Performance study of LMS based adaptive algorithms for unknown system identification

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

Javed, Shazia; Ahmad, Noor Atinah

Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signalmore » is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment.« less

Adaptation to sensory input tunes visual cortex to criticality

NASA Astrophysics Data System (ADS)

Shew, Woodrow L.; Clawson, Wesley P.; Pobst, Jeff; Karimipanah, Yahya; Wright, Nathaniel C.; Wessel, Ralf

2015-08-01

A long-standing hypothesis at the interface of physics and neuroscience is that neural networks self-organize to the critical point of a phase transition, thereby optimizing aspects of sensory information processing. This idea is partially supported by strong evidence for critical dynamics observed in the cerebral cortex, but the impact of sensory input on these dynamics is largely unknown. Thus, the foundations of this hypothesis--the self-organization process and how it manifests during strong sensory input--remain unstudied experimentally. Here we show in visual cortex and in a computational model that strong sensory input initially elicits cortical network dynamics that are not critical, but adaptive changes in the network rapidly tune the system to criticality. This conclusion is based on observations of multifaceted scaling laws predicted to occur at criticality. Our findings establish sensory adaptation as a self-organizing mechanism that maintains criticality in visual cortex during sensory information processing.

Input reconstruction for networked control systems subject to deception attacks and data losses on control signals

NASA Astrophysics Data System (ADS)

Keller, J. Y.; Chabir, K.; Sauter, D.

2016-03-01

State estimation of stochastic discrete-time linear systems subject to unknown inputs or constant biases has been widely studied but no work has been dedicated to the case where a disturbance switches between unknown input and constant bias. We show that such disturbance can affect a networked control system subject to deception attacks and data losses on the control signals transmitted by the controller to the plant. This paper proposes to estimate the switching disturbance from an augmented state version of the intermittent unknown input Kalman filter recently developed by the authors. Sufficient stochastic stability conditions are established when the arrival binary sequence of data losses follows a Bernoulli random process.

Parameter and state estimation in a Neisseria meningitidis model: A study case of Niger

NASA Astrophysics Data System (ADS)

Bowong, S.; Mountaga, L.; Bah, A.; Tewa, J. J.; Kurths, J.

2016-12-01

Neisseria meningitidis (Nm) is a major cause of bacterial meningitidis outbreaks in Africa and the Middle East. The availability of yearly reported meningitis cases in the African meningitis belt offers the opportunity to analyze the transmission dynamics and the impact of control strategies. In this paper, we propose a method for the estimation of state variables that are not accessible to measurements and an unknown parameter in a Nm model. We suppose that the yearly number of Nm induced mortality and the total population are known inputs, which can be obtained from data, and the yearly number of new Nm cases is the model output. We also suppose that the Nm transmission rate is an unknown parameter. We first show how the recruitment rate into the population can be estimated using real data of the total population and Nm induced mortality. Then, we use an auxiliary system called observer whose solutions converge exponentially to those of the original model. This observer does not use the unknown infection transmission rate but only uses the known inputs and the model output. This allows us to estimate unmeasured state variables such as the number of carriers that play an important role in the transmission of the infection and the total number of infected individuals within a human community. Finally, we also provide a simple method to estimate the unknown Nm transmission rate. In order to validate the estimation results, numerical simulations are conducted using real data of Niger.

Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs.

PubMed

Shi, Wuxi; Luo, Rui; Li, Baoquan

2017-01-01

In this study, an adaptive fuzzy prescribed performance control approach is developed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with unknown control direction and unknown dead-zone inputs. The properties of symmetric matrix are exploited to design adaptive fuzzy prescribed performance controller, and a Nussbaum-type function is incorporated in the controller to estimate the unknown control direction. This method has two prominent advantages: it does not require the priori knowledge of control direction and only three parameters need to be updated on-line for this MIMO systems. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Modulation, Adaptation, and Control of Orofacial Pathways in Healthy Adults

ERIC Educational Resources Information Center

Estep, Meredith E.

2009-01-01

Although the healthy adult possesses a large repertoire of coordinative strategies for oromotor behaviors, a range of nonverbal, speech-like movements can be observed during speech. The extent of overlap among sensorimotor speech and nonspeech neural correlates and the role of neuromodulatory inputs generated during oromotor behaviors are unknown.…

Adaptive Neural Output Feedback Control for Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis and Unknown Control Directions.

PubMed

Yu, Zhaoxu; Li, Shugang; Yu, Zhaosheng; Li, Fangfei

2018-04-01

This paper investigates the problem of output feedback adaptive stabilization for a class of nonstrict-feedback stochastic nonlinear systems with both unknown backlashlike hysteresis and unknown control directions. A new linear state transformation is applied to the original system, and then, control design for the new system becomes feasible. By combining the neural network's (NN's) parameterization, variable separation technique, and Nussbaum gain function method, an input-driven observer-based adaptive NN control scheme, which involves only one parameter to be updated, is developed for such systems. All closed-loop signals are bounded in probability and the error signals remain semiglobally bounded in the fourth moment (or mean square). Finally, the effectiveness and the applicability of the proposed control design are verified by two simulation examples.

Wideband Motion Control by Position and Acceleration Input Based Disturbance Observer

NASA Astrophysics Data System (ADS)

Irie, Kouhei; Katsura, Seiichiro; Ohishi, Kiyoshi

The disturbance observer can observe and suppress the disturbance torque within its bandwidth. Recent motion systems begin to spread in the society and they are required to have ability to contact with unknown environment. Such a haptic motion requires much wider bandwidth. However, since the conventional disturbance observer attains the acceleration response by the second order derivative of position response, the bandwidth is limited due to the derivative noise. This paper proposes a novel structure of a disturbance observer. The proposed disturbance observer uses an acceleration sensor for enlargement of bandwidth. Generally, the bandwidth of an acceleration sensor is from 1Hz to more than 1kHz. To cover DC range, the conventional position sensor based disturbance observer is integrated. Thus, the performance of the proposed Position and Acceleration input based disturbance observer (PADO) is superior to the conventional one. The PADO is applied to position control (infinity stiffness) and force control (zero stiffness). The numerical and experimental results show viability of the proposed method.

Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.

PubMed

Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang

2014-06-01

This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

Reinforcement-learning-based output-feedback control of nonstrict nonlinear discrete-time systems with application to engine emission control.

PubMed

Shih, Peter; Kaul, Brian C; Jagannathan, Sarangapani; Drallmeier, James A

2009-10-01

A novel reinforcement-learning-based output adaptive neural network (NN) controller, which is also referred to as the adaptive-critic NN controller, is developed to deliver the desired tracking performance for a class of nonlinear discrete-time systems expressed in nonstrict feedback form in the presence of bounded and unknown disturbances. The adaptive-critic NN controller consists of an observer, a critic, and two action NNs. The observer estimates the states and output, and the two action NNs provide virtual and actual control inputs to the nonlinear discrete-time system. The critic approximates a certain strategic utility function, and the action NNs minimize the strategic utility function and control inputs. All NN weights adapt online toward minimization of a performance index, utilizing the gradient-descent-based rule, in contrast with iteration-based adaptive-critic schemes. Lyapunov functions are used to show the stability of the closed-loop tracking error, weights, and observer estimates. Separation and certainty equivalence principles, persistency of excitation condition, and linearity in the unknown parameter assumption are not needed. Experimental results on a spark ignition (SI) engine operating lean at an equivalence ratio of 0.75 show a significant (25%) reduction in cyclic dispersion in heat release with control, while the average fuel input changes by less than 1% compared with the uncontrolled case. Consequently, oxides of nitrogen (NO(x)) drop by 30%, and unburned hydrocarbons drop by 16% with control. Overall, NO(x)'s are reduced by over 80% compared with stoichiometric levels.

Adaptive control of Parkinson's state based on a nonlinear computational model with unknown parameters.

PubMed

Su, Fei; Wang, Jiang; Deng, Bin; Wei, Xi-Le; Chen, Ying-Yuan; Liu, Chen; Li, Hui-Yan

2015-02-01

The objective here is to explore the use of adaptive input-output feedback linearization method to achieve an improved deep brain stimulation (DBS) algorithm for closed-loop control of Parkinson's state. The control law is based on a highly nonlinear computational model of Parkinson's disease (PD) with unknown parameters. The restoration of thalamic relay reliability is formulated as the desired outcome of the adaptive control methodology, and the DBS waveform is the control input. The control input is adjusted in real time according to estimates of unknown parameters as well as the feedback signal. Simulation results show that the proposed adaptive control algorithm succeeds in restoring the relay reliability of the thalamus, and at the same time achieves accurate estimation of unknown parameters. Our findings point to the potential value of adaptive control approach that could be used to regulate DBS waveform in more effective treatment of PD.

Adaptive non-predictor control of lower triangular uncertain nonlinear systems with an unknown time-varying delay in the input

NASA Astrophysics Data System (ADS)

Koo, Min-Sung; Choi, Ho-Lim

2018-01-01

In this paper, we consider a control problem for a class of uncertain nonlinear systems in which there exists an unknown time-varying delay in the input and lower triangular nonlinearities. Usually, in the existing results, input delays have been coupled with feedforward (or upper triangular) nonlinearities; in other words, the combination of lower triangular nonlinearities and input delay has been rare. Motivated by the existing controller for input-delayed chain of integrators with nonlinearity, we show that the control of input-delayed nonlinear systems with two particular types of lower triangular nonlinearities can be done. As a control solution, we propose a newly designed feedback controller whose main features are its dynamic gain and non-predictor approach. Three examples are given for illustration.

Switched-Observer-Based Adaptive Neural Control of MIMO Switched Nonlinear Systems With Unknown Control Gains.

PubMed

Long, Lijun; Zhao, Jun

2017-07-01

In this paper, the problem of adaptive neural output-feedback control is addressed for a class of multi-input multioutput (MIMO) switched uncertain nonlinear systems with unknown control gains. Neural networks (NNs) are used to approximate unknown nonlinear functions. In order to avoid the conservativeness caused by adoption of a common observer for all subsystems, an MIMO NN switched observer is designed to estimate unmeasurable states. A new switched observer-based adaptive neural control technique for the problem studied is then provided by exploiting the classical average dwell time (ADT) method and the backstepping method and the Nussbaum gain technique. It effectively handles the obstacle about the coexistence of multiple Nussbaum-type function terms, and improves the classical ADT method, since the exponential decline property of Lyapunov functions for individual subsystems is no longer satisfied. It is shown that the technique proposed is able to guarantee semiglobal uniformly ultimately boundedness of all the signals in the closed-loop system under a class of switching signals with ADT, and the tracking errors converge to a small neighborhood of the origin. The effectiveness of the approach proposed is illustrated by its application to a two inverted pendulum system.

Active vibration control for piezoelectricity cantilever beam: an adaptive feedforward control method

NASA Astrophysics Data System (ADS)

Zhu, Qiao; Yue, Jun-Zhou; Liu, Wei-Qun; Wang, Xu-Dong; Chen, Jun; Hu, Guang-Di

2017-04-01

This work is focused on the active vibration control of piezoelectric cantilever beam, where an adaptive feedforward controller (AFC) is utilized to reject the vibration with unknown multiple frequencies. First, the experiment setup and its mathematical model are introduced. Due to that the channel between the disturbance and the vibration output is unknown in practice, a concept of equivalent input disturbance (EID) is employed to put an equivalent disturbance into the input channel. In this situation, the vibration control can be achieved by setting the control input be the identified EID. Then, for the EID with known multiple frequencies, the AFC is introduced to perfectly reject the vibration but is sensitive to the frequencies. In order to accurately identify the unknown frequencies of EID in presence of the random disturbances and un-modeled nonlinear dynamics, the time-frequency-analysis (TFA) method is employed to precisely identify the unknown frequencies. Consequently, a TFA-based AFC algorithm is proposed to the active vibration control with unknown frequencies. Finally, four cases are given to illustrate the efficiency of the proposed TFA-based AFC algorithm by experiment.

Structural identifiability analyses of candidate models for in vitro Pitavastatin hepatic uptake.

PubMed

Grandjean, Thomas R B; Chappell, Michael J; Yates, James W T; Evans, Neil D

2014-05-01

In this paper a review of the application of four different techniques (a version of the similarity transformation approach for autonomous uncontrolled systems, a non-differential input/output observable normal form approach, the characteristic set differential algebra and a recent algebraic input/output relationship approach) to determine the structural identifiability of certain in vitro nonlinear pharmacokinetic models is provided. The Organic Anion Transporting Polypeptide (OATP) substrate, Pitavastatin, is used as a probe on freshly isolated animal and human hepatocytes. Candidate pharmacokinetic non-linear compartmental models have been derived to characterise the uptake process of Pitavastatin. As a prerequisite to parameter estimation, structural identifiability analyses are performed to establish that all unknown parameters can be identified from the experimental observations available. Copyright © 2013. Published by Elsevier Ireland Ltd.

Effects of Meteorological Data Quality on Snowpack Modeling

NASA Astrophysics Data System (ADS)

Havens, S.; Marks, D. G.; Robertson, M.; Hedrick, A. R.; Johnson, M.

2017-12-01

Detailed quality control of meteorological inputs is the most time-intensive component of running the distributed, physically-based iSnobal snow model, and the effect of data quality of the inputs on the model is unknown. The iSnobal model has been run operationally since WY2013, and is currently run in several basins in Idaho and California. The largest amount of user input during modeling is for the quality control of precipitation, temperature, relative humidity, solar radiation, wind speed and wind direction inputs. Precipitation inputs require detailed user input and are crucial to correctly model the snowpack mass. This research applies a range of quality control methods to meteorological input, from raw input with minimal cleaning, to complete user-applied quality control. The meteorological input cleaning generally falls into two categories. The first is global minimum/maximum and missing value correction that could be corrected and/or interpolated with automated processing. The second category is quality control for inputs that are not globally erroneous, yet are still unreasonable and generally indicate malfunctioning measurement equipment, such as temperature or relative humidity that remains constant, or does not correlate with daily trends observed at nearby stations. This research will determine how sensitive model outputs are to different levels of quality control and guide future operational applications.

Electrical and Optical Activation of Mesoscale Neural Circuits with Implications for Coding.

PubMed

Millard, Daniel C; Whitmire, Clarissa J; Gollnick, Clare A; Rozell, Christopher J; Stanley, Garrett B

2015-11-25

Artificial activation of neural circuitry through electrical microstimulation and optogenetic techniques is important for both scientific discovery of circuit function and for engineered approaches to alleviate various disorders of the nervous system. However, evidence suggests that neural activity generated by artificial stimuli differs dramatically from normal circuit function, in terms of both the local neuronal population activity at the site of activation and the propagation to downstream brain structures. The precise nature of these differences and the implications for information processing remain unknown. Here, we used voltage-sensitive dye imaging of primary somatosensory cortex in the anesthetized rat in response to deflections of the facial vibrissae and electrical or optogenetic stimulation of thalamic neurons that project directly to the somatosensory cortex. Although the different inputs produced responses that were similar in terms of the average cortical activation, the variability of the cortical response was strikingly different for artificial versus sensory inputs. Furthermore, electrical microstimulation resulted in highly unnatural spatial activation of cortex, whereas optical input resulted in spatial cortical activation that was similar to that induced by sensory inputs. A thalamocortical network model suggested that observed differences could be explained by differences in the way in which artificial and natural inputs modulate the magnitude and synchrony of population activity. Finally, the variability structure in the response for each case strongly influenced the optimal inputs for driving the pathway from the perspective of an ideal observer of cortical activation when considered in the context of information transmission. Artificial activation of neural circuitry through electrical microstimulation and optogenetic techniques is important for both scientific discovery and clinical translation. However, neural activity generated by these artificial means differs dramatically from normal circuit function, both locally and in the propagation to downstream brain structures. The precise nature of these differences and the implications for information processing remain unknown. The significance of this work is in quantifying the differences, elucidating likely mechanisms underlying the differences, and determining the implications for information processing. Copyright © 2015 the authors 0270-6474/15/3515702-14$15.00/0.

A Unified Approach to Adaptive Neural Control for Nonlinear Discrete-Time Systems With Nonlinear Dead-Zone Input.

PubMed

Liu, Yan-Jun; Gao, Ying; Tong, Shaocheng; Chen, C L Philip

2016-01-01

In this paper, an effective adaptive control approach is constructed to stabilize a class of nonlinear discrete-time systems, which contain unknown functions, unknown dead-zone input, and unknown control direction. Different from linear dead zone, the dead zone, in this paper, is a kind of nonlinear dead zone. To overcome the noncausal problem, which leads to the control scheme infeasible, the systems can be transformed into a m -step-ahead predictor. Due to nonlinear dead-zone appearance, the transformed predictor still contains the nonaffine function. In addition, it is assumed that the gain function of dead-zone input and the control direction are unknown. These conditions bring about the difficulties and the complicacy in the controller design. Thus, the implicit function theorem is applied to deal with nonaffine dead-zone appearance, the problem caused by the unknown control direction can be resolved through applying the discrete Nussbaum gain, and the neural networks are used to approximate the unknown function. Based on the Lyapunov theory, all the signals of the resulting closed-loop system are proved to be semiglobal uniformly ultimately bounded. Moreover, the tracking error is proved to be regulated to a small neighborhood around zero. The feasibility of the proposed approach is demonstrated by a simulation example.

State estimation of spatio-temporal phenomena

NASA Astrophysics Data System (ADS)

Yu, Dan

This dissertation addresses the state estimation problem of spatio-temporal phenomena which can be modeled by partial differential equations (PDEs), such as pollutant dispersion in the atmosphere. After discretizing the PDE, the dynamical system has a large number of degrees of freedom (DOF). State estimation using Kalman Filter (KF) is computationally intractable, and hence, a reduced order model (ROM) needs to be constructed first. Moreover, the nonlinear terms, external disturbances or unknown boundary conditions can be modeled as unknown inputs, which leads to an unknown input filtering problem. Furthermore, the performance of KF could be improved by placing sensors at feasible locations. Therefore, the sensor scheduling problem to place multiple mobile sensors is of interest. The first part of the dissertation focuses on model reduction for large scale systems with a large number of inputs/outputs. A commonly used model reduction algorithm, the balanced proper orthogonal decomposition (BPOD) algorithm, is not computationally tractable for large systems with a large number of inputs/outputs. Inspired by the BPOD and randomized algorithms, we propose a randomized proper orthogonal decomposition (RPOD) algorithm and a computationally optimal RPOD (RPOD*) algorithm, which construct an ROM to capture the input-output behaviour of the full order model, while reducing the computational cost of BPOD by orders of magnitude. It is demonstrated that the proposed RPOD* algorithm could construct the ROM in real-time, and the performance of the proposed algorithms on different advection-diffusion equations. Next, we consider the state estimation problem of linear discrete-time systems with unknown inputs which can be treated as a wide-sense stationary process with rational power spectral density, while no other prior information needs to be known. We propose an autoregressive (AR) model based unknown input realization technique which allows us to recover the input statistics from the output data by solving an appropriate least squares problem, then fit an AR model to the recovered input statistics and construct an innovations model of the unknown inputs using the eigensystem realization algorithm. The proposed algorithm outperforms the augmented two-stage Kalman Filter (ASKF) and the unbiased minimum-variance (UMV) algorithm are shown in several examples. Finally, we propose a framework to place multiple mobile sensors to optimize the long-term performance of KF in the estimation of the state of a PDE. The major challenges are that placing multiple sensors is an NP-hard problem, and the optimization problem is non-convex in general. In this dissertation, first, we construct an ROM using RPOD* algorithm, and then reduce the feasible sensor locations into a subset using the ROM. The Information Space Receding Horizon Control (I-RHC) approach and a modified Monte Carlo Tree Search (MCTS) approach are applied to solve the sensor scheduling problem using the subset. Various applications have been provided to demonstrate the performance of the proposed approach.

A note on the blind deconvolution of multiple sparse signals from unknown subspaces

NASA Astrophysics Data System (ADS)

Cosse, Augustin

2017-08-01

This note studies the recovery of multiple sparse signals, xn ∈ ℝL, n = 1, . . . , N, from the knowledge of their convolution with an unknown point spread function h ∈ ℝL. When the point spread function is known to be nonzero, |h[k]| > 0, this blind deconvolution problem can be relaxed into a linear, ill-posed inverse problem in the vector concatenating the unknown inputs xn together with the inverse of the filter, d ∈ ℝL where d[k] := 1/h[k]. When prior information is given on the input subspaces, the resulting overdetermined linear system can be solved efficiently via least squares (see Ling et al. 20161). When no information is given on those subspaces, and the inputs are only known to be sparse, it still remains possible to recover these inputs along with the filter by considering an additional l1 penalty. This note certifies exact recovery of both the unknown PSF and unknown sparse inputs, from the knowledge of their convolutions, as soon as the number of inputs N and the dimension of each input, L , satisfy L ≳ N and N ≳ T2max, up to log factors. Here Tmax = maxn{Tn} and Tn, n = 1, . . . , N denote the supports of the inputs xn. Our proof system combines the recent results on blind deconvolution via least squares to certify invertibility of the linear map encoding the convolutions, with the construction of a dual certificate following the structure first suggested in Candés et al. 2007.2 Unlike in these papers, however, it is not possible to rely on the norm ||(A*TAT)-1|| to certify recovery. We instead use a combination of the Schur Complement and Neumann series to compute an expression for the inverse (A*TAT)-1. Given this expression, it is possible to show that the poorly scaled blocks in (A*TAT)-1 are multiplied by the better scaled ones or vanish in the construction of the certificate. Recovery is certified with high probablility on the choice of the supports and distribution of the signs of each input xn on the support. The paper follows the line of previous work by Wang et al. 20163 where the authors guarantee recovery for subgaussian × Bernoulli inputs satisfying 𝔼xn|k| ∈ [1/10, 1] as soon as N ≳ L. Examples of applications include seismic imaging with unknown source or marine seismic data deghosting, magnetic resonance autocalibration or multiple channel estimation in communication. Numerical experiments are provided along with a discussion on the sample complexity tightness.

Estimating unknown input parameters when implementing the NGA ground-motion prediction equations in engineering practice

USGS Publications Warehouse

Kaklamanos, James; Baise, Laurie G.; Boore, David M.

2011-01-01

The ground-motion prediction equations (GMPEs) developed as part of the Next Generation Attenuation of Ground Motions (NGA-West) project in 2008 are becoming widely used in seismic hazard analyses. However, these new models are considerably more complicated than previous GMPEs, and they require several more input parameters. When employing the NGA models, users routinely face situations in which some of the required input parameters are unknown. In this paper, we present a framework for estimating the unknown source, path, and site parameters when implementing the NGA models in engineering practice, and we derive geometrically-based equations relating the three distance measures found in the NGA models. Our intent is for the content of this paper not only to make the NGA models more accessible, but also to help with the implementation of other present or future GMPEs.

Neuro-adaptive backstepping control of SISO non-affine systems with unknown gain sign.

PubMed

Ramezani, Zahra; Arefi, Mohammad Mehdi; Zargarzadeh, Hassan; Jahed-Motlagh, Mohammad Reza

2016-11-01

This paper presents two neuro-adaptive controllers for a class of uncertain single-input, single-output (SISO) nonlinear non-affine systems with unknown gain sign. The first approach is state feedback controller, so that a neuro-adaptive state-feedback controller is constructed based on the backstepping technique. The second approach is an observer-based controller and K-filters are designed to estimate the system states. The proposed method relaxes a priori knowledge of control gain sign and therefore by utilizing the Nussbaum-type functions this problem is addressed. In these methods, neural networks are employed to approximate the unknown nonlinear functions. The proposed adaptive control schemes guarantee that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB). Finally, the theoretical results are numerically verified through simulation examples. Simulation results show the effectiveness of the proposed methods. Copyright © 2016 ISA. All rights reserved.

Input-output identification of controlled discrete manufacturing systems

NASA Astrophysics Data System (ADS)

Estrada-Vargas, Ana Paula; López-Mellado, Ernesto; Lesage, Jean-Jacques

2014-03-01

The automated construction of discrete event models from observations of external system's behaviour is addressed. This problem, often referred to as system identification, allows obtaining models of ill-known (or even unknown) systems. In this article, an identification method for discrete event systems (DESs) controlled by a programmable logic controller is presented. The method allows processing a large quantity of observed long sequences of input/output signals generated by the controller and yields an interpreted Petri net model describing the closed-loop behaviour of the automated DESs. The proposed technique allows the identification of actual complex systems because it is sufficiently efficient and well adapted to cope with both the technological characteristics of industrial controllers and data collection requirements. Based on polynomial-time algorithms, the method is implemented as an efficient software tool which constructs and draws the model automatically; an overview of this tool is given through a case study dealing with an automated manufacturing system.

Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints.

PubMed

Chen, Ziting; Li, Zhijun; Chen, C L Philip

2017-06-01

An adaptive neural control strategy for multiple input multiple output nonlinear systems with various constraints is presented in this paper. To deal with the nonsymmetric input nonlinearity and the constrained states, the proposed adaptive neural control is combined with the backstepping method, radial basis function neural network, barrier Lyapunov function (BLF), and disturbance observer. By ensuring the boundedness of the BLF of the closed-loop system, it is demonstrated that the output tracking is achieved with all states remaining in the constraint sets and the general assumption on nonsingularity of unknown control coefficient matrices has been eliminated. The constructed adaptive neural control has been rigorously proved that it can guarantee the semiglobally uniformly ultimate boundedness of all signals in the closed-loop system. Finally, the simulation studies on a 2-DOF robotic manipulator system indicate that the designed adaptive control is effective.

Unified quantum no-go theorems and transforming of quantum pure states in a restricted set

NASA Astrophysics Data System (ADS)

Luo, Ming-Xing; Li, Hui-Ran; Lai, Hong; Wang, Xiaojun

2017-12-01

The linear superposition principle in quantum mechanics is essential for several no-go theorems such as the no-cloning theorem, the no-deleting theorem and the no-superposing theorem. In this paper, we investigate general quantum transformations forbidden or permitted by the superposition principle for various goals. First, we prove a no-encoding theorem that forbids linearly superposing of an unknown pure state and a fixed pure state in Hilbert space of a finite dimension. The new theorem is further extended for multiple copies of an unknown state as input states. These generalized results of the no-encoding theorem include the no-cloning theorem, the no-deleting theorem and the no-superposing theorem as special cases. Second, we provide a unified scheme for presenting perfect and imperfect quantum tasks (cloning and deleting) in a one-shot manner. This scheme may lead to fruitful results that are completely characterized with the linear independence of the representative vectors of input pure states. The upper bounds of the efficiency are also proved. Third, we generalize a recent superposing scheme of unknown states with a fixed overlap into new schemes when multiple copies of an unknown state are as input states.

Quasi-minimal active disturbance rejection control of MIMO perturbed linear systems based on differential neural networks and the attractive ellipsoid method.

PubMed

Salgado, Iván; Mera-Hernández, Manuel; Chairez, Isaac

2017-11-01

This study addresses the problem of designing an output-based controller to stabilize multi-input multi-output (MIMO) systems in the presence of parametric disturbances as well as uncertainties in the state model and output noise measurements. The controller design includes a linear state transformation which separates uncertainties matched to the control input and the unmatched ones. A differential neural network (DNN) observer produces a nonlinear approximation of the matched perturbation and the unknown states simultaneously in the transformed coordinates. This study proposes the use of the Attractive Ellipsoid Method (AEM) to optimize the gains of the controller and the gain observer in the DNN structure. As a consequence, the obtained control input minimizes the convergence zone for the estimation error. Moreover, the control design uses the estimated disturbance provided by the DNN to obtain a better performance in the stabilization task in comparison with a quasi-minimal output feedback controller based on a Luenberger observer and a sliding mode controller. Numerical results pointed out the advantages obtained by the nonlinear control based on the DNN observer. The first example deals with the stabilization of an academic linear MIMO perturbed system and the second example stabilizes the trajectories of a DC-motor into a predefined operation point. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Application of non-destructive testing to evaluate unknown foundations for Pennsylvania bridges.

DOT National Transportation Integrated Search

2013-08-01

Unknown bridge foundations present a unique challenge to Departments of Transportation (DOT) across the country since : foundation characteristics are a necessary input to assess scour vulnerability and to develop appropriate scour countermeasures. :...

Distributed Adaptive Neural Network Output Tracking of Leader-Following High-Order Stochastic Nonlinear Multiagent Systems With Unknown Dead-Zone Input.

PubMed

Hua, Changchun; Zhang, Liuliu; Guan, Xinping

2017-01-01

This paper studies the problem of distributed output tracking consensus control for a class of high-order stochastic nonlinear multiagent systems with unknown nonlinear dead-zone under a directed graph topology. The adaptive neural networks are used to approximate the unknown nonlinear functions and a new inequality is used to deal with the completely unknown dead-zone input. Then, we design the controllers based on backstepping method and the dynamic surface control technique. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of semiglobally uniform ultimate boundedness and the tracking errors between the leader and the followers approach to a small residual set based on Lyapunov stability theory. Finally, two simulation examples are presented to show the effectiveness and the advantages of the proposed techniques.

Prescribed performance distributed consensus control for nonlinear multi-agent systems with unknown dead-zone input

NASA Astrophysics Data System (ADS)

Cui, Guozeng; Xu, Shengyuan; Ma, Qian; Li, Yongmin; Zhang, Zhengqiang

2018-05-01

In this paper, the problem of prescribed performance distributed output consensus for higher-order non-affine nonlinear multi-agent systems with unknown dead-zone input is investigated. Fuzzy logical systems are utilised to identify the unknown nonlinearities. By introducing prescribed performance, the transient and steady performance of synchronisation errors are guaranteed. Based on Lyapunov stability theory and the dynamic surface control technique, a new distributed consensus algorithm for non-affine nonlinear multi-agent systems is proposed, which ensures cooperatively uniformly ultimately boundedness of all signals in the closed-loop systems and enables the output of each follower to synchronise with the leader within predefined bounded error. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed control scheme.

Adaptive Fuzzy Control Design for Stochastic Nonlinear Switched Systems With Arbitrary Switchings and Unmodeled Dynamics.

PubMed

Li, Yongming; Sui, Shuai; Tong, Shaocheng

2017-02-01

This paper deals with the problem of adaptive fuzzy output feedback control for a class of stochastic nonlinear switched systems. The controlled system in this paper possesses unmeasured states, completely unknown nonlinear system functions, unmodeled dynamics, and arbitrary switchings. A state observer which does not depend on the switching signal is constructed to tackle the unmeasured states. Fuzzy logic systems are employed to identify the completely unknown nonlinear system functions. Based on the common Lyapunov stability theory and stochastic small-gain theorem, a new robust adaptive fuzzy backstepping stabilization control strategy is developed. The stability of the closed-loop system on input-state-practically stable in probability is proved. The simulation results are given to verify the efficiency of the proposed fuzzy adaptive control scheme.

Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method.

PubMed

Zhang, Huaguang; Cui, Lili; Zhang, Xin; Luo, Yanhong

2011-12-01

In this paper, a novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method. In the design of the controller, only available input-output data is required instead of known system dynamics. A data-driven model is established by a recurrent neural network (NN) to reconstruct the unknown system dynamics using available input-output data. By adding a novel adjustable term related to the modeling error, the resultant modeling error is first guaranteed to converge to zero. Then, based on the obtained data-driven model, the ADP method is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Further, a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method. Based on Lyapunov approach, stability analysis of the closed-loop system is performed to show that the proposed controller guarantees the system state asymptotically tracking the desired trajectory. Additionally, the obtained control input is proven to be close to the optimal control input within a small bound. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed control scheme.

Role of primary afferents in the developmental regulation of motor axon synapse numbers on Renshaw cells

PubMed Central

Siembab, Valerie C.; Gomez-Perez, Laura; Rotterman, Travis M.; Shneider, Neil A.; Alvarez, Francisco J.

2015-01-01

Motor function in mammalian species depends on the maturation of spinal circuits formed by a large variety of interneurons that regulate motoneuron firing and motor output. Interneuron activity is in turn modulated by the organization of their synaptic inputs, but the principles governing the development of specific synaptic architectures unique to each premotor interneuron are unknown. For example, Renshaw cells receive, at least in the neonate, convergent inputs from sensory afferents (likely Ia) and motor axons raising the question of whether they interact during Renshaw cell development. In other well-studied neurons, like Purkinje cells, heterosynaptic competition between inputs from different sources shapes synaptic organization. To examine the possibility that sensory afferents modulate synaptic maturation on developing Renshaw cells, we used three animal models in which afferent inputs in the ventral horn are dramatically reduced (Er81(−/−) knockout), weakened (Egr3(−/−) knockout) or strengthened (mlcNT3(+/−) transgenic). We demonstrate that increasing the strength of sensory inputs on Renshaw cells prevents their de-selection and reduces motor axon synaptic density and, in contrast, absent or diminished sensory afferent inputs correlate with increased densities of motor axons synapses. No effects were observed on other glutamatergic inputs. We conclude that the early strength of Ia synapses influences their maintenance or weakening during later development and that heterosynaptic influences from sensory synapses during early development regulates the density and organization of motor inputs on mature Renshaw cells. PMID:26660356

Adaptive Fuzzy Output Constrained Control Design for Multi-Input Multioutput Stochastic Nonstrict-Feedback Nonlinear Systems.

PubMed

Li, Yongming; Tong, Shaocheng

2017-12-01

In this paper, an adaptive fuzzy output constrained control design approach is addressed for multi-input multioutput uncertain stochastic nonlinear systems in nonstrict-feedback form. The nonlinear systems addressed in this paper possess unstructured uncertainties, unknown gain functions and unknown stochastic disturbances. Fuzzy logic systems are utilized to tackle the problem of unknown nonlinear uncertainties. The barrier Lyapunov function technique is employed to solve the output constrained problem. In the framework of backstepping design, an adaptive fuzzy control design scheme is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.

An adaptive three-stage extended Kalman filter for nonlinear discrete-time system in presence of unknown inputs.

PubMed

Xiao, Mengli; Zhang, Yongbo; Wang, Zhihua; Fu, Huimin

2018-04-01

Considering the performances of conventional Kalman filter may seriously degrade when it suffers stochastic faults and unknown input, which is very common in engineering problems, a new type of adaptive three-stage extended Kalman filter (AThSEKF) is proposed to solve state and fault estimation in nonlinear discrete-time system under these conditions. The three-stage UV transformation and adaptive forgetting factor are introduced for derivation, and by comparing with the adaptive augmented state extended Kalman filter, it is proven to be uniformly asymptotically stable. Furthermore, the adaptive three-stage extended Kalman filter is applied to a two-dimensional radar tracking scenario to illustrate the effect, and the performance is compared with that of conventional three stage extended Kalman filter (ThSEKF) and the adaptive two-stage extended Kalman filter (ATEKF). The results show that the adaptive three-stage extended Kalman filter is more effective than these two filters when facing the nonlinear discrete-time systems with information of unknown inputs not perfectly known. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Vehicle longitudinal velocity estimation during the braking process using unknown input Kalman filter

NASA Astrophysics Data System (ADS)

Moaveni, Bijan; Khosravi Roqaye Abad, Mahdi; Nasiri, Sayyad

2015-10-01

In this paper, vehicle longitudinal velocity during the braking process is estimated by measuring the wheels speed. Here, a new algorithm based on the unknown input Kalman filter is developed to estimate the vehicle longitudinal velocity with a minimum mean square error and without using the value of braking torque in the estimation procedure. The stability and convergence of the filter are analysed and proved. Effectiveness of the method is shown by designing a real experiment and comparing the estimation result with actual longitudinal velocity computing from a three-axis accelerometer output.

Synchronization of motor unit firings: an epiphenomenon of firing rate characteristics not common inputs

PubMed Central

Kline, Joshua C.

2015-01-01

Synchronous motor unit firing instances have been attributed to anatomical inputs shared by motoneurons. Yet, there is a lack of empirical evidence confirming the notion that common inputs elicit synchronization under voluntary conditions. We tested this notion by measuring synchronization between motor unit action potential trains (MUAPTs) as their firing rates progressed within a contraction from a relatively low force level to a higher one. On average, the degree of synchronization decreased as the force increased. The common input notion provides no empirically supported explanation for the observed synchronization behavior. Therefore, we investigated a more probable explanation for synchronization. Our data set of 17,546 paired MUAPTs revealed that the degree of synchronization varies as a function of two characteristics of the motor unit firing rate: the similarity and the slope as a function of force. Both are measures of the excitation of the motoneurons. As the force generated by the muscle increases, the firing rate slope decreases, and the synchronization correspondingly decreases. Different muscles have motor units with different firing rate characteristics and display different amounts of synchronization. Although this association is not proof of causality, it consistently explains our observations and strongly suggests further investigation. So viewed, synchronization is likely an epiphenomenon, subject to countless unknown neural interactions. As such, synchronous firing instances may not be the product of a specific design and may not serve a specific physiological purpose. Our explanation for synchronization has the advantage of being supported by empirical evidence, whereas the common input does not. PMID:26490288

Synchronization of motor unit firings: an epiphenomenon of firing rate characteristics not common inputs.

PubMed

Kline, Joshua C; De Luca, Carlo J

2016-01-01

Synchronous motor unit firing instances have been attributed to anatomical inputs shared by motoneurons. Yet, there is a lack of empirical evidence confirming the notion that common inputs elicit synchronization under voluntary conditions. We tested this notion by measuring synchronization between motor unit action potential trains (MUAPTs) as their firing rates progressed within a contraction from a relatively low force level to a higher one. On average, the degree of synchronization decreased as the force increased. The common input notion provides no empirically supported explanation for the observed synchronization behavior. Therefore, we investigated a more probable explanation for synchronization. Our data set of 17,546 paired MUAPTs revealed that the degree of synchronization varies as a function of two characteristics of the motor unit firing rate: the similarity and the slope as a function of force. Both are measures of the excitation of the motoneurons. As the force generated by the muscle increases, the firing rate slope decreases, and the synchronization correspondingly decreases. Different muscles have motor units with different firing rate characteristics and display different amounts of synchronization. Although this association is not proof of causality, it consistently explains our observations and strongly suggests further investigation. So viewed, synchronization is likely an epiphenomenon, subject to countless unknown neural interactions. As such, synchronous firing instances may not be the product of a specific design and may not serve a specific physiological purpose. Our explanation for synchronization has the advantage of being supported by empirical evidence, whereas the common input does not. Copyright © 2016 the American Physiological Society.

Altered functional connectivity of the amygdaloid input nuclei in adolescents and young adults with autism spectrum disorder: a resting state fMRI study.

PubMed

Rausch, Annika; Zhang, Wei; Haak, Koen V; Mennes, Maarten; Hermans, Erno J; van Oort, Erik; van Wingen, Guido; Beckmann, Christian F; Buitelaar, Jan K; Groen, Wouter B

2016-01-01

Amygdala dysfunction is hypothesized to underlie the social deficits observed in autism spectrum disorders (ASD). However, the neurobiological basis of this hypothesis is underspecified because it is unknown whether ASD relates to abnormalities of the amygdaloid input or output nuclei. Here, we investigated the functional connectivity of the amygdaloid social-perceptual input nuclei and emotion-regulation output nuclei in ASD versus controls. We collected resting state functional magnetic resonance imaging (fMRI) data, tailored to provide optimal sensitivity in the amygdala as well as the neocortex, in 20 adolescents and young adults with ASD and 25 matched controls. We performed a regular correlation analysis between the entire amygdala (EA) and the whole brain and used a partial correlation analysis to investigate whole-brain functional connectivity uniquely related to each of the amygdaloid subregions. Between-group comparison of regular EA correlations showed significantly reduced connectivity in visuospatial and superior parietal areas in ASD compared to controls. Partial correlation analysis revealed that this effect was driven by the left superficial and right laterobasal input subregions, but not the centromedial output nuclei. These results indicate reduced connectivity of specifically the amygdaloid sensory input channels in ASD, suggesting that abnormal amygdalo-cortical connectivity can be traced down to the socio-perceptual pathways.

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

PubMed

Gao, Hui; Song, Yongduan; Wen, Changyun

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

Do Long-Term Changes in Organic Matter Inputs to Forest Soils Affect Dissolved Organic Matter Chemistry and Export?

NASA Astrophysics Data System (ADS)

Lajtha, K.; Strid, A.; Lee, B. S.

2014-12-01

Dissolved organic matter (DOM) production and transport play an important role in regulating organic matter (OM) distribution through a soil profile and ultimately, OM stabilization or export to aquatic systems. The contributions of varying OM inputs to the quality and amount of DOM as it passes through a soil profile remain relatively unknown. The Detrital Input and Removal Treatment (DIRT) site at the H. J. Andrews Experimental Forest in Oregon has undergone 17 years of litter, wood and root input manipulations and allows us to guage shifts in DOM chemistry induced by long-term changes to aboveground and belowground OM additions and exclusions. Using fluorescence and UV spectroscopy to characterize fluorescent properties, extent of decomposition, and sources of DOM in streams and soil solutions collected with lysimeters and soil extractions, we have assessed the importance of fresh OM inputs to DOM chemistry. Soil extracts from DIRT plots had a higher fluorescence index (FI) than lysimeter solutions or stream water. A high FI in surface water is generally interpreted as indicative of a high proportion of microbially-derived DOM. However, we suspect that the high FI in soil extracts is due to a higher proportion of non-aromatic DOM from fresh soil that microorganisms consume in transit through the soil profile to lysimeters or to streams. High redox index (RI) values were observed in lysimeters from the April 2014 sampling compared with the November 2013 sampling. These RI values show evidence of more reducing conditions at the end of the rainy season in the spring compared to the onset of the rainy season in the fall. Lysimeter water collected in No Input, No Litter, and No Root treatments contained high proportions of protein, suggesting the absence of carbon inputs changes activities of the microbial community. Observed variations reflect the viability of using fluorescent properties to explore the terrestrial-aquatic interface.

Differential flatness properties and adaptive control of the hypothalamic-pituitary-adrenal axis model

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos

2016-12-01

It is shown that the model of the hypothalamic-pituitary-adrenal gland axis is a differentially flat one and this permits to transform it to the so-called linear canonical form. For the new description of the system's dynamics the transformed control inputs contain unknown terms which depend on the system's parameters. To identify these terms an adaptive fuzzy approximator is used in the control loop. Thus an adaptive fuzzy control scheme is implemented in which the unknown or unmodeled system dynamics is approximated by neurofuzzy networks and next this information is used by a feedback controller that makes the state variables (CRH - corticotropin releasing hormone, adenocortocotropic hormone - ACTH, cortisol) of the hypothalamic-pituitary-adrenal gland axis model converge to the desirable levels (setpoints). This adaptive control scheme is exclusively implemented with the use of output feedback, while the state vector elements which are not directly measured are estimated with the use of a state observer that operates in the control loop. The learning rate of the adaptive fuzzy system is suitably computed from Lyapunov analysis, so as to assure that both the learning procedure for the unknown system's parameters, the dynamics of the observer and the dynamics of the control loop will remain stable. The performed Lyapunov stability analysis depends on two Riccati equations, one associated with the feedback controller and one associated with the state observer. Finally, it is proven that for the control scheme that comprises the feedback controller, the state observer and the neurofuzzy approximator, an H-infinity tracking performance can be succeeded.

Echolalic responses by a child with autism to four experimental conditions of sociolinguistic input.

PubMed

Violette, J; Swisher, L

1992-02-01

Studies of the immediate verbal imitations (IVIs) of subjects with echolalia report that features of linguistic or social input alone affect the number of IVIs elicited. This experimental study of a child with echolalia and autism controlled each of these variables while introducing a systematic change in the other. The subject produced more (p less than .05) IVIs in response to unknown lexical words presented with a high degree of directiveness (Condition D) than in response to three other conditions of stimulus presentation (e.g., unknown lexical words, minimally directive style.) Thus, an interaction between the effects of linguistic and social input was demonstrated. IVIs were produced across all conditions, primarily during first presentations of lexical stimuli. Only the IVIs elicited by first presentations of the lexical stimuli during Condition D differed significantly (p less than .05) from the number of IVIs elicited by first presentations of lexical stimuli in other conditions. These findings viewed together suggest that the occurrence of IVIs was related, at least for this child, to an uncertain or informative event and that this response was significantly greater when the lexical stimuli were unknown and presented in a highly directive style.

Entanglement in channel discrimination with restricted measurements

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

Matthews, William; Piani, Marco; Watrous, John

2010-09-15

We study the power of measurements implementable with local quantum operations and classical communication (LOCC) measurements in the setting of quantum channel discrimination. More precisely, we consider discrimination procedures that attempt to identify an unknown channel, chosen uniformly from two known alternatives, that take the following form: (i) the input to the unknown channel is prepared in a possibly entangled state with an ancillary system, (ii) the unknown channel is applied to the input system, and (iii) an LOCC measurement is performed on the output and ancillary systems, resulting in a guess for which of the two channels was given.more » The restriction of the measurement in such a procedure to be an LOCC measurement is of interest because it isolates the entanglement in the initial input-ancillary systems as a resource in the setting of channel discrimination. We prove that there exist channel discrimination problems for which restricted procedures of this sort can be at either of the two extremes: they may be optimal within the set of all discrimination procedures (and simultaneously outperform all strategies that make no use of entanglement), or they may be no better than unentangled strategies (and simultaneously suboptimal within the set of all discrimination procedures).« less

Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

NASA Astrophysics Data System (ADS)

Yang, Xiong; Liu, Derong; Wang, Ding

2014-03-01

In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

A Biophysical Neural Model To Describe Spatial Visual Attention

NASA Astrophysics Data System (ADS)

Hugues, Etienne; José, Jorge V.

2008-02-01

Visual scenes have enormous spatial and temporal information that are transduced into neural spike trains. Psychophysical experiments indicate that only a small portion of a spatial image is consciously accessible. Electrophysiological experiments in behaving monkeys have revealed a number of modulations of the neural activity in special visual area known as V4, when the animal is paying attention directly towards a particular stimulus location. The nature of the attentional input to V4, however, remains unknown as well as to the mechanisms responsible for these modulations. We use a biophysical neural network model of V4 to address these issues. We first constrain our model to reproduce the experimental results obtained for different external stimulus configurations and without paying attention. To reproduce the known neuronal response variability, we found that the neurons should receive about equal, or balanced, levels of excitatory and inhibitory inputs and whose levels are high as they are in in vivo conditions. Next we consider attentional inputs that can induce and reproduce the observed spiking modulations. We also elucidate the role played by the neural network to generate these modulations.

A Biophysical Neural Model To Describe Spatial Visual Attention

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

Hugues, Etienne; Jose, Jorge V.

2008-02-14

Visual scenes have enormous spatial and temporal information that are transduced into neural spike trains. Psychophysical experiments indicate that only a small portion of a spatial image is consciously accessible. Electrophysiological experiments in behaving monkeys have revealed a number of modulations of the neural activity in special visual area known as V4, when the animal is paying attention directly towards a particular stimulus location. The nature of the attentional input to V4, however, remains unknown as well as to the mechanisms responsible for these modulations. We use a biophysical neural network model of V4 to address these issues. We firstmore » constrain our model to reproduce the experimental results obtained for different external stimulus configurations and without paying attention. To reproduce the known neuronal response variability, we found that the neurons should receive about equal, or balanced, levels of excitatory and inhibitory inputs and whose levels are high as they are in in vivo conditions. Next we consider attentional inputs that can induce and reproduce the observed spiking modulations. We also elucidate the role played by the neural network to generate these modulations.« less

Constraining Mass Anomalies Using Trans-dimensional Gravity Inversions

NASA Astrophysics Data System (ADS)

Izquierdo, K.; Montesi, L.; Lekic, V.

2016-12-01

The density structure of planetary interiors constitutes a key constraint on their composition, temperature, and dynamics. This has motivated the development of non-invasive methods to infer 3D distribution of density anomalies within a planet's interior using gravity observations made from the surface or orbit. On Earth, this information can be supplemented by seismic and electromagnetic observations, but such data are generally not available on other planets and inferences must be made from gravity observations alone. Unfortunately, inferences of density anomalies from gravity are non-unique and even the dimensionality of the problem - i.e., the number of density anomalies detectable in the planetary interior - is unknown. In this project, we use the Reversible Jump Markov chain Monte Carlo (RJMCMC) algorithm to approach gravity inversions in a trans-dimensional way, that is, considering the magnitude of the mass, the latitude, longitude, depth and number of anomalies itself as unknowns to be constrained by the observed gravity field at the surface of a planet. Our approach builds upon previous work using trans-dimensional gravity inversions in which the density contrast between the anomaly and the surrounding material is known. We validate the algorithm by analyzing a synthetic gravity field produced by a known density structure and comparing the retrieved and input density structures. We find excellent agreement between the input and retrieved structure when working in 1D and 2D domains. However, in 3D domains, comprehensive exploration of the much larger space of possible models makes search efficiency a key ingredient in successful gravity inversion. We find that upon a sufficiently long RJMCMC run, it is possible to use statistical information to recover a predicted model that matches the real model. We argue that even more complex problems, such as those involving real gravity acceleration data of a planet as the constraint, our trans-dimensional gravity inversion algorithm provides a good option to overcome the problem of non-uniqueness while achieving parsimony in gravity inversions.

A Measure Approximation for Distributionally Robust PDE-Constrained Optimization Problems

DOE PAGES

Kouri, Drew Philip

2017-12-19

In numerous applications, scientists and engineers acquire varied forms of data that partially characterize the inputs to an underlying physical system. This data is then used to inform decisions such as controls and designs. Consequently, it is critical that the resulting control or design is robust to the inherent uncertainties associated with the unknown probabilistic characterization of the model inputs. Here in this work, we consider optimal control and design problems constrained by partial differential equations with uncertain inputs. We do not assume a known probabilistic model for the inputs, but rather we formulate the problem as a distributionally robustmore » optimization problem where the outer minimization problem determines the control or design, while the inner maximization problem determines the worst-case probability measure that matches desired characteristics of the data. We analyze the inner maximization problem in the space of measures and introduce a novel measure approximation technique, based on the approximation of continuous functions, to discretize the unknown probability measure. Finally, we prove consistency of our approximated min-max problem and conclude with numerical results.« less

Defining the computational structure of the motion detector in Drosophila

PubMed Central

Clark, Damon A.; Bursztyn, Limor; Horowitz, Mark; Schnitzer, Mark J.; Clandinin, Thomas R.

2011-01-01

SUMMARY Many animals rely on visual motion detection for survival. Motion information is extracted from spatiotemporal intensity patterns on the retina, a paradigmatic neural computation. A phenomenological model, the Hassenstein-Reichardt Correlator (HRC), relates visual inputs to neural and behavioral responses to motion, but the circuits that implement this computation remain unknown. Using cell-type specific genetic silencing, minimal motion stimuli, and in vivo calcium imaging, we examine two critical HRC inputs. These two pathways respond preferentially to light and dark moving edges. We demonstrate that these pathways perform overlapping but complementary subsets of the computations underlying the HRC. A numerical model implementing differential weighting of these operations displays the observed edge preferences. Intriguingly, these pathways are distinguished by their sensitivities to a stimulus correlation that corresponds to an illusory percept, “reverse phi”, that affects many species. Thus, this computational architecture may be widely used to achieve edge selectivity in motion detection. PMID:21689602

Observer-based state tracking control of uncertain stochastic systems via repetitive controller

NASA Astrophysics Data System (ADS)

Sakthivel, R.; Susana Ramya, L.; Selvaraj, P.

2017-08-01

This paper develops the repetitive control scheme for state tracking control of uncertain stochastic time-varying delay systems via equivalent-input-disturbance approach. The main purpose of this work is to design a repetitive controller to guarantee the tracking performance under the effects of unknown disturbances with bounded frequency and parameter variations. Specifically, a new set of linear matrix inequality (LMI)-based conditions is derived based on the suitable Lyapunov-Krasovskii functional theory for designing a repetitive controller which guarantees stability and desired tracking performance. More precisely, an equivalent-input-disturbance estimator is incorporated into the control design to reduce the effect of the external disturbances. Simulation results are provided to demonstrate the desired control system stability and their tracking performance. A practical stream water quality preserving system is also provided to show the effectiveness and advantage of the proposed approach.

Desired Accuracy Estimation of Noise Function from ECG Signal by Fuzzy Approach

PubMed Central

Vahabi, Zahra; Kermani, Saeed

2012-01-01

Unknown noise and artifacts present in medical signals with non-linear fuzzy filter will be estimated and then removed. An adaptive neuro-fuzzy interference system which has a non-linear structure presented for the noise function prediction by before Samples. This paper is about a neuro-fuzzy method to estimate unknown noise of Electrocardiogram signal. Adaptive neural combined with Fuzzy System to construct a fuzzy Predictor. For this system setting parameters such as the number of Membership Functions for each input and output, training epochs, type of MFs for each input and output, learning algorithm and etc. is determined by learning data. At the end simulated experimental results are presented for proper validation. PMID:23717810

String resistance detector

NASA Technical Reports Server (NTRS)

Hall, A. Daniel (Inventor); Davies, Francis J. (Inventor)

2007-01-01

Method and system are disclosed for determining individual string resistance in a network of strings when the current through a parallel connected string is unknown and when the voltage across a series connected string is unknown. The method/system of the invention involves connecting one or more frequency-varying impedance components with known electrical characteristics to each string and applying a frequency-varying input signal to the network of strings. The frequency-varying impedance components may be one or more capacitors, inductors, or both, and are selected so that each string is uniquely identifiable in the output signal resulting from the frequency-varying input signal. Numerical methods, such as non-linear regression, may then be used to resolve the resistance associated with each string.

Calibration of hydrological models using flow-duration curves

NASA Astrophysics Data System (ADS)

Westerberg, I. K.; Guerrero, J.-L.; Younger, P. M.; Beven, K. J.; Seibert, J.; Halldin, S.; Freer, J. E.; Xu, C.-Y.

2011-07-01

The degree of belief we have in predictions from hydrologic models will normally depend on how well they can reproduce observations. Calibrations with traditional performance measures, such as the Nash-Sutcliffe model efficiency, are challenged by problems including: (1) uncertain discharge data, (2) variable sensitivity of different performance measures to different flow magnitudes, (3) influence of unknown input/output errors and (4) inability to evaluate model performance when observation time periods for discharge and model input data do not overlap. This paper explores a calibration method using flow-duration curves (FDCs) to address these problems. The method focuses on reproducing the observed discharge frequency distribution rather than the exact hydrograph. It consists of applying limits of acceptability for selected evaluation points (EPs) on the observed uncertain FDC in the extended GLUE approach. Two ways of selecting the EPs were tested - based on equal intervals of discharge and of volume of water. The method was tested and compared to a calibration using the traditional model efficiency for the daily four-parameter WASMOD model in the Paso La Ceiba catchment in Honduras and for Dynamic TOPMODEL evaluated at an hourly time scale for the Brue catchment in Great Britain. The volume method of selecting EPs gave the best results in both catchments with better calibrated slow flow, recession and evaporation than the other criteria. Observed and simulated time series of uncertain discharges agreed better for this method both in calibration and prediction in both catchments. An advantage with the method is that the rejection criterion is based on an estimation of the uncertainty in discharge data and that the EPs of the FDC can be chosen to reflect the aims of the modelling application, e.g. using more/less EPs at high/low flows. While the method appears less sensitive to epistemic input/output errors than previous use of limits of acceptability applied directly to the time series of discharge, it still requires a reasonable representation of the distribution of inputs. Additional constraints might therefore be required in catchments subject to snow and where peak-flow timing at sub-daily time scales is of high importance. The results suggest that the calibration method can be useful when observation time periods for discharge and model input data do not overlap. The method could also be suitable for calibration to regional FDCs while taking uncertainties in the hydrological model and data into account.

Calibration of hydrological models using flow-duration curves

NASA Astrophysics Data System (ADS)

Westerberg, I. K.; Guerrero, J.-L.; Younger, P. M.; Beven, K. J.; Seibert, J.; Halldin, S.; Freer, J. E.; Xu, C.-Y.

