (United States)

    Wood, Joanne M; Black, Alex A; Mallon, Kerry; Thomas, Ravi; Owsley, Cynthia


    To comprehensively investigate the types of driving errors and locations that are most problematic for older drivers with glaucoma compared to those without glaucoma using a standardized on-road assessment. Participants included 75 drivers with glaucoma (mean = 73.2±6.0 years) with mild to moderate field loss (better-eye MD = -1.21 dB; worse-eye MD = -7.75 dB) and 70 age-matched controls without glaucoma (mean = 72.6 ± 5.0 years). On-road driving performance was assessed in a dual-brake vehicle by an occupational therapist using a standardized scoring system which assessed the types of driving errors and the locations where they were made and the number of critical errors that required an instructor intervention. Driving safety was rated on a 10-point scale. Self-reported driving ability and difficulties were recorded using the Driving Habits Questionnaire. Drivers with glaucoma were rated as significantly less safe, made more driving errors, and had almost double the rate of critical errors than those without glaucoma. Driving errors involved lane positioning and planning/approach, and were significantly more likely to occur at traffic lights and yield/give-way intersections. There were few between group differences in self-reported driving ability. Older drivers with glaucoma with even mild to moderate field loss exhibit impairments in driving ability, particularly during complex driving situations that involve tactical problems with lane-position, planning ahead and observation. These results, together with the fact that these drivers self-report their driving to be relatively good, reinforce the need for evidence-based on-road assessments for evaluating driving fitness.

  10. Pile Driving (United States)


    Machine-oriented structural engineering firm TERA, Inc. is engaged in a project to evaluate the reliability of offshore pile driving prediction methods to eventually predict the best pile driving technique for each new offshore oil platform. Phase I Pile driving records of 48 offshore platforms including such information as blow counts, soil composition and pertinent construction details were digitized. In Phase II, pile driving records were statistically compared with current methods of prediction. Result was development of modular software, the CRIPS80 Software Design Analyzer System, that companies can use to evaluate other prediction procedures or other data bases.

  11. Enrichment of skin-derived neural precursor cells from dermal cell populations by altering culture conditions. (United States)

    Bayati, Vahid; Gazor, Rohoullah; Nejatbakhsh, Reza; Negad Dehbashi, Fereshteh


    As stem cells play a critical role in tissue repair, their manipulation for being applied in regenerative medicine is of great importance. Skin-derived precursors (SKPs) may be good candidates for use in cell-based therapy as the only neural stem cells which can be isolated from an accessible tissue, skin. Herein, we presented a simple protocol to enrich neural SKPs by monolayer adherent cultivation to prove the efficacy of this method. To enrich neural SKPs from dermal cell populations, we have found that a monolayer adherent cultivation helps to increase the numbers of neural precursor cells. Indeed, we have cultured dermal cells as monolayer under serum-supplemented (control) and serum-supplemented culture, followed by serum free cultivation (test) and compared. Finally, protein markers of SKPs were assessed and compared in both experimental groups and differentiation potential was evaluated in enriched culture. The cells of enriched culture concurrently expressed fibronectin, vimentin and nestin, an intermediate filament protein expressed in neural and skeletal muscle precursors as compared to control culture. In addition, they possessed a multipotential capacity to differentiate into neurogenic, glial, adipogenic, osteogenic and skeletal myogenic cell lineages. It was concluded that serum-free adherent culture reinforced by growth factors have been shown to be effective on proliferation of skin-derived neural precursor cells (skin-NPCs) and drive their selective and rapid expansion.

  12. Reinforced sulphur concrete

    NARCIS (Netherlands)


    Reinforced sulphur concrete wherein one or more metal reinforcing members are in contact with sulphur concrete is disclosed. The reinforced sulphur concrete comprises an adhesion promoter that enhances the interaction between the sulphur and the one or more metal reinforcing members.

  13. Model analysis of adaptive car driving behavior

    NARCIS (Netherlands)

    Wewerinke, P.H.


    This paper deals with two modeling approaches to car driving. The first one is a system theoretic approach to describe adaptive human driving behavior. The second approach utilizes neural networks. As an illustrative example the overtaking task is considered and modeled in system theoretic terms.

  14. Impaired Driving (United States)

    ... Get the Facts What Works: Strategies to Increase Car Seat and Booster Seat ... narcotics. 3 That’s one percent of the 111 million self-reported episodes of alcohol-impaired driving among U.S. ...

  15. Program Helps Simulate Neural Networks (United States)

    Villarreal, James; Mcintire, Gary


    Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

  16. Evidence for a neural law of effect. (United States)

    Athalye, Vivek R; Santos, Fernando J; Carmena, Jose M; Costa, Rui M


    Thorndike's law of effect states that actions that lead to reinforcements tend to be repeated more often. Accordingly, neural activity patterns leading to reinforcement are also reentered more frequently. Reinforcement relies on dopaminergic activity in the ventral tegmental area (VTA), and animals shape their behavior to receive dopaminergic stimulation. Seeking evidence for a neural law of effect, we found that mice learn to reenter more frequently motor cortical activity patterns that trigger optogenetic VTA self-stimulation. Learning was accompanied by gradual shaping of these patterns, with participating neurons progressively increasing and aligning their covariance to that of the target pattern. Motor cortex patterns that lead to phasic dopaminergic VTA activity are progressively reinforced and shaped, suggesting a mechanism by which animals select and shape actions to reliably achieve reinforcement. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

  17. Performance in Stroke: Driving Neural Reorganization?

    Directory of Open Access Journals (Sweden)

    Roberta B. Shepherd


    increase strength, control, skill, endurance, fitness, and social readjustment. Rehabilitation services remain slow to make the changes necessary to upgrade environments, attitudes, and rehabilitation methodologies to those shown to be more scientifically rational and for which there is evidence of effectiveness.

  18. Driving things

    DEFF Research Database (Denmark)

    Nevile, Maurice Richard


    I explore how participants organise involvement with objects brought into the car, relative to the demands of driving and social activity. Objects in cars commonly include phones or other technologies, food, body care products, texts, clothing, bags and carry items, toys, and even animals...... 2004, Haddington et al. 2012). I focus here especially on how the practical and interactional work of locating, seeing, placing, handling, hearing, and relinquishing, is ordered and accomplished relative to the emerging and contingent demands of both driving and social participation......, such that involvement with objects is constituted as secondary to driving in a multiactivity setting (e.g. Haddington et al. 2014). We see how events with, for, of, and even by objects can occur as predictable, planned and even designed for (e.g. changing glasses, applying body lotion), or might be unexpected...

  19. Management of Reinforcement Corrosion

    DEFF Research Database (Denmark)

    Küter, André; Geiker, Mette Rica; Møller, Per

    Reinforcement corrosion is the most important cause for deterioration of reinforced concrete structures, both with regard to costs and consequences. Thermodynamically consistent descriptions of corrosion mechanisms are expected to allow the development of innovative concepts for the management...... of reinforcement corrosion....

  20. Reinforcement Learning in Autism Spectrum Disorder

    Directory of Open Access Journals (Sweden)

    Manuela Schuetze


    Full Text Available Early behavioral interventions are recognized as integral to standard care in autism spectrum disorder (ASD, and often focus on reinforcing desired behaviors (e.g., eye contact and reducing the presence of atypical behaviors (e.g., echoing others' phrases. However, efficacy of these programs is mixed. Reinforcement learning relies on neurocircuitry that has been reported to be atypical in ASD: prefrontal-sub-cortical circuits, amygdala, brainstem, and cerebellum. Thus, early behavioral interventions rely on neurocircuitry that may function atypically in at least a subset of individuals with ASD. Recent work has investigated physiological, behavioral, and neural responses to reinforcers to uncover differences in motivation and learning in ASD. We will synthesize this work to identify promising avenues for future research that ultimately can be used to enhance the efficacy of early intervention.

  1. Genomic Signatures of Reinforcement

    Directory of Open Access Journals (Sweden)

    Austin G. Garner


    Full Text Available Reinforcement is the process by which selection against hybridization increases reproductive isolation between taxa. Much research has focused on demonstrating the existence of reinforcement, yet relatively little is known about the genetic basis of reinforcement or the evolutionary conditions under which reinforcement can occur. Inspired by reinforcement’s characteristic phenotypic pattern of reproductive trait divergence in sympatry but not in allopatry, we discuss whether reinforcement also leaves a distinct genomic pattern. First, we describe three patterns of genetic variation we expect as a consequence of reinforcement. Then, we discuss a set of alternative processes and complicating factors that may make the identification of reinforcement at the genomic level difficult. Finally, we consider how genomic analyses can be leveraged to inform if and to what extent reinforcement evolved in the face of gene flow between sympatric lineages and between allopatric and sympatric populations of the same lineage. Our major goals are to understand if genome scans for particular patterns of genetic variation could identify reinforcement, isolate the genetic basis of reinforcement, or infer the conditions under which reinforcement evolved.

  2. Genomic Signatures of Reinforcement (United States)

    Goulet, Benjamin E.


    Reinforcement is the process by which selection against hybridization increases reproductive isolation between taxa. Much research has focused on demonstrating the existence of reinforcement, yet relatively little is known about the genetic basis of reinforcement or the evolutionary conditions under which reinforcement can occur. Inspired by reinforcement’s characteristic phenotypic pattern of reproductive trait divergence in sympatry but not in allopatry, we discuss whether reinforcement also leaves a distinct genomic pattern. First, we describe three patterns of genetic variation we expect as a consequence of reinforcement. Then, we discuss a set of alternative processes and complicating factors that may make the identification of reinforcement at the genomic level difficult. Finally, we consider how genomic analyses can be leveraged to inform if and to what extent reinforcement evolved in the face of gene flow between sympatric lineages and between allopatric and sympatric populations of the same lineage. Our major goals are to understand if genome scans for particular patterns of genetic variation could identify reinforcement, isolate the genetic basis of reinforcement, or infer the conditions under which reinforcement evolved. PMID:29614048

  3. Instructional control of reinforcement learning: a behavioral and neurocomputational investigation. (United States)

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


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

  4. Community Drive

    DEFF Research Database (Denmark)

    Magnussen, Rikke


    Schools and educational institutions are challenged by not adequately educating students for independent knowledge collaboration and solving of complex societal challenges (Bundsgaard & Hansen, 2016; Slot et al., 2017). As an alternative strategy to formal learning has Community-driven research...... opportunity to break boundaries between research institutions and surrounding communities through the involvement of new types of actors, knowledge forms and institutions (OECD, 2011). This paper presents the project Community Drive a three year cross disciplinary community-driven game– and data-based project....... In the paper we present how the project Community Drive initiated in May 2018 is based on results from pilot projects conducted from 2014 – 2017. Overall these studies showed that it is a strong motivational factor for students to be given the task to change their living conditions through redesign...

  5. Global reinforcement training of CrossNets (United States)

    Ma, Xiaolong


    Hybrid "CMOL" integrated circuits, incorporating advanced CMOS devices for neural cell bodies, nanowires as axons and dendrites, and latching switches as synapses, may be used for the hardware implementation of extremely dense (107 cells and 1012 synapses per cm2) neuromorphic networks, operating up to 10 6 times faster than their biological prototypes. We are exploring several "Cross- Net" architectures that accommodate the limitations imposed by CMOL hardware and should allow effective training of the networks without a direct external access to individual synapses. Our studies have show that CrossNets based on simple (two-terminal) crosspoint devices can work well in at least two modes: as Hop-field networks for associative memory and multilayer perceptrons for classification tasks. For more intelligent tasks (such as robot motion control or complex games), which do not have "examples" for supervised learning, more advanced training methods such as the global reinforcement learning are necessary. For application of global reinforcement training algorithms to CrossNets, we have extended Williams's REINFORCE learning principle to a more general framework and derived several learning rules that are more suitable for CrossNet hardware implementation. The results of numerical experiments have shown that these new learning rules can work well for both classification tasks and reinforcement tasks such as the cartpole balancing control problem. Some limitations imposed by the CMOL hardware need to be carefully addressed for the the successful application of in situ reinforcement training to CrossNets.

  6. Electric drives

    Energy Technology Data Exchange (ETDEWEB)


    Several electric vehicles have been tested in long-term tests, i.e. an electric passenger car (maximum speed 115 km/h) and several busses for use in pedestrians' zones, spas, airports, natural reserves, and urban transportation (DUO busses). The ICE high-speed train is discussed in some detail, i.e. its aeroacoustic and aerodynamic design, running gear, computer-controlled drives and brakes, diagnostic systems, and electrical equipment. The Berlin Maglev system is mentioned as well as current inverters in rail vehicles. (HWJ).

  7. Music mood induction and maintenance while driving : A simulator study

    NARCIS (Netherlands)

    Dijksterhuis, Chris; van der Zwaag, Marjolein; de Waard, Dick; Westerink, Joyce; Brookhuis, Karel; Mulder, Ben L. J. M.


    It is common knowledge that mood can influence our everyday behaviour and people often seek to reinforce, or to alter their mood, for example by turning on music. Music listening while driving is a common activity. However, the actual impact of music listening while driving on physical state and

  8. Dopamine-Dependent Reinforcement of Motor Skill Learning: Evidence from Gilles de la Tourette Syndrome (United States)

    Palminteri, Stefano; Lebreton, Mael; Worbe, Yulia; Hartmann, Andreas; Lehericy, Stephane; Vidailhet, Marie; Grabli, David; Pessiglione, Mathias


    Reinforcement learning theory has been extensively used to understand the neural underpinnings of instrumental behaviour. A central assumption surrounds dopamine signalling reward prediction errors, so as to update action values and ensure better choices in the future. However, educators may share the intuitive idea that reinforcements not only…

  9. Continuous Reinforced Concrete Beams

    DEFF Research Database (Denmark)

    Hoang, Cao Linh; Nielsen, Mogens Peter


    This report deals with stress and stiffness estimates of continuous reinforced concrete beams with different stiffnesses for negative and positive moments e.g. corresponding to different reinforcement areas in top and bottom. Such conditions are often met in practice.The moment distribution...

  10. In vitro reinforcement of hippocampal bursting: a search for Skinner's atoms of behavior.


    Stein, L; Xue, B G; Belluzzi, J D


    A novel "in vitro reinforcement" paradigm was used to investigate Skinner's (1953) hypotheses (a) that operant behavior is made up of infinitesimal "response elements" or "behavioral atoms" and (b) that these very small units, and not whole responses, are the functional units of reinforcement. Our tests are based on the assumption that behavioral atoms may plausibly be represented at the neural level by individual cellular responses. As a first approach, we attempted to reinforce the bursting...

  11. Tackling Error Propagation through Reinforcement Learning: A Case of Greedy Dependency Parsing


    Le, Minh; Fokkens, Antske


    Error propagation is a common problem in NLP. Reinforcement learning explores erroneous states during training and can therefore be more robust when mistakes are made early in a process. In this paper, we apply reinforcement learning to greedy dependency parsing which is known to suffer from error propagation. Reinforcement learning improves accuracy of both labeled and unlabeled dependencies of the Stanford Neural Dependency Parser, a high performance greedy parser, while maintaining its eff...

  12. Safe driving for teens (United States)

    Driving and teenagers; Teens and safe driving; Automobile safety - teenage drivers ... months before taking friends as passengers. Teenage-related driving deaths occur more often in certain conditions. OTHER SAFETY TIPS FOR TEENS Reckless driving is still a ...

  13. Algorithms for Reinforcement Learning

    CERN Document Server

    Szepesvari, Csaba


    Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms'

  14. Autonomous reinforcement learning with experience replay. (United States)

    Wawrzyński, Paweł; Tanwani, Ajay Kumar


    This paper considers the issues of efficiency and autonomy that are required to make reinforcement learning suitable for real-life control tasks. A real-time reinforcement learning algorithm is presented that repeatedly adjusts the control policy with the use of previously collected samples, and autonomously estimates the appropriate step-sizes for the learning updates. The algorithm is based on the actor-critic with experience replay whose step-sizes are determined on-line by an enhanced fixed point algorithm for on-line neural network training. An experimental study with simulated octopus arm and half-cheetah demonstrates the feasibility of the proposed algorithm to solve difficult learning control problems in an autonomous way within reasonably short time. Copyright © 2012 Elsevier Ltd. All rights reserved.

  15. Modeling reinforced concrete durability. (United States)


    This project developed a next-generation modeling approach for projecting the extent of : reinforced concrete corrosion-related damage, customized for new and existing Florida Department of : Transportation bridges and suitable for adapting to broade...

  16. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis


    The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

  17. Neural networks and statistical learning

    CERN Document Server

    Du, Ke-Lin


    Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

  18. GA-based fuzzy reinforcement learning for control of a magnetic bearing system. (United States)

    Lin, C T; Jou, C P


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

  19. Behavior of reinforced concrete beams reinforced with GFRP bars

    Directory of Open Access Journals (Sweden)

    D. H. Tavares

    Full Text Available The use of fiber reinforced polymer (FRP bars is one of the alternatives presented in recent studies to prevent the drawbacks related to the steel reinforcement in specific reinforced concrete members. In this work, six reinforced concrete beams were submitted to four point bending tests. One beam was reinforced with CA-50 steel bars and five with glass fiber reinforced polymer (GFRP bars. The tests were carried out in the Department of Structural Engineering in São Carlos Engineering School, São Paulo University. The objective of the test program was to compare strength, reinforcement deformation, displacement, and some anchorage aspects between the GFRP-reinforced concrete beams and the steel-reinforced concrete beam. The results show that, even though four GFRP-reinforced concrete beams were designed with the same internal tension force as that with steel reinforcement, their capacity was lower than that of the steel-reinforced beam. The results also show that similar flexural capacity can be achieved for the steel- and for the GFRP-reinforced concrete beams by controlling the stiffness (reinforcement modulus of elasticity multiplied by the bar cross-sectional area - EA and the tension force of the GFRP bars.

  20. Steel fiber reinforced concrete

    International Nuclear Information System (INIS)

    Baloch, S.U.


    Steel-Fiber Reinforced Concrete is constructed by adding short fibers of small cross-sectional size .to the fresh concrete. These fibers reinforce the concrete in all directions, as they are randomly oriented. The improved mechanical properties of concrete include ductility, impact-resistance, compressive, tensile and flexural strength and abrasion-resistance. These uniqlte properties of the fiber- reinforcement can be exploited to great advantage in concrete structural members containing both conventional bar-reinforcement and steel fibers. The improvements in mechanical properties of cementitious materials resulting from steel-fiber reinforcement depend on the type, geometry, volume fraction and material-properties of fibers, the matrix mix proportions and the fiber-matrix interfacial bond characteristics. Effects of steel fibers on the mechanical properties of concrete have been investigated in this paper through a comprehensive testing-programme, by varying the fiber volume fraction and the aspect-ratio (Lid) of fibers. Significant improvements are observed in compressive, tensile, flexural strength and impact-resistance of concrete, accompanied by marked improvement in ductility. optimum fiber-volume fraction and aspect-ratio of steel fibers is identified. Test results are analyzed in details and relevant conclusions drawn. The research is finally concluded with future research needs. (author)

  1. International Conference on Artificial Neural Networks (ICANN)

    CERN Document Server

    Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics


    The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

  2. Closed loop interactions between spiking neural network and robotic simulators based on MUSIC and ROS

    Directory of Open Access Journals (Sweden)

    Philipp Weidel


    Full Text Available In order to properly assess the function and computational properties of simulated neural systems, it is necessary to account for the nature of the stimuli that drive the system. However, providing stimuli that are rich and yet both reproducible and amenable to experimental manipulations is technically challenging, and even more so if a closed-loop scenario is required. In this work, we present a novel approach to solve this problem, connecting robotics and neural network simulators. We implement a middleware solution that bridges the Robotic Operating System (ROS to the Multi-Simulator Coordinator (MUSIC. This enables any robotic and neural simulators that implement the corresponding interfaces to be efficiently coupled, allowing real-time performance for a wide range of configurations. This work extends the toolset available for researchers in both neurorobotics and computational neuroscience, and creates the opportunity to perform closed-loop experiments of arbitrary complexity to address questions in multiple areas, including embodiment, agency, and reinforcement learning.

  3. Reinforced concrete tomography

    International Nuclear Information System (INIS)

    Mariscotti, M.A.J.; Morixe, M.; Tarela, P.A.; Thieberger, P.


    In this paper we describe the technique of reinforced concrete tomography, its historical background, recent technological developments and main applications. Gamma radiation sensitive plates are imprinted with radiation going through the concrete sample under study, and then processed to reveal the presence of reinforcement and defects in the material density. The three dimensional reconstruction, or tomography, of the reinforcement out of a single gammagraphy is an original development alternative to conventional methods. Re-bar diameters and positions may be determined with an accuracy of ± 1 mm 0.5-1 cm, respectively. The non-destructive character of this technique makes it particularly attractive in cases of inhabited buildings and diagnoses of balconies. (author) [es

  4. Braided reinforced composite rods for the internal reinforcement of concrete (United States)

    Gonilho Pereira, C.; Fangueiro, R.; Jalali, S.; Araujo, M.; Marques, P.


    This paper reports on the development of braided reinforced composite rods as a substitute for the steel reinforcement in concrete. The research work aims at understanding the mechanical behaviour of core-reinforced braided fabrics and braided reinforced composite rods, namely concerning the influence of the braiding angle, the type of core reinforcement fibre, and preloading and postloading conditions. The core-reinforced braided fabrics were made from polyester fibres for producing braided structures, and E-glass, carbon, HT polyethylene, and sisal fibres were used for the core reinforcement. The braided reinforced composite rods were obtained by impregnating the core-reinforced braided fabric with a vinyl ester resin. The preloading of the core-reinforced braided fabrics and the postloading of the braided reinforced composite rods were performed in three and two stages, respectively. The results of tensile tests carried out on different samples of core-reinforced braided fabrics are presented and discussed. The tensile and bending properties of the braided reinforced composite rods have been evaluated, and the results obtained are presented, discussed, and compared with those of conventional materials, such as steel.

  5. Soil reinforcement with geosynthetics

    Directory of Open Access Journals (Sweden)

    Bessaim Mohammed Mustapha


    Full Text Available The proportionality of existence of land with good bearing to erect any building or building is very small, to remedy this deficiency it is necessary to resort to techniques of reinforcement of the soils which can constitute a very important development. Among these methods of remediation, there is reinforcement by the geosynthetics which constitute an effective solution to these constraints. This process tends to stabilize the soil in question with increased load bearing capacity in civil engineering and geotechnical works such as embankments, slopes, embankments and hydraulic structures, with an inestimable gain in time, economy and durability while preserving the natural and environmental aspect.

  6. Extended driving impairs nocturnal driving performances.

    Directory of Open Access Journals (Sweden)

    Patricia Sagaspe

    Full Text Available Though fatigue and sleepiness at the wheel are well-known risk factors for traffic accidents, many drivers combine extended driving and sleep deprivation. Fatigue-related accidents occur mainly at night but there is no experimental data available to determine if the duration of prior driving affects driving performance at night. Participants drove in 3 nocturnal driving sessions (3-5 am, 1-5 am and 9 pm-5 am on open highway. Fourteen young healthy men (mean age [+/-SD] = 23.4 [+/-1.7] years participated Inappropriate line crossings (ILC in the last hour of driving of each session, sleep variables, self-perceived fatigue and sleepiness were measured. Compared to the short (3-5 am driving session, the incidence rate ratio of inappropriate line crossings increased by 2.6 (95% CI, 1.1 to 6.0; P<.05 for the intermediate (1-5 am driving session and by 4.0 (CI, 1.7 to 9.4; P<.001 for the long (9 pm-5 am driving session. Compared to the reference session (9-10 pm, the incidence rate ratio of inappropriate line crossings were 6.0 (95% CI, 2.3 to 15.5; P<.001, 15.4 (CI, 4.6 to 51.5; P<.001 and 24.3 (CI, 7.4 to 79.5; P<.001, respectively, for the three different durations of driving. Self-rated fatigue and sleepiness scores were both positively correlated to driving impairment in the intermediate and long duration sessions (P<.05 and increased significantly during the nocturnal driving sessions compared to the reference session (P<.01. At night, extended driving impairs driving performances and therefore should be limited.

  7. Reinforcement Magnitude: An Evaluation of Preference and Reinforcer Efficacy


    Trosclair-Lasserre, Nicole M; Lerman, Dorothea C; Call, Nathan A; Addison, Laura R; Kodak, Tiffany


    Consideration of reinforcer magnitude may be important for maximizing the efficacy of treatment for problem behavior. Nonetheless, relatively little is known about children's preferences for different magnitudes of social reinforcement or the extent to which preference is related to differences in reinforcer efficacy. The purpose of the current study was to evaluate the relations among reinforcer magnitude, preference, and efficacy by drawing on the procedures and results of basic experimenta...

  8. Turbomachine blade reinforcement (United States)

    Garcia Crespo, Andres Jose


    Embodiments of the present disclosure include a system having a turbomachine blade segment including a blade and a mounting segment coupled to the blade, wherein the mounting segment has a plurality of reinforcement pins laterally extending at least partially through a neck of the mounting segment.

  9. Reinforcement Magnitude: An Evaluation of Preference and Reinforcer Efficacy (United States)

    Trosclair-Lasserre, Nicole M.; Lerman, Dorothea C.; Call, Nathan A.; Addison, Laura R.; Kodak, Tiffany


    Consideration of reinforcer magnitude may be important for maximizing the efficacy of treatment for problem behavior. Nonetheless, relatively little is known about children's preferences for different magnitudes of social reinforcement or the extent to which preference is related to differences in reinforcer efficacy. The purpose of the current…

  10. Goal-seeking neural net for recall and recognition (United States)

    Omidvar, Omid M.


    Neural networks have been used to mimic cognitive processes which take place in animal brains. The learning capability inherent in neural networks makes them suitable candidates for adaptive tasks such as recall and recognition. The synaptic reinforcements create a proper condition for adaptation, which results in memorization, formation of perception, and higher order information processing activities. In this research a model of a goal seeking neural network is studied and the operation of the network with regard to recall and recognition is analyzed. In these analyses recall is defined as retrieval of stored information where little or no matching is involved. On the other hand recognition is recall with matching; therefore it involves memorizing a piece of information with complete presentation. This research takes the generalized view of reinforcement in which all the signals are potential reinforcers. The neuronal response is considered to be the source of the reinforcement. This local approach to adaptation leads to the goal seeking nature of the neurons as network components. In the proposed model all the synaptic strengths are reinforced in parallel while the reinforcement among the layers is done in a distributed fashion and pipeline mode from the last layer inward. A model of complex neuron with varying threshold is developed to account for inhibitory and excitatory behavior of real neuron. A goal seeking model of a neural network is presented. This network is utilized to perform recall and recognition tasks. The performance of the model with regard to the assigned tasks is presented.

  11. A Difficult Journey: Reflections on Driving and Driving Cessation From a Team of Clinical Researchers. (United States)

    Liddle, Jacki; Gustafsson, Louise; Mitchell, Geoffrey; Pachana, Nancy A


    Recognizing the clinical importance and safety and well-being implications for the population, a multidisciplinary team has been researching older drivers and driving cessation issues for more than 15 years. Using empirical approaches, the team has explored quality of life and participation outcomes related to driving and nondriving for older people and has developed interventions to improve outcomes after driving cessation. The team members represent occupational therapists, medical practitioners, and clinical and neuropsychologists. While building the evidence base for driving- and driving cessation-related clinical practice, the researchers have also had first-hand experiences of interruptions to their own or parents' driving; involvement of older family members in road crashes; and provision of support during family members' driving assessment and cessation. This has led to reflection on their understandings and re-evaluation and refocusing of their perspectives in driving cessation research. This work will share the narratives of the authors and note their developing perspectives and foci within research as well as their clinical practice. Personal reflections have indicated the far-reaching implications for older drivers and family members of involvement in road crashes: the potential for interruptions to driving as a time for support and future planning and the conflicting and difficult roles of family members within the driving cessation process. Overall the lived, personal experience of the authors has reinforced the complex nature of driving and changes to driving status for the driver and their support team and the need for further research and support. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail:

  12. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.


    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing


    Directory of Open Access Journals (Sweden)

    Piotr FOLĘGA


    Full Text Available The variety of types and sizes currently in production harmonic drive is a problem in their rational choice. Properly selected harmonic drive must meet certain requirements during operation, and achieve the anticipated service life. The paper discusses the problems associated with the selection of the harmonic drive. It also presents the algorithm correct choice of harmonic drive. The main objective of this study was to develop a computer program that allows the correct choice of harmonic drive by developed algorithm.

  14. Evolvable synthetic neural system (United States)

    Curtis, Steven A. (Inventor)


    An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

  15. neural control system

    International Nuclear Information System (INIS)

    Elshazly, A.A.E.


    Automatic power stabilization control is the desired objective for any reactor operation , especially, nuclear power plants. A major problem in this area is inevitable gap between a real plant ant the theory of conventional analysis and the synthesis of linear time invariant systems. in particular, the trajectory tracking control of a nonlinear plant is a class of problems in which the classical linear transfer function methods break down because no transfer function can represent the system over the entire operating region . there is a considerable amount of research on the model-inverse approach using feedback linearization technique. however, this method requires a prices plant model to implement the exact linearizing feedback, for nuclear reactor systems, this approach is not an easy task because of the uncertainty in the plant parameters and un-measurable state variables . therefore, artificial neural network (ANN) is used either in self-tuning control or in improving the conventional rule-based exper system.the main objective of this thesis is to suggest an ANN, based self-learning controller structure . this method is capable of on-line reinforcement learning and control for a nuclear reactor with a totally unknown dynamics model. previously, researches are based on back- propagation algorithm . back -propagation (BP), fast back -propagation (FBP), and levenberg-marquardt (LM), algorithms are discussed and compared for reinforcement learning. it is found that, LM algorithm is quite superior

  16. Neural network regulation driven by autonomous neural firings (United States)

    Cho, Myoung Won


    Biological neurons naturally fire spontaneously due to the existence of a noisy current. Such autonomous firings may provide a driving force for network formation because synaptic connections can be modified due to neural firings. Here, we study the effect of autonomous firings on network formation. For the temporally asymmetric Hebbian learning, bidirectional connections lose their balance easily and become unidirectional ones. Defining the difference between reciprocal connections as new variables, we could express the learning dynamics as if Ising model spins interact with each other in magnetism. We present a theoretical method to estimate the interaction between the new variables in a neural system. We apply the method to some network systems and find some tendencies of autonomous neural network regulation.

  17. The Reinforcing Event (RE) Menu (United States)

    Addison, Roger M.; Homme, Lloyd E.


    A motivational system, the Contingency Management System, uses contracts in which some amount of defined task behavior is demanded for some interval of reinforcing event. The Reinforcing Event Menu, a list of high probability reinforcing behaviors, is used in the system as a prompting device for the learner and as an aid for the administrator in…

  18. Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning


    Yue Hu; Weimin Li; Kun Xu; Taimoor Zahid; Feiyan Qin; Chenming Li


    An energy management strategy (EMS) is important for hybrid electric vehicles (HEVs) since it plays a decisive role on the performance of the vehicle. However, the variation of future driving conditions deeply influences the effectiveness of the EMS. Most existing EMS methods simply follow predefined rules that are not adaptive to different driving conditions online. Therefore, it is useful that the EMS can learn from the environment or driving cycle. In this paper, a deep reinforcement learn...

  19. Smart sensorless prediction diagnosis of electric drives (United States)

    Kruglova, TN; Glebov, NA; Shoshiashvili, ME


    In this paper, the discuss diagnostic method and prediction of the technical condition of an electrical motor using artificial intelligent method, based on the combination of fuzzy logic and neural networks, are discussed. The fuzzy sub-model determines the degree of development of each fault. The neural network determines the state of the object as a whole and the number of serviceable work periods for motors actuator. The combination of advanced techniques reduces the learning time and increases the forecasting accuracy. The experimental implementation of the method for electric drive diagnosis and associated equipment is carried out at different speeds. As a result, it was found that this method allows troubleshooting the drive at any given speed.

  20. Autoshaping Chicks with Heat Reinforcement: The Role of Stimulus-Reinforcer and Response-Reinforcer Relations (United States)

    Wasserman, Edward A.; And Others


    The present series of experiments attempted to analyze more fully the contributions of stimulus-reinforcer and response-reinforcer relations to autoshaping within a single conditioning situation. (Author)

  1. Study on reinforced concrete beams with helical transverse reinforcement (United States)

    Kaarthik Krishna, N.; Sandeep, S.; Mini, K. M.


    In a Reinforced Concrete (R.C) structure, major reinforcement is used for taking up tensile stresses acting on the structure due to applied loading. The present paper reports the behavior of reinforced concrete beams with helical reinforcement (transverse reinforcement) subjected to monotonous loading by 3-point flexure test. The results were compared with identically similar reinforced concrete beams with rectangular stirrups. During the test crack evolution, load carrying capacity and deflection of the beams were monitored, analyzed and compared. Test results indicate that the use of helical reinforcement provides enhanced load carrying capacity and a lower deflection proving to be more ductile, clearly indicating the advantage in carrying horizontal loads. An analysis was also carried out using ANSYS software in order to compare the test results of both the beams.

  2. Degradation of Waterfront Reinforced Concrete Structures

    African Journals Online (AJOL)

    Key words: Degradation, reinforced concrete, Dar es Salaam port. Abstract—One of the ... especially corrosion of the reinforcement. ... Corrosion of steel reinforcement contributes .... cracks along the line of reinforcement bars and most of the ...

  3. Adversarial Reinforcement Learning in a Cyber Security Simulation}


    Elderman, Richard; Pater, Leon; Thie, Albert; Drugan, Madalina; Wiering, Marco


    This paper focuses on cyber-security simulations in networks modeled as a Markov game with incomplete information and stochastic elements. The resulting game is an adversarial sequential decision making problem played with two agents, the attacker and defender. The two agents pit one reinforcement learning technique, like neural networks, Monte Carlo learning and Q-learning, against each other and examine their effectiveness against learning opponents. The results showed that Monte Carlo lear...

  4. South Oregon Coast Reinforcement.

    Energy Technology Data Exchange (ETDEWEB)

    United States. Bonneville Power Administration.


    The Bonneville Power Administration is proposing to build a transmission line to reinforce electrical service to the southern coast of Oregon. This FYI outlines the proposal, tells how one can learn more, and how one can share ideas and opinions. The project will reinforce Oregon`s south coast area and provide the necessary transmission for Nucor Corporation to build a new steel mill in the Coos Bay/North Bend area. The proposed plant, which would use mostly recycled scrap metal, would produce rolled steel products. The plant would require a large amount of electrical power to run the furnace used in its steel-making process. In addition to the potential steel mill, electrical loads in the south Oregon coast area are expected to continue to grow.