2010-12-01

The degree of belief we have in predictions from hydrologic models depends on how well they can reproduce observations. Calibrations with traditional performance measures such as the Nash-Sutcliffe model efficiency are challenged by problems including: (1) uncertain discharge data, (2) variable importance of the performance with flow magnitudes, (3) influence of unknown input/output errors and (4) inability to evaluate model performance when observation time periods for discharge and model input data do not overlap. A new calibration method using flow-duration curves (FDCs) was developed which addresses these problems. The method focuses on reproducing the observed discharge frequency distribution rather than the exact hydrograph. It consists of applying limits of acceptability for selected evaluation points (EPs) of the observed uncertain FDC in the extended GLUE approach. Two ways of selecting the EPs were tested - based on equal intervals of discharge and of volume of water. The method was tested and compared to a calibration using the traditional model efficiency for the daily four-parameter WASMOD model in the Paso La Ceiba catchment in Honduras and for Dynamic TOPMODEL evaluated at an hourly time scale for the Brue catchment in Great Britain. The volume method of selecting EPs gave the best results in both catchments with better calibrated slow flow, recession and evaporation than the other criteria. Observed and simulated time series of uncertain discharges agreed better for this method both in calibration and prediction in both catchments without resulting in overpredicted simulated uncertainty. An advantage with the method is that the rejection criterion is based on an estimation of the uncertainty in discharge data and that the EPs of the FDC can be chosen to reflect the aims of the modelling application e.g. using more/less EPs at high/low flows. While the new method is less sensitive to epistemic input/output errors than the normal use of limits of acceptability applied directly to the time series of discharge, it still requires a reasonable representation of the distribution of inputs. Additional constraints might therefore be required in catchments subject to snow. The results suggest that the new calibration method can be useful when observation time periods for discharge and model input data do not overlap. The new method could also be suitable for calibration to regional FDCs while taking uncertainties in the hydrological model and data into account.

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

Kato, Go

We consider the situation where s replicas of a qubit with an unknown state and its orthogonal k replicas are given as an input, and we try to make c clones of the qubit with the unknown state. As a function of s, k, and c, we obtain the optimal fidelity between the qubit with an unknown state and the clone by explicitly giving a completely positive trace-preserving (CPTP) map that represents a cloning machine. We discuss dependency of the fidelity on the values of the parameters s, k, and c.

M-MRAC Backstepping for Systems with Unknown Virtual Control Coefficients

NASA Technical Reports Server (NTRS)

Stepanyan, Vahram; Krishnakumar, Kalmanje

2015-01-01

The paper presents an over-parametrization free certainty equivalence state feedback backstepping adaptive control design method for systems of any relative degree with unmatched uncertainties and unknown virtual control coefficients. It uses a fast prediction model to estimate the unknown parameters, which is independent of the control design. It is shown that the system's input and output tracking errors can be systematically decreased by the proper choice of the design parameters. The benefits of the approach are demonstrated in numerical simulations.

An easy-to-use tool for the evaluation of leachate production at landfill sites.

PubMed

Grugnaletti, Matteo; Pantini, Sara; Verginelli, Iason; Lombardi, Francesco

2016-09-01

A simulation program for the evaluation of leachate generation at landfill sites is herein presented. The developed tool is based on a water balance model that accounts for all the key processes influencing leachate generation through analytical and empirical equations. After a short description of the tool, different simulations on four Italian landfill sites are shown. The obtained results revealed that when literature values were assumed for the unknown input parameters, the model provided a rough estimation of the leachate production measured in the field. In this case, indeed, the deviations between observed and predicted data appeared, in some cases, significant. Conversely, by performing a preliminary calibration for some of the unknown input parameters (e.g. initial moisture content of wastes, compression index), in nearly all cases the model performances significantly improved. These results although showed the potential capability of a water balance model to estimate the leachate production at landfill sites also highlighted the intrinsic limitation of a deterministic approach to accurately forecast the leachate production over time. Indeed, parameters such as the initial water content of incoming waste and the compression index, that have a great influence on the leachate production, may exhibit temporal variation due to seasonal changing of weather conditions (e.g. rainfall, air humidity) as well as to seasonal variability in the amount and type of specific waste fractions produced (e.g. yard waste, food, plastics) that make their prediction quite complicated. In this sense, we believe that a tool such as the one proposed in this work that requires a limited number of unknown parameters, can be easier handled to quantify the uncertainties. Copyright © 2016 Elsevier Ltd. All rights reserved.

Adaptive Neural Network-Based Event-Triggered Control of Single-Input Single-Output Nonlinear Discrete-Time Systems.

PubMed

Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani

2016-01-01

This paper presents a novel adaptive neural network (NN) control of single-input and single-output uncertain nonlinear discrete-time systems under event sampled NN inputs. In this control scheme, the feedback signals are transmitted, and the NN weights are tuned in an aperiodic manner at the event sampled instants. After reviewing the NN approximation property with event sampled inputs, an adaptive state estimator (SE), consisting of linearly parameterized NNs, is utilized to approximate the unknown system dynamics in an event sampled context. The SE is viewed as a model and its approximated dynamics and the state vector, during any two events, are utilized for the event-triggered controller design. An adaptive event-trigger condition is derived by using both the estimated NN weights and a dead-zone operator to determine the event sampling instants. This condition both facilitates the NN approximation and reduces the transmission of feedback signals. The ultimate boundedness of both the NN weight estimation error and the system state vector is demonstrated through the Lyapunov approach. As expected, during an initial online learning phase, events are observed more frequently. Over time with the convergence of the NN weights, the inter-event times increase, thereby lowering the number of triggered events. These claims are illustrated through the simulation results.

Knowing requires data

USGS Publications Warehouse

Naranjo, Ramon C.

2017-01-01

Groundwater-flow models are often calibrated using a limited number of observations relative to the unknown inputs required for the model. This is especially true for models that simulate groundwater surface-water interactions. In this case, subsurface temperature sensors can be an efficient means for collecting long-term data that capture the transient nature of physical processes such as seepage losses. Continuous and spatially dense network of diverse observation data can be used to improve knowledge of important physical drivers, conceptualize and calibrate variably saturated groundwater flow models. An example is presented for which the results of such analysis were used to help guide irrigation districts and water management decisions on costly upgrades to conveyance systems to improve water usage, farm productivity and restoration efforts to improve downstream water quality and ecosystems.

A robust H∞-tracking design for uncertain Takagi-Sugeno fuzzy systems with unknown premise variables using descriptor redundancy approach

NASA Astrophysics Data System (ADS)

Hassan Asemani, Mohammad; Johari Majd, Vahid

2015-12-01

This paper addresses a robust H∞ fuzzy observer-based tracking design problem for uncertain Takagi-Sugeno fuzzy systems with external disturbances. To have a practical observer-based controller, the premise variables of the system are assumed to be not measurable in general, which leads to a more complex design process. The tracker is synthesised based on a fuzzy Lyapunov function approach and non-parallel distributed compensation (non-PDC) scheme. Using the descriptor redundancy approach, the robust stability conditions are derived in the form of strict linear matrix inequalities (LMIs) even in the presence of uncertainties in the system, input, and output matrices simultaneously. Numerical simulations are provided to show the effectiveness of the proposed method.

Neural-adaptive control of single-master-multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties.

PubMed

Li, Zhijun; Su, Chun-Yi

2013-09-01

In this paper, adaptive neural network control is investigated for single-master-multiple-slaves teleoperation in consideration of time delays and input dead-zone uncertainties for multiple mobile manipulators carrying a common object in a cooperative manner. Firstly, concise dynamics of teleoperation systems consisting of a single master robot, multiple coordinated slave robots, and the object are developed in the task space. To handle asymmetric time-varying delays in communication channels and unknown asymmetric input dead zones, the nonlinear dynamics of the teleoperation system are transformed into two subsystems through feedback linearization: local master or slave dynamics including the unknown input dead zones and delayed dynamics for the purpose of synchronization. Then, a model reference neural network control strategy based on linear matrix inequalities (LMI) and adaptive techniques is proposed. The developed control approach ensures that the defined tracking errors converge to zero whereas the coordination internal force errors remain bounded and can be made arbitrarily small. Throughout this paper, stability analysis is performed via explicit Lyapunov techniques under specific LMI conditions. The proposed adaptive neural network control scheme is robust against motion disturbances, parametric uncertainties, time-varying delays, and input dead zones, which is validated by simulation studies.

Optimization of a Thermodynamic Model Using a Dakota Toolbox Interface

NASA Astrophysics Data System (ADS)

Cyrus, J.; Jafarov, E. E.; Schaefer, K. M.; Wang, K.; Clow, G. D.; Piper, M.; Overeem, I.

2016-12-01

Scientific modeling of the Earth physical processes is an important driver of modern science. The behavior of these scientific models is governed by a set of input parameters. It is crucial to choose accurate input parameters that will also preserve the corresponding physics being simulated in the model. In order to effectively simulate real world processes the models output data must be close to the observed measurements. To achieve this optimal simulation, input parameters are tuned until we have minimized the objective function, which is the error between the simulation model outputs and the observed measurements. We developed an auxiliary package, which serves as a python interface between the user and DAKOTA. The package makes it easy for the user to conduct parameter space explorations, parameter optimizations, as well as sensitivity analysis while tracking and storing results in a database. The ability to perform these analyses via a Python library also allows the users to combine analysis techniques, for example finding an approximate equilibrium with optimization then immediately explore the space around it. We used the interface to calibrate input parameters for the heat flow model, which is commonly used in permafrost science. We performed optimization on the first three layers of the permafrost model, each with two thermal conductivity coefficients input parameters. Results of parameter space explorations indicate that the objective function not always has a unique minimal value. We found that gradient-based optimization works the best for the objective functions with one minimum. Otherwise, we employ more advanced Dakota methods such as genetic optimization and mesh based convergence in order to find the optimal input parameters. We were able to recover 6 initially unknown thermal conductivity parameters within 2% accuracy of their known values. Our initial tests indicate that the developed interface for the Dakota toolbox could be used to perform analysis and optimization on a `black box' scientific model more efficiently than using just Dakota.

Neural net target-tracking system using structured laser patterns

NASA Astrophysics Data System (ADS)

Cho, Jae-Wan; Lee, Yong-Bum; Lee, Nam-Ho; Park, Soon-Yong; Lee, Jongmin; Choi, Gapchu; Baek, Sunghyun; Park, Dong-Sun

1996-06-01

In this paper, we describe a robot endeffector tracking system using sensory information from recently-announced structured pattern laser diodes, which can generate images with several different types of structured pattern. The neural network approach is employed to recognize the robot endeffector covering the situation of three types of motion: translation, scaling and rotation. Features for the neural network to detect the position of the endeffector are extracted from the preprocessed images. Artificial neural networks are used to store models and to match with unknown input features recognizing the position of the robot endeffector. Since a minimal number of samples are used for different directions of the robot endeffector in the system, an artificial neural network with the generalization capability can be utilized for unknown input features. A feedforward neural network with the generalization capability can be utilized for unknown input features. A feedforward neural network trained with the back propagation learning is used to detect the position of the robot endeffector. Another feedforward neural network module is used to estimate the motion from a sequence of images and to control movements of the robot endeffector. COmbining the tow neural networks for recognizing the robot endeffector and estimating the motion with the preprocessing stage, the whole system keeps tracking of the robot endeffector effectively.

Observer-based output consensus of a class of heterogeneous multi-agent systems with unmatched disturbances

NASA Astrophysics Data System (ADS)

Zhang, Jiancheng; Zhu, Fanglai

2018-03-01

In this paper, the output consensus of a class of linear heterogeneous multi-agent systems with unmatched disturbances is considered. Firstly, based on the relative output information among neighboring agents, we propose an asymptotic sliding-mode based consensus control scheme, under which, the output consensus error can converge to zero by removing the disturbances from output channels. Secondly, in order to reach the consensus goal, we design a novel high-order unknown input observer for each agent. It can estimate not only each agent's states and disturbances, but also the disturbances' high-order derivatives, which are required in the control scheme aforementioned above. The observer-based consensus control laws and the convergence analysis of the consensus error dynamics are given. Finally, a simulation example is provided to verify the validity of our methods.

Robust adaptive sliding mode control for uncertain systems with unknown time-varying delay input.

PubMed

Benamor, Anouar; Messaoud, Hassani

2018-05-02

This article focuses on robust adaptive sliding mode control law for uncertain discrete systems with unknown time-varying delay input, where the uncertainty is assumed unknown. The main results of this paper are divided into three phases. In the first phase, we propose a new sliding surface is derived within the Linear Matrix Inequalities (LMIs). In the second phase, using the new sliding surface, the novel Robust Sliding Mode Control (RSMC) is proposed where the upper bound of uncertainty is supposed known. Finally, the novel approach of Robust Adaptive Sliding ModeControl (RASMC) has been defined for this type of systems, where the upper limit of uncertainty which is assumed unknown. In this new approach, we have estimate the upper limit of uncertainties and we have determined the control law based on a sliding surface that will converge to zero. This novel control laws are been validated in simulation on an uncertain numerical system with good results and comparative study. This efficiency is emphasized through the application of the new controls on the two physical systems which are the process trainer PT326 and hydraulic system two tanks. Published by Elsevier Ltd.

Neural network controller development and implementation for spark ignition engines with high EGR levels.

PubMed

Vance, Jonathan Blake; Singh, Atmika; Kaul, Brian C; Jagannathan, Sarangapani; Drallmeier, James A

2007-07-01

Past research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10%-25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (see Dudek and Sain, 1989). However, under high EGR levels, the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance preventing commercial engines to operate with high EGR levels. A neural network (NN)-based output feedback controller is developed to reduce cyclic variation in the heat release under high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate control loop was designed for controlling EGR levels. The stability analysis of the closed-loop system is given and the boundedness of the control input is demonstrated by relaxing separation principle, persistency of excitation condition, certainty equivalence principle, and linear in the unknown parameter assumptions. Online training is used for the adaptive NN and no offline training phase is needed. This online learning feature and model-free approach is used to demonstrate the applicability of the controller on a different engine with minimal effort. Simulation results demonstrate that the cyclic dispersion is reduced significantly using the proposed controller when implemented on an engine model that has been validated experimentally. For a single cylinder research engine fitted with a modern four-valve head (Ricardo engine), experimental results at 15% EGR indicate that cyclic dispersion was reduced 33% by the controller, an improvement of fuel efficiency by 2%, and a 90% drop in NOx from stoichiometric operation without EGR was observed. Moreover, unburned hydrocarbons (uHC) drop by 6% due to NN control as compared to the uncontrolled scenario due to the drop in cyclic dispersion. Similar performance was observed with the controller on a different engine.

Modern control concepts in hydrology. [parameter identification in adaptive stochastic control approach

NASA Technical Reports Server (NTRS)

Duong, N.; Winn, C. B.; Johnson, G. R.

1975-01-01

Two approaches to an identification problem in hydrology are presented, based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time-invariant or time-dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and confirm the results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.

New developments of a knowledge based system (VEG) for inferring vegetation characteristics

NASA Technical Reports Server (NTRS)

Kimes, D. S.; Harrison, P. A.; Harrison, P. R.

1992-01-01

An extraction technique for inferring physical and biological surface properties of vegetation using nadir and/or directional reflectance data as input has been developed. A knowledge-based system (VEG) accepts spectral data of an unknown target as input, determines the best strategy for inferring the desired vegetation characteristic, applies the strategy to the target data, and provides a rigorous estimate of the accuracy of the inference. Progress in developing the system is presented. VEG combines methods from remote sensing and artificial intelligence, and integrates input spectral measurements with diverse knowledge bases. VEG has been developed to (1) infer spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles; (2) test and develop new extraction techniques on an internal spectral database; (3) browse, plot, or analyze directional reflectance data in the system's spectral database; (4) discriminate between user-defined vegetation classes using spectral and directional reflectance relationships; and (5) infer unknown view angles from known view angles (known as view angle extension).

Modern control concepts in hydrology

NASA Technical Reports Server (NTRS)

Duong, N.; Johnson, G. R.; Winn, C. B.

1974-01-01

Two approaches to an identification problem in hydrology are presented based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time invariant or time dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and conform with results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second, by using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.

Experimentally superposing two pure states with partial prior knowledge

NASA Astrophysics Data System (ADS)

Li, Keren; Long, Guofei; Katiyar, Hemant; Xin, Tao; Feng, Guanru; Lu, Dawei; Laflamme, Raymond

2017-02-01

Superposition, arguably the most fundamental property of quantum mechanics, lies at the heart of quantum information science. However, how to create the superposition of any two unknown pure states remains as a daunting challenge. Recently, it was proved that such a quantum protocol does not exist if the two input states are completely unknown, whereas a probabilistic protocol is still available with some prior knowledge about the input states [M. Oszmaniec et al., Phys. Rev. Lett. 116, 110403 (2016), 10.1103/PhysRevLett.116.110403]. The knowledge is that both of the two input states have nonzero overlaps with some given referential state. In this work, we experimentally realize the probabilistic protocol of superposing two pure states in a three-qubit nuclear magnetic resonance system. We demonstrate the feasibility of the protocol by preparing a families of input states, and the average fidelity between the prepared state and expected superposition state is over 99%. Moreover, we experimentally illustrate the limitation of the protocol that it is likely to fail or yields very low fidelity, if the nonzero overlaps are approaching zero. Our experimental implementation can be extended to more complex situations and other quantum systems.

Robust input design for nonlinear dynamic modeling of AUV.

PubMed

Nouri, Nowrouz Mohammad; Valadi, Mehrdad

2017-09-01

Input design has a dominant role in developing the dynamic model of autonomous underwater vehicles (AUVs) through system identification. Optimal input design is the process of generating informative inputs that can be used to generate the good quality dynamic model of AUVs. In a problem with optimal input design, the desired input signal depends on the unknown system which is intended to be identified. In this paper, the input design approach which is robust to uncertainties in model parameters is used. The Bayesian robust design strategy is applied to design input signals for dynamic modeling of AUVs. The employed approach can design multiple inputs and apply constraints on an AUV system's inputs and outputs. Particle swarm optimization (PSO) is employed to solve the constraint robust optimization problem. The presented algorithm is used for designing the input signals for an AUV, and the estimate obtained by robust input design is compared with that of the optimal input design. According to the results, proposed input design can satisfy both robustness of constraints and optimality. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Vagal innervation of the aldosterone-sensitive HSD2 neurons in the NTS

PubMed Central

Shin, Jung-Won; Geerling, Joel C.; Loewy, Arthur D.

2009-01-01

The nucleus of the solitary tract (NTS) contains a unique subpopulation of aldosterone-sensitive neurons. These neurons express the enzyme 11-β-hydroxysteroid dehydrogenase type 2 (HSD2) and are activated by sodium deprivation. They are located in the caudal NTS, a region which is densely innervated by the vagus nerve, suggesting that they could receive direct viscerosensory input from the periphery. To test this possibility, we injected the highly sensitive axonal tracer biotinylated dextran amine (BDA) into the left nodose ganglion in rats. Using confocal microscopy, we observed a sparse input from the vagus to most HSD2 neurons. Roughly 80% of the ipsilateral HSD2 neurons exhibited at least one close contact with a BDA-labeled vagal bouton, although most of these cells received only a few total contacts. Most of these contacts were axo-dendritic (~80%), while ~20% were axo-somatic. In contrast, the synaptic vesicular transporters VGLUT2 or GAD7 labeled much larger populations of boutons contacting HSD2-labeled dendrites and somata, suggesting that direct input from the vagus may only account for a minority of the information integrated by these neurons. In summary, the aldosterone-sensitive HSD2 neurons in the NTS appear to receive a small amount of direct viscerosensory input from the vagus nerve. The peripheral sites of origin and functional significance of this projection remain unknown. Combined with previously-identified central sources of input to these cells, the present finding indicates that the HSD2 neurons integrate humoral information with input from a variety of neural afferents. PMID:19010311

Sculpt test problem analysis.

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

Sweetser, John David

2013-10-01

This report details Sculpt's implementation from a user's perspective. Sculpt is an automatic hexahedral mesh generation tool developed at Sandia National Labs by Steve Owen. 54 predetermined test cases are studied while varying the input parameters (Laplace iterations, optimization iterations, optimization threshold, number of processors) and measuring the quality of the resultant mesh. This information is used to determine the optimal input parameters to use for an unknown input geometry. The overall characteristics are covered in Chapter 1. The speci c details of every case are then given in Appendix A. Finally, example Sculpt inputs are given in B.1 andmore » B.2.« less

Anoxia increases potassium conductance in hippocampal nerve cells.

PubMed

Hansen, A J; Hounsgaard, J; Jahnsen, H

1982-07-01

The effect of anoxia on nerve cell function was studied by intra- and extracellular microelectrode recordings from the CA1 and CA3 region in guinea pig hippocampal slices. Hyperpolarization and concomitant reduction of the nerve cell input resistance was observed early during anoxia. During this period the spontaneous activity first disappeared, then the evoked activity gradually disappeared. The hyperpolarization was followed by depolarization and an absence of a measurable input resistance. All the induced changes were reversed when the slice was reoxygenated. Reversal of the electro-chemical gradient for Cl- across the nerve cell membrane did not affect the course of events during anoxia. Aminopyridines blocked the anoxic hyperpolarization and attenuated the decrease of membrane resistance, but had no effect on the later depolarization. Blockers of synaptic transmission. Mn++, Mg++ and of Na+-channels (TTX) were without effect on the nerve cell changes during anoxia. It is suggested that the reduction of nerve cell excitability in anoxia is primarily due to increased K+-conductance. Thus, the nerve cells are hyperpolarized and the input resistance reduced, causing higher threshold and reduction of synaptic potentials. The mechanism of the K+-conductance activation is unknown at present.

Optimal control of nonlinear continuous-time systems in strict-feedback form.

PubMed

Zargarzadeh, Hassan; Dierks, Travis; Jagannathan, Sarangapani

2015-10-01

This paper proposes a novel optimal tracking control scheme for nonlinear continuous-time systems in strict-feedback form with uncertain dynamics. The optimal tracking problem is transformed into an equivalent optimal regulation problem through a feedforward adaptive control input that is generated by modifying the standard backstepping technique. Subsequently, a neural network-based optimal control scheme is introduced to estimate the cost, or value function, over an infinite horizon for the resulting nonlinear continuous-time systems in affine form when the internal dynamics are unknown. The estimated cost function is then used to obtain the optimal feedback control input; therefore, the overall optimal control input for the nonlinear continuous-time system in strict-feedback form includes the feedforward plus the optimal feedback terms. It is shown that the estimated cost function minimizes the Hamilton-Jacobi-Bellman estimation error in a forward-in-time manner without using any value or policy iterations. Finally, optimal output feedback control is introduced through the design of a suitable observer. Lyapunov theory is utilized to show the overall stability of the proposed schemes without requiring an initial admissible controller. Simulation examples are provided to validate the theoretical results.

Reconstruction of nonlinear wave propagation

DOEpatents

Fleischer, Jason W; Barsi, Christopher; Wan, Wenjie

2013-04-23

Disclosed are systems and methods for characterizing a nonlinear propagation environment by numerically propagating a measured output waveform resulting from a known input waveform. The numerical propagation reconstructs the input waveform, and in the process, the nonlinear environment is characterized. In certain embodiments, knowledge of the characterized nonlinear environment facilitates determination of an unknown input based on a measured output. Similarly, knowledge of the characterized nonlinear environment also facilitates formation of a desired output based on a configurable input. In both situations, the input thus characterized and the output thus obtained include features that would normally be lost in linear propagations. Such features can include evanescent waves and peripheral waves, such that an image thus obtained are inherently wide-angle, farfield form of microscopy.

Matrix completion by deep matrix factorization.

PubMed

Fan, Jicong; Cheng, Jieyu

2018-02-01

Conventional methods of matrix completion are linear methods that are not effective in handling data of nonlinear structures. Recently a few researchers attempted to incorporate nonlinear techniques into matrix completion but there still exists considerable limitations. In this paper, a novel method called deep matrix factorization (DMF) is proposed for nonlinear matrix completion. Different from conventional matrix completion methods that are based on linear latent variable models, DMF is on the basis of a nonlinear latent variable model. DMF is formulated as a deep-structure neural network, in which the inputs are the low-dimensional unknown latent variables and the outputs are the partially observed variables. In DMF, the inputs and the parameters of the multilayer neural network are simultaneously optimized to minimize the reconstruction errors for the observed entries. Then the missing entries can be readily recovered by propagating the latent variables to the output layer. DMF is compared with state-of-the-art methods of linear and nonlinear matrix completion in the tasks of toy matrix completion, image inpainting and collaborative filtering. The experimental results verify that DMF is able to provide higher matrix completion accuracy than existing methods do and DMF is applicable to large matrices. Copyright © 2017 Elsevier Ltd. All rights reserved.

Identifying known unknowns using the US EPA's CompTox Chemistry Dashboard.

PubMed

McEachran, Andrew D; Sobus, Jon R; Williams, Antony J

2017-03-01

Chemical features observed using high-resolution mass spectrometry can be tentatively identified using online chemical reference databases by searching molecular formulae and monoisotopic masses and then rank-ordering of the hits using appropriate relevance criteria. The most likely candidate "known unknowns," which are those chemicals unknown to an investigator but contained within a reference database or literature source, rise to the top of a chemical list when rank-ordered by the number of associated data sources. The U.S. EPA's CompTox Chemistry Dashboard is a curated and freely available resource for chemistry and computational toxicology research, containing more than 720,000 chemicals of relevance to environmental health science. In this research, the performance of the Dashboard for identifying known unknowns was evaluated against that of the online ChemSpider database, one of the primary resources used by mass spectrometrists, using multiple previously studied datasets reported in the peer-reviewed literature totaling 162 chemicals. These chemicals were examined using both applications via molecular formula and monoisotopic mass searches followed by rank-ordering of candidate compounds by associated references or data sources. A greater percentage of chemicals ranked in the top position when using the Dashboard, indicating an advantage of this application over ChemSpider for identifying known unknowns using data source ranking. Additional approaches are being developed for inclusion into a non-targeted analysis workflow as part of the CompTox Chemistry Dashboard. This work shows the potential for use of the Dashboard in exposure assessment and risk decision-making through significant improvements in non-targeted chemical identification. Graphical abstract Identifying known unknowns in the US EPA's CompTox Chemistry Dashboard from molecular formula and monoisotopic mass inputs.

Interactions Between Hydroclimate and Soil Properties Control the Risk For Altered Hydrologic Partitioning From Changing Snowmelt

NASA Astrophysics Data System (ADS)

Harpold, A. A.; Longley, P.; Weiss, S. G.; Kampf, S. K.; Flint, A. L.

2016-12-01

Mountain snowmelt is a critical water source for downstream human populations and local ecosystem health. Here we explore the relatively unknown hydrologic consequences of two observed trends in Western U.S. snowpack dynamics: 1) shifts from snow to rain and 2) earlier and slower snowmelt. We apply two modeling approaches to tease apart the hydrologic effects of altered winter water inputs: 1) highly resolved one-dimensional HYDRUS modeling based on the Richard's equation at intensively measured sites and 2) the distributed Basin Characterization Model (BCM) over the Southwestern U.S. with relatively simple subsurface processes. The HYDRUS model was trained using observations from ten Snow Telemetry (SNOTEL) sites to investigate drainage below the root zone under scenarios of rain only and slower snowmelt. We found that shifts to rain-only regimes and earlier snowmelt both resulted in greater fluxes below the root zone using the measured soil depths. However, drainage fluxes and differences among scenarios diminished precipitously when rooting depths were increased to account for uncertainty. Next using the BCM, we compared water partitioning during historical runs from 1940-2014 to a scenario with all precipitation as rain but identical climate. We found that ET generally increased from eliminating snowpack sublimation. Recharge and runoff exhibited diverging responses to shifting precipitation regimes; runoff typically decreased and recharge increased, with the exception of areas in western and southern California and central Arizona. The observed changes in annual runoff and recharge were primarily caused by changes in input intensity and not changes in input timing. Runoff was most sensitive in areas with wet winters and low soil water storage. Both modeling approaches corroborated the potential for diverging changes in mountain water budgets from altered winter water inputs that will be mediated precipitation regime (i.e. precipitation intensity and timing) and soil water storage. Efforts to link these results to water resources will be discussed.

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.

1991-01-01

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

Defining the computational structure of the motion detector in Drosophila.

PubMed

Clark, Damon A; Bursztyn, Limor; Horowitz, Mark A; Schnitzer, Mark J; Clandinin, Thomas R

2011-06-23

Many animals rely on visual motion detection for survival. Motion information is extracted from spatiotemporal intensity patterns on the retina, a paradigmatic neural computation. A phenomenological model, the Hassenstein-Reichardt correlator (HRC), relates visual inputs to neural activity and behavioral responses to motion, but the circuits that implement this computation remain unknown. By using cell-type specific genetic silencing, minimal motion stimuli, and in vivo calcium imaging, we examine two critical HRC inputs. These two pathways respond preferentially to light and dark moving edges. We demonstrate that these pathways perform overlapping but complementary subsets of the computations underlying the HRC. A numerical model implementing differential weighting of these operations displays the observed edge preferences. Intriguingly, these pathways are distinguished by their sensitivities to a stimulus correlation that corresponds to an illusory percept, "reverse phi," that affects many species. Thus, this computational architecture may be widely used to achieve edge selectivity in motion detection. Copyright © 2011 Elsevier Inc. All rights reserved.

An evaluation of open set recognition for FLIR images

NASA Astrophysics Data System (ADS)

Scherreik, Matthew; Rigling, Brian

2015-05-01

Typical supervised classification algorithms label inputs according to what was learned in a training phase. Thus, test inputs that were not seen in training are always given incorrect labels. Open set recognition algorithms address this issue by accounting for inputs that are not present in training and providing the classifier with an option to reject" unknown samples. A number of such techniques have been developed in the literature, many of which are based on support vector machines (SVMs). One approach, the 1-vs-set machine, constructs a slab" in feature space using the SVM hyperplane. Inputs falling on one side of the slab or within the slab belong to a training class, while inputs falling on the far side of the slab are rejected. We note that rejection of unknown inputs can be achieved by thresholding class posterior probabilities. Another recently developed approach, the Probabilistic Open Set SVM (POS-SVM), empirically determines good probability thresholds. We apply the 1-vs-set machine, POS-SVM, and closed set SVMs to FLIR images taken from the Comanche SIG dataset. Vehicles in the dataset are divided into three general classes: wheeled, armored personnel carrier (APC), and tank. For each class, a coarse pose estimate (front, rear, left, right) is taken. In a closed set sense, we analyze these algorithms for prediction of vehicle class and pose. To test open set performance, one or more vehicle classes are held out from training. By considering closed and open set performance separately, we may closely analyze both inter-class discrimination and threshold effectiveness.

Secure estimation, control and optimization of uncertain cyber-physical systems with applications to power networks

NASA Astrophysics Data System (ADS)

Taha, Ahmad Fayez

Transportation networks, wearable devices, energy systems, and the book you are reading now are all ubiquitous cyber-physical systems (CPS). These inherently uncertain systems combine physical phenomena with communication, data processing, control and optimization. Many CPSs are controlled and monitored by real-time control systems that use communication networks to transmit and receive data from systems modeled by physical processes. Existing studies have addressed a breadth of challenges related to the design of CPSs. However, there is a lack of studies on uncertain CPSs subject to dynamic unknown inputs and cyber-attacks---an artifact of the insertion of communication networks and the growing complexity of CPSs. The objective of this dissertation is to create secure, computational foundations for uncertain CPSs by establishing a framework to control, estimate and optimize the operation of these systems. With major emphasis on power networks, the dissertation deals with the design of secure computational methods for uncertain CPSs, focusing on three crucial issues---(1) cyber-security and risk-mitigation, (2) network-induced time-delays and perturbations and (3) the encompassed extreme time-scales. The dissertation consists of four parts. In the first part, we investigate dynamic state estimation (DSE) methods and rigorously examine the strengths and weaknesses of the proposed routines under dynamic attack-vectors and unknown inputs. In the second part, and utilizing high-frequency measurements in smart grids and the developed DSE methods in the first part, we present a risk mitigation strategy that minimizes the encountered threat levels, while ensuring the continual observability of the system through available, safe measurements. The developed methods in the first two parts rely on the assumption that the uncertain CPS is not experiencing time-delays, an assumption that might fail under certain conditions. To overcome this challenge, networked unknown input observers---observers/estimators for uncertain CPSs---are designed such that the effect of time-delays and cyber-induced perturbations are minimized, enabling secure DSE and risk mitigation in the first two parts. The final part deals with the extreme time-scales encompassed in CPSs, generally, and smart grids, specifically. Operational decisions for long time-scales can adversely affect the security of CPSs for faster time-scales. We present a model that jointly describes steady-state operation and transient stability by combining convex optimal power flow with semidefinite programming formulations of an optimal control problem. This approach can be jointly utilized with the aforementioned parts of the dissertation work, considering time-delays and DSE. The research contributions of this dissertation furnish CPS stakeholders with insights on the design and operation of uncertain CPSs, whilst guaranteeing the system's real-time safety. Finally, although many of the results of this dissertation are tailored to power systems, the results are general enough to be applied for a variety of uncertain CPSs.

Understanding virtual water flows: A multiregion input-output case study of Victoria

NASA Astrophysics Data System (ADS)

Lenzen, Manfred

2009-09-01

This article explains and interprets virtual water flows from the well-established perspective of input-output analysis. Using a case study of the Australian state of Victoria, it demonstrates that input-output analysis can enumerate virtual water flows without systematic and unknown truncation errors, an issue which has been largely absent from the virtual water literature. Whereas a simplified flow analysis from a producer perspective would portray Victoria as a net virtual water importer, enumerating the water embodiments across the full supply chain using input-output analysis shows Victoria as a significant net virtual water exporter. This study has succeeded in informing government policy in Australia, which is an encouraging sign that input-output analysis will be able to contribute much value to other national and international applications.

Laterodorsal Nucleus of the Thalamus: A Processor of Somatosensory Inputs

PubMed Central

BEZDUDNAYA, TATIANA; KELLER, ASAF

2009-01-01

The laterodorsal (LD) nucleus of the thalamus has been considered a “higher order” nucleus that provides inputs to limbic cortical areas. Although its functions are largely unknown, it is often considered to be involved in spatial learning and memory. Here we provide evidence that LD is part of a hitherto unknown pathway for processing somatosensory information. Juxtacellular and extracellular recordings from LD neurons reveal that they respond to vibrissa stimulation with short latency (median = 7 ms) and large magnitude responses (median = 1.2 spikes/stimulus). Most neurons (62%) had large receptive fields, responding to six and more individual vibrissae. Electrical stimulation of the trigeminal nucleus interpolaris (SpVi) evoked short latency responses (median = 3.8 ms) in vibrissa-responsive LD neurons. Labeling produced by anterograde and retrograde neuroanatomical tracers confirmed that LD neurons receive direct inputs from SpVi. Electrophysiological and neuroanatomical analyses revealed also that LD projects upon the cingulate and retrosplenial cortex, but has only sparse projections to the barrel cortex. These findings suggest that LD is part of a novel processing stream involved in spatial orientation and learning related to somatosensory cues. PMID:18273888

Manufacturing Planning Guide

NASA Technical Reports Server (NTRS)

Waid, Michael

2011-01-01

Manufacturing process, milestones and inputs are unknowns to first-time users of the manufacturing facilities. The Manufacturing Planning Guide aids in establishing expectations for both NASA and non-NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their project engineering personnel in manufacturing planning and execution. Material covered includes a roadmap of the manufacturing process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, products, and inputs necessary to define test scope, cost, and schedule are included as an appendix to the guide.

Differential inputs to striatal cholinergic and parvalbumin interneurons imply functional distinctions

PubMed Central

Klug, Jason R; Engelhardt, Max D; Cadman, Cara N; Li, Hao; Smith, Jared B; Ayala, Sarah; Williams, Elora W; Hoffman, Hilary

2018-01-01

Striatal cholinergic (ChAT) and parvalbumin (PV) interneurons exert powerful influences on striatal function in health and disease, yet little is known about the organization of their inputs. Here using rabies tracing, electrophysiology and genetic tools, we compare the whole-brain inputs to these two types of striatal interneurons and dissect their functional connectivity in mice. ChAT interneurons receive a substantial cortical input from associative regions of cortex, such as the orbitofrontal cortex. Amongst subcortical inputs, a previously unknown inhibitory thalamic reticular nucleus input to striatal PV interneurons is identified. Additionally, the external segment of the globus pallidus targets striatal ChAT interneurons, which is sufficient to inhibit tonic ChAT interneuron firing. Finally, we describe a novel excitatory pathway from the pedunculopontine nucleus that innervates ChAT interneurons. These results establish the brain-wide direct inputs of two major types of striatal interneurons and allude to distinct roles in regulating striatal activity and controlling behavior. PMID:29714166

New control concepts for uncertain water resources systems: 1. Theory

NASA Astrophysics Data System (ADS)

Georgakakos, Aris P.; Yao, Huaming

1993-06-01

A major complicating factor in water resources systems management is handling unknown inputs. Stochastic optimization provides a sound mathematical framework but requires that enough data exist to develop statistical input representations. In cases where data records are insufficient (e.g., extreme events) or atypical of future input realizations, stochastic methods are inadequate. This article presents a control approach where input variables are only expected to belong in certain sets. The objective is to determine sets of admissible control actions guaranteeing that the system will remain within desirable bounds. The solution is based on dynamic programming and derived for the case where all sets are convex polyhedra. A companion paper (Yao and Georgakakos, this issue) addresses specific applications and problems in relation to reservoir system management.

Model-Based Analysis of the Long-Term Effects of Fertilization Management on Cropland Soil Acidification.

PubMed

Zeng, Mufan; de Vries, Wim; Bonten, Luc T C; Zhu, Qichao; Hao, Tianxiang; Liu, Xuejun; Xu, Minggang; Shi, Xiaojun; Zhang, Fusuo; Shen, Jianbo

2017-04-04

Agricultural soil acidification in China is known to be caused by the over-application of nitrogen (N) fertilizers, but the long-term impacts of different fertilization practices on intensive cropland soil acidification are largely unknown. Here, we further developed the soil acidification model VSD+ for intensive agricultural systems and validated it against observed data from three long-term fertilization experiments in China. The model simulated well the changes in soil pH and base saturation over the last 20 years. The validated model was adopted to quantify the contribution of N and base cation (BC) fluxes to soil acidification. The net NO 3 - leaching and NO 4 + input accounted for 80% of the proton production under N application, whereas one-third of acid was produced by BC uptake when N was not applied. The simulated long-term (1990-2050) effects of different fertilizations on soil acidification showed that balanced N application combined with manure application avoids reduction of both soil pH and base saturation, while application of calcium nitrate and liming increases these two soil properties. Reducing NH 4 + input and NO 3 - leaching by optimizing N management and increasing BC inputs by manure application thus already seem to be effective approaches to mitigating soil acidification in intensive cropland systems.

Absence of S-cone input in human blindsight following hemispherectomy.

PubMed

Leh, Sandra E; Mullen, Kathy T; Ptito, Alain

2006-11-01

Destruction of the occipital cortex presumably leads to permanent blindness in the contralateral visual field. Residual abilities to respond to visual stimuli in the blind field without consciously experiencing them have, however, been described in cortically blind patients and are termed 'blindsight'. Although the neuronal basis of blindsight remains unknown, possible neuronal correlates have been proposed based on the nature of the residual vision observed. The most prominent but still controversial hypothesis postulates the involvement of the superior colliculi in blindsight. Here we demonstrate, using a computer-based reaction time test in a group of hemispherectomized subjects, that human 'attention-blindsight' can be measured for achromatic stimuli but disappears for stimuli that solely activate S-cones. Given that primate data have shown that the superior colliculi lacks input from S-cones, our results lend strong support to the hypothesis that 'attention-blindsight' is mediated through a collicular pathway. The contribution of a direct geniculo-extrastriate-koniocellular projection was ruled out by testing hemispherectomized subjects in whom a whole hemisphere has been removed or disconnected for the treatment of epilepsy. A direct retino-pulvinar-cortical connection is also unlikely as the pulvinar nucleus is known to receive input from S-cones as well as from L/M-cone-driven colour-opponent ganglion cells.

Constructing general partial differential equations using polynomial and neural networks.

PubMed

Zjavka, Ladislav; Pedrycz, Witold

2016-01-01

Sum fraction terms can approximate multi-variable functions on the basis of discrete observations, replacing a partial differential equation definition with polynomial elementary data relation descriptions. Artificial neural networks commonly transform the weighted sum of inputs to describe overall similarity relationships of trained and new testing input patterns. Differential polynomial neural networks form a new class of neural networks, which construct and solve an unknown general partial differential equation of a function of interest with selected substitution relative terms using non-linear multi-variable composite polynomials. The layers of the network generate simple and composite relative substitution terms whose convergent series combinations can describe partial dependent derivative changes of the input variables. This regression is based on trained generalized partial derivative data relations, decomposed into a multi-layer polynomial network structure. The sigmoidal function, commonly used as a nonlinear activation of artificial neurons, may transform some polynomial items together with the parameters with the aim to improve the polynomial derivative term series ability to approximate complicated periodic functions, as simple low order polynomials are not able to fully make up for the complete cycles. The similarity analysis facilitates substitutions for differential equations or can form dimensional units from data samples to describe real-world problems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Quantum pattern recognition with multi-neuron interactions

NASA Astrophysics Data System (ADS)

Fard, E. Rezaei; Aghayar, K.; Amniat-Talab, M.

2018-03-01

We present a quantum neural network with multi-neuron interactions for pattern recognition tasks by a combination of extended classic Hopfield network and adiabatic quantum computation. This scheme can be used as an associative memory to retrieve partial patterns with any number of unknown bits. Also, we propose a preprocessing approach to classifying the pattern space S to suppress spurious patterns. The results of pattern clustering show that for pattern association, the number of weights (η ) should equal the numbers of unknown bits in the input pattern ( d). It is also remarkable that associative memory function depends on the location of unknown bits apart from the d and load parameter α.

Proof-of-Concept Studies in Novel Guided Wave Methods for Metallic Structural Condition

DTIC Science & Technology

2009-03-01

stiffness tensor which is general can be complex (viscoelastic behavior). More details on the compatibility operator can be found in (Gopalakrishnan...structure was in when these AR coefficients were recorded is scored as the " vote " for the unknown condition using that particular input signal. This...signals that are imparted to the structure in its unknown state. The votes for each condition are then summed and the condition with the plurality of

Differential Acute and Chronic Effects of Leptin on Hypothalamic Astrocyte Morphology and Synaptic Protein Levels

PubMed Central

García-Cáceres, Cristina; Fuente-Martín, Esther; Burgos-Ramos, Emma; Granado, Miriam; Frago, Laura M.; Barrios, Vicente; Horvath, Tamas

2011-01-01

Astrocytes participate in neuroendocrine functions partially through modulation of synaptic input density in the hypothalamus. Indeed, glial ensheathing of neurons is modified by specific hormones, thus determining the availability of neuronal membrane space for synaptic inputs, with the loss of this plasticity possibly being involved in pathological processes. Leptin modulates synaptic inputs in the hypothalamus, but whether astrocytes participate in this action is unknown. Here we report that astrocyte structural proteins, such as glial fibrillary acidic protein (GFAP) and vimentin, are induced and astrocyte morphology modified by chronic leptin administration (intracerebroventricular, 2 wk), with these changes being inversely related to modifications in synaptic protein densities. Similar changes in glial structural proteins were observed in adult male rats that had increased body weight and circulating leptin levels due to neonatal overnutrition (overnutrition: four pups/litter vs. control: 12 pups/litter). However, acute leptin treatment reduced hypothalamic GFAP levels and induced synaptic protein levels 1 h after administration, with no effect on vimentin. In primary hypothalamic astrocyte cultures leptin also reduced GFAP levels at 1 h, with an induction at 24 h, indicating a possible direct effect of leptin. Hence, one mechanism by which leptin may affect metabolism is by modifying hypothalamic astrocyte morphology, which in turn could alter synaptic inputs to hypothalamic neurons. Furthermore, the responses to acute and chronic leptin exposure are inverse, raising the possibility that increased glial activation in response to chronic leptin exposure could be involved in central leptin resistance. PMID:21343257

Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-Learning.

PubMed

Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

2018-02-01

This paper proposes a combined Virtual Reference Feedback Tuning-Q-learning model-free control approach, which tunes nonlinear static state feedback controllers to achieve output model reference tracking in an optimal control framework. The novel iterative Batch Fitted Q-learning strategy uses two neural networks to represent the value function (critic) and the controller (actor), and it is referred to as a mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach. Learning convergence of the Q-learning schemes generally depends, among other settings, on the efficient exploration of the state-action space. Handcrafting test signals for efficient exploration is difficult even for input-output stable unknown processes. Virtual Reference Feedback Tuning can ensure an initial stabilizing controller to be learned from few input-output data and it can be next used to collect substantially more input-state data in a controlled mode, in a constrained environment, by compensating the process dynamics. This data is used to learn significantly superior nonlinear state feedback neural networks controllers for model reference tracking, using the proposed Batch Fitted Q-learning iterative tuning strategy, motivating the original combination of the two techniques. The mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach is experimentally validated for water level control of a multi input-multi output nonlinear constrained coupled two-tank system. Discussions on the observed control behavior are offered. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Synaptic Scaling in Combination with Many Generic Plasticity Mechanisms Stabilizes Circuit Connectivity

PubMed Central

Tetzlaff, Christian; Kolodziejski, Christoph; Timme, Marc; Wörgötter, Florentin

2011-01-01

Synaptic scaling is a slow process that modifies synapses, keeping the firing rate of neural circuits in specific regimes. Together with other processes, such as conventional synaptic plasticity in the form of long term depression and potentiation, synaptic scaling changes the synaptic patterns in a network, ensuring diverse, functionally relevant, stable, and input-dependent connectivity. How synaptic patterns are generated and stabilized, however, is largely unknown. Here we formally describe and analyze synaptic scaling based on results from experimental studies and demonstrate that the combination of different conventional plasticity mechanisms and synaptic scaling provides a powerful general framework for regulating network connectivity. In addition, we design several simple models that reproduce experimentally observed synaptic distributions as well as the observed synaptic modifications during sustained activity changes. These models predict that the combination of plasticity with scaling generates globally stable, input-controlled synaptic patterns, also in recurrent networks. Thus, in combination with other forms of plasticity, synaptic scaling can robustly yield neuronal circuits with high synaptic diversity, which potentially enables robust dynamic storage of complex activation patterns. This mechanism is even more pronounced when considering networks with a realistic degree of inhibition. Synaptic scaling combined with plasticity could thus be the basis for learning structured behavior even in initially random networks. PMID:22203799

Antenna Test Facility (ATF): User Test Planning Guide

NASA Technical Reports Server (NTRS)

Lin, Greg

2011-01-01

Test process, milestones and inputs are unknowns to first-time users of the ATF. The User Test Planning Guide aids in establishing expectations for both NASA and non-NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their test engineering personnel in test planning and execution. Material covered includes a roadmap of the test process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, test article interfaces, and inputs necessary to define test scope, cost, and schedule are included as an appendix to the guide.

Chamber B Thermal/Vacuum Chamber: User Test Planning Guide

NASA Technical Reports Server (NTRS)

Montz, Mike E.

2012-01-01

Test process, milestones and inputs are unknowns to first-time users of Chamber B. The User Test Planning Guide aids in establishing expectations for both NASA and non-NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their test engineering personnel in test planning and execution. Material covered includes a roadmap of the test process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, test article interfaces, and inputs necessary to define test scope, cost, and schedule are included as an appendix to the guide.

Audio Development Laboratory (ADL) User Test Planning Guide

NASA Technical Reports Server (NTRS)

Romero, Andy

2012-01-01

Test process, milestones and inputs are unknowns to first-time users of the ADL. The User Test Planning Guide aids in establishing expectations for both NASA and non-NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their test engineering personnel in test planning and execution. Material covered includes a roadmap of the test process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, test article interfaces, and inputs necessary to define test scope, cost, and schedule are included as an appendix to the guide.

Radiant Heat Test Facility (RHTF): User Test Planning Guide

NASA Technical Reports Server (NTRS)

DelPapa, Steven

2011-01-01

Test process, milestones and inputs are unknowns to first-time users of the RHTF. The User Test Planning Guide aids in establishing expectations for both NASA and non- NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their test engineering personnel in test planning and execution. Material covered includes a roadmap of the test process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, test article interfaces, and inputs necessary to define test scope, cost, and schedule are included as an appendix to the guide.

Electronic Systems Test Laboratory (ESTL) User Test Planning Guide

NASA Technical Reports Server (NTRS)

Robinson, Neil

2011-01-01

Test process, milestones and inputs are unknowns to first-time users of the ESTL. The User Test Planning Guide aids in establishing expectations for both NASA and non-NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their test engineering personnel in test planning and execution. Material covered includes a roadmap of the test process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, test article interfaces, and inputs necessary to define test scope, cost, and schedule are included as an appendix to the guide.

Communication Systems Simulation Laboratory (CSSL): Simulation Planning Guide

NASA Technical Reports Server (NTRS)

Schlesinger, Adam

2012-01-01

The simulation process, milestones and inputs are unknowns to first-time users of the CSSL. The Simulation Planning Guide aids in establishing expectations for both NASA and non-NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their engineering personnel in simulation planning and execution. Material covered includes a roadmap of the simulation process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, facility interfaces, and inputs necessary to define scope, cost, and schedule are included as an appendix to the guide.

Advanced Materials Laboratory User Test Planning Guide

NASA Technical Reports Server (NTRS)

Orndoff, Evelyne

2012-01-01

Test process, milestones and inputs are unknowns to first-time users of the Advanced Materials Laboratory. The User Test Planning Guide aids in establishing expectations for both NASA and non-NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their test engineering personnel in test planning and execution. Material covered includes a roadmap of the test process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, test article interfaces, and inputs necessary to define test scope, cost, and schedule are included as an appendix to the guide.

Structures Test Laboratory (STL). User Test Planning Guide

NASA Technical Reports Server (NTRS)

Zipay, John J.

2011-01-01

Test process, milestones and inputs are unknowns to first-time users of the STL. The User Test Planning Guide aids in establishing expectations for both NASA and non-NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their test engineering personnel in test planning and execution. Material covered includes a roadmap of the test process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, test article interfaces, and inputs necessary to define test scope, cost, and schedule are included as an appendix to the guide.

Systems Engineering Simulator (SES) Simulator Planning Guide

NASA Technical Reports Server (NTRS)

McFarlane, Michael

2011-01-01

The simulation process, milestones and inputs are unknowns to first-time users of the SES. The Simulator Planning Guide aids in establishing expectations for both NASA and non-NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their engineering personnel in simulation planning and execution. Material covered includes a roadmap of the simulation process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, facility interfaces, and inputs necessary to define scope, cost, and schedule are included as an appendix to the guide.

Computational Electromagnetics (CEM) Laboratory: Simulation Planning Guide

NASA Technical Reports Server (NTRS)

Khayat, Michael A.

2011-01-01

The simulation process, milestones and inputs are unknowns to first-time users of the CEM Laboratory. The Simulation Planning Guide aids in establishing expectations for both NASA and non-NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their engineering personnel in simulation planning and execution. Material covered includes a roadmap of the simulation process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, facility interfaces, and inputs necessary to define scope, cost, and schedule are included as an appendix to the guide.

Explicit least squares system parameter identification for exact differential input/output models

NASA Technical Reports Server (NTRS)

Pearson, A. E.

1993-01-01

The equation error for a class of systems modeled by input/output differential operator equations has the potential to be integrated exactly, given the input/output data on a finite time interval, thereby opening up the possibility of using an explicit least squares estimation technique for system parameter identification. The paper delineates the class of models for which this is possible and shows how the explicit least squares cost function can be obtained in a way that obviates dealing with unknown initial and boundary conditions. The approach is illustrated by two examples: a second order chemical kinetics model and a third order system of Lorenz equations.

Training a Network of Electronic Neurons for Control of a Mobile Robot

NASA Astrophysics Data System (ADS)

Vromen, T. G. M.; Steur, E.; Nijmeijer, H.

An adaptive training procedure is developed for a network of electronic neurons, which controls a mobile robot driving around in an unknown environment while avoiding obstacles. The neuronal network controls the angular velocity of the wheels of the robot based on the sensor readings. The nodes in the neuronal network controller are clusters of neurons rather than single neurons. The adaptive training procedure ensures that the input-output behavior of the clusters is identical, even though the constituting neurons are nonidentical and have, in isolation, nonidentical responses to the same input. In particular, we let the neurons interact via a diffusive coupling, and the proposed training procedure modifies the diffusion interaction weights such that the neurons behave synchronously with a predefined response. The working principle of the training procedure is experimentally validated and results of an experiment with a mobile robot that is completely autonomously driving in an unknown environment with obstacles are presented.

Laterodorsal nucleus of the thalamus: A processor of somatosensory inputs.

PubMed

Bezdudnaya, Tatiana; Keller, Asaf

2008-04-20

The laterodorsal (LD) nucleus of the thalamus has been considered a "higher order" nucleus that provides inputs to limbic cortical areas. Although its functions are largely unknown, it is often considered to be involved in spatial learning and memory. Here we provide evidence that LD is part of a hitherto unknown pathway for processing somatosensory information. Juxtacellular and extracellular recordings from LD neurons reveal that they respond to vibrissa stimulation with short latency (median = 7 ms) and large magnitude responses (median = 1.2 spikes/stimulus). Most neurons (62%) had large receptive fields, responding to six and more individual vibrissae. Electrical stimulation of the trigeminal nucleus interpolaris (SpVi) evoked short latency responses (median = 3.8 ms) in vibrissa-responsive LD neurons. Labeling produced by anterograde and retrograde neuroanatomical tracers confirmed that LD neurons receive direct inputs from SpVi. Electrophysiological and neuroanatomical analyses revealed also that LD projects upon the cingulate and retrosplenial cortex, but has only sparse projections to the barrel cortex. These findings suggest that LD is part of a novel processing stream involved in spatial orientation and learning related to somatosensory cues. (c) 2008 Wiley-Liss, Inc.

Bottom-up and Top-down Input Augment the Variability of Cortical Neurons

PubMed Central

Nassi, Jonathan J.; Kreiman, Gabriel; Born, Richard T.

2016-01-01

SUMMARY Neurons in the cerebral cortex respond inconsistently to a repeated sensory stimulus, yet they underlie our stable sensory experiences. Although the nature of this variability is unknown, its ubiquity has encouraged the general view that each cell produces random spike patterns that noisily represent its response rate. In contrast, here we show that reversibly inactivating distant sources of either bottom-up or top-down input to cortical visual areas in the alert primate reduces both the spike train irregularity and the trial-to-trial variability of single neurons. A simple model in which a fraction of the pre-synaptic input is silenced can reproduce this reduction in variability, provided that there exist temporal correlations primarily within, but not between, excitatory and inhibitory input pools. A large component of the variability of cortical neurons may therefore arise from synchronous input produced by signals arriving from multiple sources. PMID:27427459

Role of ionotropic GABA, glutamate and glycine receptors in the tonic and reflex control of cardiac vagal outflow in the rat

PubMed Central

2010-01-01

Background Cardiac vagal preganglionic neurons (CVPN) are responsible for the tonic, reflex and respiratory modulation of heart rate (HR). Although CVPN receive GABAergic and glutamatergic inputs, likely involved in respiratory and reflex modulation of HR respectively, little else is known regarding the functions controlled by ionotropic inputs. Activation of g-protein coupled receptors (GPCR) alters these inputs, but the functional consequence is largely unknown. The present study aimed to delineate how ionotropic GABAergic, glycinergic and glutamatergic inputs contribute to the tonic and reflex control of HR and in particular determine which receptor subtypes were involved. Furthermore, we wished to establish how activation of the 5-HT1A GPCR affects tonic and reflex control of HR and what ionotropic interactions this might involve. Results Microinjection of the GABAA antagonist picrotoxin into CVPN decreased HR but did not affect baroreflex bradycardia. The glycine antagonist strychnine did not alter HR or baroreflex bradycardia. Combined microinjection of the NMDA antagonist, MK801, and AMPA antagonist, CNQX, into CVPN evoked a small bradycardia and abolished baroreflex bradycardia. MK801 attenuated whereas CNQX abolished baroreceptor bradycardia. Control intravenous injections of the 5-HT1A agonist 8-OH-DPAT evoked a small bradycardia and potentiated baroreflex bradycardia. These effects were still observed following microinjection of picrotoxin but not strychnine into CVPN. Conclusions We conclude that activation of GABAA receptors set the level of HR whereas AMPA to a greater extent than NMDA receptors elicit baroreflex changes in HR. Furthermore, activation of 5-HT1A receptors evokes bradycardia and enhances baroreflex changes in HR due to interactions with glycinergic neurons involving strychnine receptors. This study provides reference for future studies investigating how diseases alter neurochemical inputs to CVPN. PMID:20939929

Effect of gravity waves on the North Atlantic circulation

NASA Astrophysics Data System (ADS)

Eden, Carsten

2017-04-01

The recently proposed IDEMIX (Internal wave Dissipation, Energy and MIXing) parameterisation for the effect of gravity waves offers the possibility to construct consistent ocean models with a closed energy cycle. This means that the energy available for interior mixing in the ocean is only controlled by external energy input from the atmosphere and the tidal system and by internal exchanges. A central difficulty is the unknown fate of meso-scale eddy energy. In different scenarios for that eddy dissipation, the parameterized internal wave field provides between 2 and 3 TW for interior mixing from the total external energy input of about 4 TW, such that a transfer between 0.3 and 0.4 TW into mean potential energy contributes to drive the large-scale circulation in the model. The impact of the different mixing on the meridional overturning in the North Atlantic is discussed and compared to hydrographic observations. Furthermore, the direct energy exchange of the wave field with the geostrophic flow is parameterized in extended IDEMIX versions and the sensitivity of the North Atlantic circulation by this gravity wave drag is discussed.

Non-predictor control of a class of feedforward nonlinear systems with unknown time-varying delays

NASA Astrophysics Data System (ADS)

Koo, Min-Sung; Choi, Ho-Lim

2016-08-01

This paper generalises the several recent results on the control of feedforward time-delay nonlinear systems. First, in view of system formulation, there are unknown time-varying delays in both states and main control input. Also, the considered nonlinear system has extended feedforward nonlinearities. Second, in view of control solution, our proposed controller is a non-predictor feedback controller whereas smith-predictor type controllers are used in the several existing results. Moreover, our controller does not need any information on the unknown delays except their upper bounds. Thus, our result has certain merits in both system formulation and control solution perspective. The analysis and example are given for clear illustration.

"Who" is saying "what"? Brain-based decoding of human voice and speech.

PubMed

Formisano, Elia; De Martino, Federico; Bonte, Milene; Goebel, Rainer

2008-11-07

Can we decipher speech content ("what" is being said) and speaker identity ("who" is saying it) from observations of brain activity of a listener? Here, we combine functional magnetic resonance imaging with a data-mining algorithm and retrieve what and whom a person is listening to from the neural fingerprints that speech and voice signals elicit in the listener's auditory cortex. These cortical fingerprints are spatially distributed and insensitive to acoustic variations of the input so as to permit the brain-based recognition of learned speech from unknown speakers and of learned voices from previously unheard utterances. Our findings unravel the detailed cortical layout and computational properties of the neural populations at the basis of human speech recognition and speaker identification.

13-fold resolution gain through turbid layer via translated unknown speckle illumination

PubMed Central

Guo, Kaikai; Zhang, Zibang; Jiang, Shaowei; Liao, Jun; Zhong, Jingang; Eldar, Yonina C.; Zheng, Guoan

2017-01-01

Fluorescence imaging through a turbid layer holds great promise for various biophotonics applications. Conventional wavefront shaping techniques aim to create and scan a focus spot through the turbid layer. Finding the correct input wavefront without direct access to the target plane remains a critical challenge. In this paper, we explore a new strategy for imaging through turbid layer with a large field of view. In our setup, a fluorescence sample is sandwiched between two turbid layers. Instead of generating one focus spot via wavefront shaping, we use an unshaped beam to illuminate the turbid layer and generate an unknown speckle pattern at the target plane over a wide field of view. By tilting the input wavefront, we raster scan the unknown speckle pattern via the memory effect and capture the corresponding low-resolution fluorescence images through the turbid layer. Different from the wavefront-shaping-based single-spot scanning, the proposed approach employs many spots (i.e., speckles) in parallel for extending the field of view. Based on all captured images, we jointly recover the fluorescence object, the unknown optical transfer function of the turbid layer, the translated step size, and the unknown speckle pattern. Without direct access to the object plane or knowledge of the turbid layer, we demonstrate a 13-fold resolution gain through the turbid layer using the reported strategy. We also demonstrate the use of this technique to improve the resolution of a low numerical aperture objective lens allowing to obtain both large field of view and high resolution at the same time. The reported method provides insight for developing new fluorescence imaging platforms and may find applications in deep-tissue imaging. PMID:29359102

Performance Analysis for Channel Estimation With 1-Bit ADC and Unknown Quantization Threshold

NASA Astrophysics Data System (ADS)

Stein, Manuel S.; Bar, Shahar; Nossek, Josef A.; Tabrikian, Joseph

2018-05-01

In this work, the problem of signal parameter estimation from measurements acquired by a low-complexity analog-to-digital converter (ADC) with $1$-bit output resolution and an unknown quantization threshold is considered. Single-comparator ADCs are energy-efficient and can be operated at ultra-high sampling rates. For analysis of such systems, a fixed and known quantization threshold is usually assumed. In the symmetric case, i.e., zero hard-limiting offset, it is known that in the low signal-to-noise ratio (SNR) regime the signal processing performance degrades moderately by ${2}/{\\pi}$ ($-1.96$ dB) when comparing to an ideal $\\infty$-bit converter. Due to hardware imperfections, low-complexity $1$-bit ADCs will in practice exhibit an unknown threshold different from zero. Therefore, we study the accuracy which can be obtained with receive data processed by a hard-limiter with unknown quantization level by using asymptotically optimal channel estimation algorithms. To characterize the estimation performance of these nonlinear algorithms, we employ analytic error expressions for different setups while modeling the offset as a nuisance parameter. In the low SNR regime, we establish the necessary condition for a vanishing loss due to missing offset knowledge at the receiver. As an application, we consider the estimation of single-input single-output wireless channels with inter-symbol interference and validate our analysis by comparing the analytic and experimental performance of the studied estimation algorithms. Finally, we comment on the extension to multiple-input multiple-output channel models.

Vibration and Acoustic Test Facility (VATF): User Test Planning Guide

NASA Technical Reports Server (NTRS)

Fantasia, Peter M.

2011-01-01

Test process, milestones and inputs are unknowns to first-time users of the VATF. The User Test Planning Guide aids in establishing expectations for both NASA and non-NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their test engineering personnel in test planning and execution. Material covered includes a roadmap of the test process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, test article interfaces, and inputs necessary to define test scope, cost, and schedule are included as an appendix to the guide.

Six-Degree-of-Freedom Dynamic Test System (SDTS) User Test Planning Guide

NASA Technical Reports Server (NTRS)

Stokes, LeBarian

2012-01-01

Test process, milestones and inputs are unknowns to first-time users of the SDTS. The User Test Planning Guide aids in establishing expectations for both NASA and non- NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their test engineering personnel in test planning and execution. Material covered includes a roadmap of the test process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, test article interfaces, and inputs necessary to define test scope, cost, and schedule are included as an appendix to the guide.

Materials and Nondestructive Evaluation Laboratoriers: User Test Planning Guide

NASA Technical Reports Server (NTRS)

Schaschl, Leslie

2011-01-01

The Materials and Nondestructive Evaluation Laboratory process, milestones and inputs are unknowns to first-time users. The Materials and Nondestructive Evaluation Laboratory Planning Guide aids in establishing expectations for both NASA and non- NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware developers. It is intended to assist their project engineering personnel in materials analysis planning and execution. Material covered includes a roadmap of the analysis process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, products, and inputs necessary to define scope of analysis, cost, and schedule are included as an appendix to the guide.

Specialized Environmental Chamber Test Complex: User Test Planning Guide

NASA Technical Reports Server (NTRS)

Montz, Michael E.

2011-01-01

Test process, milestones and inputs are unknowns to first-time users of the Specialized Environmental Test Complex. The User Test Planning Guide aids in establishing expectations for both NASA and non-NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their test engineering personnel in test planning and execution. Material covered includes a roadmap of the test process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, test article interfaces, and inputs necessary to define test scope, cost, and schedule are included as an appendix to the guide.

Atmospheric Reentry Materials and Structures Evaluation Facility (ARMSEF). User Test Planning Guide

NASA Technical Reports Server (NTRS)

2011-01-01

Test process, milestones and inputs are unknowns to first-time users of the ARMSEF. The User Test Planning Guide aids in establishing expectations for both NASA and non-NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their test engineering personnel in test planning and execution. Material covered includes a roadmap of the test process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, test article interfaces, and inputs necessary to define test scope, cost, and schedule are included as an appendix to the guide.

Energy Systems Test Area (ESTA) Battery Test Operations User Test Planning Guide

NASA Technical Reports Server (NTRS)

Salinas, Michael

2012-01-01

Test process, milestones and inputs are unknowns to first-time users of the ESTA Battery Test Operations. The User Test Planning Guide aids in establishing expectations for both NASA and non-NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their test engineering personnel in test planning and execution. Material covered includes a roadmap of the test process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, test article interfaces, and inputs necessary to define test scope, cost, and schedule are included as an appendix to the guide.

Electromagnetic Interference/Compatibility (EMI/EMC) Control Test and Measurement Facility: User Test Planning Guide

NASA Technical Reports Server (NTRS)

Scully, Robert C.

2011-01-01

Test process, milestones and inputs are unknowns to first-time users of the EMI/EMC Test Facility. The User Test Planning Guide aids in establishing expectations for both NASA and non-NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their test engineering personnel in test planning and execution. Material covered includes a roadmap of the test process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, test article interfaces, and inputs necessary to define test scope, cost, and schedule are included as an appendix to the guide.

Assessment of model behavior and acceptable forcing data uncertainty in the context of land surface soil moisture estimation

NASA Astrophysics Data System (ADS)

Dumedah, Gift; Walker, Jeffrey P.

2017-03-01

The sources of uncertainty in land surface models are numerous and varied, from inaccuracies in forcing data to uncertainties in model structure and parameterizations. Majority of these uncertainties are strongly tied to the overall makeup of the model, but the input forcing data set is independent with its accuracy usually defined by the monitoring or the observation system. The impact of input forcing data on model estimation accuracy has been collectively acknowledged to be significant, yet its quantification and the level of uncertainty that is acceptable in the context of the land surface model to obtain a competitive estimation remain mostly unknown. A better understanding is needed about how models respond to input forcing data and what changes in these forcing variables can be accommodated without deteriorating optimal estimation of the model. As a result, this study determines the level of forcing data uncertainty that is acceptable in the Joint UK Land Environment Simulator (JULES) to competitively estimate soil moisture in the Yanco area in south eastern Australia. The study employs hydro genomic mapping to examine the temporal evolution of model decision variables from an archive of values obtained from soil moisture data assimilation. The data assimilation (DA) was undertaken using the advanced Evolutionary Data Assimilation. Our findings show that the input forcing data have significant impact on model output, 35% in root mean square error (RMSE) for 5cm depth of soil moisture and 15% in RMSE for 15cm depth of soil moisture. This specific quantification is crucial to illustrate the significance of input forcing data spread. The acceptable uncertainty determined based on dominant pathway has been validated and shown to be reliable for all forcing variables, so as to provide optimal soil moisture. These findings are crucial for DA in order to account for uncertainties that are meaningful from the model standpoint. Moreover, our results point to a proper treatment of input forcing data in general land surface and hydrological model estimation.

Dual physiological rate measurement instrument

NASA Technical Reports Server (NTRS)

Cooper, Tommy G. (Inventor)

1990-01-01

The object of the invention is to provide an instrument for converting a physiological pulse rate into a corresponding linear output voltage. The instrument which accurately measures the rate of an unknown rectangular pulse wave over an extended range of values comprises a phase-locked loop including a phase comparator, a filtering network, and a voltage-controlled oscillator, arranged in cascade. The phase comparator has a first input responsive to the pulse wave and a second input responsive to the output signal of the voltage-controlled oscillator. The comparator provides a signal dependent on the difference in phase and frequency between the signals appearing on the first and second inputs. A high-input impedance amplifier accepts an output from the filtering network and provides an amplified output DC signal to a utilization device for providing a measurement of the rate of the pulse wave.

A Dasymetric-Based Monte Carlo Simulation Approach to the Probabilistic Analysis of Spatial Variables

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

Morton, April M; Piburn, Jesse O; McManamay, Ryan A

2017-01-01

Monte Carlo simulation is a popular numerical experimentation technique used in a range of scientific fields to obtain the statistics of unknown random output variables. Despite its widespread applicability, it can be difficult to infer required input probability distributions when they are related to population counts unknown at desired spatial resolutions. To overcome this challenge, we propose a framework that uses a dasymetric model to infer the probability distributions needed for a specific class of Monte Carlo simulations which depend on population counts.

Parameterisation of Biome BGC to assess forest ecosystems in Africa

NASA Astrophysics Data System (ADS)

Gautam, Sishir; Pietsch, Stephan A.

2010-05-01

African forest ecosystems are an important environmental and economic resource. Several studies show that tropical forests are critical to society as economic, environmental and societal resources. Tropical forests are carbon dense and thus play a key role in climate change mitigation. Unfortunately, the response of tropical forests to environmental change is largely unknown owing to insufficient spatially extensive observations. Developing regions like Africa where records of forest management for long periods are unavailable the process-based ecosystem simulation model - BIOME BGC could be a suitable tool to explain forest ecosystem dynamics. This ecosystem simulation model uses descriptive input parameters to establish the physiology, biochemistry, structure, and allocation patterns within vegetation functional types, or biomes. Undocumented parameters for larger-resolution simulations are currently the major limitations to regional modelling in African forest ecosystems. This study was conducted to document input parameters for BIOME-BGC for major natural tropical forests in the Congo basin. Based on available literature and field measurements updated values for turnover and mortality, allometry, carbon to nitrogen ratios, allocation of plant material to labile, cellulose, and lignin pools, tree morphology and other relevant factors were assigned. Daily climate input data for the model applications were generated using the statistical weather generator MarkSim. The forest was inventoried at various sites and soil samples of corresponding stands across Gabon were collected. Carbon and nitrogen in the collected soil samples were determined from soil analysis. The observed tree volume, soil carbon and soil nitrogen were then compared with the simulated model outputs to evaluate the model performance. Furthermore, the simulation using Congo Basin specific parameters and generalised BIOME BGC parameters for tropical evergreen broadleaved tree species were also executed and the simulated results compared. Once the model was optimised for forests in the Congo basin it was validated against observed tree volume, soil carbon and soil nitrogen from a set of independent plots.

Nonlinear unbiased minimum-variance filter for Mars entry autonomous navigation under large uncertainties and unknown measurement bias.

PubMed

Xiao, Mengli; Zhang, Yongbo; Fu, Huimin; Wang, Zhihua

2018-05-01

High-precision navigation algorithm is essential for the future Mars pinpoint landing mission. The unknown inputs caused by large uncertainties of atmospheric density and aerodynamic coefficients as well as unknown measurement biases may cause large estimation errors of conventional Kalman filters. This paper proposes a derivative-free version of nonlinear unbiased minimum variance filter for Mars entry navigation. This filter has been designed to solve this problem by estimating the state and unknown measurement biases simultaneously with derivative-free character, leading to a high-precision algorithm for the Mars entry navigation. IMU/radio beacons integrated navigation is introduced in the simulation, and the result shows that with or without radio blackout, our proposed filter could achieve an accurate state estimation, much better than the conventional unscented Kalman filter, showing the ability of high-precision Mars entry navigation algorithm. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Output feedback control of a quadrotor UAV using neural networks.

PubMed

Dierks, Travis; Jagannathan, Sarangapani

2010-01-01

In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.

Adaptive Identification of Fluid-Dynamic Systems

DTIC Science & Technology

2001-06-14

Fig. 1. Unknown System Adaptive Filter Σ _ + Input u Filter Output y Desired Output d Error e Fig. 1. Modeling of a SISO system using...2J E e n =   (12) Here [ ]. E is the expectation operator and ( ) ( ) ( ) e n d n y n= − is the error between the desired system output and...B … input vector ( ) ( ) ( ) ( )[ ], , ,1 1 Tn u n u n u n N= − − +U … output and error ( ) ( ) ( ) ( ) ( ) ( ) ( ) T T y n n n e n d n n n

Distributed adaptive asymptotically consensus tracking control of uncertain Euler-Lagrange systems under directed graph condition.

PubMed

Wang, Wei; Wen, Changyun; Huang, Jiangshuai; Fan, Huijin

2017-11-01

In this paper, a backstepping based distributed adaptive control scheme is proposed for multiple uncertain Euler-Lagrange systems under directed graph condition. The common desired trajectory is allowed totally unknown by part of the subsystems and the linearly parameterized trajectory model assumed in currently available results is no longer needed. To compensate the effects due to unknown trajectory information, a smooth function of consensus errors and certain positive integrable functions are introduced in designing virtual control inputs. Besides, to overcome the difficulty of completely counteracting the coupling terms of distributed consensus errors and parameter estimation errors in the presence of asymmetric Laplacian matrix, extra information transmission of local parameter estimates are introduced among linked subsystem and adaptive gain technique is adopted to generate distributed torque inputs. It is shown that with the proposed distributed adaptive control scheme, global uniform boundedness of all the closed-loop signals and asymptotically output consensus tracking can be achieved. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Simulations of Brady's-Type Fault Undergoing CO2 Push-Pull: Pressure-Transient and Sensitivity Analysis

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

Jung, Yoojin; Doughty, Christine

Input and output files used for fault characterization through numerical simulation using iTOUGH2. The synthetic data for the push period are generated by running a forward simulation (input parameters are provided in iTOUGH2 Brady GF6 Input Parameters.txt [InvExt6i.txt]). In general, the permeability of the fault gouge, damage zone, and matrix are assumed to be unknown. The input and output files are for the inversion scenario where only pressure transients are available at the monitoring well located 200 m above the injection well and only the fault gouge permeability is estimated. The input files are named InvExt6i, INPUT.tpl, FOFT.ins, CO2TAB, andmore » the output files are InvExt6i.out, pest.fof, and pest.sav (names below are display names). The table graphic in the data files below summarizes the inversion results, and indicates the fault gouge permeability can be estimated even if imperfect guesses are used for matrix and damage zone permeabilities, and permeability anisotropy is not taken into account.« less

Energy Systems Test Area (ESTA) Electrical Power Systems Test Operations: User Test Planning Guide

NASA Technical Reports Server (NTRS)

Salinas, Michael J.

2012-01-01

Test process, milestones and inputs are unknowns to first-time users of the ESTA Electrical Power Systems Test Laboratory. The User Test Planning Guide aids in establishing expectations for both NASA and non-NASA facility customers. The potential audience for this guide includes both internal and commercial spaceflight hardware/software developers. It is intended to assist their test engineering personnel in test planning and execution. Material covered includes a roadmap of the test process, roles and responsibilities of facility and user, major milestones, facility capabilities, and inputs required by the facility. Samples of deliverables, test article interfaces, and inputs necessary to define test scope, cost, and schedule are included as an appendix to the guide.

Impedance properties of circular microstrip antenna

NASA Technical Reports Server (NTRS)

Deshpande, M. D.; Bailey, M. C.

1983-01-01

A moment method solution to the input impedance of a circular microstrip antenna excited by either a microstrip feed or a coaxial probe is presented. Using the exact dyadic Green's function and the Fourier transform the problem is formulated in terms of Richmond's reaction integral equation from which the unknown patch current can be solved for. The patch current is expanded in terms of regular surface patch modes and an attachment mode (for probe excited case) which insures continuity of the current at probe/patch junction, proper polarization and p-dependance of patch current in the vicinity of the probe. The input impedance of a circular microstrip antenna is computed and compared with earlier results. Effect of attachment mode on the input impedance is also discussed.

Sensory experience modifies feature map relationships in visual cortex

PubMed Central

Cloherty, Shaun L; Hughes, Nicholas J; Hietanen, Markus A; Bhagavatula, Partha S

2016-01-01

The extent to which brain structure is influenced by sensory input during development is a critical but controversial question. A paradigmatic system for studying this is the mammalian visual cortex. Maps of orientation preference (OP) and ocular dominance (OD) in the primary visual cortex of ferrets, cats and monkeys can be individually changed by altered visual input. However, the spatial relationship between OP and OD maps has appeared immutable. Using a computational model we predicted that biasing the visual input to orthogonal orientation in the two eyes should cause a shift of OP pinwheels towards the border of OD columns. We then confirmed this prediction by rearing cats wearing orthogonally oriented cylindrical lenses over each eye. Thus, the spatial relationship between OP and OD maps can be modified by visual experience, revealing a previously unknown degree of brain plasticity in response to sensory input. DOI: http://dx.doi.org/10.7554/eLife.13911.001 PMID:27310531

Non-intrusive parameter identification procedure user's guide

NASA Technical Reports Server (NTRS)

Hanson, G. D.; Jewell, W. F.

1983-01-01

Written in standard FORTRAN, NAS is capable of identifying linear as well as nonlinear relations between input and output parameters; the only restriction is that the input/output relation be linear with respect to the unknown coefficients of the estimation equations. The output of the identification algorithm can be specified to be in either the time domain (i.e., the estimation equation coefficients) or in the frequency domain (i.e., a frequency response of the estimation equation). The frame length ("window") over which the identification procedure is to take place can be specified to be any portion of the input time history, thereby allowing the freedom to start and stop the identification procedure within a time history. There also is an option which allows a sliding window, which gives a moving average over the time history. The NAS software also includes the ability to identify several assumed solutions simultaneously for the same or different input data.

Current Sensor Fault Diagnosis Based on a Sliding Mode Observer for PMSM Driven Systems

PubMed Central

Huang, Gang; Luo, Yi-Ping; Zhang, Chang-Fan; Huang, Yi-Shan; Zhao, Kai-Hui

2015-01-01

This paper proposes a current sensor fault detection method based on a sliding mode observer for the torque closed-loop control system of interior permanent magnet synchronous motors. First, a sliding mode observer based on the extended flux linkage is built to simplify the motor model, which effectively eliminates the phenomenon of salient poles and the dependence on the direct axis inductance parameter, and can also be used for real-time calculation of feedback torque. Then a sliding mode current observer is constructed in αβ coordinates to generate the fault residuals of the phase current sensors. The method can accurately identify abrupt gain faults and slow-variation offset faults in real time in faulty sensors, and the generated residuals of the designed fault detection system are not affected by the unknown input, the structure of the observer, and the theoretical derivation and the stability proof process are concise and simple. The RT-LAB real-time simulation is used to build a simulation model of the hardware in the loop. The simulation and experimental results demonstrate the feasibility and effectiveness of the proposed method. PMID:25970258

Experimental Detection of Quantum Channel Capacities.

PubMed

Cuevas, Álvaro; Proietti, Massimiliano; Ciampini, Mario Arnolfo; Duranti, Stefano; Mataloni, Paolo; Sacchi, Massimiliano F; Macchiavello, Chiara

2017-09-08

We present an efficient experimental procedure that certifies nonvanishing quantum capacities for qubit noisy channels. Our method is based on the use of a fixed bipartite entangled state, where the system qubit is sent to the channel input. A particular set of local measurements is performed at the channel output and the ancilla qubit mode, obtaining lower bounds to the quantum capacities for any unknown channel with no need of quantum process tomography. The entangled qubits have a Bell state configuration and are encoded in photon polarization. The lower bounds are found by estimating the Shannon and von Neumann entropies at the output using an optimized basis, whose statistics is obtained by measuring only the three observables σ_{x}⊗σ_{x}, σ_{y}⊗σ_{y}, and σ_{z}⊗σ_{z}.

Contrasting effect of Saharan dust and UVR on autotrophic picoplankton in nearshore versus offshore waters of Mediterranean Sea

NASA Astrophysics Data System (ADS)

González-Olalla, J. M.; Medina-Sánchez, J. M.; Cabrerizo, M. J.; Villar-Argáiz, Manuel; Sánchez-Castillo, Pedro M.; Carrillo, Presentación

2017-08-01

Autotrophic picoplankton (APP) is responsible for the vast majority of primary production in oligotrophic marine areas, such as the Alboran Sea. The increase in atmospheric dust deposition (e.g., from Sahara Desert) associated with global warming, together with the high UV radiation (UVR) on these ecosystems, may generate effects on APP hitherto unknown. We performed an observational study across the Alboran Sea to establish which factors control the abundance and distribution of APP, and we made a microcosm experiment in two distinct areas, nearshore and offshore, to predict the joint UVR × dust impact on APP at midterm scales. Our observational study showed that temperature (T) was the main factor explaining the APP distribution whereas total dissolved nitrogen positively correlated with APP abundance. Our experimental study revealed that Saharan dust inputs reduced or inverted the UVR damage on the photosynthetic quantum yield (ΦPSII) and picoplanktonic primary production (PPP) in the nearshore area but accentuated it in the offshore. This contrasting effect is partially explained by the nonphotochemical quenching, acting as a photorepair mechanism. Picoeukaryotes reflected the observed effects on the physiological and metabolic variables, and Synechococcus was the only picoprokaryotic group that showed a positive response under UVR × dust conditions. Our study highlights a dual sensitivity of nearshore versus offshore picoplankton to dust inputs and UVR fluxes, just at the time in which these two global-change factors show their highest intensities and may recreate a potential future response of the microbial food web under global-change conditions.

Evolution of optimal Hill coefficients in nonlinear public goods games.

PubMed

Archetti, Marco; Scheuring, István

2016-10-07

In evolutionary game theory, the effect of public goods like diffusible molecules has been modelled using linear, concave, sigmoid and step functions. The observation that biological systems are often sigmoid input-output functions, as described by the Hill equation, suggests that a sigmoid function is more realistic. The Michaelis-Menten model of enzyme kinetics, however, predicts a concave function, and while mechanistic explanations of sigmoid kinetics exist, we lack an adaptive explanation: what is the evolutionary advantage of a sigmoid benefit function? We analyse public goods games in which the shape of the benefit function can evolve, in order to determine the optimal and evolutionarily stable Hill coefficients. We find that, while the dynamics depends on whether output is controlled at the level of the individual or the population, intermediate or high Hill coefficients often evolve, leading to sigmoid input-output functions that for some parameters are so steep to resemble a step function (an on-off switch). Our results suggest that, even when the shape of the benefit function is unknown, biological public goods should be modelled using a sigmoid or step function rather than a linear or concave function. Copyright © 2016 Elsevier Ltd. All rights reserved.

National Dam Inspection Program. Jennings Pond Dam (NDI I.D. PA-0891 DER I.D. 066-012) Susquehanna River Basin, Little Mehoopany Creek, Wyoming County, Pennsylvania. Phase I Inspection Report,

DTIC Science & Technology

1981-03-19

Area 7.9 square miles(1) b. Discharge at Dam Site ( cfs ) Maximum known flood at dam site Unknown Outlet conduit at maximum pool Unknown Gated spillway...700 cfs , based on the available 2.4-foot freeboard relative to the low spot on the left abutment. b. Experience Data. As previously stated, Jennings...in Appendix D. The inflow hydrograph for one-half PMF was found to have a peak flow of 6835 cfs . Computer input and summary of computer output are

Shape Models of Asteroids as a Missing Input for Bulk Density Determinations

NASA Astrophysics Data System (ADS)

Hanuš, Josef

2015-07-01

To determine a meaningful bulk density of an asteroid, both accurate volume and mass estimates are necessary. The volume can be computed by scaling the size of the 3D shape model to fit the disk-resolved images or stellar occultation profiles, which are available in the literature or through collaborations. This work provides a list of asteroids, for which (i) there are already mass estimates with reported uncertainties better than 20% or their mass will be most likely determined in the future from Gaia astrometric observations, and (ii) their 3D shape models are currently unknown. Additional optical lightcurves are necessary to determine the convex shape models of these asteroids. The main aim of this article is to motivate the observers to obtain lightcurves of these asteroids, and thus contribute to their shape model determinations. Moreover, a web page https://asteroid-obs.oca.eu, which maintains an up-to-date list of these objects to assure efficiency and to avoid any overlapping efforts, was created.

Statistical inference of empirical constituents in partitioned analysis from integral-effect experiments: An application in thermo-mechanical coupling

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

Stevens, Garrison N.; Atamturktur, Sez; Brown, D. Andrew

Rapid advancements in parallel computing over the last two decades have enabled simulations of complex, coupled systems through partitioning. In partitioned analysis, independently developed constituent models communicate, representing dependencies between multiple physical phenomena that occur in the full system. Figure 1 schematically demonstrates a coupled system with two constituent models, each resolving different physical behavior. In this figure, the constituent model, denoted as the “consumer,” relies upon some input parameter that is being provided by the constituent model acting as a “feeder”. The role of the feeder model is to map operating conditions (i.e. those that are stimulating the process)more » to consumer inputs, thus providing functional inputs to the consumer model*. Problems arise if the feeder model cannot be built–a challenge that is prevalent for highly complex systems in extreme operational conditions that push the limits of our understanding of underlying physical behavior. Often, these are also the situations where separate-effect experiments isolating the physical phenomena are not available; meaning that experimentally determining the unknown constituent behavior is not possible (Bauer and Holland, 1995; Unal et al., 2013), and that integral-effect experiments that reflect the behavior of the complete system tend to be the only available observations. In this paper, the authors advocate for the usefulness of integral-effect experiments in furthering a model developer’s knowledge of the physics principles governing the system behavior of interest.« less

Statistical inference of empirical constituents in partitioned analysis from integral-effect experiments: An application in thermo-mechanical coupling

DOE PAGES

Stevens, Garrison N.; Atamturktur, Sez; Brown, D. Andrew; ...

2018-04-16

Rapid advancements in parallel computing over the last two decades have enabled simulations of complex, coupled systems through partitioning. In partitioned analysis, independently developed constituent models communicate, representing dependencies between multiple physical phenomena that occur in the full system. Figure 1 schematically demonstrates a coupled system with two constituent models, each resolving different physical behavior. In this figure, the constituent model, denoted as the “consumer,” relies upon some input parameter that is being provided by the constituent model acting as a “feeder”. The role of the feeder model is to map operating conditions (i.e. those that are stimulating the process)more » to consumer inputs, thus providing functional inputs to the consumer model*. Problems arise if the feeder model cannot be built–a challenge that is prevalent for highly complex systems in extreme operational conditions that push the limits of our understanding of underlying physical behavior. Often, these are also the situations where separate-effect experiments isolating the physical phenomena are not available; meaning that experimentally determining the unknown constituent behavior is not possible (Bauer and Holland, 1995; Unal et al., 2013), and that integral-effect experiments that reflect the behavior of the complete system tend to be the only available observations. In this paper, the authors advocate for the usefulness of integral-effect experiments in furthering a model developer’s knowledge of the physics principles governing the system behavior of interest.« less

An Error-Entropy Minimization Algorithm for Tracking Control of Nonlinear Stochastic Systems with Non-Gaussian Variables

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

Liu, Yunlong; Wang, Aiping; Guo, Lei

This paper presents an error-entropy minimization tracking control algorithm for a class of dynamic stochastic system. The system is represented by a set of time-varying discrete nonlinear equations with non-Gaussian stochastic input, where the statistical properties of stochastic input are unknown. By using Parzen windowing with Gaussian kernel to estimate the probability densities of errors, recursive algorithms are then proposed to design the controller such that the tracking error can be minimized. The performance of the error-entropy minimization criterion is compared with the mean-square-error minimization in the simulation results.

Reexamination of optimal quantum state estimation of pure states

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

Hayashi, A.; Hashimoto, T.; Horibe, M.

2005-09-15

A direct derivation is given for the optimal mean fidelity of quantum state estimation of a d-dimensional unknown pure state with its N copies given as input, which was first obtained by Hayashi in terms of an infinite set of covariant positive operator valued measures (POVM's) and by Bruss and Macchiavello establishing a connection to optimal quantum cloning. An explicit condition for POVM measurement operators for optimal estimators is obtained, by which we construct optimal estimators with finite POVMs using exact quadratures on a hypersphere. These finite optimal estimators are not generally universal, where universality means the fidelity is independentmore » of input states. However, any optimal estimator with finite POVM for M(>N) copies is universal if it is used for N copies as input.« less

A Novel Approach to Implement Takagi-Sugeno Fuzzy Models.

PubMed

Chang, Chia-Wen; Tao, Chin-Wang

2017-09-01

This paper proposes new algorithms based on the fuzzy c-regressing model algorithm for Takagi-Sugeno (T-S) fuzzy modeling of the complex nonlinear systems. A fuzzy c-regression state model (FCRSM) algorithm is a T-S fuzzy model in which the functional antecedent and the state-space-model-type consequent are considered with the available input-output data. The antecedent and consequent forms of the proposed FCRSM consists mainly of two advantages: one is that the FCRSM has low computation load due to only one input variable is considered in the antecedent part; another is that the unknown system can be modeled to not only the polynomial form but also the state-space form. Moreover, the FCRSM can be extended to FCRSM-ND and FCRSM-Free algorithms. An algorithm FCRSM-ND is presented to find the T-S fuzzy state-space model of the nonlinear system when the input-output data cannot be precollected and an assumed effective controller is available. In the practical applications, the mathematical model of controller may be hard to be obtained. In this case, an online tuning algorithm, FCRSM-FREE, is designed such that the parameters of a T-S fuzzy controller and the T-S fuzzy state model of an unknown system can be online tuned simultaneously. Four numerical simulations are given to demonstrate the effectiveness of the proposed approach.

Interacting with notebook input devices: an analysis of motor performance and users' expertise.

PubMed

Sutter, Christine; Ziefle, Martina

2005-01-01

In the present study the usability of two different types of notebook input devices was examined. The independent variables were input device (touchpad vs. mini-joystick) and user expertise (expert vs. novice state). There were 30 participants, of whom 15 were touchpad experts and the other 15 were mini-joystick experts. The experimental tasks were a point-click task (Experiment 1) and a point-drag-drop task (Experiment 2). Dependent variables were the time and accuracy of cursor control. To assess carryover effects, we had the participants complete both experiments, using not only the input device for which they were experts but also the device for which they were novices. Results showed the touchpad performance to be clearly superior to mini-joystick performance. Overall, experts showed better performance than did novices. The significant interaction of input device and expertise showed that the use of an unknown device is difficult, but only for touchpad experts, who were remarkably slower and less accurate when using a mini-joystick. Actual and potential applications of this research include an evaluation of current notebook input devices. The outcomes allow ergonomic guidelines to be derived for optimized usage and design of the mini-joystick and touchpad devices.

How to Assess the Signature of the Data: Catchments and Aquifers as Input Processing Systems

NASA Astrophysics Data System (ADS)

Lischeid, G.

2010-12-01

It has been argued recently that hydrological models should not only mimic observed data, but should reproduce the signatures of the data appropriately. However, there is no consent how these signatures could be assessed. In general, hydrological models aim at predicting groundwater head dynamics or hydrograph response to input signals (e.g., groundwater recharge, effective rain), based on information about structural properties of the system, like e.g., transmissivity fields, soil hydraulic conductivity, or size of the catchment water storage. That approach usually faces substantial spatial heterogeneities and nonlinear feedbacks. Here, an alternative approach is suggested for characterizing catchments or aquifers as input signal processing systems. The concept was developed for remote areas where direct anthropogenic effects (groundwater withdrawal, injection wells, etc.), plant water uptake and evaporation from groundwater and streams are negligible. Then, any increase of groundwater head or discharge is related to a corresponding input signal, i.e., groundwater recharge or effective rainfall. That signal propagates through the system and is increasingly attenuated and decelerated with increasing flowpath length. This attenuation differs from simple low-pass-filtering. E.g., different input signals propagate at different velocities, depending on rainfall intensity, antecedent soil moisture, etc. The new approach is based on a principal component analysis of time series of groundwater or lake water level, soil water content, or discharge at different sites. This information is used to for assessing the functional properties of the system rather than its structural heterogeneity at different measurement sites, and to assess first order controls on its spatial patterns. Thus, hydrologic measurements provide a mean to measure the functional properties of the system. It is suggested to use this as signatures of the data. In a next step, model structure can be optimized, focusing on representing these signatures. Furthermore, even the unknown input signal can be assessed, making the catchment or aquifer a giant effective rain sampler. Examples will be presented including heterogeneous and sparse data sets, and an extension to a more complex system with various production wells of a large water supply work.

Recursive Inversion By Finite-Impulse-Response Filters

NASA Technical Reports Server (NTRS)

Bach, Ralph E., Jr.; Baram, Yoram

1991-01-01

Recursive approximation gives least-squares best fit to exact response. Algorithm yields finite-impulse-response approximation of unknown single-input/single-output, causal, time-invariant, linear, real system, response of which is sequence of impulses. Applicable to such system-inversion problems as suppression of echoes and identification of target from its scatter response to incident impulse.

Distributed Time-Varying Formation Robust Tracking for General Linear Multiagent Systems With Parameter Uncertainties and External Disturbances.

PubMed

Hua, Yongzhao; Dong, Xiwang; Li, Qingdong; Ren, Zhang

2017-05-18

This paper investigates the time-varying formation robust tracking problems for high-order linear multiagent systems with a leader of unknown control input in the presence of heterogeneous parameter uncertainties and external disturbances. The followers need to accomplish an expected time-varying formation in the state space and track the state trajectory produced by the leader simultaneously. First, a time-varying formation robust tracking protocol with a totally distributed form is proposed utilizing the neighborhood state information. With the adaptive updating mechanism, neither any global knowledge about the communication topology nor the upper bounds of the parameter uncertainties, external disturbances and leader's unknown input are required in the proposed protocol. Then, in order to determine the control parameters, an algorithm with four steps is presented, where feasible conditions for the followers to accomplish the expected time-varying formation tracking are provided. Furthermore, based on the Lyapunov-like analysis theory, it is proved that the formation tracking error can converge to zero asymptotically. Finally, the effectiveness of the theoretical results is verified by simulation examples.

Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP.

PubMed

Wei, Qinglai; Song, Ruizhuo; Yan, Pengfei

2016-02-01

This paper is concerned with a new data-driven zero-sum neuro-optimal control problem for continuous-time unknown nonlinear systems with disturbance. According to the input-output data of the nonlinear system, an effective recurrent neural network is introduced to reconstruct the dynamics of the nonlinear system. Considering the system disturbance as a control input, a two-player zero-sum optimal control problem is established. Adaptive dynamic programming (ADP) is developed to obtain the optimal control under the worst case of the disturbance. Three single-layer neural networks, including one critic and two action networks, are employed to approximate the performance index function, the optimal control law, and the disturbance, respectively, for facilitating the implementation of the ADP method. Convergence properties of the ADP method are developed to show that the system state will converge to a finite neighborhood of the equilibrium. The weight matrices of the critic and the two action networks are also convergent to finite neighborhoods of their optimal ones. Finally, the simulation results will show the effectiveness of the developed data-driven ADP methods.

A novel sensitivity-based method for damage detection of structures under unknown periodic excitations

NASA Astrophysics Data System (ADS)

Naseralavi, S. S.; Salajegheh, E.; Fadaee, M. J.; Salajegheh, J.

2014-06-01

This paper presents a technique for damage detection in structures under unknown periodic excitations using the transient displacement response. The method is capable of identifying the damage parameters without finding the input excitations. We first define the concept of displacement space as a linear space in which each point represents displacements of structure under an excitation and initial condition. Roughly speaking, the method is based on the fact that structural displacements under free and forced vibrations are associated with two parallel subspaces in the displacement space. Considering this novel geometrical viewpoint, an equation called kernel parallelization equation (KPE) is derived for damage detection under unknown periodic excitations and a sensitivity-based algorithm for solving KPE is proposed accordingly. The method is evaluated via three case studies under periodic excitations, which confirm the efficiency of the proposed method.

On experimental damage localization by SP2E: Application of H∞ estimation and oblique projections

NASA Astrophysics Data System (ADS)

Lenzen, Armin; Vollmering, Max

2018-05-01

In this article experimental damage localization based on H∞ estimation and state projection estimation error (SP2E) is studied. Based on an introduced difference process, a state space representation is derived for advantageous numerical solvability. Because real structural excitations are presumed to be unknown, a general input is applied therein, which allows synchronization and normalization. Furthermore, state projections are introduced to enhance damage identification. While first experiments to verify method SP2E have already been conducted and published, further laboratory results are analyzed here. Therefore, SP2E is used to experimentally localize stiffness degradations and mass alterations. Furthermore, the influence of projection techniques is analyzed. In summary, method SP2E is able to localize structural alterations, which has been observed by results of laboratory experiments.

Efficiency Improvement of Action Acquisition in Two-Link Robot Arm Using Fuzzy ART with Genetic Algorithm

NASA Astrophysics Data System (ADS)

Kotani, Naoki; Taniguchi, Kenji

An efficient learning method using Fuzzy ART with Genetic Algorithm is proposed. The proposed method reduces the number of trials by using a policy acquired in other tasks because a reinforcement learning needs a lot of the number of trials until an agent acquires appropriate actions. Fuzzy ART is an incremental unsupervised learning algorithm in responce to arbitrary sequences of analog or binary input vectors. Our proposed method gives a policy by crossover or mutation when an agent observes unknown states. Selection controls the category proliferation problem of Fuzzy ART. The effectiveness of the proposed method was verified with the simulation of the reaching problem for the two-link robot arm. The proposed method achieves a reduction of both the number of trials and the number of states.

Simplified Interval Observer Scheme: A New Approach for Fault Diagnosis in Instruments

PubMed Central

Martínez-Sibaja, Albino; Astorga-Zaragoza, Carlos M.; Alvarado-Lassman, Alejandro; Posada-Gómez, Rubén; Aguila-Rodríguez, Gerardo; Rodríguez-Jarquin, José P.; Adam-Medina, Manuel

2011-01-01

There are different schemes based on observers to detect and isolate faults in dynamic processes. In the case of fault diagnosis in instruments (FDI) there are different diagnosis schemes based on the number of observers: the Simplified Observer Scheme (SOS) only requires one observer, uses all the inputs and only one output, detecting faults in one detector; the Dedicated Observer Scheme (DOS), which again uses all the inputs and just one output, but this time there is a bank of observers capable of locating multiple faults in sensors, and the Generalized Observer Scheme (GOS) which involves a reduced bank of observers, where each observer uses all the inputs and m-1 outputs, and allows the localization of unique faults. This work proposes a new scheme named Simplified Interval Observer SIOS-FDI, which does not requires the measurement of any input and just with just one output allows the detection of unique faults in sensors and because it does not require any input, it simplifies in an important way the diagnosis of faults in processes in which it is difficult to measure all the inputs, as in the case of biologic reactors. PMID:22346593

Improved prescribed performance control for air-breathing hypersonic vehicles with unknown deadzone input nonlinearity.

PubMed

Wang, Yingyang; Hu, Jianbo

2018-05-19

An improved prescribed performance controller is proposed for the longitudinal model of an air-breathing hypersonic vehicle (AHV) subject to uncertain dynamics and input nonlinearity. Different from the traditional non-affine model requiring non-affine functions to be differentiable, this paper utilizes a semi-decomposed non-affine model with non-affine functions being locally semi-bounded and possibly in-differentiable. A new error transformation combined with novel prescribed performance functions is proposed to bypass complex deductions caused by conventional error constraint approaches and circumvent high frequency chattering in control inputs. On the basis of backstepping technique, the improved prescribed performance controller with low structural and computational complexity is designed. The methodology guarantees the altitude and velocity tracking error within transient and steady state performance envelopes and presents excellent robustness against uncertain dynamics and deadzone input nonlinearity. Simulation results demonstrate the efficacy of the proposed method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Light adaptation alters the source of inhibition to the mouse retinal OFF pathway

PubMed Central

Mazade, Reece E.

2013-01-01

Sensory systems must avoid saturation to encode a wide range of stimulus intensities. One way the retina accomplishes this is by using both dim-light-sensing rod and bright-light-sensing cone photoreceptor circuits. OFF cone bipolar cells are a key point in this process, as they receive both excitatory input from cones and inhibitory input from AII amacrine cells via the rod pathway. However, in addition to AII amacrine cell input, other inhibitory inputs from cone pathways also modulate OFF cone bipolar cell light signals. It is unknown how these inhibitory inputs to OFF cone bipolar cells change when switching between rod and cone pathways or whether all OFF cone bipolar cells receive rod pathway input. We found that one group of OFF cone bipolar cells (types 1, 2, and 4) receive rod-mediated inhibitory inputs that likely come from the rod-AII amacrine cell pathway, while another group of OFF cone bipolar cells (type 3) do not. In both cases, dark-adapted rod-dominant light responses showed a significant contribution of glycinergic inhibition, which decreased with light adaptation and was, surprisingly, compensated by an increase in GABAergic inhibition. As GABAergic input has distinct timing and spatial spread from glycinergic input, a shift from glycinergic to GABAergic inhibition could significantly alter OFF cone bipolar cell signaling to downstream OFF ganglion cells. Larger GABAergic input could reflect an adjustment of OFF bipolar cell spatial inhibition, which may be one mechanism that contributes to retinal spatial sensitivity in the light. PMID:23926034

The grammatical morpheme deficit in moderate hearing impairment.

PubMed

McGuckian, Maria; Henry, Alison

2007-03-01

Much remains unknown about grammatical morpheme (GM) acquisition by children with moderate hearing impairment (HI) acquiring spoken English. To investigate how moderate HI impacts on the use of GMs in speech and to provide an explanation for the pattern of findings. Elicited and spontaneous speech data were collected from children with moderate HI (n = 10; mean age = 7;4 years) and a control group of typically developing children (n = 10; mean age = 3;2 years) with equivalent mean length of utterance (MLU). The data were analysed to determine the use of ten GMs of English. Comparisons were made between the groups for rates of correct GM production, for types and rates of GM errors, and for order of GM accuracy. The findings revealed significant differences between the HI group and the control group for correct production of five GMs. The differences were not all in the same direction. The HI group produced possessive -s and plural -s significantly less frequently than the controls (this is not simply explained by the perceptual saliency of -s) and produced progressive -ing, articles and irregular past tense significantly more frequently than the controls. Moreover, the order of GM accuracy for the HI group did not correlate with that observed for the control group. Various factors were analysed in an attempt to explain order of GM accuracy for the HI group (i.e. perceptual saliency, syntactic category, semantics and frequency of GMs in input). Frequency of GMs in input was the most successful explanation for the overall pattern of GM accuracy. Interestingly, the order of GM accuracy for the HI group (acquiring spoken English as a first language) was characteristic of that reported for individuals learning English as a second language. An explanation for the findings is drawn from a factor that connects these different groups of language learners, i.e. limited access to spoken English input. It is argued that, because of hearing factors, the children with HI are below a threshold for intake of spoken language input (a threshold easily reached by the controls). Thus, the children with HI are more input-dependent at the point in development studied and as such are more sensitive to input frequency effects. The findings suggest that optimizing or indeed increasing auditory input of GMs may have a positive impact on GM development for children with moderate HI.

Groebner Basis Solutions to Satellite Trajectory Control by Pole Placement

NASA Astrophysics Data System (ADS)

Kukelova, Z.; Krsek, P.; Smutny, V.; Pajdla, T.

2013-09-01

Satellites play an important role, e.g., in telecommunication, navigation and weather monitoring. Controlling their trajectories is an important problem. In [1], an approach to the pole placement for the synthesis of a linear controller has been presented. It leads to solving five polynomial equations in nine unknown elements of the state space matrices of a compensator. This is an underconstrained system and therefore four of the unknown elements need to be considered as free parameters and set to some prior values to obtain a system of five equations in five unknowns. In [1], this system was solved for one chosen set of free parameters with the help of Dixon resultants. In this work, we study and present Groebner basis solutions to this problem of computation of a dynamic compensator for the satellite for different combinations of input free parameters. We show that the Groebner basis method for solving systems of polynomial equations leads to very simple solutions for all combinations of free parameters. These solutions require to perform only the Gauss-Jordan elimination of a small matrix and computation of roots of a single variable polynomial. The maximum degree of this polynomial is not greater than six in general but for most combinations of the input free parameters its degree is even lower. [1] B. Palancz. Application of Dixon resultant to satellite trajectory control by pole placement. Journal of Symbolic Computation, Volume 50, March 2013, Pages 79-99, Elsevier.

A potato model intercomparison across varying climates and productivity levels.

PubMed

Fleisher, David H; Condori, Bruno; Quiroz, Roberto; Alva, Ashok; Asseng, Senthold; Barreda, Carolina; Bindi, Marco; Boote, Kenneth J; Ferrise, Roberto; Franke, Angelinus C; Govindakrishnan, Panamanna M; Harahagazwe, Dieudonne; Hoogenboom, Gerrit; Naresh Kumar, Soora; Merante, Paolo; Nendel, Claas; Olesen, Jorgen E; Parker, Phillip S; Raes, Dirk; Raymundo, Rubi; Ruane, Alex C; Stockle, Claudio; Supit, Iwan; Vanuytrecht, Eline; Wolf, Joost; Woli, Prem

2017-03-01

A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low-input (Chinoli, Bolivia and Gisozi, Burundi)- and high-input (Jyndevad, Denmark and Washington, United States) management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with interannual variability, and predictions for all agronomic variables were significantly different from one model to another (P < 0.001). Uncertainty averaged 15% higher for low- vs. high-input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET, respectively, as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for nonirrigated sites). Differences in predictions due to model representation of light utilization were significant (P < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach. © 2016 John Wiley & Sons Ltd.

Prediction Errors but Not Sharpened Signals Simulate Multivoxel fMRI Patterns during Speech Perception

PubMed Central

Davis, Matthew H.

2016-01-01

Successful perception depends on combining sensory input with prior knowledge. However, the underlying mechanism by which these two sources of information are combined is unknown. In speech perception, as in other domains, two functionally distinct coding schemes have been proposed for how expectations influence representation of sensory evidence. Traditional models suggest that expected features of the speech input are enhanced or sharpened via interactive activation (Sharpened Signals). Conversely, Predictive Coding suggests that expected features are suppressed so that unexpected features of the speech input (Prediction Errors) are processed further. The present work is aimed at distinguishing between these two accounts of how prior knowledge influences speech perception. By combining behavioural, univariate, and multivariate fMRI measures of how sensory detail and prior expectations influence speech perception with computational modelling, we provide evidence in favour of Prediction Error computations. Increased sensory detail and informative expectations have additive behavioural and univariate neural effects because they both improve the accuracy of word report and reduce the BOLD signal in lateral temporal lobe regions. However, sensory detail and informative expectations have interacting effects on speech representations shown by multivariate fMRI in the posterior superior temporal sulcus. When prior knowledge was absent, increased sensory detail enhanced the amount of speech information measured in superior temporal multivoxel patterns, but with informative expectations, increased sensory detail reduced the amount of measured information. Computational simulations of Sharpened Signals and Prediction Errors during speech perception could both explain these behavioural and univariate fMRI observations. However, the multivariate fMRI observations were uniquely simulated by a Prediction Error and not a Sharpened Signal model. The interaction between prior expectation and sensory detail provides evidence for a Predictive Coding account of speech perception. Our work establishes methods that can be used to distinguish representations of Prediction Error and Sharpened Signals in other perceptual domains. PMID:27846209

On the estimation of stellar parameters with uncertainty prediction from Generative Artificial Neural Networks: application to Gaia RVS simulated spectra

NASA Astrophysics Data System (ADS)

Dafonte, C.; Fustes, D.; Manteiga, M.; Garabato, D.; Álvarez, M. A.; Ulla, A.; Allende Prieto, C.

2016-10-01

Aims: We present an innovative artificial neural network (ANN) architecture, called Generative ANN (GANN), that computes the forward model, that is it learns the function that relates the unknown outputs (stellar atmospheric parameters, in this case) to the given inputs (spectra). Such a model can be integrated in a Bayesian framework to estimate the posterior distribution of the outputs. Methods: The architecture of the GANN follows the same scheme as a normal ANN, but with the inputs and outputs inverted. We train the network with the set of atmospheric parameters (Teff, log g, [Fe/H] and [α/ Fe]), obtaining the stellar spectra for such inputs. The residuals between the spectra in the grid and the estimated spectra are minimized using a validation dataset to keep solutions as general as possible. Results: The performance of both conventional ANNs and GANNs to estimate the stellar parameters as a function of the star brightness is presented and compared for different Galactic populations. GANNs provide significantly improved parameterizations for early and intermediate spectral types with rich and intermediate metallicities. The behaviour of both algorithms is very similar for our sample of late-type stars, obtaining residuals in the derivation of [Fe/H] and [α/ Fe] below 0.1 dex for stars with Gaia magnitude Grvs < 12, which accounts for a number in the order of four million stars to be observed by the Radial Velocity Spectrograph of the Gaia satellite. Conclusions: Uncertainty estimation of computed astrophysical parameters is crucial for the validation of the parameterization itself and for the subsequent exploitation by the astronomical community. GANNs produce not only the parameters for a given spectrum, but a goodness-of-fit between the observed spectrum and the predicted one for a given set of parameters. Moreover, they allow us to obtain the full posterior distribution over the astrophysical parameters space once a noise model is assumed. This can be used for novelty detection and quality assessment.

Field evaluation of potential weed-suppressive traits in an indica x tropical japonica mapping population

USDA-ARS?s Scientific Manuscript database

The indica rice accession, PI 312777 (a.k.a. WC 4644), is highly productive and can suppress barnyardgrass (Echinochloa crus-galli) in reduced-input systems, but the genetic control of this weed suppression is unknown. A set of 330 recombinant inbred lines (RILs) was developed using single seed desc...

From Atmospheric Neutrinos to the Neutrino Mass Hierarchy

NASA Astrophysics Data System (ADS)

Kappes, A.

2015-08-01

After a brief introduction to neutrino oscillation, the article discusses how proposed detectors like PINGU and ORCA can use atmospheric neutrinos in the GeV range to determine the neutrino mass hierarchy, one of the crucial unknowns in the neutrino sector of particle physics, and what uncertainties on external input parameters have to be taken into account.

Echolalic Responses by a Child with Autism to Four Experimental Conditions of Sociolinguistic Input.

ERIC Educational Resources Information Center

Violette, Joseph; Swisher, Linda

1992-01-01

The immediate verbal imitations (IVIs) of a boy (age five) with autism and echolalia were studied, with variables of linguistic familiarity and instructor's style of directiveness being manipulated. The occurrence of IVIs was related to uncertain or informative events, and was significantly greater when lexical stimuli were unknown and presented…

Forest productivity predicts invertebrate biomass and ovenbird (Seriurus Aurocapillus) reproduction in Appalachian landscapes

Treesearch

Steven W. Seagle; Brian R. Sturtevant

2005-01-01

Forest-floor detrital food webs are sustained by annual inputs of leaf fall. However, it is unknown whether this bottom-up effect extends to vertebrates feeding on the detrital food web. We hypothesized that reproductive success of Ovenbirds (Seiurus aurocapillus L.) is a function of acroinvertebrate biomass within the detrital food web, and that...

An offline approach for output-only Bayesian identification of stochastic nonlinear systems using unscented Kalman filtering

NASA Astrophysics Data System (ADS)

Erazo, Kalil; Nagarajaiah, Satish

2017-06-01

In this paper an offline approach for output-only Bayesian identification of stochastic nonlinear systems is presented. The approach is based on a re-parameterization of the joint posterior distribution of the parameters that define a postulated state-space stochastic model class. In the re-parameterization the state predictive distribution is included, marginalized, and estimated recursively in a state estimation step using an unscented Kalman filter, bypassing state augmentation as required by existing online methods. In applications expectations of functions of the parameters are of interest, which requires the evaluation of potentially high-dimensional integrals; Markov chain Monte Carlo is adopted to sample the posterior distribution and estimate the expectations. The proposed approach is suitable for nonlinear systems subjected to non-stationary inputs whose realization is unknown, and that are modeled as stochastic processes. Numerical verification and experimental validation examples illustrate the effectiveness and advantages of the approach, including: (i) an increased numerical stability with respect to augmented-state unscented Kalman filtering, avoiding divergence of the estimates when the forcing input is unmeasured; (ii) the ability to handle arbitrary prior and posterior distributions. The experimental validation of the approach is conducted using data from a large-scale structure tested on a shake table. It is shown that the approach is robust to inherent modeling errors in the description of the system and forcing input, providing accurate prediction of the dynamic response when the excitation history is unknown.

Indirect adaptive fuzzy fault-tolerant tracking control for MIMO nonlinear systems with actuator and sensor failures.

PubMed

Bounemeur, Abdelhamid; Chemachema, Mohamed; Essounbouli, Najib

2018-05-10

In this paper, an active fuzzy fault tolerant tracking control (AFFTTC) scheme is developed for a class of multi-input multi-output (MIMO) unknown nonlinear systems in the presence of unknown actuator faults, sensor failures and external disturbance. The developed control scheme deals with four kinds of faults for both sensors and actuators. The bias, drift, and loss of accuracy additive faults are considered along with the loss of effectiveness multiplicative fault. A fuzzy adaptive controller based on back-stepping design is developed to deal with actuator failures and unknown system dynamics. However, an additional robust control term is added to deal with sensor faults, approximation errors, and external disturbances. Lyapunov theory is used to prove the stability of the closed loop system. Numerical simulations on a quadrotor are presented to show the effectiveness of the proposed approach. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive output feedback NN control of a class of discrete-time MIMO nonlinear systems with unknown control directions.