  5. Nanostructured composite reinforced material (United States)

    Seals, Roland D [Oak Ridge, TN; Ripley, Edward B [Knoxville, TN; Ludtka, Gerard M [Oak Ridge, TN


    A family of materials wherein nanostructures and/or nanotubes are incorporated into a multi-component material arrangement, such as a metallic or ceramic alloy or composite/aggregate, producing a new material or metallic/ceramic alloy. The new material has significantly increased strength, up to several thousands of times normal and perhaps substantially more, as well as significantly decreased weight. The new materials may be manufactured into a component where the nanostructure or nanostructure reinforcement is incorporated into the bulk and/or matrix material, or as a coating where the nanostructure or nanostructure reinforcement is incorporated into the coating or surface of a "normal" substrate material. The nanostructures are incorporated into the material structure either randomly or aligned, within grains, or along or across grain boundaries.

  6. Wrinkles in reinforced membranes (United States)

    Takei, Atsushi; Brau, Fabian; Roman, Benoît; Bico, José.


    We study, through model experiments, the buckling under tension of an elastic membrane reinforced with a more rigid strip or a fiber. In these systems, the compression of the rigid layer is induced through Poisson contraction as the membrane is stretched perpendicularly to the strip. Although strips always lead to out-of-plane wrinkles, we observe a transition from out-of-plane to in plane wrinkles beyond a critical strain in the case of fibers embedded into the elastic membranes. The same transition is also found when the membrane is reinforced with a wall of the same material depending on the aspect ratio of the wall. We describe through scaling laws the evolution of the morphology of the wrinkles and the different transitions as a function of material properties and stretching strain.

  7. Prediction of punching shear capacities of two-way concrete slabs reinforced with FRP bars

    Directory of Open Access Journals (Sweden)

    Ibrahim M. Metwally


    Full Text Available Where corrosion of steel reinforcement is a concern, fiber-reinforced polymer (FRP reinforcing bar or grid reinforcement provides an alternative reinforcement for concrete flat slabs. The existing provisions for punching of slabs in most international design standards for reinforced concrete are based on tests of steel reinforced slabs. The elastic stiffness and bonding characteristics of FRP reinforcement are sufficiently different from those of steel to affect punching strength [1]. This paper evaluates the punching shear strength of concrete flat slabs reinforced with different types of fiber-reinforced polymer (FRP. A total of 59 full-size slabs were constructed and tested collected from the literature of FRP bars reinforced concrete slabs. The test parameters were the amount of FRP reinforcing bars, Young’s modulus of FRP bars, slab thickness, loaded areas and concrete compressive strength. The experimental punching shear strengths were compared with the available theoretical predictions, including the ACI 318 Code, BS 8110 Code, ACI 440 design guidelines, and a number of models proposed by some researchers in the literature. Two approaches for predicting the punching strength of FRP-reinforced slabs are examined. The first is an empirical new model which is considered as a modification of El-Gamal et al. [2] model. The second is a Neural Networks Technique; which has been developed to predict the punching shear capacity of FRP reinforced concrete slabs. The accuracies of both methods were evaluated against the experimental test data. They attained excellent agreement with available test results compared to the existing design formulas.

  8. Deep Reinforcement Fuzzing


    Böttinger, Konstantin; Godefroid, Patrice; Singh, Rishabh


    Fuzzing is the process of finding security vulnerabilities in input-processing code by repeatedly testing the code with modified inputs. In this paper, we formalize fuzzing as a reinforcement learning problem using the concept of Markov decision processes. This in turn allows us to apply state-of-the-art deep Q-learning algorithms that optimize rewards, which we define from runtime properties of the program under test. By observing the rewards caused by mutating with a specific set of actions...

  9. artificial neural network model for low strength rc beam shear capacity

    African Journals Online (AJOL)


    RESEARCH PAPER. Keywords: Shear strength, reinforced concrete, Artificial Neural Network, design equations ... searchers using artificial intelligence to im- prove on theoretical ...... benefit to humanity or a waste of time?” The. Structural ...

  10. Human-level control through deep reinforcement learning (United States)

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


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

  11. Human-level control through deep reinforcement learning. (United States)

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


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

  12. Human reinforcement learning subdivides structured action spaces by learning effector-specific values


    Gershman, Samuel J.; Pesaran, Bijan; Daw, Nathaniel D.


    Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable, due to the high dimensionality of the action space. An unresolved question is how neural systems for reinforcement learning – such as prediction error signals for action valuation associated with dopamine and the striatum – can cope with this “curse of dimensionality...

  13. Electric Vehicle - Economical driving

    DEFF Research Database (Denmark)

    Jensen, VCE, Steen V.; Schøn, Henriette


    Instruct the reader in getting most satisfaction out of an EV, especially concerning driving and loading.......Instruct the reader in getting most satisfaction out of an EV, especially concerning driving and loading....

  14. Dementia and driving (United States)

    ... this page: // Dementia and driving To use the sharing features on ... please enable JavaScript. If your loved one has dementia , deciding when they can no longer drive may ...

  15. Neural basis of decision making guided by emotional outcomes. (United States)

    Katahira, Kentaro; Matsuda, Yoshi-Taka; Fujimura, Tomomi; Ueno, Kenichi; Asamizuya, Takeshi; Suzuki, Chisato; Cheng, Kang; Okanoya, Kazuo; Okada, Masato


    Emotional events resulting from a choice influence an individual's subsequent decision making. Although the relationship between emotion and decision making has been widely discussed, previous studies have mainly investigated decision outcomes that can easily be mapped to reward and punishment, including monetary gain/loss, gustatory stimuli, and pain. These studies regard emotion as a modulator of decision making that can be made rationally in the absence of emotions. In our daily lives, however, we often encounter various emotional events that affect decisions by themselves, and mapping the events to a reward or punishment is often not straightforward. In this study, we investigated the neural substrates of how such emotional decision outcomes affect subsequent decision making. By using functional magnetic resonance imaging (fMRI), we measured brain activities of humans during a stochastic decision-making task in which various emotional pictures were presented as decision outcomes. We found that pleasant pictures differentially activated the midbrain, fusiform gyrus, and parahippocampal gyrus, whereas unpleasant pictures differentially activated the ventral striatum, compared with neutral pictures. We assumed that the emotional decision outcomes affect the subsequent decision by updating the value of the options, a process modeled by reinforcement learning models, and that the brain regions representing the prediction error that drives the reinforcement learning are involved in guiding subsequent decisions. We found that some regions of the striatum and the insula were separately correlated with the prediction error for either pleasant pictures or unpleasant pictures, whereas the precuneus was correlated with prediction errors for both pleasant and unpleasant pictures. Copyright © 2015 the American Physiological Society.

  16. Neural basis of decision making guided by emotional outcomes (United States)

    Matsuda, Yoshi-Taka; Fujimura, Tomomi; Ueno, Kenichi; Asamizuya, Takeshi; Suzuki, Chisato; Cheng, Kang; Okanoya, Kazuo; Okada, Masato


    Emotional events resulting from a choice influence an individual's subsequent decision making. Although the relationship between emotion and decision making has been widely discussed, previous studies have mainly investigated decision outcomes that can easily be mapped to reward and punishment, including monetary gain/loss, gustatory stimuli, and pain. These studies regard emotion as a modulator of decision making that can be made rationally in the absence of emotions. In our daily lives, however, we often encounter various emotional events that affect decisions by themselves, and mapping the events to a reward or punishment is often not straightforward. In this study, we investigated the neural substrates of how such emotional decision outcomes affect subsequent decision making. By using functional magnetic resonance imaging (fMRI), we measured brain activities of humans during a stochastic decision-making task in which various emotional pictures were presented as decision outcomes. We found that pleasant pictures differentially activated the midbrain, fusiform gyrus, and parahippocampal gyrus, whereas unpleasant pictures differentially activated the ventral striatum, compared with neutral pictures. We assumed that the emotional decision outcomes affect the subsequent decision by updating the value of the options, a process modeled by reinforcement learning models, and that the brain regions representing the prediction error that drives the reinforcement learning are involved in guiding subsequent decisions. We found that some regions of the striatum and the insula were separately correlated with the prediction error for either pleasant pictures or unpleasant pictures, whereas the precuneus was correlated with prediction errors for both pleasant and unpleasant pictures. PMID:25695644

  17. Gear bearing drive (United States)

    Mavroidis, Constantinos (Inventor); Vranish, John M. (Inventor); Weinberg, Brian (Inventor)


    A gear bearing drive provides a compact mechanism that operates as an actuator providing torque and as a joint providing support. The drive includes a gear arrangement integrating an external rotor DC motor within a sun gear. Locking surfaces maintain the components of the drive in alignment and provide support for axial loads and moments. The gear bearing drive has a variety of applications, including as a joint in robotic arms and prosthetic limbs.

  18. Batch Policy Gradient Methods for Improving Neural Conversation Models


    Kandasamy, Kirthevasan; Bachrach, Yoram; Tomioka, Ryota; Tarlow, Daniel; Carter, David


    We study reinforcement learning of chatbots with recurrent neural network architectures when the rewards are noisy and expensive to obtain. For instance, a chatbot used in automated customer service support can be scored by quality assurance agents, but this process can be expensive, time consuming and noisy. Previous reinforcement learning work for natural language processing uses on-policy updates and/or is designed for on-line learning settings. We demonstrate empirically that such strateg...

  19. Antihistamines and driving safety. (United States)

    O'Hanlon, J F


    The results of two placebo-controlled driving performance studies confirm laboratory data showing that the nonsedating antihistamine terfenadine does not influence the driving performance of users. The amplitude of vehicle weaving calculated for drivers who received this agent did not differ from control values. Neither terfenadine nor loratadine, another nonsedating antihistamine, potentiated the adverse effects of alcohol on driving performance.

  20. Driving After a Stroke (United States)

    ... 23,2015 Can I drive after a stroke? Driving is often a major concern after someone has a stroke. It’s not unusual for stroke survivors to want to drive. Being able to get around after a stroke is important. Safety behind the wheel is even more important after ...

  1. Sequential Dependencies in Driving (United States)

    Doshi, Anup; Tran, Cuong; Wilder, Matthew H.; Mozer, Michael C.; Trivedi, Mohan M.


    The effect of recent experience on current behavior has been studied extensively in simple laboratory tasks. We explore the nature of sequential effects in the more naturalistic setting of automobile driving. Driving is a safety-critical task in which delayed response times may have severe consequences. Using a realistic driving simulator, we find…

  2. Simple Driving Techniques

    DEFF Research Database (Denmark)

    Rosendahl, Mads


    -like language. Our aim is to extract a simple notion of driving and show that even in this tamed form it has much of the power of more general notions of driving. Our driving technique may be used to simplify functional programs which use function composition and will often be able to remove intermediate data...

  3. Kernel Temporal Differences for Neural Decoding (United States)

    Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.


    We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

  4. Driving range estimation for electric vehicles based on driving condition identification and forecast (United States)

    Pan, Chaofeng; Dai, Wei; Chen, Liao; Chen, Long; Wang, Limei


    With the impact of serious environmental pollution in our cities combined with the ongoing depletion of oil resources, electric vehicles are becoming highly favored as means of transport. Not only for the advantage of low noise, but for their high energy efficiency and zero pollution. The Power battery is used as the energy source of electric vehicles. However, it does currently still have a few shortcomings, noticeably the low energy density, with high costs and short cycle life results in limited mileage compared with conventional passenger vehicles. There is great difference in vehicle energy consumption rate under different environment and driving conditions. Estimation error of current driving range is relatively large due to without considering the effects of environmental temperature and driving conditions. The development of a driving range estimation method will have a great impact on the electric vehicles. A new driving range estimation model based on the combination of driving cycle identification and prediction is proposed and investigated. This model can effectively eliminate mileage errors and has good convergence with added robustness. Initially the identification of the driving cycle is based on Kernel Principal Component feature parameters and fuzzy C referring to clustering algorithm. Secondly, a fuzzy rule between the characteristic parameters and energy consumption is established under MATLAB/Simulink environment. Furthermore the Markov algorithm and BP(Back Propagation) neural network method is utilized to predict the future driving conditions to improve the accuracy of the remaining range estimation. Finally, driving range estimation method is carried out under the ECE 15 condition by using the rotary drum test bench, and the experimental results are compared with the estimation results. Results now show that the proposed driving range estimation method can not only estimate the remaining mileage, but also eliminate the fluctuation of the

  5. Reinforced seal component

    International Nuclear Information System (INIS)

    Jeanson, G.M.; Odent, R.P.


    The invention concerns a seal component of the kind comprising a soft sheath and a flexible reinforcement housed throughout the entire length of the sheath. The invention enables O ring seals to be made capable of providing a radial seal, that is to say between two sides or flat collars of two cylindrical mechanical parts, or an axial seal, that is to say between two co-axial axisymmetrical areas. The seal so ensured is relative, but it remains adequately sufficient for many uses, for instance, to ensure the separation of two successive fixed blading compartments of axial compressors used in gas diffusion isotope concentration facilities [fr

  6. Manifold Regularized Reinforcement Learning. (United States)

    Li, Hongliang; Liu, Derong; Wang, Ding


    This paper introduces a novel manifold regularized reinforcement learning scheme for continuous Markov decision processes. Smooth feature representations for value function approximation can be automatically learned using the unsupervised manifold regularization method. The learned features are data-driven, and can be adapted to the geometry of the state space. Furthermore, the scheme provides a direct basis representation extension for novel samples during policy learning and control. The performance of the proposed scheme is evaluated on two benchmark control tasks, i.e., the inverted pendulum and the energy storage problem. Simulation results illustrate the concepts of the proposed scheme and show that it can obtain excellent performance.

  7. Modelling reinforcement corrosion in concrete

    DEFF Research Database (Denmark)

    Michel, Alexander; Geiker, Mette Rica; Stang, Henrik


    A physio-chemical model for the simulation of reinforcement corrosion in concrete struc-tures was developed. The model allows for simulation of initiation and subsequent propaga-tion of reinforcement corrosion. Corrosion is assumed to be initiated once a defined critical chloride threshold......, a numerical example is pre-sented, that illustrates the formation of corrosion cells as well as propagation of corrosion in a reinforced concrete structure....

  8. The Reinforcement Learning Competition 2014


    Dimitrakakis, Christos; Li, Guangliang; Tziortziotis, Nikoalos


    Reinforcement learning is one of the most general problems in artificial intelligence. It has been used to model problems in automated experiment design, control, economics, game playing, scheduling and telecommunications. The aim of the reinforcement learning competition is to encourage the development of very general learning agents for arbitrary reinforcement learning problems and to provide a test-bed for the unbiased evaluation of algorithms.

  9. Optimizing microstimulation using a reinforcement learning framework. (United States)

    Brockmeier, Austin J; Choi, John S; Distasio, Marcello M; Francis, Joseph T; Príncipe, José C


    The ability to provide sensory feedback is desired to enhance the functionality of neuroprosthetics. Somatosensory feedback provides closed-loop control to the motor system, which is lacking in feedforward neuroprosthetics. In the case of existing somatosensory function, a template of the natural response can be used as a template of desired response elicited by electrical microstimulation. In the case of no initial training data, microstimulation parameters that produce responses close to the template must be selected in an online manner. We propose using reinforcement learning as a framework to balance the exploration of the parameter space and the continued selection of promising parameters for further stimulation. This approach avoids an explicit model of the neural response from stimulation. We explore a preliminary architecture--treating the task as a k-armed bandit--using offline data recorded for natural touch and thalamic microstimulation, and we examine the methods efficiency in exploring the parameter space while concentrating on promising parameter forms. The best matching stimulation parameters, from k = 68 different forms, are selected by the reinforcement learning algorithm consistently after 334 realizations.

  10. Recycling of Reinforced Plastics (United States)

    Adams, R. D.; Collins, Andrew; Cooper, Duncan; Wingfield-Digby, Mark; Watts-Farmer, Archibald; Laurence, Anna; Patel, Kayur; Stevens, Mark; Watkins, Rhodri


    This work has shown is that it is possible to recycle continuous and short fibre reinforced thermosetting resins while keeping almost the whole of the original material, both fibres and matrix, within the recyclate. By splitting, crushing hot or cold, and hot forming, it is possible to create a recyclable material, which we designate a Remat, which can then be used to remanufacture other shapes, examples of plates and tubes being demonstrated. Not only can remanufacturing be done, but it has been shown that over 50 % of the original mechanical properties, such as the E modulus, tensile strength, and interlaminar shear strength, can be retained. Four different forms of composite were investigated, a random mat Glass Fibre Reinforced Plastic (GFRP) bathroom component and boat hull, woven glass and carbon fibre cloth impregnated with an epoxy resin, and unidirectional carbon fibre pre-preg. One of the main factors found to affect composite recyclability was the type of resin matrix used in the composite. Thermoset resins tested were shown to have a temperature range around the Glass Transition Temperature (Tg) where they exhibit ductile behaviour, hence aiding reforming of the material. The high-grade carbon fibre prepreg was found to be less easy to recycle than the woven of random fibre laminates. One method of remanufacturing was by heating the Remat to above its glass transition temperature, bending it to shape, and then cooling it. However, unless precautions are taken, the geometric form may revert. This does not happen with the crushed material.

  11. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome


    Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

  12. Control rod drive

    International Nuclear Information System (INIS)

    Okutani, Tetsuro.


    Purpose: To provide a simple and economical control rod drive using a control circuit requiring no pulse circuit. Constitution: Control rods in a BWR type reactor are driven by hydraulic pressure and inserted or withdrawn in the direction of applying the hydraulic pressure. The direction of the hydraulic pressure is controlled by a direction control valve. Since the driving for the control rod is extremely important in view of the operation, a self diagnosis function is disposed for rapid inspection of possible abnormality. In the present invention, two driving contacts are disposed each by one between the both ends of a solenoid valve of the direction control valve for driving the control rod and the driving power source, and diagnosis is conducted by alternately operating them. Therefore, since it is only necessary that the control circuit issues a driving instruction only to one of the two driving contacts, the pulse circuit is no more required. Further, since the control rod driving is conducted upon alignment of the two driving instructions, the reliability of the control rod drive can be improved. (Horiuchi, T.)

  13. Using driving simulators to assess driving safety. (United States)

    Boyle, Linda Ng; Lee, John D


    Changes in drivers, vehicles, and roadways pose substantial challenges to the transportation safety community. Crash records and naturalistic driving data are useful for examining the influence of past or existing technology on drivers, and the associations between risk factors and crashes. However, they are limited because causation cannot be established and technology not yet installed in production vehicles cannot be assessed. Driving simulators have become an increasingly widespread tool to understand evolving and novel technologies. The ability to manipulate independent variables in a randomized, controlled setting also provides the added benefit of identifying causal links. This paper introduces a special issue on simulator-based safety studies. The special issue comprises 25 papers that demonstrate the use of driving simulators to address pressing transportation safety problems and includes topics as diverse as neurological dysfunction, work zone design, and driver distraction. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

  14. Superluminal warp drive

    Energy Technology Data Exchange (ETDEWEB)

    Gonzalez-Diaz, Pedro F. [Colina de los Chopos, Centro de Fisica ' Miguel A. Catalan' , Instituto de Matematicas y Fisica Fundamental, Consejo Superior de Investigaciones Cientificas, Serrano 121, 28006 Madrid (Spain)], E-mail:


    In this Letter we consider a warp drive spacetime resulting from that suggested by Alcubierre when the spaceship can only travel faster than light. Restricting to the two dimensions that retains most of the physics, we derive the thermodynamic properties of the warp drive and show that the temperature of the spaceship rises up as its apparent velocity increases. We also find that the warp drive spacetime can be exhibited in a manifestly cosmological form.

  15. Constitutive model for reinforced concrete

    NARCIS (Netherlands)

    Feenstra, P.H.; Borst, de R.


    A numerical model is proposed for reinforced-concrete behavior that combines the commonly accepted ideas from modeling plain concrete, reinforcement, and interaction behavior in a consistent manner. The behavior of plain concrete is govern by fracture-energy-level-based formulation both in tension

  16. Quenched Reinforcement Exposed to Fire

    DEFF Research Database (Denmark)

    Hertz, Kristian Dahl


    .0% is seldom found in “slack” (not prestressed) reinforcement, but 2.0% stresses might be relevant for reinforcement in T shaped cross sections and for prestressed structures, where large strains can be applied. All data are provided in a “HOT” condition during a fire and in a “COLD” condition after a fire...

  17. Tangible Reinforcers: Bonuses or Bribes? (United States)

    O'Leary, K. Daniel; And Others


    Objections to the use of tangible reinforcers, such as prizes, candy, cigarettes, and money, are discussed. Treatment programs using tangible reinforcers are recommended as powerful modifers of behavior to be implemented only after less powerful means of modification have been tried. (Author)

  18. The probability of reinforcement per trial affects posttrial responding and subsequent extinction but not within-trial responding. (United States)

    Harris, Justin A; Kwok, Dorothy W S


    During magazine approach conditioning, rats do not discriminate between a conditional stimulus (CS) that is consistently reinforced with food and a CS that is occasionally (partially) reinforced, as long as the CSs have the same overall reinforcement rate per second. This implies that rats are indifferent to the probability of reinforcement per trial. However, in the same rats, the per-trial reinforcement rate will affect subsequent extinction-responding extinguishes more rapidly for a CS that was consistently reinforced than for a partially reinforced CS. Here, we trained rats with consistently and partially reinforced CSs that were matched for overall reinforcement rate per second. We measured conditioned responding both during and immediately after the CSs. Differences in the per-trial probability of reinforcement did not affect the acquisition of responding during the CS but did affect subsequent extinction of that responding, and also affected the post-CS response rates during conditioning. Indeed, CSs with the same probability of reinforcement per trial evoked the same amount of post-CS responding even when they differed in overall reinforcement rate and thus evoked different amounts of responding during the CS. We conclude that reinforcement rate per second controls rats' acquisition of responding during the CS, but at the same time, rats also learn specifically about the probability of reinforcement per trial. The latter learning affects the rats' expectation of reinforcement as an outcome of the trial, which influences their ability to detect retrospectively that an opportunity for reinforcement was missed, and, in turn, drives extinction. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  19. Medications and impaired driving. (United States)

    Hetland, Amanda; Carr, David B


    To describe the association of specific medication classes with driving outcomes and provide clinical recommendations. The MEDLINE and EMBASE databases were searched for articles published from January 1973 to June 2013 on classes of medications associated with driving impairment. The search included outcome terms such as automobile driving, motor vehicle crash, driving simulator, and road tests. Only English-language articles that contained findings from observational or interventional designs with ≥ 10 participants were included in this review. Cross-sectional studies, case series, and case reports were excluded. Driving is an important task and activity for the majority of adults. Some commonly prescribed medications have been associated with driving impairment measured by road performance, driving simulation, and/or motor vehicle crashes. This review of 30 studies identified findings with barbiturates, benzodiazepines, hypnotics, antidepressants, opioid and nonsteroidal analgesics, anticonvulsants, antipsychotics, antiparkinsonian agents, skeletal muscle relaxants, antihistamines, anticholinergic medications, and hypoglycemic agents. Additional studies of medication impact on sedation, sleep latency, and psychomotor function, as well as the role of alcohol, are also discussed. Psychotropic agents and those with central nervous system side effects were associated with measures of impaired driving performance. It is difficult to determine if such associations are actually a result of medication use or the medical diagnosis itself. Regardless, clinicians should be aware of the increased risk of impaired driving with specific classes of medications, educate their patients, and/or consider safer alternatives.

  20. Universal Drive Train Facility (United States)

    Federal Laboratory Consortium — This vehicle drive train research facility is capable of evaluating helicopter and ground vehicle power transmission technologies in a system level environment. The...

  1. Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior. (United States)

    Panzeri, Stefano; Harvey, Christopher D; Piasini, Eugenio; Latham, Peter E; Fellin, Tommaso


    The two basic processes underlying perceptual decisions-how neural responses encode stimuli, and how they inform behavioral choices-have mainly been studied separately. Thus, although many spatiotemporal features of neural population activity, or "neural codes," have been shown to carry sensory information, it is often unknown whether the brain uses these features for perception. To address this issue, we propose a new framework centered on redefining the neural code as the neural features that carry sensory information used by the animal to drive appropriate behavior; that is, the features that have an intersection between sensory and choice information. We show how this framework leads to a new statistical analysis of neural activity recorded during behavior that can identify such neural codes, and we discuss how to combine intersection-based analysis of neural recordings with intervention on neural activity to determine definitively whether specific neural activity features are involved in a task. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. The combination of appetitive and aversive reinforcers and the nature of their interaction during auditory learning. (United States)

    Ilango, A; Wetzel, W; Scheich, H; Ohl, F W


    Learned changes in behavior can be elicited by either appetitive or aversive reinforcers. It is, however, not clear whether the two types of motivation, (approaching appetitive stimuli and avoiding aversive stimuli) drive learning in the same or different ways, nor is their interaction understood in situations where the two types are combined in a single experiment. To investigate this question we have developed a novel learning paradigm for Mongolian gerbils, which not only allows rewards and punishments to be presented in isolation or in combination with each other, but also can use these opposite reinforcers to drive the same learned behavior. Specifically, we studied learning of tone-conditioned hurdle crossing in a shuttle box driven by either an appetitive reinforcer (brain stimulation reward) or an aversive reinforcer (electrical footshock), or by a combination of both. Combination of the two reinforcers potentiated speed of acquisition, led to maximum possible performance, and delayed extinction as compared to either reinforcer alone. Additional experiments, using partial reinforcement protocols and experiments in which one of the reinforcers was omitted after the animals had been previously trained with the combination of both reinforcers, indicated that appetitive and aversive reinforcers operated together but acted in different ways: in this particular experimental context, punishment appeared to be more effective for initial acquisition and reward more effective to maintain a high level of conditioned responses (CRs). The results imply that learning mechanisms in problem solving were maximally effective when the initial punishment of mistakes was combined with the subsequent rewarding of correct performance. Copyright 2010 IBRO. Published by Elsevier Ltd. All rights reserved.

  3. Towards autonomous neuroprosthetic control using Hebbian reinforcement learning. (United States)

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


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

  4. Piezoelectric drive circuit (United States)

    Treu, C.A. Jr.


    A piezoelectric motor drive circuit is provided which utilizes the piezoelectric elements as oscillators and a Meacham half-bridge approach to develop feedback from the motor ground circuit to produce a signal to drive amplifiers to power the motor. The circuit automatically compensates for shifts in harmonic frequency of the piezoelectric elements due to pressure and temperature changes. 7 figs.

  5. Wrong-way driving.

    NARCIS (Netherlands)


    Wrong-way driving is a phenomenon that mainly happens on motorways. Although the number of wrong-way crashes is relatively limited, their consequences are much more severe than the consequences of other motorway injury crashes. The groups most often causing wrong-way driving accidents are young,

  6. Recognizing driving in haste

    NARCIS (Netherlands)

    Rendón-Vélez, E.


    One can often hear people discussing the reasons why a road accident has happened: “She had to pick up her kids in the school before four o’clock and she was driving in haste and careless”, “He was stressed, he wanted to reach the beginning of the football match, tried to drive faster and didn't

  7. Control rod drives

    International Nuclear Information System (INIS)

    Futatsugi, Masao.


    Purpose: To secure the reactor operation safety by the provision of a fluid pressure detecting section for control rod driving fluid and a control rod interlock at the midway of the flow pass for supplying driving fluid to the control rod drives. Constitution: Between a driving line and a direction control valve are provided a pressure detecting portion, an alarm generating device, and a control rod inhibition interlock. The driving fluid from a driving fluid source is discharged by way of a pump and a manual valve into the reactor in which the control rods and reactor fuels are contained. In addition, when the direction control valve is switched and the control rods are inserted and extracted by the control rod drives, the pressure in the driving line is always detected by the pressure detection section, whereby if abnormal pressure is resulted, the alarm generating device is actuated to warn the abnormality and the control rod inhibition interlock is actuated to lock the direction control valve thereby secure the safety operation of the reactor. (Seki, T.)

  8. Switched reluctance motor drives

    Indian Academy of Sciences (India)

    Davis RM, Ray WF, Blake RJ 1981 Inverter drive for switched reluctance: circuits and component ratings. Inst. Elec. Eng. Proc. B128: 126-136. Ehsani M. 1991 Position Sensor elimination technique for the switched reluctance motor drive. US Patent No. 5,072,166. Ehsani M, Ramani K R 1993 Direct control strategies based ...

  9. Self-driving carsickness

    NARCIS (Netherlands)

    Diels, C.; Bos, J.E.


    This paper discusses the predicted increase in the occurrence and severity of motion sickness in self-driving cars. Self-driving cars have the potential to lead to significant benefits. From the driver's perspective, the direct benefits of this technology are considered increased comfort and

  10. Self-driving carsickness.

    NARCIS (Netherlands)

    Diels, C.; Bos, J.E.


    This paper discusses the predicted increase in the occurrence and severity of motion sickness in self-driving cars. Self-driving cars have the potential to lead to significant benefits. From the driver's perspective, the direct benefits of this technology are considered increased comfort and

  11. Fundamentals of electrical drives

    CERN Document Server

    Veltman, André; De Doncker, Rik W


    Provides a comprehensive introduction to various aspects of electrical drive systems. This volume provides a presentation of dynamic generic models that cover all major electrical machine types and modulation/control components of a drive as well as dynamic and steady state analysis of transformers and electrical machines.

  12. Electric Vehicle - Economical driving

    DEFF Research Database (Denmark)

    Jensen, VCE, Steen V.; Schøn, Henriette


    How do you reduce the energy-wast when driving and loading EV's - or rather: How do I get more km/l out of an EV......How do you reduce the energy-wast when driving and loading EV's - or rather: How do I get more km/l out of an EV...

  13. BAS-drive trait modulates dorsomedial striatum activity during reward response-outcome associations. (United States)

    Costumero, Víctor; Barrós-Loscertales, Alfonso; Fuentes, Paola; Rosell-Negre, Patricia; Bustamante, Juan Carlos; Ávila, César


    According to the Reinforcement Sensitivity Theory, behavioral studies have found that individuals with stronger reward sensitivity easily detect cues of reward and establish faster associations between instrumental responses and reward. Neuroimaging studies have shown that processing anticipatory cues of reward is accompanied by stronger ventral striatum activity in individuals with stronger reward sensitivity. Even though establishing response-outcome contingencies has been consistently associated with dorsal striatum, individual differences in this process are poorly understood. Here, we aimed to study the relation between reward sensitivity and brain activity while processing response-reward contingencies. Forty-five participants completed the BIS/BAS questionnaire and performed a gambling task paradigm in which they received monetary rewards or punishments. Overall, our task replicated previous results that have related processing high reward outcomes with activation of striatum and medial frontal areas, whereas processing high punishment outcomes was associated with stronger activity in insula and middle cingulate. As expected, the individual differences in the activity of dorsomedial striatum correlated positively with BAS-Drive. Our results agree with previous studies that have related the dorsomedial striatum with instrumental performance, and suggest that the individual differences in this area may form part of the neural substrate responsible for modulating instrumental conditioning by reward sensitivity.

  14. Control rod drives

    International Nuclear Information System (INIS)

    Nakamura, Akira.


    Purpose: To enable to monitor the coupling state between a control rod and a control rod drive. Constitution: After the completion of a control rod withdrawal, a coolant pressure is applied to a control rod drive being adjusted so as to raise only the control rod drive and, in a case where the coupling between the control rod drive and the control rod is detached, the former is elevated till it contacts the control rod and then stopped. The actual stopping position is detected by an actual position detection circuit and compared with a predetermined position stored in a predetermined position detection circuit. If both of the positions are not aligned with each other, it is judged by a judging circuit that the control rod and the control rod drives are not combined. (Sekiya, K.)

  15. Nonequilibrium landscape theory of neural networks. (United States)

    Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin


    The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape-flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments.

  16. Nonequilibrium landscape theory of neural networks (United States)

    Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin


    The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape–flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments. PMID:24145451

  17. Ceramic fiber reinforced filter (United States)

    Stinton, David P.; McLaughlin, Jerry C.; Lowden, Richard A.


    A filter for removing particulate matter from high temperature flowing fluids, and in particular gases, that is reinforced with ceramic fibers. The filter has a ceramic base fiber material in the form of a fabric, felt, paper of the like, with the refractory fibers thereof coated with a thin layer of a protective and bonding refractory applied by chemical vapor deposition techniques. This coating causes each fiber to be physically joined to adjoining fibers so as to prevent movement of the fibers during use and to increase the strength and toughness of the composite filter. Further, the coating can be selected to minimize any reactions between the constituents of the fluids and the fibers. A description is given of the formation of a composite filter using a felt preform of commercial silicon carbide fibers together with the coating of these fibers with pure silicon carbide. Filter efficiency approaching 100% has been demonstrated with these filters. The fiber base material is alternately made from aluminosilicate fibers, zirconia fibers and alumina fibers. Coating with Al.sub.2 O.sub.3 is also described. Advanced configurations for the composite filter are suggested.

  18. Simulation-based optimization parametric optimization techniques and reinforcement learning

    CERN Document Server

    Gosavi, Abhijit


    Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: *An accessible introduction to reinforcement learning and parametric-optimization techniques. *A step-by-step description of several algorithms of simulation-based optimization. *A clear and simple introduction to the methodology of neural networks. *A gentle introduction to converg...

  19. Neural responses to exclusion predict susceptibility to social influence. (United States)

    Falk, Emily B; Cascio, Christopher N; O'Donnell, Matthew Brook; Carp, Joshua; Tinney, Francis J; Bingham, C Raymond; Shope, Jean T; Ouimet, Marie Claude; Pradhan, Anuj K; Simons-Morton, Bruce G


    Social influence is prominent across the lifespan, but sensitivity to influence is especially high during adolescence and is often associated with increased risk taking. Such risk taking can have dire consequences. For example, in American adolescents, traffic-related crashes are leading causes of nonfatal injury and death. Neural measures may be especially useful in understanding the basic mechanisms of adolescents' vulnerability to peer influence. We examined neural responses to social exclusion as potential predictors of risk taking in the presence of peers in recently licensed adolescent drivers. Risk taking was assessed in a driving simulator session occurring approximately 1 week after the neuroimaging session. Increased activity in neural systems associated with the distress of social exclusion and mentalizing during an exclusion episode predicted increased risk taking in the presence of a peer (controlling for solo risk behavior) during a driving simulator session outside the neuroimaging laboratory 1 week later. These neural measures predicted risky driving behavior above and beyond self-reports of susceptibility to peer pressure and distress during exclusion. These results address the neural bases of social influence and risk taking; contribute to our understanding of social and emotional function in the adolescent brain; and link neural activity in specific, hypothesized, regions to risk-relevant outcomes beyond the neuroimaging laboratory. Results of this investigation are discussed in terms of the mechanisms underlying risk taking in adolescents and the public health implications for adolescent driving. Copyright © 2014 Society for Adolescent Health and Medicine. All rights reserved.