PubMed

Li, Yanan; Yang, Chenguang; Ge, Shuzhi Sam; Lee, Tong Heng

2011-04-01

In this paper, adaptive neural network (NN) control is investigated for a class of block triangular multiinput-multioutput nonlinear discrete-time systems with each subsystem in pure-feedback form with unknown control directions. These systems are of couplings in every equation of each subsystem, and different subsystems may have different orders. To avoid the noncausal problem in the control design, the system is transformed into a predictor form by rigorous derivation. By exploring the properties of the block triangular form, implicit controls are developed for each subsystem such that the couplings of inputs and states among subsystems have been completely decoupled. The radial basis function NN is employed to approximate the unknown control. Each subsystem achieves a semiglobal uniformly ultimately bounded stability with the proposed control, and simulation results are presented to demonstrate its efficiency.

Adaptive NN control for discrete-time pure-feedback systems with unknown control direction under amplitude and rate actuator constraints.

PubMed

Chen, Weisheng

2009-07-01

This paper focuses on the problem of adaptive neural network tracking control for a class of discrete-time pure-feedback systems with unknown control direction under amplitude and rate actuator constraints. Two novel state-feedback and output-feedback dynamic control laws are established where the function tanh(.) is employed to solve the saturation constraint problem. Implicit function theorem and mean value theorem are exploited to deal with non-affine variables that are used as actual control. Radial basis function neural networks are used to approximate the desired input function. Discrete Nussbaum gain is used to estimate the unknown sign of control gain. The uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. A simulation example is provided to illustrate the effectiveness of control schemes proposed in this paper.

Learning Spatially-Smooth Mappings in Non-Rigid Structure from Motion

PubMed Central

Hamsici, Onur C.; Gotardo, Paulo F.U.; Martinez, Aleix M.

2013-01-01

Non-rigid structure from motion (NRSFM) is a classical underconstrained problem in computer vision. A common approach to make NRSFM more tractable is to constrain 3D shape deformation to be smooth over time. This constraint has been used to compress the deformation model and reduce the number of unknowns that are estimated. However, temporal smoothness cannot be enforced when the data lacks temporal ordering and its benefits are less evident when objects undergo abrupt deformations. This paper proposes a new NRSFM method that addresses these problems by considering deformations as spatial variations in shape space and then enforcing spatial, rather than temporal, smoothness. This is done by modeling each 3D shape coefficient as a function of its input 2D shape. This mapping is learned in the feature space of a rotation invariant kernel, where spatial smoothness is intrinsically defined by the mapping function. As a result, our model represents shape variations compactly using custom-built coefficient bases learned from the input data, rather than a pre-specified set such as the Discrete Cosine Transform. The resulting kernel-based mapping is a by-product of the NRSFM solution and leads to another fundamental advantage of our approach: for a newly observed 2D shape, its 3D shape is recovered by simply evaluating the learned function. PMID:23946937

Learning Spatially-Smooth Mappings in Non-Rigid Structure from Motion.

PubMed

Hamsici, Onur C; Gotardo, Paulo F U; Martinez, Aleix M

2012-01-01

Non-rigid structure from motion (NRSFM) is a classical underconstrained problem in computer vision. A common approach to make NRSFM more tractable is to constrain 3D shape deformation to be smooth over time. This constraint has been used to compress the deformation model and reduce the number of unknowns that are estimated. However, temporal smoothness cannot be enforced when the data lacks temporal ordering and its benefits are less evident when objects undergo abrupt deformations. This paper proposes a new NRSFM method that addresses these problems by considering deformations as spatial variations in shape space and then enforcing spatial, rather than temporal, smoothness. This is done by modeling each 3D shape coefficient as a function of its input 2D shape. This mapping is learned in the feature space of a rotation invariant kernel, where spatial smoothness is intrinsically defined by the mapping function. As a result, our model represents shape variations compactly using custom-built coefficient bases learned from the input data, rather than a pre-specified set such as the Discrete Cosine Transform. The resulting kernel-based mapping is a by-product of the NRSFM solution and leads to another fundamental advantage of our approach: for a newly observed 2D shape, its 3D shape is recovered by simply evaluating the learned function.

Nitrogen and phosphorus fluxes from watersheds of the northeast U.S. from 1930 to 2000: Role of anthropogenic nutrient inputs, infrastructure, and runoff

NASA Astrophysics Data System (ADS)

Hale, Rebecca L.; Grimm, Nancy B.; Vörösmarty, Charles J.; Fekete, Balazs

2015-03-01

An ongoing challenge for society is to harness the benefits of nutrients, nitrogen (N) and phosphorus (P), while minimizing their negative effects on ecosystems. While there is a good understanding of the mechanisms of nutrient delivery at small scales, it is unknown how nutrient transport and processing scale up to larger watersheds and whole regions over long time periods. We used a model that incorporates nutrient inputs to watersheds, hydrology, and infrastructure (sewers, wastewater treatment plants, and reservoirs) to reconstruct historic nutrient yields for the northeastern U.S. from 1930 to 2002. Over the study period, yields of nutrients increased significantly from some watersheds and decreased in others. As a result, at the regional scale, the total yield of N and P from the region did not change significantly. Temporal variation in regional N and P yields was correlated with runoff coefficient, but not with nutrient inputs. Spatial patterns of N and P yields were best predicted by nutrient inputs, but the correlation between inputs and yields across watersheds decreased over the study period. The effect of infrastructure on yields was minimal relative to the importance of soils and rivers. However, infrastructure appeared to alter the relationships between inputs and yields. The role of infrastructure changed over time and was important in creating spatial and temporal heterogeneity in nutrient input-yield relationships.

Identification of input variables for feature based artificial neural networks-saccade detection in EOG recordings.

PubMed

Tigges, P; Kathmann, N; Engel, R R

1997-07-01

Though artificial neural networks (ANN) are excellent tools for pattern recognition problems when signal to noise ratio is low, the identification of decision relevant features for ANN input data is still a crucial issue. The experience of the ANN designer and the existing knowledge and understanding of the problem seem to be the only links for a specific construction. In the present study a backpropagation ANN based on modified raw data inputs showed encouraging results. Investigating the specific influences of prototypical input patterns on a specially designed ANN led to a new sparse and efficient input data presentation. This data coding obtained by a semiautomatic procedure combining existing expert knowledge and the internal representation structures of the raw data based ANN yielded a list of feature vectors, each representing the relevant information for saccade identification. The feature based ANN produced a reduction of the error rate of nearly 40% compared with the raw data ANN. An overall correct classification of 92% of so far unknown data was realized. The proposed method of extracting internal ANN knowledge for the production of a better input data representation is not restricted to EOG recordings, and could be used in various fields of signal analysis.

Drivers of the primate thalamus

PubMed Central

Rovó, Zita; Ulbert, István; Acsády, László

2012-01-01

The activity of thalamocortical neurons is largely determined by giant excitatory terminals, called drivers. These afferents may arise from neocortex or from subcortical centers; however their exact distribution, segregation or putative absence in given thalamic nuclei are unknown. To unravel the nucleus-specific composition of drivers, we mapped the entire macaque thalamus utilizing vesicular glutamate transporters 1 and 2 to label cortical and subcortical afferents, respectively. Large thalamic territories were innervated exclusively either by giant vGLUT2- or vGLUT1-positive boutons. Co-distribution of drivers with different origin was not abundant. In several thalamic regions, no giant terminals of any type could be detected at light microscopic level. Electron microscopic observation of these territories revealed either the complete absence of large multisynaptic excitatory terminals (basal ganglia-recipient nuclei) or the presence of both vGLUT1- and vGLUT2-positive terminals, which were significantly smaller than their giant counterparts (intralaminar nuclei, medial pulvinar). In the basal ganglia-recipient thalamus, giant inhibitory terminals replaced the excitatory driver inputs. The pulvinar and the mediodorsal nucleus displayed subnuclear heterogeneity in their driver assemblies. These results show that distinct thalamic territories can be under pure subcortical or cortical control; however there is significant variability in the composition of major excitatory inputs in several thalamic regions. Since thalamic information transfer depends on the origin and complexity of the excitatory inputs, this suggests that the computations performed by individual thalamic regions display considerable variability. Finally, the map of driver distribution may help to resolve the morphological basis of human diseases involving different parts of the thalamus. PMID:23223308

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

PubMed

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

2014-07-01

In this paper, a reinforcement-learning-based direct adaptive control is developed to deliver a desired tracking performance for a class of discrete-time (DT) nonlinear systems with unknown bounded disturbances. We investigate multi-input-multi-output unknown nonaffine nonlinear DT systems and employ two neural networks (NNs). By using Implicit Function Theorem, an action NN is used to generate the control signal and it is also designed to cancel the nonlinearity of unknown DT systems, for purpose of utilizing feedback linearization methods. On the other hand, a critic NN is applied to estimate the cost function, which satisfies the recursive equations derived from heuristic dynamic programming. The weights of both the action NN and the critic NN are directly updated online instead of offline training. By utilizing Lyapunov's direct method, the closed-loop tracking errors and the NN estimated weights are demonstrated to be uniformly ultimately bounded. Two numerical examples are provided to show the effectiveness of the present approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

Approximation-Based Adaptive Neural Tracking Control of Nonlinear MIMO Unknown Time-Varying Delay Systems With Full State Constraints.

PubMed

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

2017-10-01

This paper deals with the tracking control problem for a class of nonlinear multiple input multiple output unknown time-varying delay systems with full state constraints. To overcome the challenges which cause by the appearances of the unknown time-varying delays and full-state constraints simultaneously in the systems, an adaptive control method is presented for such systems for the first time. The appropriate Lyapunov-Krasovskii functions and a separation technique are employed to eliminate the effect of unknown time-varying delays. The barrier Lyapunov functions are employed to prevent the violation of the full state constraints. The singular problems are dealt with by introducing the signal function. Finally, it is proven that the proposed method can both guarantee the good tracking performance of the systems output, all states are remained in the constrained interval and all the closed-loop signals are bounded in the design process based on choosing appropriate design parameters. The practicability of the proposed control technique is demonstrated by a simulation study in this paper.

Sequential reconstruction of driving-forces from nonlinear nonstationary dynamics

NASA Astrophysics Data System (ADS)

Güntürkün, Ulaş

2010-07-01

This paper describes a functional analysis-based method for the estimation of driving-forces from nonlinear dynamic systems. The driving-forces account for the perturbation inputs induced by the external environment or the secular variations in the internal variables of the system. The proposed algorithm is applicable to the problems for which there is too little or no prior knowledge to build a rigorous mathematical model of the unknown dynamics. We derive the estimator conditioned on the differentiability of the unknown system’s mapping, and smoothness of the driving-force. The proposed algorithm is an adaptive sequential realization of the blind prediction error method, where the basic idea is to predict the observables, and retrieve the driving-force from the prediction error. Our realization of this idea is embodied by predicting the observables one-step into the future using a bank of echo state networks (ESN) in an online fashion, and then extracting the raw estimates from the prediction error and smoothing these estimates in two adaptive filtering stages. The adaptive nature of the algorithm enables to retrieve both slowly and rapidly varying driving-forces accurately, which are illustrated by simulations. Logistic and Moran-Ricker maps are studied in controlled experiments, exemplifying chaotic state and stochastic measurement models. The algorithm is also applied to the estimation of a driving-force from another nonlinear dynamic system that is stochastic in both state and measurement equations. The results are judged by the posterior Cramer-Rao lower bounds. The method is finally put into test on a real-world application; extracting sun’s magnetic flux from the sunspot time series.

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits.

PubMed

Ujfalussy, Balázs B; Makara, Judit K; Branco, Tiago; Lengyel, Máté

2015-12-24

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits.

Retinal Origin of Direction Selectivity in the Superior Colliculus

PubMed Central

Shi, Xuefeng; Barchini, Jad; Ledesma, Hector Acaron; Koren, David; Jin, Yanjiao; Liu, Xiaorong; Wei, Wei; Cang, Jianhua

2017-01-01

Detecting visual features in the environment such as motion direction is crucial for survival. The circuit mechanisms that give rise to direction selectivity in a major visual center, the superior colliculus (SC), are entirely unknown. Here, we optogenetically isolate the retinal inputs that individual direction-selective SC neurons receive and find that they are already selective as a result of precisely converging inputs from similarly-tuned retinal ganglion cells. The direction selective retinal input is linearly amplified by the intracollicular circuits without changing its preferred direction or level of selectivity. Finally, using 2-photon calcium imaging, we show that SC direction selectivity is dramatically reduced in transgenic mice that have decreased retinal selectivity. Together, our studies demonstrate a retinal origin of direction selectivity in the SC, and reveal a central visual deficit as a consequence of altered feature selectivity in the retina. PMID:28192394

Adaptive fuzzy predictive sliding control of uncertain nonlinear systems with bound-known input delay.

PubMed

Khazaee, Mostafa; Markazi, Amir H D; Omidi, Ehsan

2015-11-01

In this paper, a new Adaptive Fuzzy Predictive Sliding Mode Control (AFP-SMC) is presented for nonlinear systems with uncertain dynamics and unknown input delay. The control unit consists of a fuzzy inference system to approximate the ideal linearization control, together with a switching strategy to compensate for the estimation errors. Also, an adaptive fuzzy predictor is used to estimate the future values of the system states to compensate for the time delay. The adaptation laws are used to tune the controller and predictor parameters, which guarantee the stability based on a Lyapunov-Krasovskii functional. To evaluate the method effectiveness, the simulation and experiment on an overhead crane system are presented. According to the obtained results, AFP-SMC can effectively control the uncertain nonlinear systems, subject to input delays of known bound. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

NMDA Receptor Regulation Prevents Regression of Visual Cortical Function in the Absence of Mecp2

PubMed Central

Durand, Severine; Patrizi, Annarita; Quast, Kathleen B.; Hachigian, Lea; Pavlyuk, Roman; Saxena, Alka; Carninci, Piero; Hensch, Takao K.; Fagiolini, Michela

2012-01-01

SUMMARY Brain function is shaped by postnatal experience and vulnerable to disruption of Methyl-CpG-binding protein, Mecp2, in multiple neurodevelopmental disorders. How Mecp2 contributes to the experience-dependent refinement of specific cortical circuits and their impairment remains unknown. We analyzed vision in gene-targeted mice and observed an initial normal development in the absence of Mecp2. Visual acuity then rapidly regressed after postnatal day P35–40 and cortical circuits largely fell silent by P55-60. Enhanced inhibitory gating and an excess of parvalbumin-positive, perisomatic input preceded the loss of vision. Both cortical function and inhibitory hyperconnectivity were strikingly rescued independent of Mecp2 by early sensory deprivation or genetic deletion of the excitatory NMDA receptor subunit, NR2A. Thus, vision is a sensitive biomarker of progressive cortical dysfunction and may guide novel, circuit-based therapies for Mecp2 deficiency. PMID:23259945

The Drosophila Extradenticle and Homothorax selector proteins control branchless/FGF expression in mesodermal bridge-cells.

PubMed

Merabet, Samir; Ebner, Andreas; Affolter, Markus

2005-08-01

The stereotyped outgrowth of tubular branches of the Drosophila tracheal system is orchestrated by the local and highly dynamic expression profile of branchless (bnl), which encodes a secreted fibroblast growth factor (FGF)-like molecule. Despite the importance of the spatial and temporal bnl regulation, little is known about the upstream mechanisms that establish its complex expression pattern. Here, we show that the Extradenticle and Homothorax selector proteins control bnl transcription in a single cell per segment, the mesodermal bridge-cell. In addition, we observed that a key determinant of bridge-cell specification, the transcription factor Hunchback, is also required for bnl expression. Therefore, we propose that one of the functions of the bridge-cell is to synthesize and secrete the chemoattractant Bnl. These findings provide a hitherto unknown and interesting link between combinatorial inputs of transcription factors, cell-specific ligand expression and organ morphogenesis.

Controller design for a class of nonlinear MIMO coupled system using multiple models and second level adaptation.

PubMed

Pandey, Vinay Kumar; Kar, Indrani; Mahanta, Chitralekha

2017-07-01

In this paper, an adaptive control method using multiple models with second level adaptation is proposed for a class of nonlinear multi-input multi-output (MIMO) coupled systems. Multiple estimation models are used to tune the unknown parameters at the first level. The second level adaptation provides a single parameter vector for the controller. A feedback linearization technique is used to design a state feedback control. The efficacy of the designed controller is validated by conducting real time experiment on a laboratory setup of twin rotor MIMO system (TRMS). The TRMS setup is discussed in detail and the experiments were performed for regulation and tracking problem for pitch and yaw control using different reference signals. An Extended Kalman Filter (EKF) has been used to observe the unavailable states of the TRMS. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Time-response shaping using output to input saturation transformation

NASA Astrophysics Data System (ADS)

Chambon, E.; Burlion, L.; Apkarian, P.

2018-03-01

For linear systems, the control law design is often performed so that the resulting closed loop meets specific frequency-domain requirements. However, in many cases, it may be observed that the obtained controller does not enforce time-domain requirements amongst which the objective of keeping a scalar output variable in a given interval. In this article, a transformation is proposed to convert prescribed bounds on an output variable into time-varying saturations on the synthesised linear scalar control law. This transformation uses some well-chosen time-varying coefficients so that the resulting time-varying saturation bounds do not overlap in the presence of disturbances. Using an anti-windup approach, it is obtained that the origin of the resulting closed loop is globally asymptotically stable and that the constrained output variable satisfies the time-domain constraints in the presence of an unknown finite-energy-bounded disturbance. An application to a linear ball and beam model is presented.

Structural Aspects of System Identification

NASA Technical Reports Server (NTRS)

Glover, Keith

1973-01-01

The problem of identifying linear dynamical systems is studied by considering structural and deterministic properties of linear systems that have an impact on stochastic identification algorithms. In particular considered is parametrization of linear systems so that there is a unique solution and all systems in appropriate class can be represented. It is assumed that a parametrization of system matrices has been established from a priori knowledge of the system, and the question is considered of when the unknown parameters of this system can be identified from input/output observations. It is assumed that the transfer function can be asymptotically identified, and the conditions are derived for the local, global and partial identifiability of the parametrization. Then it is shown that, with the right formulation, identifiability in the presence of feedback can be treated in the same way. Similarly the identifiability of parametrizations of systems driven by unobserved white noise is considered using the results from the theory of spectral factorization.

MoCha: Molecular Characterization of Unknown Pathways.

PubMed

Lobo, Daniel; Hammelman, Jennifer; Levin, Michael

2016-04-01

Automated methods for the reverse-engineering of complex regulatory networks are paving the way for the inference of mechanistic comprehensive models directly from experimental data. These novel methods can infer not only the relations and parameters of the known molecules defined in their input datasets, but also unknown components and pathways identified as necessary by the automated algorithms. Identifying the molecular nature of these unknown components is a crucial step for making testable predictions and experimentally validating the models, yet no specific and efficient tools exist to aid in this process. To this end, we present here MoCha (Molecular Characterization), a tool optimized for the search of unknown proteins and their pathways from a given set of known interacting proteins. MoCha uses the comprehensive dataset of protein-protein interactions provided by the STRING database, which currently includes more than a billion interactions from over 2,000 organisms. MoCha is highly optimized, performing typical searches within seconds. We demonstrate the use of MoCha with the characterization of unknown components from reverse-engineered models from the literature. MoCha is useful for working on network models by hand or as a downstream step of a model inference engine workflow and represents a valuable and efficient tool for the characterization of unknown pathways using known data from thousands of organisms. MoCha and its source code are freely available online under the GPLv3 license.

Learning to Estimate Dynamical State with Probabilistic Population Codes.

PubMed

Makin, Joseph G; Dichter, Benjamin K; Sabes, Philip N

2015-11-01

Tracking moving objects, including one's own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, "probabilistic population codes." We show that a recurrent neural network-a modified form of an exponential family harmonium (EFH)-that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.

Learning to Estimate Dynamical State with Probabilistic Population Codes

PubMed Central

Sabes, Philip N.

2015-01-01

Tracking moving objects, including one’s own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, “probabilistic population codes.” We show that a recurrent neural network—a modified form of an exponential family harmonium (EFH)—that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states. PMID:26540152

Adaptive NN controller design for a class of nonlinear MIMO discrete-time systems.

PubMed

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

2015-05-01

An adaptive neural network tracking control is studied for a class of multiple-input multiple-output (MIMO) nonlinear systems. The studied systems are in discrete-time form and the discretized dead-zone inputs are considered. In addition, the studied MIMO systems are composed of N subsystems, and each subsystem contains unknown functions and external disturbance. Due to the complicated framework of the discrete-time systems, the existence of the dead zone and the noncausal problem in discrete-time, it brings about difficulties for controlling such a class of systems. To overcome the noncausal problem, by defining the coordinate transformations, the studied systems are transformed into a special form, which is suitable for the backstepping design. The radial basis functions NNs are utilized to approximate the unknown functions of the systems. The adaptation laws and the controllers are designed based on the transformed systems. By using the Lyapunov method, it is proved that the closed-loop system is stable in the sense that the semiglobally uniformly ultimately bounded of all the signals and the tracking errors converge to a bounded compact set. The simulation examples and the comparisons with previous approaches are provided to illustrate the effectiveness of the proposed control algorithm.

A matlab framework for estimation of NLME models using stochastic differential equations: applications for estimation of insulin secretion rates.

PubMed

Mortensen, Stig B; Klim, Søren; Dammann, Bernd; Kristensen, Niels R; Madsen, Henrik; Overgaard, Rune V

2007-10-01

The non-linear mixed-effects model based on stochastic differential equations (SDEs) provides an attractive residual error model, that is able to handle serially correlated residuals typically arising from structural mis-specification of the true underlying model. The use of SDEs also opens up for new tools for model development and easily allows for tracking of unknown inputs and parameters over time. An algorithm for maximum likelihood estimation of the model has earlier been proposed, and the present paper presents the first general implementation of this algorithm. The implementation is done in Matlab and also demonstrates the use of parallel computing for improved estimation times. The use of the implementation is illustrated by two examples of application which focus on the ability of the model to estimate unknown inputs facilitated by the extension to SDEs. The first application is a deconvolution-type estimation of the insulin secretion rate based on a linear two-compartment model for C-peptide measurements. In the second application the model is extended to also give an estimate of the time varying liver extraction based on both C-peptide and insulin measurements.

Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

PubMed

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

2013-10-01

This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.

A radial map of multi-whisker correlation selectivity in the rat barrel cortex

PubMed Central

Estebanez, Luc; Bertherat, Julien; Shulz, Daniel E.; Bourdieu, Laurent; Léger, Jean- François

2016-01-01

In the barrel cortex, several features of single-whisker stimuli are organized in functional maps. The barrel cortex also encodes spatio-temporal correlation patterns of multi-whisker inputs, but so far the cortical mapping of neurons tuned to such input statistics is unknown. Here we report that layer 2/3 of the rat barrel cortex contains an additional functional map based on neuronal tuning to correlated versus uncorrelated multi-whisker stimuli: neuron responses to uncorrelated multi-whisker stimulation are strongest above barrel centres, whereas neuron responses to correlated and anti-correlated multi-whisker stimulation peak above the barrel–septal borders, forming rings of multi-whisker synchrony-preferring cells. PMID:27869114

A radial map of multi-whisker correlation selectivity in the rat barrel cortex.

PubMed

Estebanez, Luc; Bertherat, Julien; Shulz, Daniel E; Bourdieu, Laurent; Léger, Jean-François

2016-11-21

In the barrel cortex, several features of single-whisker stimuli are organized in functional maps. The barrel cortex also encodes spatio-temporal correlation patterns of multi-whisker inputs, but so far the cortical mapping of neurons tuned to such input statistics is unknown. Here we report that layer 2/3 of the rat barrel cortex contains an additional functional map based on neuronal tuning to correlated versus uncorrelated multi-whisker stimuli: neuron responses to uncorrelated multi-whisker stimulation are strongest above barrel centres, whereas neuron responses to correlated and anti-correlated multi-whisker stimulation peak above the barrel-septal borders, forming rings of multi-whisker synchrony-preferring cells.

Translating PI observing proposals into ALMA observing scripts

NASA Astrophysics Data System (ADS)

Liszt, Harvey S.

2014-08-01

The ALMA telescope is a complex 66-antenna array working in the specialized domain of mm- and sub-mm aperture synthesis imaging. To make ALMA accessible to technically inexperienced but scientifically expert users, the ALMA Observing Tool (OT) has been developed. Using the OT, scientifically oriented user input is formatted as observing proposals that are packaged for peer-review and assessment of technical feasibility. If accepted, the proposal's scientifically oriented inputs are translated by the OT into scheduling blocks, which function as input to observing scripts for the telescope's online control system. Here I describe the processes and practices by which this translation from PI scientific goals to online control input and schedule block execution actually occurs.

Schizophrenia-Related Microdeletion Impairs Emotional Memory through MicroRNA-Dependent Disruption of Thalamic Inputs to the Amygdala.

PubMed

Eom, Tae-Yeon; Bayazitov, Ildar T; Anderson, Kara; Yu, Jing; Zakharenko, Stanislav S

2017-05-23

Individuals with 22q11.2 deletion syndrome (22q11DS) are at high risk of developing psychiatric diseases such as schizophrenia. Individuals with 22q11DS and schizophrenia are impaired in emotional memory, anticipating, recalling, and assigning a correct context to emotions. The neuronal circuits responsible for these emotional memory deficits are unknown. Here, we show that 22q11DS mouse models have disrupted synaptic transmission at thalamic inputs to the lateral amygdala (thalamo-LA projections). This synaptic deficit is caused by haploinsufficiency of the 22q11DS gene Dgcr8, which is involved in microRNA processing, and is mediated by the increased dopamine receptor Drd2 levels in the thalamus and by reduced probability of glutamate release from thalamic inputs. This deficit in thalamo-LA synaptic transmission is sufficient to cause fear memory deficits. Our results suggest that dysregulation of the Dgcr8-Drd2 mechanism at thalamic inputs to the amygdala underlies emotional memory deficits in 22q11DS. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Adaptive fuzzy control of a class of nonaffine nonlinear system with input saturation based on passivity theorem.

PubMed

Molavi, Ali; Jalali, Aliakbar; Ghasemi Naraghi, Mahdi

2017-07-01

In this paper, based on the passivity theorem, an adaptive fuzzy controller is designed for a class of unknown nonaffine nonlinear systems with arbitrary relative degree and saturation input nonlinearity to track the desired trajectory. The system equations are in normal form and its unforced dynamic may be unstable. As relative degree one is a structural obstacle in system passivation approach, in this paper, backstepping method is used to circumvent this obstacle and passivate the system step by step. Because of the existence of uncertainty and disturbance in the system, exact passivation and reference tracking cannot be tackled, so the approximate passivation or passivation with respect to a set is obtained to hold the tracking error in a neighborhood around zero. Furthermore, in order to overcome the non-smoothness of the saturation input nonlinearity, a parametric smooth nonlinear function with arbitrary approximation error is used to approximate the input saturation. Finally, the simulation results for the theoretical and practical examples are given to validate the proposed controller. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Atomic entanglement purification and concentration using coherent state input-output process in low-Q cavity QED regime.

PubMed

Cao, Cong; Wang, Chuan; He, Ling-Yan; Zhang, Ru

2013-02-25

We investigate an atomic entanglement purification protocol based on the coherent state input-output process by working in low-Q cavity in the atom-cavity intermediate coupling region. The information of entangled states are encoded in three-level configured single atoms confined in separated one-side optical micro-cavities. Using the coherent state input-output process, we design a two-qubit parity check module (PCM), which allows the quantum nondemolition measurement for the atomic qubits, and show its use for remote parities to distill a high-fidelity atomic entangled ensemble from an initial mixed state ensemble nonlocally. The proposed scheme can further be used for unknown atomic states entanglement concentration. Also by exploiting the PCM, we describe a modified scheme for atomic entanglement concentration by introducing ancillary single atoms. As the coherent state input-output process is robust and scalable in realistic applications, and the detection in the PCM is based on the intensity of outgoing coherent state, the present protocols may be widely used in large-scaled and solid-based quantum repeater and quantum information processing.

Input Shaping enhanced Active Disturbance Rejection Control for a twin rotor multi-input multi-output system (TRMS).

PubMed

Yang, Xiaoyan; Cui, Jianwei; Lao, Dazhong; Li, Donghai; Chen, Junhui

2016-05-01

In this paper, a composite control based on Active Disturbance Rejection Control (ADRC) and Input Shaping is presented for TRMS with two degrees of freedom (DOF). The control tasks consist of accurately tracking desired trajectories and obtaining disturbance rejection in both horizontal and vertical planes. Due to un-measurable states as well as uncertainties stemming from modeling uncertainty and unknown disturbance torques, ADRC is employed, and feed-forward Input Shaping is used to improve the dynamical response. In the proposed approach, because the coupling effects are maintained in controller derivation, there is no requirement to decouple the TRMS into horizontal and vertical subsystems, which is usually performed in the literature. Finally, the proposed method is implemented on the TRMS platform, and the results are compared with those of PID and ADRC in a similar structure. The experimental results demonstrate the effectiveness of the proposed method. The operation of the controller allows for an excellent set-point tracking behavior and disturbance rejection with system nonlinearity and complex coupling conditions. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Structural damage identification using piezoelectric impedance measurement with sparse inverse analysis

NASA Astrophysics Data System (ADS)

Cao, Pei; Qi, Shuai; Tang, J.

2018-03-01

The impedance/admittance measurements of a piezoelectric transducer bonded to or embedded in a host structure can be used as damage indicator. When a credible model of the healthy structure, such as the finite element model, is available, using the impedance/admittance change information as input, it is possible to identify both the location and severity of damage. The inverse analysis, however, may be under-determined as the number of unknowns in high-frequency analysis is usually large while available input information is limited. The fundamental challenge thus is how to find a small set of solutions that cover the true damage scenario. In this research we cast the damage identification problem into a multi-objective optimization framework to tackle this challenge. With damage locations and severities as unknown variables, one of the objective functions is the difference between impedance-based model prediction in the parametric space and the actual measurements. Considering that damage occurrence generally affects only a small number of elements, we choose the sparsity of the unknown variables as another objective function, deliberately, the l 0 norm. Subsequently, a multi-objective Dividing RECTangles (DIRECT) algorithm is developed to facilitate the inverse analysis where the sparsity is further emphasized by sigmoid transformation. As a deterministic technique, this approach yields results that are repeatable and conclusive. In addition, only one algorithmic parameter, the number of function evaluations, is needed. Numerical and experimental case studies demonstrate that the proposed framework is capable of obtaining high-quality damage identification solutions with limited measurement information.

Resilience to the contralateral visual field bias as a window into object representations

PubMed Central

Garcea, Frank E.; Kristensen, Stephanie; Almeida, Jorge; Mahon, Bradford Z.

2016-01-01

Viewing images of manipulable objects elicits differential blood oxygen level-dependent (BOLD) contrast across parietal and dorsal occipital areas of the human brain that support object-directed reaching, grasping, and complex object manipulation. However, it is unknown which object-selective regions of parietal cortex receive their principal inputs from the ventral object-processing pathway and which receive their inputs from the dorsal object-processing pathway. Parietal areas that receive their inputs from the ventral visual pathway, rather than from the dorsal stream, will have inputs that are already filtered through object categorization and identification processes. This predicts that parietal regions that receive inputs from the ventral visual pathway should exhibit object-selective responses that are resilient to contralateral visual field biases. To test this hypothesis, adult participants viewed images of tools and animals that were presented to the left or right visual fields during functional magnetic resonance imaging (fMRI). We found that the left inferior parietal lobule showed robust tool preferences independently of the visual field in which tool stimuli were presented. In contrast, a region in posterior parietal/dorsal occipital cortex in the right hemisphere exhibited an interaction between visual field and category: tool-preferences were strongest contralateral to the stimulus. These findings suggest that action knowledge accessed in the left inferior parietal lobule operates over inputs that are abstracted from the visual input and contingent on analysis by the ventral visual pathway, consistent with its putative role in supporting object manipulation knowledge. PMID:27160998

Enhanced litter input rather than changes in litter chemistry drive soil carbon and nitrogen cycles under elevated CO2: a microcosm study

Treesearch

Lingli Lui; John S. King; Fitzgerald L. Booker; Christian P. Giardina; H. Lee Allen; Shuijin Hu

2009-01-01

Elevated CO2 has been shown to stimulate plant productivity and change litter chemistry. These changes in substrate availability may then alter soil microbial processes and possibly lead to feedback effects on N availability. However, the strength of this feedback, and even its direction, remains unknown. Further, uncertainty remains whether...

Dopamine D1 Receptors in the Anterior Cingulate Cortex Regulate Effort-Based Decision Making

ERIC Educational Resources Information Center

Schweimer, Judith; Hauber, Wolfgang

2006-01-01

The anterior cingulate cortex (ACC) has been implicated in encoding whether or not an action is worth performing in view of the expected benefit and the cost of performing the action. Dopamine input to the ACC may be critical for this form of effort-based decision making; however, the role of distinct ACC dopamine receptors is yet unknown.…

40 CFR Appendix A to Part 72 - Methodology for Annualization of Emissions Limits

Code of Federal Regulations, 2010 CFR

2010-07-01

... expressed in pounds of SO2 per million British Thermal Unit of heat input (lb/mmBtu) and expressed on an...) than annually. Because of the variability of sulfur in coal and, in some cases, scrubber performance... Not specified 0.93 0.89 At all times 0.93 0.89 Coal unit: No Federal limit or limit unknown 1.00 1.00 ...

supernovae: Photometric classification of supernovae

NASA Astrophysics Data System (ADS)

Charnock, Tom; Moss, Adam

2017-05-01

Supernovae classifies supernovae using their light curves directly as inputs to a deep recurrent neural network, which learns information from the sequence of observations. Observational time and filter fluxes are used as inputs; since the inputs are agnostic, additional data such as host galaxy information can also be included.

Stochastic reduced order models for inverse problems under uncertainty

PubMed Central

Warner, James E.; Aquino, Wilkins; Grigoriu, Mircea D.

2014-01-01

This work presents a novel methodology for solving inverse problems under uncertainty using stochastic reduced order models (SROMs). Given statistical information about an observed state variable in a system, unknown parameters are estimated probabilistically through the solution of a model-constrained, stochastic optimization problem. The point of departure and crux of the proposed framework is the representation of a random quantity using a SROM - a low dimensional, discrete approximation to a continuous random element that permits e cient and non-intrusive stochastic computations. Characterizing the uncertainties with SROMs transforms the stochastic optimization problem into a deterministic one. The non-intrusive nature of SROMs facilitates e cient gradient computations for random vector unknowns and relies entirely on calls to existing deterministic solvers. Furthermore, the method is naturally extended to handle multiple sources of uncertainty in cases where state variable data, system parameters, and boundary conditions are all considered random. The new and widely-applicable SROM framework is formulated for a general stochastic optimization problem in terms of an abstract objective function and constraining model. For demonstration purposes, however, we study its performance in the specific case of inverse identification of random material parameters in elastodynamics. We demonstrate the ability to efficiently recover random shear moduli given material displacement statistics as input data. We also show that the approach remains effective for the case where the loading in the problem is random as well. PMID:25558115

Application of a Fuzzy Neural Network Model in Predicting Polycyclic Aromatic Hydrocarbon- Mediated Perturbations of the Cyp1b1 Transcriptional Regulatory Network in Mouse Skin

PubMed Central

Larkin, Andrew; Siddens, Lisbeth K.; Krueger, Sharon K.; Tilton, Susan C.; Waters, Katrina M.; Williams, David E.; Baird, William M.

2013-01-01

Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdani logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave one out cross-validation. Predictions were within 1 log2 fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights. PMID:23274566

Capacitance measuring device

DOEpatents

Andrews, W.H. Jr.

1984-08-01

A capacitance measuring circuit is provided in which an unknown capacitance is measured by comparing the charge stored in the unknown capacitor with that stored in a known capacitance. Equal and opposite voltages are repetitively simultaneously switched onto the capacitors through an electronic switch driven by a pulse generator to charge the capacitors during the ''on'' portion of the cycle. The stored charge is compared by summing discharge currents flowing through matched resistors at the input of a current sensor during the ''off'' portion of the switching cycle. The net current measured is thus proportional to the difference in value of the two capacitances. The circuit is capable of providing much needed accuracy and stability to a great variety of capacitance-based measurement devices at a relatively low cost.

Microstructural and Electrochemical Evaluation of Fusion Welded Low-Nickel and 304 SS at Different Heat Input

NASA Astrophysics Data System (ADS)

Bansod, Ankur V.; Patil, Awanikumar P.; Moon, Abhijeet P.; Shukla, Sourabh

2017-12-01

The present research study investigates the effect of heat input using E 308 electrode (controlled by welding current, i.e., 70, 85 and 100 A) on microstructure, mechanical properties and corrosion behavior of low-nickel and 304 stainless steel (SS) weldments produced by shielded metal arc welding technique. SEM investigation shows that with the higher heat input, δ-ferrite content was reduced. Dendrite and inter-dendritic length is also reduced by lowering the heat input. For all the heat inputs, it is observed that δ-ferrite content was higher in 304 stainless steel (SS) as compared to that of low-nickel austenitic stainless steel (Cr-Mn SS). Considering the heat input for Cr-Mn SS, coarse grains were observed in the heat-affected zone region. For low heat input (LHI), tensile fracture surface has exhibited river-like pattern with dimple appearance. Corrosion studies show better pitting resistance for low heat input (LHI) samples due to higher δ-ferrite present in the weld region. Similarly, higher interphase corrosion resistance is observed in both the SS grades causing more dissolution in the LHI samples.

A reduced adaptive observer for multivariable systems. [using reduced dynamic ordering

NASA Technical Reports Server (NTRS)

Carroll, R. L.; Lindorff, D. P.

1973-01-01

An adaptive observer for multivariable systems is presented for which the dynamic order of the observer is reduced, subject to mild restrictions. The observer structure depends directly upon the multivariable structure of the system rather than a transformation to a single-output system. The number of adaptive gains is at most the sum of the order of the system and the number of input parameters being adapted. Moreover, for the relatively frequent specific cases for which the number of required adaptive gains is less than the sum of system order and input parameters, the number of these gains is easily determined by inspection of the system structure. This adaptive observer possesses all the properties ascribed to the single-input single-output adpative observer. Like the other adaptive observers some restriction is required of the allowable system command input to guarantee convergence of the adaptive algorithm, but the restriction is more lenient than that required by the full-order multivariable observer. This reduced observer is not restricted to cycle systems.

Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering

PubMed Central

Havlicek, Martin; Friston, Karl J.; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D.

2011-01-01

This paper presents a new approach to inverting (fitting) models of coupled dynamical systems based on state-of-the-art (cubature) Kalman filtering. Crucially, this inversion furnishes posterior estimates of both the hidden states and parameters of a system, including any unknown exogenous input. Because the underlying generative model is formulated in continuous time (with a discrete observation process) it can be applied to a wide variety of models specified with either ordinary or stochastic differential equations. These are an important class of models that are particularly appropriate for biological time-series, where the underlying system is specified in terms of kinetics or dynamics (i.e., dynamic causal models). We provide comparative evaluations with generalized Bayesian filtering (dynamic expectation maximization) and demonstrate marked improvements in accuracy and computational efficiency. We compare the schemes using a series of difficult (nonlinear) toy examples and conclude with a special focus on hemodynamic models of evoked brain responses in fMRI. Our scheme promises to provide a significant advance in characterizing the functional architectures of distributed neuronal systems, even in the absence of known exogenous (experimental) input; e.g., resting state fMRI studies and spontaneous fluctuations in electrophysiological studies. Importantly, unlike current Bayesian filters (e.g. DEM), our scheme provides estimates of time-varying parameters, which we will exploit in future work on the adaptation and enabling of connections in the brain. PMID:21396454

Geochemical modeling of magma mixing and magma reservoir volumes during early episodes of Kīlauea Volcano's Pu`u `Ō`ō eruption

NASA Astrophysics Data System (ADS)

Shamberger, Patrick J.; Garcia, Michael O.

2007-02-01

Geochemical modeling of magma mixing allows for evaluation of volumes of magma storage reservoirs and magma plumbing configurations. A new analytical expression is derived for a simple two-component box-mixing model describing the proportions of mixing components in erupted lavas as a function of time. Four versions of this model are applied to a mixing trend spanning episodes 3 31 of Kilauea Volcano’s Puu Oo eruption, each testing different constraints on magma reservoir input and output fluxes. Unknown parameters (e.g., magma reservoir influx rate, initial reservoir volume) are optimized for each model using a non-linear least squares technique to fit model trends to geochemical time-series data. The modeled mixing trend closely reproduces the observed compositional trend. The two models that match measured lava effusion rates have constant magma input and output fluxes and suggest a large pre-mixing magma reservoir (46±2 and 49±1 million m3), with little or no volume change over time. This volume is much larger than a previous estimate for the shallow, dike-shaped magma reservoir under the Puu Oo vent, which grew from ˜3 to ˜10 12 million m3. These volumetric differences are interpreted as indicating that mixing occurred first in a larger, deeper reservoir before the magma was injected into the overlying smaller reservoir.

Investigating Response from Turbulent Boundary Layer Excitations on a Real Launch Vehicle using SEA

NASA Technical Reports Server (NTRS)

Harrison, Phillip; LaVerde,Bruce; Teague, David

2009-01-01

Statistical Energy Analysis (SEA) response has been fairly well anchored to test observations for Diffuse Acoustic Field (DAF) loading by others. Meanwhile, not many examples can be found in the literature anchoring the SEA vehicle panel response results to Turbulent Boundary Layer (TBL) fluctuating pressure excitations. This deficiency is especially true for supersonic trajectories such as those required by this nation s launch vehicles. Space Shuttle response and excitation data recorded from vehicle flight measurements during the development flights were used in a trial to assess the capability of the SEA tool to predict similar responses. Various known/measured inputs were used. These were supplemented with a range of assumed values in order to cover unknown parameters of the flight. This comparison is presented as "Part A" of the study. A secondary, but perhaps more important, objective is to provide more clarity concerning the accuracy and conservatism that can be expected from response estimates of TBL-excited vehicle models in SEA (Part B). What range of parameters must be included in such an analysis in order to land on the conservative side in response predictions? What is the sensitivity of changes in these input parameters on the results? The TBL fluid structure loading model used for this study is provided by the SEA module of the commercial code VA One.

Sliding Mode Control of a Thermal Mixing Process

NASA Technical Reports Server (NTRS)

Richter, Hanz; Figueroa, Fernando

2004-01-01

In this paper we consider the robust control of a thermal mixer using multivariable Sliding Mode Control (SMC). The mixer consists of a mixing chamber, hot and cold fluid valves, and an exit valve. The commanded positions of the three valves are the available control inputs, while the controlled variables are total mass flow rate, chamber pressure and the density of the mixture inside the chamber. Unsteady thermodynamics and linear valve models are used in deriving a 5th order nonlinear system with three inputs and three outputs, An SMC controller is designed to achieve robust output tracking in the presence of unknown energy losses between the chamber and the environment. The usefulness of the technique is illustrated with a simulation.

Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs: Supplemental Material

NASA Technical Reports Server (NTRS)

Glotter, Michael J.; Ruane, Alex C.; Moyer, Elisabeth J.; Elliott, Joshua W.

2015-01-01

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but under sensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves.

Evaluating the sensitivity of agricultural model performance to different climate inputs

PubMed Central

Glotter, Michael J.; Moyer, Elisabeth J.; Ruane, Alex C.; Elliott, Joshua W.

2017-01-01

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled to observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections, but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely-used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources – reanalysis, reanalysis bias-corrected with observed climate, and a control dataset – and compared to observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by un-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. However, some issues persist for all choices of climate inputs: crop yields appear oversensitive to precipitation fluctuations but undersensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves. PMID:29097985

A Bayesian Retrieval of Greenland Ice Sheet Internal Temperature from Ultra-wideband Software-defined Microwave Radiometer (UWBRAD) Measurements

NASA Astrophysics Data System (ADS)

Duan, Y.; Durand, M. T.; Jezek, K. C.; Yardim, C.; Bringer, A.; Aksoy, M.; Johnson, J. T.

2017-12-01

The ultra-wideband software-defined microwave radiometer (UWBRAD) is designed to provide ice sheet internal temperature product via measuring low frequency microwave emission. Twelve channels ranging from 0.5 to 2.0 GHz are covered by the instrument. A Greenland air-borne demonstration was demonstrated in September 2016, provided first demonstration of Ultra-wideband radiometer observations of geophysical scenes, including ice sheets. Another flight is planned for September 2017 for acquiring measurements in central ice sheet. A Bayesian framework is designed to retrieve the ice sheet internal temperature from simulated UWBRAD brightness temperature (Tb) measurements over Greenland flight path with limited prior information of the ground. A 1-D heat-flow model, the Robin Model, was used to model the ice sheet internal temperature profile with ground information. Synthetic UWBRAD Tb observations was generated via the partially coherent radiation transfer model, which utilizes the Robin model temperature profile and an exponential fit of ice density from Borehole measurement as input, and corrupted with noise. The effective surface temperature, geothermal heat flux, the variance of upper layer ice density, and the variance of fine scale density variation at deeper ice sheet were treated as unknown variables within the retrieval framework. Each parameter is defined with its possible range and set to be uniformly distributed. The Markov Chain Monte Carlo (MCMC) approach is applied to make the unknown parameters randomly walk in the parameter space. We investigate whether the variables can be improved over priors using the MCMC approach and contribute to the temperature retrieval theoretically. UWBRAD measurements near camp century from 2016 was also treated with the MCMC to examine the framework with scattering effect. The fine scale density fluctuation is an important parameter. It is the most sensitive yet highly unknown parameter in the estimation framework. Including the fine scale density fluctuation greatly improved the retrieval results. The ice sheet vertical temperature profile, especially the 10m temperature, can be well retrieved via the MCMC process. Future retrieval work will apply the Bayesian approach to UWBRAD airborne measurements.

Kalman filters for assimilating near-surface observations in the Richards equation - Part 2: A dual filter approach for simultaneous retrieval of states and parameters

NASA Astrophysics Data System (ADS)

Medina, H.; Romano, N.; Chirico, G. B.

2012-12-01

We present a dual Kalman Filter (KF) approach for retrieving states and parameters controlling soil water dynamics in a homogenous soil column by using near-surface state observations. The dual Kalman filter couples a standard KF algorithm for retrieving the states and an unscented KF algorithm for retrieving the parameters. We examine the performance of the dual Kalman Filter applied to two alternative state-space formulations of the Richards equation, respectively differentiated by the type of variable employed for representing the states: either the soil water content (θ) or the soil matric pressure head (h). We use a synthetic time-series series of true states and noise corrupted observations and a synthetic time-series of meteorological forcing. The performance analyses account for the effect of the input parameters, the observation depth and the assimilation frequency as well as the relationship between the retrieved states and the assimilated variables. We show that the identifiability of the parameters is strongly conditioned by several factors, such as the initial guess of the unknown parameters, the wet or dry range of the retrieved states, the boundary conditions, as well as the form (h-based or θ-based) of the state-space formulation. State identifiability is instead efficient even with a relatively coarse time-resolution of the assimilated observation. The accuracy of the retrieved states exhibits limited sensitivity to the observation depth and the assimilation frequency.

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits

PubMed Central

Ujfalussy, Balázs B; Makara, Judit K; Branco, Tiago; Lengyel, Máté

2015-01-01

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits. DOI: http://dx.doi.org/10.7554/eLife.10056.001 PMID:26705334

Identification of modal parameters including unmeasured forces and transient effects

NASA Astrophysics Data System (ADS)

Cauberghe, B.; Guillaume, P.; Verboven, P.; Parloo, E.

2003-08-01

In this paper, a frequency-domain method to estimate modal parameters from short data records with known input (measured) forces and unknown input forces is presented. The method can be used for an experimental modal analysis, an operational modal analysis (output-only data) and the combination of both. A traditional experimental and operational modal analysis in the frequency domain starts respectively, from frequency response functions and spectral density functions. To estimate these functions accurately sufficient data have to be available. The technique developed in this paper estimates the modal parameters directly from the Fourier spectra of the outputs and the known input. Instead of using Hanning windows on these short data records the transient effects are estimated simultaneously with the modal parameters. The method is illustrated, tested and validated by Monte Carlo simulations and experiments. The presented method to process short data sequences leads to unbiased estimates with a small variance in comparison to the more traditional approaches.

Direct Midbrain Dopamine Input to the Suprachiasmatic Nucleus Accelerates Circadian Entrainment.

PubMed

Grippo, Ryan M; Purohit, Aarti M; Zhang, Qi; Zweifel, Larry S; Güler, Ali D

2017-08-21

Dopamine (DA) neurotransmission controls behaviors important for survival, including voluntary movement, reward processing, and detection of salient events, such as food or mate availability. Dopaminergic tone also influences circadian physiology and behavior. Although the evolutionary significance of this input is appreciated, its precise neurophysiological architecture remains unknown. Here, we identify a novel, direct connection between the DA neurons of the ventral tegmental area (VTA) and the suprachiasmatic nucleus (SCN). We demonstrate that D1 dopamine receptor (Drd1) signaling within the SCN is necessary for properly timed resynchronization of activity rhythms to phase-shifted light:dark cycles and that elevation of DA tone through selective activation of VTA DA neurons accelerates photoentrainment. Our findings demonstrate a previously unappreciated role for direct DA input to the master circadian clock and highlight the importance of an evolutionarily significant relationship between the circadian system and the neuromodulatory circuits that govern motivational behaviors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Identifiability Results for Several Classes of Linear Compartment Models.

PubMed

Meshkat, Nicolette; Sullivant, Seth; Eisenberg, Marisa

2015-08-01

Identifiability concerns finding which unknown parameters of a model can be estimated, uniquely or otherwise, from given input-output data. If some subset of the parameters of a model cannot be determined given input-output data, then we say the model is unidentifiable. In this work, we study linear compartment models, which are a class of biological models commonly used in pharmacokinetics, physiology, and ecology. In past work, we used commutative algebra and graph theory to identify a class of linear compartment models that we call identifiable cycle models, which are unidentifiable but have the simplest possible identifiable functions (so-called monomial cycles). Here we show how to modify identifiable cycle models by adding inputs, adding outputs, or removing leaks, in such a way that we obtain an identifiable model. We also prove a constructive result on how to combine identifiable models, each corresponding to strongly connected graphs, into a larger identifiable model. We apply these theoretical results to several real-world biological models from physiology, cell biology, and ecology.

A mechanism of wave drag reduction in the thermal energy deposition experiments

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

Markhotok, A., E-mail: amarhotk@phys.washington.edu

2015-06-15

Many experimental studies report reduced wave drag when thermal energy is deposited in the supersonic flow upstream of a body. Though a large amount of research on this topic has been accumulated, the exact mechanism of the drag reduction is still unknown. This paper is to fill the gap in the understanding connecting multiple stages of the observed phenomena with a single mechanism. The proposed model provides an insight on the origin of the chain of subsequent transformations in the flow leading to the reduction in wave drag, such as typical deformations of the front, changes in the gas pressuremore » and density in front of the body, the odd shapes of the deflection signals, and the shock wave extinction in the plasma area. The results of numerical simulation based on the model are presented for three types of plasma parameter distribution. The spherical and cylindrical geometry has been used to match the data with the experimental observations. The results demonstrate full ability of the model to exactly explain all the features observed in the drag reduction experiments. Analytical expressions used in the model allow separating out a number of adjustment parameters that can be used to optimize thermal energy input and thus achieve fundamentally lower drag values than that of conventional approaches.« less

Impact of simulated herbivory on water relations of aspen (Populus tremuloides) seedlings: the role of new tissue in the hydraulic conductivity recovery cycle

Treesearch

David A. Galvez; M.T. Tyree

2009-01-01

Physiological mechanisms behind plant-herbivore interactions are commonly approached as input-output systems where the role of plant physiology is viewed as a black box. Studies evaluating impacts of defoliation on plant physiology have mostly focused on changes in photosynthesis while the overall impact on plant water relations is largely unknown. Stem hydraulic...

Optical neural net for classifying imaging spectrometer data

NASA Technical Reports Server (NTRS)

Barnard, Etienne; Casasent, David P.

1989-01-01

The problem of determining the composition of an unknown input mixture from its measured spectrum, given the spectra of a number of elements, is studied. The Hopfield minimization procedure was used to express the determination of the compositions as a problem suitable for solution by neural nets. A mathematical description of the problem was developed and used as a basis for a neural network solution and an optical implementation.

The Blood-Testis Barrier and Male Sexual Dysfunction following Spinal Cord Injury

DTIC Science & Technology

2014-10-01

antigenic sperm and sperm cell-containing compartments within the testis. We also demonstrated that once failed, the BTB remains permeable, essentially...input into the male sexual organs. SCI-dependent male infertility is characterized by a significant reduction in numbers and quality of functional... sperm . The mechanism(s) underlying this deficit has previously been unknown. My laboratory has explored the effects of spinal trauma on tissues that

Improved water resource management using three dimensional groundwater modelling for a highly complex environmental

NASA Astrophysics Data System (ADS)

Moeck, Christian; Affolter, Annette; Radny, Dirk; Auckenthaler, Adrian; Huggenberger, Peter; Schirmer, Mario

2017-04-01

Proper allocation and management of groundwater is an important and critical challenge under rising water demands of various environmental sectors but good groundwater quality is often limited because of urbanization and contamination of aquifers. Given the predictive capability of groundwater models, they are often the only viable means of providing input to water management decisions. However, modelling flow and transport processes can be difficult due to their unknown subsurface heterogeneity and typically unknown distribution of contaminants. As a result water resource management tasks are based on uncertain assumption on contaminants patterns and this uncertainty is typically not incorporated into the assessment of risks associated with different proposed management scenarios. A three-dimensional groundwater model was used to improve water resource management for a study area, where drinking water production is close to different former landfills and industrial areas. To avoid drinking water contamination, artificial groundwater recharge with surface water into the gravel aquifer is used to create a hydraulic barrier between contaminated sites and drinking water extraction wells. The model was used for simulating existing and proposed water management strategies as a tool to ensure the utmost security for drinking water. A systematic evaluation of the flow direction and magnitude between existing observation points using a newly developed three point estimation method for a large amount of scenarios was carried out. Due to the numerous observation points 32 triangles (three-points) were created which cover the entire area around the Hardwald. We demonstrated that systematically applying our developed methodology helps to identify important locations which are sensitive to changing boundary conditions and where additional protection is required without highly computational demanding transport modelling. The presented integrated approach using the flow direction between observation points can be easily transferred to a variety of hydrological settings to evaluate systematically groundwater modelling scenarios.

A robust momentum management and attitude control system for the space station

NASA Technical Reports Server (NTRS)

Speyer, J. L.; Rhee, Ihnseok

1991-01-01

A game theoretic controller is synthesized for momentum management and attitude control of the Space Station in the presence of uncertainties in the moments of inertia. Full state information is assumed since attitude rates are assumed to be very assurately measured. By an input-output decomposition of the uncertainty in the system matrices, the parameter uncertainties in the dynamic system are represented as an unknown gain associated with an internal feedback loop (IFL). The input and output matrices associated with the IFL form directions through which the uncertain parameters affect system response. If the quadratic form of the IFL output augments the cost criterion, then enhanced parameter robustness is anticipated. By considering the input and the input disturbance from the IFL as two noncooperative players, a linear-quadratic differential game is constructed. The solution in the form of a linear controller is used for synthesis. Inclusion of the external disturbance torques results in a dynamic feedback controller which consists of conventional PID (proportional integral derivative) control and cyclic disturbance rejection filters. It is shown that the game theoretic design allows large variations in the inertias in directions of importance.

Robust fault-tolerant tracking control design for spacecraft under control input saturation.

PubMed

Bustan, Danyal; Pariz, Naser; Sani, Seyyed Kamal Hosseini

2014-07-01

In this paper, a continuous globally stable tracking control algorithm is proposed for a spacecraft in the presence of unknown actuator failure, control input saturation, uncertainty in inertial matrix and external disturbances. The design method is based on variable structure control and has the following properties: (1) fast and accurate response in the presence of bounded disturbances; (2) robust to the partial loss of actuator effectiveness; (3) explicit consideration of control input saturation; and (4) robust to uncertainty in inertial matrix. In contrast to traditional fault-tolerant control methods, the proposed controller does not require knowledge of the actuator faults and is implemented without explicit fault detection and isolation processes. In the proposed controller a single parameter is adjusted dynamically in such a way that it is possible to prove that both attitude and angular velocity errors will tend to zero asymptotically. The stability proof is based on a Lyapunov analysis and the properties of the singularity free quaternion representation of spacecraft dynamics. Results of numerical simulations state that the proposed controller is successful in achieving high attitude performance in the presence of external disturbances, actuator failures, and control input saturation. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Robustness of sensory-evoked excitation is increased by inhibitory inputs to distal apical tuft dendrites

PubMed Central

Egger, Robert; Schmitt, Arno C.; Wallace, Damian J.; Sakmann, Bert; Oberlaender, Marcel; Kerr, Jason N. D.

2015-01-01

Cortical inhibitory interneurons (INs) are subdivided into a variety of morphologically and functionally specialized cell types. How the respective specific properties translate into mechanisms that regulate sensory-evoked responses of pyramidal neurons (PNs) remains unknown. Here, we investigated how INs located in cortical layer 1 (L1) of rat barrel cortex affect whisker-evoked responses of L2 PNs. To do so we combined in vivo electrophysiology and morphological reconstructions with computational modeling. We show that whisker-evoked membrane depolarization in L2 PNs arises from highly specialized spatiotemporal synaptic input patterns. Temporally L1 INs and L2–5 PNs provide near synchronous synaptic input. Spatially synaptic contacts from L1 INs target distal apical tuft dendrites, whereas PNs primarily innervate basal and proximal apical dendrites. Simulations of such constrained synaptic input patterns predicted that inactivation of L1 INs increases trial-to-trial variability of whisker-evoked responses in L2 PNs. The in silico predictions were confirmed in vivo by L1-specific pharmacological manipulations. We present a mechanism—consistent with the theory of distal dendritic shunting—that can regulate the robustness of sensory-evoked responses in PNs without affecting response amplitude or latency. PMID:26512104

Actor-critic-based optimal tracking for partially unknown nonlinear discrete-time systems.

PubMed

Kiumarsi, Bahare; Lewis, Frank L

2015-01-01

This paper presents a partially model-free adaptive optimal control solution to the deterministic nonlinear discrete-time (DT) tracking control problem in the presence of input constraints. The tracking error dynamics and reference trajectory dynamics are first combined to form an augmented system. Then, a new discounted performance function based on the augmented system is presented for the optimal nonlinear tracking problem. In contrast to the standard solution, which finds the feedforward and feedback terms of the control input separately, the minimization of the proposed discounted performance function gives both feedback and feedforward parts of the control input simultaneously. This enables us to encode the input constraints into the optimization problem using a nonquadratic performance function. The DT tracking Bellman equation and tracking Hamilton-Jacobi-Bellman (HJB) are derived. An actor-critic-based reinforcement learning algorithm is used to learn the solution to the tracking HJB equation online without requiring knowledge of the system drift dynamics. That is, two neural networks (NNs), namely, actor NN and critic NN, are tuned online and simultaneously to generate the optimal bounded control policy. A simulation example is given to show the effectiveness of the proposed method.

Robust momentum management and attitude control system for the Space Station

NASA Technical Reports Server (NTRS)

Rhee, Ihnseok; Speyer, Jason L.

1992-01-01

A game theoretic controller is synthesized for momentum management and attitude control of the Space Station in the presence of uncertainties in the moments of inertia. Full state information is assumed since attitude rates are assumed to be very accurately measured. By an input-output decomposition of the uncertainty in the system matrices, the parameter uncertainties in the dynamic system are represented as an unknown gain associated with an internal feedback loop (IFL). The input and output matrices associated with the IFL form directions through which the uncertain parameters affect system response. If the quadratic form of the IFL output augments the cost criterion, then enhanced parameter robustness is anticipated. By considering the input and the input disturbance from the IFL as two noncooperative players, a linear-quadratic differential game is constructed. The solution in the form of a linear controller is used for synthesis. Inclusion of the external disturbance torques results in a dynamic feedback controller which consists of conventional PID (proportional integral derivative) control and cyclic disturbance rejection filters. It is shown that the game theoretic design allows large variations in the inertias in directions of importance.

High-order motor cortex in rats receives somatosensory inputs from the primary motor cortex via cortico-cortical pathways.

PubMed

Kunori, Nobuo; Takashima, Ichiro

2016-12-01

The motor cortex of rats contains two forelimb motor areas; the caudal forelimb area (CFA) and the rostral forelimb area (RFA). Although the RFA is thought to correspond to the premotor and/or supplementary motor cortices of primates, which are higher-order motor areas that receive somatosensory inputs, it is unknown whether the RFA of rats receives somatosensory inputs in the same manner. To investigate this issue, voltage-sensitive dye (VSD) imaging was used to assess the motor cortex in rats following a brief electrical stimulation of the forelimb. This procedure was followed by intracortical microstimulation (ICMS) mapping to identify the motor representations in the imaged cortex. The combined use of VSD imaging and ICMS revealed that both the CFA and RFA received excitatory synaptic inputs after forelimb stimulation. Further evaluation of the sensory input pathway to the RFA revealed that the forelimb-evoked RFA response was abolished either by the pharmacological inactivation of the CFA or a cortical transection between the CFA and RFA. These results suggest that forelimb-related sensory inputs would be transmitted to the RFA from the CFA via the cortico-cortical pathway. Thus, the present findings imply that sensory information processed in the RFA may be used for the generation of coordinated forelimb movements, which would be similar to the function of the higher-order motor cortex in primates. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Moss-cyanobacteria associations as biogenic sources of nitrogen in boreal forest ecosystems.

PubMed

Rousk, Kathrin; Jones, Davey L; Deluca, Thomas H

2013-01-01

The biological fixation of atmospheric nitrogen (N) is a major pathway for available N entering ecosystems. In N-limited boreal forests, a significant amount of N2 is fixed by cyanobacteria living in association with mosses, contributing up to 50% to the total N input. In this review, we synthesize reports on the drivers of N2 fixation in feather moss-cyanobacteria associations to gain a deeper understanding of their role for ecosystem-N-cycling. Nitrogen fixation in moss-cyanobacteria associations is inhibited by N inputs and therefore, significant fixation occurs only in low N-deposition areas. While it has been shown that artificial N additions in the laboratory as well as in the field inhibit N2 fixation in moss-cyanobacteria associations, the type, as well as the amounts of N that enters the system, affect N2 fixation differently. Another major driver of N2 fixation is the moisture status of the cyanobacteria-hosting moss, wherein moist conditions promote N2 fixation. Mosses experience large fluctuations in their hydrological status, undergoing significant natural drying and rewetting cycles over the course of only a few hours, especially in summer, which likely compromises the N input to the system via N2 fixation. Perhaps the most central question, however, that remains unanswered is the fate of the fixed N2 in mosses. The cyanobacteria are likely to leak N, but whether this N is transferred to the soil and if so, at which rates and timescales, is unknown. Despite our increasing understanding of the drivers of N2 fixation, the role moss-cyanobacteria associations play in ecosystem-N-cycling remains unresolved. Further, the relationship mosses and cyanobacteria share is unknown to date and warrants further investigation.

Speaker identification for the improvement of the security communication between law enforcement units

NASA Astrophysics Data System (ADS)

Tovarek, Jaromir; Partila, Pavol

2017-05-01

This article discusses the speaker identification for the improvement of the security communication between law enforcement units. The main task of this research was to develop the text-independent speaker identification system which can be used for real-time recognition. This system is designed for identification in the open set. It means that the unknown speaker can be anyone. Communication itself is secured, but we have to check the authorization of the communication parties. We have to decide if the unknown speaker is the authorized for the given action. The calls are recorded by IP telephony server and then these recordings are evaluate using classification If the system evaluates that the speaker is not authorized, it sends a warning message to the administrator. This message can detect, for example a stolen phone or other unusual situation. The administrator then performs the appropriate actions. Our novel proposal system uses multilayer neural network for classification and it consists of three layers (input layer, hidden layer, and output layer). A number of neurons in input layer corresponds with the length of speech features. Output layer then represents classified speakers. Artificial Neural Network classifies speech signal frame by frame, but the final decision is done over the complete record. This rule substantially increases accuracy of the classification. Input data for the neural network are a thirteen Mel-frequency cepstral coefficients, which describe the behavior of the vocal tract. These parameters are the most used for speaker recognition. Parameters for training, testing and validation were extracted from recordings of authorized users. Recording conditions for training data correspond with the real traffic of the system (sampling frequency, bit rate). The main benefit of the research is the system developed for text-independent speaker identification which is applied to secure communication between law enforcement units.

Intrinsically organized network for word processing during the resting state.

PubMed

Zhao, Jizheng; Liu, Jiangang; Li, Jun; Liang, Jimin; Feng, Lu; Ai, Lin; Lee, Kang; Tian, Jie

2011-01-03

Neural mechanisms underlying word processing have been extensively studied. It has been revealed that when individuals are engaged in active word processing, a complex network of cortical regions is activated. However, it is entirely unknown whether the word-processing regions are intrinsically organized without any explicit processing tasks during the resting state. The present study investigated the intrinsic functional connectivity between word-processing regions during the resting state with the use of fMRI methodology. The low-frequency fluctuations were observed between the left middle fusiform gyrus and a number of cortical regions. They included the left angular gyrus, left supramarginal gyrus, bilateral pars opercularis, and left pars triangularis of the inferior frontal gyrus, which have been implicated in phonological and semantic processing. Additionally, the activations were also observed in the bilateral superior parietal lobule and dorsal lateral prefrontal cortex, which have been suggested to provide top-down monitoring on the visual-spatial processing of words. The findings of our study indicate an intrinsically organized network during the resting state that likely prepares the visual system to anticipate the highly probable word input for ready and effective processing. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

The Approximate Bayesian Computation methods in the localization of the atmospheric contamination source

NASA Astrophysics Data System (ADS)

Kopka, P.; Wawrzynczak, A.; Borysiewicz, M.

2015-09-01

In many areas of application, a central problem is a solution to the inverse problem, especially estimation of the unknown model parameters to model the underlying dynamics of a physical system precisely. In this situation, the Bayesian inference is a powerful tool to combine observed data with prior knowledge to gain the probability distribution of searched parameters. We have applied the modern methodology named Sequential Approximate Bayesian Computation (S-ABC) to the problem of tracing the atmospheric contaminant source. The ABC is technique commonly used in the Bayesian analysis of complex models and dynamic system. Sequential methods can significantly increase the efficiency of the ABC. In the presented algorithm, the input data are the on-line arriving concentrations of released substance registered by distributed sensor network from OVER-LAND ATMOSPHERIC DISPERSION (OLAD) experiment. The algorithm output are the probability distributions of a contamination source parameters i.e. its particular location, release rate, speed and direction of the movement, start time and duration. The stochastic approach presented in this paper is completely general and can be used in other fields where the parameters of the model bet fitted to the observable data should be found.

Dendrites are dispensable for basic motoneuron function but essential for fine tuning of behavior.

PubMed

Ryglewski, Stefanie; Kadas, Dimitrios; Hutchinson, Katie; Schuetzler, Natalie; Vonhoff, Fernando; Duch, Carsten

2014-12-16

Dendrites are highly complex 3D structures that define neuronal morphology and connectivity and are the predominant sites for synaptic input. Defects in dendritic structure are highly consistent correlates of brain diseases. However, the precise consequences of dendritic structure defects for neuronal function and behavioral performance remain unknown. Here we probe dendritic function by using genetic tools to selectively abolish dendrites in identified Drosophila wing motoneurons without affecting other neuronal properties. We find that these motoneuron dendrites are unexpectedly dispensable for synaptic targeting, qualitatively normal neuronal activity patterns during behavior, and basic behavioral performance. However, significant performance deficits in sophisticated motor behaviors, such as flight altitude control and switching between discrete courtship song elements, scale with the degree of dendritic defect. To our knowledge, our observations provide the first direct evidence that complex dendrite architecture is critically required for fine-tuning and adaptability within robust, evolutionarily constrained behavioral programs that are vital for mating success and survival. We speculate that the observed scaling of performance deficits with the degree of structural defect is consistent with gradual increases in intellectual disability during continuously advancing structural deficiencies in progressive neurological disorders.

Visual Perceptual Echo Reflects Learning of Regularities in Rapid Luminance Sequences.

PubMed

Chang, Acer Y-C; Schwartzman, David J; VanRullen, Rufin; Kanai, Ryota; Seth, Anil K

2017-08-30

A novel neural signature of active visual processing has recently been described in the form of the "perceptual echo", in which the cross-correlation between a sequence of randomly fluctuating luminance values and occipital electrophysiological signals exhibits a long-lasting periodic (∼100 ms cycle) reverberation of the input stimulus (VanRullen and Macdonald, 2012). As yet, however, the mechanisms underlying the perceptual echo and its function remain unknown. Reasoning that natural visual signals often contain temporally predictable, though nonperiodic features, we hypothesized that the perceptual echo may reflect a periodic process associated with regularity learning. To test this hypothesis, we presented subjects with successive repetitions of a rapid nonperiodic luminance sequence, and examined the effects on the perceptual echo, finding that echo amplitude linearly increased with the number of presentations of a given luminance sequence. These data suggest that the perceptual echo reflects a neural signature of regularity learning.Furthermore, when a set of repeated sequences was followed by a sequence with inverted luminance polarities, the echo amplitude decreased to the same level evoked by a novel stimulus sequence. Crucially, when the original stimulus sequence was re-presented, the echo amplitude returned to a level consistent with the number of presentations of this sequence, indicating that the visual system retained sequence-specific information, for many seconds, even in the presence of intervening visual input. Altogether, our results reveal a previously undiscovered regularity learning mechanism within the human visual system, reflected by the perceptual echo. SIGNIFICANCE STATEMENT How the brain encodes and learns fast-changing but nonperiodic visual input remains unknown, even though such visual input characterizes natural scenes. We investigated whether the phenomenon of "perceptual echo" might index such learning. The perceptual echo is a long-lasting reverberation between a rapidly changing visual input and evoked neural activity, apparent in cross-correlations between occipital EEG and stimulus sequences, peaking in the alpha (∼10 Hz) range. We indeed found that perceptual echo is enhanced by repeatedly presenting the same visual sequence, indicating that the human visual system can rapidly and automatically learn regularities embedded within fast-changing dynamic sequences. These results point to a previously undiscovered regularity learning mechanism, operating at a rate defined by the alpha frequency. Copyright © 2017 the authors 0270-6474/17/378486-12$15.00/0.

Functional dissociation of stimulus intensity encoding and predictive coding of pain in the insula

PubMed Central

Geuter, Stephan; Boll, Sabrina; Eippert, Falk; Büchel, Christian

2017-01-01

The computational principles by which the brain creates a painful experience from nociception are still unknown. Classic theories suggest that cortical regions either reflect stimulus intensity or additive effects of intensity and expectations, respectively. By contrast, predictive coding theories provide a unified framework explaining how perception is shaped by the integration of beliefs about the world with mismatches resulting from the comparison of these beliefs against sensory input. Using functional magnetic resonance imaging during a probabilistic heat pain paradigm, we investigated which computations underlie pain perception. Skin conductance, pupil dilation, and anterior insula responses to cued pain stimuli strictly followed the response patterns hypothesized by the predictive coding model, whereas posterior insula encoded stimulus intensity. This novel functional dissociation of pain processing within the insula together with previously observed alterations in chronic pain offer a novel interpretation of aberrant pain processing as disturbed weighting of predictions and prediction errors. DOI: http://dx.doi.org/10.7554/eLife.24770.001 PMID:28524817

Interplay between population firing stability and single neuron dynamics in hippocampal networks

PubMed Central

Slomowitz, Edden; Styr, Boaz; Vertkin, Irena; Milshtein-Parush, Hila; Nelken, Israel; Slutsky, Michael; Slutsky, Inna

2015-01-01

Neuronal circuits' ability to maintain the delicate balance between stability and flexibility in changing environments is critical for normal neuronal functioning. However, to what extent individual neurons and neuronal populations maintain internal firing properties remains largely unknown. In this study, we show that distributions of spontaneous population firing rates and synchrony are subject to accurate homeostatic control following increase of synaptic inhibition in cultured hippocampal networks. Reduction in firing rate triggered synaptic and intrinsic adaptive responses operating as global homeostatic mechanisms to maintain firing macro-stability, without achieving local homeostasis at the single-neuron level. Adaptive mechanisms, while stabilizing population firing properties, reduced short-term facilitation essential for synaptic discrimination of input patterns. Thus, invariant ongoing population dynamics emerge from intrinsically unstable activity patterns of individual neurons and synapses. The observed differences in the precision of homeostatic control at different spatial scales challenge cell-autonomous theory of network homeostasis and suggest the existence of network-wide regulation rules. DOI: http://dx.doi.org/10.7554/eLife.04378.001 PMID:25556699

A time delay controller for magnetic bearings

NASA Technical Reports Server (NTRS)

Youcef-Toumi, K.; Reddy, S.

1991-01-01

The control of systems with unknown dynamics and unpredictable disturbances has raised some challenging problems. This is particularly important when high system performance needs to be guaranteed at all times. Recently, the Time Delay Control has been suggested as an alternative control scheme. The proposed control system does not require an explicit plant model nor does it depend on the estimation of specific plant parameters. Rather, it combines adaptation with past observations to directly estimate the effect of the plant dynamics. A control law is formulated for a class of dynamic systems and a sufficient condition is presented for control systems stability. The derivation is based on the bounded input-bounded output stability approach using L sub infinity function norms. The control scheme is implemented on a five degrees of freedom high speed and high precision magnetic bearing. The control performance is evaluated using step responses, frequency responses, and disturbance rejection properties. The experimental data show an excellent control performance despite the system complexity.

The Energetics and Physiological Impact of Cohesin Extrusion.

PubMed

Vian, Laura; Pękowska, Aleksandra; Rao, Suhas S P; Kieffer-Kwon, Kyong-Rim; Jung, Seolkyoung; Baranello, Laura; Huang, Su-Chen; El Khattabi, Laila; Dose, Marei; Pruett, Nathanael; Sanborn, Adrian L; Canela, Andres; Maman, Yaakov; Oksanen, Anna; Resch, Wolfgang; Li, Xingwang; Lee, Byoungkoo; Kovalchuk, Alexander L; Tang, Zhonghui; Nelson, Steevenson; Di Pierro, Michele; Cheng, Ryan R; Machol, Ido; St Hilaire, Brian Glenn; Durand, Neva C; Shamim, Muhammad S; Stamenova, Elena K; Onuchic, José N; Ruan, Yijun; Nussenzweig, Andre; Levens, David; Aiden, Erez Lieberman; Casellas, Rafael

2018-05-17

Cohesin extrusion is thought to play a central role in establishing the architecture of mammalian genomes. However, extrusion has not been visualized in vivo, and thus, its functional impact and energetics are unknown. Using ultra-deep Hi-C, we show that loop domains form by a process that requires cohesin ATPases. Once formed, however, loops and compartments are maintained for hours without energy input. Strikingly, without ATP, we observe the emergence of hundreds of CTCF-independent loops that link regulatory DNA. We also identify architectural "stripes," where a loop anchor interacts with entire domains at high frequency. Stripes often tether super-enhancers to cognate promoters, and in B cells, they facilitate Igh transcription and recombination. Stripe anchors represent major hotspots for topoisomerase-mediated lesions, which promote chromosomal translocations and cancer. In plasmacytomas, stripes can deregulate Igh-translocated oncogenes. We propose that higher organisms have coopted cohesin extrusion to enhance transcription and recombination, with implications for tumor development. Copyright © 2018 Elsevier Inc. All rights reserved.

Data mining: childhood injury control and beyond.

PubMed

Tepas, Joseph J

2009-08-01

Data mining is defined as the automatic extraction of useful, often previously unknown information from large databases or data sets. It has become a major part of modern life and is extensively used in industry, banking, government, and health care delivery. The process requires a data collection system that integrates input from multiple sources containing critical elements that define outcomes of interest. Appropriately designed data mining processes identify and adjust for confounding variables. The statistical modeling used to manipulate accumulated data may involve any number of techniques. As predicted results are periodically analyzed against those observed, the model is consistently refined to optimize precision and accuracy. Whether applying integrated sources of clinical data to inferential probabilistic prediction of risk of ventilator-associated pneumonia or population surveillance for signs of bioterrorism, it is essential that modern health care providers have at least a rudimentary understanding of what the concept means, how it basically works, and what it means to current and future health care.

A three-layer model of natural image statistics.

PubMed

Gutmann, Michael U; Hyvärinen, Aapo

2013-11-01

An important property of visual systems is to be simultaneously both selective to specific patterns found in the sensory input and invariant to possible variations. Selectivity and invariance (tolerance) are opposing requirements. It has been suggested that they could be joined by iterating a sequence of elementary selectivity and tolerance computations. It is, however, unknown what should be selected or tolerated at each level of the hierarchy. We approach this issue by learning the computations from natural images. We propose and estimate a probabilistic model of natural images that consists of three processing layers. Two natural image data sets are considered: image patches, and complete visual scenes downsampled to the size of small patches. For both data sets, we find that in the first two layers, simple and complex cell-like computations are performed. In the third layer, we mainly find selectivity to longer contours; for patch data, we further find some selectivity to texture, while for the downsampled complete scenes, some selectivity to curvature is observed. Copyright © 2013 Elsevier Ltd. All rights reserved.

Simulation of Combustion Systems with Realistic g-Jitter

NASA Technical Reports Server (NTRS)

Mell, W. E.; McGrattan, K. B.; Nakamura, Y.; Baum, H. R.

2001-01-01

A number of facilities are available for microgravity combustion experiments: aircraft, drop towers, sounding rockets, the space shuttle, and, in the future, the International Space Station (ISS). Acceleration disturbances or g-jitter about the background level of reduced gravity exist in all these microgravity facilities. While g-jitter is routinely measured, a quantitative comparison of the quality of g-jitter among the different microgravity facilities, in terms of its affects on combustion experiments, has not been compiled. Low frequency g-jitter (< 1 Hz) has been repeatedly observed to disturb a number of combustion systems. Guidelines regarding tolerable levels of acceleration disturbances for combustion experiments have been developed for use in the design of ISS experiments. The validity of these guidelines, however, remains unknown. In this project a transient, 3-D numerical model is under development to simulate the effects of realistic g-jitter on a number of combustion systems. The measured acceleration vector or some representation of it can be used as input to the simulation.

Delta-Isobar Production in the Hard Photodisintegration of a Deuteron

NASA Astrophysics Data System (ADS)

Granados, Carlos; Sargsian, Misak

2010-02-01

Hard photodisintegration of the deuteron in delta-isobar production channels is proposed as a useful process in identifying the quark structure of hadrons and of hadronic interactions at large momentum and energy transfer. The reactions are modeled using the hard re scattering model, HRM, following previous works on hard breakup of a nucleon nucleon (NN) system in light nuclei. Here,quantitative predictions through the HRM require the numerical input of fits of experimental NN hard elastic scattering cross sections. Because of the lack of data in hard NN scattering into δ-isobar channels, the cross section of the corresponding photodisintegration processes cannot be predicted in the same way. Instead, the corresponding NN scattering process is modeled through the quark interchange mechanism, QIM, leaving an unknown normalization parameter. The observables of interest are ratios of differential cross sections of δ-isobar production channels to NN breakup in deuteron photodisintegration. Both entries in these ratios are derived through the HRM and QIM so that normalization parameters cancel out and numerical predictions can be obtained. )

Sensorimotor nucleus NIf is necessary for auditory processing but not vocal motor output in the avian song system.

PubMed

Cardin, Jessica A; Raksin, Jonathan N; Schmidt, Marc F

2005-04-01

Sensorimotor integration in the avian song system is crucial for both learning and maintenance of song, a vocal motor behavior. Although a number of song system areas demonstrate both sensory and motor characteristics, their exact roles in auditory and premotor processing are unclear. In particular, it is unknown whether input from the forebrain nucleus interface of the nidopallium (NIf), which exhibits both sensory and premotor activity, is necessary for both auditory and premotor processing in its target, HVC. Here we show that bilateral NIf lesions result in long-term loss of HVC auditory activity but do not impair song production. NIf is thus a major source of auditory input to HVC, but an intact NIf is not necessary for motor output in adult zebra finches.

Dual control and prevention of the turn-off phenomenon in a class of mimo systems

NASA Technical Reports Server (NTRS)

Mookerjee, P.; Bar-Shalom, Y.; Molusis, J. A.

1985-01-01

A recently developed methodology of adaptive dual control based upon sensitivity functions is applied here to a multivariable input-output model. The plant has constant but unknown parameters. It represents a simplified linear version of the relationship between the vibration output and the higher harmonic control input for a helicopter. The cautious and the new dual controller are examined. In many instances, the cautious controller is seen to turn off. The new dual controller modifies the cautious control design by numerator and denominator correction terms which depend upon the sensitivity functions of the expected future cost and avoids the turn-off and burst phenomena. Monte Carlo simulations and statistical tests of significance indicate the superiority of the dual controller over the cautious and the heuristic certainity equivalence controllers.

A Stochastic Total Least Squares Solution of Adaptive Filtering Problem

PubMed Central

Ahmad, Noor Atinah

2014-01-01

An efficient and computationally linear algorithm is derived for total least squares solution of adaptive filtering problem, when both input and output signals are contaminated by noise. The proposed total least mean squares (TLMS) algorithm is designed by recursively computing an optimal solution of adaptive TLS problem by minimizing instantaneous value of weighted cost function. Convergence analysis of the algorithm is given to show the global convergence of the proposed algorithm, provided that the stepsize parameter is appropriately chosen. The TLMS algorithm is computationally simpler than the other TLS algorithms and demonstrates a better performance as compared with the least mean square (LMS) and normalized least mean square (NLMS) algorithms. It provides minimum mean square deviation by exhibiting better convergence in misalignment for unknown system identification under noisy inputs. PMID:24688412

Polynomial-Time Approximation Algorithm for the Problem of Cardinality-Weighted Variance-Based 2-Clustering with a Given Center

NASA Astrophysics Data System (ADS)

Kel'manov, A. V.; Motkova, A. V.

2018-01-01

A strongly NP-hard problem of partitioning a finite set of points of Euclidean space into two clusters is considered. The solution criterion is the minimum of the sum (over both clusters) of weighted sums of squared distances from the elements of each cluster to its geometric center. The weights of the sums are equal to the cardinalities of the desired clusters. The center of one cluster is given as input, while the center of the other is unknown and is determined as the point of space equal to the mean of the cluster elements. A version of the problem is analyzed in which the cardinalities of the clusters are given as input. A polynomial-time 2-approximation algorithm for solving the problem is constructed.

Methods in Computational Neuroscience Course: Student Project Descriptions

DTIC Science & Technology

1989-09-02

form of a "peri Geniculate Nucleus"). Currently, for some yet unknown reason (probably the lateral inhibition) layer 6 shows symmetrical end-inhibition...40 neurons each) in the inferior colliculus served as inputs to a sheet of 100 cells within the medial geniculate body where combination sensitivity is...tertiary dendritic function in the bushy cells, as well as lateral inhibition in the AVCN stellate cells yielded the results that feedback inhibition

Optical study of interactions among propagation waves of neural excitation in the rat somatosensory cortex evoked by forelimb and hindlimb stimuli.

PubMed

Hama, Noriyuki; Kawai, Minako; Ito, Shin-Ichi; Hirota, Akihiko

2018-05-01

Multisite optical recording has revealed that the neural excitation wave induced by a sensory stimulation begins at a focus and propagates in the cortex. This wave is considered to be important for computation in the sensory cortex, particularly the integration of sensory information; however, the nature of this wave remains largely unknown. In the present study, we examined the interaction between two waves in the rat sensory cortex induced by hindlimb and forelimb stimuli with different interstimulus intervals. We classified the resultant patterns as follows: 1) the collision of two waves, 2) the hindlimb response being evoked while the forelimb-induced wave is passing the hindlimb focus, and 3) the hindlimb response being evoked after the forelimb-induced wave has passed the hindlimb focus. In pattern 1, the two waves fused into a single wave, but the propagation pattern differed from that predicted by the superimposition of two singly induced propagation courses. In pattern 2, the state of the interaction between the two waves varied depending on the phase of optical signals constituting the forelimb-induced wave around the hindlimb focus. Although no hindlimb-induced wave was observed in the rising phase, the propagating velocity of the forelimb-induced wave increased. At the peak, neither the hindlimb-induced response nor a modulatory effect on the forelimb-induced wave was detected. In pattern 3, the hindlimb-induced wave showed a reduced amplitude and spatial extent. These results indicate that the state of the interaction between waves was strongly influenced by the relative timing of sensory inputs. NEW & NOTEWORTHY Sensory stimulation-induced cortical excitation propagates as a wave and spreads over a wide area of the sensory cortex. To elucidate the characteristics of this relatively unknown phenomenon, we examined the interaction between two individually induced waves in the somatosensory cortex. Either the waves collided or the preceding wave affected the emergence of the following one. Our results indicate that the state of the interaction was strongly influenced by the relative timing of sensory inputs.

Effect of heat input on dissimilar welds of ultra high strength steel and duplex stainless steel: Microstructural and compositional analysis

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

Tasalloti, H., E-mail: hamed.tasalloti.kashani@stu

The effect of heat input on the microstructure and compositional heterogeneity of welds of direct-quenched ultra high strength steel (Optim 960 QC) and duplex stainless steel (UNS S32205) was studied. The dissimilar welds were made using GMAW with a fully austenitic filler wire. In addition to grain coarsening in the heat affected zone (HAZ) of the ferritic side, it was found that an increase in heat input correlatively increased the proportional volume of bainitic to martensitic phases. Coarse ferritic grains were observed in the duplex HAZ. Higher heat input, however, had a beneficial effect on the nucleation of austenite inmore » the HAZ. Heat input had a regulatory effect on grain growth within the austenitic weld and more favorable equiaxed austenite was obtained with higher heat input. On the ferritic side of the welds, macrosegregation in the form of a martensitic intermediate zone was observed for all the cooling rates studied. However, on the duplex side, macrosegregation in the fusion boundary was only noticed with higher cooling rates. Microstructural observations and compositional analysis suggest that higher heat input could be beneficial for the structural integrity of the weld despite higher heat input increasing the extent of adverse coarse grains in the HAZ, especially on the ferritic side. - Highlights: •The effect of heat input on dissimilar welds of UHSS and DSS was studied. •Transmutation of the microstructure was discussed in detail. •The influence of heat input on compositional heterogeneity of welds was described. •Higher heat input enhanced bainitic transformation on the ferritic side. •Macrosegregation was affected by the amount of heat input on the DSS side.« less

Olfactory Bulb Deep Short-Axon Cells Mediate Widespread Inhibition of Tufted Cell Apical Dendrites

PubMed Central

LaRocca, Greg

2017-01-01

In the main olfactory bulb (MOB), the first station of sensory processing in the olfactory system, GABAergic interneuron signaling shapes principal neuron activity to regulate olfaction. However, a lack of known selective markers for MOB interneurons has strongly impeded cell-type-selective investigation of interneuron function. Here, we identify the first selective marker of glomerular layer-projecting deep short-axon cells (GL-dSACs) and investigate systematically the structure, abundance, intrinsic physiology, feedforward sensory input, neuromodulation, synaptic output, and functional role of GL-dSACs in the mouse MOB circuit. GL-dSACs are located in the internal plexiform layer, where they integrate centrifugal cholinergic input with highly convergent feedforward sensory input. GL-dSAC axons arborize extensively across the glomerular layer to provide highly divergent yet selective output onto interneurons and principal tufted cells. GL-dSACs are thus capable of shifting the balance of principal tufted versus mitral cell activity across large expanses of the MOB in response to diverse sensory and top-down neuromodulatory input. SIGNIFICANCE STATEMENT The identification of cell-type-selective molecular markers has fostered tremendous insight into how distinct interneurons shape sensory processing and behavior. In the main olfactory bulb (MOB), inhibitory circuits regulate the activity of principal cells precisely to drive olfactory-guided behavior. However, selective markers for MOB interneurons remain largely unknown, limiting mechanistic understanding of olfaction. Here, we identify the first selective marker of a novel population of deep short-axon cell interneurons with superficial axonal projections to the sensory input layer of the MOB. Using this marker, together with immunohistochemistry, acute slice electrophysiology, and optogenetic circuit mapping, we reveal that this novel interneuron population integrates centrifugal cholinergic input with broadly tuned feedforward sensory input to modulate principal cell activity selectively. PMID:28003347

An efficient and flexible Abel-inversion method for noisy data

NASA Astrophysics Data System (ADS)

Antokhin, Igor I.

2016-12-01

We propose an efficient and flexible method for solving the Abel integral equation of the first kind, frequently appearing in many fields of astrophysics, physics, chemistry, and applied sciences. This equation represents an ill-posed problem, thus solving it requires some kind of regularization. Our method is based on solving the equation on a so-called compact set of functions and/or using Tikhonov's regularization. A priori constraints on the unknown function, defining a compact set, are very loose and can be set using simple physical considerations. Tikhonov's regularization in itself does not require any explicit a priori constraints on the unknown function and can be used independently of such constraints or in combination with them. Various target degrees of smoothness of the unknown function may be set, as required by the problem at hand. The advantage of the method, apart from its flexibility, is that it gives uniform convergence of the approximate solution to the exact solution, as the errors of input data tend to zero. The method is illustrated on several simulated models with known solutions. An example of astrophysical application of the method is also given.

Adaptive iterative learning control of a class of nonlinear time-delay systems with unknown backlash-like hysteresis input and control direction.

PubMed

Wei, Jianming; Zhang, Youan; Sun, Meimei; Geng, Baoliang

2017-09-01

This paper presents an adaptive iterative learning control scheme for a class of nonlinear systems with unknown time-varying delays and control direction preceded by unknown nonlinear backlash-like hysteresis. Boundary layer function is introduced to construct an auxiliary error variable, which relaxes the identical initial condition assumption of iterative learning control. For the controller design, integral Lyapunov function candidate is used, which avoids the possible singularity problem by introducing hyperbolic tangent funciton. After compensating for uncertainties with time-varying delays by combining appropriate Lyapunov-Krasovskii function with Young's inequality, an adaptive iterative learning control scheme is designed through neural approximation technique and Nussbaum function method. On the basis of the hyperbolic tangent function's characteristics, the system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapunov-like composite energy function (CEF) in two cases, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Should nevirapine be used to prevent mother-to-child transmission of HIV among women of unknown serostatus?

PubMed Central

Sint, Tin Tin; Dabis, François; Kamenga, Claude; Shaffer, Nathan; de Zoysa, Isabelle F.

2005-01-01

At present, HIV testing and counselling during pregnancy represent the key entry point for women to learn their serostatus and for them to access, if they are HIV-positive, specific interventions to reduce mother-to-child transmission (MTCT) of HIV. However, the provision and uptake of testing and counselling services are inadequate, and many pregnant women in countries most affected by the HIV/AIDS epidemic remain unaware of their HIV status. The offer of single-dose nevirapine prophylaxis to women whose HIV status is unknown at the time of delivery has been proposed to circumvent these problems in high-prevalence settings. The potential advantages and disadvantages of three different programme approaches are considered: targeted programmes in which antiretroviral drugs are offered only to women who are known to be HIV-positive; combined programmes in which nevirapine prophylaxis is offered to women whose serostatus remains unknown at the time of delivery despite targeted programme inputs; and universal nevirapine prophylaxis programmes in which HIV testing and counselling are not available and all pregnant women, regardless of their serostatus, are offered nevirapine prophylaxis. PMID:15798847

Neuromodulation to the Rescue: Compensation of Temperature-Induced Breakdown of Rhythmic Motor Patterns via Extrinsic Neuromodulatory Input

PubMed Central

Städele, Carola; Heigele, Stefanie; Stein, Wolfgang

2015-01-01

Stable rhythmic neural activity depends on the well-coordinated interplay of synaptic and cell-intrinsic conductances. Since all biophysical processes are temperature dependent, this interplay is challenged during temperature fluctuations. How the nervous system remains functional during temperature perturbations remains mostly unknown. We present a hitherto unknown mechanism of how temperature-induced changes in neural networks are compensated by changing their neuromodulatory state: activation of neuromodulatory pathways establishes a dynamic coregulation of synaptic and intrinsic conductances with opposing effects on neuronal activity when temperature changes, hence rescuing neuronal activity. Using the well-studied gastric mill pattern generator of the crab, we show that modest temperature increase can abolish rhythmic activity in isolated neural circuits due to increased leak currents in rhythm-generating neurons. Dynamic clamp-mediated addition of leak currents was sufficient to stop neuronal oscillations at low temperatures, and subtraction of additional leak currents at elevated temperatures was sufficient to rescue the rhythm. Despite the apparent sensitivity of the isolated nervous system to temperature fluctuations, the rhythm could be stabilized by activating extrinsic neuromodulatory inputs from descending projection neurons, a strategy that we indeed found to be implemented in intact animals. In the isolated nervous system, temperature compensation was achieved by stronger extrinsic neuromodulatory input from projection neurons or by augmenting projection neuron influence via bath application of the peptide cotransmitter Cancer borealis tachykinin-related peptide Ia (CabTRP Ia). CabTRP Ia activates the modulator-induced current IMI (a nonlinear voltage-gated inward current) that effectively acted as a negative leak current and counterbalanced the temperature-induced leak to rescue neuronal oscillations. Computational modelling revealed the ability of IMI to reduce detrimental leak-current influences on neuronal networks over a broad conductance range and indicated that leak and IMI are closely coregulated in the biological system to enable stable motor patterns. In conclusion, these results show that temperature compensation does not need to be implemented within the network itself but can be conditionally provided by extrinsic neuromodulatory input that counterbalances temperature-induced modifications of circuit-intrinsic properties. PMID:26417944

Constraining the Fore-Arc Flux Along the Central America Margin

NASA Astrophysics Data System (ADS)

Hilton, D. R.; Barry, P. H.; Ramirez, C. J.; Kulongoski, J. T.; Patel, B. S.; Blackmon, K.

2014-12-01

The transport of carbon to the deep mantle via subduction zones is interrupted by outputs via the fore-arc, volcanic front, and back-arc regions. Whereas output fluxes for the front and back-arc locales are well constrained for Central America (CA) [1], the fore-arc flux via cold seeps and groundwaters is virtually unknown. We present new He and CO2 data for the inner fore-arc of Costa Rica and western Panama to complement our study [2] of offshore CO2fluxes on the outer-forearc. On the Nicoya Peninsula, the Costa Rica Pacific coastline (including the Oso Peninsula) and the Talamanca Mountain Range, as well as coastal seeps in Panama, coupled CO2-He studies allow recognition of mantle (3He/4He up to 6RA) and crustal inputs to the volatile inventory. We associate the crustal component with CO2 derived from limestone (L) and organic sediments (S) on the subducting slab, and see a decrease in the L/S ratio trench-ward with the lowest values akin to those of diatomaceous ooze in the uppermost sequence of the subducting sediment package. This observation is consistent with the removal of the uppermost organic-rich sediment from deep subduction by under-plating. As the input carbon fluxes of the individual sedimentary layers are well constrained [3], we can limit the potential steady-state flux of carbon loss at the subaerial fore-arc to ~ 6 × 107 gCkm-1yr-1, equivalent to ~88% of the input flux of C associated with the ooze, or <4% of the total incoming sedimentary C. This study confirms that the greatest loss of slab-derived carbon at the CA margin occurs at the volcanic front with recycling efficiencies between 12% (Costa Rica) and 29% (El Salvador) of the sedimentary input [1]. It also demonstrates the utility of the coupled He-CO2approach for mass balance studies at subduction zones. [1] De Leeuw et al., EPSL, 2007; [2] Furi et al., G-cubed, 2010; [3] Li and Bebout, JGR, 2005.

Integration of Canal and Otolith Inputs by Central Vestibular Neurons Is Subadditive for Both Active and Passive Self-Motion: Implication for Perception

PubMed Central

Carriot, Jerome; Jamali, Mohsen; Brooks, Jessica X.

2015-01-01

Traditionally, the neural encoding of vestibular information is studied by applying either passive rotations or translations in isolation. However, natural vestibular stimuli are typically more complex. During everyday life, our self-motion is generally not restricted to one dimension, but rather comprises both rotational and translational motion that will simultaneously stimulate receptors in the semicircular canals and otoliths. In addition, natural self-motion is the result of self-generated and externally generated movements. However, to date, it remains unknown how information about rotational and translational components of self-motion is integrated by vestibular pathways during active and/or passive motion. Accordingly, here, we compared the responses of neurons at the first central stage of vestibular processing to rotation, translation, and combined motion. Recordings were made in alert macaques from neurons in the vestibular nuclei involved in postural control and self-motion perception. In response to passive stimulation, neurons did not combine canal and otolith afferent information linearly. Instead, inputs were subadditively integrated with a weighting that was frequency dependent. Although canal inputs were more heavily weighted at low frequencies, the weighting of otolith input increased with frequency. In response to active stimulation, neuronal modulation was significantly attenuated (∼70%) relative to passive stimulation for rotations and translations and even more profoundly attenuated for combined motion due to subadditive input integration. Together, these findings provide insights into neural computations underlying the integration of semicircular canal and otolith inputs required for accurate posture and motor control, as well as perceptual stability, during everyday life. PMID:25716854

Sliding Mode Observer-Based Current Sensor Fault Reconstruction and Unknown Load Disturbance Estimation for PMSM Driven System.

PubMed

Zhao, Kaihui; Li, Peng; Zhang, Changfan; Li, Xiangfei; He, Jing; Lin, Yuliang

2017-12-06

This paper proposes a new scheme of reconstructing current sensor faults and estimating unknown load disturbance for a permanent magnet synchronous motor (PMSM)-driven system. First, the original PMSM system is transformed into two subsystems; the first subsystem has unknown system load disturbances, which are unrelated to sensor faults, and the second subsystem has sensor faults, but is free from unknown load disturbances. Introducing a new state variable, the augmented subsystem that has sensor faults can be transformed into having actuator faults. Second, two sliding mode observers (SMOs) are designed: the unknown load disturbance is estimated by the first SMO in the subsystem, which has unknown load disturbance, and the sensor faults can be reconstructed using the second SMO in the augmented subsystem, which has sensor faults. The gains of the proposed SMOs and their stability analysis are developed via the solution of linear matrix inequality (LMI). Finally, the effectiveness of the proposed scheme was verified by simulations and experiments. The results demonstrate that the proposed scheme can reconstruct current sensor faults and estimate unknown load disturbance for the PMSM-driven system.

Adaptive Fault-Tolerant Control of Uncertain Nonlinear Large-Scale Systems With Unknown Dead Zone.

PubMed

Chen, Mou; Tao, Gang

2016-08-01

In this paper, an adaptive neural fault-tolerant control scheme is proposed and analyzed for a class of uncertain nonlinear large-scale systems with unknown dead zone and external disturbances. To tackle the unknown nonlinear interaction functions in the large-scale system, the radial basis function neural network (RBFNN) is employed to approximate them. To further handle the unknown approximation errors and the effects of the unknown dead zone and external disturbances, integrated as the compounded disturbances, the corresponding disturbance observers are developed for their estimations. Based on the outputs of the RBFNN and the disturbance observer, the adaptive neural fault-tolerant control scheme is designed for uncertain nonlinear large-scale systems by using a decentralized backstepping technique. The closed-loop stability of the adaptive control system is rigorously proved via Lyapunov analysis and the satisfactory tracking performance is achieved under the integrated effects of unknown dead zone, actuator fault, and unknown external disturbances. Simulation results of a mass-spring-damper system are given to illustrate the effectiveness of the proposed adaptive neural fault-tolerant control scheme for uncertain nonlinear large-scale systems.

Variable input observer for state estimation of high-rate dynamics

NASA Astrophysics Data System (ADS)

Hong, Jonathan; Cao, Liang; Laflamme, Simon; Dodson, Jacob

2017-04-01

High-rate systems operating in the 10 μs to 10 ms timescale are likely to experience damaging effects due to rapid environmental changes (e.g., turbulence, ballistic impact). Some of these systems could benefit from real-time state estimation to enable their full potential. Examples of such systems include blast mitigation strategies, automotive airbag technologies, and hypersonic vehicles. Particular challenges in high-rate state estimation include: 1) complex time varying nonlinearities of system (e.g. noise, uncertainty, and disturbance); 2) rapid environmental changes; 3) requirement of high convergence rate. Here, we propose using a Variable Input Observer (VIO) concept to vary the input space as the event unfolds. When systems experience high-rate dynamics, rapid changes in the system occur. To investigate the VIO's potential, a VIO-based neuro-observer is constructed and studied using experimental data collected from a laboratory impact test. Results demonstrate that the input space is unique to different impact conditions, and that adjusting the input space throughout the dynamic event produces better estimations than using a traditional fixed input space strategy.

Structure and Composition of Isolated Core-Shell (In ,Ga )N /GaN Rods Based on Nanofocus X-Ray Diffraction and Scanning Transmission Electron Microscopy

NASA Astrophysics Data System (ADS)

Krause, Thilo; Hanke, Michael; Nicolai, Lars; Cheng, Zongzhe; Niehle, Michael; Trampert, Achim; Kahnt, Maik; Falkenberg, Gerald; Schroer, Christian G.; Hartmann, Jana; Zhou, Hao; Wehmann, Hergo-Heinrich; Waag, Andreas

2017-02-01

Nanofocus x-ray diffraction is used to investigate the structure and local strain field of an isolated (In ,Ga )N /GaN core-shell microrod. Because the high spatial resolution of the x-ray beam is only 80 ×90 nm2, we are able to investigate several distinct volumes on one individual side facet. Here, we find a drastic increase in thickness of the outer GaN shell along the rod height. Additionally, we performed high-angle annular dark-field scanning-transmission-electron-microscopy measurements on several rods from the same sample showing that (In,Ga)N double-quantum-well and GaN barrier thicknesses also increase strongly along the height. Moreover, plastic relaxation is observed in the top part of the rod. Based on the experimentally obtained structural parameters, we simulate the strain-induced deformation using the finite-element method, which serves as the input for subsequent kinematic scattering simulations. The simulations reveal a significant increase of elastic in-plane relaxation along the rod height. However, at a certain height, the occurrence of plastic relaxation yields a decrease of the elastic strain. Because of the experimentally obtained structural input for the finite-element simulations, we can exclude unknown structural influences on the strain distribution, and we are able to translate the elastic relaxation into an indium concentration which increases by a factor of 4 from the bottom to the height where plastic relaxation occurs.

Syntax-induced pattern deafness

PubMed Central

Endress, Ansgar D.; Hauser, Marc D.

2009-01-01

Perceptual systems often force systematically biased interpretations upon sensory input. These interpretations are obligatory, inaccessible to conscious control, and prevent observers from perceiving alternative percepts. Here we report a similarly impenetrable phenomenon in the domain of language, where the syntactic system prevents listeners from detecting a simple perceptual pattern. Healthy human adults listened to three-word sequences conforming to patterns readily learned even by honeybees, rats, and sleeping human neonates. Specifically, sequences either started or ended with two words from the same syntactic category (e.g., noun–noun–verb or verb–verb–noun). Although participants readily processed the categories and learned repetition patterns over nonsyntactic categories (e.g., animal–animal–clothes), they failed to learn the repetition pattern over syntactic categories, even when explicitly instructed to look for it. Further experiments revealed that participants successfully learned the repetition patterns only when they were consistent with syntactically possible structures, irrespective of whether these structures were attested in English or in other languages unknown to the participants. When the repetition patterns did not match such syntactically possible structures, participants failed to learn them. Our results suggest that when human adults hear a string of nouns and verbs, their syntactic system obligatorily attempts an interpretation (e.g., in terms of subjects, objects, and predicates). As a result, subjects fail to perceive the simpler pattern of repetitions—a form of syntax-induced pattern deafness that is reminiscent of how other perceptual systems force specific interpretations upon sensory input. PMID:19920182

High visual resolution matters in audiovisual speech perception, but only for some.

PubMed

Alsius, Agnès; Wayne, Rachel V; Paré, Martin; Munhall, Kevin G

2016-07-01

The basis for individual differences in the degree to which visual speech input enhances comprehension of acoustically degraded speech is largely unknown. Previous research indicates that fine facial detail is not critical for visual enhancement when auditory information is available; however, these studies did not examine individual differences in ability to make use of fine facial detail in relation to audiovisual speech perception ability. Here, we compare participants based on their ability to benefit from visual speech information in the presence of an auditory signal degraded with noise, modulating the resolution of the visual signal through low-pass spatial frequency filtering and monitoring gaze behavior. Participants who benefited most from the addition of visual information (high visual gain) were more adversely affected by the removal of high spatial frequency information, compared to participants with low visual gain, for materials with both poor and rich contextual cues (i.e., words and sentences, respectively). Differences as a function of gaze behavior between participants with the highest and lowest visual gains were observed only for words, with participants with the highest visual gain fixating longer on the mouth region. Our results indicate that the individual variance in audiovisual speech in noise performance can be accounted for, in part, by better use of fine facial detail information extracted from the visual signal and increased fixation on mouth regions for short stimuli. Thus, for some, audiovisual speech perception may suffer when the visual input (in addition to the auditory signal) is less than perfect.

Lateral Spread of Orientation Selectivity in V1 is Controlled by Intracortical Cooperativity

PubMed Central

Chavane, Frédéric; Sharon, Dahlia; Jancke, Dirk; Marre, Olivier; Frégnac, Yves; Grinvald, Amiram

2011-01-01

Neurons in the primary visual cortex receive subliminal information originating from the periphery of their receptive fields (RF) through a variety of cortical connections. In the cat primary visual cortex, long-range horizontal axons have been reported to preferentially bind to distant columns of similar orientation preferences, whereas feedback connections from higher visual areas provide a more diverse functional input. To understand the role of these lateral interactions, it is crucial to characterize their effective functional connectivity and tuning properties. However, the overall functional impact of cortical lateral connections, whatever their anatomical origin, is unknown since it has never been directly characterized. Using direct measurements of postsynaptic integration in cat areas 17 and 18, we performed multi-scale assessments of the functional impact of visually driven lateral networks. Voltage-sensitive dye imaging showed that local oriented stimuli evoke an orientation-selective activity that remains confined to the cortical feedforward imprint of the stimulus. Beyond a distance of one hypercolumn, the lateral spread of cortical activity gradually lost its orientation preference approximated as an exponential with a space constant of about 1 mm. Intracellular recordings showed that this loss of orientation selectivity arises from the diversity of converging synaptic input patterns originating from outside the classical RF. In contrast, when the stimulus size was increased, we observed orientation-selective spread of activation beyond the feedforward imprint. We conclude that stimulus-induced cooperativity enhances the long-range orientation-selective spread. PMID:21629708

Long-term Priming-induced Changes in Permafrost Soil Organic Matter Decomposition

NASA Astrophysics Data System (ADS)

Pegoraro, E.; Bracho, R. G.; Schuur, E.

2016-12-01

Warming of tundra ecosystems due to climate change is predicted to thaw permafrost and increase plant biomass and litter input to soil. Additional input of easily decomposable carbon can stimulate microbial activity, consequently increasing soil organic matter decomposition rates. This phenomenon, known as the priming effect, can exacerbate the effects of climate change by releasing more CO2 from permafrost soils; however, the extent to which it could decrease soil carbon stocks in the Arctic is unknown. Most priming incubation studies are conducted for a short period of time, making it difficult to assess if priming is a short-term phenomenon, or could persist over the long-term. We incubated permafrost soil from a moist acidic tundra site in Healy, Alaska for 456 days at 15° C. Soil from surface and deep layers were amended with three pulses of uniformly 13C labeled glucose, a fast decomposing substrate, every 152 days. We also quantified the proportion of old carbon respired by measuring 14CO2. Substrate addition resulted in higher respiration rates in glucose amended soils; however, positive priming was only observed in deep layers, where on average 9%, 57%, and 25% more soil-derived C was respired at 45-55, 65-75, and 75-85 cm depth increments for the duration of the experiment. This suggests that microbes in deep layers are limited in energy, and the addition of easily decomposable carbon increases native soil organic matter decomposition.

Input clustering in the normal and learned circuits of adult barn owls.

PubMed

McBride, Thomas J; DeBello, William M

2015-05-01

Experience-dependent formation of synaptic input clusters can occur in juvenile brains. Whether this also occurs in adults is largely unknown. We previously reconstructed the normal and learned circuits of prism-adapted barn owls and found that changes in clustering of axo-dendritic contacts (putative synapses) predicted functional circuit strength. Here we asked whether comparable changes occurred in normal and prism-removed adults. Across all anatomical zones, no systematic differences in the primary metrics for within-branch or between-branch clustering were observed: 95-99% of contacts resided within clusters (<10-20 μm from nearest neighbor) regardless of circuit strength. Bouton volumes, a proxy measure of synaptic strength, were on average larger in the functionally strong zones, indicating that changes in synaptic efficacy contributed to the differences in circuit strength. Bootstrap analysis showed that the distribution of inter-contact distances strongly deviated from random not in the functionally strong zones but in those that had been strong during the sensitive period (60-250 d), indicating that clusters formed early in life were preserved regardless of current value. While cluster formation in juveniles appeared to require the production of new synapses, cluster formation in adults did not. In total, these results support a model in which high cluster dynamics in juveniles sculpt a potential connectivity map that is refined in adulthood. We propose that preservation of clusters in functionally weak adult circuits provides a storage mechanism for disused but potentially useful pathways. Copyright © 2015 Elsevier Inc. All rights reserved.

Effects of Extreme Events on Arsenic Cycling in Salt Marshes

NASA Astrophysics Data System (ADS)

Northrup, Kristy; Capooci, Margaret; Seyfferth, Angelia L.

2018-03-01

Extreme events such as storm surges, intense precipitation, and supermoons cause anomalous and large fluctuations in water level in tidal salt marshes, which impacts the sediment biogeochemistry that dictates arsenic (As) cycling. In addition to changes in water level, which impacts soil redox potential, these extreme events may also change salinity due to freshwater inputs from precipitation or saltwater inputs due to surge. It is currently unknown how As mobility in tidal salt marshes will be impacted by extreme events, as fluctuations in salinity and redox potential may act synergistically to mobilize As. To investigate impacts of extreme events on As cycling in tidal salt marshes, we conducted a combined laboratory and field investigation. We monitored pore water and soil samples before, during, and after two extreme events: a supermoon lunar eclipse followed by a storm surge and precipitation induced by Hurricane Joaquin in fall 2015 at the St. Jones Reserve in Dover, Delaware, a representative tidal salt marsh in the Mid-Atlantic United States. We also conducted soil incubations of marsh sediments in batch and in flow-through experiments in which redox potential and/or salinity were manipulated. Field investigations showed that pore water As was inversely proportional to redox potential. During the extreme events, a distinct pulse of As was observed in the pore water with maximum salinity. Combined field and laboratory investigations revealed that this As pulse is likely due to rapid changes in salinity. These results have implications for As mobility in the face of extreme weather variability.

Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering.

PubMed

Havlicek, Martin; Friston, Karl J; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D

2011-06-15

This paper presents a new approach to inverting (fitting) models of coupled dynamical systems based on state-of-the-art (cubature) Kalman filtering. Crucially, this inversion furnishes posterior estimates of both the hidden states and parameters of a system, including any unknown exogenous input. Because the underlying generative model is formulated in continuous time (with a discrete observation process) it can be applied to a wide variety of models specified with either ordinary or stochastic differential equations. These are an important class of models that are particularly appropriate for biological time-series, where the underlying system is specified in terms of kinetics or dynamics (i.e., dynamic causal models). We provide comparative evaluations with generalized Bayesian filtering (dynamic expectation maximization) and demonstrate marked improvements in accuracy and computational efficiency. We compare the schemes using a series of difficult (nonlinear) toy examples and conclude with a special focus on hemodynamic models of evoked brain responses in fMRI. Our scheme promises to provide a significant advance in characterizing the functional architectures of distributed neuronal systems, even in the absence of known exogenous (experimental) input; e.g., resting state fMRI studies and spontaneous fluctuations in electrophysiological studies. Importantly, unlike current Bayesian filters (e.g. DEM), our scheme provides estimates of time-varying parameters, which we will exploit in future work on the adaptation and enabling of connections in the brain. Copyright © 2011 Elsevier Inc. All rights reserved.

Global identifiability of linear compartmental models--a computer algebra algorithm.