  20. Turbulent current drive mechanisms (United States)

    McDevitt, Christopher J.; Tang, Xian-Zhu; Guo, Zehua


    Mechanisms through which plasma microturbulence can drive a mean electron plasma current are derived. The efficiency through which these turbulent contributions can drive deviations from neoclassical predictions of the electron current profile is computed by employing a linearized Coulomb collision operator. It is found that a non-diffusive contribution to the electron momentum flux as well as an anomalous electron-ion momentum exchange term provide the most efficient means through which turbulence can modify the mean electron current for the cases considered. Such turbulent contributions appear as an effective EMF within Ohm's law and hence provide an ideal means for driving deviations from neoclassical predictions.

  1. Fast wave current drive

    International Nuclear Information System (INIS)

    Goree, J.; Ono, M.; Colestock, P.; Horton, R.; McNeill, D.; Park, H.


    Fast wave current drive is demonstrated in the Princeton ACT-I toroidal device. The fast Alfven wave, in the range of high ion-cyclotron harmonics, produced 40 A of current from 1 kW of rf power coupled into the plasma by fast wave loop antenna. This wave excites a steady current by damping on the energetic tail of the electron distribution function in the same way as lower-hybrid current drive, except that fast wave current drive is appropriate for higher plasma densities

  2. Modeling reinforced concrete durability : [summary]. (United States)


    Many Florida bridges are built of steel-reinforced concrete. Floridas humid and marine : environments subject steel in these structures : to corrosion once water and salt penetrate the : concrete and contact the steel. Corroded steel : takes up mo...

  3. Evolutionary computation for reinforcement learning

    NARCIS (Netherlands)

    Whiteson, S.; Wiering, M.; van Otterlo, M.


    Algorithms for evolutionary computation, which simulate the process of natural selection to solve optimization problems, are an effective tool for discovering high-performing reinforcement-learning policies. Because they can automatically find good representations, handle continuous action spaces,

  4. Identifying Method of Drunk Driving Based on Driving Behavior

    Directory of Open Access Journals (Sweden)

    Xiaohua Zhao


    Full Text Available Drunk driving is one of the leading causes contributing to traffic crashes. There are numerous issues that need to be resolved with the current method of identifying drunk driving. Driving behavior, with the characteristic of real-time, was extensively researched to identify impaired driving behaviors. In this paper, the drives with BACs above 0.05% were defined as drunk driving state. A detailed comparison was made between normal driving and drunk driving. The experiment in driving simulator was designed to collect the driving performance data of the groups. According to the characteristics analysis for the effect of alcohol on driving performance, seven significant indicators were extracted and the drunk driving was identified by the Fisher Discriminant Method. The discriminant function demonstrated a high accuracy of classification. The optimal critical score to differentiate normal from drinking state was found to be 0. The evaluation result verifies the accuracy of classification method.

  5. Deep Reinforcement Learning: An Overview


    Li, Yuxi


    We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsuperv...

  6. Linear step drive

    International Nuclear Information System (INIS)

    Haniger, L.; Elger, R.; Kocandrle, L.; Zdebor, J.


    A linear step drive is described developed in Czechoslovak-Soviet cooperation and intended for driving WWER-1000 control rods. The functional principle is explained of the motor and the mechanical and electrical parts of the drive, power control, and the indicator of position are described. The motor has latches situated in the reactor at a distance of 3 m from magnetic armatures, it has a low structural height above the reactor cover, which suggests its suitability for seismic localities. Its magnetic circuits use counterpoles; the mechanical shocks at the completion of each step are damped using special design features. The position indicator is of a special design and evaluates motor position within ±1% of total travel. A drive diagram and the flow chart of both the control electronics and the position indicator are presented. (author) 4 figs

  7. Fundamentals of electrical drives

    NARCIS (Netherlands)

    Veltman, A.; Pulle, D.W.J.; de Doncker, R.W.


    Comprehensive, user-friendly, color illustrated introductory text for electrical drive systems that simplifies the understanding of electrical machine principles Updated edition covers innovations in machine design, power semi-conductors, digital signal processors and simulation software Presents

  8. Science of driving. (United States)


    The Science of Driving project focused on developing a collaborative relationship to develop curriculum units for middle school and high school students to engage them in exciting real-world scenarios. This effort involved faculty, staff, and student...

  9. Drugs and driving

    NARCIS (Netherlands)

    Walsh, J. Michael; De Gier, Johan J.; Christopherson, Asbjørg S.; Verstraete, Alain G.

    The authors present a global overview on the issue of drugs and driving covering four major areas: (1) Epidemiology and Prevalence-which reviews epidemiological research, summarizes available information, discusses the methodological shortcomings of extant studies, and makes recommendations for

  10. Moods as ups and downs of the motivation pendulum: Revisiting Reinforcement Sensitivity Theory (RST in Bipolar Disorder

    Directory of Open Access Journals (Sweden)

    Tal eGonen


    Full Text Available Motivation is a key neurobehavioral concept underlying adaptive responses to environmental incentives and threats. As such, dysregulation of motivational processes may be critical in the formation of abnormal behavioral patterns/tendencies. According to the long standing model of the Reinforcement Sensitivity Theory (RST, motivation behaviors are driven by three neurobehavioral systems mediating the sensitivity to punishment, reward or goal-conflict. Corresponding to current neurobehavioral theories in psychiatry, this theory links abnormal motivational drives to abnormal behavior; viewing depression and mania as two abnormal extremes of reward driven processes leading to either under or over approach tendencies, respectively. We revisit the RST framework in the context of bipolar disorder (BD and challenge this concept by suggesting that dysregulated interactions of both punishment and reward related processes better account for the psychological and neural abnormalities observed in BD. We further present an integrative model positing that the three parallel motivation systems currently proposed by the RST model, can be viewed as subsystems in a large-scale neurobehavioral network of motivational decision making.

  11. Instant Google Drive starter

    CERN Document Server

    Procopio, Mike


    This book is a Starter which teaches you how to use Google Drive practically. This book is perfect for people of all skill levels who want to enjoy the benefits of using Google Drive to safely store their files online and in the cloud. It's also great for anyone looking to learn more about cloud computing in general. Readers are expected to have an Internet connection and basic knowledge of using the internet.

  12. Control rod driving mechanism

    International Nuclear Information System (INIS)

    Ooshima, Yoshio.


    Purpose: To perform reliable scram operation, even if abnormality should occur in a system instructing scram operation in FBR type reactors. Constitution: An aluminum alloy member to be melt at a predetermined temperature (about 600sup(o)C) is disposed to a connection part between a control rod and a driving mechanism, whereby the control rod is detached from the driving mechanism and gravitationally fallen to the reactor core. (Ikeda, J.)

  13. Modulated Current Drive Measurements

    International Nuclear Information System (INIS)

    Petty, C.C.; Lohr, J.; Luce, T.C.; Prater, R.; Cox, W.A.; Forest, C.B.; Jayakumar, R.J.; Makowski, M.A.


    A new measurement approach is presented which directly determines the noninductive current profile from the periodic response of the motional Stark effect (MSE) signals to the slow modulation of the external current drive source. A Fourier transform of the poloidal magnetic flux diffusion equation is used to analyze the MSE data. An example of this measurement technique is shown using modulated electron cyclotron current drive (ECCD) discharges from the DIII-D tokamak

  14. Belt drive construction improvement

    Directory of Open Access Journals (Sweden)

    I.Yu. Khomenko


    Full Text Available The possibility of the traction capacity increase of the belt drive TRK is examined. This was done for the purpose of air conditioning system of passenger car with double-generator system energy supplying. Belts XPC (made by the German firm «Continental ContiTech» testing were conducted. The results confirmed the possibility of their usage in order to improve belt drive TRK characteristics.

  15. Self-driving carsickness. (United States)

    Diels, Cyriel; Bos, Jelte E


    This paper discusses the predicted increase in the occurrence and severity of motion sickness in self-driving cars. Self-driving cars have the potential to lead to significant benefits. From the driver's perspective, the direct benefits of this technology are considered increased comfort and productivity. However, we here show that the envisaged scenarios all lead to an increased risk of motion sickness. As such, the benefits this technology is assumed to bring may not be capitalised on, in particular by those already susceptible to motion sickness. This can negatively affect user acceptance and uptake and, in turn, limit the potential socioeconomic benefits that this emerging technology may provide. Following a discussion on the causes of motion sickness in the context of self-driving cars, we present guidelines to steer the design and development of automated vehicle technologies. The aim is to limit or avoid the impact of motion sickness and ultimately promote the uptake of self-driving cars. Attention is also given to less well known consequences of motion sickness, in particular negative aftereffects such as postural instability, and detrimental effects on task performance and how this may impact the use and design of self-driving cars. We conclude that basic perceptual mechanisms need to be considered in the design process whereby self-driving cars cannot simply be thought of as living rooms, offices, or entertainment venues on wheels. Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.

  16. Dementia and driving. (United States)

    O'Neill, D; Neubauer, K; Boyle, M; Gerrard, J; Surmon, D; Wilcock, G K


    Many European countries test cars, but not their drivers, as they age. There is evidence to suggest that human factors are more important than vehicular factors as causes of motor crashes. The elderly also are involved in more accidents per distance travelled than middle-aged drivers. As the UK relies on self-certification of health by drivers over the age of 70 years, we examined the driving practices of patients with dementia attending a Memory Clinic. Nearly one-fifth of 329 patients with documented dementia continued to drive after the onset of dementia, and impaired driving ability was noted in two-thirds of these. Their families experienced great difficulty in persuading patients to stop driving, and had to invoke outside help in many cases. Neuropsychological tests did not help to identify those who drove badly while activity of daily living scores were related to driving ability. These findings suggest that many patients with dementia drive in an unsafe fashion after the onset of the illness. The present system of self-certification of health by the elderly for driver-licensing purposes needs to be reassessed.

  17. Methods for producing reinforced carbon nanotubes (United States)

    Ren, Zhifen [Newton, MA; Wen, Jian Guo [Newton, MA; Lao, Jing Y [Chestnut Hill, MA; Li, Wenzhi [Brookline, MA


    Methods for producing reinforced carbon nanotubes having a plurality of microparticulate carbide or oxide materials formed substantially on the surface of such reinforced carbon nanotubes composite materials are disclosed. In particular, the present invention provides reinforced carbon nanotubes (CNTs) having a plurality of boron carbide nanolumps formed substantially on a surface of the reinforced CNTs that provide a reinforcing effect on CNTs, enabling their use as effective reinforcing fillers for matrix materials to give high-strength composites. The present invention also provides methods for producing such carbide reinforced CNTs.

  18. Within- and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory. (United States)

    Collins, Anne G E; Frank, Michael J


    Learning from rewards and punishments is essential to survival and facilitates flexible human behavior. It is widely appreciated that multiple cognitive and reinforcement learning systems contribute to decision-making, but the nature of their interactions is elusive. Here, we leverage methods for extracting trial-by-trial indices of reinforcement learning (RL) and working memory (WM) in human electro-encephalography to reveal single-trial computations beyond that afforded by behavior alone. Neural dynamics confirmed that increases in neural expectation were predictive of reduced neural surprise in the following feedback period, supporting central tenets of RL models. Within- and cross-trial dynamics revealed a cooperative interplay between systems for learning, in which WM contributes expectations to guide RL, despite competition between systems during choice. Together, these results provide a deeper understanding of how multiple neural systems interact for learning and decision-making and facilitate analysis of their disruption in clinical populations.

  19. Empathic concern drives costly altruism (United States)

    FeldmanHall, Oriel; Dalgleish, Tim; Evans, Davy; Mobbs, Dean


    Why do we self-sacrifice to help others in distress? Two competing theories have emerged, one suggesting that prosocial behavior is primarily motivated by feelings of empathic other-oriented concern, the other that we help mainly because we are egoistically focused on reducing our own discomfort. Here we explore the relationship between costly altruism and these two sub-processes of empathy, specifically drawing on the caregiving model to test the theory that trait empathic concern (e.g. general tendency to have sympathy for another) and trait personal distress (e.g. predisposition to experiencing aversive arousal states) may differentially drive altruistic behavior. We find that trait empathic concern – and not trait personal distress – motivates costly altruism, and this relationship is supported by activity in the ventral tegmental area, caudate and subgenual anterior cingulate, key regions for promoting social attachment and caregiving. Together, this data helps identify the behavioral and neural mechanisms motivating costly altruism, while demonstrating that individual differences in empathic concern-related brain responses can predict real prosocial choice. PMID:25462694

  20. Estimating the behavior of RC beams strengthened with NSM system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Seyed Rohollah Hosseini Vaez


    Full Text Available In the last decade, conventional materials such as steel and concrete are being replaced by fiber reinforced polymer (FRP materials for the strengthening of concrete structures. Among the strengthening techniques based on Fiber Reinforced Polymer composites, the use of near-surface mounted (NSM FRP rods is emerging as a promising technology for increasing flexural and shear strength of deficient concrete, masonry and timber members. An artificial neural network is an information processing tool that is inspired by the way biological nervous systems (such as the brain process the information. The key element of this tool is the novel structure of the information processing system. In engineering applications, a neural network can be a vector mapper which maps an input vector to an output one. In the present study, a new approach is developed to predict the behavior of strengthened concrete beam using a large number of experimental data by applying artificial neural networks. Having parameters used as input nodes in ANN modeling such as elastic modulus of the FRP reinforcement, the ratio of the steel longitudinal reinforcement, dimensions of the beam section, the ratio of the NSM-FRP reinforcement and characteristics of concrete, the output node was the flexural strength of beams. The idealized neural network was employed to generate empirical charts and equations to be used in design. The aim of this study is to investigate the behavior of strengthened RC beam using artificial neural networks.

  1. Reinforcement-driven spread of innovations and fads

    International Nuclear Information System (INIS)

    Krapivsky, P L; Redner, S; Volovik, D


    We investigate how social reinforcement drives the spread of permanent innovations and transient fads. We account for social reinforcement by endowing each individual with M + 1 possible awareness states 0, 1, 2,..., M, with state M corresponding to adopting an innovation. An individual with awareness k 1−1/M for M > 1. When individuals can abandon the innovation at rate λ, the population fraction that remains clueless about the fad undergoes a phase transition at a critical rate λ c ; this transition is second order for M = 1 and first order for M > 1, with macroscopic fluctuations accompanying the latter. The time for the fad to disappear has an intriguing non-monotonic dependence on λ

  2. Variability in prescription opioid intake and reinforcement amongst 129 substrains. (United States)

    Jimenez, S M; Healy, A F; Coelho, M A; Brown, C N; Kippin, T E; Szumlinski, K K


    Opioid abuse in the United States has reached epidemic proportions, with treatment admissions and deaths associated with prescription opioid abuse quadrupling over the past 10 years. Although genetics are theorized to contribute substantially to inter-individual variability in the development, severity and treatment outcomes of opioid abuse/addiction, little direct preclinical study has focused on the behavioral genetics of prescription opioid reinforcement and drug-taking. Herein, we employed different 129 substrains of mice currently available from The Jackson Laboratory (129S1/SvlmJ, 129X1/SvJ, 129S4/SvJaeJ and 129P3/J) as a model system of genetic variation and assayed mice for oral opioid intake and reinforcement, as well as behavioral and somatic signs of dependence. All substrains exhibited a dose-dependent increase in oral oxycodone and heroin preference and intake under limited-access procedures and all, but 129S1/SvlmJ mice, exhibited oxycodone reinforcement. Relative to the other substrains, 129P3/J mice exhibited higher heroin and oxycodone intake. While 129X1/SvJ exhibited the highest anxiety-like behavior during natural opioid withdrawal, somatic and behavior signs of precipitated withdrawal were most robust in 129P3/J mice. These results demonstrate the feasibility and relative sensitivity of our oral opioid self-administration procedures for detecting substrain differences in drug reinforcement/intake among 129 mice, of relevance to the identification of genetic variants contributing to high vs. low oxycodone reinforcement and intake. © 2017 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

  3. Control rod drive

    International Nuclear Information System (INIS)

    Hawke, B.C.


    A reactor core, one or more control rods, and a control rod drive are described for selectively inserting and withdrawing the one or more control rods into and from the reactor core, which consists of: a support structure secured beneath the reactor core; control rod positioning means supported by the support structure for movably supporting the control rod for movement between a lower position wherein the control rod is located substantially beneath the reactor core and an upper position wherein at least an upper portion of the control rod extends into the reactor core; transmission means; primary drive means connected with the control rod positioning means by the transmission means for positioning the control rod under normal operating conditions; emergency drive means for moving the control rod from the lower position to the upper position under emergency conditions, the emergency drive means including a weight movable between an upper and a lower position, means for movably supporting the weight, and means for transmitting gravitational force exerted on the weight to the control rod positioning means to move the control rod upwardly when the weight is pulled downwardly by gravity; the transmission means connecting the control rod positioning means with the emergency drive means so that the primary drive means effects movement of the weight and the control rod in opposite directions under normal conditions, thus providing counterbalancing to reduce the force required for upward movement of the control rod under normal conditions; and restraint means for restraining the fall of the weight under normal operating conditions and disengaging the primary drive means to release the weight under emergency conditions

  4. Brain Electrodynamic and Hemodynamic Signatures Against Fatigue During Driving

    Directory of Open Access Journals (Sweden)

    Chun-Hsiang Chuang


    Full Text Available Fatigue is likely to be gradually cumulated in a prolonged and attention-demanding task that may adversely affect task performance. To address the brain dynamics during a driving task, this study recruited 16 subjects to participate in an event-related lane-departure driving experiment. Each subject was instructed to maintain attention and task performance throughout an hour-long driving experiment. The subjects' brain electrodynamics and hemodynamics were simultaneously recorded via 32-channel electroencephalography (EEG and 8-source/16-detector functional near-infrared spectroscopy (fNIRS. The behavior performance demonstrated that all subjects were able to promptly respond to lane-deviation events, even if the sign of fatigue arose in the brain, which suggests that the subjects were fighting fatigue during the driving experiment. The EEG event-related analysis showed strengthening alpha suppression in the occipital cortex, a common brain region of fatigue. Furthermore, we noted increasing oxygenated hemoglobin (HbO of the brain to fight driving fatigue in the frontal cortex, primary motor cortex, parieto-occipital cortex and supplementary motor area. In conclusion, the increasing neural activity and cortical activations were aimed at maintaining driving performance when fatigue emerged. The electrodynamic and hemodynamic signatures of fatigue fighting contribute to our understanding of the brain dynamics of driving fatigue and address driving safety issues through the maintenance of attention and behavioral performance.

  5. Corrosion of reinforcement bars in steel ibre reinforced concrete structures

    DEFF Research Database (Denmark)

    Solgaard, Anders Ole Stubbe

    and the influence of steel fibres on initiation and propagation of cracks in concrete. Moreover, the impact of fibres on corrosion-induced cover cracking was covered. The impact of steel fibres on propagation of reinforcement corrosion was investigated through studies of their impact on the electrical resistivity...... of concrete, which is known to affect the corrosion process of embedded reinforcement. The work concerning the impact of steel fibres on initiation and propagation of cracks was linked to corrosion initiation and propagation of embedded reinforcement bars via additional studies. Cracks in the concrete cover...... are known to alter the ingress rate of depassivating substances and thereby influence the corrosion process. The Ph.D. study covered numerical as well as experimental studies. Electrochemically passive steel fibres are electrically isolating thus not changing the electrical resistivity of concrete, whereas...

  6. The Neural Representation of Goal-Directed Actions and Outcomes in the Ventral Striatum's Olfactory Tubercle (United States)

    Gadziola, Marie A.


    The ventral striatum is critical for evaluating reward information and the initiation of goal-directed behaviors. The many cellular, afferent, and efferent similarities between the ventral striatum's nucleus accumbens and olfactory tubercle (OT) suggests the distributed involvement of neurons within the ventral striatopallidal complex in motivated behaviors. Although the nucleus accumbens has an established role in representing goal-directed actions and their outcomes, it is not known whether this function is localized within the nucleus accumbens or distributed also within the OT. Answering such a fundamental question will expand our understanding of the neural mechanisms underlying motivated behaviors. Here we address whether the OT encodes natural reinforcers and serves as a substrate for motivational information processing. In recordings from mice engaged in a novel water-motivated instrumental task, we report that OT neurons modulate their firing rate during initiation and progression of the instrumental licking behavior, with some activity being internally generated and preceding the first lick. We further found that as motivational drive decreases throughout a session, the activity of OT neurons is enhanced earlier relative to the behavioral action. Additionally, OT neurons discriminate the types and magnitudes of fluid reinforcers. Together, these data suggest that the processing of reward information and the orchestration of goal-directed behaviors is a global principle of the ventral striatum and have important implications for understanding the neural systems subserving addiction and mood disorders. SIGNIFICANCE STATEMENT Goal-directed behaviors are widespread among animals and underlie complex behaviors ranging from food intake, social behavior, and even pathological conditions, such as gambling and drug addiction. The ventral striatum is a neural system critical for evaluating reward information and the initiation of goal-directed behaviors. Here we

  7. Central neural pathways for thermoregulation (United States)

    Morrison, Shaun F.; Nakamura, Kazuhiro


    Central neural circuits orchestrate a homeostatic repertoire to maintain body temperature during environmental temperature challenges and to alter body temperature during the inflammatory response. This review summarizes the functional organization of the neural pathways through which cutaneous thermal receptors alter thermoregulatory effectors: the cutaneous circulation for heat loss, the brown adipose tissue, skeletal muscle and heart for thermogenesis and species-dependent mechanisms (sweating, panting and saliva spreading) for evaporative heat loss. These effectors are regulated by parallel but distinct, effector-specific neural pathways that share a common peripheral thermal sensory input. The thermal afferent circuits include cutaneous thermal receptors, spinal dorsal horn neurons and lateral parabrachial nucleus neurons projecting to the preoptic area to influence warm-sensitive, inhibitory output neurons which control thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus neurons controlling cutaneous vasoconstriction. PMID:21196160

  8. Control rod drives

    International Nuclear Information System (INIS)

    Hayakawa, Hiroyasu.


    Purpose: To enable rapid control in a simple circuit by providing a motor control device having an electric capacity capable of simultaneously driving all of the control rods rapidly only in the inserting direction as well as a motor controlling device capable of fine control for the insertion and extraction at usual operation. Constitution: The control rod drives comprise a first motor control device capable of finely controlling the control rods both in inserting and extracting directions, a second motor control device capable of rapidly driving the control rods only in the inserting direction, and a first motor switching circuit and a second motor switching circuit switched by switches. Upon issue of a rapid insertion instruction for the control rods, the second motor switching circuit is closed by the switch and the second motor control circuit and driving motors are connected. Thus, each of the control rod driving motors is driven at a high speed in the inserting direction to rapidly insert all of the control rods. (Yoshino, Y.)

  9. Epilepsy and driving

    Directory of Open Access Journals (Sweden)

    Matej Mavrič


    Full Text Available Epilepsy poses a risk for all participants in road traffic; therefore people with epilepsy do not meet the criteria for an unlimited driving license. Their driving is affected not only by epileptic seizures causing impaired consciousness and involuntary movements, but also by antiepileptic drugs with their many unwanted affects. The experts have not yet agreed on whether people with epilepsy have an increased risk of experiencing a road traffic accident. However, recent data suggests that the overall risk is lower compared to other medical conditions. Scientific evidence forms the basis of legislation, which by limiting people with epilepsy, enables all participants in road traffic to drive in the safest possible environment. The legislation that governs epilepsy and driving in Slovenia has been recently thoroughly reformed and thus allows a less discriminatory management of people with epilepsy. Although people with epilepsy experience many issues in their daily life, including their personal relationships and employment, they often list the need for driving as a top concern in surveys. General physicians play an important role in managing the issues of people with epilepsy.

  10. Self-rated Driving and Driving Safety in Older Adults


    Ross, Lesley A.; Dodson, Joan; Edwards, Jerri D.; Ackerman, Michelle L.; Ball, Karlene


    Many U.S. states rely on older adults to self-regulate their driving and determine when driving is no longer a safe option. However, the relationship of older adults’ self-rated driving in terms of actual driving competency outcomes is unclear. The current study investigates self-rated driving in terms of (1) systematic differences between older adults with high (good/excellent) versus low (poor/fair/average) self-ratings, and (2) the predictive nature of self-rated driving to adverse driving...

  11. Traffic sign recognition with deep convolutional neural networks


    Karamatić, Boris


    The problem of detection and recognition of traffic signs is becoming an important problem when it comes to the development of self driving cars and advanced driver assistance systems. In this thesis we will develop a system for detection and recognition of traffic signs. For the problem of detection we will use aggregate channel features and for the problem of recognition we will use a deep convolutional neural network. We will describe how convolutional neural networks work, how they are co...

  12. On the Application of TLS Techniques to AC Electrical Drives

    Directory of Open Access Journals (Sweden)

    M. Cirrincione


    Full Text Available This paper deals with the application of a new neuron, the TLS EXIN neuron, to AC induction motor drives. In particular, it addresses two important subjects of AC induction motor drives: the on-line estimation of the electrical parameters of the machine and the speed estimation in sensorless drives. On this basis, this work summarizes the parameter estimation and sensorless techniques already developed by the authors over these last few years, all based on the TLS EXIN. With regard to sensorless, two techniques are proposed: one based on the MRAS and the other based on the full-order Luenberger observer. The work show some of the most significant results obtained by the authors in these fields and stresses the important potentiality of this new neural technique in AC induction machine drives.

  13. Carbon Fiber Reinforced Polymer Grids for Shear and End Zone Reinforcement in Bridge Beams (United States)


    Corrosion of reinforcing steel reduces life spans of bridges throughout the United States; therefore, using non-corroding carbon fiber reinforced polymer (CFRP) reinforcement is seen as a way to increase service life. The use of CFRP as the flexural ...

  14. Health monitoring of precast bridge deck panels reinforced with glass fiber reinforced polymer (GFRP) bars. (United States)


    The present research project investigates monitoring concrete precast panels for bridge decks that are reinforced with Glass Fiber Reinforced Polymer (GFRP) bars. Due to the lack of long term research on concrete members reinforced with GFRP bars, lo...

  15. The power reinforcement framework revisited

    DEFF Research Database (Denmark)

    Nielsen, Jeppe; Andersen, Kim Normann; Danziger, James N.


    Whereas digital technologies are often depicted as being capable of disrupting long-standing power structures and facilitating new governance mechanisms, the power reinforcement framework suggests that information and communications technologies tend to strengthen existing power arrangements within...... public organizations. This article revisits the 30-yearold power reinforcement framework by means of an empirical analysis on the use of mobile technology in a large-scale programme in Danish public sector home care. It explores whether and to what extent administrative management has controlled decision......-making and gained most benefits from mobile technology use, relative to the effects of the technology on the street-level workers who deliver services. Current mobile technology-in-use might be less likely to be power reinforcing because it is far more decentralized and individualized than the mainly expert...

  16. Prebiotics Supplementation Impact on the Reinforcing and Motivational Aspect of Feeding

    Directory of Open Access Journals (Sweden)

    Anne-Sophie Delbès


    (regular chow or HFHS as well as the timing at which prebiotic supplementation is introduced (preventive or curative greatly influence the efficacy of the gut–microbiota–brain axis. This crosstalk selectively alters the hedonic or motivational drive to eat and triggers molecular changes in neural substrates involved in the homeostatic and non-homeostatic control of body weight.

  17. Gears and gear drives

    CERN Document Server

    Jelaska, Damir T


    Understanding how gears are formed and how they interact or 'mesh' with each other is essential when designing equipment that uses gears or gear trains. The way in which gear teeth are formed and how they mesh is determined by their geometry and kinematics, which is the topic of this book.  Gears and Gear Drives provides the reader with comprehensive coverage of gears and gear drives. Spur, helical, bevel, worm and planetary gears are all covered, with consideration given to their classification, geometry, kinematics, accuracy control, load capacity and manufacturing. Cylindric

  18. Toyota hybrid synergy drive

    Energy Technology Data Exchange (ETDEWEB)

    Gautschi, H.


    This presentation made at the Swiss 2008 research conference on traffic by Hannes Gautschi, director of service and training at the Toyota company in Switzerland, takes a look at Toyota's hybrid drive vehicles. The construction of the vehicles and their combined combustion engines and electric generators and drives is presented and the combined operation of these components is described. Braking and energy recovery are discussed. Figures on the performance, fuel consumption and CO{sub 2} output of the hybrid vehicles are compared with those of conventional vehicles.

  19. Biologically Inspired Modular Neural Control for a Leg-Wheel Hybrid Robot

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Wörgötter, Florentin; Laksanacharoen, Pudit


    In this article we present modular neural control for a leg-wheel hybrid robot consisting of three legs with omnidirectional wheels. This neural control has four main modules having their functional origin in biological neural systems. A minimal recurrent control (MRC) module is for sensory signal...... processing and state memorization. Its outputs drive two front wheels while the rear wheel is controlled through a velocity regulating network (VRN) module. In parallel, a neural oscillator network module serves as a central pattern generator (CPG) controls leg movements for sidestepping. Stepping directions...... or they can serve as useful modules for other module-based neural control applications....

  20. Modeling Avoidance in Mood and Anxiety Disorders Using Reinforcement Learning. (United States)

    Mkrtchian, Anahit; Aylward, Jessica; Dayan, Peter; Roiser, Jonathan P; Robinson, Oliver J


    Serious and debilitating symptoms of anxiety are the most common mental health problem worldwide, accounting for around 5% of all adult years lived with disability in the developed world. Avoidance behavior-avoiding social situations for fear of embarrassment, for instance-is a core feature of such anxiety. However, as for many other psychiatric symptoms the biological mechanisms underlying avoidance remain unclear. Reinforcement learning models provide formal and testable characterizations of the mechanisms of decision making; here, we examine avoidance in these terms. A total of 101 healthy participants and individuals with mood and anxiety disorders completed an approach-avoidance go/no-go task under stress induced by threat of unpredictable shock. We show an increased reliance in the mood and anxiety group on a parameter of our reinforcement learning model that characterizes a prepotent (pavlovian) bias to withhold responding in the face of negative outcomes. This was particularly the case when the mood and anxiety group was under stress. This formal description of avoidance within the reinforcement learning framework provides a new means of linking clinical symptoms with biophysically plausible models of neural circuitry and, as such, takes us closer to a mechanistic understanding of mood and anxiety disorders. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  1. Comparing Expert and Novice Driving Behavior in a Driving Simulator

    Directory of Open Access Journals (Sweden)

    Hiran B. Ekanayake


    Full Text Available This paper presents a study focused on comparing driving behavior of expert and novice drivers in a mid-range driving simulator with the intention of evaluating the validity of driving simulators for driver training. For the investigation, measurements of performance, psychophysiological measurements, and self-reported user experience under different conditions of driving tracks and driving sessions were analyzed. We calculated correlations between quantitative and qualitative measures to enhance the reliability of the findings. The experiment was conducted involving 14 experienced drivers and 17 novice drivers. The results indicate that driving behaviors of expert and novice drivers differ from each other in several ways but it heavily depends on the characteristics of the task. Moreover, our belief is that the analytical framework proposed in this paper can be used as a tool for selecting appropriate driving tasks as well as for evaluating driving performance in driving simulators.

  2. Deep learning in neural networks: an overview. (United States)

    Schmidhuber, Jürgen


    In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

  3. Driving skills after whiplash. (United States)

    Gimse, R; Bjørgen, I A; Straume, A


    Previous studies have shown that some persons with longlasting problems after whiplash have changed eye movements. These changes have been related to disturbance of the posture control system. The question raised in the present study is whether such disturbances can influence daily life functions connected with balance, position and external movements, such as car driving. A group of 23 persons with disturbed eye movements due to whiplash injury, was tested in a driving simulator, together with a closely matched control group. The results revealed significant differences between the two groups with respect to response times to the traffic signs presented, identification of type of sign, as well as steering precision while the subjects' attention was directed to the process of identifying the signs. Alternative explanations such as driving experience, pain, medication or malingering are at least partly controlled for, but cannot completely be ruled out. A distorted posture control system leading to disturbance of eye movements seems to be the most likely primary causative factor, but these disturbances are most certainly complexly determined. Reduced attention capacity is considered to be a mediating secondary factor. Registration of eye movements may be a useful diagnostic tool to evaluate driving skill after whiplash.

  4. Gaze-controlled Driving

    DEFF Research Database (Denmark)

    Tall, Martin; Alapetite, Alexandre; San Agustin, Javier


    We investigate if the gaze (point of regard) can control a remote vehicle driving on a racing track. Five different input devices (on-screen buttons, mouse-pointing low-cost webcam eye tracker and two commercial eye tracking systems) provide heading and speed control on the scene view transmitted...

  5. Gas turbine drives

    Energy Technology Data Exchange (ETDEWEB)


    Developments in gas turbine drives are reviewed, e.g., low weight per unit power and thrust-weight ratio, fast availability of the maximum speed, absolute resistance to cold and to droplet formation vibrationeless run, and low exhaust gas temperatures. Applications in aeronautic engineering (turbofan), power stations, marine propulsion systems, railways and road transportation vehicles are mentioned.

  6. Chaos in drive systems

    Directory of Open Access Journals (Sweden)

    Kratochvíl C.


    Full Text Available The purpose of this article is to provide an elementary introduction to the subject of chaos in the electromechanical drive systems. In this article, we explore chaotic solutions of maps and continuous time systems. These solutions are also bounded like equilibrium, periodic and quasiperiodic solutions.

  7. Electric Drive Study (United States)


    compound promises to reduce weight of future permanent magnet motors by 20 to 30 percent; a similar reduction is expected in size (approximately systems. The AC permanent magnet (brushless DC motor) is rapidly evolving and will replace most electrically excited machines. Permanent magnet motors using

  8. Electric-Drive Vehicles

    Energy Technology Data Exchange (ETDEWEB)

    Septon, Kendall K [National Renewable Energy Laboratory (NREL), Golden, CO (United States)


    Electric-drive vehicles use electricity as their primary fuel or to improve the efficiency of conventional vehicle designs. These vehicles can be divided into three categories: Hybrid electric vehicles (HEVs), Plug-in hybrid electric vehicles (PHEVs), All-electric vehicles (EVs). Together, PHEVs and EVs can also be referred to as plug-in electric vehicles (PEVs).

  9. Electric-Drive Vehicles

    Energy Technology Data Exchange (ETDEWEB)



    Electric-drive vehicles use electricity as their primary fuel or to improve the efficiency of conventional vehicle designs. These vehicles can be divided into three categories: Hybrid electric vehicles (HEVs), Plug-in hybrid electric vehicles (PHEVs), All-electric vehicles (EVs). Together, PHEVs and EVs can also be referred to as plug-in electric vehicles (PEVs).