PubMed

Audoly, S; D'Angiò, L; Saccomani, M P; Cobelli, C

1998-01-01

A priori global identifiability deals with the uniqueness of the solution for the unknown parameters of a model and is, thus, a prerequisite for parameter estimation of biological dynamic models. Global identifiability is however difficult to test, since it requires solving a system of algebraic nonlinear equations which increases both in nonlinearity degree and number of terms and unknowns with increasing model order. In this paper, a computer algebra tool, GLOBI (GLOBal Identifiability) is presented, which combines the topological transfer function method with the Buchberger algorithm, to test global identifiability of linear compartmental models. GLOBI allows for the automatic testing of a priori global identifiability of general structure compartmental models from general multi input-multi output experiments. Examples of usage of GLOBI to analyze a priori global identifiability of some complex biological compartmental models are provided.

A Report on Applying EEGnet to Discriminate Human State Effects on Task Performance

DTIC Science & Technology

2018-01-01

whether we could identify what task the participant was performing from differences in the recorded brain time series . We modeled the relationship...between input data (brain time series ) and output labels (task A and task B) as an unknown function, and we found an optimal approximation of that...this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. Citation of

Application of AI techniques to infer vegetation characteristics from directional reflectance(s)

NASA Technical Reports Server (NTRS)

Kimes, D. S.; Smith, J. A.; Harrison, P. A.; Harrison, P. R.

1994-01-01

Traditionally, the remote sensing community has relied totally on spectral knowledge to extract vegetation characteristics. However, there are other knowledge bases (KB's) that can be used to significantly improve the accuracy and robustness of inference techniques. Using AI (artificial intelligence) techniques a KB system (VEG) was developed that integrates input spectral measurements with diverse KB's. These KB's consist of data sets of directional reflectance measurements, knowledge from literature, and knowledge from experts which are combined into an intelligent and efficient system for making vegetation inferences. VEG accepts spectral data of an unknown target as input, determines the best techniques for inferring the desired vegetation characteristic(s), applies the techniques to the target data, and provides a rigorous estimate of the accuracy of the inference. VEG was developed to: infer spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles; infer percent ground cover from any combination of nadir and/or off-nadir view angles; infer unknown view angle(s) from known view angle(s) (known as view angle extension); and discriminate between user defined vegetation classes using spectral and directional reflectance relationships developed from an automated learning algorithm. The errors for these techniques were generally very good ranging between 2 to 15% (proportional root mean square). The system is designed to aid scientists in developing, testing, and applying new inference techniques using directional reflectance data.

Significance of microbial asynchronous anabolism to soil carbon dynamics driven by litter inputs

PubMed Central

Fan, Zhaosheng; Liang, Chao

2015-01-01

Soil organic carbon (SOC) plays an important role in the global carbon cycle. However, it remains largely unknown how plant litter inputs impact magnitude, composition and source configuration of the SOC stocks over long term through microbial catabolism and anabolism, mostly due to uncoupled research on litter decomposition and SOC formation. This limits our ability to predict soil system responses to changes in land-use and climate. Here, we examine how microbes act as a valve controlling carbon sequestrated from plant litters versus released to the atmosphere in natural ecosystems amended with plant litters varying in quantity and quality. We find that litter quality – not quantity – regulates long-term SOC dynamics under different plausible scenarios. Long-term changes in bulk SOC stock occur only when the quality of carbon inputs causes asynchronous change in a microbial physiological trait, defined as “microbial biosynthesis acceleration” (MBA). This is the first theoretical demonstration that the response of the SOC stocks to litter inputs is critically determined by the microbial physiology. Our work suggests that total SOC at an equilibrium state may be an intrinsic property of a given ecosystem, which ultimately is controlled by the asynchronous MBA between microbial functional groups. PMID:25849864

Significance of microbial asynchronous anabolism to soil carbon dynamics driven by litter inputs

DOE PAGES

Fan, Zhaosheng; Liang, Chao

2015-04-02

Soil organic carbon (SOC) plays an important role in the global carbon cycle. However, it remains largely unknown how plant litter inputs impact magnitude, composition and source configuration of the SOC stocks over long term through microbial catabolism and anabolism, mostly due to uncoupled research on litter decomposition and SOC formation. This limits our ability to predict soil system responses to changes in land-use and climate. Here, we examine how microbes act as a valve controlling carbon sequestrated from plant litters versus released to the atmosphere in natural ecosystems amended with plant litters varying in quantity and quality. We findmore » that litter quality – not quantity – regulates long-term SOC dynamics under different plausible scenarios. Long-term changes in bulk SOC stock occur only when the quality of carbon inputs causes asynchronous change in a microbial physiological trait, defined as ‘‘microbial biosynthesis acceleration’’ (MBA). This is the first theoretical demonstration that the response of the SOC stocks to litter inputs is critically determined by the microbial physiology. Our work suggests that total SOC at an equilibrium state may be an intrinsic property of a given ecosystem, which ultimately is controlled by the asynchronous MBA between microbial functional groups.« less

Analyzing the sensitivity of a flood risk assessment model towards its input data

NASA Astrophysics Data System (ADS)

Glas, Hanne; Deruyter, Greet; De Maeyer, Philippe; Mandal, Arpita; James-Williamson, Sherene

2016-11-01

The Small Island Developing States are characterized by an unstable economy and low-lying, densely populated cities, resulting in a high vulnerability to natural hazards. Flooding affects more people than any other hazard. To limit the consequences of these hazards, adequate risk assessments are indispensable. Satisfactory input data for these assessments are hard to acquire, especially in developing countries. Therefore, in this study, a methodology was developed and evaluated to test the sensitivity of a flood model towards its input data in order to determine a minimum set of indispensable data. In a first step, a flood damage assessment model was created for the case study of Annotto Bay, Jamaica. This model generates a damage map for the region based on the flood extent map of the 2001 inundations caused by Tropical Storm Michelle. Three damages were taken into account: building, road and crop damage. Twelve scenarios were generated, each with a different combination of input data, testing one of the three damage calculations for its sensitivity. One main conclusion was that population density, in combination with an average number of people per household, is a good parameter in determining the building damage when exact building locations are unknown. Furthermore, the importance of roads for an accurate visual result was demonstrated.

Adaptive Control via Neural Output Feedback for a Class of Nonlinear Discrete-Time Systems in a Nested Interconnected Form.

PubMed

Li, Dong-Juan; Li, Da-Peng

2017-09-14

In this paper, an adaptive output feedback control is framed for uncertain nonlinear discrete-time systems. The considered systems are a class of multi-input multioutput nonaffine nonlinear systems, and they are in the nested lower triangular form. Furthermore, the unknown dead-zone inputs are nonlinearly embedded into the systems. These properties of the systems will make it very difficult and challenging to construct a stable controller. By introducing a new diffeomorphism coordinate transformation, the controlled system is first transformed into a state-output model. By introducing a group of new variables, an input-output model is finally obtained. Based on the transformed model, the implicit function theorem is used to determine the existence of the ideal controllers and the approximators are employed to approximate the ideal controllers. By using the mean value theorem, the nonaffine functions of systems can become an affine structure but nonaffine terms still exist. The adaptation auxiliary terms are skillfully designed to cancel the effect of the dead-zone input. Based on the Lyapunov difference theorem, the boundedness of all the signals in the closed-loop system can be ensured and the tracking errors are kept in a bounded compact set. The effectiveness of the proposed technique is checked by a simulation study.

Disturbance observer-based adaptive sliding mode hybrid projective synchronisation of identical fractional-order financial systems

NASA Astrophysics Data System (ADS)

Khan, Ayub; Tyagi, Arti

2018-05-01

In this paper, we have studied the hybrid projective synchronisation for incommensurate, integer and commensurate fractional-order financial systems with unknown disturbance. To tackle the problem of unknown bounded disturbance, fractional-order disturbance observer is designed to approximate the unknown disturbance. Further, we have introduced simple sliding mode surface and designed adaptive sliding mode controllers incorporating with the designed fractional-order disturbance observer to achieve a bounded hybrid projective synchronisation between two identical fractional-order financial model with different initial conditions. It is shown that the slave system with disturbance can be synchronised with the projection of the master system generated through state transformation. Simulation results are presented to ensure the validity and effectiveness of the proposed sliding mode control scheme in the presence of external bounded unknown disturbance. Also, synchronisation error for commensurate, integer and incommensurate fractional-order financial systems is studied in numerical simulation.

Direct Measurements of Iceberg Melt in Greenland Tidewater Glacier Fjords

NASA Astrophysics Data System (ADS)

Schild, K. M.; Sutherland, D.; Straneo, F.; Elosegui, P.

2017-12-01

The increasing input of freshwater to the subpolar North Atlantic, both through glacier meltwater runoff and the melting of calved icebergs, has significant implications for the Atlantic meridional overturning circulation and regional scale circulation. However, the magnitude and timing of this meltwater input has been challenging to quantify because iceberg melt rates are largely unknown. Here we use data from a simultaneous glaciological and oceanographic field campaign conducted in Sermilik Fjord, southeast Greenland, during July 2017 to map the surface and submarine geometry of large icebergs and use repeat surveys to directly measure iceberg melt rates. We use a combination of coincident ship-based multibeam submarine scans, ocean hydrography measurements, aerial drone mapping, and high precision iceberg-mounted GPS measurements to construct a detailed picture of iceberg geometry and melt. This synthesis of in situ iceberg melt measurements is amongst the first of its kind. Here, we will discuss the results of the 2017 field campaign, the implications of variable iceberg meltwater input throughout the water column, and comparisons to standard melt rate parameterizations and tidewater glacier submarine melt rate calculations.

Novel adaptive neural control design for a constrained flexible air-breathing hypersonic vehicle based on actuator compensation

NASA Astrophysics Data System (ADS)

Bu, Xiangwei; Wu, Xiaoyan; He, Guangjun; Huang, Jiaqi

2016-03-01

This paper investigates the design of a novel adaptive neural controller for the longitudinal dynamics of a flexible air-breathing hypersonic vehicle with control input constraints. To reduce the complexity of controller design, the vehicle dynamics is decomposed into the velocity subsystem and the altitude subsystem, respectively. For each subsystem, only one neural network is utilized to approach the lumped unknown function. By employing a minimal-learning parameter method to estimate the norm of ideal weight vectors rather than their elements, there are only two adaptive parameters required for neural approximation. Thus, the computational burden is lower than the ones derived from neural back-stepping schemes. Specially, to deal with the control input constraints, additional systems are exploited to compensate the actuators. Lyapunov synthesis proves that all the closed-loop signals involved are uniformly ultimately bounded. Finally, simulation results show that the adopted compensation scheme can tackle actuator constraint effectively and moreover velocity and altitude can stably track their reference trajectories even when the physical limitations on control inputs are in effect.

Single-image super-resolution based on Markov random field and contourlet transform

NASA Astrophysics Data System (ADS)

Wu, Wei; Liu, Zheng; Gueaieb, Wail; He, Xiaohai

2011-04-01

Learning-based methods are well adopted in image super-resolution. In this paper, we propose a new learning-based approach using contourlet transform and Markov random field. The proposed algorithm employs contourlet transform rather than the conventional wavelet to represent image features and takes into account the correlation between adjacent pixels or image patches through the Markov random field (MRF) model. The input low-resolution (LR) image is decomposed with the contourlet transform and fed to the MRF model together with the contourlet transform coefficients from the low- and high-resolution image pairs in the training set. The unknown high-frequency components/coefficients for the input low-resolution image are inferred by a belief propagation algorithm. Finally, the inverse contourlet transform converts the LR input and the inferred high-frequency coefficients into the super-resolved image. The effectiveness of the proposed method is demonstrated with the experiments on facial, vehicle plate, and real scene images. A better visual quality is achieved in terms of peak signal to noise ratio and the image structural similarity measurement.

Adaptive NN tracking control of uncertain nonlinear discrete-time systems with nonaffine dead-zone input.

PubMed

Liu, Yan-Jun; Tong, Shaocheng

2015-03-01

In the paper, an adaptive tracking control design is studied for a class of nonlinear discrete-time systems with dead-zone input. The considered systems are of the nonaffine pure-feedback form and the dead-zone input appears nonlinearly in the systems. The contributions of the paper are that: 1) it is for the first time to investigate the control problem for this class of discrete-time systems with dead-zone; 2) there are major difficulties for stabilizing such systems and in order to overcome the difficulties, the systems are transformed into an n-step-ahead predictor but nonaffine function is still existent; and 3) an adaptive compensative term is constructed to compensate for the parameters of the dead-zone. The neural networks are used to approximate the unknown functions in the transformed systems. Based on the Lyapunov theory, it is proven that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to a small neighborhood of zero. Two simulation examples are provided to verify the effectiveness of the control approach in the paper.

Organization of Functional Long-Range Circuits Controlling the Activity of Serotonergic Neurons in the Dorsal Raphe Nucleus.

PubMed

Zhou, Li; Liu, Ming-Zhe; Li, Qing; Deng, Juan; Mu, Di; Sun, Yan-Gang

2017-03-21

Serotonergic neurons play key roles in various biological processes. However, circuit mechanisms underlying tight control of serotonergic neurons remain largely unknown. Here, we systematically investigated the organization of long-range synaptic inputs to serotonergic neurons and GABAergic neurons in the dorsal raphe nucleus (DRN) of mice with a combination of viral tracing, slice electrophysiological, and optogenetic techniques. We found that DRN serotonergic neurons and GABAergic neurons receive largely comparable synaptic inputs from six major upstream brain areas. Upon further analysis of the fine functional circuit structures, we found both bilateral and ipsilateral patterns of topographic connectivity in the DRN for the axons from different inputs. Moreover, the upstream brain areas were found to bidirectionally control the activity of DRN serotonergic neurons by recruiting feedforward inhibition or via a push-pull mechanism. Our study provides a framework for further deciphering the functional roles of long-range circuits controlling the activity of serotonergic neurons in the DRN. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Assessing the performance of a motion tracking system based on optical joint transform correlation

NASA Astrophysics Data System (ADS)

Elbouz, M.; Alfalou, A.; Brosseau, C.; Ben Haj Yahia, N.; Alam, M. S.

2015-08-01

We present an optimized system specially designed for the tracking and recognition of moving subjects in a confined environment (such as an elderly remaining at home). In the first step of our study, we use a VanderLugt correlator (VLC) with an adapted pre-processing treatment of the input plane and a postprocessing of the correlation plane via a nonlinear function allowing us to make a robust decision. The second step is based on an optical joint transform correlation (JTC)-based system (NZ-NL-correlation JTC) for achieving improved detection and tracking of moving persons in a confined space. The proposed system has been found to have significantly superior discrimination and robustness capabilities allowing to detect an unknown target in an input scene and to determine the target's trajectory when this target is in motion. This system offers robust tracking performance of a moving target in several scenarios, such as rotational variation of input faces. Test results obtained using various real life video sequences show that the proposed system is particularly suitable for real-time detection and tracking of moving objects.

Neonatal Restriction of Tactile Inputs Leads to Long-Lasting Impairments of Cross-Modal Processing

PubMed Central

Röder, Brigitte; Hanganu-Opatz, Ileana L.

2015-01-01

Optimal behavior relies on the combination of inputs from multiple senses through complex interactions within neocortical networks. The ontogeny of this multisensory interplay is still unknown. Here, we identify critical factors that control the development of visual-tactile processing by combining in vivo electrophysiology with anatomical/functional assessment of cortico-cortical communication and behavioral investigation of pigmented rats. We demonstrate that the transient reduction of unimodal (tactile) inputs during a short period of neonatal development prior to the first cross-modal experience affects feed-forward subcortico-cortical interactions by attenuating the cross-modal enhancement of evoked responses in the adult primary somatosensory cortex. Moreover, the neonatal manipulation alters cortico-cortical interactions by decreasing the cross-modal synchrony and directionality in line with the sparsification of direct projections between primary somatosensory and visual cortices. At the behavioral level, these functional and structural deficits resulted in lower cross-modal matching abilities. Thus, neonatal unimodal experience during defined developmental stages is necessary for setting up the neuronal networks of multisensory processing. PMID:26600123

ELECTRONIC SYSTEM

DOEpatents

Robison, G.H. et al.

1960-11-15

An electronic system is described for indicating the occurrence of a plurality of electrically detectable events within predetermined time intervals. It is comprised of separate input means electrically associated with the events under observation: an electronic channel associated with each input means including control means and indicating means; timing means associated with each of the input means and the control means and adapted to derive a signal from the input means and apply it after a predetermined time to the control means to effect deactivation of each of the channels; and means for resetting the system to its initial condition after observation of each group of events.

Pulsar-aided SETI experimental observations

NASA Technical Reports Server (NTRS)

Heidmann, J.; Biraud, F.; Tarter, J.

1989-01-01

The rotational frequencies of pulsars are used to select preferred radio frequencies for SETI. Pulsar rotational frequencies are converted into SETI frequencies in the 1-10 GHz Galactic radio window. Experimental observations using the frequencies are conducted for target stars closer than 25 parsecs, unknown targets in a globular cluster, and unknown targets in the Galaxy closer than 2.5 kpc. The status of these observations is discussed.

Transformation of nonlinear discrete-time system into the extended observer form

NASA Astrophysics Data System (ADS)

Kaparin, V.; Kotta, Ü.

2018-04-01

The paper addresses the problem of transforming discrete-time single-input single-output nonlinear state equations into the extended observer form, which, besides the input and output, also depends on a finite number of their past values. Necessary and sufficient conditions for the existence of both the extended coordinate and output transformations, solving the problem, are formulated in terms of differential one-forms, associated with the input-output equation, corresponding to the state equations. An algorithm for transformation of state equations into the extended observer form is proposed and illustrated by an example. Moreover, the considered approach is compared with the method of dynamic observer error linearisation, which likewise is intended to enlarge the class of systems transformable into an observer form.

Robust interval-based regulation for anaerobic digestion processes.

PubMed

Alcaraz-González, V; Harmand, J; Rapaport, A; Steyer, J P; González-Alvarez, V; Pelayo-Ortiz, C

2005-01-01

A robust regulation law is applied to the stabilization of a class of biochemical reactors exhibiting partially known highly nonlinear dynamic behavior. An uncertain environment with the presence of unknown inputs is considered. Based on some structural and operational conditions, this regulation law is shown to exponentially stabilize the aforementioned bioreactors around a desired set-point. This approach is experimentally applied and validated on a pilot-scale (1 m3) anaerobic digestion process for the treatment of raw industrial wine distillery wastewater where the objective is the regulation of the chemical oxygen demand (COD) by using the dilution rate as the manipulated variable. Despite large disturbances on the input COD and state and parametric uncertainties, this regulation law gave excellent performances leading the output COD towards its set-point and keeping it inside a pre-specified interval.

How much to trust the senses: Likelihood learning

PubMed Central

Sato, Yoshiyuki; Kording, Konrad P.

2014-01-01

Our brain often needs to estimate unknown variables from imperfect information. Our knowledge about the statistical distributions of quantities in our environment (called priors) and currently available information from sensory inputs (called likelihood) are the basis of all Bayesian models of perception and action. While we know that priors are learned, most studies of prior-likelihood integration simply assume that subjects know about the likelihood. However, as the quality of sensory inputs change over time, we also need to learn about new likelihoods. Here, we show that human subjects readily learn the distribution of visual cues (likelihood function) in a way that can be predicted by models of statistically optimal learning. Using a likelihood that depended on color context, we found that a learned likelihood generalized to new priors. Thus, we conclude that subjects learn about likelihood. PMID:25398975

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Event-Triggered Distributed Approximate Optimal State and Output Control of Affine Nonlinear Interconnected Systems.

PubMed

Narayanan, Vignesh; Jagannathan, Sarangapani

2017-06-08

This paper presents an approximate optimal distributed control scheme for a known interconnected system composed of input affine nonlinear subsystems using event-triggered state and output feedback via a novel hybrid learning scheme. First, the cost function for the overall system is redefined as the sum of cost functions of individual subsystems. A distributed optimal control policy for the interconnected system is developed using the optimal value function of each subsystem. To generate the optimal control policy, forward-in-time, neural networks are employed to reconstruct the unknown optimal value function at each subsystem online. In order to retain the advantages of event-triggered feedback for an adaptive optimal controller, a novel hybrid learning scheme is proposed to reduce the convergence time for the learning algorithm. The development is based on the observation that, in the event-triggered feedback, the sampling instants are dynamic and results in variable interevent time. To relax the requirement of entire state measurements, an extended nonlinear observer is designed at each subsystem to recover the system internal states from the measurable feedback. Using a Lyapunov-based analysis, it is demonstrated that the system states and the observer errors remain locally uniformly ultimately bounded and the control policy converges to a neighborhood of the optimal policy. Simulation results are presented to demonstrate the performance of the developed controller.

Bayesian estimation of a source term of radiation release with approximately known nuclide ratios

NASA Astrophysics Data System (ADS)

Tichý, Ondřej; Šmídl, Václav; Hofman, Radek

2016-04-01

We are concerned with estimation of a source term in case of an accidental release from a known location, e.g. a power plant. Usually, the source term of an accidental release of radiation comprises of a mixture of nuclide. The gamma dose rate measurements do not provide a direct information on the source term composition. However, physical properties of respective nuclide (deposition properties, decay half-life) can be used when uncertain information on nuclide ratios is available, e.g. from known reactor inventory. The proposed method is based on linear inverse model where the observation vector y arise as a linear combination y = Mx of a source-receptor-sensitivity (SRS) matrix M and the source term x. The task is to estimate the unknown source term x. The problem is ill-conditioned and further regularization is needed to obtain a reasonable solution. In this contribution, we assume that nuclide ratios of the release is known with some degree of uncertainty. This knowledge is used to form the prior covariance matrix of the source term x. Due to uncertainty in the ratios the diagonal elements of the covariance matrix are considered to be unknown. Positivity of the source term estimate is guaranteed by using multivariate truncated Gaussian distribution. Following Bayesian approach, we estimate all parameters of the model from the data so that y, M, and known ratios are the only inputs of the method. Since the inference of the model is intractable, we follow the Variational Bayes method yielding an iterative algorithm for estimation of all model parameters. Performance of the method is studied on simulated 6 hour power plant release where 3 nuclide are released and 2 nuclide ratios are approximately known. The comparison with method with unknown nuclide ratios will be given to prove the usefulness of the proposed approach. This research is supported by EEA/Norwegian Financial Mechanism under project MSMT-28477/2014 Source-Term Determination of Radionuclide Releases by Inverse Atmospheric Dispersion Modelling (STRADI).

Synchronization properties of coupled chaotic neurons: The role of random shared input

NASA Astrophysics Data System (ADS)

Kumar, Rupesh; Bilal, Shakir; Ramaswamy, Ram

2016-06-01

Spike-time correlations of neighbouring neurons depend on their intrinsic firing properties as well as on the inputs they share. Studies have shown that periodically firing neurons, when subjected to random shared input, exhibit asynchronicity. Here, we study the effect of random shared input on the synchronization of weakly coupled chaotic neurons. The cases of so-called electrical and chemical coupling are both considered, and we observe a wide range of synchronization behaviour. When subjected to identical shared random input, there is a decrease in the threshold coupling strength needed for chaotic neurons to synchronize in-phase. The system also supports lag-synchronous states, and for these, we find that shared input can cause desynchronization. We carry out a master stability function analysis for a network of such neurons and show agreement with the numerical simulations. The contrasting role of shared random input for complete and lag synchronized neurons is useful in understanding spike-time correlations observed in many areas of the brain.

Synchronization properties of coupled chaotic neurons: The role of random shared input

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

Kumar, Rupesh; Bilal, Shakir; Ramaswamy, Ram

Spike-time correlations of neighbouring neurons depend on their intrinsic firing properties as well as on the inputs they share. Studies have shown that periodically firing neurons, when subjected to random shared input, exhibit asynchronicity. Here, we study the effect of random shared input on the synchronization of weakly coupled chaotic neurons. The cases of so-called electrical and chemical coupling are both considered, and we observe a wide range of synchronization behaviour. When subjected to identical shared random input, there is a decrease in the threshold coupling strength needed for chaotic neurons to synchronize in-phase. The system also supports lag–synchronous states,more » and for these, we find that shared input can cause desynchronization. We carry out a master stability function analysis for a network of such neurons and show agreement with the numerical simulations. The contrasting role of shared random input for complete and lag synchronized neurons is useful in understanding spike-time correlations observed in many areas of the brain.« less

Observer-based perturbation extremum seeking control with input constraints for direct-contact membrane distillation process

NASA Astrophysics Data System (ADS)

Eleiwi, Fadi; Laleg-Kirati, Taous Meriem

2018-06-01

An observer-based perturbation extremum seeking control is proposed for a direct-contact membrane distillation (DCMD) process. The process is described with a dynamic model that is based on a 2D advection-diffusion equation model which has pump flow rates as process inputs. The objective of the controller is to optimise the trade-off between the permeate mass flux and the energy consumption by the pumps inside the process. Cases of single and multiple control inputs are considered through the use of only the feed pump flow rate or both the feed and the permeate pump flow rates. A nonlinear Lyapunov-based observer is designed to provide an estimation for the temperature distribution all over the designated domain of the DCMD process. Moreover, control inputs are constrained with an anti-windup technique to be within feasible and physical ranges. Performance of the proposed structure is analysed, and simulations based on real DCMD process parameters for each control input are provided.

An analysis of input errors in precipitation-runoff models using regression with errors in the independent variables

USGS Publications Warehouse

Troutman, Brent M.

1982-01-01

Errors in runoff prediction caused by input data errors are analyzed by treating precipitation-runoff models as regression (conditional expectation) models. Independent variables of the regression consist of precipitation and other input measurements; the dependent variable is runoff. In models using erroneous input data, prediction errors are inflated and estimates of expected storm runoff for given observed input variables are biased. This bias in expected runoff estimation results in biased parameter estimates if these parameter estimates are obtained by a least squares fit of predicted to observed runoff values. The problems of error inflation and bias are examined in detail for a simple linear regression of runoff on rainfall and for a nonlinear U.S. Geological Survey precipitation-runoff model. Some implications for flood frequency analysis are considered. A case study using a set of data from Turtle Creek near Dallas, Texas illustrates the problems of model input errors.

Sliding Mode Observer-Based Current Sensor Fault Reconstruction and Unknown Load Disturbance Estimation for PMSM Driven System

PubMed Central

Li, Xiangfei; Lin, Yuliang

2017-01-01

This paper proposes a new scheme of reconstructing current sensor faults and estimating unknown load disturbance for a permanent magnet synchronous motor (PMSM)-driven system. First, the original PMSM system is transformed into two subsystems; the first subsystem has unknown system load disturbances, which are unrelated to sensor faults, and the second subsystem has sensor faults, but is free from unknown load disturbances. Introducing a new state variable, the augmented subsystem that has sensor faults can be transformed into having actuator faults. Second, two sliding mode observers (SMOs) are designed: the unknown load disturbance is estimated by the first SMO in the subsystem, which has unknown load disturbance, and the sensor faults can be reconstructed using the second SMO in the augmented subsystem, which has sensor faults. The gains of the proposed SMOs and their stability analysis are developed via the solution of linear matrix inequality (LMI). Finally, the effectiveness of the proposed scheme was verified by simulations and experiments. The results demonstrate that the proposed scheme can reconstruct current sensor faults and estimate unknown load disturbance for the PMSM-driven system. PMID:29211017

Multivariable Methods for the Design, Identification and Control of Large Space Structures. Volume 2. Optimal

DTIC Science & Technology

1989-07-01

the Government may have formulated or in any way supplied the said drawings, specifications, or other data, is not to be regarded by implication, or...of these gains gives a measure of the total amount of damping supplied by the actuators and colocated velocity sensors. In this sense, the sum of the...disturbances are assumed to be unknown but bounded time-varying pro - cesses. Second, inequality constraints on outputs (measurements) and inputs (con

Efficient dynamic optimization of logic programs

NASA Technical Reports Server (NTRS)

Laird, Phil

1992-01-01

A summary is given of the dynamic optimization approach to speed up learning for logic programs. The problem is to restructure a recursive program into an equivalent program whose expected performance is optimal for an unknown but fixed population of problem instances. We define the term 'optimal' relative to the source of input instances and sketch an algorithm that can come within a logarithmic factor of optimal with high probability. Finally, we show that finding high-utility unfolding operations (such as EBG) can be reduced to clause reordering.

Force Method Optimization II. Volume II. User’s Manual.

DTIC Science & Technology

1982-11-01

column labels ICC Iteration counter ICHECK Vector for intermediate output, identifying the convergence status of unknowns, 0 = has not converged, 1...NDC,NW,SIG,ND,IDYN,UP,LOW,IAREA,IMU, ALAMBDW,WARAY,NSN.,NDCNL,NXNL,NWNL,NDNNL, NSENLIRST, ICHECK ,WDYN,PR1,MAXIT,WS,ARAY) 8. Input Tapes: None 9. Output...IMUSL,IMUDL,IAREA, IMU, P,NDN,UP,LOW,IX,IDYN,NW,IMUXL,IMUWL,ICC,ALAMBD, AMIN,WT,KL,NODE,ND,COND, IDEL .NSNL,NDCNL, NXNL, NWNL, ICHECK ,WDYN,PRI,WS,MAXT

Direct Adaptive Control of Systems with Actuator Failures: State of the Art and Continuing Challenges

NASA Technical Reports Server (NTRS)

Tao, Gang; Joshi, Suresh M.

2008-01-01

In this paper, the problem of controlling systems with failures and faults is introduced, and an overview of recent work on direct adaptive control for compensation of uncertain actuator failures is presented. Actuator failures may be characterized by some unknown system inputs being stuck at some unknown (fixed or varying) values at unknown time instants, that cannot be influenced by the control signals. The key task of adaptive compensation is to design the control signals in such a manner that the remaining actuators can automatically and seamlessly take over for the failed ones, and achieve desired stability and asymptotic tracking. A certain degree of redundancy is necessary to accomplish failure compensation. The objective of adaptive control design is to effectively use the available actuation redundancy to handle failures without the knowledge of the failure patterns, parameters, and time of occurrence. This is a challenging problem because failures introduce large uncertainties in the dynamic structure of the system, in addition to parametric uncertainties and unknown disturbances. The paper addresses some theoretical issues in adaptive actuator failure compensation: actuator failure modeling, redundant actuation requirements, plant-model matching, error system dynamics, adaptation laws, and stability, tracking, and performance analysis. Adaptive control designs can be shown to effectively handle uncertain actuator failures without explicit failure detection. Some open technical challenges and research problems in this important research area are discussed.

A Subtype of Olfactory Bulb Interneurons Is Required for Odor Detection and Discrimination Behaviors.

PubMed

Takahashi, Hiroo; Ogawa, Yoichi; Yoshihara, Sei-Ichi; Asahina, Ryo; Kinoshita, Masahito; Kitano, Tatsuro; Kitsuki, Michiko; Tatsumi, Kana; Okuda, Mamiko; Tatsumi, Kouko; Wanaka, Akio; Hirai, Hirokazu; Stern, Peter L; Tsuboi, Akio

2016-08-03

Neural circuits that undergo reorganization by newborn interneurons in the olfactory bulb (OB) are necessary for odor detection and discrimination, olfactory memory, and innate olfactory responses, including predator avoidance and sexual behaviors. The OB possesses many interneurons, including various types of granule cells (GCs); however, the contribution that each type of interneuron makes to olfactory behavioral control remains unknown. Here, we investigated the in vivo functional role of oncofetal trophoblast glycoprotein 5T4, a regulator for dendritic arborization of 5T4-expressing GCs (5T4 GCs), the level of which is reduced in the OB of 5T4 knock-out (KO) mice. Electrophysiological recordings with acute OB slices indicated that external tufted cells (ETCs) can be divided into two types, bursting and nonbursting. Optogenetic stimulation of 5T4 GCs revealed their connection to both bursting and nonbursting ETCs, as well as to mitral cells (MCs). Interestingly, nonbursting ETCs received fewer inhibitory inputs from GCs in 5T4 KO mice than from those in wild-type (WT) mice, whereas bursting ETCs and MCs received similar inputs in both mice. Furthermore, 5T4 GCs received significantly fewer excitatory inputs in 5T4 KO mice. Remarkably, in olfactory behavior tests, 5T4 KO mice had higher odor detection thresholds than the WT, as well as defects in odor discrimination learning. Therefore, the loss of 5T4 attenuates inhibitory inputs from 5T4 GCs to nonbursting ETCs and excitatory inputs to 5T4 GCs, contributing to disturbances in olfactory behavior. Our novel findings suggest that, among the various types of OB interneurons, the 5T4 GC subtype is required for odor detection and discrimination behaviors. Neuronal circuits in the brain include glutamatergic principal neurons and GABAergic interneurons. Although the latter is a minority cell type, they are vital for normal brain function because they regulate the activity of principal neurons. If interneuron function is impaired, brain function may be damaged, leading to behavior disorder. The olfactory bulb (OB) possesses various types of interneurons, including granule cells (GCs); however, the contribution that each type of interneuron makes to the control of olfactory behavior remains unknown. Here, we analyzed electrophysiologically and behaviorally the function of oncofetal trophoblast glycoprotein 5T4, a regulator for dendritic branching in OB GCs. We found that, among the various types of OB interneuron, the 5T4 GC subtype is required for odor detection and odor discrimination behaviors. Copyright © 2016 the authors 0270-6474/16/368211-18$15.00/0.

Structural control and health monitoring of building structures with unknown ground excitations: Experimental investigation

NASA Astrophysics Data System (ADS)

He, Jia; Xu, You-Lin; Zhan, Sheng; Huang, Qin

2017-03-01

When health monitoring system and vibration control system both are required for a building structure, it will be beneficial and cost-effective to integrate these two systems together for creating a smart building structure. Recently, on the basis of extended Kalman filter (EKF), a time-domain integrated approach was proposed for the identification of structural parameters of the controlled buildings with unknown ground excitations. The identified physical parameters and structural state vectors were then utilized to determine the control force for vibration suppression. In this paper, the possibility of establishing such a smart building structure with the function of simultaneous damage detection and vibration suppression was explored experimentally. A five-story shear building structure equipped with three magneto-rheological (MR) dampers was built. Four additional columns were added to the building model, and several damage scenarios were then simulated by symmetrically cutting off these columns in certain stories. Two sets of earthquakes, i.e. Kobe earthquake and Northridge earthquake, were considered as seismic input and assumed to be unknown during the tests. The structural parameters and the unknown ground excitations were identified during the tests by using the proposed identification method with the measured control forces. Based on the identified structural parameters and system states, a switching control law was employed to adjust the current applied to the MR dampers for the purpose of vibration attenuation. The experimental results show that the presented approach is capable of satisfactorily identifying structural damages and unknown excitations on one hand and significantly mitigating the structural vibration on the other hand.

Detecting Unknown Artificial Urban Surface Materials Based on Spectral Dissimilarity Analysis.

PubMed

Jilge, Marianne; Heiden, Uta; Habermeyer, Martin; Mende, André; Juergens, Carsten

2017-08-08

High resolution imaging spectroscopy data have been recognised as a valuable data resource for augmenting detailed material inventories that serve as input for various urban applications. Image-specific urban spectral libraries are successfully used in urban imaging spectroscopy studies. However, the regional- and sensor-specific transferability of such libraries is limited due to the wide range of different surface materials. With the developed methodology, incomplete urban spectral libraries can be utilised by assuming that unknown surface material spectra are dissimilar to the known spectra in a basic spectral library (BSL). The similarity measure SID-SCA (Spectral Information Divergence-Spectral Correlation Angle) is applied to detect image-specific unknown urban surfaces while avoiding spectral mixtures. These detected unknown materials are categorised into distinct and identifiable material classes based on their spectral and spatial metrics. Experimental results demonstrate a successful redetection of material classes that had been previously erased in order to simulate an incomplete BSL. Additionally, completely new materials e.g., solar panels were identified in the data. It is further shown that the level of incompleteness of the BSL and the defined dissimilarity threshold are decisive for the detection of unknown material classes and the degree of spectral intra-class variability. A detailed accuracy assessment of the pre-classification results, aiming to separate natural and artificial materials, demonstrates spectral confusions between spectrally similar materials utilizing SID-SCA. However, most spectral confusions occur between natural or artificial materials which are not affecting the overall aim. The dissimilarity analysis overcomes the limitations of working with incomplete urban spectral libraries and enables the generation of image-specific training databases.

Detecting Unknown Artificial Urban Surface Materials Based on Spectral Dissimilarity Analysis

PubMed Central

Jilge, Marianne; Heiden, Uta; Habermeyer, Martin; Mende, André; Juergens, Carsten

2017-01-01

High resolution imaging spectroscopy data have been recognised as a valuable data resource for augmenting detailed material inventories that serve as input for various urban applications. Image-specific urban spectral libraries are successfully used in urban imaging spectroscopy studies. However, the regional- and sensor-specific transferability of such libraries is limited due to the wide range of different surface materials. With the developed methodology, incomplete urban spectral libraries can be utilised by assuming that unknown surface material spectra are dissimilar to the known spectra in a basic spectral library (BSL). The similarity measure SID-SCA (Spectral Information Divergence-Spectral Correlation Angle) is applied to detect image-specific unknown urban surfaces while avoiding spectral mixtures. These detected unknown materials are categorised into distinct and identifiable material classes based on their spectral and spatial metrics. Experimental results demonstrate a successful redetection of material classes that had been previously erased in order to simulate an incomplete BSL. Additionally, completely new materials e.g., solar panels were identified in the data. It is further shown that the level of incompleteness of the BSL and the defined dissimilarity threshold are decisive for the detection of unknown material classes and the degree of spectral intra-class variability. A detailed accuracy assessment of the pre-classification results, aiming to separate natural and artificial materials, demonstrates spectral confusions between spectrally similar materials utilizing SID-SCA. However, most spectral confusions occur between natural or artificial materials which are not affecting the overall aim. The dissimilarity analysis overcomes the limitations of working with incomplete urban spectral libraries and enables the generation of image-specific training databases. PMID:28786947

[The effect of 18beta-glycyrrhetinic acid on gap junction among cerebral arteriolar smooth muscle cells in Wistar rat and spontaneously hypertensive rat].

PubMed

Chen, Xin-Yan; Si, Jun-Qiang; Li, Li; Zhao, Lei; Wei, Li-Li; Jiang, Xue-Wei; Ma, Ke-Tao

2013-05-01

This study compared Wistar rat with spontaneously hypertensive rat (SHR) on the electrophysiology and coupling force of the smooth muscle cells in the cerebral arteriolar segments and observe the influence of 18beta-glycyrrhetinic acid(18beta-GA) on the gap junctions between the arterial smooth muscle cells. The outer layer's connective tissue of the cerebral arteriolar segments was removed. Whole-cell patch clamp recordings were used to observe the 18beta-GA's impaction on the arteriolar segment membrane's input capacitance (C(input)), input conductance (G(input)) and input resistance (R(input)) of the smooth muscle cells. (1) The C(input) and G(input) of the SHR arteriolar segment smooth muscle cells was much higher than the Wistar rats, there was significant difference (P < 0.05). (2) 18beta-GA concentration-dependently reduced C(input) and G(input) (or increase R(input)) on smooth muscle cells in arteriolar segment. IC50 of 18beta-GA suppression's G(input) of the Wistar rat and SHR were 1.7 and 2.0 micromol/L respectively, there was not significant difference (P > 0.05). After application of 18beta-GA concentration > or = 100 micrmol/L, the C(input), G(input) and R(input) of the single smooth muscle cells was very close. Gap junctional coupling is enhanced in the SHR cerebral arterial smooth muscle cells. 18beta-GA concentration-dependent inhibits Wistar rat's and SHR cerebral arteriolar gap junctions between arterial smooth muscle cells. The inhibitory potency is similar between the two different rats. When 18beta-GA concentration is > or = 100 micromol/L, it can completely block gap junctions between arteriolar smooth muscle cells.

Thalamic amplification of cortical connectivity sustains attentional control

PubMed Central

Schmitt, L. Ian; Wimmer, Ralf D.; Nakajima, Miho; Happ, Michael; Mofakham, Sima; Halassa, Michael M.

2017-01-01

While interactions between the thalamus and cortex are critical for cognitive function1–3, the exact contribution of the thalamus to these interactions is often unclear. Recent studies have shown diverse connectivity patterns across the thalamus 4,5, but whether this diversity translates to thalamic functions beyond relaying information to or between cortical regions6 is unknown. Here, by investigating prefrontal cortical (PFC) representation of two rules used to guide attention, we find that the mediodorsal thalamus (MD) sustains these representations without relaying categorical information. Specifically, MD input amplifies local PFC connectivity, enabling rule-specific neural sequences to emerge and thereby maintain rule representations. Consistent with this notion, broadly enhancing PFC excitability diminishes rule specificity and behavioral performance, while enhancing MD excitability improves both. Overall, our results define a previously unknown principle in neuroscience; thalamic control of functional cortical connectivity. This function indicates that the thalamus plays much more central roles in cognition than previously thought. PMID:28467827

Extracting scene feature vectors through modeling, volume 3

NASA Technical Reports Server (NTRS)

Berry, J. K.; Smith, J. A.

1976-01-01

The remote estimation of the leaf area index of winter wheat at Finney County, Kansas was studied. The procedure developed consists of three activities: (1) field measurements; (2) model simulations; and (3) response classifications. The first activity is designed to identify model input parameters and develop a model evaluation data set. A stochastic plant canopy reflectance model is employed to simulate reflectance in the LANDSAT bands as a function of leaf area index for two phenological stages. An atmospheric model is used to translate these surface reflectances into simulated satellite radiance. A divergence classifier determines the relative similarity between model derived spectral responses and those of areas with unknown leaf area index. The unknown areas are assigned the index associated with the closest model response. This research demonstrated that the SRVC canopy reflectance model is appropriate for wheat scenes and that broad categories of leaf area index can be inferred from the procedure developed.

Exploring the Behaviour of Emerging Contaminants in the Water Cycle using the Capabilities of High Resolution Mass Spectrometry.

PubMed

Hollender, Juliane; Bourgin, Marc; Fenner, Kathrin B; Longrée, Philipp; Mcardell, Christa S; Moschet, Christoph; Ruff, Matthias; Schymanski, Emma L; Singer, Heinz P

2014-11-01

To characterize a broad range of organic contaminants and their transformation products (TPs) as well as their loads, input pathways and fate in the water cycle, the Department of Environmental Chemistry (Uchem) at Eawag applies and develops high-performance liquid chromatography (LC) methods combined with high-resolution tandem mass spectrometry (HRMS/MS). In this article, the background and state-of-the-art of LC-HRMS/MS for detection of i) known targets, ii) suspected compounds like TPs, and iii) unknown emerging compounds are introduced briefly. Examples for each approach are taken from recent research projects conducted within the department. These include the detection of trace organic contaminants and their TPs in wastewater, pesticides and their TPs in surface water, identification of new TPs in laboratory degradation studies and ozonation experiments and finally the screening for unknown compounds in the catchment of the river Rhine.

Neural network L1 adaptive control of MIMO systems with nonlinear uncertainty.

PubMed

Zhen, Hong-tao; Qi, Xiao-hui; Li, Jie; Tian, Qing-min

2014-01-01

An indirect adaptive controller is developed for a class of multiple-input multiple-output (MIMO) nonlinear systems with unknown uncertainties. This control system is comprised of an L 1 adaptive controller and an auxiliary neural network (NN) compensation controller. The L 1 adaptive controller has guaranteed transient response in addition to stable tracking. In this architecture, a low-pass filter is adopted to guarantee fast adaptive rate without generating high-frequency oscillations in control signals. The auxiliary compensation controller is designed to approximate the unknown nonlinear functions by MIMO RBF neural networks to suppress the influence of uncertainties. NN weights are tuned on-line with no prior training and the project operator ensures the weights bounded. The global stability of the closed-system is derived based on the Lyapunov function. Numerical simulations of an MIMO system coupled with nonlinear uncertainties are used to illustrate the practical potential of our theoretical results.

A new method for determining acoustic-liner admittance in a rectangular duct with grazing flow from experimental data

NASA Technical Reports Server (NTRS)

Watson, W. R.

1984-01-01

A method is developed for determining acoustic liner admittance in a rectangular duct with grazing flow. The axial propagation constant, cross mode order, and mean flow profile is measured. These measured data are then input into an analytical program which determines the unknown admittance value. The analytical program is based upon a finite element discretization of the acoustic field and a reposing of the unknown admittance value as a linear eigenvalue problem on the admittance value. Gaussian elimination is employed to solve this eigenvalue problem. The method used is extendable to grazing flows with boundary layers in both transverse directions of an impedance tube (or duct). Predicted admittance values are compared both with exact values that can be obtained for uniform mean flow profiles and with those from a Runge Kutta integration technique for cases involving a one dimensional boundary layer.

A Novel Extreme Learning Control Framework of Unmanned Surface Vehicles.

PubMed

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

2016-05-01

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

Regression Equations for Monthly and Annual Mean and Selected Percentile Streamflows for Ungaged Rivers in Maine

USGS Publications Warehouse

Dudley, Robert W.

2015-12-03

The largest average errors of prediction are associated with regression equations for the lowest streamflows derived for months during which the lowest streamflows of the year occur (such as the 5 and 1 monthly percentiles for August and September). The regression equations have been derived on the basis of streamflow and basin characteristics data for unregulated, rural drainage basins without substantial streamflow or drainage modifications (for example, diversions and (or) regulation by dams or reservoirs, tile drainage, irrigation, channelization, and impervious paved surfaces), therefore using the equations for regulated or urbanized basins with substantial streamflow or drainage modifications will yield results of unknown error. Input basin characteristics derived using techniques or datasets other than those documented in this report or using values outside the ranges used to develop these regression equations also will yield results of unknown error.

Variable input observer for structural health monitoring of high-rate systems

NASA Astrophysics Data System (ADS)

Hong, Jonathan; Laflamme, Simon; Cao, Liang; Dodson, Jacob

2017-02-01

The development of high-rate structural health monitoring methods is intended to provide damage detection on timescales of 10 µs -10ms where speed of detection is critical to maintain structural integrity. Here, a novel Variable Input Observer (VIO) coupled with an adaptive observer is proposed as a potential solution for complex high-rate problems. The VIO is designed to adapt its input space based on real-time identification of the system's essential dynamics. By selecting appropriate time-delayed coordinates defined by both a time delay and an embedding dimension, the proper input space is chosen which allows more accurate estimations of the current state and a reduction of the convergence rate. The optimal time-delay is estimated based on mutual information, and the embedding dimension is based on false nearest neighbors. A simulation of the VIO is conducted on a two degree-of-freedom system with simulated damage. Results are compared with an adaptive Luenberger observer, a fixed time-delay observer, and a Kalman Filter. Under its preliminary design, the VIO converges significantly faster than the Luenberger and fixed observer. It performed similarly to the Kalman Filter in terms of convergence, but with greater accuracy.

Electronic system

DOEpatents

Robison, G H; Dickson, J F

1960-11-15

An electronic system is designed for indicating the occurrence of a plurality of electrically detectable events within predetermined time intervals. The system comprises separate input means electrically associated with the events under observation an electronic channel associated with each input means, including control means and indicating means; timing means adapted to apply a signal from the input means after a predetermined time to the control means to deactivate each of the channels; and means for resetting the system to its initial condition after the observation of each group of events. (D.L.C.)

System/observer/controller identification toolbox

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Horta, Lucas G.; Phan, Minh

1992-01-01

System Identification is the process of constructing a mathematical model from input and output data for a system under testing, and characterizing the system uncertainties and measurement noises. The mathematical model structure can take various forms depending upon the intended use. The SYSTEM/OBSERVER/CONTROLLER IDENTIFICATION TOOLBOX (SOCIT) is a collection of functions, written in MATLAB language and expressed in M-files, that implements a variety of modern system identification techniques. For an open loop system, the central features of the SOCIT are functions for identification of a system model and its corresponding forward and backward observers directly from input and output data. The system and observers are represented by a discrete model. The identified model and observers may be used for controller design of linear systems as well as identification of modal parameters such as dampings, frequencies, and mode shapes. For a closed-loop system, an observer and its corresponding controller gain directly from input and output data.

The modal surface interpolation method for damage localization

NASA Astrophysics Data System (ADS)

Pina Limongelli, Maria

2017-05-01

The Interpolation Method (IM) has been previously proposed and successfully applied for damage localization in plate like structures. The method is based on the detection of localized reductions of smoothness in the Operational Deformed Shapes (ODSs) of the structure. The IM can be applied to any type of structure provided the ODSs are estimated accurately in the original and in the damaged configurations. If the latter circumstance fails to occur, for example when the structure is subjected to an unknown input(s) or if the structural responses are strongly corrupted by noise, both false and missing alarms occur when the IM is applied to localize a concentrated damage. In order to overcome these drawbacks a modification of the method is herein investigated. An ODS is the deformed shape of a structure subjected to a harmonic excitation: at resonances the ODS are dominated by the relevant mode shapes. The effect of noise at resonance is usually lower with respect to other frequency values hence the relevant ODS are estimated with higher reliability. Several methods have been proposed to reliably estimate modal shapes in case of unknown input. These two circumstances can be exploited to improve the reliability of the IM. In order to reduce or eliminate the drawbacks related to the estimation of the ODSs in case of noisy signals, in this paper is investigated a modified version of the method based on a damage feature calculated considering the interpolation error relevant only to the modal shapes and not to all the operational shapes in the significant frequency range. Herein will be reported the comparison between the results of the IM in its actual version (with the interpolation error calculated summing up the contributions of all the operational shapes) and in the new proposed version (with the estimation of the interpolation error limited to the modal shapes).

Tonic or Phasic Stimulation of Dopaminergic Projections to Prefrontal Cortex Causes Mice to Maintain or Deviate from Previously Learned Behavioral Strategies

PubMed Central

Ellwood, Ian T.; Patel, Tosha; Wadia, Varun; Lee, Anthony T.; Liptak, Alayna T.

2017-01-01

Dopamine neurons in the ventral tegmental area (VTA) encode reward prediction errors and can drive reinforcement learning through their projections to striatum, but much less is known about their projections to prefrontal cortex (PFC). Here, we studied these projections and observed phasic VTA–PFC fiber photometry signals after the delivery of rewards. Next, we studied how optogenetic stimulation of these projections affects behavior using conditioned place preference and a task in which mice learn associations between cues and food rewards and then use those associations to make choices. Neither phasic nor tonic stimulation of dopaminergic VTA–PFC projections elicited place preference. Furthermore, substituting phasic VTA–PFC stimulation for food rewards was not sufficient to reinforce new cue–reward associations nor maintain previously learned ones. However, the same patterns of stimulation that failed to reinforce place preference or cue–reward associations were able to modify behavior in other ways. First, continuous tonic stimulation maintained previously learned cue–reward associations even after they ceased being valid. Second, delivering phasic stimulation either continuously or after choices not previously associated with reward induced mice to make choices that deviated from previously learned associations. In summary, despite the fact that dopaminergic VTA–PFC projections exhibit phasic increases in activity that are time locked to the delivery of rewards, phasic activation of these projections does not necessarily reinforce specific actions. Rather, dopaminergic VTA–PFC activity can control whether mice maintain or deviate from previously learned cue–reward associations. SIGNIFICANCE STATEMENT Dopaminergic inputs from ventral tegmental area (VTA) to striatum encode reward prediction errors and reinforce specific actions; however, it is currently unknown whether dopaminergic inputs to prefrontal cortex (PFC) play similar or distinct roles. Here, we used bulk Ca2+ imaging to show that unexpected rewards or reward-predicting cues elicit phasic increases in the activity of dopaminergic VTA–PFC fibers. However, in multiple behavioral paradigms, we failed to observe reinforcing effects after stimulation of these fibers. In these same experiments, we did find that tonic or phasic patterns of stimulation caused mice to maintain or deviate from previously learned cue–reward associations, respectively. Therefore, although they may exhibit similar patterns of activity, dopaminergic inputs to striatum and PFC can elicit divergent behavioral effects. PMID:28739583

Postselection technique for quantum channels with applications to quantum cryptography.

PubMed

Christandl, Matthias; König, Robert; Renner, Renato

2009-01-16

We propose a general method for studying properties of quantum channels acting on an n-partite system, whose action is invariant under permutations of the subsystems. Our main result is that, in order to prove that a certain property holds for an arbitrary input, it is sufficient to consider the case where the input is a particular de Finetti-type state, i.e., a state which consists of n identical and independent copies of an (unknown) state on a single subsystem. Our technique can be applied to the analysis of information-theoretic problems. For example, in quantum cryptography, we get a simple proof for the fact that security of a discrete-variable quantum key distribution protocol against collective attacks implies security of the protocol against the most general attacks. The resulting security bounds are tighter than previously known bounds obtained with help of the exponential de Finetti theorem.

Modal identification of structures from the responses and random decrement signatures

NASA Technical Reports Server (NTRS)

Brahim, S. R.; Goglia, G. L.

1977-01-01

The theory and application of a method which utilizes the free response of a structure to determine its vibration parameters is described. The time-domain free response is digitized and used in a digital computer program to determine the number of modes excited, the natural frequencies, the damping factors, and the modal vectors. The technique is applied to a complex generalized payload model previously tested using sine sweep method and analyzed by NASTRAN. Ten modes of the payload model are identified. In case free decay response is not readily available, an algorithm is developed to obtain the free responses of a structure from its random responses, due to some unknown or known random input or inputs, using the random decrement technique without changing time correlation between signals. The algorithm is tested using random responses from a generalized payload model and from the space shuttle model.

Distributed Adaptive Containment Control for a Class of Nonlinear Multiagent Systems With Input Quantization.

PubMed

Wang, Chenliang; Wen, Changyun; Hu, Qinglei; Wang, Wei; Zhang, Xiuyu

2018-06-01

This paper is devoted to distributed adaptive containment control for a class of nonlinear multiagent systems with input quantization. By employing a matrix factorization and a novel matrix normalization technique, some assumptions involving control gain matrices in existing results are relaxed. By fusing the techniques of sliding mode control and backstepping control, a two-step design method is proposed to construct controllers and, with the aid of neural networks, all system nonlinearities are allowed to be unknown. Moreover, a linear time-varying model and a similarity transformation are introduced to circumvent the obstacle brought by quantization, and the controllers need no information about the quantizer parameters. The proposed scheme is able to ensure the boundedness of all closed-loop signals and steer the containment errors into an arbitrarily small residual set. The simulation results illustrate the effectiveness of the scheme.

Dopaminergic inputs in the dentate gyrus direct the choice of memory encoding.

PubMed

Du, Huiyun; Deng, Wei; Aimone, James B; Ge, Minyan; Parylak, Sarah; Walch, Keenan; Zhang, Wei; Cook, Jonathan; Song, Huina; Wang, Liping; Gage, Fred H; Mu, Yangling

2016-09-13

Rewarding experiences are often well remembered, and such memory formation is known to be dependent on dopamine modulation of the neural substrates engaged in learning and memory; however, it is unknown how and where in the brain dopamine signals bias episodic memory toward preceding rather than subsequent events. Here we found that photostimulation of channelrhodopsin-2-expressing dopaminergic fibers in the dentate gyrus induced a long-term depression of cortical inputs, diminished theta oscillations, and impaired subsequent contextual learning. Computational modeling based on this dopamine modulation indicated an asymmetric association of events occurring before and after reward in memory tasks. In subsequent behavioral experiments, preexposure to a natural reward suppressed hippocampus-dependent memory formation, with an effective time window consistent with the duration of dopamine-induced changes of dentate activity. Overall, our results suggest a mechanism by which dopamine enables the hippocampus to encode memory with reduced interference from subsequent experience.

The relationship between sensory-processing patterns and occupational engagement among older persons.

PubMed

Engel-Yeger, Batya; Rosenblum, Sara

2017-02-01

Meaningful occupational engagement is essential for successful aging. Sensory-processing abilities that are known to deteriorate with age may reduce occupational engagement. However, the relationship between sensory-processing abilities and occupational engagement among older persons in daily life is unknown. This study examined the relationship between sensory-processing patterns and occupational engagement among older persons. Participants were 180 people, ages 50 to 73 years, in good health, who lived in their homes. All participants completed the Adolescent/Adult Sensory Profile and the Activity Card Sort. Better registration of sensory input and greater sensory seeking were related to greater occupational engagement. Sensory-processing abilities among older persons and their relation to occupational engagement in various life settings should receive attention in research and practice. Occupational therapists should encourage older people to seek sensory input and provide them with rich sensory environments for enhancing meaningful engagement in real life.