  10. Driving While Intoxicated. (United States)

    Brick, John

    Alcohol intoxication increases the risk of highway accidents, the relative risk of crash probability increasing as a function of blood alcohol content (BAC). Because alcohol use is more prevalent than use of other drugs, more is known about the relationship between alcohol use and driving. Most states presume a BAC of .10% to be evidence of drunk…

  11. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)


    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

  12. Neural Tube Defects (United States)

    Neural tube defects are birth defects of the brain, spine, or spinal cord. They happen in the ... that she is pregnant. The two most common neural tube defects are spina bifida and anencephaly. In ...

  13. Nanocellulose reinforcement of Transparent Composites (United States)

    Joshua Steele; Hong Dong; James F. Snyder; Josh A. Orlicki; Richard S. Reiner; Alan W. Rudie


    In this work, we evaluate the impact of nanocellulose reinforcement on transparent composite properties. Due to the small diameter, high modulus, and high strength of cellulose nanocrystals, transparent composites that utilize these materials should show improvement in bulk mechanical performances without a corresponding reduction in optical properties. In this study...

  14. Silica reinforced triblock copolymer gels

    DEFF Research Database (Denmark)

    Theunissen, E.; Overbergh, N.; Reynaers, H.


    The effect of silica and polymer coated silica particles as reinforcing agents on the structural and mechanical properties of polystyrene-poly(ethylene/butylene)-polystyrene (PS-PEB-PS) triblock gel has been investigated. Different types of chemically modified silica have been compared in order...

  15. Reinforcement learning in supply chains. (United States)

    Valluri, Annapurna; North, Michael J; Macal, Charles M


    Effective management of supply chains creates value and can strategically position companies. In practice, human beings have been found to be both surprisingly successful and disappointingly inept at managing supply chains. The related fields of cognitive psychology and artificial intelligence have postulated a variety of potential mechanisms to explain this behavior. One of the leading candidates is reinforcement learning. This paper applies agent-based modeling to investigate the comparative behavioral consequences of three simple reinforcement learning algorithms in a multi-stage supply chain. For the first time, our findings show that the specific algorithm that is employed can have dramatic effects on the results obtained. Reinforcement learning is found to be valuable in multi-stage supply chains with several learning agents, as independent agents can learn to coordinate their behavior. However, learning in multi-stage supply chains using these postulated approaches from cognitive psychology and artificial intelligence take extremely long time periods to achieve stability which raises questions about their ability to explain behavior in real supply chains. The fact that it takes thousands of periods for agents to learn in this simple multi-agent setting provides new evidence that real world decision makers are unlikely to be using strict reinforcement learning in practice.

  16. Stability of reinforced cemented backfills

    International Nuclear Information System (INIS)

    Mitchell, R.J.; Stone, D.M.


    Mining with backfill has been the subject of several international meetings in recent years and a considerable research effort is being applied to improve both mining economics and ore recovery by using backfill for ground support. Classified mill tailings sands are the most commonly used backfill material but these fine sands must be stabilized before full ore pillar recovery can be achieved. Normal portland cement is generally used for stabilization but the high cost of cement prohibits high cement usage. This paper considers the use of reinforcements in cemented fill to reduce the cement usage. It is concluded that strong cemented layers at typical spacings of about 3 meters in a low cement content bulk fill can reinforce the fill and reduce the overall cement usage. Fibre reinforcements introduced into strong layers or into bulk fills are also known to be effective in reducing cement usage. Some development work is needed to produce the ideal type of anchored fibre in order to realize economic gains from fibre-reinforced fills

  17. Adaptive representations for reinforcement learning

    NARCIS (Netherlands)

    Whiteson, S.


    This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own

  18. Brain Inspired Cognitive Model with Attention for Self-Driving Cars


    Chen, Shitao; Zhang, Songyi; Shang, Jinghao; Chen, Badong; Zheng, Nanning


    Perception-driven approach and end-to-end system are two major vision-based frameworks for self-driving cars. However, it is difficult to introduce attention and historical information of autonomous driving process, which are the essential factors for achieving human-like driving into these two methods. In this paper, we propose a novel model for self-driving cars named brain-inspired cognitive model with attention (CMA). This model consists of three parts: a convolutional neural network for ...

  19. Tunnel Ventilation Control Using Reinforcement Learning Methodology (United States)

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

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

  20. Depression, Activity, and Evaluation of Reinforcement (United States)

    Hammen, Constance L.; Glass, David R., Jr.


    This research attempted to find the causal relation between mood and level of reinforcement. An effort was made to learn what mood change might occur if depressed subjects increased their levels of participation in reinforcing activities. (Author/RK)

  1. Neural tissue-spheres

    DEFF Research Database (Denmark)

    Andersen, Rikke K; Johansen, Mathias; Blaabjerg, Morten


    By combining new and established protocols we have developed a procedure for isolation and propagation of neural precursor cells from the forebrain subventricular zone (SVZ) of newborn rats. Small tissue blocks of the SVZ were dissected and propagated en bloc as free-floating neural tissue...... content, thus allowing experimental studies of neural precursor cells and their niche...

  2. Rod drive and latching mechanism

    International Nuclear Information System (INIS)

    Veronesi, L.; Sherwood, D.G.


    Hydraulic drive and latching mechanisms for driving reactivity control mechanisms in nuclear reactors are described. Preferably, the pressurized reactor coolant is utilized to raise the drive rod into contact with and to pivot the latching mechanism so as to allow the drive rod to pass the latching mechanism. The pressure in the housing may then be equalized which allows the drive rod to move downwardly into contact with the latching mechanism but to hold the shaft in a raised position with respect to the reactor core. Once again, the reactor coolant pressure may be utilized to raise the drive rod and thus pivot the latching mechanism so that the drive rod passes above the latching mechanism. Again, the mechanism pressure can be equalized which allows the drive rod to fall and pass by the latching mechanism so that the drive rod approaches the reactor core. (author)

  3. Hybrid computing using a neural network with dynamic external memory. (United States)

    Graves, Alex; Wayne, Greg; Reynolds, Malcolm; Harley, Tim; Danihelka, Ivo; Grabska-Barwińska, Agnieszka; Colmenarejo, Sergio Gómez; Grefenstette, Edward; Ramalho, Tiago; Agapiou, John; Badia, Adrià Puigdomènech; Hermann, Karl Moritz; Zwols, Yori; Ostrovski, Georg; Cain, Adam; King, Helen; Summerfield, Christopher; Blunsom, Phil; Kavukcuoglu, Koray; Hassabis, Demis


    Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read-write memory.

  4. The Neural Foundations of Reaction and Action in Aversive Motivation. (United States)

    Campese, Vincent D; Sears, Robert M; Moscarello, Justin M; Diaz-Mataix, Lorenzo; Cain, Christopher K; LeDoux, Joseph E


    Much of the early research in aversive learning concerned motivation and reinforcement in avoidance conditioning and related paradigms. When the field transitioned toward the focus on Pavlovian threat conditioning in isolation, this paved the way for the clear understanding of the psychological principles and neural and molecular mechanisms responsible for this type of learning and memory that has unfolded over recent decades. Currently, avoidance conditioning is being revisited, and with what has been learned about associative aversive learning, rapid progress is being made. We review, below, the literature on the neural substrates critical for learning in instrumental active avoidance tasks and conditioned aversive motivation.

  5. Evolving Neural Turing Machines for Reward-based Learning

    DEFF Research Database (Denmark)

    Greve, Rasmus Boll; Jacobsen, Emil Juul; Risi, Sebastian


    An unsolved problem in neuroevolution (NE) is to evolve artificial neural networks (ANN) that can store and use information to change their behavior online. While plastic neural networks have shown promise in this context, they have difficulties retaining information over longer periods of time...... version of the double T-Maze, a complex reinforcement-like learning problem. In the T-Maze learning task the agent uses the memory bank to display adaptive behavior that normally requires a plastic ANN, thereby suggesting a complementary and effective mechanism for adaptive behavior in NE....

  6. Electrical drives for direct drive renewable energy systems

    CERN Document Server

    Mueller, Markus


    Wind turbine gearboxes present major reliability issues, leading to great interest in the current development of gearless direct-drive wind energy systems. Offering high reliability, high efficiency and low maintenance, developments in these direct-drive systems point the way to the next generation of wind power, and Electrical drives for direct drive renewable energy systems is an authoritative guide to their design, development and operation. Part one outlines electrical drive technology, beginning with an overview of electrical generators for direct drive systems. Principles of electrical design for permanent magnet generators are discussed, followed by electrical, thermal and structural generator design and systems integration. A review of power electronic converter technology and power electronic converter systems for direct drive renewable energy applications is then conducted. Part two then focuses on wind and marine applications, beginning with a commercial overview of wind turbine drive systems and a...

  7. Conditioned Reinforcement Value and Resistance to Change (United States)

    Shahan, Timothy A.; Podlesnik, Christopher A.


    Three experiments examined the effects of conditioned reinforcement value and primary reinforcement rate on resistance to change using a multiple schedule of observing-response procedures with pigeons. In the absence of observing responses in both components, unsignaled periods of variable-interval (VI) schedule food reinforcement alternated with…

  8. Simple artificial neural networks that match probability and exploit and explore when confronting a multiarmed bandit. (United States)

    Dawson, Michael R W; Dupuis, Brian; Spetch, Marcia L; Kelly, Debbie M


    The matching law (Herrnstein 1961) states that response rates become proportional to reinforcement rates; this is related to the empirical phenomenon called probability matching (Vulkan 2000). Here, we show that a simple artificial neural network generates responses consistent with probability matching. This behavior was then used to create an operant procedure for network learning. We use the multiarmed bandit (Gittins 1989), a classic problem of choice behavior, to illustrate that operant training balances exploiting the bandit arm expected to pay off most frequently with exploring other arms. Perceptrons provide a medium for relating results from neural networks, genetic algorithms, animal learning, contingency theory, reinforcement learning, and theories of choice.

  9. Driving towards ecotechnologies. (United States)

    Najjar, Devora A; Normandin, Avery M; Strait, Elizabeth A; Esvelt, Kevin M


    The prospect of using genetic methods to target vector, parasite, and reservoir species offers tremendous potential benefits to public health, but the use of genome editing to alter the shared environment will require special attention to public perception and community governance in order to benefit the world. Public skepticism combined with the media scrutiny of gene drive systems could easily derail unpopular projects entirely, especially given the potential for trade barriers to be raised against countries that employ self-propagating gene drives. Hence, open and community-guided development of thoughtfully chosen applications is not only the most ethical approach, but also the most likely to overcome the economic, social, and diplomatic barriers. Here we review current and past attempts to alter ecosystems using biological methods, identify key determinants of social acceptance, and chart a stepwise path for developers towards safe and widely supported use.

  10. Control rod drives

    International Nuclear Information System (INIS)

    Asano, Hiromitsu.


    Purpose: To drive control rods at an optimum safety speed corresponding to the reactor core output. Constitution: The reactor power is detected by a neutron detector and the output signal is applied to a process computer. The process computer issues a signal representing the reactor core output, which is converted through a function generator into a signal representing the safety speed of control rods. The converted signal is further supplied to a V/F converter and converted into a pulse signal. The pulse signal is inputted to a step motor driving circuit, which actuates a step motor to operate the control rods always at a safety speed corresponding to the reactor core power. (Furukawa, Y.)

  11. Drive-by-Downloads

    Energy Technology Data Exchange (ETDEWEB)

    Narvaez, Julia; Endicott-Popovsky, Barbara E.; Seifert, Christian; Aval, Chiraag U.; Frincke, Deborah A.


    Abstract: Drive-by-downloads are malware that push, and then execute, malicious code on a client system without the user's consent. The purpose of this paper is to introduce a discussion of the usefulness of antivirus software for detecting the installation of such malware, providing groundwork for future studies. Client honeypots collected drive-by malware which was then evaluated using common antivirus products. Initial analysis showed that most of such antivirus products identified less than 70% of these highly polymorphic malware programs. Also, it was observed that the antivirus products tested, even when successfully detecting this malware, often failed to classify it, leading to the conclusion that further work could involve not only developing new behavioral detection technologies, but also empirical studies that improve general understanding of these threats. Toward that end, one example of malicious code was analyzed behaviorally to provide insight into next steps for the future direction of this research.

  12. Safety rod driving device

    International Nuclear Information System (INIS)

    Murakami, Kiyonobu; Kurosaki, Akira.


    Purpose: To rapidly insert safety rods for a criticality experiment device into a reactor core container to stop the criticality reaction thereby prevent reactivity accidents. Constitution: A cylinder device having a safety rod as a cylinder rod attached with a piston at one end is constituted. The piston is elevated by pressurized air and attracted and fixed by an electromagnet which is a stationary device disposed at the upper portion of the cylinder. If the current supply to the electromagnet is disconnected, the safety rod constituting the cylinder rod is fallen together with the piston to the lower portion of the cylinder. Since the cylinder rod driving device has neither electrical motor nor driving screw as in the conventional device, necessary space can be reduced and the weight is decreased. In addition, since the inside of the nuclear reactor can easily be shielded completely from the external atmosphere, leakage of radioactive materials can be prevented. (Horiuchi, T.)

  13. Control rod drive mechanism

    International Nuclear Information System (INIS)

    Futatsugi, Masao; Goto, Mikihiko.


    Purpose: To provide a control rod drive mechanism using water as an operating source, which prevents a phenomenon for forming two-layers of water in the neighbourhood of a return nozzle in a reactor to limit formation of excessive thermal stress to improve a safety. Constitution: In the control rod drive mechanism of the present invention, a heating device is installed in the neighbourhood of a pressure container for a reactor. This heating device is provided to heat return water in the reactor to a level equal to the temperature of reactor water thereby preventing a phenomenon for forming two-layers of water in the reactor. This limits formation of thermal stress in the return nozzle in the reactor. Accordingly, it is possible to minimize damages in the return nozzle portion and yet a possibility of failure in reactor water. (Kawakami, Y.)

  14. A rotary drive

    International Nuclear Information System (INIS)

    Causer, R.


    A rotary drive for a manipulator or teleoperator comprises a ring member freely rotatable about an eccentric boss extending from an input driver shaft. The ring member has a tapered rim portion wedged between two resiliently biassed friction rings of larger diameter than the ring member and coaxial with the driver shaft, and the ring member is rotatably connected to an output driven shaft. The rotary drive provides a considerable velocity ratio, and also provides a safety feature in that friction between the rim portion and the friction rings only causes rotation of the driven shaft if the load on the driven shaft is less than a certain limiting value. This limiting value may be varied by adjusting the resilient bias on the friction rings. (author)

  15. Driving and engine cycles

    CERN Document Server

    Giakoumis, Evangelos G


    This book presents in detail the most important driving and engine cycles used for the certification and testing of new vehicles and engines around the world. It covers chassis and engine-dynamometer cycles for passenger cars, light-duty vans, heavy-duty engines, non-road engines and motorcycles, offering detailed historical information and critical review. The book also provides detailed examples from SI and diesel engines and vehicles operating during various cycles, with a focus on how the engine behaves during transients and how this is reflected in emitted pollutants, CO2 and after-treatment systems operation. It describes the measurement methods for the testing of new vehicles and essential information on the procedure for creating a driving cycle. Lastly, it presents detailed technical specifications on the most important chassis-dynamometer cycles around the world, together with a direct comparison of those cycles.

  16. Sensation and perception of sucrose and fat stimuli predict the reinforcing value of food. (United States)

    Panek-Scarborough, Leah M; Dewey, Amber M; Temple, Jennifer L


    Chronic overeating can lead to weight gain and obesity. Sensory system function may play a role in the types of foods people select and the amount of food people eat. Several studies have shown that the orosensory components of eating play a strong role in driving food intake and food selection. In addition, previous work has shown that motivation to get food, or the reinforcing value of food, is a predictor of energy intake. The purpose of this study was to test the hypothesis that higher detection thresholds and lower suprathreshold intensity ratings of sweet and fat stimuli are associated with greater reinforcing value of food. In addition, we sought to determine if the sensory ratings of the stimuli would differ depending on whether they were expectorated or swallowed. The reinforcing value of food was measured by having participants perform operant responses for food on progressive ratio schedules of reinforcement. Taste detection thresholds and suprathresholds for solutions containing varied concentrations of sucrose and fat were also measured in two different Experiments. In Experiment 1, we found that sucrose, but not fat, detection predicted the reinforcing value of food with the reinforcing value of food increasing as sucrose detection threshold increased (indicating poorer detection). In Experiment 2, we found that lower suprathreshold ratings of expectorated fat and sucrose predicted greater reinforcing value of food. In addition, higher detection thresholds for fat stimuli (indicating poorer detection) were associated with greater reinforcing value of food. When taken together, these studies suggest that there is a relationship between taste detection and perception and reinforcing value of food and that these relationships vary based on whether the stimulus is swallowed or expectorated. Copyright © 2012 Elsevier Inc. All rights reserved.

  17. Driving profile modeling and recognition based on soft computing approach. (United States)

    Wahab, Abdul; Quek, Chai; Tan, Chin Keong; Takeda, Kazuya


    Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection. In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models (GMMs) are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network (FNN) system to ascertain the identity of the driver. Two fuzzy neural networks, namely, the evolving fuzzy neural network (EFuNN) and the adaptive network-based fuzzy inference system (ANFIS), are used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network (NN) implementation using the multilayer perceptron (MLP) network and a statistical method based on the GMM. Extensive testing was conducted and the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.

  18. Driving electrostatic transducers

    DEFF Research Database (Denmark)

    Nielsen, Dennis; Knott, Arnold; Andersen, Michael A. E.


    Electrostatic transducers represent a very interesting alternative to the traditional inefficient electrodynamic transducers. In order to establish the full potential of these transducers, power amplifiers which fulfill the strict requirements imposed by such loads (high impedance, frequency...... depended, nonlinear and high bias voltage for linearization) must be developed. This paper analyzes power stages and bias configurations suitable for driving an electrostatic transducer. Measurement results of a 300 V prototype amplifier are shown. Measuring THD across a high impedance source is discussed...

  19. Additive manufacturing of short and mixed fibre-reinforced polymer (United States)

    Lewicki, James; Duoss, Eric B.; Rodriguez, Jennifer Nicole; Worsley, Marcus A.; King, Michael J.


    Additive manufacturing of a fiber-reinforced polymer (FRP) product using an additive manufacturing print head; a reservoir in the additive manufacturing print head; short carbon fibers in the reservoir, wherein the short carbon fibers are randomly aligned in the reservoir; an acrylate, methacrylate, epoxy, cyanate ester or isocyanate resin in the reservoir, wherein the short carbon fibers are dispersed in the acrylate, methacrylate, epoxy, cyanate ester or isocyanate resin; a tapered nozzle in the additive manufacturing print head operatively connected to the reservoir, the tapered nozzle produces an extruded material that forms the fiber-reinforced polymer product; baffles in the tapered nozzle that receive the acrylate, methacrylate, epoxy, cyanate ester or isocyanate resin with the short carbon fibers dispersed in the acrylate, methacrylate, epoxy, cyanate ester or isocyanate resin; and a system for driving the acrylate, methacrylate, epoxy, cyanate ester or isocyanate resin with the short carbon fibers dispersed in the acrylate, methacrylate, epoxy, cyanate ester or isocyanate resin from the reservoir through the tapered nozzle wherein the randomly aligned short carbon fibers in the acrylate, methacrylate, epoxy, cyanate ester or isocyanate resin are aligned by the baffles and wherein the extruded material has the short carbon fibers aligned in the acrylate, methacrylate, epoxy, cyanate ester or isocyanate resin that forms the fiber-reinforced polymer product.

  20. Reinforcement shapes clines in female mate discrimination in Drosophila subquinaria (United States)

    Bewick, Emily R.; Dyer, Kelly A.


    Reinforcement of species boundaries may alter mate recognition in a way that also affects patterns of mate preference among conspecific populations. In the fly Drosophila subquinaria, females sympatric with the closely related species D. recens reject mating with heterospecific males as well as with conspecific males from allopatric populations. Here, we assess geographic variation in behavioral isolation within and among populations of D. subquinaria and use cline theory to understand patterns of selection on reinforced discrimination and its consequences for sexual isolation within species. We find that selection has fixed rejection of D. recens males in sympatry, while significant genetic variation in this behavior occurs within allopatric populations. In conspecific matings sexual isolation is also asymmetric and stronger in populations that are sympatric with D. recens. The clines in behavioral discrimination within and between species are similar in shape and are maintained by strong selection in the face of gene flow, and we show that some of their genetic basis may be either shared or linked. Thus, while reinforcement can drive extremely strong phenotypic divergence, the long-term consequences for incipient speciation depend on gene flow, genetic linkage of discrimination traits, and the cost of these behaviors in allopatry. PMID:25163510

  1. Neural electrical activity and neural network growth. (United States)

    Gafarov, F M


    The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

  2. Cognitive deficits caused by prefrontal cortical and hippocampal neural disinhibition. (United States)

    Bast, Tobias; Pezze, Marie; McGarrity, Stephanie


    We review recent evidence concerning the significance of inhibitory GABA transmission and of neural disinhibition, that is, deficient GABA transmission, within the prefrontal cortex and the hippocampus, for clinically relevant cognitive functions. Both regions support important cognitive functions, including attention and memory, and their dysfunction has been implicated in cognitive deficits characterizing neuropsychiatric disorders. GABAergic inhibition shapes cortico-hippocampal neural activity, and, recently, prefrontal and hippocampal neural disinhibition has emerged as a pathophysiological feature of major neuropsychiatric disorders, especially schizophrenia and age-related cognitive decline. Regional neural disinhibition, disrupting spatio-temporal control of neural activity and causing aberrant drive of projections, may disrupt processing within the disinhibited region and efferent regions. Recent studies in rats showed that prefrontal and hippocampal neural disinhibition (by local GABA antagonist microinfusion) dysregulates burst firing, which has been associated with important aspects of neural information processing. Using translational tests of clinically relevant cognitive functions, these studies showed that prefrontal and hippocampal neural disinhibition disrupts regional cognitive functions (including prefrontal attention and hippocampal memory function). Moreover, hippocampal neural disinhibition disrupted attentional performance, which does not require the hippocampus but requires prefrontal-striatal circuits modulated by the hippocampus. However, some prefrontal and hippocampal functions (including inhibitory response control) are spared by regional disinhibition. We consider conceptual implications of these findings, regarding the distinct relationships of distinct cognitive functions to prefrontal and hippocampal GABA tone and neural activity. Moreover, the findings support the proposition that prefrontal and hippocampal neural disinhibition

  3. Control rod drive mechanism

    International Nuclear Information System (INIS)

    Mizuno, Katsuyuki.


    Object: To restrict the reduction in performance due to stress corrosion cracks by making use of condensate produced in a turbine steam condenser. Structure: Water produced in a turbine steam condenser is forced into a condensed water desalting unit by low pressure condensate pump. The condensate is purified and then forced by a high pressure condensate pump into a feedwater heater for heating before it is returned to the reactor by a feedwater pump. Part of the condensate issuing from the condensate desalting unit is branched from the remaining portion at a point upstream the pump and is withdrawn into a control rod drive water pump after passing through a motordriven bypass valve, an orifice and a condenser water level control valve, is pressurized in the control rod drive water desalting unit and supplied to a control rod drive water pressure system. The control rod is vertically moved by the valve operation of the water pressure system. Since water of high oxygen concentration does not enter during normal operation, it is possible to prevent the stress cracking of the stainless steel apparatus. (Nakamura, S.)

  4. Motor car driving; Kraftfahrzeugfuehrung

    Energy Technology Data Exchange (ETDEWEB)

    Juergensohn, T. [Technische Univ. Berlin (Germany). ISS-Fahrzeugtechnik; Timpe, K.P. (eds.) [Technische Univ. Berlin (DE). Zentrum Mensch-Maschine-Systeme (ZMMS)


    This is the first comprehensive book on motor car driving, i.e. all aspects of motor car technology that cannot be looked at separately from the needs, characteristics and limitations of the human driver. This includes ergonomics as well as the design of the driver interface in consideration of the findings of cognitive science, problems of driving simulation in the context of simulation of technical systems, problems relating to optimal car automation up to traffic psychology. The book is in honour of Prof. Dr. Willumeit who died in summer 2000. Prof. Willumeit was one of the few scientists in Germany who had been an expert on all aspects of motor car driving for many years. [German] Erstmalig wird das Thema der Fahrzeugfuehrung geschlossen dargestellt. Die Thematik der 'Kraftfahrzeugfuehrung' umfasst in diesem Zusammenhang alle Aspekte der Kraftfahrzeugtechnik, die nicht isoliert von den Erfordernissen, Eigenschaften und Grenzen des menschlichen Fahrers betrachtet werden koennen. Dies beinhaltet u.a. Probleme der Ergonomie, aber auch Fragen nach einer kognitionswissenschaftlich unterstuetzten Schnittstellengestaltung, Fragen der Simulation des Fahrverhalten im Kontext der Simulation technischer Systeme oder Fragen einer optimalen Fahrzeugautomatisierung bis hin zu verkehrspsychologischen Aspekten. Das Buch ist als Gedenkband fuer Prof. Dr. Willumeit konzipiert, der im Sommer 2000 verstarb. Prof. Willumeit war einer der wenigen Wissenschaftler in Deutschland, der ueber viele Jahre diese Thematik der Kraftfahrzeugfuehrung in ihrer vollen Breite verfolgte. (orig.)

  5. Control rod drive

    International Nuclear Information System (INIS)

    Watando, Kosaku; Tanaka, Yuzo; Mizumura, Yasuhiro; Hosono, Kazuya.


    Object: To provide a simple and compact construction of an apparatus for driving a drive shaft inside with a magnetic force from the outside of the primary system water side. Structure: The weight of a plunger provided with an attraction plate is supported by a plunger lift spring means so as to provide a buffer action at the time of momentary movement while also permitting the load on lift coil to be constituted solely by the load on the drive shaft. In addition, by arranging the attraction plate and lift coil so that they face each other with a small gap there-between, it is made possible to reduce the size and permit efficient utilization of the attracting force. Because of the small size, cooling can be simply carried out. Further, since there is no mechanical penetration portion, there is no possibility of leakage of the primary system water. Furthermore, concentration of load on a latch pin is prevented by arranging so that with a structure the load of the control rod to be directly beared through the scrum latch. (Kamimura, M.)

  6. Effects of partial reinforcement and time between reinforced trials on terminal response rate in pigeon autoshaping. (United States)

    Gottlieb, Daniel A


    Partial reinforcement often leads to asymptotically higher rates of responding and number of trials with a response than does continuous reinforcement in pigeon autoshaping. However, comparisons typically involve a partial reinforcement schedule that differs from the continuous reinforcement schedule in both time between reinforced trials and probability of reinforcement. Two experiments examined the relative contributions of these two manipulations to asymptotic response rate. Results suggest that the greater responding previously seen with partial reinforcement is primarily due to differential probability of reinforcement and not differential time between reinforced trials. Further, once established, differences in responding are resistant to a change in stimulus and contingency. Secondary response theories of autoshaped responding (theories that posit additional response-augmenting or response-attenuating mechanisms specific to partial or continuous reinforcement) cannot fully accommodate the current body of data. It is suggested that researchers who study pigeon autoshaping train animals on a common task prior to training them under different conditions.

  7. Pragmatically Framed Cross-Situational Noun Learning Using Computational Reinforcement Models. (United States)

    Najnin, Shamima; Banerjee, Bonny


    Cross-situational learning and social pragmatic theories are prominent mechanisms for learning word meanings (i.e., word-object pairs). In this paper, the role of reinforcement is investigated for early word-learning by an artificial agent. When exposed to a group of speakers, the agent comes to understand an initial set of vocabulary items belonging to the language used by the group. Both cross-situational learning and social pragmatic theory are taken into account. As social cues, joint attention and prosodic cues in caregiver's speech are considered. During agent-caregiver interaction, the agent selects a word from the caregiver's utterance and learns the relations between that word and the objects in its visual environment. The "novel words to novel objects" language-specific constraint is assumed for computing rewards. The models are learned by maximizing the expected reward using reinforcement learning algorithms [i.e., table-based algorithms: Q-learning, SARSA, SARSA-λ, and neural network-based algorithms: Q-learning for neural network (Q-NN), neural-fitted Q-network (NFQ), and deep Q-network (DQN)]. Neural network-based reinforcement learning models are chosen over table-based models for better generalization and quicker convergence. Simulations are carried out using mother-infant interaction CHILDES dataset for learning word-object pairings. Reinforcement is modeled in two cross-situational learning cases: (1) with joint attention (Attentional models), and (2) with joint attention and prosodic cues (Attentional-prosodic models). Attentional-prosodic models manifest superior performance to Attentional ones for the task of word-learning. The Attentional-prosodic DQN outperforms existing word-learning models for the same task.

  8. TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow


    Hafner, Danijar; Davidson, James; Vanhoucke, Vincent


    We introduce TensorFlow Agents, an efficient infrastructure paradigm for building parallel reinforcement learning algorithms in TensorFlow. We simulate multiple environments in parallel, and group them to perform the neural network computation on a batch rather than individual observations. This allows the TensorFlow execution engine to parallelize computation, without the need for manual synchronization. Environments are stepped in separate Python processes to progress them in parallel witho...

  9. On the Drive Specificity of Freudian Drives for the Generation of SEEKING Activities: The Importance of the Underestimated Imperative Motor Factor

    Directory of Open Access Journals (Sweden)

    Michael Kirsch


    Full Text Available Doubters of Freud’s theory of drives frequently mentioned that his approach is outdated and therefore cannot be useful for solving current problems in patients with mental disorders. At present, many scientists believe that affects rather than drives are of utmost importance for the emotional life and the theoretical framework of affective neuroscience, developed by Panksepp, strongly underpinned this view. Panksepp evaluated seven so-called command systems and the SEEKING system is therein of central importance. Panksepp used Pankseppian drives as inputs for the SEEKING system but noted the missing explanation of drive-specific generation of SEEKING activities in his description. Drive specificity requires dual action of the drive: the activation of a drive-specific brain area and the release of the neurotransmitter dopamine. Noticeably, as Freud claimed drive specificity too, it was here analyzed whether a Freudian drive can evoke the generation of drive-specific SEEKING activities. Special importance was addressed to the imperative motor factor in Freud’s drive theory because Panksepp’s formulations focused on neural pathways without specifying underlying neurotransmitter/endocrine factors impelling motor activity. As Panksepp claimed sleep as a Pankseppian drive, we firstly had to classified sleep as a Freudian drive by using three evaluated criteria for a Freudian drive. After that it was possible to identify the imperative motor factors of hunger, thirst, sex, and sleep. Most importantly, all of these imperative motor factors can both activate a drive-specific brain area and release dopamine from dopaminergic neurons, i.e., they can achieve the so-called drive specificity. Surprisingly, an impaired Freudian drive can alter via endocrinological pathways the concentration of the imperative motor factor of a second Freudian drive, obviously in some independence to the level of the metabolic deficit, thereby offering the possibility to

  10. Self-rated driving and driving safety in older adults. (United States)

    Ross, Lesley A; Dodson, Joan E; Edwards, Jerri D; Ackerman, Michelle L; Ball, Karlene


    Many U.S. states rely on older adults to self-regulate their driving and determine when driving is no longer a safe option. However, the relationship of older adults' self-rated driving in terms of actual driving competency outcomes is unclear. The current study investigates self-rated driving in terms of (1) systematic differences between older adults with high (good/excellent) versus low (poor/fair/average) self-ratings, and (2) the predictive nature of self-rated driving to adverse driving outcomes in older adults (n=350; mean age 73.9, SD=5.25, range 65-91). Adverse driving outcomes included self-reported incidences of (1) being pulled over by the police, (2) receiving a citation, (3) receiving a recommendation to cease or limit driving, (4) crashes, and (5) state-reported crashes. Results found that older drivers with low self-ratings reported more medical conditions, less driving frequency, and had been given more suggestions to stop/limit their driving; there were no other significant differences between low and high self-raters. Logistic regression revealed older drivers were more likely to have a state-reported crash and receive a suggestion to stop or limit driving. Men were more likely to report all adverse driving outcomes except for receiving a suggestion to stop or limit driving. Regarding self-rated driving, older adults with high ratings were 66% less likely (OR=0.34, 95% CI=0.14-0.85) to have received suggestions to limit or stop driving after accounting for demographics, health and driving frequency. Self-ratings were not predictive of other driving outcomes (being pulled over by the police, receiving a citation, self-reported crashes, or state-reported crashes, ps>0.05). Most older drivers (85.14%) rated themselves as either good or excellent drivers regardless of their actual previous citation or crash rates. Self-rated driving is likely not related to actual driving proficiency as indicated by previous crash involvement in older adults

  11. hmmr mediates anterior neural tube closure and morphogenesis in the frog Xenopus. (United States)

    Prager, Angela; Hagenlocher, Cathrin; Ott, Tim; Schambony, Alexandra; Feistel, Kerstin


    Development of the central nervous system requires orchestration of morphogenetic processes which drive elevation and apposition of the neural folds and their fusion into a neural tube. The newly formed tube gives rise to the brain in anterior regions and continues to develop into the spinal cord posteriorly. Conspicuous differences between the anterior and posterior neural tube become visible already during neural tube closure (NTC). Planar cell polarity (PCP)-mediated convergent extension (CE) movements are restricted to the posterior neural plate, i.e. hindbrain and spinal cord, where they propagate neural fold apposition. The lack of CE in the anterior neural plate correlates with a much slower mode of neural fold apposition anteriorly. The morphogenetic processes driving anterior NTC have not been addressed in detail. Here, we report a novel role for the breast cancer susceptibility gene and microtubule (MT) binding protein Hmmr (Hyaluronan-mediated motility receptor, RHAMM) in anterior neurulation and forebrain development in Xenopus laevis. Loss of hmmr function resulted in a lack of telencephalic hemisphere separation, arising from defective roof plate formation, which in turn was caused by impaired neural tissue narrowing. hmmr regulated polarization of neural cells, a function which was dependent on the MT binding domains. hmmr cooperated with the core PCP component vangl2 in regulating cell polarity and neural morphogenesis. Disrupted cell polarization and elongation in hmmr and vangl2 morphants prevented radial intercalation (RI), a cell behavior essential for neural morphogenesis. Our results pinpoint a novel role of hmmr in anterior neural development and support the notion that RI is a major driving force for anterior neurulation and forebrain morphogenesis. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. Low Sex Drive in Women (United States)

    Low sex drive in women Overview Women's sexual desires naturally fluctuate over the years. Highs and lows commonly coincide ... used for mood disorders also can cause low sex drive in women. If your lack of interest ...

  13. Marijuana and actual driving performance (United States)


    This report concerns the effects of marijuana smoking on actual driving performance. It presents the results of one pilot and three actual driving studies. The pilot study's major purpose was to establish the THC dose current marijuana users smoke to...