Dopaminergic inputs in the dentate gyrus direct the choice of memory encoding

PubMed Central

Du, Huiyun; Deng, Wei; Aimone, James B.; Ge, Minyan; Parylak, Sarah; Walch, Keenan; Zhang, Wei; Cook, Jonathan; Song, Huina; Wang, Liping; Gage, Fred H.; Mu, Yangling

2016-01-01

Rewarding experiences are often well remembered, and such memory formation is known to be dependent on dopamine modulation of the neural substrates engaged in learning and memory; however, it is unknown how and where in the brain dopamine signals bias episodic memory toward preceding rather than subsequent events. Here we found that photostimulation of channelrhodopsin-2–expressing dopaminergic fibers in the dentate gyrus induced a long-term depression of cortical inputs, diminished theta oscillations, and impaired subsequent contextual learning. Computational modeling based on this dopamine modulation indicated an asymmetric association of events occurring before and after reward in memory tasks. In subsequent behavioral experiments, preexposure to a natural reward suppressed hippocampus-dependent memory formation, with an effective time window consistent with the duration of dopamine-induced changes of dentate activity. Overall, our results suggest a mechanism by which dopamine enables the hippocampus to encode memory with reduced interference from subsequent experience. PMID:27573822

Top-down cortical input during NREM sleep consolidates perceptual memory.

PubMed

Miyamoto, D; Hirai, D; Fung, C C A; Inutsuka, A; Odagawa, M; Suzuki, T; Boehringer, R; Adaikkan, C; Matsubara, C; Matsuki, N; Fukai, T; McHugh, T J; Yamanaka, A; Murayama, M

2016-06-10

During tactile perception, long-range intracortical top-down axonal projections are essential for processing sensory information. Whether these projections regulate sleep-dependent long-term memory consolidation is unknown. We altered top-down inputs from higher-order cortex to sensory cortex during sleep and examined the consolidation of memories acquired earlier during awake texture perception. Mice learned novel textures and consolidated them during sleep. Within the first hour of non-rapid eye movement (NREM) sleep, optogenetic inhibition of top-down projecting axons from secondary motor cortex (M2) to primary somatosensory cortex (S1) impaired sleep-dependent reactivation of S1 neurons and memory consolidation. In NREM sleep and sleep-deprivation states, closed-loop asynchronous or synchronous M2-S1 coactivation, respectively, reduced or prolonged memory retention. Top-down cortical information flow in NREM sleep is thus required for perceptual memory consolidation. Copyright © 2016, American Association for the Advancement of Science.

Removing flicker based on sparse color correspondences in old film restoration

NASA Astrophysics Data System (ADS)

Huang, Xi; Ding, Youdong; Yu, Bing; Xia, Tianran

2018-04-01

In the long history of human civilization, archived film is an indispensable part of it, and using digital method to repair damaged film is also a mainstream trend nowadays. In this paper, we propose a sparse color correspondences based technique to remove fading flicker for old films. Our model, combined with multi frame images to establish a simple correction model, includes three key steps. Firstly, we recover sparse color correspondences in the input frames to build a matrix with many missing entries. Secondly, we present a low-rank matrix factorization approach to estimate the unknown parameters of this model. Finally, we adopt a two-step strategy that divide the estimated parameters into reference frame parameters for color recovery correction and other frame parameters for color consistency correction to remove flicker. Our method combined multi-frames takes continuity of the input sequence into account, and the experimental results show the method can remove fading flicker efficiently.

Effect of Welding Heat Input on Microstructure and Texture of Inconel 625 Weld Overlay Studied Using the Electron Backscatter Diffraction Method

NASA Astrophysics Data System (ADS)

Kim, Joon-Suk; Lee, Hae-Woo

2016-12-01

The grain size and the texture of three specimens prepared at different heat inputs were determined using optical microscopy and the electron backscatter diffraction method of scanning electron microscopy. Each specimen was equally divided into fusion line zone (FLZ), columnar dendrite zone (CDZ), and surface zone (SZ), according to the location of the weld. Fine dendrites were observed in the FLZ, coarse dendrites in the CDZ, and dendrites grew perpendicular to the FLZ and CDZ. As the heat input increased, the melted zone in the vicinity of the FLZ widened due to the higher Fe content. A lower image quality value was observed for the FLZ compared to the other zones. The results of grain size measurement in each zone showed that the grain size of the SZ became larger as the heat input increased. From the inverse pole figure (IPF) map in the normal direction (ND) and the rolling direction (RD), as the heat input increased, a specific orientation was formed. However, a dominant [001] direction was observed in the RD IPF map.

Retinal input to efferent target amacrine cells in the avian retina

PubMed Central

Lindstrom, Sarah H.; Azizi, Nason; Weller, Cynthia; Wilson, Martin

2012-01-01

The bird visual system includes a substantial projection, of unknown function, from a midbrain nucleus to the contralateral retina. Every centrifugal, or efferent, neuron originating in the midbrain nucleus makes synaptic contact with the soma of a single, unique amacrine cell, the target cell (TC). By labeling efferent neurons in the midbrain we have been able to identify their terminals in retinal slices and make patch clamp recordings from TCs. TCs generate Na+ based action potentials triggered by spontaneous EPSPs originating from multiple classes of presynaptic neurons. Exogenously applied glutamate elicited inward currents having the mixed pharmacology of NMDA, kainate and inward rectifying AMPA receptors. Exogenously applied GABA elicited currents entirely suppressed by GABAzine, and therefore mediated by GABAA receptors. Immunohistochemistry showed the vesicular glutamate transporter, vGluT2, to be present in the characteristic synaptic boutons of efferent terminals, whereas the GABA synthetic enzyme, GAD, was present in much smaller processes of intrinsic retinal neurons. Extracellular recording showed that exogenously applied GABA was directly excitatory to TCs and, consistent with this, NKCC, the Cl− transporter often associated with excitatory GABAergic synapses, was identified in TCs by antibody staining. The presence of excitatory retinal input to TCs implies that TCs are not merely slaves to their midbrain input; instead, their output reflects local retinal activity and descending input from the midbrain. PMID:20650017

On the frequency response of a Wenglor particle-counting system for aeolian transport measurements

NASA Astrophysics Data System (ADS)

Bauer, Bernard O.; Davidson-Arnott, Robin G. D.; Hilton, Michael J.; Fraser, Douglas

2018-06-01

A commonly deployed particle-counting system for aeolian saltation flux, consisting of a Wenglor fork sensor and an Onset Hobo Pulse Input Adapter linked to an Onset Hobo Energy Logger Pro data logger, was tested for frequency response. The Wenglor fork sensor is an optical gate device that has very fast switching capacity that can accommodate the time of flight of saltating sand particles through the sensing volume with the exception of very fine sand or silt and very quickly moving particles. The Pulse Input Adapter, in contrast, imposes limitations on the frequency response of the system. The manufacturer of the pulse adapter specifies an upper limit of 120 Hz, although bench tests with an electronic pulse generator indicate that the frequency response of the Pulse Input Adapter, in isolation, is excellent up to 3000 Hz, with only small error (less than 1.6%) due to under-counting during data transfer intervals. A mechanical test of the integrated system (fork sensor, pulse input adapter, and data logger) demonstrates excellent performance up to about 700 Hz (less than 2% error), but significant under-counting above 1000 Hz for unknown reasons. This specific particle-counting system therefore has a frequency response that is well suited for investigation of the dynamics of aeolian saltation as typically encountered in most field conditions on coastal beaches with the exception of extreme wind events and very small particle sizes.

Early uneven ear input induces long-lasting differences in left-right motor function.

PubMed

Antoine, Michelle W; Zhu, Xiaoxia; Dieterich, Marianne; Brandt, Thomas; Vijayakumar, Sarath; McKeehan, Nicholas; Arezzo, Joseph C; Zukin, R Suzanne; Borkholder, David A; Jones, Sherri M; Frisina, Robert D; Hébert, Jean M

2018-03-01

How asymmetries in motor behavior become established normally or atypically in mammals remains unclear. An established model for motor asymmetry that is conserved across mammals can be obtained by experimentally inducing asymmetric striatal dopamine activity. However, the factors that can cause motor asymmetries in the absence of experimental manipulations to the brain remain unknown. Here, we show that mice with inner ear dysfunction display a robust left or right rotational preference, and this motor preference reflects an atypical asymmetry in cortico-striatal neurotransmission. By unilaterally targeting striatal activity with an antagonist of extracellular signal-regulated kinase (ERK), a downstream integrator of striatal neurotransmitter signaling, we can reverse or exaggerate rotational preference in these mice. By surgically biasing vestibular failure to one ear, we can dictate the direction of motor preference, illustrating the influence of uneven vestibular failure in establishing the outward asymmetries in motor preference. The inner ear-induced striatal asymmetries identified here intersect with non-ear-induced asymmetries previously linked to lateralized motor behavior across species and suggest that aspects of left-right brain function in mammals can be ontogenetically influenced by inner ear input. Consistent with inner ear input contributing to motor asymmetry, we also show that, in humans with normal ear function, the motor-dominant hemisphere, measured as handedness, is ipsilateral to the ear with weaker vestibular input.

Synaptic input correlations leading to membrane potential decorrelation of spontaneous activity in cortex.

PubMed

Graupner, Michael; Reyes, Alex D

2013-09-18

Correlations in the spiking activity of neurons have been found in many regions of the cortex under multiple experimental conditions and are postulated to have important consequences for neural population coding. While there is a large body of extracellular data reporting correlations of various strengths, the subthreshold events underlying the origin and magnitude of signal-independent correlations (called noise or spike count correlations) are unknown. Here we investigate, using intracellular recordings, how synaptic input correlations from shared presynaptic neurons translate into membrane potential and spike-output correlations. Using a pharmacologically activated thalamocortical slice preparation, we perform simultaneous recordings from pairs of layer IV neurons in the auditory cortex of mice and measure synaptic potentials/currents, membrane potentials, and spiking outputs. We calculate cross-correlations between excitatory and inhibitory inputs to investigate correlations emerging from the network. We furthermore evaluate membrane potential correlations near resting potential to study how excitation and inhibition combine and affect spike-output correlations. We demonstrate directly that excitation is correlated with inhibition thereby partially canceling each other and resulting in weak membrane potential and spiking correlations between neurons. Our data suggest that cortical networks are set up to partially cancel correlations emerging from the connections between neurons. This active decorrelation is achieved because excitation and inhibition closely track each other. Our results suggest that the numerous shared presynaptic inputs do not automatically lead to increased spiking correlations.

Inferring topologies via driving-based generalized synchronization of two-layer networks

NASA Astrophysics Data System (ADS)

Wang, Yingfei; Wu, Xiaoqun; Feng, Hui; Lu, Jun-an; Xu, Yuhua

2016-05-01

The interaction topology among the constituents of a complex network plays a crucial role in the network’s evolutionary mechanisms and functional behaviors. However, some network topologies are usually unknown or uncertain. Meanwhile, coupling delays are ubiquitous in various man-made and natural networks. Hence, it is necessary to gain knowledge of the whole or partial topology of a complex dynamical network by taking into consideration communication delay. In this paper, topology identification of complex dynamical networks is investigated via generalized synchronization of a two-layer network. Particularly, based on the LaSalle-type invariance principle of stochastic differential delay equations, an adaptive control technique is proposed by constructing an auxiliary layer and designing proper control input and updating laws so that the unknown topology can be recovered upon successful generalized synchronization. Numerical simulations are provided to illustrate the effectiveness of the proposed method. The technique provides a certain theoretical basis for topology inference of complex networks. In particular, when the considered network is composed of systems with high-dimension or complicated dynamics, a simpler response layer can be constructed, which is conducive to circuit design. Moreover, it is practical to take into consideration perturbations caused by control input. Finally, the method is applicable to infer topology of a subnetwork embedded within a complex system and locate hidden sources. We hope the results can provide basic insight into further research endeavors on understanding practical and economical topology inference of networks.

Olfactory Bulb Deep Short-Axon Cells Mediate Widespread Inhibition of Tufted Cell Apical Dendrites.

PubMed

Burton, Shawn D; LaRocca, Greg; Liu, Annie; Cheetham, Claire E J; Urban, Nathaniel N

2017-02-01

In the main olfactory bulb (MOB), the first station of sensory processing in the olfactory system, GABAergic interneuron signaling shapes principal neuron activity to regulate olfaction. However, a lack of known selective markers for MOB interneurons has strongly impeded cell-type-selective investigation of interneuron function. Here, we identify the first selective marker of glomerular layer-projecting deep short-axon cells (GL-dSACs) and investigate systematically the structure, abundance, intrinsic physiology, feedforward sensory input, neuromodulation, synaptic output, and functional role of GL-dSACs in the mouse MOB circuit. GL-dSACs are located in the internal plexiform layer, where they integrate centrifugal cholinergic input with highly convergent feedforward sensory input. GL-dSAC axons arborize extensively across the glomerular layer to provide highly divergent yet selective output onto interneurons and principal tufted cells. GL-dSACs are thus capable of shifting the balance of principal tufted versus mitral cell activity across large expanses of the MOB in response to diverse sensory and top-down neuromodulatory input. The identification of cell-type-selective molecular markers has fostered tremendous insight into how distinct interneurons shape sensory processing and behavior. In the main olfactory bulb (MOB), inhibitory circuits regulate the activity of principal cells precisely to drive olfactory-guided behavior. However, selective markers for MOB interneurons remain largely unknown, limiting mechanistic understanding of olfaction. Here, we identify the first selective marker of a novel population of deep short-axon cell interneurons with superficial axonal projections to the sensory input layer of the MOB. Using this marker, together with immunohistochemistry, acute slice electrophysiology, and optogenetic circuit mapping, we reveal that this novel interneuron population integrates centrifugal cholinergic input with broadly tuned feedforward sensory input to modulate principal cell activity selectively. Copyright © 2017 the authors 0270-6474/17/371117-22$15.00/0.

Neural Correlates of Sensory Substitution in Vestibular Pathways Following Complete Vestibular Loss

PubMed Central

Sadeghi, Soroush G.; Minor, Lloyd B.; Cullen, Kathleen E.

2012-01-01

Sensory substitution is the term typically used in reference to sensory prosthetic devices designed to replace input from one defective modality with input from another modality. Such devices allow an alternative encoding of sensory information that is no longer directly provided by the defective modality in a purposeful and goal-directed manner. The behavioral recovery that follows complete vestibular loss is impressive and has long been thought to take advantage of a natural form of sensory substitution in which head motion information is no longer provided by vestibular inputs, but instead by extra-vestibular inputs such as proprioceptive and motor efference copy signals. Here we examined the neuronal correlates of this behavioral recovery after complete vestibular loss in alert behaving monkeys (Macaca mulata). We show for the first time that extra-vestibular inputs substitute for the vestibular inputs to stabilize gaze at the level of single neurons in the VOR premotor circuitry. The summed weighting of neck proprioceptive and efference copy information was sufficient to explain simultaneously observed behavioral improvements in gaze stability. Furthermore, by altering correspondence between intended and actual head movement we revealed a four-fold increase in the weight of neck motor efference copy signals consistent with the enhanced behavioral recovery observed when head movements are voluntary versus unexpected. Thus, taken together our results provide direct evidence that the substitution by extra-vestibular inputs in vestibular pathways provides a neural correlate for the improvements in gaze stability that are observed following the total loss of vestibular inputs. PMID:23077054

Deposition modes in the paleo-lake Colônia (São Paulo, SE / Brazil): detrital input and bio-geochemical processes

NASA Astrophysics Data System (ADS)

Roeser, Patricia; Ledru, Marie-Pierre; Thouveny, Nicolas; Tachikawa, Kazuyo; Rostek, Frauke; Garcia, Marta; Struck, Ulrich; Sawakuchi, André; Favier, Charly; Bard, Edouard

2017-04-01

Colônia, a geomorphological circular structure in southeast Brazil, probably originated from an meteor impact with still unknown age. The structure, situated 40 km south of the center of the mega city São Paulo, has ca. 3.6 km in diameter and a surrounding rim elevated by ca. 120 meters. At present, the inner part of the structure contains a swampy alluvial plain. Sediment columns recovered in September 2014 have shown that below a circa 8 meter thick peat deposit, sediments are lacustrine and characterized by light-gray bands (cm scale). According to a preliminary age-depth model, based on radiocarbon ages, luminescence ages and paleomagnetism, the transition between lake to peat deposition seems to relate to climate boundary conditions from glacial towards interglacial conditions. In the lacustrine fine-grained sediments, the banded gray layers have distinct grain size, as macroscopically observed from mica grains/plates. Correlated to high-resolution geochemical data, lighter colored bands hold increased amounts of K and Si [XRF counts], originating from detrital input from the basin, e.g. flood events during tropical storms. Potassium is mainly contained in the crystalline structure of muscovite, whereas silica is additionally contained in kaolinite and quartz, thereby completing the minerals that make out the major mineral assemblage found in the sediments. Pyrite is found as an accessory mineral with average concentrations between 1 and 2%, peaking at 5% up to 10% in covariance to Fe/Ti [XRF count ratio]. Overall a covariance pattern, with or without phase lag, between pyrite, ∂13C (of TOC) and the concentrations of the biomarker hopane is observed in the lacustrine sediments. These relationships likely originate from stratification conditions in the paleo-lake, such that a more stable stratification eventually led to anoxic lake bottom conditions, favoring authigenic/microbial pyrite precipitation, better preservation of organic matter and affecting gas exchange between the water and the atmosphere.

Radius Determination of Solar-type Stars Using Asteroseismology: What to Expect from the Kepler Mission

NASA Astrophysics Data System (ADS)

Stello, Dennis; Chaplin, William J.; Bruntt, Hans; Creevey, Orlagh L.; García-Hernández, Antonio; Monteiro, Mario J. P. F. G.; Moya, Andrés; Quirion, Pierre-Olivier; Sousa, Sergio G.; Suárez, Juan-Carlos; Appourchaux, Thierry; Arentoft, Torben; Ballot, Jerome; Bedding, Timothy R.; Christensen-Dalsgaard, Jørgen; Elsworth, Yvonne; Fletcher, Stephen T.; García, Rafael A.; Houdek, Günter; Jiménez-Reyes, Sebastian J.; Kjeldsen, Hans; New, Roger; Régulo, Clara; Salabert, David; Toutain, Thierry

2009-08-01

For distant stars, as observed by the NASA Kepler satellite, parallax information is currently of fairly low quality and is not complete. This limits the precision with which the absolute sizes of the stars and their potential transiting planets can be determined by traditional methods. Asteroseismology will be used to aid the radius determination of stars observed during NASA's Kepler mission. We report on the recent asteroFLAG hare-and-hounds Exercise#2, where a group of "hares" simulated data of F-K main-sequence stars that a group of "hounds" sought to analyze, aimed at determining the stellar radii. We investigated stars in the range 9 < V < 15, both with and without parallaxes. We further test different uncertainties in T eff, and compare results with and without using asteroseismic constraints. Based on the asteroseismic large frequency spacing, obtained from simulations of 4 yr time series data from the Kepler mission, we demonstrate that the stellar radii can be correctly and precisely determined, when combined with traditional stellar parameters from the Kepler Input Catalogue. The radii found by the various methods used by each independent hound generally agree with the true values of the artificial stars to within 3%, when the large frequency spacing is used. This is 5-10 times better than the results where seismology is not applied. These results give strong confidence that radius estimation can be performed to better than 3% for solar-like stars using automatic pipeline reduction. Even when the stellar distance and luminosity are unknown we can obtain the same level of agreement. Given the uncertainties used for this exercise we find that the input log g and parallax do not help to constrain the radius, and that T eff and metallicity are the only parameters we need in addition to the large frequency spacing. It is the uncertainty in the metallicity that dominates the uncertainty in the radius.

Comparing taxi clearance input layouts for advancements in flight deck automation for surface operations

NASA Astrophysics Data System (ADS)

Cheng, Lara W. S.

Airport moving maps (AMMs) have been shown to decrease navigation errors, increase taxiing speed, and reduce workload when they depict airport layout, current aircraft position, and the cleared taxi route. However, current technologies are limited in their ability to depict the cleared taxi route due to the unavailability of datacomm or other means of electronically transmitting clearances from ATC to the flight deck. This study examined methods by which pilots can input ATC-issued taxi clearances to support taxi route depictions on the AMM. Sixteen general aviation (GA) pilots used a touchscreen monitor to input taxi clearances using two input layouts, softkeys and QWERTY, each with and without feedforward (graying out invalid inputs). QWERTY yielded more taxi route input errors than the softkeys layout. The presence of feedforward did not produce fewer taxi route input errors than in the non-feedforward condition. The QWERTY layout did reduce taxi clearance input times relative to the softkeys layout, but when feedforward was present this effect was observed only for the longer, 6-segment taxi clearances. It was observed that with the softkeys layout, feedforward reduced input times compared to non-feedforward but only for the 4-segment clearances. Feedforward did not support faster taxi clearance input times for the QWERTY layout. Based on the results and analyses of the present study, it is concluded that for taxi clearance inputs, (1) QWERTY remain the standard for alphanumeric inputs, and (2) feedforward be investigated further, with a focus on participant preference and performance of black-gray contrast of keys.

Translocation of CaMKII to dendritic microtubules supports the plasticity of local synapses

PubMed Central

Lemieux, Mado; Labrecque, Simon; Tardif, Christian; Labrie-Dion, Étienne; LeBel, Éric

2012-01-01

The processing of excitatory synaptic inputs involves compartmentalized dendritic Ca2+ oscillations. The downstream signaling evoked by these local Ca2+ transients and their impact on local synaptic development and remodeling are unknown. Ca2+/calmodulin-dependent protein kinase II (CaMKII) is an important decoder of Ca2+ signals and mediator of synaptic plasticity. In addition to its known accumulation at spines, we observed with live imaging the dynamic recruitment of CaMKII to dendritic subdomains adjacent to activated synapses in cultured hippocampal neurons. This localized and transient enrichment of CaMKII to dendritic sites coincided spatially and temporally with dendritic Ca2+ transients. We show that it involved an interaction with microtubular elements, required activation of the kinase, and led to localized dendritic CaMKII autophosphorylation. This process was accompanied by the adjacent remodeling of spines and synaptic AMPA receptor insertion. Replacement of endogenous CaMKII with a mutant that cannot translocate within dendrites lessened this activity-dependent synaptic plasticity. Thus, CaMKII could decode compartmental dendritic Ca2+ transients to support remodeling of local synapses. PMID:22965911

A user-friendly one-dimensional model for wet volcanic plumes

USGS Publications Warehouse

Mastin, Larry G.

2007-01-01

This paper presents a user-friendly graphically based numerical model of one-dimensional steady state homogeneous volcanic plumes that calculates and plots profiles of upward velocity, plume density, radius, temperature, and other parameters as a function of height. The model considers effects of water condensation and ice formation on plume dynamics as well as the effect of water added to the plume at the vent. Atmospheric conditions may be specified through input parameters of constant lapse rates and relative humidity, or by loading profiles of actual atmospheric soundings. To illustrate the utility of the model, we compare calculations with field-based estimates of plume height (∼9 km) and eruption rate (>∼4 × 105 kg/s) during a brief tephra eruption at Mount St. Helens on 8 March 2005. Results show that the atmospheric conditions on that day boosted plume height by 1–3 km over that in a standard dry atmosphere. Although the eruption temperature was unknown, model calculations most closely match the observations for a temperature that is below magmatic but above 100°C.

Sensor fault diagnosis of singular delayed LPV systems with inexact parameters: an uncertain system approach

NASA Astrophysics Data System (ADS)

Hassanabadi, Amir Hossein; Shafiee, Masoud; Puig, Vicenc

2018-01-01

In this paper, sensor fault diagnosis of a singular delayed linear parameter varying (LPV) system is considered. In the considered system, the model matrices are dependent on some parameters which are real-time measurable. The case of inexact parameter measurements is considered which is close to real situations. Fault diagnosis in this system is achieved via fault estimation. For this purpose, an augmented system is created by including sensor faults as additional system states. Then, an unknown input observer (UIO) is designed which estimates both the system states and the faults in the presence of measurement noise, disturbances and uncertainty induced by inexact measured parameters. Error dynamics and the original system constitute an uncertain system due to inconsistencies between real and measured values of the parameters. Then, the robust estimation of the system states and the faults are achieved with H∞ performance and formulated with a set of linear matrix inequalities (LMIs). The designed UIO is also applicable for fault diagnosis of singular delayed LPV systems with unmeasurable scheduling variables. The efficiency of the proposed approach is illustrated with an example.

Ground Motion Prediction Model Using Artificial Neural Network

NASA Astrophysics Data System (ADS)

Dhanya, J.; Raghukanth, S. T. G.

2018-03-01

This article focuses on developing a ground motion prediction equation based on artificial neural network (ANN) technique for shallow crustal earthquakes. A hybrid technique combining genetic algorithm and Levenberg-Marquardt technique is used for training the model. The present model is developed to predict peak ground velocity, and 5% damped spectral acceleration. The input parameters for the prediction are moment magnitude ( M w), closest distance to rupture plane ( R rup), shear wave velocity in the region ( V s30) and focal mechanism ( F). A total of 13,552 ground motion records from 288 earthquakes provided by the updated NGA-West2 database released by Pacific Engineering Research Center are utilized to develop the model. The ANN architecture considered for the model consists of 192 unknowns including weights and biases of all the interconnected nodes. The performance of the model is observed to be within the prescribed error limits. In addition, the results from the study are found to be comparable with the existing relations in the global database. The developed model is further demonstrated by estimating site-specific response spectra for Shimla city located in Himalayan region.

Compressive MIMO Beamforming of Data Collected in a Refractive Environment

NASA Astrophysics Data System (ADS)

Wagner, Mark; Nannuru, Santosh; Gerstoft, Peter

2017-12-01

The phenomenon of ducting is caused by abnormal atmospheric refractivity patterns and is known to allow electromagnetic waves to propagate over the horizon with unusually low propagation loss. It is unknown what effect ducting has on multiple input multiple output (MIMO) channels, particularly its effect on multipath propagation in MIMO channels. A high-accuracy angle-of-arrival and angle-of-departure estimation technique for MIMO communications, which we will refer to as compressive MIMO beamforming, was tested on simulated data then applied to experimental data taken from an over the horizon MIMO test bed located in a known ducting hot spot in Southern California. The multipath channel was estimated from the receiver data recorded over a period of 18 days, and an analysis was performed on the recorded data. The goal is to observe the evolution of the MIMO multipath channel as atmospheric ducts form and dissipate to gain some understanding of the behavior of channels in a refractive environment. This work is motivated by the idea that some multipath characteristics of MIMO channels within atmospheric ducts could yield important information about the duct.

16S rRNA Gene-Based Metagenomic Analysis of Ozark Cave Bacteria.

PubMed

Oliveira, Cássia; Gunderman, Lauren; Coles, Cathryn A; Lochmann, Jason; Parks, Megan; Ballard, Ethan; Glazko, Galina; Rahmatallah, Yasir; Tackett, Alan J; Thomas, David J

2017-09-01

The microbial diversity within cave ecosystems is largely unknown. Ozark caves maintain a year-round stable temperature (12-14 °C), but most parts of the caves experience complete darkness. The lack of sunlight and geological isolation from surface-energy inputs generate nutrient-poor conditions that may limit species diversity in such environments. Although microorganisms play a crucial role in sustaining life on Earth and impacting human health, little is known about their diversity, ecology, and evolution in community structures. We used five Ozark region caves as test sites for exploring bacterial diversity and monitoring long-term biodiversity. Illumina MiSeq sequencing of five cave soil samples and a control sample revealed a total of 49 bacterial phyla, with seven major phyla: Proteobacteria, Acidobacteria, Actinobacteria, Firmicutes, Chloroflexi, Bacteroidetes, and Nitrospirae. Variation in bacterial composition was observed among the five caves studied. Sandtown Cave had the lowest richness and most divergent community composition. 16S rRNA gene-based metagenomic analysis of cave-dwelling microbial communities in the Ozark caves revealed that species abundance and diversity are vast and included ecologically, agriculturally, and economically relevant taxa.

Genetically increased cell-intrinsic excitability enhances neuronal integration into adult brain circuits

PubMed Central

Lin, Chia-Wei; Sim, Shuyin; Ainsworth, Alice; Okada, Masayoshi; Kelsch, Wolfgang; Lois, Carlos

2009-01-01

New neurons are added to the adult brain throughout life, but only half ultimately integrate into existing circuits. Sensory experience is an important regulator of the selection of new neurons but it remains unknown whether experience provides specific patterns of synaptic input, or simply a minimum level of overall membrane depolarization critical for integration. To investigate this issue, we genetically modified intrinsic electrical properties of adult-generated neurons in the mammalian olfactory bulb. First, we observed that suppressing levels of cell-intrinsic neuronal activity via expression of ESKir2.1 potassium channels decreases, whereas enhancing activity via expression of NaChBac sodium channels increases survival of new neurons. Neither of these modulations affects synaptic formation. Furthermore, even when neurons are induced to fire dramatically altered patterns of action potentials, increased levels of cell-intrinsic activity completely blocks cell death triggered by NMDA receptor deletion. These findings demonstrate that overall levels of cell-intrinsic activity govern survival of new neurons and precise firing patterns are not essential for neuronal integration into existing brain circuits. PMID:20152111

Iron from a submarine source impacts the productive layer of the Western Tropical South Pacific (WTSP).

PubMed

Guieu, Cécile; Bonnet, Sophie; Petrenko, Anne; Menkes, Christophe; Chavagnac, Valérie; Desboeufs, Karine; Maes, Christophe; Moutin, Thierry

2018-06-13

In the Western Tropical South Pacific, patches of high chlorophyll concentrations linked to the occurrence of N 2 -fixing organisms are found in the vicinity of volcanic islands. The survival of these organisms relies on a high bioavailable iron supply whose origin and fluxes remain unknown. Here, we measured high dissolved iron (DFe) concentrations (up to 66 nM) in the euphotic layer, extending zonally over 10 degrees longitude (174 E-175 W) at ∼20°S latitude. DFe atmospheric fluxes were at the lower end of reported values of the remote ocean and could not explain the high DFe concentrations measured in the water column in the vicinity of Tonga. We argue that the high DFe concentrations may be sustained by a submarine source, also characterized by freshwater input and recorded as salinity anomalies by Argo float in situ measurements and atlas data. The observed negative salinity anomalies are reproduced by simulations from a general ocean circulation model. Submarine iron sources reaching the euphotic layer may impact nitrogen fixation across the whole region.

The interactive effects of excess reactive nitrogen and climate change on aquatic ecosystems and water resources of the United States

USGS Publications Warehouse

Baron, Jill S.; Hall, E.K.; Nolan, B.T.; Finlay, J.C.; Bernhardt, E.S.; Harrison, J.A.; Chan, F.; Boyer, E.W.

2012-01-01

Nearly all freshwaters and coastal zones of the US are degraded from inputs of excess reactive nitrogen (Nr), sources of which are runoff, atmospheric N deposition, and imported food and feed. Some major adverse effects include harmful algal blooms, hypoxia of fresh and coastal waters, ocean acidification, long-term harm to human health, and increased emissions of greenhouse gases. Nitrogen fluxes to coastal areas and emissions of nitrous oxide from waters have increased in response to N inputs. Denitrification and sedimentation of organic N to sediments are important processes that divert N from downstream transport. Aquatic ecosystems are particularly important denitrification hotspots. Carbon storage in sediments is enhanced by Nr, but whether carbon is permanently buried is unknown. The effect of climate change on N transport and processing in fresh and coastal waters will be felt most strongly through changes to the hydrologic cycle, whereas N loading is mostly climate-independent. Alterations in precipitation amount and dynamics will alter runoff, thereby influencing both rates of Nr inputs to aquatic ecosystems and groundwater and the water residence times that affect Nr removal within aquatic systems. Both infrastructure and climate change alter the landscape connectivity and hydrologic residence time that are essential to denitrification. While Nr inputs to and removal rates from aquatic systems are influenced by climate and management, reduction of N inputs from their source will be the most effective means to prevent or to minimize environmental and economic impacts of excess Nr to the nation’s water resources.

The anatomy of clinical decision-making in multidisciplinary cancer meetings

PubMed Central

Soukup, Tayana; Petrides, Konstantinos V.; Lamb, Benjamin W.; Sarkar, Somita; Arora, Sonal; Shah, Sujay; Darzi, Ara; Green, James S. A.; Sevdalis, Nick

2016-01-01

Abstract In the UK, treatment recommendations for patients with cancer are routinely made by multidisciplinary teams in weekly meetings. However, their performance is variable. The aim of this study was to explore the underlying structure of multidisciplinary decision-making process, and examine how it relates to team ability to reach a decision. This is a cross-sectional observational study consisting of 1045 patient reviews across 4 multidisciplinary cancer teams from teaching and community hospitals in London, UK, from 2010 to 2014. Meetings were chaired by surgeons. We used a validated observational instrument (Metric for the Observation of Decision-making in Cancer Multidisciplinary Meetings) consisting of 13 items to assess the decision-making process of each patient discussion. Rated on a 5-point scale, the items measured quality of presented patient information, and contributions to review by individual disciplines. A dichotomous outcome (yes/no) measured team ability to reach a decision. Ratings were submitted to Exploratory Factor Analysis and regression analysis. The exploratory factor analysis produced 4 factors, labeled “Holistic and Clinical inputs” (patient views, psychosocial aspects, patient history, comorbidities, oncologists’, nurses’, and surgeons’ inputs), “Radiology” (radiology results, radiologists’ inputs), “Pathology” (pathology results, pathologists’ inputs), and “Meeting Management” (meeting chairs’ and coordinators’ inputs). A negative cross-loading was observed from surgeons’ input on the fourth factor with a follow-up analysis showing negative correlation (r = −0.19, P < 0.001). In logistic regression, all 4 factors predicted team ability to reach a decision (P < 0.001). Hawthorne effect is the main limitation of the study. The decision-making process in cancer meetings is driven by 4 underlying factors representing the complete patient profile and contributions to case review by all core disciplines. Evidence of dual-task interference was observed in relation to the meeting chairs’ input and their corresponding surgical input into case reviews. PMID:27310981

Altered Excitability and Local Connectivity of mPFC-PAG Neurons in a Mouse Model of Neuropathic Pain.

PubMed

Cheriyan, John; Sheets, Patrick L

2018-05-16

The medial prefrontal cortex (mPFC) plays a major role in both sensory and affective aspects of pain. There is extensive evidence that chronic pain produces functional changes within the mPFC. However, our understanding of local circuit changes to defined subpopulations of mPFC neurons in chronic pain models remains unclear. A major subpopulation of mPFC neurons project to the periaqueductal gray (PAG), which is a key midbrain structure involved in endogenous pain suppression and facilitation. Here, we used laser scanning photostimulation of caged glutamate to map cortical circuits of retrogradely labeled cortico-PAG (CP) neurons in layer 5 (L5) of mPFC in brain slices prepared from male mice having undergone chronic constriction injury (CCI) of the sciatic nerve. Whole-cell recordings revealed a significant reduction in excitability for L5 CP neurons contralateral to CCI in the prelimbic (PL), but not infralimbic (IL), region of mPFC. Circuit mapping showed that excitatory inputs to L5 CP neurons in both PL and IL arose primarily from layer 2/3 (L2/3) and were significantly reduced in CCI mice. Glutamate stimulation of L2/3 and L5 elicited inhibitory inputs to CP neurons in both PL and IL, but only L2/3 input was significantly reduced in CP neurons of CCI mice. We also observed significant reduction in excitability and L2/3 inhibitory input to CP neurons ipsilateral to CCI. These results demonstrating region and laminar specific changes to mPFC-PAG neurons suggest that a unilateral CCI bilaterally alters cortical circuits upstream of the endogenous analgesic network, which may contribute to persistence of chronic pain. SIGNIFICANCE STATEMENT Chronic pain is a significant unresolved medical problem that is refractory to traditional analgesics and can negatively affect emotional health. The role of central circuits in mediating the persistent nature of chronic pain remains unclear. Local circuits within the medial prefrontal cortex (mPFC) process ascending pain inputs and can modulate endogenous analgesia via direct projections to the periaqueductal gray (PAG). However, the mechanisms by which chronic pain alters intracortical circuitry of mPFC-PAG neurons are unknown. Here, we report specific changes to local circuits of mPFC-PAG neurons in mice displaying chronic pain behavior after nerve injury. These findings provide evidence for a neural mechanism by which chronic pain disrupts the descending analgesic system via functional changes to cortical circuits. Copyright © 2018 the authors 0270-6474/18/384829-11$15.00/0.

Spinal cord processing of cardiac nociception: are there sex differences between male and proestrous female rats?

PubMed

Little, Janine M; Qin, Chao; Farber, Jay P; Foreman, Robert D

2011-09-21

Sex differences in the characteristics of cardiac pain have been reported from clinical studies. For example, women experience chest pain less frequently than men. Women describe their chest pain as sharp and stabbing, while men have chest pain that is felt as a pressure or heaviness. Pain is also referred to the back more often in women than men. The mechanisms underlying sex differences in cardiac pain are unknown. One possible mechanism for the observed differences could be related to plasma estradiol. This study investigated the actions of estradiol on the activity of T(3) spinal neurons that process cardiosomatic information in male and female rats. Extracellular potentials of T(3) spinal neurons were recorded in response to mechanical somatic stimulation and noxious chemical cardiac stimulation in pentobarbital-anesthetized male and proestrous female rats. Fifty one percent and fifty percent of neurons responded to intrapericardial algogenic chemicals (0.2 ml) in male and female rats, respectively. Somatic fields were located by applying brush, pressure, and pinch to the upper body. Of those neurons receiving cardiac input, 54% in female and 55% in male rats also received somatic input. In both male and female rats, 81% of neurons responding to somatic stimuli had somatic fields located on the side of the upper body, while 19% of neurons had somatic fields located on the chest. These results indicate there are no significant differences in the responses of T(3) spinal neurons to cardiosomatic stimulation between male and proestrous female rats, despite differences in estradiol levels. Published by Elsevier B.V.

Comparison of several maneuvering target tracking models

NASA Astrophysics Data System (ADS)

McIntyre, Gregory A.; Hintz, Kenneth J.

1998-07-01

The tracking of maneuvering targets is complicated by the fact that acceleration is not directly observable or measurable. Additionally, acceleration can be induced by a variety of sources including human input, autonomous guidance, or atmospheric disturbances. The approaches to tracking maneuvering targets can be divided into two categories both of which assume that the maneuver input command is unknown. One approach is to model the maneuver as a random process. The other approach assumes that the maneuver is not random and that it is either detected or estimated in real time. The random process models generally assume one of two statistical properties, either white noise or an autocorrelated noise. The multiple-model approach is generally used with the white noise model while a zero-mean, exponentially correlated acceleration approach is used with the autocorrelated noise model. The nonrandom approach uses maneuver detection to correct the state estimate or a variable dimension filter to augment the state estimate with an extra state component during a detected maneuver. Another issue with the tracking of maneuvering target is whether to perform the Kalman filter in Polar or Cartesian coordinates. This paper will examine and compare several exponentially correlated acceleration approaches in both Polar and Cartesian coordinates for accuracy and computational complexity. They include the Singer model in both Polar and Cartesian coordinates, the Singer model in Polar coordinates converted to Cartesian coordinates, Helferty's third order rational approximation of the Singer model and the Bar-Shalom and Fortmann model. This paper shows that these models all provide very accurate position estimates with only minor differences in velocity estimates and compares the computational complexity of the models.

Seaglider surveys at Ocean Station Papa: Diagnosis of upper-ocean heat and salt balances using least squares with inequality constraints

NASA Astrophysics Data System (ADS)

Pelland, Noel A.; Eriksen, Charles C.; Cronin, Meghan F.

2017-06-01

Heat and salt balances in the upper 200 m are examined using data from Seaglider spatial surveys June 2008 to January 2010 surrounding a NOAA surface mooring at Ocean Station Papa (OSP; 50°N, 145°W). A least-squares approach is applied to repeat Seaglider survey and moored measurements to solve for unknown or uncertain monthly three-dimensional circulation and vertical diffusivity. Within the surface boundary layer, the estimated heat and salt balances are dominated throughout the surveys by turbulent flux, vertical advection, and for heat, radiative absorption. When vertically integrated balances are considered, an estimated upwelling of cool water balances the net surface input of heat, while the corresponding large import of salt across the halocline due to upwelling and diffusion is balanced by surface moisture input and horizontal import of fresh water. Measurement of horizontal gradients allows the estimation of unresolved vertical terms over more than one annual cycle; diffusivity in the upper-ocean transition layer decreases rapidly to the depth of the maximum near-surface stratification in all months, with weak seasonal modulation in the rate of decrease and profile amplitude. Vertical velocity is estimated to be on average upward but with important monthly variations. Results support and expand existing evidence concerning the importance of horizontal advection in the balances of heat and salt in the Gulf of Alaska, highlight time and depth variability in difficult-to-measure vertical transports in the upper ocean, and suggest avenues of further study in future observational work at OSP.

A Parametric Approach to Numerical Modeling of TKR Contact Forces

PubMed Central

Lundberg, Hannah J.; Foucher, Kharma C.; Wimmer, Markus A.

2009-01-01

In vivo knee contact forces are difficult to determine using numerical methods because there are more unknown forces than equilibrium equations available. We developed parametric methods for computing contact forces across the knee joint during the stance phase of level walking. Three-dimensional contact forces were calculated at two points of contact between the tibia and the femur, one on the lateral aspect of the tibial plateau, and one on the medial side. Muscle activations were parametrically varied over their physiologic range resulting in a solution space of contact forces. The obtained solution space was reasonably small and the resulting force pattern compared well to a previous model from the literature for kinematics and external kinetics from the same patient. Peak forces of the parametric model and the previous model were similar for the first half of the stance phase, but differed for the second half. The previous model did not take into account the transverse external moment about the knee and could not calculate muscle activation levels. Ultimately, the parametric model will result in more accurate contact force inputs for total knee simulators, as current inputs are not generally based on kinematics and kinetics inputs from TKR patients. PMID:19155015

Tropical Andean Forests Are Highly Susceptible to Nutrient Inputs—Rapid Effects of Experimental N and P Addition to an Ecuadorian Montane Forest

PubMed Central

Homeier, Jürgen; Hertel, Dietrich; Camenzind, Tessa; Cumbicus, Nixon L.; Maraun, Mark; Martinson, Guntars O.; Poma, L. Nohemy; Rillig, Matthias C.; Sandmann, Dorothee; Scheu, Stefan; Veldkamp, Edzo; Wilcke, Wolfgang; Wullaert, Hans; Leuschner, Christoph

2012-01-01

Tropical regions are facing increasing atmospheric inputs of nutrients, which will have unknown consequences for the structure and functioning of these systems. Here, we show that Neotropical montane rainforests respond rapidly to moderate additions of N (50 kg ha−1 yr−1) and P (10 kg ha−1 yr−1). Monitoring of nutrient fluxes demonstrated that the majority of added nutrients remained in the system, in either soil or vegetation. N and P additions led to not only an increase in foliar N and P concentrations, but also altered soil microbial biomass, standing fine root biomass, stem growth, and litterfall. The different effects suggest that trees are primarily limited by P, whereas some processes—notably aboveground productivity—are limited by both N and P. Highly variable and partly contrasting responses of different tree species suggest marked changes in species composition and diversity of these forests by nutrient inputs in the long term. The unexpectedly fast response of the ecosystem to moderate nutrient additions suggests high vulnerability of tropical montane forests to the expected increase in nutrient inputs. PMID:23071734

MERRA-2 Input Observations: Summary and Assessment

NASA Technical Reports Server (NTRS)

Koster, Randal D. (Editor); McCarty, Will; Coy, Lawrence; Gelaro, Ronald; Huang, Albert; Merkova, Dagmar; Smith, Edmond B.; Sienkiewicz, Meta; Wargan, Krzysztof

2016-01-01

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is an atmospheric reanalysis, spanning 1980 through near-realtime, that uses state-of-the-art processing of observations from the continually evolving global observing system. The effectiveness of any reanalysis is a function not only of the input observations themselves, but also of how the observations are handled in the assimilation procedure. Relevant issues to consider include, but are not limited to, data selection, data preprocessing, quality control, bias correction procedures, and blacklisting. As the assimilation algorithm and earth system models are fundamentally fixed in a reanalysis, it is often a change in the character of the observations, and their feedbacks on the system, that cause changes in the character of the reanalysis. It is therefore important to provide documentation of the observing system so that its discontinuities and transitions can be readily linked to discontinuities seen in the gridded atmospheric fields of the reanalysis. With this in mind, this document provides an exhaustive list of the input observations, the context under which they are assimilated, and an initial assessment of selected core observations fundamental to the reanalysis.

Observations of the directional distribution of the wind energy input function over swell waves

NASA Astrophysics Data System (ADS)

Shabani, Behnam; Babanin, Alex V.; Baldock, Tom E.

2016-02-01

Field measurements of wind stress over shallow water swell traveling in different directions relative to the wind are presented. The directional distribution of the measured stresses is used to confirm the previously proposed but unverified directional distribution of the wind energy input function. The observed wind energy input function is found to follow a much narrower distribution (β∝cos⁡3.6θ) than the Plant (1982) cosine distribution. The observation of negative stress angles at large wind-wave angles, however, indicates that the onset of negative wind shearing occurs at about θ≈ 50°, and supports the use of the Snyder et al. (1981) directional distribution. Taking into account the reverse momentum transfer from swell to the wind, Snyder's proposed parameterization is found to perform exceptionally well in explaining the observed narrow directional distribution of the wind energy input function, and predicting the wind drag coefficients. The empirical coefficient (ɛ) in Snyder's parameterization is hypothesised to be a function of the wave shape parameter, with ɛ value increasing as the wave shape changes between sinusoidal, sawtooth, and sharp-crested shoaling waves.

Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Mousavi, Shahram

2016-05-01

Uncertainties of the field parameters, noise of the observed data and unknown boundary conditions are the main factors involved in the groundwater level (GL) time series which limit the modeling and simulation of GL. This paper presents a hybrid artificial intelligence-meshless model for spatiotemporal GL modeling. In this way firstly time series of GL observed in different piezometers were de-noised using threshold-based wavelet method and the impact of de-noised and noisy data was compared in temporal GL modeling by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). In the second step, both ANN and ANFIS models were calibrated and verified using GL data of each piezometer, rainfall and runoff considering various input scenarios to predict the GL at one month ahead. In the final step, the simulated GLs in the second step of modeling were considered as interior conditions for the multiquadric radial basis function (RBF) based solve of governing partial differential equation of groundwater flow to estimate GL at any desired point within the plain where there is not any observation. In order to evaluate and compare the GL pattern at different time scales, the cross-wavelet coherence was also applied to GL time series of piezometers. The results showed that the threshold-based wavelet de-noising approach can enhance the performance of the modeling up to 13.4%. Also it was found that the accuracy of ANFIS-RBF model is more reliable than ANN-RBF model in both calibration and validation steps.

Estimation of soft sediment thickness in Kuala Lumpur based on microtremor observation data

NASA Astrophysics Data System (ADS)

Chiew, Chang Chyau; Cheah, Yi Ben; Tan, Chin Guan; Lau, Tze Liang

2017-10-01

Seismic site effect is one of the major concerns in earthquake engineering. Soft ground tends to amplify the seismic wave in surficial geological layers. The determination of soft ground thickness on the surface layers of the earth is an important input for seismic hazard assessment. This paper presents an easy and convenient approach to estimate the soft sediment thickness at the site using microtremor observation technique. A total number of 133 survey points were conducted in selected sites around Kuala Lumpur area using a microtremor measuring instrument, but only 103 survey points contributed to the seismic microzonation and sediment thickness plots. The bedrock of Kuala Lumpur area is formed by Kenny Hill Formation, limestone, granite, and the Hawthornden Schist; however, the thickness of surface soft ground formed by alluvial deposits, mine tailings, and residual soils remains unknown. Hence, the predominant frequency of the ground in each site was determined based on Nakamura method. A total number of 14 sites with known depth to bedrock from the supply of geotechnical reports in the study area were determined. An empirical correlation was developed to relate the ground predominant frequency and soft ground thickness. This correlation may contribute to local soil underlying the subsurface of Kuala Lumpur area. The finding provides an important relationship for engineers to estimate the soft ground thickness in Kuala Lumpur area based on the dynamic characteristics of the ground measured from microtremor observation.

Bayesian methods for characterizing unknown parameters of material models

DOE PAGES

Emery, J. M.; Grigoriu, M. D.; Field Jr., R. V.

2016-02-04

A Bayesian framework is developed for characterizing the unknown parameters of probabilistic models for material properties. In this framework, the unknown parameters are viewed as random and described by their posterior distributions obtained from prior information and measurements of quantities of interest that are observable and depend on the unknown parameters. The proposed Bayesian method is applied to characterize an unknown spatial correlation of the conductivity field in the definition of a stochastic transport equation and to solve this equation by Monte Carlo simulation and stochastic reduced order models (SROMs). As a result, the Bayesian method is also employed tomore » characterize unknown parameters of material properties for laser welds from measurements of peak forces sustained by these welds.« less

Bayesian methods for characterizing unknown parameters of material models

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

Emery, J. M.; Grigoriu, M. D.; Field Jr., R. V.

A Bayesian framework is developed for characterizing the unknown parameters of probabilistic models for material properties. In this framework, the unknown parameters are viewed as random and described by their posterior distributions obtained from prior information and measurements of quantities of interest that are observable and depend on the unknown parameters. The proposed Bayesian method is applied to characterize an unknown spatial correlation of the conductivity field in the definition of a stochastic transport equation and to solve this equation by Monte Carlo simulation and stochastic reduced order models (SROMs). As a result, the Bayesian method is also employed tomore » characterize unknown parameters of material properties for laser welds from measurements of peak forces sustained by these welds.« less

Vascular involvement in systemic sclerosis (scleroderma)

PubMed Central

Pattanaik, Debendra; Brown, Monica; Postlethwaite, Arnold E

2011-01-01

Systemic sclerosis (SSc) is an acquired multiorgan connective tissue disease with variable mortality and morbidity dictated by clinical subset type. The etiology of the basic disease and pathogenesis of the systemic autoimmunity, fibrosis, and fibroproliferative vasculopathy are unknown and debated. In this review, the spectrum of vascular abnormalities and the options currently available to treat the vascular manifestations of SSc are discussed. Also discussed is how the hallmark pathologies (ie, how autoimmunity, vasculopathy, and fibrosis of the disease) might be effected and interconnected with modulatory input from lysophospholipids, sphingosine 1-phosphate, and lysophosphatidic acid. PMID:22096374

Global SWOT Data Assimilation of River Hydrodynamic Model; the Twin Simulation Test of CaMa-Flood

NASA Astrophysics Data System (ADS)

Ikeshima, D.; Yamazaki, D.; Kanae, S.

2016-12-01

CaMa-Flood is a global scale model for simulating hydrodynamics in large scale rivers. It can simulate river hydrodynamics such as river discharge, flooded area, water depth and so on by inputting water runoff derived from land surface model. Recently many improvements at parameters or terrestrial data are under process to enhance the reproducibility of true natural phenomena. However, there are still some errors between nature and simulated result due to uncertainties in each model. SWOT (Surface water and Ocean Topography) is a satellite, which is going to be launched in 2021, can measure open water surface elevation. SWOT observed data can be used to calibrate hydrodynamics model at river flow forecasting and is expected to improve model's accuracy. Combining observation data into model to calibrate is called data assimilation. In this research, we developed data-assimilated river flow simulation system in global scale, using CaMa-Flood as river hydrodynamics model and simulated SWOT as observation data. Generally at data assimilation, calibrating "model value" with "observation value" makes "assimilated value". However, the observed data of SWOT satellite will not be available until its launch in 2021. Instead, we simulated the SWOT observed data using CaMa-Flood. Putting "pure input" into CaMa-Flood produce "true water storage". Extracting actual daily swath of SWOT from "true water storage" made simulated observation. For "model value", we made "disturbed water storage" by putting "noise disturbed input" to CaMa-Flood. Since both "model value" and "observation value" are made by same model, we named this twin simulation. At twin simulation, simulated observation of "true water storage" is combined with "disturbed water storage" to make "assimilated value". As the data assimilation method, we used ensemble Kalman filter. If "assimilated value" is closer to "true water storage" than "disturbed water storage", the data assimilation can be marked effective. Also by changing the input disturbance of "disturbed water storage", acceptable rate of uncertainty at the input may be discussed.

Effect of Heat Input on Microstructural Changes and Corrosion Behavior of Commercially Pure Titanium Welds in Nitric Acid Medium

NASA Astrophysics Data System (ADS)

Ravi Shankar, A.; Gopalakrishnan, G.; Balusamy, V.; Kamachi Mudali, U.

2009-11-01

Commercially pure titanium (Ti) has been selected for the fabrication of dissolver for the proposed fast reactor fuel reprocessing plant at Kalpakkam, India. In the present investigation, microstructural changes and corrosion behavior of tungsten inert gas (TIG) welds of Ti grade-1 and grade-2 with different heat inputs were carried out. A wider heat affected zone was observed with higher heat inputs and coarse grains were observed from base metal toward the weld zone with increasing heat input. Fine and more equiaxed prior β grains were observed at lower heat input and the grain size increased toward fusion zone. The results indicated that Ti grade-1 and grade-2 with different heat inputs and different microstructures were insensitive to corrosion in liquid, vapor, and condensate phases of 11.5 M nitric acid tested up to 240 h. The corrosion rate in boiling liquid phase (0.60-0.76 mm/year) was higher than that in vapor (0.012-0.039 mm/year) and condensate phases (0.04-0.12 mm/year) of nitric acid for Ti grade-1 and grade-2, as well as for base metal for all heat inputs. Potentiodynamic polarization experiment carried out at room temperature indicated higher current densities and better passivation in 11.5 M nitric acid. SEM examination of Ti grade-1 welds for all heat inputs exposed to liquid phase after 240 h showed corrosion attack on the surface, exposing Widmanstatten microstructure containing acicular alpha. The continuous dissolution of the liquid-exposed samples was attributed to the heterogeneous microstructure and non-protective passive film formation.

Synchrony measure for a neuron driven by excitatory and inhibitory inputs and its adaptation to experimentally-recorded data.

PubMed

Zavou, Christina; Kkoushi, Antria; Koutsou, Achilleas; Christodoulou, Chris

2017-11-01

The aim of the current work is twofold: firstly to adapt an existing method measuring the input synchrony of a neuron driven only by excitatory inputs in such a way so as to account for inhibitory inputs as well and secondly to further appropriately adapt this measure so as to be correctly utilised on experimentally-recorded data. The existing method uses the normalized pre-spike slope (NPSS) of the membrane potential, resulting from observing the slope of depolarization of the membrane potential of a neuron prior to the moment of crossing the threshold within a short period of time, to identify the response-relevant input synchrony and through it to infer the operational mode of a neuron. The first adaptation of NPSS is made such that its upper bound calculation accommodates for the higher possible slope values caused by the lower average and minimum membrane potential values due to inhibitory inputs. Results indicate that when the input spike trains arrive randomly, the modified NPSS works as expected inferring that the neuron is operating as a temporal integrator. When the input spike trains arrive in perfect synchrony though, the modified NPSS works as expected only when the level of inhibition is much higher than the level of excitation. This suggests that calculation of the upper bound of the NPSS should be a function of the ratio between excitatory and inhibitory inputs in order to be able to correctly capture perfect synchrony at a neuron's input. In addition, we effectively demonstrate a process which has to be followed when aiming to use the NPSS on real neuron recordings. This process, which relies on empirical observations of the slope of depolarisation for estimating the bounds for the range of observed interspike interval lengths, is successfully applied to experimentally-recorded data showing that through it both a real neuron's operational mode and the amount of input synchrony that caused its firing can be inferred. Copyright © 2017 Elsevier B.V. All rights reserved.

Probabilistic Open Set Recognition

NASA Astrophysics Data System (ADS)

Jain, Lalit Prithviraj

Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary support vector machines. Building from the success of statistical EVT based recognition methods such as PI-SVM and W-SVM on the open set problem, we present a new general supervised learning algorithm for multi-class classification and multi-class open set recognition called the Extreme Value Local Basis (EVLB). The design of this algorithm is motivated by the observation that extrema from known negative class distributions are the closest negative points to any positive sample during training, and thus should be used to define the parameters of a probabilistic decision model. In the EVLB, the kernel distribution for each positive training sample is estimated via an EVT distribution fit over the distances to the separating hyperplane between positive training sample and closest negative samples, with a subset of the overall positive training data retained to form a probabilistic decision boundary. Using this subset as a frame of reference, the probability of a sample at test time decreases as it moves away from the positive class. Possessing this property, the EVLB is well-suited to open set recognition problems where samples from unknown or novel classes are encountered at test. Our experimental evaluation shows that the EVLB provides a substantial improvement in scalability compared to standard radial basis function kernel machines, as well as P I-SVM and W-SVM, with improved accuracy in many cases. We evaluate our algorithm on open set variations of the standard visual learning benchmarks, as well as with an open subset of classes from Caltech 256 and ImageNet. Our experiments show that PI-SVM, WSVM and EVLB provide significant advances over the previous state-of-the-art solutions for the same tasks.