  14. Distracted Driving Raises Crash Risk (United States)

    ... this issue Health Capsule Distracted Driving Raises Crash Risk En español Send us your comments Video technology ... distracted driving, especially among new drivers, raises the risk for car crashes and near crashes. The study ...

  15. Woodflour as Reinforcement of Polypropylene

    Directory of Open Access Journals (Sweden)

    José Cláudio Caraschi


    Full Text Available The effect of the filler content and size, as well as accelerated aging on the mechanical properties of polypropylene composites reinforced with woodflour (WF/PP were evaluated. The composites were prepared by the extrusion of polypropylene with woodflour (Pinus elliotti based on following ratios: 15, 25 and 40 wt% with two different granulometries. The specimens were injection molded according to ASTM standards. The composite properties did not show significant differences as a function of the filler granulometry. We also observed that by increasing the filler content, both the mechanical properties and the melt flow index (MFI decreased, and the elasticity modulus, hardness and density increased. Concerning the accelerated aging, the composite presented a reduction in tensile properties. The results showed that the composite properties are extremely favorable when compared to other commercial systems reinforced by inorganic fillers.

  16. Homeostatic scaling of excitability in recurrent neural networks.

    NARCIS (Netherlands)

    Remme, M.W.H.; Wadman, W.J.


    Neurons adjust their intrinsic excitability when experiencing a persistent change in synaptic drive. This process can prevent neural activity from moving into either a quiescent state or a saturated state in the face of ongoing plasticity, and is thought to promote stability of the network in which

  17. Carbon fiber reinforced asphalt concrete

    International Nuclear Information System (INIS)

    Jahromi, Saeed G.


    Fibers are often used in the manufacture of other materials. For many years, they have been utilized extensively in numerous applications in civil engineering. Fiber-reinforcement refers to incorporating materials with desired properties within some other materials lacking those properties. Use of fibers is not a new phenomenon, as the technique of fiber-reinforced bitumen began early as 1950. In all industrialized countries today, nearly all concretes used in construction are reinforced. A multitude of fibers and fiber materials are being introduced in the market regularly. The present paper presents characteristics and properties of carbon fiber-reinforced asphalt mixtures, which improve the performance of pavements. To evaluate the effect of fiber contents on bituminous mixtures, laboratory investigations were carried out on the samples with and without fibers. During the course of this study, various tests were undertaken, applying Marshall Test indirect tensile test, creep test and resistance to fatigue cracking by using repeated load indirect tensile test. Carbon fiber exhibited consistency in results and as such it was observed that the addition of fiber does affect the properties of bituminous mixtures, i.e. an increase in its stability and decrease in the flow value as well as an increase in voids in the mix. Results indicate that fibers have the potential to resist structural distress in pavement, in the wake of growing traffic loads and thus improve fatigue by increasing resistance to cracks or permanent deformation. On the whole, the results show that the addition of carbon fiber will improve some of the mechanical properties like fatigue and deformation in the flexible pavement. (author)

  18. FRP reinforcement of timber structures


    Schober, Kay-Uwe; Harte, Annette M.; Kliger, Robert; Jockwer, Robert; Xu, Qingfeng; Chen, Jian-Fei


    Timber engineering has advanced over recent decades to offer an alternative to traditional materials and methods. The bonding of fibre reinforced plastics (FRP) with adhesives to timber structures for repair and strengthening has many advantages. However, the lack of established design rules has strongly restrained the use of FRP strengthening in many situations, where these could be a preferable option to most traditional techniques. A significant body of research has been carried out in rec...

  19. Continuous carbon nanotube reinforced composites. (United States)

    Ci, L; Suhr, J; Pushparaj, V; Zhang, X; Ajayan, P M


    Carbon nanotubes are considered short fibers, and polymer composites with nanotube fillers are always analogues of random, short fiber composites. The real structural carbon fiber composites, on the other hand, always contain carbon fiber reinforcements where fibers run continuously through the composite matrix. With the recent optimization in aligned nanotube growth, samples of nanotubes in macroscopic lengths have become available, and this allows the creation of composites that are similar to the continuous fiber composites with individual nanotubes running continuously through the composite body. This allows the proper utilization of the extreme high modulus and strength predicted for nanotubes in structural composites. Here, we fabricate such continuous nanotube polymer composites with continuous nanotube reinforcements and report that under compressive loadings, the nanotube composites can generate more than an order of magnitude improvement in the longitudinal modulus (up to 3,300%) as well as damping capability (up to 2,100%). It is also observed that composites with a random distribution of nanotubes of same length and similar filler fraction provide three times less effective reinforcement in composites.

  20. Mechanics of fiber reinforced materials (United States)

    Sun, Huiyu

    This dissertation is dedicated to mechanics of fiber reinforced materials and the woven reinforcement and composed of four parts of research: analytical characterization of the interfaces in laminated composites; micromechanics of braided composites; shear deformation, and Poisson's ratios of woven fabric reinforcements. A new approach to evaluate the mechanical characteristics of interfaces between composite laminae based on a modified laminate theory is proposed. By including an interface as a special lamina termed the "bonding-layer" in the analysis, the mechanical properties of the interfaces are obtained. A numerical illustration is given. For micro-mechanical properties of three-dimensionally braided composite materials, a new method via homogenization theory and incompatible multivariable FEM is developed. Results from the hybrid stress element approach compare more favorably with the experimental data than other existing numerical methods widely used. To evaluate the shearing properties for woven fabrics, a new mechanical model is proposed during the initial slip region. Analytical results show that this model provides better agreement with the experiments for both the initial shear modulus and the slipping angle than the existing models. Finally, another mechanical model for a woven fabric made of extensible yarns is employed to calculate the fabric Poisson's ratios. Theoretical results are compared with the available experimental data. A thorough examination on the influences of various mechanical properties of yarns and structural parameters of fabrics on the Poisson's ratios of a woven fabric is given at the end.

  1. Reinforcement learning in computer vision (United States)

    Bernstein, A. V.; Burnaev, E. V.


    Nowadays, machine learning has become one of the basic technologies used in solving various computer vision tasks such as feature detection, image segmentation, object recognition and tracking. In many applications, various complex systems such as robots are equipped with visual sensors from which they learn state of surrounding environment by solving corresponding computer vision tasks. Solutions of these tasks are used for making decisions about possible future actions. It is not surprising that when solving computer vision tasks we should take into account special aspects of their subsequent application in model-based predictive control. Reinforcement learning is one of modern machine learning technologies in which learning is carried out through interaction with the environment. In recent years, Reinforcement learning has been used both for solving such applied tasks as processing and analysis of visual information, and for solving specific computer vision problems such as filtering, extracting image features, localizing objects in scenes, and many others. The paper describes shortly the Reinforcement learning technology and its use for solving computer vision problems.

  2. Nuclear refueling platform drive system

    International Nuclear Information System (INIS)

    Busch, F.R.; Faulstich, D.L.


    This patent describes a drive system. It comprises: a gantry including a bridge having longitudinal and transverse axes and supported by spaced first and second end frames joined to fist and second end frames joined to first and second drive trucks for moving the bridge along the transverse axis; first means for driving the first drive truck; second means for driving the second drive truck being independent from the first driving means; and means for controlling the first and second driving means for reducing differential transverse travel between the first and second drive trucks, due to a skewing torque acting on the bridge, to less than a predetermined maximum, the controlling means being in the form of an electrical central processing unit and including: a closed-loop first velocity control means for controlling velocity of the first drive truck by providing a first command signal to the first driver means; a close loop second velocity control means for controlling velocity of the second drive truck by providing a second command signal to the second driving means; and an auxiliary closed-loop travel control means

  3. Chaotic diagonal recurrent neural network

    International Nuclear Information System (INIS)

    Wang Xing-Yuan; Zhang Yi


    We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

  4. Vicarious reinforcement learning signals when instructing others. (United States)

    Apps, Matthew A J; Lesage, Elise; Ramnani, Narender


    Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action-outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors. Copyright © 2015 Apps et al.

  5. Lung Nodule Detection via Deep Reinforcement Learning

    Directory of Open Access Journals (Sweden)

    Issa Ali


    Full Text Available Lung cancer is the most common cause of cancer-related death globally. As a preventive measure, the United States Preventive Services Task Force (USPSTF recommends annual screening of high risk individuals with low-dose computed tomography (CT. The resulting volume of CT scans from millions of people will pose a significant challenge for radiologists to interpret. To fill this gap, computer-aided detection (CAD algorithms may prove to be the most promising solution. A crucial first step in the analysis of lung cancer screening results using CAD is the detection of pulmonary nodules, which may represent early-stage lung cancer. The objective of this work is to develop and validate a reinforcement learning model based on deep artificial neural networks for early detection of lung nodules in thoracic CT images. Inspired by the AlphaGo system, our deep learning algorithm takes a raw CT image as input and views it as a collection of states, and output a classification of whether a nodule is present or not. The dataset used to train our model is the LIDC/IDRI database hosted by the lung nodule analysis (LUNA challenge. In total, there are 888 CT scans with annotations based on agreement from at least three out of four radiologists. As a result, there are 590 individuals having one or more nodules, and 298 having none. Our training results yielded an overall accuracy of 99.1% [sensitivity 99.2%, specificity 99.1%, positive predictive value (PPV 99.1%, negative predictive value (NPV 99.2%]. In our test, the results yielded an overall accuracy of 64.4% (sensitivity 58.9%, specificity 55.3%, PPV 54.2%, and NPV 60.0%. These early results show promise in solving the major issue of false positives in CT screening of lung nodules, and may help to save unnecessary follow-up tests and expenditures.

  6. Evolvable Neural Software System (United States)

    Curtis, Steven A.


    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  7. Offset Compound Gear Drive (United States)

    Stevens, Mark A.; Handschuh, Robert F.; Lewicki, David G.


    The Offset Compound Gear Drive is an in-line, discrete, two-speed device utilizing a special offset compound gear that has both an internal tooth configuration on the input end and external tooth configuration on the output end, thus allowing it to mesh in series, simultaneously, with both a smaller external tooth input gear and a larger internal tooth output gear. This unique geometry and offset axis permits the compound gear to mesh with the smaller diameter input gear and the larger diameter output gear, both of which are on the same central, or primary, centerline. This configuration results in a compact in-line reduction gear set consisting of fewer gears and bearings than a conventional planetary gear train. Switching between the two output ratios is accomplished through a main control clutch and sprag. Power flow to the above is transmitted through concentric power paths. Low-speed operation is accomplished in two meshes. For the purpose of illustrating the low-speed output operation, the following example pitch diameters are given. A 5.0 pitch diameter (PD) input gear to 7.50 PD (internal tooth) intermediate gear (0.667 reduction mesh), and a 7.50 PD (external tooth) intermediate gear to a 10.00 PD output gear (0.750 reduction mesh). Note that it is not required that the intermediate gears on the offset axis be of the same diameter. For this example, the resultant low-speed ratio is 2:1 (output speed = 0.500; product of stage one 0.667 reduction and stage two 0.750 stage reduction). The design is not restricted to the example pitch diameters, or output ratio. From the output gear, power is transmitted through a hollow drive shaft, which, in turn, drives a sprag during which time the main clutch is disengaged.

  8. Electrical machines and drives

    CERN Document Server

    Hindmarsh, John


    Recent years have brought substantial developments in electrical drive technology, with the appearance of highly rated, very-high-speed power-electronic switches, combined with microcomputer control systems.This popular textbook has been thoroughly revised and updated in the light of these changes. It retains its successful formula of teaching through worked examples, which are put in context with concise explanations of theory, revision of equations and discussion of the engineering implications. Numerous problems are also provided, with answers supplied.The third edition in

  9. Electrical machines & drives

    CERN Document Server

    Hammond, P


    Containing approximately 200 problems (100 worked), the text covers a wide range of topics concerning electrical machines, placing particular emphasis upon electrical-machine drive applications. The theory is concisely reviewed and focuses on features common to all machine types. The problems are arranged in order of increasing levels of complexity and discussions of the solutions are included where appropriate to illustrate the engineering implications. This second edition includes an important new chapter on mathematical and computer simulation of machine systems and revised discussions o

  10. Measurement of Driving Terms

    CERN Document Server

    Schmidt, F; Faus-Golfe, A


    In 2000 a series of MDs has been performed at the SPS to measure resonance driving terms. Theory predicts that these terms can be determined by harmonic analysis of BPM data recorded after applying single kicks at various amplitudes. Strong sextupoles were introduced to create a sizeable amount of nonlinearities. Experiments at injection energy (26 GeV) with single bunch as well as one experiment at 120 GeV with 84 bunches were carried out. The expected nonlinear content is compared to the experimenteal observation.

  11. Cognitive impairment and driving safety. (United States)

    Eby, David W; Molnar, Lisa J


    As the populations of many countries continue to age, cognitive impairment will likely become more common. Individuals with cognitive impairment pose special challenges for families, health professionals, driving safety professionals, and the larger community, particularly if these older adults depend on driving as their primary means of community mobility. It is vital that we continue to extend our knowledge about the driving behavior of individuals' with cognitive impairment, as well as try to develop effective means of screening and assessing these individuals for fitness to drive and help facilitate their transition to non-driving when appropriate. This special issue is intended to provide researchers and practitioners an opportunity to present the most recent research findings on driving-related issues among older adults with cognitive impairment. The issue contains 11 original contributions from seven countries. The topics covered by these papers are: crash risks; screening, assessment, and fitness to drive; driving performance using a driving simulator; and driving behaviors and driving-related decisions of people with cognitive impairments. Copyright © 2012. Published by Elsevier Ltd.

  12. Parkinson's disease and driving ability (United States)

    Singh, Rajiv; Pentland, Brian; Hunter, John; Provan, Frances


    Objectives To explore the driving problems associated with Parkinson's disease (PD) and to ascertain whether any clinical features or tests predict driver safety. Methods The driving ability of 154 individuals with PD referred to a driving assessment centre was determined by a combination of clinical tests, reaction times on a test rig and an in‐car driving test. Results The majority of cases (104, 66%) were able to continue driving although 46 individuals required an automatic transmission and 10 others needed car modifications. Ability to drive was predicted by the severity of physical disease, age, presence of other associated medical conditions, particularly dementia, duration of disease, brake reaction, time on a test rig and score on a driving test (all pautomatic transmission. A combination of clinical tests and in‐car driving assessment will establish safety to drive, and a number of clinical correlates can be shown to predict the likely outcome and may assist in the decision process. This is the largest series of consecutive patients seen at a driving assessment centre reported to date, and the first to devise a scoring system for on‐road driving assessment. PMID:17178820

  13. A qualitative exploration of driving stress and driving discourtesy. (United States)

    Scott-Parker, B; Jones, C M; Rune, K; Tucker, J


    Driving courtesy, and conversely driving discourtesy, recently has been of great interest in the public domain. In addition, there has been increasing recognition of the negative impact of stress upon the individual's health and wellbeing, with a plethora of interventions aimed at minimising stress more generally. The research literature regarding driving dis/courtesy, in comparison, is scant, with a handful of studies examining the dis/courteous driving behaviour of road users, and the relationship between driving discourtesy and driving stress. To examine courteous and discourteous driving experiences, and to explore the impact of stress associated with such driving experiences. Thirty-eight drivers (20 females) from the Sunshine Coast region volunteered to participate in one of four 1-1.5 h focus groups. Content analysis used the verbatim utterances captured via an Mp3 device. Three themes pertaining to stressful and discourteous interactions were identified. Theme one pertained to the driving context: road infrastructure (eg, roundabouts, roadwork), vehicles (eg, features), location (eg, country vs city, unfamiliar areas), and temporal aspects (eg, holidays). Theme two pertained to other road users: their behaviour (eg, tailgating, merging), and unknown factors (eg, illicit and licit drug use). Theme three pertained to the self as road user: their own behaviours (eg, deliberate intimidation), and their emotions (eg, angry reaction to other drivers, being in control). Driving dis/courtesy and driving stress is a complex phenomenon, suggesting complex intervention efforts are required. Driving discourtesy was reported as being highly stressful, therefore intervention efforts which encourage driving courtesy and which foster emotional capacity to cope with stressful circumstances appear warranted. Copyright © 2018. Published by Elsevier Ltd.

  14. Control rod drives

    International Nuclear Information System (INIS)

    Hayakawa, Hiroyasu; Kawamura, Atsuo.


    Purpose: To reduce pellet-clad mechanical interactions, as well as improve the fuel safety. Constitution: In the rod drive of a bwr type reactor, an electric motor operated upon intermittent input such as of pulse signals is connected to a control rod. A resolver for converting the rotational angle of the motor to electric signals is connected to the rotational shaft of the motor and the phase difference between the output signal from the resolver and a reference signal is adapted to detect by a comparator. Based on the detection result, the controller is actuated to control a motor for control rod drive so that fine control for the movement of the control rod is made possible. This can reduce the moving distance of the control rod, decrease the thermal stress applied to the control rod and decrease the pellet clad mechanical interaction failures due to thermal expansion between the cladding tube and the pellets caused by abrupt changes in the generated power. (Furukawa, Y.)

  15. Control rod drives

    International Nuclear Information System (INIS)

    Oonuki, Koji.


    Purpose: To increase the driving speed of control rods at rapid insertion with an elongate control rod and an extension pipe while ensuring sufficient buffering performance in a short buffering distance, by providing a plurality of buffers to an extension pipe between a control rod drive source and a control rod in LMFBR type reactor. Constitution: First, second and third buffers are respectively provided to an acceleration piston, an extension pipe and a control rod respectively and the insertion positions for each of the buffers are displaced orderly from above to below. Upon disconnection of energizing current for an electromagnet, the acceleration piston, the extension pipe and the control rod are rapidly inserted in one body. The first, second and third buffers are respectively actuated at each of their falling strokes upon rapid insertion respectively, and the acceleration piston, the extension pipe and the control rod receive the deceleration effect in the order correspondingly. Although the compression force is applied to the control rod only near the stroke end, it does not cause deformation. (Kawakami, Y.)

  16. Origins of food reinforcement in infants12345 (United States)

    Kong, Kai Ling; Feda, Denise M; Eiden, Rina D; Epstein, Leonard H


    Background: Rapid weight gain in infancy is associated with a higher risk of obesity in children and adults. A high relative reinforcing value of food is cross-sectionally related to obesity; lean children find nonfood alternatives more reinforcing than do overweight/obese children. However, to our knowledge, there is no research on how and when food reinforcement develops. Objective: This study was designed to assess whether the reinforcing value of food and nonfood alternatives could be tested in 9- to 18-mo-old infants and whether the reinforcing value of food and nonfood alternatives is differentially related to infant weight status. Design: Reinforcing values were assessed by using absolute progressive ratio schedules of reinforcement, with presentation of food and nonfood alternatives counterbalanced in 2 separate studies. Two nonfood reinforcers [Baby Einstein–Baby MacDonald shows (study 1, n = 27) or bubbles (study 2, n = 30)] were tested against the baby’s favorite food. Food reinforcing ratio (FRR) was quantified by measuring the reinforcing value of food (Food Pmax) in proportion to the total reinforcing value of food and a nonfood alternative (DVD Pmax or BUB Pmax). Results: Greater weight-for-length z score was associated with a greater FRR of a favorite food in study 1 (FRR-DVD) (r = 0.60, P positively associated with FRR-DVD (r = 0.57, P = 0.009) and FRR-BUB (r = 0.37, P = 0.047). Conclusions: Our newly developed paradigm, which tested 2 different nonfood alternatives, demonstrated that lean infants find nonfood alternatives more reinforcing than do overweight/obese infants. This observation suggests that strengthening the alternative reinforcers may have a protective effect against childhood obesity. This research was registered at as NCT02229552. PMID:25733636

  17. Diverse Teams Drive Leadership Development

    DEFF Research Database (Denmark)

    Holck, Lotte; Hjortlund Andersen, Lotte

    New research from ISS Denmark shows that leading diverse teams strengthens leaders’ competencies within communication, relationship building and talent development and ensures inclusion. This has a reinforcing effect as the better the leadership, the better the heterogeneous team will function....

  18. A neural flow estimator

    DEFF Research Database (Denmark)

    Jørgensen, Ivan Harald Holger; Bogason, Gudmundur; Bruun, Erik


    This paper proposes a new way to estimate the flow in a micromechanical flow channel. A neural network is used to estimate the delay of random temperature fluctuations induced in a fluid. The design and implementation of a hardware efficient neural flow estimator is described. The system...... is implemented using switched-current technique and is capable of estimating flow in the μl/s range. The neural estimator is built around a multiplierless neural network, containing 96 synaptic weights which are updated using the LMS1-algorithm. An experimental chip has been designed that operates at 5 V...

  19. Neural Systems Laboratory (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  20. Function of FEZF1 during early neural differentiation of human embryonic stem cells. (United States)

    Liu, Xin; Su, Pei; Lu, Lisha; Feng, Zicen; Wang, Hongtao; Zhou, Jiaxi


    The understanding of the mechanism underlying human neural development has been hampered due to lack of a cellular system and complicated ethical issues. Human embryonic stem cells (hESCs) provide an invaluable model for dissecting human development because of unlimited self-renewal and the capacity to differentiate into nearly all cell types in the human body. In this study, using a chemical defined neural induction protocol and molecular profiling, we identified Fez family zinc finger 1 (FEZF1) as a potential regulator of early human neural development. FEZF1 is rapidly up-regulated during neural differentiation in hESCs and expressed before PAX6, a well-established marker of early human neural induction. We generated FEZF1-knockout H1 hESC lines using CRISPR-CAS9 technology and found that depletion of FEZF1 abrogates neural differentiation of hESCs. Moreover, loss of FEZF1 impairs the pluripotency exit of hESCs during neural specification, which partially explains the neural induction defect caused by FEZF1 deletion. However, enforced expression of FEZF1 itself fails to drive neural differentiation in hESCs, suggesting that FEZF1 is necessary but not sufficient for neural differentiation from hESCs. Taken together, our findings identify one of the earliest regulators expressed upon neural induction and provide insight into early neural development in human.

  1. Brain activation during fast driving in a driving simulator: the role of the lateral prefrontal cortex. (United States)

    Jäncke, Lutz; Brunner, Béatrice; Esslen, Michaela


    Little is currently known about the neural underpinnings of the cognitive control of driving behavior in realistic situations and of the driver's speeding behavior in particular. In this study, participants drove in realistic scenarios presented in a high-end driving simulator. Scalp-recorded EEG oscillations in the alpha-band (8-13 Hz) with a 30-electrode montage were recorded while the participants drove under different conditions: (i) excessively fast (Fast), (ii) in a controlled manner at a safe speed (Correct), and (iii) impatiently in the context of testing traffic conditions (Impatient). Intracerebral sources of alpha-band activation were estimated using low resolution electrical tomography. Given that previous studies have shown a strong negative correlation between the Bold response in the frontal cortex and the alpha-band power, we used alpha-band-related activity as an estimation of frontal activation. Statistical analysis revealed more alpha-band-related activity (i.e. less neuronal activation) in the right lateral prefrontal cortex, including the dorsolateral prefrontal cortex, during fast driving. Those participants who speeded most and exhibited greater risk-taking behavior demonstrated stronger alpha-related activity (i.e. less neuronal activation) in the left anterior lateral prefrontal cortex. These findings are discussed in the context of current theories about the role of the lateral prefrontal cortex in controlling risk-taking behavior, task switching, and multitasking.

  2. Repair of reinforced concrete beams using carbon fiber reinforced polymer

    Directory of Open Access Journals (Sweden)

    Karzad Abdul Saboor


    Full Text Available This research paper is part of an ongoing research on the behaviour of Reinforced Concrete (RC beams retrofitted with Externally Bonded Carbon Fiber Reinforced Polymer (EB-CFRP. A total of 5 large-scale rectangular beams, previously damaged due to shear loading, were repaired and strengthened with EB-CFRP and tested in this study. The major cracks of the damaged beams were injected with epoxy and the beams were wrapped with 2 layers of EB-CFRP discrete strips with 100mm width and 150mm center to center spacing. The beams were instrumented and tested to failure under three points loading in simply supported configuration. The measured test parameters were the beams deflection, maximum load, and the strain in the FRP strips. The failure mode was also observed. The results showed that applying EB-FRP strips increased the shear strength significantly relative to the original shear capacity of the beam. The results demonstrate that the application of EB-FRP strips used in this study is an effective repair method that can be used to repair and strengthen damaged beams.

  3. What Can Reinforcement Learning Teach Us About Non-Equilibrium Quantum Dynamics (United States)

    Bukov, Marin; Day, Alexandre; Sels, Dries; Weinberg, Phillip; Polkovnikov, Anatoli; Mehta, Pankaj

    Equilibrium thermodynamics and statistical physics are the building blocks of modern science and technology. Yet, our understanding of thermodynamic processes away from equilibrium is largely missing. In this talk, I will reveal the potential of what artificial intelligence can teach us about the complex behaviour of non-equilibrium systems. Specifically, I will discuss the problem of finding optimal drive protocols to prepare a desired target state in quantum mechanical systems by applying ideas from Reinforcement Learning [one can think of Reinforcement Learning as the study of how an agent (e.g. a robot) can learn and perfect a given policy through interactions with an environment.]. The driving protocols learnt by our agent suggest that the non-equilibrium world features possibilities easily defying intuition based on equilibrium physics.

  4. Anti-synchronization control of BAM memristive neural networks with multiple proportional delays and stochastic perturbations (United States)

    Wang, Weiping; Yuan, Manman; Luo, Xiong; Liu, Linlin; Zhang, Yao


    Proportional delay is a class of unbounded time-varying delay. A class of bidirectional associative memory (BAM) memristive neural networks with multiple proportional delays is concerned in this paper. First, we propose the model of BAM memristive neural networks with multiple proportional delays and stochastic perturbations. Furthermore, by choosing suitable nonlinear variable transformations, the BAM memristive neural networks with multiple proportional delays can be transformed into the BAM memristive neural networks with constant delays. Based on the drive-response system concept, differential inclusions theory and Lyapunov stability theory, some anti-synchronization criteria are obtained. Finally, the effectiveness of proposed criteria are demonstrated through numerical examples.

  5. Human Embryonic Stem Cells: A Model for the Study of Neural Development and Neurological Diseases

    Directory of Open Access Journals (Sweden)

    Piya Prajumwongs


    Full Text Available Although the mechanism of neurogenesis has been well documented in other organisms, there might be fundamental differences between human and those species referring to species-specific context. Based on principles learned from other systems, it is found that the signaling pathways required for neural induction and specification of human embryonic stem cells (hESCs recapitulated those in the early embryo development in vivo at certain degree. This underscores the usefulness of hESCs in understanding early human neural development and reinforces the need to integrate the principles of developmental biology and hESC biology for an efficient neural differentiation.

  6. Dimensions of driving anger and their relationships with aberrant driving. (United States)

    Zhang, Tingru; Chan, Alan H S; Zhang, Wei


    The purpose of this study was to investigate the relationship between driving anger and aberrant driving behaviours. An internet-based questionnaire survey was administered to a sample of Chinese drivers, with driving anger measured by a 14-item short Driving Anger Scale (DAS) and the aberrant driving behaviours measured by a 23-item Driver Behaviour Questionnaire (DBQ). The results of Confirmatory Factor Analysis demonstrated that the three-factor model (hostile gesture, arrival-blocking and safety-blocking) of the DAS fitted the driving anger data well. The Exploratory Factor Analysis on DBQ data differentiated four types of aberrant driving, viz. emotional violation, error, deliberate violation and maintaining progress violation. For the anger-aberration relation, it was found that only "arrival-blocking" anger was a significant positive predictor for all four types of aberrant driving behaviours. The "safety-blocking" anger revealed a negative impact on deliberate violations, a finding different from previously established positive anger-aberration relation. These results suggest that drivers with different patterns of driving anger would show different behavioural tendencies and as a result intervention strategies may be differentially effective for drivers of different profiles. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Integrating neural network technology and noise analysis

    International Nuclear Information System (INIS)

    Uhrig, R.E.; Oak Ridge National Lab., TN


    The integrated use of neural network and noise analysis technologies offers advantages not available by the use of either technology alone. The application of neural network technology to noise analysis offers an opportunity to expand the scope of problems where noise analysis is useful and unique ways in which the integration of these technologies can be used productively. The two-sensor technique, in which the responses of two sensors to an unknown driving source are related, is used to demonstration such integration. The relationship between power spectral densities (PSDs) of accelerometer signals is derived theoretically using noise analysis to demonstrate its uniqueness. This relationship is modeled from experimental data using a neural network when the system is working properly, and the actual PSD of one sensor is compared with the PSD of that sensor predicted by the neural network using the PSD of the other sensor as an input. A significant deviation between the actual and predicted PSDs indicate that system is changing (i.e., failing). Experiments carried out on check values and bearings illustrate the usefulness of the methodology developed. (Author)

  8. Neural Networks: Implementations and Applications


    Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.


    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  9. Control rod drive mechanism

    International Nuclear Information System (INIS)

    Nakamura, Akira.


    Purpose: To ensure the scram operation of a control rod by the reliable detection for the position of control rods. Constitution: A permanent magnet is provided to the lower portion of a connecting rod in engagement with a control rod and a tube having a plurality of lead switches arranged axially therein in a predetermined pitch is disposed outside of the control rod drives. When the control rod moves upwardly in the scram operation, the lead switches are closed successively upon passage of the permanent magnet to operate the electrical circuit provided by way of each of the lead switches. Thus, the position for the control rod during the scram can reliably be determined and the scram characteristic of the control rod can be recognized. (Furukawa, Y.)

  10. [Epilepsy and driving]. (United States)

    Matsuura, Masato


    In Japan, the Road Traffic Act was amended in June 2013, including new penalty to false statement in a disease condition declaration form, and new voluntary notification system for a doctor who is aware that a person is at high risk for traffic accident and in possession of a driver license. Moreover, New Criminal Law Act was established in November 2013, including a prison sentence of up to 15 years for persons, who under the influence of specific drugs or diseases, causing death or injury to other persons by driving a motor vehicle. Both laws are supposed to be enforced during 2014, after additional resolutions including the review of the laws after five years, considerations so as not to create discrimination due to diseases, etc are examined.

  11. Control rod drives

    International Nuclear Information System (INIS)

    Furumitsu, Yutaka.


    Purpose: To improve the reliability of a device for driving an LMFBR type reactor control rod by providing a buffer unit having a stationary electromagnetic coil and a movable electromagnetic coil in the device to thereby avord impact stress at scram time and to simplify the structure of the buffer unit. Constitution: A non-contact type buffer unit is constructed with a stationary electromagnetic coil, a cable for the stationary coil, a movable electromagnetic coil, a spring cable for the movable coil, and a backup coil spring or the like. Force produced at scram time is delivered without impact by the attracting or repelling force between the stationary coil and the movable coil of the buffer unit. Accordingly, since the buffer unit is of a non-contact type, there is no mechanical impact and thus no large impact stress, and as it has simple configuration, the reliability is improved and the maintenance can be conducted more easily. (Yoshihara, H.)

  12. Control rod drives

    International Nuclear Information System (INIS)

    Yamanaka, Toshikatsu.


    Purpose: To protect bellows against failures due to negative pressure to prevent the loss of pressure balance caused by the expansion of the bellows upon scram. Constitution: An expansion pipe connected to the control rod drive is driven along a guide pipe to insert a control rod into the reactor core. Expansible bellows are provided at the step between the expansion pipe and the guide pipe. Further, a plurality of bore holes or slits are formed on the side wall of the guide pipe corresponding to the expansion portion of the bellows. In such an arrangement, when the expansion pipe falls rapidly and the bellows are expanded upon scram, the volume between each of the pipes of the bellows and the guide pipe is increased to produce a negative pressure, but the effect of the negative pressure on the bellows can be eliminated by the flowing-in of coolants corresponding to that pressure through the bore holes or the slits. (Furukawa, Y.)

  13. Do emotions drive psychosis?

    Directory of Open Access Journals (Sweden)

    João G. Ribeiro


    Full Text Available Introduction: How important is the emotional life of persons who manifest psychotic symptoms? Aims: The aim of this paper is to review evidence on a causal role for emotions in psychotic processes. Methods: Selective review of literature on affective symptoms in psychoses, on emotions in the production of psychotic symptoms and on dopaminergic models of psychosis. Results: Affective symptoms are relevant across psychoses. Persons with schizophrenia have high levels of emotional reactivity and the intensification of negative affects not only is associated with but also precedes the intensification of psychotic symptoms, which is evidence that negative emotions drive the course of psychotic symptoms. Negative self‑representations are central in psychotic processes and can be the link between negative emotions and psychosis. Evidence favours the notion that persecutory delusions are consistent with negative affects and self‑representations, while grandiose delusions are consistent with a defensive amplification of positive affects and self‑representations. Shame has been proposed as the core emotional experience of psychosis, one in which the self becomes vulnerable to the external world, which is consistent with persecutory experiences. Assaults on the self, under the form of hostility in the family environment and society, are strong predictors of relapse and development of schizophrenia. Assaults on the self which induce social defeat are also strong stimulants of mesolimbic dopaminergic pathways, whose hyperactivity is associated with acute psychotic episodes and the experience of “aberrant salience”, put forward as a dopaminergic model of psychosis. Conclusions: The “defeat of the self” emerges as a central link that binds the experience of negative emotions to the expression of psychotic symptoms and its psychological and neurobiological correlates. The hypothesis gains support that the emotions related to that defeat control

  14. Limit analysis of solid reinforced concrete structures

    DEFF Research Database (Denmark)

    Larsen, Kasper Paaske


    Recent studies have shown that Semidefinite Programming (SDP) can be used effectively for limit analysis of isotropic cohesive-frictional continuums using the classical Mohr-Coulomb yield criterion. In this paper we expand on this previous research by adding reinforcement to the model and a solid...... reinforcement and it is therefore possible to analyze structures with complex reinforcement layouts. Tests are conducted to validate the method against well-known analytical solutions....

  15. Strength Characteristics of Reinforced Sandy Soil


    S. N. Bannikov; Mahamed Al Fayez


    Laboratory tests on determination of reinforced sandy soil strength characteristics (angle of internal friction, specific cohesive force) have been carried out with the help of a specially designed instrument and proposed methodology. Analysis of the obtained results has revealed that cohesive forces are brought about in reinforced sandy soil and an angle of internal soil friction becomes larger in comparison with non-reinforced soil.

  16. Behavior of reinforced concrete at elevated temperatures

    International Nuclear Information System (INIS)

    Freskakis, G.N.


    A study is presented concerning the behavior of reinforced concrete sections at elevated temperatures. Material properties of concrete and reinforcing steel are discussed. Behavior studies are made by means of moment-curvature-axial force relationships. Particular attention is given to the load carrying capacity, thermal forces and moments, and deformation capacity. The effects on these properties of variations in the strength properties, the temperature level and distribution, the amount of reinforcing steel, and limiting values of strains are considered

  17. Reinforcement Learning in Repeated Portfolio Decisions


    Diao, Linan; Rieskamp, Jörg


    How do people make investment decisions when they receive outcome feedback? We examined how well the standard mean-variance model and two reinforcement models predict people's portfolio decisions. The basic reinforcement model predicts a learning process that relies solely on the portfolio's overall return, whereas the proposed extended reinforcement model also takes the risk and covariance of the investments into account. The experimental results illustrate that people reacted sensitively to...

  18. A canonical neural mechanism for behavioral variability (United States)

    Darshan, Ran; Wood, William E.; Peters, Susan; Leblois, Arthur; Hansel, David


    The ability to generate variable movements is essential for learning and adjusting complex behaviours. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioural variability is unclear. Here we combine modelling, electrophysiological and behavioural studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviours. Simultaneous recordings of neurons in singing finches reveal that neural correlations increase across the circuit driving song variability, in agreement with the model predictions. Analysing behavioural data, we find remarkable similarities in the babbling statistics of 5-6-month-old human infants and juveniles from three songbird species and show that our model naturally accounts for these `universal' statistics.

  19. Disrupted expected value signaling in youth with disruptive behavior disorders to environmental reinforcers. (United States)

    White, Stuart F; Fowler, Katherine A; Sinclair, Stephen; Schechter, Julia C; Majestic, Catherine M; Pine, Daniel S; Blair, R James


    Youth with disruptive behavior disorders (DBD), including conduct disorder (CD) and oppositional defiant disorder (ODD), have difficulties in reinforcement-based decision making, the neural basis of which is poorly understood. Studies examining decision making in youth with DBD have revealed reduced reward responses within the ventromedial prefrontal cortex/orbitofrontal cortex (vmPFC/OFC), increased responses to unexpected punishment within the vmPFC and striatum, and reduced use of expected value information in the anterior insula cortex and dorsal anterior cingulate cortex during the avoidance of suboptimal choices. Previous work has used only monetary reinforcement. The current study examined whether dysfunction in youth with DBD during decision making extended to environmental reinforcers. A total of 30 youth (15 healthy youth and 15 youth with DBD) completed a novel reinforcement-learning paradigm using environmental reinforcers (physical threat images, e.g., striking snake image; contamination threat images, e.g., rotting food; appetitive images, e.g., puppies) while undergoing functional magnetic resonance imaging (fMRI). Behaviorally, healthy youth were significantly more likely to avoid physical threat, but not contamination threat, stimuli than youth with DBD. Imaging results revealed that youth with DBD showed significantly reduced use of expected value information in the bilateral caudate, thalamus, and posterior cingulate cortex during the avoidance of suboptimal responses. The current data suggest that youth with DBD show deficits to environmental reinforcers similar to the deficits seen to monetary reinforcers. Importantly, this deficit was unrelated to callous-unemotional (CU) traits, suggesting that caudate impairment may be a common deficit across youth with DBD. Published by Elsevier Inc.

  20. Design of reinforced concrete plates and shells

    International Nuclear Information System (INIS)

    Schulz, M.


    Nowadays, the internal forces of reinforced concrete laminar structures can be easily evaluated by the finite element procedures. The longitudinal design in each direction is not adequate, since the whole set of internal forces in each point must be concomitantly considered. The classic formulation for the design and new design charts which bring reduction of the amount of necessary reinforcement are presented. A rational reinforced concrete mathematical theory which makes possible the limit state design of plates and shells is discussed. This model can also be applied to define the constitutive relationships of laminar finite elements of reinforced concrete. (Author) [pt

  1. Reinforcement of RC structure by carbon fibers

    Directory of Open Access Journals (Sweden)

    Kissi B.


    Full Text Available In recent years, rehabilitation has been the subject of extensive research due to the increased spending on building maintenance work and restoration of built works. In all cases, it is essential to carry out methods of reinforcement or maintenance of structural elements, following an inspection analysis and methodology of a correct diagnosis. This research focuses on the calculation of the necessary reinforcement sections of carbon fiber for structural elements with reinforced concrete in order to improve their load bearing capacity and rigidity. The different results obtained reveal a considerable gain in resistance and deformation capacity of reinforced sections without significant increase in the weight of the rehabilitated elements.

  2. Methodology of shell structure reinforcement layout optimization (United States)

    Szafrański, Tomasz; Małachowski, Jerzy; Damaziak, Krzysztof


    This paper presents an optimization process of a reinforced shell diffuser intended for a small wind turbine (rated power of 3 kW). The diffuser structure consists of multiple reinforcement and metal skin. This kind of structure is suitable for optimization in terms of selection of reinforcement density, stringers cross sections, sheet thickness, etc. The optimisation approach assumes the reduction of the amount of work to be done between the optimization process and the final product design. The proposed optimization methodology is based on application of a genetic algorithm to generate the optimal reinforcement layout. The obtained results are the basis for modifying the existing Small Wind Turbine (SWT) design.

  3. Functional Contour-following via Haptic Perception and Reinforcement Learning. (United States)

    Hellman, Randall B; Tekin, Cem; van der Schaar, Mihaela; Santos, Veronica J


    Many tasks involve the fine manipulation of objects despite limited visual feedback. In such scenarios, tactile and proprioceptive feedback can be leveraged for task completion. We present an approach for real-time haptic perception and decision-making for a haptics-driven, functional contour-following task: the closure of a ziplock bag. This task is challenging for robots because the bag is deformable, transparent, and visually occluded by artificial fingertip sensors that are also compliant. A deep neural net classifier was trained to estimate the state of a zipper within a robot's pinch grasp. A Contextual Multi-Armed Bandit (C-MAB) reinforcement learning algorithm was implemented to maximize cumulative rewards by balancing exploration versus exploitation of the state-action space. The C-MAB learner outperformed a benchmark Q-learner by more efficiently exploring the state-action space while learning a hard-to-code task. The learned C-MAB policy was tested with novel ziplock bag scenarios and contours (wire, rope). Importantly, this work contributes to the development of reinforcement learning approaches that account for limited resources such as hardware life and researcher time. As robots are used to perform complex, physically interactive tasks in unstructured or unmodeled environments, it becomes important to develop methods that enable efficient and effective learning with physical testbeds.

  4. CLIC Drive Beam Phase Stabilisation

    CERN Document Server

    Gerbershagen, Alexander; Schulte, Daniel

    The thesis presents phase stability studies for the Compact Linear Collider (CLIC) and focuses in particular on CLIC Drive Beam longitudinal phase stabilisation. This topic constitutes one of the main feasibility challenges for CLIC construction and is an essential component of the current CLIC stabilisation campaign. The studies are divided into two large interrelated sections: the simulation studies for the CLIC Drive Beam stability, and measurements, data analysis and simulations of the CLIC Test Facility (CTF3) Drive Beam phase errors. A dedicated software tool has been developed for a step-by-step analysis of the error propagation through the CLIC Drive Beam. It uses realistic RF potential and beam loading amplitude functions for the Drive and Main Beam accelerating structures, complete models of the recombination scheme and compressor chicane as well as of further CLIC Drive Beam modules. The tool has been tested extensively and its functionality has been verified. The phase error propagation at CLIC h...

  5. H1 antihistamines and driving. (United States)

    Popescu, Florin Dan


    Driving performances depend on cognitive, psychomotor and perception functions. The CNS adverse effects of some H1 antihistamines can alter the patient ability to drive. Data from studies using standardized objective cognitive and psychomotor tests (Choice Reaction Time, Critical Flicker Fusion. Digital Symbol Substitution Test), functional brain imaging (Positron Emission Tomography, functional Magnetic Resonance Imaging), neurophysiological studies (Multiple Sleep Latency Test, auditory and visual evoked potentials), experimental simulated driving (driving simulators) and real driving studies (the Highway Driving Test, with the evaluation of the Standard Deviation Lateral Position, and the Car Following Test, with the measurement of the Brake Reaction Time) must be discussed in order to classify a H1 antihistamine as a true non-sedating one.

  6. Reinforcement learning or active inference? (United States)

    Friston, Karl J; Daunizeau, Jean; Kiebel, Stefan J


    This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

  7. Reinforcement learning or active inference?

    Directory of Open Access Journals (Sweden)

    Karl J Friston


    Full Text Available This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

  8. Monitoring device for reinforced concrete

    International Nuclear Information System (INIS)

    Matsuzaki, Tetsuo; Saito, Koichi; Furukawa, Hideyasu.


    A reactor container made of reinforced concretes is monitored for the temperature at each of portions upon placing concretes under construction of a plant, upon pressure-proof test and during plant operation. That is, optical fibers are uniformly laid spirally throughout the inside of the concretes. Pulses are injected from one end of the optical fibers, and the temperature at a reflection point can be measured by measuring specific rays (Raman scattering rays) among lights reflected after a predetermined period of time. According to the present invention, measurement for an optional position within a range where one fiber cable is laid can be conducted. Accordingly, it is possible to conduct temperature control upon concrete placing and apply temperature compensation for the measurement for stresses of the concretes and the reinforcing steels upon container pressure-proof. Further, during plant operation, if the temperature of the concretes rises due to thermal conduction of the temperature in the container, integrity of the concretes can be ensured by a countermeasures such as air conditioning. (I.S.)

  9. Enhancing corrosion resistance of reinforced concrete structures with hybrid fiber reinforced concrete

    International Nuclear Information System (INIS)

    Blunt, J.; Jen, G.; Ostertag, C.P.


    Highlights: • Reinforced concrete beams were subjected to cyclic flexural loading. • Hybrid fiber reinforced composites were effective in reducing corrosion rates. • Crack resistance due to fibers increased corrosion resistance of steel rebar. • Galvanic corrosion measurements underestimated corrosion rates. • Polarization resistance measurements predicted mass loss more accurately. - Abstract: Service loads well below the yield strength of steel reinforcing bars lead to cracking of reinforced concrete. This paper investigates whether the crack resistance of Hybrid Fiber Reinforced Concrete (HyFRC) reduces the corrosion rate of steel reinforcing bars in concrete after cyclic flexural loading. The reinforcing bars were extracted to examine their surface for corrosion and compare microcell and macrocell corrosion mass loss estimates against direct gravimetric measurements. A delay in corrosion initiation and lower active corrosion rates were observed in the HyFRC beam specimens when compared to reinforced specimens containing plain concrete matrices cycled at the same flexural load

  10. Reinforcement learning for optimal control of low exergy buildings

    International Nuclear Information System (INIS)

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


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

  11. Critical Branching Neural Networks (United States)

    Kello, Christopher T.


    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

  12. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

    changes or to abandon the strong identity thesis altogether. Were one to pursue a theory according to which consciousness is not an epiphenomenon to brain processes, consciousness may in fact affect its own neural basis. The neural correlate of consciousness is often seen as a stable structure, that is...

  13. Failure Prediction for Autonomous Driving


    Hecker, Simon; Dai, Dengxin; Van Gool, Luc


    The primary focus of autonomous driving research is to improve driving accuracy. While great progress has been made, state-of-the-art algorithms still fail at times. Such failures may have catastrophic consequences. It therefore is important that automated cars foresee problems ahead as early as possible. This is also of paramount importance if the driver will be asked to take over. We conjecture that failures do not occur randomly. For instance, driving models may fail more likely at places ...

  14. The Drive-Wise Project: Driving Simulator Training increases real driving performance in healthy older drivers

    Directory of Open Access Journals (Sweden)

    Gianclaudio eCasutt


    Full Text Available Background: Age-related cognitive decline is often associated with unsafe driving behavior. We hypothesized that 10 active training sessions in a driving simulator increase cognitive and on-road driving performance. In addition, driving simulator training should outperform cognitive training.Methods: Ninety-one healthy active drivers (62 – 87 years were randomly assigned to either (1 a driving simulator training group, (2 an attention training group (vigilance and selective attention, or (3 a control group. The main outcome variables were on-road driving and cognitive performance. Seventy-seven participants (85% completed the training and were included in the analyses. Training gains were analyzed using a multiple regression analysis with planned comparisons.Results: The driving simulator training group showed an improvement in on-road driving performance compared to the attention training group. In addition, both training groups increased cognitive performance compared to the control group. Conclusion: Driving simulator training offers the potential to enhance driving skills in older drivers. Compared to the attention training, the simulator training seems to be a more powerful program for increasing older drivers’ safety on the road.

  15. Noninductive current drive in tokamaks

    International Nuclear Information System (INIS)

    Uckan, N.A.


    Various current drive mechanisms may be grouped into four classes: (1) injection of energetic particle beams; (2) launching of rf waves; (3) hybrid schemes, which are combinations of various rf schemes (rf plus beams, rf and/or beam plus ohmic heating, etc.); and (4) other schemes, some of which are specific to reactor plasma conditions requiring the presence of alpha particle or intense synchrotron radiation. Particle injection schemes include current drive by neutral beams and relativistic electron beams. The rf schemes include current drive by the lower hybrid (LH) waves, the electron waves, the waves in the ion cyclotron range of frequencies, etc. Only a few of these approaches, however, have been tested experimentally, with the broadest data base available for LH waves. Included in this report are (1) efficiency criteria for current drive, (2) current drive by neutral beam injection, (3) LH current drive, (4) electron cyclotron current drive, (5) current drive by ion cyclotron waves - minority species heating, and (6) current drive by other schemes (such as hybrids and low frequency waves)

  16. Distracted driving in elderly and middle-aged drivers. (United States)

    Thompson, Kelsey R; Johnson, Amy M; Emerson, Jamie L; Dawson, Jeffrey D; Boer, Erwin R; Rizzo, Matthew


    Automobile driving is a safety-critical real-world example of multitasking. A variety of roadway and in-vehicle distracter tasks create information processing loads that compete for the neural resources needed to drive safely. Drivers with mind and brain aging may be particularly susceptible to distraction due to waning cognitive resources and control over attention. This study examined distracted driving performance in an instrumented vehicle (IV) in 86 elderly (mean=72.5 years, SD=5.0 years) and 51 middle-aged drivers (mean=53.7 years, SD=9.3 year) under a concurrent auditory-verbal processing load created by the Paced Auditory Serial Addition Task (PASAT). Compared to baseline (no-task) driving performance, distraction was associated with reduced steering control in both groups, with middle-aged drivers showing a greater increase in steering variability. The elderly drove slower and showed decreased speed variability during distraction compared to middle-aged drivers. They also tended to "freeze up", spending significantly more time holding the gas pedal steady, another tactic that may mitigate time pressured integration and control of information, thereby freeing mental resources to maintain situation awareness. While 39% of elderly and 43% of middle-aged drivers committed significantly more driving safety errors during distraction, 28% and 18%, respectively, actually improved, compatible with allocation of attention resources to safety critical tasks under a cognitive load. Copyright © 2011 Elsevier Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Zehui Kong

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

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

    Kong, Zehui; Zou, Yuan; Liu, Teng


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

  19. Applications of neural networks to mechanics

    International Nuclear Information System (INIS)


    Neural networks have become powerful tools in engineer's techniques. The aim of this conference was to present their application to concrete cases in the domain of mechanics, including the preparation and use of materials. Artificial neurons are non-linear organs which provide an output signal that depends on several differently weighted input signals. Their connection into networks allows to solve problems for which the driving laws are not well known. The applications discussed during this conference deal with: the driving of machines or processes, the control of machines, materials or products, the simulation and forecasting, and the optimization. Three papers dealing with the control of spark ignition engines, the regulation of heating floors and the optimization of energy consumptions in industrial buildings were selected for ETDE and one paper dealing with the optimization of the management of a reprocessed plutonium stock was selected for INIS. (J.S.)

  20. Investigations of timing during the schedule and reinforcement intervals with wheel-running reinforcement. (United States)

    Belke, Terry W; Christie-Fougere, Melissa M


    Across two experiments, a peak procedure was used to assess the timing of the onset and offset of an opportunity to run as a reinforcer. The first experiment investigated the effect of reinforcer duration on temporal discrimination of the onset of the reinforcement interval. Three male Wistar rats were exposed to fixed-interval (FI) 30-s schedules of wheel-running reinforcement and the duration of the opportunity to run was varied across values of 15, 30, and 60s. Each session consisted of 50 reinforcers and 10 probe trials. Results showed that as reinforcer duration increased, the percentage of postreinforcement pauses longer than the 30-s schedule interval increased. On probe trials, peak response rates occurred near the time of reinforcer delivery and peak times varied with reinforcer duration. In a second experiment, seven female Long-Evans rats were exposed to FI 30-s schedules leading to 30-s opportunities to run. Timing of the onset and offset of the reinforcement period was assessed by probe trials during the schedule interval and during the reinforcement interval in separate conditions. The results provided evidence of timing of the onset, but not the offset of the wheel-running reinforcement period. Further research is required to assess if timing occurs during a wheel-running reinforcement period.

  1. Experimental analysis of reinforced concrete beams strengthened in bending with carbon fiber reinforced polymer

    Directory of Open Access Journals (Sweden)

    M. M. VIEIRA

    Full Text Available The use of carbon fiber reinforced polymer (CFRP has been widely used for the reinforcement of concrete structures due to its practicality and versatility in application, low weight, high tensile strength and corrosion resistance. Some construction companies use CFRP in flexural strengthening of reinforced concrete beams, but without anchor systems. Therefore, the aim of this study is analyze, through an experimental program, the structural behavior of reinforced concrete beams flexural strengthened by CFRP without anchor fibers, varying steel reinforcement and the amount of carbon fibers reinforcement layers. Thus, two groups of reinforced concrete beams were produced with the same geometric feature but with different steel reinforcement. Each group had five beams: one that is not reinforced with CFRP (reference and other reinforced with two, three, four and five layers of carbon fibers. Beams were designed using a computational routine developed in MAPLE software and subsequently tested in 4-point points flexural test up to collapse. Experimental tests have confirmed the effectiveness of the reinforcement, ratifying that beams collapse at higher loads and lower deformation as the amount of fibers in the reinforcing layers increased. However, the increase in the number of layers did not provide a significant increase in the performance of strengthened beams, indicating that it was not possible to take full advantage of strengthening applied due to the occurrence of premature failure mode in the strengthened beams for pullout of the cover that could have been avoided through the use of a suitable anchoring system for CFRP.

  2. Novel Adaptive Forward Neural MIMO NARX Model for the Identification of Industrial 3-DOF Robot Arm Kinematics


    Ho Pham Huy Anh; Nguyen Thanh Nam


    In this paper, a novel forward adaptive neural MIMO NARX model is used for modelling and identifying the forward kinematics of an industrial 3‐DOF robot arm system. The nonlinear features of the forward kinematics of the industrial robot arm drive are thoroughly modelled based on the forward adaptive neural NARX model‐based identification process using experimental input‐output training data. This paper proposes a novel use of a back propagation (BP) algorithm to generate the forward neural M...

  3. Electric motor drive unit, especially adjustment drive for vehicles

    Energy Technology Data Exchange (ETDEWEB)

    Litterst, P


    An electric motor drive unit, particularly an adjustment drive for vehicles with at least two parallel drive shafts is described, which is compact and saves space, and whose manufacturing costs are low compared with those of well-known drive units of this type. The drive unit contains a suitable number of magnet systems, preferably permanent magnet systems, whose pole axes are spaced and run parallel. The two pole magnet systems have diametrically opposite shell-shaped segments, to which the poles are fixed. In at least one magnet system the two segments are connected by diametrically opposite flat walls parallel to the pole axes to form a single magnetic circuit pole housing. The segments of at least one other magnet system are arranged on this pole housing so that one of these flat walls is a magnetically conducting, connecting component of the magnetic circuit of the other magnet system.

  4. Reading the Freudian theory of sexual drives from a functional neuroimaging perspective

    Directory of Open Access Journals (Sweden)

    Serge eStoléru


    Full Text Available One of the essential tasks of neuropsychoanalysis is to investigate the neural correlates of sexual drives. Here, we consider the four defining characteristics of sexual drives as delineated by Freud: their pressure, aim, object, and source. We systematically examine the relations between these characteristics and the four-component neurophenomenological model that we have proposed based on functional neuroimaging studies, which comprises a cognitive, a motivational, an emotional and an autonomic/neuroendocrine component. Functional neuroimaging studies of sexual arousal have thrown a new light on the four fundamental characteristics of sexual drives by identifying their potential neural correlates. While these studies are essentally consistent with the Freudian model of drives, the main difference emerging between the functional neuroimaging perspective on sexual drives and the Freudian theory relates to the source of drives. From a functional neuroimaging perspective sources of sexual drives, conceived by psychoanalysis as processes of excitation occurring in a peripheral organ, do not seem, at least in adult subjects, to be an essential part of the determinants of sexual arousal. It is rather the central processing of visual or genital stimuli that gives to these stimuli their sexually arousing and sexually pleasurable character.

  5. Dynamic Sensor Tasking for Space Situational Awareness via Reinforcement Learning (United States)

    Linares, R.; Furfaro, R.


    This paper studies the Sensor Management (SM) problem for optical Space Object (SO) tracking. The tasking problem is formulated as a Markov Decision Process (MDP) and solved using Reinforcement Learning (RL). The RL problem is solved using the actor-critic policy gradient approach. The actor provides a policy which is random over actions and given by a parametric probability density function (pdf). The critic evaluates the policy by calculating the estimated total reward or the value function for the problem. The parameters of the policy action pdf are optimized using gradients with respect to the reward function. Both the critic and the actor are modeled using deep neural networks (multi-layer neural networks). The policy neural network takes the current state as input and outputs probabilities for each possible action. This policy is random, and can be evaluated by sampling random actions using the probabilities determined by the policy neural network's outputs. The critic approximates the total reward using a neural network. The estimated total reward is used to approximate the gradient of the policy network with respect to the network parameters. This approach is used to find the non-myopic optimal policy for tasking optical sensors to estimate SO orbits. The reward function is based on reducing the uncertainty for the overall catalog to below a user specified uncertainty threshold. This work uses a 30 km total position error for the uncertainty threshold. This work provides the RL method with a negative reward as long as any SO has a total position error above the uncertainty threshold. This penalizes policies that take longer to achieve the desired accuracy. A positive reward is provided when all SOs are below the catalog uncertainty threshold. An optimal policy is sought that takes actions to achieve the desired catalog uncertainty in minimum time. This work trains the policy in simulation by letting it task a single sensor to "learn" from its performance

  6. Fiber breakage phenomena in long fiber reinforced plastic preparation

    International Nuclear Information System (INIS)

    Huang, Chao-Tsai; Tseng, Huan-Chang; Chang, Rong-Yeu; Vlcek, Jiri


    Due to the high demand of smart green, the lightweight technologies have become the driving force for the development of automotives and other industries in recent years. Among those technologies, using short and long fiber-reinforced plastics (FRP) to replace some metal components can reduce the weight of an automotive significantly. However, the microstructures of fibers inside plastic matrix are too complicated to manage and control during the injection molding through the screw, the runner, the gate, and then into the cavity. This study focuses on the fiber breakage phenomena during the screw plastification. Results show that fiber breakage is strongly dependent on screw design and operation. When the screw geometry changes, the fiber breakage could be larger even with lower compression ratio. (paper)

  7. Control rod drives

    International Nuclear Information System (INIS)

    Ikakura, Hiroaki.


    Purpose: To enable to direct disconnection of control rods upon abnormal temperature rise in the reactor thereby improve the reliability for the disconnecting operation in control rod drives for FBR type reactors upon emergency. Constitution: A diaphragm is disposed to the upper opening of a sealing vessel inserted to the hollow portion of an electromagnet and a rod is secured to the central position of the upper surface. A spring contacts are attached by way of an insulator to the inner surface at the lower portion of an extension pipe and connected with cables for supplying electric power sources respectively to a magnet. If the temperature in the reactor abnormally rises, liquid metals in the sealing vessel are expanded tending to extend the bellows downwardly. However, since they are attracted by the electromagnet, the thermal expansion of the liquid metals exert on the diaphragm prior to the bellows. Thus, the switch between the spring contacts is made open to attain the deenergized state to thereby disconnect the control rod and shutdown the neclear reactor. (Horiuchi, T.)

  8. Driving for shorter outages

    International Nuclear Information System (INIS)

    Tritch, S.


    Nuclear plant outages are necessary to complete activities that cannot be completed during the operating cycle, such as steam generator inspection and testing, refueling, installing modifications, and performing maintenance tests. The time devoted to performing outages is normally the largest contributor to plant unavailability. Similarly, outage costs are a sizable portion of the total plant budget. The scope and quality of work done during outages directly affects operating reliability and the number of unplanned outages. Improved management and planning of outages enhances the margin of safety during the outage and results in increased plant reliability. The detailed planning and in-depth preparation that has become a necessity for driving shorter outage durations has also produced safer outages and improved post-outage reliability. Short outages require both plant and vendor management to focus on all aspects of the outage. Short outage durations, such as 26 days at South Texas or 29 days at North Anna, require power plant inter-department and intra-department teamwork and communication and vendor participation. In this paper shorter and safer outage at the 3-loop plants in the United States are explained. (J.P.N.)

  9. Control rod drive

    International Nuclear Information System (INIS)

    Kojima, Akira.


    In the control rod drive for a BWR type reactor, etc., according to this invention, the lower limit flow rate is set so as to keep the restriction for stability upon spectral shift operation. The setting condition for keeping the restriction is the lowest pump speed and the lower limit for the automatic control of the flow rate, which are considered to be important in view of the stablility from the actual power state. In view of the above, it is possible to keep the reactor core stably even in a case where such a transient phenomenon occurs that the recycling flow rate has to be run back to the lowest pump speed during spectral shift opeeration or in a case where the load demand is reduced and the flow rate is decreased by an automatic mode as in night operation. Accordingly, in the case of conducting the spectral shift operation according to this invention, the operation region capable of keeping the reactor core state stably during operation can be extended. (I.S.)

  10. Failure Criteria for Reinforced Materials

    DEFF Research Database (Denmark)

    Rathkjen, Arne

    Failure of materials is often characterized as ductile yielding, brittle fracture, creep rupture, etc., and different criteria given in terms of different parameters have been used to describe different types of failure. Only criteria expressing failure in terms of stress are considered in what...... place until the matrix, the continuous component of the composite, fails. When an isotropic matrix is reinforced as described above, the result is an anisotropic composite material. Even if the material is anisotropic, it usually exhibits a rather high degree of symmetry and such symmetries place...... certain restrictions on the form of the failure criteria for anisotropic materials. In section 2, some failure criteria for homogenous materials are reviewed. Both isotropic and anisotropic materials are described, and in particular the constraints imposed on the criteria from the symmetries orthotropy...

  11. Corrosion of reinforcement induced by environment containing ...

    Indian Academy of Sciences (India)


    carbonation and chlorides causing corrosion of steel reinforcement. ... interesting and important when the evaluation of the service life of the ... preferably in the areas of industrial and transport activities. ... For controlling the embedded corrosion sensors, elec- .... danger of corrosion of reinforcement seems to be more.

  12. Reinforcement, Behavior Constraint, and the Overjustification Effect. (United States)

    Williams, Bruce W.


    Four levels of the behavior constraint-reinforcement variable were manipulated: attractive reward, unattractive reward, request to perform, and a no-reward control. Only the unattractive reward and request groups showed the performance decrements that suggest the overjustification effect. It is concluded that reinforcement does not cause the…

  13. Continuous jute fibre reinforced laminated paper composite

    Indian Academy of Sciences (India)

    Jute fibre; laminated paper composite; plastic bag pollution. Abstract. Plastic bags create a serious environmental problem. The proposed jute fibre reinforced laminated paper composite and reinforcement-fibre free paper laminate may help to combat the war against this pollutant to certain extent. The paper laminate ...

  14. Durability of fibre reinforced concrete structures

    DEFF Research Database (Denmark)

    Hansen, Ernst Jan De Place; Hansen, Kurt Kielsgaard


    The planned research will indicate, whether fibre reinforced concrete has better or worse durability than normal concrete. Durability specimens will be measured on cracked as well as uncracked specimens. Also the pore structure in the concrete will be characterized.Keywords: Fibre reinforced...... concrete, durability, pore structure, mechanical load...

  15. Rotational Capacity of Reinforced Concrete Beams

    DEFF Research Database (Denmark)

    Ulfkjær, J. P.; Henriksen, M. S.; Brincker, Rune


    programme where 120 reinforced concrete beams, 54 plain concrete beams and 324 concrete cylinders are tested. For the reinforced concrete beams four different parar meters are varied. The slenderness is 6, 12 and 18, the beam depth is 100 mm, 200 mm and 400 mm giving nine different geometries, five...

  16. Control rod drive shaft latch

    International Nuclear Information System (INIS)

    Thorp, A.G. II.


    A latch mechanism is operated by differential pressure on a piston to engage the drive shaft for a control rod in a nuclear reactor, thereby preventing the control rod from being ejected from the reactor in case of failure of the control rod drive mechanism housing which is subjected to the internal pressure in the reactor vessel. 6 claims, 4 drawing figures



    This research project aims to examine the eco-driving modeling capabilities of different traffic modeling tools available and to develop a driver-simulator-based eco-driving modeling tool to evaluate driver behavior and to reliably estimate or measur...

  18. Real-world driving behaviour

    NARCIS (Netherlands)

    Rijkeboer, R.C.; Hendriksen, P.; Gense, N.L.J.


    With increasing complexity of engine management system there is a tendency for traditional driving cyles to become further and further removed from reality. So for a sensible evaluation of emissions and fuel consumption of road vehicles in the field there is an urgent need for 'real-world' driving

  19. H1 antihistamines and driving


    Florin-Dan, Popescu


    Driving performances depend on cognitive, psychomotor and perception functions. The CNS adverse effects of some H1 antihistamines can alter the patient ability to drive. Data from studies using standardized objective cognitive and psychomotor tests (Choice Reaction Time, Critical Flicker Fusion, Digital Symbol Substitution Test), functional brain imaging (Positron Emission Tomography, functional Magnetic Resonance Imaging), neurophysiological studies (Multiple Sleep Latency Test, auditory and...

  20. Dynamics of neural cryptography. (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido


    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  1. Dynamics of neural cryptography

    International Nuclear Information System (INIS)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido


    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible

  2. Dynamics of neural cryptography (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido


    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  3. Structural Behavior of Concrete Beams Reinforced with Basalt Fiber Reinforced Polymer (BFRP) Bars (United States)

    Ovitigala, Thilan

    The main challenge for civil engineers is to provide sustainable, environmentally friendly and financially feasible structures to the society. Finding new materials such as fiber reinforced polymer (FRP) material that can fulfill the above requirements is a must. FRP material was expensive and it was limited to niche markets such as space shuttles and air industry in the 1960s. Over the time, it became cheaper and spread to other industries such as sporting goods in the 1980-1990, and then towards the infrastructure industry. Design and construction guidelines are available for carbon fiber reinforced polymer (CFRP), aramid fiber reinforced polymer (AFRP) and glass fiber reinforced polymer (GFRP) and they are currently used in structural applications. Since FRP is linear elastic brittle material, design guidelines for the steel reinforcement are not valid for FRP materials. Corrosion of steel reinforcement affects the durability of the concrete structures. FRP reinforcement is identified as an alternative to steel reinforcement in corrosive environments. Although basalt fiber reinforced polymer (BFRP) has many advantages over other FRP materials, but limited studies have been done. These studies didn't include larger BFRP bar diameters that are mostly used in practice. Therefore, larger beam sizes with larger BFRP reinforcement bar diameters are needed to investigate the flexural and shear behavior of BFRP reinforced concrete beams. Also, shear behavior of BFRP reinforced concrete beams was not yet studied. Experimental testing of mechanical properties and bond strength of BFRP bars and flexural and shear behavior of BFRP reinforced concrete beams are needed to include BFRP reinforcement bars in the design codes. This study mainly focuses on the use of BFRP bars as internal reinforcement. The test results of the mechanical properties of BFRP reinforcement bars, the bond strength of BFRP reinforcement bars, and the flexural and shear behavior of concrete beams

  4. Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning. (United States)

    Doll, Bradley B; Bath, Kevin G; Daw, Nathaniel D; Frank, Michael J


    Considerable evidence suggests that multiple learning systems can drive behavior. Choice can proceed reflexively from previous actions and their associated outcomes, as captured by "model-free" learning algorithms, or flexibly from prospective consideration of outcomes that might occur, as captured by "model-based" learning algorithms. However, differential contributions of dopamine to these systems are poorly understood. Dopamine is widely thought to support model-free learning by modulating plasticity in striatum. Model-based learning may also be affected by these striatal effects, or by other dopaminergic effects elsewhere, notably on prefrontal working memory function. Indeed, prominent demonstrations linking striatal dopamine to putatively model-free learning did not rule out model-based effects, whereas other studies have reported dopaminergic modulation of verifiably model-based learning, but without distinguishing a prefrontal versus striatal locus. To clarify the relationships between dopamine, neural systems, and learning strategies, we combine a genetic association approach in humans with two well-studied reinforcement learning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspects of striatal plasticity. Prefrontal function was indexed by a polymorphism in the COMT gene, differences of which reflect dopamine levels in the prefrontal cortex. This polymorphism has been associated with differences in prefrontal activity and working memory. Striatal function was indexed by a gene coding for DARPP-32, which is densely expressed in the striatum where it is necessary for synaptic plasticity. We found evidence for our hypothesis that variations in prefrontal dopamine relate to model-based learning, whereas variations in striatal dopamine function relate to model-free learning. Decisions can stem reflexively from their previously associated outcomes or flexibly from deliberative consideration of potential choice outcomes




    Acquisition performance of 22 rats in a straight alley runway was examined. The animals were subjected to partial reinforcement when the alley was black (B+/-) and continuous reinforcement when it was white (W+). The results indicated (a) higher terminal performance, for partial as against continuous reinforcement conditions, for starting-time and running-time measures, and (b) lower terminal performance under partial conditions for a goal-entry-time measure. These results confirm within subjects an effect previously demonstrated, in the runway, only in between-groups tests, where one group is run under partial reinforcement and a separate group is run under continuous reinforcement in the presence of the same external stimuli. Differences between the runway situation, employing a discrete-trial procedure and performance measures at three points in the response chain, and the Skinner box situation, used in its free-operant mode with a single performance measure, are discussed in relation to the present findings.

  6. Reinforcement Learning State-of-the-Art

    CERN Document Server

    Wiering, Marco


    Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together the...


    International Nuclear Information System (INIS)

    Braverman, J.I.; Miller, C.A.; Ellingwood, B.R.; Naus, D.J.; Hofmayer, C.H.; Bezler, P.; Chang, T.Y.


    This paper describes the results of a study to evaluate, in probabilistic terms, the effects of age-related degradation on the structural performance of reinforced concrete members at nuclear power plants. The paper focuses on degradation of reinforced concrete flexural members and shear walls due to the loss of steel reinforcing area and loss of concrete area (cracking/spalling). Loss of steel area is typically caused by corrosion while cracking and spalling can be caused by corrosion of reinforcing steel, freeze-thaw, or aggressive chemical attack. Structural performance in the presence of uncertainties is depicted by a fragility (or conditional probability of failure). The effects of degradation on the fragility of reinforced concrete members are calculated to assess the potential significance of various levels of degradation. The fragility modeling procedures applied to degraded concrete members can be used to assess the effects of degradation on plant risk and can lead to the development of probability-based degradation acceptance limits

  8. [Driving and health at work]. (United States)

    Giorgio, Marie-Thérèse


    The role of the occupational physician is to prevent occupational accidents and diseases. Therefore, he is the one to decide if a worker is fit to drive in the context of his professional activity, including in cases where no specific driving license is required (e.g. forklift truck, mobile crane). This decision is an important one, as two thirds of fatal occupational accidents occur on the road. The decision is made on the basis of both a medical examination and the regulation, which indicates all contraindications to driving. The physician's responsibility is involved, as is the employer's, as he must ensure that his employee is fit to drive and possesses a valid driving license at all times. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  9. EEG Artifact Removal Using a Wavelet Neural Network (United States)

    Nguyen, Hoang-Anh T.; Musson, John; Li, Jiang; McKenzie, Frederick; Zhang, Guangfan; Xu, Roger; Richey, Carl; Schnell, Tom


    !n this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We. compared the WNN algorithm with .the ICA technique ,and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.

  10. Axial Compression Tests on Corroded Reinforced Concrete Columns Consolidated with Fibre Reinforced Polymers

    Directory of Open Access Journals (Sweden)

    Bin Ding


    Full Text Available Reinforced concrete structure featured by strong bearing capacity, high rigidity, good integrity, good fire resistance, and extensive applicability occupies a mainstream position in contemporary architecture. However, with the development of social economy, people need higher requirements on architectural structure; durability, especially, has been extensively researched. Because of the higher requirement on building material, ordinary reinforced concrete structure has not been able to satisfy the demand. As a result, some new materials and structures have emerged, for example, fibre reinforced polymers. Compared to steel reinforcement, fibre reinforced polymers have many advantages, such as high tensile strength, good durability, good shock absorption, low weight, and simple construction. The application of fibre reinforced polymers in architectural structure can effectively improve the durability of the concrete structure and lower the maintenance, reinforcement, and construction costs in severe environments. Based on the concepts of steel tube concrete, fibre reinforced composite material confined concrete, and fibre reinforced composite material tubed concrete, this study proposes a novel composite structure, i.e., fibre reinforced composite material and steel tube concrete composite structure. The structure was developed by pasting fibre around steel tube concrete and restraining core concrete using fibre reinforced composite material and steel tubes. The bearing capacity and ultimate deformation capacity of the structure was tested using column axial compression test.

  11. ANT Advanced Neural Tool

    Energy Technology Data Exchange (ETDEWEB)

    Labrador, I.; Carrasco, R.; Martinez, L.


    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs.

  12. ANT Advanced Neural Tool

    International Nuclear Information System (INIS)

    Labrador, I.; Carrasco, R.; Martinez, L.


    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs

  13. Driving the Landscape (United States)

    Haff, P. K.


    Technological modification of the earth's surface (e.g., agriculture, urbanization) is an old story in human history, but what about the future? The future of landscape in an accelerating technological world, beyond a relatively short time horizon, lies hidden behind an impenetrable veil of complexity. Sufficiently complex dynamics generates not only the trajectory of a variable of interest (e.g., vegetation cover) but also the environment in which that variable evolves (e.g., background climate). There is no way to anticipate what variables will define that environment—the dynamics creates its own variables. We are always open to surprise by a change of conditions we thought or assumed were fixed or by the appearance of new phenomena of whose possible existence we had been unaware or thought unlikely. This is especially true under the influence of technology, where novelty is the rule. Lack of direct long-term predictability of landscape change does not, however, mean we cannot say anything about its future. The presence of persistence (finite time scales) in a system means that prediction by a calibrated numerical model should be good for a limited period of time barring bad luck or faulty implementation. Short-term prediction, despite its limitations, provides an option for dealing with the longer-term future. If a computer-controlled car tries to drive itself from New York to Los Angeles, no conceivable (or possible) stand-alone software can be constructed to predict a priori the space-time trajectory of the vehicle. Yet the drive is normally completed easily by most drivers. The trip is successfully completed because each in a series of very short (linear) steps can be "corrected" on the fly by the driver, who takes her cues from the environment to keep the car on the road and headed toward its destination. This metaphor differs in a fundamental way from the usual notion of predicting geomorphic change, because it involves a goal—to reach a desired

  14. One central oscillatory drive is compatible with experimental motor unit behaviour in essential and Parkinsonian tremor (United States)

    Dideriksen, Jakob L.; Gallego, Juan A.; Holobar, Ales; Rocon, Eduardo; Pons, Jose L.; Farina, Dario


    Objective. Pathological tremors are symptomatic to several neurological disorders that are difficult to differentiate and the way by which central oscillatory networks entrain tremorogenic contractions is unknown. We considered the alternative hypotheses that tremor arises from one oscillator (at the tremor frequency) or, as suggested by recent findings from the superimposition of two separate inputs (at the tremor frequency and twice that frequency). Approach. Assuming one central oscillatory network we estimated analytically the relative amplitude of the harmonics of the tremor frequency in the motor neuron output for different temporal behaviors of the oscillator. Next, we analyzed the bias in the relative harmonics amplitude introduced by superimposing oscillations at twice the tremor frequency. These findings were validated using experimental measurements of wrist angular velocity and surface electromyography (EMG) from 22 patients (11 essential tremor, 11 Parkinson’s disease). The ensemble motor unit action potential trains identified from the EMG represented the neural drive to the muscles. Main results. The analytical results showed that the relative power of the tremor harmonics in the analytical models of the neural drive was determined by the variability and duration of the tremor bursts and the presence of the second oscillator biased this power towards higher values. The experimental findings accurately matched the analytical model assuming one oscillator, indicating a negligible functional role of secondary oscillatory inputs. Furthermore, a significant difference in the relative power of harmonics in the neural drive was found across the patient groups, suggesting a diagnostic value of this measure (classification accuracy: 86%). This diagnostic power decreased substantially when estimated from limb acceleration or the EMG. Signficance. The results indicate that the neural drive in pathological tremor is compatible with one central network

  15. Long-term performance of GFRP reinforcement : technical report. (United States)


    Significant research has been performed on glass fiber-reinforced polymer (GFRP) concrete reinforcement. : This research has shown that GFRP reinforcement exhibits high strengths, is lightweight, can decrease time of : construction, and is corrosion ...

  16. Reinforced dynamics for enhanced sampling in large atomic and molecular systems (United States)

    Zhang, Linfeng; Wang, Han; E, Weinan


    A new approach for efficiently exploring the configuration space and computing the free energy of large atomic and molecular systems is proposed, motivated by an analogy with reinforcement learning. There are two major components in this new approach. Like metadynamics, it allows for an efficient exploration of the configuration space by adding an adaptively computed biasing potential to the original dynamics. Like deep reinforcement learning, this biasing potential is trained on the fly using deep neural networks, with data collected judiciously from the exploration and an uncertainty indicator from the neural network model playing the role of the reward function. Parameterization using neural networks makes it feasible to handle cases with a large set of collective variables. This has the potential advantage that selecting precisely the right set of collective variables has now become less critical for capturing the structural transformations of the system. The method is illustrated by studying the full-atom explicit solvent models of alanine dipeptide and tripeptide, as well as the system of a polyalanine-10 molecule with 20 collective variables.

  17. Optimal reinforcing of reticular structures Optimal reinforcing of reticular structures

    Directory of Open Access Journals (Sweden)

    Juan Santiago Mejía


    Full Text Available This article presents an application of Genetic Algorithms (GA and Finite Element Analysis (FEA to solve a structural optimisation problem on reticular plastic structures. Structural optimisation is used to modify the original shape by placing reinforcements at optimum locations. As a result, a reduction in the maximum stress by 14,70% for a structure with a final volume increase of 8,36% was achieved. This procedure solves the structural optimisation problem by adjusting the original mold and thereby avoiding the re-construction of a new one.Este artículo presenta una aplicación de Algoritmos Genéticos (GA y Análisis por Elementos Finitos (FEA a la solución de un problema de optimización estructural en estructuras reticulares plásticas. Optimización estructurales usada para modificar la forma original colocando refuerzos en posiciones óptimas. Como resultado se obtuvo una reducción en el esfuerzo máximo de 14,70% para una estructura cuyo volumen original aumento en 8,36%. Este procedimiento soluciona el problema de optimización estructural ajustando el molde original y evitando la manufactura de un nuevo molde.

  18. Hidden neural networks

    DEFF Research Database (Denmark)

    Krogh, Anders Stærmose; Riis, Søren Kamaric


    A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...

  19. Neural networks for aircraft control (United States)

    Linse, Dennis


    Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

  20. Active Neural Localization


    Chaplot, Devendra Singh; Parisotto, Emilio; Salakhutdinov, Ruslan


    Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize accurately and efficiently. The proposed model incorporates ideas of tradition...

  1. Neural cryptography with feedback. (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido


    Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

  2. Automated driving safer and more efficient future driving

    CERN Document Server

    Horn, Martin


    The main topics of this book include advanced control, cognitive data processing, high performance computing, functional safety, and comprehensive validation. These topics are seen as technological bricks to drive forward automated driving. The current state of the art of automated vehicle research, development and innovation is given. The book also addresses industry-driven roadmaps for major new technology advances as well as collaborative European initiatives supporting the evolvement of automated driving. Various examples highlight the state of development of automated driving as well as the way forward. The book will be of interest to academics and researchers within engineering, graduate students, automotive engineers at OEMs and suppliers, ICT and software engineers, managers, and other decision-makers.

  3. Improved Object Proposals with Geometrical Features for Autonomous Driving

    Directory of Open Access Journals (Sweden)

    Yiliu Feng


    Full Text Available This paper aims at generating high-quality object proposals for object detection in autonomous driving. Most existing proposal generation methods are designed for the general object detection, which may not perform well in a particular scene. We propose several geometrical features suited for autonomous driving and integrate them into state-of-the-art general proposal generation methods. In particular, we formulate the integration as a feature fusion problem by fusing the geometrical features with existing proposal generation methods in a Bayesian framework. Experiments on the challenging KITTI benchmark demonstrate that our approach improves the existing methods significantly. Combined with a convolutional neural net detector, our approach achieves state-of-the-art performance on all three KITTI object classes.



    Sanaz Motamedi; Jyh-Hone Wang


    In an increasingly mobile era, the wide availability of technology for texting and the prevalence of hands-free form have introduced a new safety concern for drivers. To assess this concern, a questionnaire was first deployed online to gain an understanding of drivers’ text driving experiences as well as their demographic information. The results from 232 people revealed that the majority of drivers are aware of the associated risks with texting while driving. However, more than one-fourth of...

  5. Emergence of gamma motor activity in an artificial neural network model of the corticospinal system. (United States)

    Grandjean, Bernard; Maier, Marc A


    Muscle spindle discharge during active movement is a function of mechanical and neural parameters. Muscle length changes (and their derivatives) represent its primary mechanical, fusimotor drive its neural component. However, neither the action nor the function of fusimotor and in particular of γ-drive, have been clearly established, since γ-motor activity during voluntary, non-locomotor movements remains largely unknown. Here, using a computational approach, we explored whether γ-drive emerges in an artificial neural network model of the corticospinal system linked to a biomechanical antagonist wrist simulator. The wrist simulator included length-sensitive and γ-drive-dependent type Ia and type II muscle spindle activity. Network activity and connectivity were derived by a gradient descent algorithm to generate reciprocal, known target α-motor unit activity during wrist flexion-extension (F/E) movements. Two tasks were simulated: an alternating F/E task and a slow F/E tracking task. Emergence of γ-motor activity in the alternating F/E network was a function of α-motor unit drive: if muscle afferent (together with supraspinal) input was required for driving α-motor units, then γ-drive emerged in the form of α-γ coactivation, as predicted by empirical studies. In the slow F/E tracking network, γ-drive emerged in the form of α-γ dissociation and provided critical, bidirectional muscle afferent activity to the cortical network, containing known bidirectional target units. The model thus demonstrates the complementary aspects of spindle output and hence γ-drive: i) muscle spindle activity as a driving force of α-motor unit activity, and ii) afferent activity providing continuous sensory information, both of which crucially depend on γ-drive.

  6. Semiclassical instability of warp drives

    Energy Technology Data Exchange (ETDEWEB)

    Barcelo, C [Instituto de Astrofisica de Andalucia, IAA-CSIC, Glorieta de la Astronomia s/n, 18008 Granada (Spain); Finazzi, S; Liberati, S, E-mail: carlos@iaa.e, E-mail: finazzi@sissa.i, E-mail: liberati@sissa.i


    Warp drives, at least theoretically, provide a way to travel at superluminal speeds. However, even if one succeeded in providing the necessary exotic matter to construct them, it would still be necessary to check whether they would survive to the switching on of quantum effects. In this contribution we will report on the behaviour of the Renormalized Stress-Energy Tensor (RSET) in the spacetimes associated with superluminal warp drives. We find that the RSET will exponentially grow in time close to the front wall of the superluminal bubble, hence strongly supporting the conclusion that the warp-drive geometries are unstable against semiclassical back-reaction.

  7. Driving Safety and Fitness to Drive in Sleep Disorders. (United States)

    Tippin, Jon; Dyken, Mark Eric


    Driving an automobile while sleepy increases the risk of crash-related injury and death. Neurologists see patients with sleepiness due to obstructive sleep apnea, narcolepsy, and a wide variety of neurologic disorders. When addressing fitness to drive, the physician must weigh patient and societal health risks and regional legal mandates. The Driver Fitness Medical Guidelines published by the National Highway Traffic Safety Administration (NHTSA) and the American Association of Motor Vehicle Administrators (AAMVA) provide assistance to clinicians. Drivers with obstructive sleep apnea may continue to drive if they have no excessive daytime sleepiness and their apnea-hypopnea index is less than 20 per hour. Those with excessive daytime sleepiness or an apnea-hypopnea index of 20 per hour or more may not drive until their condition is effectively treated. Drivers with sleep disorders amenable to pharmaceutical treatment (eg, narcolepsy) may resume driving as long as the therapy has eliminated excessive daytime sleepiness. Following these guidelines, documenting compliance to recommended therapy, and using the Epworth Sleepiness Scale to assess subjective sleepiness can be helpful in determining patients' fitness to drive.


    Directory of Open Access Journals (Sweden)

    Zhukov Aleksey Dmitrievich


    Full Text Available The authors demonstrate that the foam concrete performance can be improved by dispersed reinforcement, including methods that involve basalt fibres. They address the results of the foam concrete modeling technology and assess the importance of technology-related parameters. Reinforcement efficiency criteria are also provided in the article. Dispersed reinforcement improves the plasticity of the concrete mix and reduces the settlement crack formation rate. Conventional reinforcement that involves metal laths and rods demonstrates its limited application in the production of concrete used for thermal insulation and structural purposes. Dispersed reinforcement is preferable. This technology contemplates the infusion of fibres into porous mixes. Metal, polymeric, basalt and glass fibres are used as reinforcing components. It has been identified that products reinforced by polypropylene fibres demonstrate substantial abradability and deformability rates even under the influence of minor tensile stresses due to the low adhesion strength of polypropylene in the cement matrix. The objective of the research was to develop the type of polypropylene of D500 grade that would demonstrate the operating properties similar to those of Hebel and Ytong polypropylenes. Dispersed reinforcement was performed by the basalt fibre. This project contemplates an autoclave-free technology to optimize the consumption of electricity. Dispersed reinforcement is aimed at the reduction of the block settlement in the course of hardening at early stages of their operation, the improvement of their strength and other operating properties. Reduction in the humidity rate of the mix is based on the plasticizing properties of fibres, as well as the application of the dry mineralization method. Selection of optimal parameters of the process-related technology was performed with the help of G-BAT-2011 Software, developed at Moscow State University of Civil Engineering. The authors also

  9. Alumina Fiber-Reinforced 9310 Steel Metal Matrix Composite for Rotorcraft Drive System Components, Phase I (United States)

    National Aeronautics and Space Administration — AISI 9310 nickel-chromium-molybdenum alloy steel is used extensively in military helicopter rotor shafts and gears. This reliable alloy provides excellent fatigue...

  10. Geo synthetic-reinforced Pavement systems

    International Nuclear Information System (INIS)

    Zornberg, J. G.


    Geo synthetics have been used as reinforcement inclusions to improve pavement performance. while there are clear field evidence of the benefit of using geo synthetic reinforcements, the specific conditions or mechanisms that govern the reinforcement of pavements are, at best, unclear and have remained largely unmeasured. Significant research has been recently conducted with the objectives of: (i) determining the relevant properties of geo synthetics that contribute to the enhanced performance of pavement systems, (ii) developing appropriate analytical, laboratory and field methods capable of quantifying the pavement performance, and (iii) enabling the prediction of pavement performance as a function of the properties of the various types of geo synthetics. (Author)

  11. Fatigue Performance of Fiber Reinforced Concrete

    DEFF Research Database (Denmark)

    Jun, Zhang; Stang, Henrik


    The objective of the present study is to obtain basic data of fibre reinforced concrete under fatigue load and to set up a theoretical model based on micromechanics. In this study, the bridging stress in fiber reinforced concrete under cyclic tensile load was investigted in details. The damage...... mechanism of the interface between fiber and matrix was proposed and a rational model given. Finally, the response of a steel fiber reinforced concrete beam under fatigue loading was predicted based on this model and compared with experimental results....

  12. Seismic Stability of Reinforced Soil Slopes

    DEFF Research Database (Denmark)

    Tzavara, I.; Zania, Varvara; Tsompanakis, Y.


    Over recent decades increased research interest has been observed on the dynamic response and stability issues of earth walls and reinforced soil structures. The current study aims to provide an insight into the dynamic response of reinforced soil structures and the potential of the geosynthetics...... to prevent the development of slope instability taking advantage of their reinforcing effect. For this purpose, a onedimensional (SDOF) model, based on Newmark’s sliding block model as well as a two-dimensional (plane-strain) dynamic finite-element analyses are conducted in order to investigate the impact...

  13. Human reinforcement learning subdivides structured action spaces by learning effector-specific values. (United States)

    Gershman, Samuel J; Pesaran, Bijan; Daw, Nathaniel D


    Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable because of the high dimensionality of the action space. An unresolved question is how neural systems for reinforcement learning-such as prediction error signals for action valuation associated with dopamine and the striatum-can cope with this "curse of dimensionality." We propose a reinforcement learning framework that allows for learned action valuations to be decomposed into effector-specific components when appropriate to a task, and test it by studying to what extent human behavior and blood oxygen level-dependent (BOLD) activity can exploit such a decomposition in a multieffector choice task. Subjects made simultaneous decisions with their left and right hands and received separate reward feedback for each hand movement. We found that choice behavior was better described by a learning model that decomposed the values of bimanual movements into separate values for each effector, rather than a traditional model that treated the bimanual actions as unitary with a single value. A decomposition of value into effector-specific components was also observed in value-related BOLD signaling, in the form of lateralized biases in striatal correlates of prediction error and anticipatory value correlates in the intraparietal sulcus. These results suggest that the human brain can use decomposed value representations to "divide and conquer" reinforcement learning over high-dimensional action spaces.

  14. The drift diffusion model as the choice rule in reinforcement learning. (United States)

    Pedersen, Mads Lund; Frank, Michael J; Biele, Guido


    Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyperactivity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups.

  15. Elastomer Reinforced with Carbon Nanotubes (United States)

    Hudson, Jared L.; Krishnamoorti, Ramanan


    Elastomers are reinforced with functionalized, single-walled carbon nanotubes (SWNTs) giving them high-breaking strain levels and low densities. Cross-linked elastomers are prepared using amine-terminated, poly(dimethylsiloxane) (PDMS), with an average molecular weight of 5,000 daltons, and a functionalized SWNT. Cross-link densities, estimated on the basis of swelling data in toluene (a dispersing solvent) indicated that the polymer underwent cross-linking at the ends of the chains. This thermally initiated cross-linking was found to occur only in the presence of the aryl alcohol functionalized SWNTs. The cross-link could have been via a hydrogen-bonding mechanism between the amine and the free hydroxyl group, or via attack of the amine on the ester linage to form an amide. Tensile properties examined at room temperature indicate a three-fold increase in the tensile modulus of the elastomer, with rupture and failure of the elastomer occurring at a strain of 6.5.

  16. Polymer reinforcement of cement systems

    International Nuclear Information System (INIS)

    Swamy, R.N.


    In the last couple of decades several cement- and concrete-based composites have come into prominence. Of these, cement-polymer composites, like cement-fibre composites, have been recognised as very promising, and considerable research and development on their properties, fabrication methods and application are in progress. Of the three types of concrete materials which incorporate polymers to form composites, polymer impregnated concrete forms a major development in which hardened concrete is impregnated with a liquid monomer which is subsequently polymerized to form a rigid polymer network in the pores of the parent material. In this first part of the extensive review of the polymer reinforcement of cement systems, the process technology of the various monomer impregnation techniques and the properties of the impregnated composite are assessed critically. It is shown that the high durability and superior performance of polymer impregnated concrete can provide an economic and competitive alternative in in situ strengthening, and in other areas where conventional concrete can only at best provide adequate performance. The review includes a section on radiation-induced polymerization. (author)

  17. Neural signals of vicarious extinction learning. (United States)

    Golkar, Armita; Haaker, Jan; Selbing, Ida; Olsson, Andreas


    Social transmission of both threat and safety is ubiquitous, but little is known about the neural circuitry underlying vicarious safety learning. This is surprising given that these processes are critical to flexibly adapt to a changeable environment. To address how the expression of previously learned fears can be modified by the transmission of social information, two conditioned stimuli (CS + s) were paired with shock and the third was not. During extinction, we held constant the amount of direct, non-reinforced, exposure to the CSs (i.e. direct extinction), and critically varied whether another individual-acting as a demonstrator-experienced safety (CS + vic safety) or aversive reinforcement (CS + vic reinf). During extinction, ventromedial prefrontal cortex (vmPFC) responses to the CS + vic reinf increased but decreased to the CS + vic safety This pattern of vmPFC activity was reversed during a subsequent fear reinstatement test, suggesting a temporal shift in the involvement of the vmPFC. Moreover, only the CS + vic reinf association recovered. Our data suggest that vicarious extinction prevents the return of conditioned fear responses, and that this efficacy is reflected by diminished vmPFC involvement during extinction learning. The present findings may have important implications for understanding how social information influences the persistence of fear memories in individuals suffering from emotional disorders. © The Author (2016). Published by Oxford University Press. For Permissions, please email:

  18. Facilitating tolerance of delayed reinforcement during functional communication training. (United States)

    Fisher, W W; Thompson, R H; Hagopian, L P; Bowman, L G; Krug, A


    Few clinical investigations have addressed the problem of delayed reinforcement. In this investigation, three individuals whose destructive behavior was maintained by positive reinforcement were treated using functional communication training (FCT) with extinction (EXT). Next, procedures used in the basic literature on delayed reinforcement and self-control (reinforcer delay fading, punishment of impulsive responding, and provision of an alternative activity during reinforcer delay) were used to teach participants to tolerate delayed reinforcement. With the first case, reinforcer delay fading alone was effective at maintaining low rates of destructive behavior while introducing delayed reinforcement. In the second case, the addition of a punishment component reduced destructive behavior to near-zero levels and facilitated reinforcer delay fading. With the third case, reinforcer delay fading was associated with increases in masturbation and head rolling, but prompting and praising the individual for completing work during the delay interval reduced all problem behaviors and facilitated reinforcer delay fading.

  19. Reinforcement function design and bias for efficient learning in mobile robots

    International Nuclear Information System (INIS)

    Touzet, C.; Santos, J.M.


    The main paradigm in sub-symbolic learning robot domain is the reinforcement learning method. Various techniques have been developed to deal with the memorization/generalization problem, demonstrating the superior ability of artificial neural network implementations. In this paper, the authors address the issue of designing the reinforcement so as to optimize the exploration part of the learning. They also present and summarize works relative to the use of bias intended to achieve the effective synthesis of the desired behavior. Demonstrative experiments involving a self-organizing map implementation of the Q-learning and real mobile robots (Nomad 200 and Khepera) in a task of obstacle avoidance behavior synthesis are described. 3 figs., 5 tabs

  20. Quantum effects in warp drives

    Directory of Open Access Journals (Sweden)

    Finazzi Stefano


    Full Text Available Warp drives are interesting configurations that, at least theoretically, provide a way to travel at superluminal speed. Unfortunately, several issues seem to forbid their realization. First, a huge amount of exotic matter is required to build them. Second, the presence of quantum fields propagating in superluminal warp-drive geometries makes them semiclassically unstable. Indeed, a Hawking-like high-temperature flux of particles is generated inside the warp-drive bubble, which causes an exponential growth of the energy density measured at the front wall of the bubble by freely falling observers. Moreover, superluminal warp drives remain unstable even if the Lorentz symmetry is broken by the introduction of regulating higher order terms in the Lagrangian of the quantum field. If the dispersion relation of the quantum field is subluminal, a black-hole laser phenomenon yields an exponential amplification of the emitted flux. If it is superluminal, infrared effects cause a linear growth of this flux.

  1. Multidisciplinary design of electrical drives

    Energy Technology Data Exchange (ETDEWEB)

    Blaabjerg, F.; Rasmussen, P.O.; Pedersen, J.K.


    Traditionally, simulation tools for drives can simulate electrical parameters, torque and different loads. Those parameters are in many cases appropriate. However, power electronics in drives will also influence on torque ripple, temperature, vibration and acoustical noise from the motor and it is necessary to include those phenomena in the next generation of simulation tools for electrical drives. This paper describes a new design program where acoustic noise of electromagnetic origin can be simulated and even be heard by the motor and drives designer. The design program is based on a simple vibrational/acoustic model where the parameters can be calculated based on the geometry of the motor. Examples of vibrational/acoustical modelling are included and verified in both time and frequency domain. Special emphasis is on the switched reluctance motor. (au)

  2. Clinical Action against Drunk Driving.

    Directory of Open Access Journals (Sweden)

    Donald A Redelmeier


    Full Text Available In advance of a safety campaign on 17 March 2017, Donald Redelmeier and Allan Detsky call on physicians and clinical colleagues to reduce the chances that patients will drive drunk.

  3. Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data

    Directory of Open Access Journals (Sweden)

    Jinshuan Peng


    Full Text Available Risky driving behavior is a major cause of traffic conflicts, which can develop into road traffic accidents, making the timely and accurate identification of such behavior essential to road safety. A platform was therefore established for analyzing the driving behavior of 20 professional drivers in field tests, in which overclose car following and lane departure were used as typical risky driving behaviors. Characterization parameters for identification were screened and used to determine threshold values and an appropriate time window for identification. A neural network-Bayesian filter identification model was established and data samples were selected to identify risky driving behavior and evaluate the identification efficiency of the model. The results obtained indicated a successful identification rate of 83.6% when the neural network model was solely used to identify risky driving behavior, but this could be increased to 92.46% once corrected by the Bayesian filter. This has important theoretical and practical significance in relation to evaluating the efficiency of existing driver assist systems, as well as the development of future intelligent driving systems.

  4. Synchronization of chaotic recurrent neural networks with time-varying delays using nonlinear feedback control

    International Nuclear Information System (INIS)

    Cui Baotong; Lou Xuyang


    In this paper, a new method to synchronize two identical chaotic recurrent neural networks is proposed. Using the drive-response concept, a nonlinear feedback control law is derived to achieve the state synchronization of the two identical chaotic neural networks. Furthermore, based on the Lyapunov method, a delay independent sufficient synchronization condition in terms of linear matrix inequality (LMI) is obtained. A numerical example with graphical illustrations is given to illuminate the presented synchronization scheme

  5. Driving safety in Parkinson's disease. (United States)

    Zesiewicz, T A; Cimino, C R; Malek, A R; Gardner, N; Leaverton, P L; Dunne, P B; Hauser, R A


    In this study, 39 patients with PD and 25 control subjects without neurologic disease completed testing in a driving simulator. PD patients had more total collisions on the driving simulator than control subjects (t = -3.7, p < 0.01). In PD patients, collisions were associated with Hoehn and Yahr stage (chi(2) = 12.4, p = 0.006) and correlated with Unified Parkinson's Disease Rating Scale score (r = 0.5, p < 0.01).

  6. Warp drive with zero expansion

    Energy Technology Data Exchange (ETDEWEB)

    Natario, Jose [Department of Mathematics, Instituto Superior Tecnico (Portugal)


    It is commonly believed that Alcubierre's warp drive works by contracting space in front of the warp bubble and expanding the space behind it. We show that this contraction/expansion is but a marginal consequence of the choice made by Alcubierre and explicitly construct a similar spacetime where no contraction/expansion occurs. Global and optical properties of warp-drive spacetimes are also discussed.

  7. Selective Perception for Robot Driving (United States)


    models are theories of human cognitive activity during driving. Van der Molen and Botticher recently reviewed several of these models [ van der Molen to represent driving knowledge, how to perceive traffic situations, or how to process information to obtain actions. Van der Molen and Botticher...attempted to compare the operations of various models objectively on the same task [Rothengatter 88, van der Molen 87], but the models could be

  8. Low backlash direct drive actuator (United States)

    Kuklo, Thomas C.


    A low backlash direct drive actuator is described which comprises a motor such as a stepper motor having at least 200 steps per revolution; a two part hub assembly comprising a drive hub coaxially attached to the shaft of the motor and having a plurality of drive pins; a driven hub having a plurality of bores in one end thereof in alignment with the drive pins in the drive hub and a threaded shaft coaxially mounted in an opposite end of the driven hub; and a housing having a central bore therein into which are fitted the drive hub and driven hub, the housing having a motor mount on one end thereof to which is mounted the stepper motor, and a closed end portion with a threaded opening therein coaxial with the central bore in the housing and receiving therein the threaded shaft attached to the driven hub. Limit switches mounted to the housing cooperate with an enlarged lip on the driven hub to limit the lateral travel of the driven hub in the housing, which also acts to limit the lateral travel of the threaded shaft which functions as a lead screw.

  9. Influence of reinforcement's corrosion into hyperstatic reinforced concrete beams: a probabilistic failure scenarios analysis

    Directory of Open Access Journals (Sweden)


    Full Text Available AbstractThis work aims to study the mechanical effects of reinforcement's corrosion in hyperstatic reinforced concrete beams. The focus is the probabilistic determination of individual failure scenarios change as well as global failure change along time. The limit state functions assumed describe analytically bending and shear resistance of reinforced concrete rectangular cross sections as a function of steel and concrete resistance and section dimensions. It was incorporated empirical laws that penalize the steel yield stress and the reinforcement's area along time in addition to Fick's law, which models the chloride penetration into concrete pores. The reliability theory was applied based on Monte Carlo simulation method, which assesses each individual probability of failure. The probability of global structural failure was determined based in the concept of failure tree. The results of a hyperstatic reinforced concrete beam showed that reinforcements corrosion make change into the failure scenarios modes. Therefore, unimportant failure modes in design phase become important after corrosion start.

  10. Spinal interneurons differentiate sequentially from those driving the fastest swimming movements in larval zebrafish to those driving the slowest ones. (United States)

    McLean, David L; Fetcho, Joseph R


    Studies of neuronal networks have revealed few general principles that link patterns of development with later functional roles. While investigating the neural control of movements, we recently discovered a topographic map in the spinal cord of larval zebrafish that relates the position of motoneurons and interneurons to their order of recruitment during swimming. Here, we show that the map reflects an orderly pattern of differentiation of neurons driving different movements. First, we use high-speed filming to show that large-amplitude swimming movements with bending along much of the body appear first, with smaller, regional swimming movements emerging later. Next, using whole-cell patch recordings, we demonstrate that the excitatory circuits that drive large-amplitude, fast swimming movements at larval stages are present and functional early on in embryos. Finally, we systematically assess the orderly emergence of spinal circuits according to swimming speed using transgenic fish expressing the photoconvertible protein Kaede to track neuronal differentiation in vivo. We conclude that a simple principle governs the development of spinal networks in which the neurons driving the fastest, most powerful swimming in larvae develop first with ones that drive increasingly weaker and slower larval movements layered on over time. Because the neurons are arranged by time of differentiation in the spinal cord, the result is a topographic map that represents the speed/strength of movements at which neurons are recruited and the temporal emergence of networks. This pattern may represent a general feature of neuronal network development throughout the brain and spinal cord.

  11. The role of the lateral amygdala in the retrieval and maintenance of fear-memories formed by probabilistic reinforcement

    Directory of Open Access Journals (Sweden)

    Jeffrey C. Erlich


    Full Text Available The lateral nucleus of the amygdala (LA is a key element in the neural circuit subserving Pavlovian fear conditioning, an animal model of fear and anxiety. Most studies have focused on the role of the LA in fear acquisition and extinction, i.e. how neural plasticity results from changing contingencies between a neutral conditioned stimulus (e.g. a tone and an aversive unconditioned stimulus (e.g. a shock. However, outside of the lab, fear memories are often the result of repeated and unpredictable experiences. Examples include domestic violence, child abuse or combat. To better understand the role of the LA in the expression of fear resulting from repeated and uncertain reinforcement, rats experienced a 30% partial reinforcement fear-conditioning schedule four days a week for four weeks. Rats reached asymptotic levels of conditioned fear expression after the first week. We then manipulated LA activity with drug (or vehicle infusions once a week, for the next three weeks, before the training session. LA infusions of muscimol, a GABA-A agonist that inhibits neural activity, reduced conditioned stimulus (CS evoked fear behavior to pre-conditioning levels. LA infusions of pentagastrin, a cholecystokinin-2 (CCK agonist that increases neural excitability, resulted in CS-evoked fear behavior that continued past the offset of the CS. This suggests that neural activity in the LA is required for the retrieval of fear memories that stem from repeated and uncertain reinforcement, and that CCK signaling in the LA plays a role in the recovery from fear after the removal of the fear-evoking stimulus.

  12. Modeling of geosynthetic reinforced capping systems

    International Nuclear Information System (INIS)

    Viswanadham, B.V.S.; Koenig, D.; Jessberger, H.L.


    The investigation deals with the influence of a geosynthetic reinforcement on the deformation behavior and sealing efficiency of the reinforced mineral sealing layer at the onset of non-uniform settlements. The research program is mainly concentrated in studying the influence of reinforcement inclusion in restraining cracks and crack propagation due to soil-geosynthetic bond efficiency. Centrifuge model tests are conducted in the 500 gt capacity balanced beam Bochum geotechnical Centrifuge (Z1) simulating a differential deformation of a mineral sealing layer of a landfill with the help of trap-door arrangement. By comparing the performance of the deformed mineral sealing layer with and without geogrid, the reinforcement ability of the geogrid in controlling the crack propagation and permeability of the mineral swing layer is evaluated

  13. Chemical modification of flax reinforced polypropylene composites

    CSIR Research Space (South Africa)

    Jacob John, Maya


    Full Text Available This paper presents an experimental study on the static and dynamic mechanical properties of nonwoven based flax fibre reinforced polypropylene composites. The effect of zein modification on flax fibres is also reported. Flax nonwovens were treated...

  14. fatigue strength of reinforced concrete flexural members

    African Journals Online (AJOL)

    Dr Obe


    Mar 1, 1980 ... cyclic loads behave differently compared with static bending and can collapse due to the fatigue of concrete, reinforcement or both when maximum fatigue stresses of ... under low and medium load levels, than under high load ...

  15. Thin fiber and textile reinforced cementitious systems

    National Research Council Canada - National Science Library

    Aldea, Corina-Maria


    This Special Publication (SP) contains ten papers which provide insight on the topics of state of the art of thin fiber and textile-reinforced cementitious systems both in academia and the industry...


    African Journals Online (AJOL)


    mechanical and thermal properties of chitin reinforced composites. ..... with crabyon fiber and normal viscose filaments. Also. Zhang et al.,[65] successfully blended chitin/cellulose using two different coagulating systems (immersed in 5.

  17. Corrosion resistant alloys for reinforced concrete [2009 (United States)


    Deterioration of concrete bridges because of reinforcing steel corrosion has been recognized for four-plus decades as a major technical and economic challenge for the United States. As an option for addressing this problem, renewed interest has focus...

  18. Belief reward shaping in reinforcement learning

    CSIR Research Space (South Africa)

    Marom, O


    Full Text Available A key challenge in many reinforcement learning problems is delayed rewards, which can significantly slow down learning. Although reward shaping has previously been introduced to accelerate learning by bootstrapping an agent with additional...

  19. Reinforced Conductive Polyaniline-Paper Composites

    Directory of Open Access Journals (Sweden)

    Jinhua Yan


    Full Text Available A method for direct aniline interfacial polymerization on polyamideamine-epichlorohydrin (PAE-reinforced paper substrate is introduced in this paper. Cellulose-based papers with and without reinforcement were considered. The polyaniline (PANI-paper composites had surface resistivity lower than 100 Ω/sq after more than 3 polymerizations. Their mechanical strength and thermal stability were analyzed by tensile tests and thermogravimetric analysis (TGA. Fourier transform infrared (FTIR results revealed that there was strong interaction between NH groups in aniline monomers and OH groups in fibers, which did not disappear until after 3 polymerizations. Scanning electron microscopy (SEM and field emission (FE SEM images showed morphological differences between composites using reinforced and untreated base papers. Conductive composites made with PAE-reinforced base paper had both good thermal stability and good mechanical strength, with high conductivity and a smaller PANI amount.

  20. Corrosion resistant alloys for reinforced concrete [2007 (United States)


    Deterioration of concrete bridges because of reinforcing steel corrosion has been recognized for 4-plus decades as a major technical and economic challenge for the United States. As an option for addressing this problem, renewed interest has focused ...

  1. Fiber reinforced polymer bridge decks : [technical summary]. (United States)


    A number of researchers have addressed the use of Fiber Reinforced Polymer (FRP) deck as a replacement solution for deteriorated bridge decks made of traditional materials. The use of new, advanced materials such as FRP is advantageous when the bridg...

  2. Finite element modeling of reinforced concrete beams with a hybrid combination of steel and aramid reinforcement

    International Nuclear Information System (INIS)

    Hawileh, R.A.


    Highlights: • Modeling of concrete beams reinforced steel and FRP bars. • Developed finite element models achieved good results. • The models are validated via comparison with experimental results. • Parametric studies are performed. - Abstract: Corrosion of steel bars has an adverse effect on the life-span of reinforced concrete (RC) members and is usually associated with crack development in RC beams. Fiber reinforced polymer (FRP) bars have been recently used to reinforce concrete members in flexure due to their high tensile strength and superior corrosion resistance properties. However, FRP materials are brittle in nature, thus RC beams reinforced with such materials would exhibit a less ductile behavior when compared to similar members reinforced with conventional steel reinforcement. Recently, researchers investigated the performance of concrete beams reinforced with a hybrid combination of steel and Aramid Fiber Reinforced Polymer (AFRP) reinforcement to maintain a reasonable level of ductility in such members. The function of the AFRP bars is to increase the load-carrying capacity, while the function of the steel bars is to ensure ductility of the flexural member upon yielding in tension. This paper presents a three-dimensional (3D) finite element (FE) model that predicted the load versus mid-span deflection response of tested RC beams conducted by other researchers with a hybrid combination of steel and AFRP bars. The developed FE models account for the constituent material nonlinearities and bond–slip behavior between the reinforcing bars and adjacent concrete surfaces. It was concluded that the developed models can accurately capture the behavior and predicts the load-carrying capacity of such RC members. In addition, a parametric study is conducted using the validated models to investigate the effect of AFRP bar size, FRP material type, bond–slip action, and concrete compressive strength on the performance of concrete beams when reinforced

  3. Combining noncontingent reinforcement and differential reinforcement schedules as treatment for aberrant behavior.


    Marcus, B A; Vollmer, T R


    Research has shown that noncontingent reinforcement (NCR) can be an effective behavior-reduction procedure when based on a functional analysis. The effects of NCR may be a result of elimination of the contingency between aberrant behavior and reinforcing consequences (extinction) or frequent and free access to reinforcers that may reduce the participant's motivation to engage in aberrant behaviors or mands. If motivation is momentarily reduced, behavior such as mands may not be sensitive to p...

  4. Reinforcement Schedules in a Verbal Reinforcement Combination and Renection-Impulsivity


    TAMASE, Koji; UEDA, Masako


    It was predicted that higher proportion of the negative reinforcement "Wrong" than that of the positive reinforcement "Right" in a reinforcement combination will produce higher proportion of the correct response and this trend will be greater in reflective children than in impulsive children. From 140 kindergarten children 30 reflective and 30 impulsive children were selected and they were given a two-hole marble-dropping task. The best performance in the ratio of correct responses was obtain...

  5. Modelling root reinforcement in shallow forest soils (United States)

    Skaugset, Arne E.


    A hypothesis used to explain the relationship between timber harvesting and landslides is that tree roots add mechanical support to soil, thus increasing soil strength. Upon harvest, the tree roots decay which reduces soil strength and increases the risk of management -induced landslides. The technical literature does not adequately support this hypothesis. Soil strength values attributed to root reinforcement that are in the technical literature are such that forested sites can't fail and all high risk, harvested sites must fail. Both unstable forested sites and stable harvested sites exist, in abundance, in the real world thus, the literature does not adequately describe the real world. An analytical model was developed to calculate soil strength increase due to root reinforcement. Conceptually, the model is composed of a reinforcing element with high tensile strength, i.e. a conifer root, embedded in a material with little tensile strength, i.e. a soil. As the soil fails and deforms, the reinforcing element also deforms and stretches. The lateral deformation of the reinforcing element is treated analytically as a laterally loaded pile in a flexible foundation and the axial deformation is treated as an axially loaded pile. The governing differential equations are solved using finite-difference approximation techniques. The root reinforcement model was tested by comparing the final shape of steel and aluminum rods, parachute cord, wooden dowels, and pine roots in direct shear with predicted shapes from the output of the root reinforcement model. The comparisons were generally satisfactory, were best for parachute cord and wooden dowels, and were poorest for steel and aluminum rods. A parameter study was performed on the root reinforcement model which showed reinforced soil strength increased with increasing root diameter and soil depth. Output from the root reinforcement model showed a strain incompatibility between large and small diameter roots. The peak

  6. Gaze-contingent reinforcement learning reveals incentive value of social signals in young children and adults. (United States)

    Vernetti, Angélina; Smith, Tim J; Senju, Atsushi


    While numerous studies have demonstrated that infants and adults preferentially orient to social stimuli, it remains unclear as to what drives such preferential orienting. It has been suggested that the learned association between social cues and subsequent reward delivery might shape such social orienting. Using a novel, spontaneous indication of reinforcement learning (with the use of a gaze contingent reward-learning task), we investigated whether children and adults' orienting towards social and non-social visual cues can be elicited by the association between participants' visual attention and a rewarding outcome. Critically, we assessed whether the engaging nature of the social cues influences the process of reinforcement learning. Both children and adults learned to orient more often to the visual cues associated with reward delivery, demonstrating that cue-reward association reinforced visual orienting. More importantly, when the reward-predictive cue was social and engaging, both children and adults learned the cue-reward association faster and more efficiently than when the reward-predictive cue was social but non-engaging. These new findings indicate that social engaging cues have a positive incentive value. This could possibly be because they usually coincide with positive outcomes in real life, which could partly drive the development of social orienting. © 2017 The Authors.

  7. Synchronization of chaotic neural networks via output or state coupling

    International Nuclear Information System (INIS)

    Lu Hongtao; Leeuwen, C. van


    We consider the problem of global exponential synchronization between two identical chaotic neural networks that are linearly and unidirectionally coupled. We formulate a general framework for the synchronization problem in which one chaotic neural network, working as the driving system (or master), sends its output or state values to the other, which serves as the response system (or slave). We use Lyapunov functions to establish general theoretical conditions for designing the coupling matrix. Neither symmetry nor negative (positive) definiteness of the coupling matrix are required; under less restrictive conditions, the two coupled chaotic neural networks can achieve global exponential synchronization regardless of their initial states. Detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws

  8. Stimulus-dependent suppression of chaos in recurrent neural networks

    International Nuclear Information System (INIS)

    Rajan, Kanaka; Abbott, L. F.; Sompolinsky, Haim


    Neuronal activity arises from an interaction between ongoing firing generated spontaneously by neural circuits and responses driven by external stimuli. Using mean-field analysis, we ask how a neural network that intrinsically generates chaotic patterns of activity can remain sensitive to extrinsic input. We find that inputs not only drive network responses, but they also actively suppress ongoing activity, ultimately leading to a phase transition in which chaos is completely eliminated. The critical input intensity at the phase transition is a nonmonotonic function of stimulus frequency, revealing a 'resonant' frequency at which the input is most effective at suppressing chaos even though the power spectrum of the spontaneous activity peaks at zero and falls exponentially. A prediction of our analysis is that the variance of neural responses should be most strongly suppressed at frequencies matching the range over which many sensory systems operate.

  9. Neural Behavior Chain Learning of Mobile Robot Actions

    Directory of Open Access Journals (Sweden)

    Lejla Banjanovic-Mehmedovic


    Full Text Available This paper presents a visual/motor behavior learning approach, based on neural networks. We propose Behavior Chain Model (BCM in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are derived. Second, a multilayer neural network for offline learning of the mapping relations is used. This learning structure through neural network training process represents a connection between the visual perceptions and motor sequence of actions in order to grip a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot behavior for a variety of similar tasks in similar environment. Prediction results suggest that the methodology is adequate and could be recognized as an idea for designing different mobile robot behaviour assistance.

  10. The Recent Research on Bamboo Reinforced Concrete

    Directory of Open Access Journals (Sweden)

    Dewi Sri Murni


    Full Text Available The paper presents the last research on bamboo reinforced concrete in Brawijaya University Indonesia. Three kinds of structures studied in recent year, the mounting of pegs on reinforcement, the use of lightweight brick to reduce the weight of the beams, and the use the light weight aggregate for bamboo concrete composite frame. All that experiments overcome some problems exist in using bamboo as environmental acceptance structures.

  11. Reinforced concrete wall under hydrogen detonation

    International Nuclear Information System (INIS)

    Saarenheimo, A.


    The structural integrity of a reinforced concrete wall in the BWR reactor building under hydrogen detonation conditions has been analysed. Of particular interest is whether the containment integrity can be jeopardised by an external hydrogen detonation. The load carrying capacity of a reinforced concrete wall was studied. The detonation pressure loads were estimated with computerised hand calculations assuming a direct initiation of detonation and applying the strong explosion theory. The results can be considered as rough and conservative estimates for the first shock pressure impact induced by a reflecting detonation wave. Structural integrity may be endangered due to slow pressurisation or dynamic impulse loads associated with local detonations. The static pressure following the passage of a shock front may be relatively high, thus this static or slowly decreasing pressure after a detonation may damage the structure severely. The mitigating effects of the opening of a door on pressure history and structural response were also studied. The non-linear behaviour of the wall was studied under detonations corresponding a detonable hydrogen mass of 0.5 kg and 1.428 kg. Non-linear finite element analyses of the reinforced concrete structure were carried out by the ABAQUS/Explicit program. The reinforcement and its non-linear material behaviour and the tensile cracking of concrete were modelled. Reinforcement was defined as layers of uniformly spaced reinforcing bars in shell elements. In these studies the surrounding structures of the non-linearly modelled reinforced concrete wall were modelled using idealised boundary conditions. Especially concrete cracking and yielding of the reinforcement was monitored during the numerical simulation. (au)

  12. Shaking Table Tests of Reinforced Concrete Frames

    DEFF Research Database (Denmark)

    Skjærbæk, P. S.; Kirkegaard, Poul Henning; Nielsen, Søren R.K.

    -varying systems and to verify various methods for damage assessment of reinforced concrete structures from soft motion measurements. In this study the maximum softening concept will be evaluated. In the paper the assessment obtained by this method is compared to visual damage assessment. The structures considered...... vector ARMA model is suitable for modal identification of degrading reinforced concrete structures and the maximum softening damage index calculated from the obtained identification provides a valuable tool for assessment of the damage state of the structure....

  13. Design Methods for Fibre Reinforced Concrete

    DEFF Research Database (Denmark)

    Stang, Henrik


    The present paper describes the outline of a research project on Fibre Reinforced Concrete (FRC) currently being carried out in Denmark under the supervision of Danish Council of Technology, Danish Technical Research Council and Danish Natural Science Research Counsil.......The present paper describes the outline of a research project on Fibre Reinforced Concrete (FRC) currently being carried out in Denmark under the supervision of Danish Council of Technology, Danish Technical Research Council and Danish Natural Science Research Counsil....

  14. How we learn to make decisions: rapid propagation of reinforcement learning prediction errors in humans. (United States)

    Krigolson, Olav E; Hassall, Cameron D; Handy, Todd C


    Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward

  15. Deep reinforcement learning for automated radiation adaptation in lung cancer. (United States)

    Tseng, Huan-Hsin; Luo, Yi; Cui, Sunan; Chien, Jen-Tzung; Ten Haken, Randall K; Naqa, Issam El


    To investigate deep reinforcement learning (DRL) based on historical treatment plans for developing automated radiation adaptation protocols for nonsmall cell lung cancer (NSCLC) patients that aim to maximize tumor local control at reduced rates of radiation pneumonitis grade 2 (RP2). In a retrospective population of 114 NSCLC patients who received radiotherapy, a three-component neural networks framework was developed for deep reinforcement learning (DRL) of dose fractionation adaptation. Large-scale patient characteristics included clinical, genetic, and imaging radiomics features in addition to tumor and lung dosimetric variables. First, a generative adversarial network (GAN) was employed to learn patient population characteristics necessary for DRL training from a relatively limited sample size. Second, a radiotherapy artificial environment (RAE) was reconstructed by a deep neural network (DNN) utilizing both original and synthetic data (by GAN) to estimate the transition probabilities for adaptation of personalized radiotherapy patients' treatment courses. Third, a deep Q-network (DQN) was applied to the RAE for choosing the optimal dose in a response-adapted treatment setting. This multicomponent reinforcement learning approach was benchmarked against real clinical decisions that were applied in an adaptive dose escalation clinical protocol. In which, 34 patients were treated based on avid PET signal in the tumor and constrained by a 17.2% normal tissue complication probability (NTCP) limit for RP2. The uncomplicated cure probability (P+) was used as a baseline reward function in the DRL. Taking our adaptive dose escalation protocol as a blueprint for the proposed DRL (GAN + RAE + DQN) architecture, we obtained an automated dose adaptation estimate for use at ∼2/3 of the way into the radiotherapy treatment course. By letting the DQN component freely control the estimated adaptive dose per fraction (ranging from 1-5 Gy), the DRL automatically favored dose

  16. Dynamic pricing and automated resource allocation for complex information services reinforcement learning and combinatorial auctions

    CERN Document Server

    Schwind, Michael; Fandel, G


    Many firms provide their customers with online information products which require limited resources such as server capacity. This book develops allocation mechanisms that aim to ensure an efficient resource allocation in modern IT-services. Recent methods of artificial intelligence, such as neural networks and reinforcement learning, and nature-oriented optimization methods, such as genetic algorithms and simulated annealing, are advanced and applied to allocation processes in distributed IT-infrastructures, e.g. grid systems. The author presents two methods, both of which using the users??? w

  17. High-power converters and AC drives

    CERN Document Server

    Wu, Bin


    This new edition reflects the recent technological advancements in the MV drive industry, such as advanced multilevel converters and drive configurations. It includes three new chapters, Control of Synchronous Motor Drives, Transformerless MV Drives, and Matrix Converter Fed Drives. In addition, there are extensively revised chapters on Multilevel Voltage Source Inverters and Voltage Source Inverter-Fed Drives. This book includes a systematic analysis on a variety of high-power multilevel converters, illustrates important concepts with simulations and experiments, introduces various megawatt drives produced by world leading drive manufacturers, and addresses practical problems and their mitigations methods.

  18. Efficient Driving of Piezoelectric Transducers Using a Biaxial Driving Technique.

    Directory of Open Access Journals (Sweden)

    Samuel Pichardo

    Full Text Available Efficient driving of piezoelectric materials is desirable when operating transducers for biomedical applications such as high intensity focused ultrasound (HIFU or ultrasound imaging. More efficient operation reduces the electric power required to produce the desired bioeffect or contrast. Our preliminary work [Cole et al. Journal of Physics: Condensed Matter. 2014;26(13:135901.] suggested that driving transducers by applying orthogonal electric fields can significantly reduce the coercivity that opposes ferroelectric switching. We present here the experimental validation of this biaxial driving technique using piezoelectric ceramics typically used in HIFU. A set of narrow-band transducers was fabricated with two sets of electrodes placed in an orthogonal configuration (following the propagation and the lateral mode. The geometry of the ceramic was chosen to have a resonance frequency similar for the propagation and the lateral mode. The average (± s.d. resonance frequency of the samples was 465.1 (± 1.5 kHz. Experiments were conducted in which each pair of electrodes was driven independently and measurements of effective acoustic power were obtained using the radiation force method. The efficiency (acoustic/electric power of the biaxial driving method was compared to the results obtained when driving the ceramic using electrodes placed only in the pole direction. Our results indicate that the biaxial method increases efficiency from 50% to 125% relative to the using a single electric field.

  19. Zero-reinforcement vessel closures

    International Nuclear Information System (INIS)

    McClellan, G.; Mou, Y.


    Access to the secondary side of a nuclear steam generator is required in order to properly inspect and maintain critical components throughout the life. For the most part, it is only on newer units that sufficient openings have been provided. Older units must be field modified to provide access to the tube bundle and internal lateral support components for inspection and penetration by cleaning equipment. In order to avoid post weld heat treatment after welding on some materials it would be desirable to machine the opening directly into the pressure boundary without providing weld build-up to compensate for the material removed. In such a case, the pressure boundary may be locally thinned below the minimum thickness required by the ASME code. As a result it is not possible to meet reinforcement limits or elastic primary stress limited of the code. However, the ASME code permits justification of the design by utilizing elastic-plastic methods. Elastic-plastic analysis can be utilized to demonstrate shake-down to elastic action and to demonstrate that small deformations in the region of the gasket seating surfaces, or any loss of bolt preload, have not compromised leak tightness. Employing the technique developed by the authors for application in ANSYS, it is feasible to carry-out such a design analysis including the effects of time varying thermal stress. This paper presents the highlights of such an analysis. It is important to note that the method also permits the analysis of openings in locations formerly considered too restrictive, such as near support and major structural discontinuities. (author)

  20. Parallel consensual neural networks. (United States)

    Benediktsson, J A; Sveinsson, J R; Ersoy, O K; Swain, P H


    A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.

  1. Learning to trade via direct reinforcement. (United States)

    Moody, J; Saffell, M


    We present methods for optimizing portfolios, asset allocations, and trading systems based on direct reinforcement (DR). In this approach, investment decision-making is viewed as a stochastic control problem, and strategies are discovered directly. We present an adaptive algorithm called recurrent reinforcement learning (RRL) for discovering investment policies. The need to build forecasting models is eliminated, and better trading performance is obtained. The direct reinforcement approach differs from dynamic programming and reinforcement algorithms such as TD-learning and Q-learning, which attempt to estimate a value function for the control problem. We find that the RRL direct reinforcement framework enables a simpler problem representation, avoids Bellman's curse of dimensionality and offers compelling advantages in efficiency. We demonstrate how direct reinforcement can be used to optimize risk-adjusted investment returns (including the differential Sharpe ratio), while accounting for the effects of transaction costs. In extensive simulation work using real financial data, we find that our approach based on RRL produces better trading strategies than systems utilizing Q-learning (a value function method). Real-world applications include an intra-daily currency trader and a monthly asset allocation system for the S&P 500 Stock Index and T-Bills.

  2. Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

    Directory of Open Access Journals (Sweden)

    Andrea Maesani


    Full Text Available The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.

  3. Memory Transformation Enhances Reinforcement Learning in Dynamic Environments. (United States)

    Santoro, Adam; Frankland, Paul W; Richards, Blake A


    Over the course of systems consolidation, there is a switch from a reliance on detailed episodic memories to generalized schematic memories. This switch is sometimes referred to as "memory transformation." Here we demonstrate a previously unappreciated benefit of memory transformation, namely, its ability to enhance reinforcement learning in a dynamic environment. We developed a neural network that is trained to find rewards in a foraging task where reward locations are continuously changing. The network can use memories for specific locations (episodic memories) and statistical patterns of locations (schematic memories) to guide its search. We find that switching from an episodic to a schematic strategy over time leads to enhanced performance due to the tendency for the reward location to be highly correlated with itself in the short-term, but regress to a stable distribution in the long-term. We also show that the statistics of the environment determine the optimal utilization of both types of memory. Our work recasts the theoretical question of why memory transformation occurs, shifting the focus from the avoidance of memory interference toward the enhancement of reinforcement learning across multiple timescales. As time passes, memories transform from a highly detailed state to a more gist-like state, in a process called "memory transformation." Theories of memory transformation speak to its advantages in terms of reducing memory interference, increasing memory robustness, and building models of the environment. However, the role of memory transformation from the perspective of an agent that continuously acts and receives reward in its environment is not well explored. In this work, we demonstrate a view of memory transformation that defines it as a way of optimizing behavior across multiple timescales. Copyright © 2016 the authors 0270-6474/16/3612228-15$15.00/0.

  4. Introduction to Concrete Reinforcing. Instructor Edition. Introduction to Construction Series. (United States)

    Oklahoma State Dept. of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

    This module on concrete reinforcing is one of a series of modules designed to teach basic skills necessary for entry-level employment in this field. This module contains three instructional units that cover the following topics: (1) concrete reinforcing materials; (2) concrete reinforcing tools; and (3) concrete reinforcing basic skills. Each…

  5. Presentations of the PTAC driving safety technology forum

    Energy Technology Data Exchange (ETDEWEB)



    Issues concerning road safety and driver training in relation to the petroleum industry were the focus of this conference. The results of a driving safety survey from the Petroleum Technology Alliance Canada were presented. High risk driver identification methods were discussed along with coaching case studies, including details of continuous and advanced driver training by various industry members. Road hazard analysis was discussed as an effective means of reducing vehicular incidents. The dangers posed by cell phones were reviewed, as well as issues concerning motor vehicle event data recorders and personal privacy. The safety aspects of global positioning system (GPS) enabled fleet management systems were evaluated. Tactical plans for Alberta's traffic collision fatality and injury reduction strategy were examined. An overview of the Schlumberger Canada Driving Program was presented. The systems and processes used within Halliburton Canada to reinforce safe driving behaviors were reviewed. Real time personnel monitoring was discussed, as well as various asset management technologies. It was suggested that new communications technologies for mobile resource management can improve oilfield fleet safety and productivity. The forum featured 12 presentations, of which 8 have been catalogued separately for inclusion in this database.

  6. i3Drive, a 3D interactive driving simulator. (United States)

    Ambroz, Miha; Prebil, Ivan


    i3Drive, a wheeled-vehicle simulator, can accurately simulate vehicles of various configurations with up to eight wheels in real time on a desktop PC. It presents the vehicle dynamics as an interactive animation in a virtual 3D environment. The application is fully GUI-controlled, giving users an easy overview of the simulation parameters and letting them adjust those parameters interactively. It models all relevant vehicle systems, including the mechanical models of the suspension, power train, and braking and steering systems. The simulation results generally correspond well with actual measurements, making the system useful for studying vehicle performance in various driving scenarios. i3Drive is thus a worthy complement to other, more complex tools for vehicle-dynamics simulation and analysis.

  7. Driving performance at lateral system limits during partially automated driving. (United States)

    Naujoks, Frederik; Purucker, Christian; Wiedemann, Katharina; Neukum, Alexandra; Wolter, Stefan; Steiger, Reid


    This study investigated driver performance during system limits of partially automated driving. Using a motion-based driving simulator, drivers encountered different situations in which a partially automated vehicle could no longer safely keep the lateral guidance. Drivers were distracted by a non-driving related task on a touch display or driving without an additional secondary task. While driving in partially automated mode drivers could either take their hands off the steering wheel for only a short period of time (10s, so-called 'Hands-on' variant) or for an extended period of time (120s, so-called 'Hands-off' variant). When the system limit was reached (e.g., when entering a work zone with temporary lines), the lateral vehicle control by the automation was suddenly discontinued and a take-over request was issued to the drivers. Regardless of the hands-off interval and the availability of a secondary task, all drivers managed the transition to manual driving safely. No lane exceedances were observed and the situations were rated as 'harmless' by the drivers. The lack of difference between the hands-off intervals can be partly attributed to the fact that most of the drivers kept contact to the steering wheel, even in the hands-off condition. Although all drivers were able to control the system limits, most of them could not explain why exactly the take-over request was issued. The average helpfulness of the take-over request was rated on an intermediate level. Consequently, providing drivers with information about the reason for a system limit can be recommended. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Exploring Forensic Implications of the Fusion Drive

    Directory of Open Access Journals (Sweden)

    Shruti Gupta


    Full Text Available This paper explores the forensic implications of Apple's Fusion Drive. The Fusion Drive is an example of auto-tiered storage. It uses a combination of a flash drive and a magnetic drive. Data is moved between the drives automatically to maximize system performance. This is different from traditional caches because data is moved and not simply copied. The research included understanding the drive structure, populating the drive, and then accessing data in a controlled setting to observe data migration strategies. It was observed that all the data is first written to the flash drive with 4 GB of free space always maintained. If data on the magnetic drive is frequently accessed, it is promoted to the flash drive while demoting other information. Data is moved at a block-level and not a file-level. The Fusion Drive didn't alter the timestamps of files with data migration.

  9. Adaptive exponential synchronization of delayed neural networks with reaction-diffusion terms

    International Nuclear Information System (INIS)

    Sheng Li; Yang Huizhong; Lou Xuyang


    This paper presents an exponential synchronization scheme for a class of neural networks with time-varying and distributed delays and reaction-diffusion terms. An adaptive synchronization controller is derived to achieve the exponential synchronization of the drive-response structure of neural networks by using the Lyapunov stability theory. At the same time, the update laws of parameters are proposed to guarantee the synchronization of delayed neural networks with all parameters unknown. It is shown that the approaches developed here extend and improve the ideas presented in recent literatures.

  10. Neural Architectures for Control (United States)

    Peterson, James K.


    The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.

  11. Sacred or Neural?

    DEFF Research Database (Denmark)

    Runehov, Anne Leona Cesarine

    Are religious spiritual experiences merely the product of the human nervous system? Anne L.C. Runehov investigates the potential of contemporary neuroscience to explain religious experiences. Following the footsteps of Michael Persinger, Andrew Newberg and Eugene d'Aquili she defines...... the terminological bounderies of "religious experiences" and explores the relevant criteria for the proper evaluation of scientific research, with a particular focus on the validity of reductionist models. Runehov's theis is that the perspectives looked at do not necessarily exclude each other but can be merged....... The question "sacred or neural?" becomes a statement "sacred and neural". The synergies thus produced provide manifold opportunities for interdisciplinary dialogue and research....

  12. Chitosan derived co-spheroids of neural stem cells and mesenchymal stem cells for neural regeneration. (United States)

    Han, Hao-Wei; Hsu, Shan-Hui


    Chitosan has been considered as candidate biomaterials for neural applications. The effective treatment of neurodegeneration or injury to the central nervous system (CNS) is still in lack nowadays. Adult neural stem cells (NSCs) represents a promising cell source to treat the CNS diseases but they are limited in number. Here, we developed the core-shell spheroids of NSCs (shell) and mesenchymal stem cells (MSCs, core) by co-culturing cells on the chitosan surface. The NSCs in chitosan derived co-spheroids displayed a higher survival rate than those in NSC homo-spheroids. The direct interaction of NSCs with MSCs in the co-spheroids increased the Notch activity and differentiation tendency of NSCs. Meanwhile, the differentiation potential of MSCs in chitosan derived co-spheroids was significantly enhanced toward neural lineages. Furthermore, NSC homo-spheroids and NSC/MSC co-spheroids derived on chitosan were evaluated for their in vivo efficacy by the embryonic and adult zebrafish brain injury models. The locomotion activity of zebrafish receiving chitosan derived NSC homo-spheroids or NSC/MSC co-spheroids was partially rescued in both models. Meanwhile, the higher survival rate was observed in the group of adult zebrafish implanted with chitosan derived NSC/MSC co-spheroids as compared to NSC homo-spheroids. These evidences indicate that chitosan may provide an extracellular matrix-like environment to drive the interaction and the morphological assembly between NSCs and MSCs and promote their neural differentiation capacities, which can be used for neural regeneration. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Deconvolution using a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Lehman, S.K.


    Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.

  14. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan


    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  15. Upset Prediction in Friction Welding Using Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Wei Liu


    Full Text Available This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW, a radial basis function (RBF neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW and continuous drive friction welding (CDFW. The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.

  16. Positive behavioral contrast across food and alcohol reinforcers.


    McSweeney, F K; Melville, C L; Higa, J


    The present study examined behavioral contrast during concurrent and multiple schedules that provided food and alcohol reinforcers. Concurrent-schedule contrast occurred in the responding reinforced by food when alcohol reinforcers were removed. It also occurred in the responding reinforced by alcohol when food was removed. Multiple-schedule contrast appeared for food when alcohol reinforcers were removed, but not for alcohol when food was removed. These results show that behavioral contrast ...

  17. Shear behaviour of reinforced phyllite concrete beams

    International Nuclear Information System (INIS)

    Adom-Asamoah, Mark; Owusu Afrifa, Russell


    Highlights: ► Phyllite concrete beams often exhibited shear with anchorage bond failure. ► Different shear design provisions for reinforced phyllite beams are compared. ► Predicted shear capacity of phyllite beams must be modified by a reduction factor. -- Abstract: The shear behaviour of concrete beams made from phyllite aggregates subjected to monotonic and cyclic loading is reported. First diagonal shear crack load of beams with and without shear reinforcement was between 42–58% and 42–92% of the failure loads respectively. The phyllite concrete beams without shear links had lower post-diagonal cracking shear resistance compared to corresponding phyllite beams with shear links. As a result of hysteretic energy dissipation, limited cyclic loading affected the stiffness, strength and deformation of the phyllite beams with shear reinforcement. Generally, beams with and without shear reinforcement showed anchorage bond failure in addition to the shear failure due to high stress concentration near the supports. The ACI, BS and EC codes are conservative for the prediction of phyllite concrete beams without shear reinforcement but they all overestimate the shear strength of phyllite concrete beams with shear reinforcement. It is recommended that the predicted shear capacity of phyllite beams reinforced with steel stirrups be modified by a reduction factor of 0.7 in order to specify a high enough safety factor on their ultimate strength. It is also recommended that susceptibility of phyllite concrete beams to undergo anchorage bond failure is averted in design by the provision of greater anchorage lengths than usually permitted.

  18. Cost efficient carbon fibre reinforced thermoplastics with in-situ polymerization of polyamide (United States)

    Köhler, T.; Akdere, M.; Röding, T.; Gries, T.; Seide, G.


    Lightweight design has gained more and more relevance over the last decades. Especially in automotive industry it is of paramount importance to reduce weight and save fuel. At the same time the demand for safety and performance increases the components’ weight. To reach a trade-off between driving comfort and efficiency new lightweight materials have to be developed. One possible solution is the usage of carbon fibre reinforced thermoplastics (CFRTP) as a lightweight substitute material. In contrast to conventional carbon fibre reinforced plastics (CFRP), CFRTPs are cheaper and have a higher impact resistance. Furthermore they are characterized by hot forming ability, weldability and recyclability. However, the impregnation of the textile requires high pressure, because of the melted polymer’s high viscosity. A new innovative approach for CFRTP is the usage of in-situ polymerization with ɛ-caprolactam as matrix, which has a much lower viscosity and thus requires much lower pressure for impregnation and consolidation.

  19. Driving safety in elderly individuals. (United States)

    Marottoli, R A


    Driving safety in elderly individuals is becoming an increasingly important issue in geriatrics and in medical practice. The number of elderly drivers is increasing as the population ages, and especially as current generations of female drivers age. Concern is raised about their safe operation of motor vehicles because of the increasing likelihood with advancing age of developing conditions that may adversely affect the visual, cognitive, and motor abilities integral to driving. But this issue is not only a medical one, since there are social and political components as well. This discussion will describe the background of this issue, focus on the changes that may occur with aging and their potential relationship to driving ability, and, finally, will outline an approach that physicians may employ in their practice.

  20. Drive: Theory and Construct Validation. (United States)

    Siegling, Alex B; Petrides, K V


    This article explicates the theory of drive and describes the development and validation of two measures. A representative set of drive facets was derived from an extensive corpus of human attributes (Study 1). Operationalised using an International Personality Item Pool version (the Drive:IPIP), a three-factor model was extracted from the facets in two samples and confirmed on a third sample (Study 2). The multi-item IPIP measure showed congruence with a short form, based on single-item ratings of the facets, and both demonstrated cross-informant reliability. Evidence also supported the measures' convergent, discriminant, concurrent, and incremental validity (Study 3). Based on very promising findings, the authors hope to initiate a stream of research in what is argued to be a rather neglected niche of individual differences and non-cognitive assessment.