Effects of heat input on the pitting resistance of Inconel 625 welds by overlay welding

NASA Astrophysics Data System (ADS)

Kim, Jun Seok; Park, Young IL; Lee, Hae Woo

2015-03-01

The objective of this study was to establish the relationship between the dilution ratio of the weld zone and pitting resistance depending on the heat input to welding of the Inconel alloy. Each specimen was produced by electroslag welding using Inconel 625 as the filler metal. In the weld zone of each specimen, dendrite grains were observed near the fusion line and equiaxed grains were observed on the surface. It was also observed that a melted zone with a high Fe content was formed around the fusion line, which became wider as the welding heat input increased. In order to evaluate the pitting resistance, potentiodynamic polarization tests and CPT tests were conducted. The results of these tests confirmed that there is no difference between the pitting resistances of each specimen, as the structures of the surfaces were identical despite the effect of the differences in the welding heat input for each specimen and the minor dilution effect on the surface.

Observer-based robust finite time H∞ sliding mode control for Markovian switching systems with mode-dependent time-varying delay and incomplete transition rate.

PubMed

Gao, Lijun; Jiang, Xiaoxiao; Wang, Dandan

2016-03-01

This paper investigates the problem of robust finite time H∞ sliding mode control for a class of Markovian switching systems. The system is subjected to the mode-dependent time-varying delay, partly unknown transition rate and unmeasurable state. The main difficulty is that, a sliding mode surface cannot be designed based on the unknown transition rate and unmeasurable state directly. To overcome this obstacle, the set of modes is firstly divided into two subsets standing for known transition rate subset and unknown one, based on which a state observer is established. A component robust finite-time sliding mode controller is also designed to cope with the effect of partially unknown transition rate. It is illustrated that the reachability, finite-time stability, finite-time boundedness, finite-time H∞ state feedback stabilization of sliding mode dynamics can be ensured despite the unknown transition rate. Finally, the simulation results verify the effectiveness of robust finite time control problem. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Effects of uncertainties in hydrological modelling. A case study of a mountainous catchment in Southern Norway

NASA Astrophysics Data System (ADS)

Engeland, Kolbjørn; Steinsland, Ingelin; Johansen, Stian Solvang; Petersen-Øverleir, Asgeir; Kolberg, Sjur

2016-05-01

In this study, we explore the effect of uncertainty and poor observation quality on hydrological model calibration and predictions. The Osali catchment in Western Norway was selected as case study and an elevation distributed HBV-model was used. We systematically evaluated the effect of accounting for uncertainty in parameters, precipitation input, temperature input and streamflow observations. For precipitation and temperature we accounted for the interpolation uncertainty, and for streamflow we accounted for rating curve uncertainty. Further, the effects of poorer quality of precipitation input and streamflow observations were explored. Less information about precipitation was obtained by excluding the nearest precipitation station from the analysis, while reduced information about the streamflow was obtained by omitting the highest and lowest streamflow observations when estimating the rating curve. The results showed that including uncertainty in the precipitation and temperature inputs has a negligible effect on the posterior distribution of parameters and for the Nash-Sutcliffe (NS) efficiency for the predicted flows, while the reliability and the continuous rank probability score (CRPS) improves. Less information in precipitation input resulted in a shift in the water balance parameter Pcorr, a model producing smoother streamflow predictions, giving poorer NS and CRPS, but higher reliability. The effect of calibrating the hydrological model using streamflow observations based on different rating curves is mainly seen as variability in the water balance parameter Pcorr. When evaluating predictions, the best evaluation scores were not achieved for the rating curve used for calibration, but for rating curves giving smoother streamflow observations. Less information in streamflow influenced the water balance parameter Pcorr, and increased the spread in evaluation scores by giving both better and worse scores.

A robust adaptive observer for a class of singular nonlinear uncertain systems

NASA Astrophysics Data System (ADS)

Arefinia, Elaheh; Talebi, Heidar Ali; Doustmohammadi, Ali

2017-05-01

This paper proposes a robust adaptive observer for a class of singular nonlinear non-autonomous uncertain systems with unstructured unknown system and derivative matrices, and unknown bounded nonlinearities. Unlike many existing observers, no strong assumption such as Lipschitz condition is imposed on the recommended system. An augmented system is constructed, and the unknown bounds are calculated online using adaptive bounding technique. Considering the continuous nonlinear gain removes the chattering which may appear in practical applications such as analysis of electrical circuits and estimation of interaction force in beating heart robotic-assisted surgery. Moreover, a simple yet precise structure is attained which is easy to implement in many systems with significant uncertainties. The existence conditions of the standard form observer are obtained in terms of linear matrix inequality and the constrained generalised Sylvester's equations, and global stability is ensured. Finally, simulation results are obtained to evaluate the performance of the proposed estimator and demonstrate the effectiveness of the developed scheme.

Open set recognition of aircraft in aerial imagery using synthetic template models

NASA Astrophysics Data System (ADS)

Bapst, Aleksander B.; Tran, Jonathan; Koch, Mark W.; Moya, Mary M.; Swahn, Robert

2017-05-01

Fast, accurate and robust automatic target recognition (ATR) in optical aerial imagery can provide game-changing advantages to military commanders and personnel. ATR algorithms must reject non-targets with a high degree of confidence in a world with an infinite number of possible input images. Furthermore, they must learn to recognize new targets without requiring massive data collections. Whereas most machine learning algorithms classify data in a closed set manner by mapping inputs to a fixed set of training classes, open set recognizers incorporate constraints that allow for inputs to be labelled as unknown. We have adapted two template-based open set recognizers to use computer generated synthetic images of military aircraft as training data, to provide a baseline for military-grade ATR: (1) a frequentist approach based on probabilistic fusion of extracted image features, and (2) an open set extension to the one-class support vector machine (SVM). These algorithms both use histograms of oriented gradients (HOG) as features as well as artificial augmentation of both real and synthetic image chips to take advantage of minimal training data. Our results show that open set recognizers trained with synthetic data and tested with real data can successfully discriminate real target inputs from non-targets. However, there is still a requirement for some knowledge of the real target in order to calibrate the relationship between synthetic template and target score distributions. We conclude by proposing algorithm modifications that may improve the ability of synthetic data to represent real data.

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

Weimar, Mark R.; Daly, Don S.; Wood, Thomas W.

Both nuclear power and nuclear weapons programs should have (related) economic signatures which are detectible at some scale. We evaluated this premise in a series of studies using national economic input/output (IO) data. Statistical discrimination models using economic IO tables predict with a high probability whether a country with an unknown predilection for nuclear weapons proliferation is in fact engaged in nuclear power development or nuclear weapons proliferation. We analyzed 93 IO tables, spanning the years 1993 to 2005 for 37 countries that are either members or associates of the Organization for Economic Cooperation and Development (OECD). The 2009 OECDmore » input/output tables featured 48 industrial sectors based on International Standard Industrial Classification (ISIC) Revision 3, and described the respective economies in current country-of-origin valued currency. We converted and transformed these reported values to US 2005 dollars using appropriate exchange rates and implicit price deflators, and addressed discrepancies in reported industrial sectors across tables. We then classified countries with Random Forest using either the adjusted or industry-normalized values. Random Forest, a classification tree technique, separates and categorizes countries using a very small, select subset of the 2304 individual cells in the IO table. A nation’s efforts in nuclear power, be it for electricity or nuclear weapons, are an enterprise with a large economic footprint -- an effort so large that it should discernibly perturb coarse country-level economics data such as that found in yearly input-output economic tables. The neoclassical economic input-output model describes a country’s or region’s economy in terms of the requirements of industries to produce the current level of economic output. An IO table row shows the distribution of an industry’s output to the industrial sectors while a table column shows the input required of each industrial sector by a given industry.« less

Synaptic inputs from stroke-injured brain to grafted human stem cell-derived neurons activated by sensory stimuli.

PubMed

Tornero, Daniel; Tsupykov, Oleg; Granmo, Marcus; Rodriguez, Cristina; Grønning-Hansen, Marita; Thelin, Jonas; Smozhanik, Ekaterina; Laterza, Cecilia; Wattananit, Somsak; Ge, Ruimin; Tatarishvili, Jemal; Grealish, Shane; Brüstle, Oliver; Skibo, Galina; Parmar, Malin; Schouenborg, Jens; Lindvall, Olle; Kokaia, Zaal

2017-03-01

Transplanted neurons derived from stem cells have been proposed to improve function in animal models of human disease by various mechanisms such as neuronal replacement. However, whether the grafted neurons receive functional synaptic inputs from the recipient's brain and integrate into host neural circuitry is unknown. Here we studied the synaptic inputs from the host brain to grafted cortical neurons derived from human induced pluripotent stem cells after transplantation into stroke-injured rat cerebral cortex. Using the rabies virus-based trans-synaptic tracing method and immunoelectron microscopy, we demonstrate that the grafted neurons receive direct synaptic inputs from neurons in different host brain areas located in a pattern similar to that of neurons projecting to the corresponding endogenous cortical neurons in the intact brain. Electrophysiological in vivo recordings from the cortical implants show that physiological sensory stimuli, i.e. cutaneous stimulation of nose and paw, can activate or inhibit spontaneous activity in grafted neurons, indicating that at least some of the afferent inputs are functional. In agreement, we find using patch-clamp recordings that a portion of grafted neurons respond to photostimulation of virally transfected, channelrhodopsin-2-expressing thalamo-cortical axons in acute brain slices. The present study demonstrates, for the first time, that the host brain regulates the activity of grafted neurons, providing strong evidence that transplanted human induced pluripotent stem cell-derived cortical neurons can become incorporated into injured cortical circuitry. Our findings support the idea that these neurons could contribute to functional recovery in stroke and other conditions causing neuronal loss in cerebral cortex. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Morphological Constraints on Cerebellar Granule Cell Combinatorial Diversity.

PubMed

Gilmer, Jesse I; Person, Abigail L

2017-12-13

Combinatorial expansion by the cerebellar granule cell layer (GCL) is fundamental to theories of cerebellar contributions to motor control and learning. Granule cells (GrCs) sample approximately four mossy fiber inputs and are thought to form a combinatorial code useful for pattern separation and learning. We constructed a spatially realistic model of the cerebellar GCL and examined how GCL architecture contributes to GrC combinatorial diversity. We found that GrC combinatorial diversity saturates quickly as mossy fiber input diversity increases, and that this saturation is in part a consequence of short dendrites, which limit access to diverse inputs and favor dense sampling of local inputs. This local sampling also produced GrCs that were combinatorially redundant, even when input diversity was extremely high. In addition, we found that mossy fiber clustering, which is a common anatomical pattern, also led to increased redundancy of GrC input combinations. We related this redundancy to hypothesized roles of temporal expansion of GrC information encoding in service of learned timing, and we show that GCL architecture produces GrC populations that support both temporal and combinatorial expansion. Finally, we used novel anatomical measurements from mice of either sex to inform modeling of sparse and filopodia-bearing mossy fibers, finding that these circuit features uniquely contribute to enhancing GrC diversification and redundancy. Our results complement information theoretic studies of granule layer structure and provide insight into the contributions of granule layer anatomical features to afferent mixing. SIGNIFICANCE STATEMENT Cerebellar granule cells are among the simplest neurons, with tiny somata and, on average, just four dendrites. These characteristics, along with their dense organization, inspired influential theoretical work on the granule cell layer as a combinatorial expander, where each granule cell represents a unique combination of inputs. Despite the centrality of these theories to cerebellar physiology, the degree of expansion supported by anatomically realistic patterns of inputs is unknown. Using modeling and anatomy, we show that realistic input patterns constrain combinatorial diversity by producing redundant combinations, which nevertheless could support temporal diversification of like combinations, suitable for learned timing. Our study suggests a neural substrate for producing high levels of both combinatorial and temporal diversity in the granule cell layer. Copyright © 2017 the authors 0270-6474/17/3712153-14$15.00/0.

Theoretic aspects of the identification of the parameters in the optimal control model

NASA Technical Reports Server (NTRS)

Vanwijk, R. A.; Kok, J. J.

1977-01-01

The identification of the parameters of the optimal control model from input-output data of the human operator is considered. Accepting the basic structure of the model as a cascade of a full-order observer and a feedback law, and suppressing the inherent optimality of the human controller, the parameters to be identified are the feedback matrix, the observer gain matrix, and the intensity matrices of the observation noise and the motor noise. The identification of the parameters is a statistical problem, because the system and output are corrupted by noise, and therefore the solution must be based on the statistics (probability density function) of the input and output data of the human operator. However, based on the statistics of the input-output data of the human operator, no distinction can be made between the observation and the motor noise, which shows that the model suffers from overparameterization.

Stochastic filtering for damage identification through nonlinear structural finite element model updating

NASA Astrophysics Data System (ADS)

Astroza, Rodrigo; Ebrahimian, Hamed; Conte, Joel P.

2015-03-01

This paper describes a novel framework that combines advanced mechanics-based nonlinear (hysteretic) finite element (FE) models and stochastic filtering techniques to estimate unknown time-invariant parameters of nonlinear inelastic material models used in the FE model. Using input-output data recorded during earthquake events, the proposed framework updates the nonlinear FE model of the structure. The updated FE model can be directly used for damage identification and further used for damage prognosis. To update the unknown time-invariant parameters of the FE model, two alternative stochastic filtering methods are used: the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). A three-dimensional, 5-story, 2-by-1 bay reinforced concrete (RC) frame is used to verify the proposed framework. The RC frame is modeled using fiber-section displacement-based beam-column elements with distributed plasticity and is subjected to the ground motion recorded at the Sylmar station during the 1994 Northridge earthquake. The results indicate that the proposed framework accurately estimate the unknown material parameters of the nonlinear FE model. The UKF outperforms the EKF when the relative root-mean-square error of the recorded responses are compared. In addition, the results suggest that the convergence of the estimate of modeling parameters is smoother and faster when the UKF is utilized.

Adaptive backstepping fault-tolerant control for flexible spacecraft with unknown bounded disturbances and actuator failures.

PubMed

Jiang, Ye; Hu, Qinglei; Ma, Guangfu

2010-01-01

In this paper, a robust adaptive fault-tolerant control approach to attitude tracking of flexible spacecraft is proposed for use in situations when there are reaction wheel/actuator failures, persistent bounded disturbances and unknown inertia parameter uncertainties. The controller is designed based on an adaptive backstepping sliding mode control scheme, and a sufficient condition under which this control law can render the system semi-globally input-to-state stable is also provided such that the closed-loop system is robust with respect to any disturbance within a quantifiable restriction on the amplitude, as well as the set of initial conditions, if the control gains are designed appropriately. Moreover, in the design, the control law does not need a fault detection and isolation mechanism even if the failure time instants, patterns and values on actuator failures are also unknown for the designers, as motivated from a practical spacecraft control application. In addition to detailed derivations of the new controller design and a rigorous sketch of all the associated stability and attitude error convergence proofs, illustrative simulation results of an application to flexible spacecraft show that high precise attitude control and vibration suppression are successfully achieved using various scenarios of controlling effective failures. 2009. Published by Elsevier Ltd.

Finite-time sliding surface constrained control for a robot manipulator with an unknown deadzone and disturbance.

PubMed

Ik Han, Seong; Lee, Jangmyung

2016-11-01

This paper presents finite-time sliding mode control (FSMC) with predefined constraints for the tracking error and sliding surface in order to obtain robust positioning of a robot manipulator with input nonlinearity due to an unknown deadzone and external disturbance. An assumed model feedforward FSMC was designed to avoid tedious identification procedures for the manipulator parameters and to obtain a fast response time. Two constraint switching control functions based on the tracking error and finite-time sliding surface were added to the FSMC to guarantee the predefined tracking performance despite the presence of an unknown deadzone and disturbance. The tracking error due to the deadzone and disturbance can be suppressed within the predefined error boundary simply by tuning the gain value of the constraint switching function and without the addition of an extra compensator. Therefore, the designed constraint controller has a simpler structure than conventional transformed error constraint methods and the sliding surface constraint scheme can also indirectly guarantee the tracking error constraint while being more stable than the tracking error constraint control. A simulation and experiment were performed on an articulated robot manipulator to validate the proposed control schemes. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Perceptual learning improves visual performance in juvenile amblyopia.

PubMed

Li, Roger W; Young, Karen G; Hoenig, Pia; Levi, Dennis M

2005-09-01

To determine whether practicing a position-discrimination task improves visual performance in children with amblyopia and to determine the mechanism(s) of improvement. Five children (age range, 7-10 years) with amblyopia practiced a positional acuity task in which they had to judge which of three pairs of lines was misaligned. Positional noise was produced by distributing the individual patches of each line segment according to a Gaussian probability function. Observers were trained at three noise levels (including 0), with each observer performing between 3000 and 4000 responses in 7 to 10 sessions. Trial-by-trial feedback was provided. Four of the five observers showed significant improvement in positional acuity. In those four observers, on average, positional acuity with no noise improved by approximately 32% and with high noise by approximately 26%. A position-averaging model was used to parse the improvement into an increase in efficiency or a decrease in equivalent input noise. Two observers showed increased efficiency (51% and 117% improvements) with no significant change in equivalent input noise across sessions. The other two observers showed both a decrease in equivalent input noise (18% and 29%) and an increase in efficiency (17% and 71%). All five observers showed substantial improvement in Snellen acuity (approximately 26%) after practice. Perceptual learning can improve visual performance in amblyopic children. The improvement can be parsed into two important factors: decreased equivalent input noise and increased efficiency. Perceptual learning techniques may add an effective new method to the armamentarium of amblyopia treatments.

Observation-Based Dissipation and Input Terms for Spectral Wave Models, with End-User Testing

DTIC Science & Technology

2014-09-30

scale influence of the Great barrier reef matrix on wave attenuation, Coral Reefs [published, refereed] Ghantous, M., and A.V. Babanin, 2014: One...Observation-Based Dissipation and Input Terms for Spectral Wave Models...functions, based on advanced understanding of physics of air-sea interactions, wave breaking and swell attenuation, in wave - forecast models. OBJECTIVES The

Metallurgy and mechanical properties variation with heat input,during dissimilar metal welding between stainless and carbon steel

NASA Astrophysics Data System (ADS)

Ramdan, RD; Koswara, AL; Surasno; Wirawan, R.; Faturohman, F.; Widyanto, B.; Suratman, R.

2018-02-01

The present research focus on the metallurgy and mechanical aspect of dissimilar metal welding.One of the common parameters that significantly contribute to the metallurgical aspect on the metal during welding is heat input. Regarding this point, in the present research, voltage, current and the welding speed has been varied in order to observe the effect of heat input on the metallurgical and mechanical aspect of both welded metals. Welding was conducted by Gas Metal Arc Welding (GMAW) on stainless and carbon steel with filler metal of ER 309. After welding, hardness test (micro-Vickers), tensile test, macro and micro-structure characterization and Energy Dispersive Spectroscopy (EDS) characterization were performed. It was observed no brittle martensite observed at HAZ of carbon steel, whereas sensitization was observed at the HAZ of stainless steel for all heat input variation at the present research. Generally, both HAZ at carbon steel and stainless steel did not affect tensile test result, however the formation of chromium carbide at the grain boundary of HAZ structure (sensitization) of stainless steel, indicate that better process and control of welding is required for dissimilar metal welding, especially to overcome this issue.

CORNAS: coverage-dependent RNA-Seq analysis of gene expression data without biological replicates.

PubMed

Low, Joel Z B; Khang, Tsung Fei; Tammi, Martti T

2017-12-28

In current statistical methods for calling differentially expressed genes in RNA-Seq experiments, the assumption is that an adjusted observed gene count represents an unknown true gene count. This adjustment usually consists of a normalization step to account for heterogeneous sample library sizes, and then the resulting normalized gene counts are used as input for parametric or non-parametric differential gene expression tests. A distribution of true gene counts, each with a different probability, can result in the same observed gene count. Importantly, sequencing coverage information is currently not explicitly incorporated into any of the statistical models used for RNA-Seq analysis. We developed a fast Bayesian method which uses the sequencing coverage information determined from the concentration of an RNA sample to estimate the posterior distribution of a true gene count. Our method has better or comparable performance compared to NOISeq and GFOLD, according to the results from simulations and experiments with real unreplicated data. We incorporated a previously unused sequencing coverage parameter into a procedure for differential gene expression analysis with RNA-Seq data. Our results suggest that our method can be used to overcome analytical bottlenecks in experiments with limited number of replicates and low sequencing coverage. The method is implemented in CORNAS (Coverage-dependent RNA-Seq), and is available at https://github.com/joel-lzb/CORNAS .

Primate translational vestibuloocular reflexes. IV. Changes after unilateral labyrinthectomy

NASA Technical Reports Server (NTRS)

Angelaki, D. E.; Newlands, S. D.; Dickman, J. D.

2000-01-01

The effects of unilateral labyrinthectomy on the properties of the translational vestibuloocular reflexes (trVORs) were investigated in rhesus monkeys trained to fixate near targets. Translational motion stimuli consisted of either steady-state lateral and fore-aft sinusoidal oscillations or short-lasting transient displacements. During small-amplitude, steady-state sinusoidal lateral oscillations, a small decrease in the horizontal trVOR sensitivity and its dependence on viewing distance was observed during the first week after labyrinthectomy. These deficits gradually recovered over time. In addition, the vertical response component increased, causing a tilt of the eye velocity vector toward the lesioned side. During large, transient lateral displacements, the deficits were larger and longer lasting. Responses after labyrinthectomy were asymmetric, with eye velocity during movements toward the side of the lesion being more compromised. The most profound effect of the lesions was observed during fore-aft motion. Whereas responses were kinematically appropriate for fixation away from the side of the lesion (e.g., to the left after right labyrinthectomy), horizontal responses were anticompensatory during fixation at targets located ipsilateral to the side of the lesion (e.g., for targets to the right after right labyrinthectomy). This deficit showed little recovery during the 3-mo post-labyrinthectomy testing period. These results suggest that inputs from both labyrinths are important for the proper function of the trVORs, although the details of how bilateral signals are processed and integrated remain unknown.

Summation of visual motion across eye movements reflects a nonspatial decision mechanism.

PubMed

Morris, Adam P; Liu, Charles C; Cropper, Simon J; Forte, Jason D; Krekelberg, Bart; Mattingley, Jason B

2010-07-21

Human vision remains perceptually stable even though retinal inputs change rapidly with each eye movement. Although the neural basis of visual stability remains unknown, a recent psychophysical study pointed to the existence of visual feature-representations anchored in environmental rather than retinal coordinates (e.g., "spatiotopic" receptive fields; Melcher and Morrone, 2003). In that study, sensitivity to a moving stimulus presented after a saccadic eye movement was enhanced when preceded by another moving stimulus at the same spatial location before the saccade. The finding is consistent with spatiotopic sensory integration, but it could also have arisen from a probabilistic improvement in performance due to the presence of more than one motion signal for the perceptual decision. Here we show that this statistical advantage accounts completely for summation effects in this task. We first demonstrate that measurements of summation are confounded by noise related to an observer's uncertainty about motion onset times. When this uncertainty is minimized, comparable summation is observed regardless of whether two motion signals occupy the same or different locations in space, and whether they contain the same or opposite directions of motion. These results are incompatible with the tuning properties of motion-sensitive sensory neurons and provide no evidence for a spatiotopic representation of visual motion. Instead, summation in this context reflects a decision mechanism that uses abstract representations of sensory events to optimize choice behavior.

GABAergic Projections from the Medial Septum Selectively Inhibit Interneurons in the Medial Entorhinal Cortex

PubMed Central

Gonzalez-Sulser, Alfredo; Parthier, Daniel; Candela, Antonio; McClure, Christina; Pastoll, Hugh; Garden, Derek; Sürmeli, Gülşen

2014-01-01

The medial septum (MS) is required for theta rhythmic oscillations and grid cell firing in the medial entorhinal cortex (MEC). While GABAergic, glutamatergic, and cholinergic neurons project from the MS to the MEC, their synaptic targets are unknown. To investigate whether MS neurons innervate specific layers and cell types in the MEC, we expressed channelrhodopsin-2 in mouse MS neurons and used patch-clamp recording in brain slices to determine the response to light activation of identified cells in the MEC. Following activation of MS axons, we observed fast monosynaptic GABAergic IPSPs in the majority (>60%) of fast-spiking (FS) and low-threshold-spiking (LTS) interneurons in all layers of the MEC, but in only 1.5% of nonstellate principal cells (NSPCs) and in no stellate cells. We also observed fast glutamatergic responses to MS activation in a minority (<5%) of NSPCs, FS, and LTS interneurons. During stimulation of MS inputs at theta frequency (10 Hz), the amplitude of GABAergic IPSPs was maintained, and spike output from LTS and FS interneurons was entrained at low (25–60 Hz) and high (60–180 Hz) gamma frequencies, respectively. By demonstrating cell type-specific targeting of the GABAergic projection from the MS to the MEC, our results support the idea that the MS controls theta frequency activity in the MEC through coordination of inhibitory circuits. PMID:25505326

Upon the opportunity to apply ART2 Neural Network for clusterization of biodiesel fuels

NASA Astrophysics Data System (ADS)

Petkov, T.; Mustafa, Z.; Sotirov, S.; Milina, R.; Moskovkina, M.

2016-03-01

A chemometric approach using artificial neural network for clusterization of biodiesels was developed. It is based on artificial ART2 neural network. Gas chromatography (GC) and Gas Chromatography - mass spectrometry (GC-MS) were used for quantitative and qualitative analysis of biodiesels, produced from different feedstocks, and FAME (fatty acid methyl esters) profiles were determined. Totally 96 analytical results for 7 different classes of biofuel plants: sunflower, rapeseed, corn, soybean, palm, peanut, "unknown" were used as objects. The analysis of biodiesels showed the content of five major FAME (C16:0, C18:0, C18:1, C18:2, C18:3) and those components were used like inputs in the model. After training with 6 samples, for which the origin was known, ANN was verified and tested with ninety "unknown" samples. The present research demonstrated the successful application of neural network for recognition of biodiesels according to their feedstock which give information upon their properties and handling.

Listening to music reduces eye movements.

PubMed

Schäfer, Thomas; Fachner, Jörg

2015-02-01

Listening to music can change the way that people visually experience the environment, probably as a result of an inwardly directed shift of attention. We investigated whether this attentional shift can be demonstrated by reduced eye movement activity, and if so, whether that reduction depends on absorption. Participants listened to their preferred music, to unknown neutral music, or to no music while viewing a visual stimulus (a picture or a film clip). Preference and absorption were significantly higher for the preferred music than for the unknown music. Participants exhibited longer fixations, fewer saccades, and more blinks when they listened to music than when they sat in silence. However, no differences emerged between the preferred music condition and the neutral music condition. Thus, music significantly reduces eye movement activity, but an attentional shift from the outer to the inner world (i.e., to the emotions and memories evoked by the music) emerged as only one potential explanation. Other explanations, such as a shift of attention from visual to auditory input, are discussed.

Adaptive Neural Tracking Control for Switched High-Order Stochastic Nonlinear Systems.

PubMed

Zhao, Xudong; Wang, Xinyong; Zong, Guangdeng; Zheng, Xiaolong

2017-10-01

This paper deals with adaptive neural tracking control design for a class of switched high-order stochastic nonlinear systems with unknown uncertainties and arbitrary deterministic switching. The considered issues are: 1) completely unknown uncertainties; 2) stochastic disturbances; and 3) high-order nonstrict-feedback system structure. The considered mathematical models can represent many practical systems in the actual engineering. By adopting the approximation ability of neural networks, common stochastic Lyapunov function method together with adding an improved power integrator technique, an adaptive state feedback controller with multiple adaptive laws is systematically designed for the systems. Subsequently, a controller with only two adaptive laws is proposed to solve the problem of over parameterization. Under the designed controllers, all the signals in the closed-loop system are bounded-input bounded-output stable in probability, and the system output can almost surely track the target trajectory within a specified bounded error. Finally, simulation results are presented to show the effectiveness of the proposed approaches.

A new reduced-order observer for the synchronization of nonlinear chaotic systems: An application to secure communications

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

Castro-Ramírez, Joel, E-mail: ingcastro.7@gmail.com; Martínez-Guerra, Rafael, E-mail: rguerra@ctrl.cinvestav.mx; Cruz-Victoria, Juan Crescenciano, E-mail: juancrescenciano.cruz@uptlax.edu.mx

2015-10-15

This paper deals with the master-slave synchronization scheme for partially known nonlinear chaotic systems, where the unknown dynamics is considered as the master system and we propose the slave system structure which estimates the unknown states. It introduced a new reduced order observer, using the concept of Algebraic Observability; we applied the results to a Sundarapandian chaotic system, and by means of some numerical simulations we show the effectiveness of the suggested approach. Finally, the proposed observer is utilized for encryption, where encryption key is the master system and decryption key is the slave system.

Coordination of heterogeneous nonlinear multi-agent systems with prescribed behaviours

NASA Astrophysics Data System (ADS)

Tang, Yutao

2017-10-01

In this paper, we consider a coordination problem for a class of heterogeneous nonlinear multi-agent systems with a prescribed input-output behaviour which was represented by another input-driven system. In contrast to most existing multi-agent coordination results with an autonomous (virtual) leader, this formulation takes possible control inputs of the leader into consideration. First, the coordination was achieved by utilising a group of distributed observers based on conventional assumptions of model matching problem. Then, a fully distributed adaptive extension was proposed without using the input of this input-output behaviour. An example was given to verify their effectiveness.

Drift simulation of MH370 debris using superensemble techniques

NASA Astrophysics Data System (ADS)

Jansen, Eric; Coppini, Giovanni; Pinardi, Nadia

2016-07-01

On 7 March 2014 (UTC), Malaysia Airlines flight 370 vanished without a trace. The aircraft is believed to have crashed in the southern Indian Ocean, but despite extensive search operations the location of the wreckage is still unknown. The first tangible evidence of the accident was discovered almost 17 months after the disappearance. On 29 July 2015, a small piece of the right wing of the aircraft was found washed up on the island of Réunion, approximately 4000 km from the assumed crash site. Since then a number of other parts have been found in Mozambique, South Africa and on Rodrigues Island. This paper presents a numerical simulation using high-resolution oceanographic and meteorological data to predict the movement of floating debris from the accident. Multiple model realisations are used with different starting locations and wind drag parameters. The model realisations are combined into a superensemble, adjusting the model weights to best represent the discovered debris. The superensemble is then used to predict the distribution of marine debris at various moments in time. This approach can be easily generalised to other drift simulations where observations are available to constrain unknown input parameters. The distribution at the time of the accident shows that the discovered debris most likely originated from the wide search area between 28 and 35° S. This partially overlaps with the current underwater search area, but extends further towards the north. Results at later times show that the most probable locations to discover washed-up debris are along the African east coast, especially in the area around Madagascar. The debris remaining at sea in 2016 is spread out over a wide area and its distribution changes only slowly.

A Tephra Database With an Intelligent Correlation System, Mono-Inyo Volcanic Chain, CA

NASA Astrophysics Data System (ADS)

Bursik, M.; Rogova, G.

2004-12-01

We are assembling a web-accessible, relational database of information on past eruptions of the Mono-Inyo volcanic chain, eastern California. The PostgreSQL database structure follows the North American Data Model and CordLink. The database allows us to extract the features diagnostic of particular pyroclastic layers, as well as lava domes and flows. The features include depth in the section, layer thickness and internal stratigraphy, mineral assemblage, major and trace element composition, tephra componentry and granulometry, and radiocarbon age. Our working hypotheses are that 1) the database will prove useful for unraveling the complex recent volcanic history of the Mono-Inyo chain 2) aided by the use of an intelligent correlation system integrated into the database system. The Mono-Inyo chain consists of domes, craters and flows that stretch for 50 km north-south, subparallel to the Sierran range front fault system. Almost all eruptions within the chain probably occurred less than 50,000 years ago. Because of the variety of magma and eruption types, and the migration of source regions in time and space, it is nontrivial to discern patterns of behaviour. We have explored the use of multiple artificial neural networks combined within the framework of the Dempster-Shafer theory of evidence to construct a hybrid information processing system as an aid in the correlation of Mono-Inyo pyroclastic layers. It is hoped that such a system could provide information useful to discerning eruptive patterns that would otherwise be difficult to sort and categorize. In a test case on tephra layers at known sites, the intelligent correlation system was able to categorize observations correctly 96% of the time. In a test case with layers at one unknown site, and using a pairwise comparison of the unknown site with the known sites, a one-to-one correlation between the unknown site and the known sites was found to sometimes be poor. Such a result could be used to aid a stratigrapher in rethinking or questioning a proposed correlation. This rethinking might not happen without the input from the intelligent system.

Hierarchical Time-Lagged Independent Component Analysis: Computing Slow Modes and Reaction Coordinates for Large Molecular Systems.

PubMed

Pérez-Hernández, Guillermo; Noé, Frank

2016-12-13

Analysis of molecular dynamics, for example using Markov models, often requires the identification of order parameters that are good indicators of the rare events, i.e. good reaction coordinates. Recently, it has been shown that the time-lagged independent component analysis (TICA) finds the linear combinations of input coordinates that optimally represent the slow kinetic modes and may serve in order to define reaction coordinates between the metastable states of the molecular system. A limitation of the method is that both computing time and memory requirements scale with the square of the number of input features. For large protein systems, this exacerbates the use of extensive feature sets such as the distances between all pairs of residues or even heavy atoms. Here we derive a hierarchical TICA (hTICA) method that approximates the full TICA solution by a hierarchical, divide-and-conquer calculation. By using hTICA on distances between heavy atoms we identify previously unknown relaxation processes in the bovine pancreatic trypsin inhibitor.

Analysis of rainfall distribution in Kelantan river basin, Malaysia

NASA Astrophysics Data System (ADS)

Che Ros, Faizah; Tosaka, Hiroyuki

2018-03-01

Using rainfall gauge on its own as input carries great uncertainties regarding runoff estimation, especially when the area is large and the rainfall is measured and recorded at irregular spaced gauging stations. Hence spatial interpolation is the key to obtain continuous and orderly rainfall distribution at unknown points to be the input to the rainfall runoff processes for distributed and semi-distributed numerical modelling. It is crucial to study and predict the behaviour of rainfall and river runoff to reduce flood damages of the affected area along the Kelantan river. Thus, a good knowledge on rainfall distribution is essential in early flood prediction studies. Forty six rainfall stations and their daily time-series were used to interpolate gridded rainfall surfaces using inverse-distance weighting (IDW), inverse-distance and elevation weighting (IDEW) methods and average rainfall distribution. Sensitivity analysis for distance and elevation parameters were conducted to see the variation produced. The accuracy of these interpolated datasets was examined using cross-validation assessment.

Sparse Coding and Lateral Inhibition Arising from Balanced and Unbalanced Dendrodendritic Excitation and Inhibition

PubMed Central

Migliore, Michele; Hines, Michael L.; Shepherd, Gordon M.

2014-01-01

The precise mechanism by which synaptic excitation and inhibition interact with each other in odor coding through the unique dendrodendritic synaptic microcircuits present in olfactory bulb is unknown. Here a scaled-up model of the mitral–granule cell network in the rodent olfactory bulb is used to analyze dendrodendritic processing of experimentally determined odor patterns. We found that the interaction between excitation and inhibition is responsible for two fundamental computational mechanisms: (1) a balanced excitation/inhibition in strongly activated mitral cells, leading to a sparse representation of odorant input, and (2) an unbalanced excitation/inhibition (inhibition dominated) in surrounding weakly activated mitral cells, leading to lateral inhibition. These results suggest how both mechanisms can carry information about the input patterns, with optimal level of synaptic excitation and inhibition producing the highest level of sparseness and decorrelation in the network response. The results suggest how the learning process, through the emergent development of these mechanisms, can enhance odor representation of olfactory bulb. PMID:25297097

A Novel Population of Wake-Promoting GABAergic Neurons in the Ventral Lateral Hypothalamus.

PubMed

Venner, Anne; Anaclet, Christelle; Broadhurst, Rebecca Y; Saper, Clifford B; Fuller, Patrick M

2016-08-22

The largest synaptic input to the sleep-promoting ventrolateral preoptic area (VLPO) [1] arises from the lateral hypothalamus [2], a brain area associated with arousal [3-5]. However, the neurochemical identity of the majority of these VLPO-projecting neurons within the lateral hypothalamus (LH), as well as their function in the arousal network, remains unknown. Herein we describe a population of VLPO-projecting neurons in the LH that express the vesicular GABA transporter (VGAT; a marker for GABA-releasing neurons). In addition to the VLPO, these neurons also project to several other established sleep and arousal nodes, including the tuberomammillary nucleus, ventral periaqueductal gray, and locus coeruleus. Selective and acute chemogenetic activation of LH VGAT(+) neurons was profoundly wake promoting, whereas acute inhibition increased sleep. Because of its direct and massive inputs to the VLPO, this population may play a particularly important role in sleep-wake switching. Copyright © 2016 Elsevier Ltd. All rights reserved.

Quantization-Based Adaptive Actor-Critic Tracking Control With Tracking Error Constraints.

PubMed

Fan, Quan-Yong; Yang, Guang-Hong; Ye, Dan

2018-04-01

In this paper, the problem of adaptive actor-critic (AC) tracking control is investigated for a class of continuous-time nonlinear systems with unknown nonlinearities and quantized inputs. Different from the existing results based on reinforcement learning, the tracking error constraints are considered and new critic functions are constructed to improve the performance further. To ensure that the tracking errors keep within the predefined time-varying boundaries, a tracking error transformation technique is used to constitute an augmented error system. Specific critic functions, rather than the long-term cost function, are introduced to supervise the tracking performance and tune the weights of the AC neural networks (NNs). A novel adaptive controller with a special structure is designed to reduce the effect of the NN reconstruction errors, input quantization, and disturbances. Based on the Lyapunov stability theory, the boundedness of the closed-loop signals and the desired tracking performance can be guaranteed. Finally, simulations on two connected inverted pendulums are given to illustrate the effectiveness of the proposed method.

A Computational Model of Spatial Development

NASA Astrophysics Data System (ADS)

Hiraki, Kazuo; Sashima, Akio; Phillips, Steven

Psychological experiments on children's development of spatial knowledge suggest experience at self-locomotion with visual tracking as important factors. Yet, the mechanism underlying development is unknown. We propose a robot that learns to mentally track a target object (i.e., maintaining a representation of an object's position when outside the field-of-view) as a model for spatial development. Mental tracking is considered as prediction of an object's position given the previous environmental state and motor commands, and the current environment state resulting from movement. Following Jordan & Rumelhart's (1992) forward modeling architecture the system consists of two components: an inverse model of sensory input to desired motor commands; and a forward model of motor commands to desired sensory input (goals). The robot was tested on the `three cups' paradigm (where children are required to select the cup containing the hidden object under various movement conditions). Consistent with child development, without the capacity for self-locomotion the robot's errors are self-center based. When given the ability of self-locomotion the robot responds allocentrically.

Quantum computation over the butterfly network

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

Soeda, Akihito; Kinjo, Yoshiyuki; Turner, Peter S.

2011-07-15

In order to investigate distributed quantum computation under restricted network resources, we introduce a quantum computation task over the butterfly network where both quantum and classical communications are limited. We consider deterministically performing a two-qubit global unitary operation on two unknown inputs given at different nodes, with outputs at two distinct nodes. By using a particular resource setting introduced by M. Hayashi [Phys. Rev. A 76, 040301(R) (2007)], which is capable of performing a swap operation by adding two maximally entangled qubits (ebits) between the two input nodes, we show that unitary operations can be performed without adding any entanglementmore » resource, if and only if the unitary operations are locally unitary equivalent to controlled unitary operations. Our protocol is optimal in the sense that the unitary operations cannot be implemented if we relax the specifications of any of the channels. We also construct protocols for performing controlled traceless unitary operations with a 1-ebit resource and for performing global Clifford operations with a 2-ebit resource.« less

Encoding of Discriminative Fear Memory by Input-Specific LTP in the Amygdala.

PubMed

Kim, Woong Bin; Cho, Jun-Hyeong

2017-08-30

In auditory fear conditioning, experimental subjects learn to associate an auditory conditioned stimulus (CS) with an aversive unconditioned stimulus. With sufficient training, animals fear conditioned to an auditory CS show fear response to the CS, but not to irrelevant auditory stimuli. Although long-term potentiation (LTP) in the lateral amygdala (LA) plays an essential role in auditory fear conditioning, it is unknown whether LTP is induced selectively in the neural pathways conveying specific CS information to the LA in discriminative fear learning. Here, we show that postsynaptically expressed LTP is induced selectively in the CS-specific auditory pathways to the LA in a mouse model of auditory discriminative fear conditioning. Moreover, optogenetically induced depotentiation of the CS-specific auditory pathways to the LA suppressed conditioned fear responses to the CS. Our results suggest that input-specific LTP in the LA contributes to fear memory specificity, enabling adaptive fear responses only to the relevant sensory cue. VIDEO ABSTRACT. Copyright © 2017 Elsevier Inc. All rights reserved.

Fasting Activation of AgRP Neurons Requires NMDA Receptors and Involves Spinogenesis and Increased Excitatory Tone

PubMed Central

Liu, Tiemin; Kong, Dong; Shah, Bhavik P.; Ye, Chianping; Koda, Shuichi; Saunders, Arpiar; Ding, Jun B.; Yang, Zongfang; Sabatini, Bernardo L.; Lowell, Bradford B.

2012-01-01

SUMMARY AgRP neuron activity drives feeding and weight gain while that of nearby POMC neurons does the opposite. However, the role of excitatory glutamatergic input in controlling these neurons is unknown. To address this question, we generated mice lacking NMDA receptors (NMDARs) on either AgRP or POMC neurons. Deletion of NMDARs from AgRP neurons markedly reduced weight, body fat and food intake whereas deletion from POMC neurons had no effect. Activation of AgRP neurons by fasting, as assessed by c-Fos, Agrp and Npy mRNA expression, AMPA receptor-mediated EPSCs, depolarization and firing rates, required NMDARs. Furthermore, AgRP but not POMC neurons have dendritic spines and increased glutamatergic input onto AgRP neurons caused by fasting was paralleled by an increase in spines, suggesting fasting induced synaptogenesis and spinogenesis. Thus glutamatergic synaptic transmission and its modulation by NMDARs play key roles in controlling AgRP neurons and determining the cellular and behavioral response to fasting. PMID:22325203

Dopaminergic inputs in the dentate gyrus direct the choice of memory encoding

DOE PAGES

Du, Huiyun; Deng, Wei; Aimone, James B.; ...

2016-09-13

Rewarding experiences are often well remembered, and such memory formation is known to be dependent on dopamine modulation of the neural substrates engaged in learning and memory; however, it is unknown how and where in the brain dopamine signals bias episodic memory toward preceding rather than subsequent events. Here we found that photostimulation of channelrhodopsin-2–expressing dopaminergic fibers in the dentate gyrus induced a long-term depression of cortical inputs, diminished theta oscillations, and impaired subsequent contextual learning. Computational modeling based on this dopamine modulation indicated an asymmetric association of events occurring before and after reward in memory tasks. In subsequent behavioralmore » experiments, preexposure to a natural reward suppressed hippocampus-dependent memory formation, with an effective time window consistent with the duration of dopamine-induced changes of dentate activity. Altogether, our results suggest a mechanism by which dopamine enables the hippocampus to encode memory with reduced interference from subsequent experience.« less

The Effect of Number and Presentation Order of High-Constraint Sentences on Second Language Word Learning.

PubMed

Ma, Tengfei; Chen, Ran; Dunlap, Susan; Chen, Baoguo

2016-01-01

This paper presents the results of an experiment that investigated the effects of number and presentation order of high-constraint sentences on semantic processing of unknown second language (L2) words (pseudowords) through reading. All participants were Chinese native speakers who learned English as a foreign language. In the experiment, sentence constraint and order of different constraint sentences were manipulated in English sentences, as well as L2 proficiency level of participants. We found that the number of high-constraint sentences was supportive for L2 word learning except in the condition in which high-constraint exposure was presented first. Moreover, when the number of high-constraint sentences was the same, learning was significantly better when the first exposure was a high-constraint exposure. And no proficiency level effects were found. Our results provided direct evidence that L2 word learning benefited from high quality language input and first presentations of high quality language input.

Dopaminergic inputs in the dentate gyrus direct the choice of memory encoding

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

Du, Huiyun; Deng, Wei; Aimone, James B.

Rewarding experiences are often well remembered, and such memory formation is known to be dependent on dopamine modulation of the neural substrates engaged in learning and memory; however, it is unknown how and where in the brain dopamine signals bias episodic memory toward preceding rather than subsequent events. Here we found that photostimulation of channelrhodopsin-2–expressing dopaminergic fibers in the dentate gyrus induced a long-term depression of cortical inputs, diminished theta oscillations, and impaired subsequent contextual learning. Computational modeling based on this dopamine modulation indicated an asymmetric association of events occurring before and after reward in memory tasks. In subsequent behavioralmore » experiments, preexposure to a natural reward suppressed hippocampus-dependent memory formation, with an effective time window consistent with the duration of dopamine-induced changes of dentate activity. Altogether, our results suggest a mechanism by which dopamine enables the hippocampus to encode memory with reduced interference from subsequent experience.« less

Human Age Recognition by Electrocardiogram Signal Based on Artificial Neural Network

NASA Astrophysics Data System (ADS)

Dasgupta, Hirak

2016-12-01

The objective of this work is to make a neural network function approximation model to detect human age from the electrocardiogram (ECG) signal. The input vectors of the neural network are the Katz fractal dimension of the ECG signal, frequencies in the QRS complex, male or female (represented by numeric constant) and the average of successive R-R peak distance of a particular ECG signal. The QRS complex has been detected by short time Fourier transform algorithm. The successive R peak has been detected by, first cutting the signal into periods by auto-correlation method and then finding the absolute of the highest point in each period. The neural network used in this problem consists of two layers, with Sigmoid neuron in the input and linear neuron in the output layer. The result shows the mean of errors as -0.49, 1.03, 0.79 years and the standard deviation of errors as 1.81, 1.77, 2.70 years during training, cross validation and testing with unknown data sets, respectively.

Astrocytes refine cortical connectivity at dendritic spines

PubMed Central

Risher, W Christopher; Patel, Sagar; Kim, Il Hwan; Uezu, Akiyoshi; Bhagat, Srishti; Wilton, Daniel K; Pilaz, Louis-Jan; Singh Alvarado, Jonnathan; Calhan, Osman Y; Silver, Debra L; Stevens, Beth; Calakos, Nicole; Soderling, Scott H; Eroglu, Cagla

2014-01-01

During cortical synaptic development, thalamic axons must establish synaptic connections despite the presence of the more abundant intracortical projections. How thalamocortical synapses are formed and maintained in this competitive environment is unknown. Here, we show that astrocyte-secreted protein hevin is required for normal thalamocortical synaptic connectivity in the mouse cortex. Absence of hevin results in a profound, long-lasting reduction in thalamocortical synapses accompanied by a transient increase in intracortical excitatory connections. Three-dimensional reconstructions of cortical neurons from serial section electron microscopy (ssEM) revealed that, during early postnatal development, dendritic spines often receive multiple excitatory inputs. Immuno-EM and confocal analyses revealed that majority of the spines with multiple excitatory contacts (SMECs) receive simultaneous thalamic and cortical inputs. Proportion of SMECs diminishes as the brain develops, but SMECs remain abundant in Hevin-null mice. These findings reveal that, through secretion of hevin, astrocytes control an important developmental synaptic refinement process at dendritic spines. DOI: http://dx.doi.org/10.7554/eLife.04047.001 PMID:25517933

Augmented Reality versus Virtual Reality for 3D Object Manipulation.

PubMed

Krichenbauer, Max; Yamamoto, Goshiro; Taketom, Takafumi; Sandor, Christian; Kato, Hirokazu

2018-02-01

Virtual Reality (VR) Head-Mounted Displays (HMDs) are on the verge of becoming commodity hardware available to the average user and feasible to use as a tool for 3D work. Some HMDs include front-facing cameras, enabling Augmented Reality (AR) functionality. Apart from avoiding collisions with the environment, interaction with virtual objects may also be affected by seeing the real environment. However, whether these effects are positive or negative has not yet been studied extensively. For most tasks it is unknown whether AR has any advantage over VR. In this work we present the results of a user study in which we compared user performance measured in task completion time on a 9 degrees of freedom object selection and transformation task performed either in AR or VR, both with a 3D input device and a mouse. Our results show faster task completion time in AR over VR. When using a 3D input device, a purely VR environment increased task completion time by 22.5 percent on average compared to AR ( ). Surprisingly, a similar effect occurred when using a mouse: users were about 17.3 percent slower in VR than in AR ( ). Mouse and 3D input device produced similar task completion times in each condition (AR or VR) respectively. We further found no differences in reported comfort.

The Mediodorsal Thalamus Drives Feedforward Inhibition in the Anterior Cingulate Cortex via Parvalbumin Interneurons

PubMed Central

Delevich, Kristen; Tucciarone, Jason; Huang, Z. Josh

2015-01-01

Although the medial prefrontal cortex (mPFC) is classically defined by its reciprocal connections with the mediodorsal thalamic nucleus (MD), the nature of information transfer between MD and mPFC is poorly understood. In sensory thalamocortical pathways, thalamic recruitment of feedforward inhibition mediated by fast-spiking, putative parvalbumin-expressing (PV) interneurons is a key feature that enables cortical neurons to represent sensory stimuli with high temporal fidelity. Whether a similar circuit mechanism is in place for the projection from the MD (a higher-order thalamic nucleus that does not receive direct input from the periphery) to the mPFC is unknown. Here we show in mice that inputs from the MD drive disynaptic feedforward inhibition in the dorsal anterior cingulate cortex (dACC) subregion of the mPFC. In particular, we demonstrate that axons arising from MD neurons directly synapse onto and excite PV interneurons that in turn mediate feedforward inhibition of pyramidal neurons in layer 3 of the dACC. This feedforward inhibition in the dACC limits the time window during which pyramidal neurons integrate excitatory synaptic inputs and fire action potentials, but in a manner that allows for greater flexibility than in sensory cortex. These findings provide a foundation for understanding the role of MD-PFC circuit function in cognition. PMID:25855185

Microwave signal processing with photorefractive dynamic holography

NASA Astrophysics Data System (ADS)

Fotheringham, Edeline B.

Have you ever found yourself listening to the music playing from the closest stereo rather than to the bromidic (uninspiring) person speaking to you? Your ears receive information from two sources but your brain listens to only one. What if your cell phone could distinguish among signals sharing the same bandwidth too? There would be no "full" channels to stop you from placing or receiving a call. This thesis presents a nonlinear optical circuit capable of distinguishing uncorrelated signals that have overlapping temporal bandwidths. This so called autotuning filter is the size of a U.S. quarter dollar and requires less than 3 mW of optical power to operate. It is basically an oscillator in which the losses are compensated with dynamic holographic gain. The combination of two photorefractive crystals in the resonator governs the filter's winner-take-all dynamics through signal-competition for gain. This physical circuit extracts what is mathematically referred to as the largest principal component of its spatio-temporal input space. The circuit's practicality is demonstrated by its incorporation in an RF-photonic system. An unknown mixture of unknown microwave signals, received by an antenna array, constitutes the input to the system. The output electronically returns one of the original microwave signals. The front-end of the system down converts the 10 GHz microwave signals and amplifies them before the signals phase modulate optical beams. The optical carrier is suppressed from these beams so that it may not be considered as a signal itself to the autotuning filter. The suppression is achieved with two-beam coupling in a single photorefractive crystal. The filter extracts the more intense of the signals present on the carrier-suppressed input beams. The detection of the extracted signal restores the microwave signal to an electronic form. The system, without the receiving antenna array, is packaged in a 13 x 18 x 6″ briefcase. Its power consumption equals that of a regular 50 W household light bulb. The system was shipped to different parts of the country for real-time demonstrations of signal separation thus also validating its claim to robustness.

Long-term effects of climate change on carbon storage and tree species composition in a dry deciduous forest.

PubMed

Fekete, István; Lajtha, Kate; Kotroczó, Zsolt; Várbíró, Gábor; Varga, Csaba; Tóth, János Attila; Demeter, Ibolya; Veperdi, Gábor; Berki, Imre

2017-08-01

Forest vegetation and soils have been suggested as potentially important sinks for carbon (C) with appropriate management and thus are implicated as effective tools in stabilizing climate even with increasing anthropogenic release of CO 2 . Drought, however, which is often predicted to increase in models of future climate change, may limit net primary productio (NPP) of dry forest types, with unknown effects on soil C storage. We studied C dynamics of a deciduous temperate forest of Hungary that has been subject to significant decreases in precipitation and increases in temperature in recent decades. We resampled plots that were established in 1972 and repeated the full C inventory by analyzing more than 4 decades of data on the number of living trees, biomass of trees and shrubs, and soil C content. Our analyses show that the decline in number and biomass of oaks started around the end of the 1970s with a 71% reduction in the number of sessile oak stems by 2014. Projected growth in this forest, based on the yield table's data for Hungary, was 4.6 kg C/m 2 . Although new species emerged, this new growth and small increases in oak biomass resulted in only 1.9 kg C/m 2 increase over 41 years. The death of oaks increased inputs of coarse woody debris to the surface of the soil, much of which is still identifiable, and caused an increase of 15.5%, or 2.6 kg C/m 2 , in the top 1 m of soil. Stability of this fresh organic matter input to surface soil is unknown, but is likely to be low based on the results of a colocated woody litter decomposition study. The effects of a warmer and drier climate on the C balance of forests in this region will be felt for decades to come as woody litter inputs decay, and forest growth remains impeded. © 2017 John Wiley & Sons Ltd.

Contributions of Rod and Cone Pathways to Retinal Direction Selectivity Through Development

PubMed Central

Rosa, Juliana M.; Morrie, Ryan D.; Baertsch, Hans C.

2016-01-01

Direction selectivity is a robust computation across a broad stimulus space that is mediated by activity of both rod and cone photoreceptors through the ON and OFF pathways. However, rods, S-cones, and M-cones activate the ON and OFF circuits via distinct pathways and the relative contribution of each to direction selectivity is unknown. Using a variety of stimulation paradigms, pharmacological agents, and knockout mice that lack rod transduction, we found that inputs from the ON pathway were critical for strong direction-selective (DS) tuning in the OFF pathway. For UV light stimulation, the ON pathway inputs to the OFF pathway originated with rod signaling, whereas for visible stimulation, the ON pathway inputs to the OFF pathway originated with both rod and M-cone signaling. Whole-cell voltage-clamp recordings revealed that blocking the ON pathway reduced directional tuning in the OFF pathway via a reduction in null-side inhibition, which is provided by OFF starburst amacrine cells (SACs). Consistent with this, our recordings from OFF SACs confirmed that signals originating in the ON pathway contribute to their excitation. Finally, we observed that, for UV stimulation, ON contributions to OFF DS tuning matured earlier than direct signaling via the OFF pathway. These data indicate that the retina uses multiple strategies for computing DS responses across different colors and stages of development. SIGNIFICANCE STATEMENT The retina uses parallel pathways to encode different features of the visual scene. In some cases, these distinct pathways converge on circuits that mediate a distinct computation. For example, rod and cone pathways enable direction-selective (DS) ganglion cells to encode motion over a wide range of light intensities. Here, we show that although direction selectivity is robust across light intensities, motion discrimination for OFF signals is dependent upon ON signaling. At eye opening, ON directional tuning is mature, whereas OFF DS tuning is significantly reduced due to a delayed maturation of S-cone to OFF cone bipolar signaling. These results provide evidence that the retina uses multiple strategies for computing DS responses across different stimulus conditions. PMID:27629718

Application of a fuzzy neural network model in predicting polycyclic aromatic hydrocarbon-mediated perturbations of the Cyp1b1 transcriptional regulatory network in mouse skin

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

Larkin, Andrew; Department of Statistics, Oregon State University; Superfund Research Center, Oregon State University

2013-03-01

Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdanimore » logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave-one-out cross-validation. Predictions were within 1 log{sub 2} fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights. - Highlights: ► Tested a model to predict PAH mixture-mediated changes in Cyp1b1 expression ► Quantitative predictions in agreement with microarrays for Cyp1b1 induction ► Unexpected difference in expression between DBC and other treatments predicted ► Model predictions for combining PAH mixtures in agreement with microarrays ► Predictions highly dependent on aryl hydrocarbon receptor repressor expression.« less

Parameter optimization on the convergence surface of path simulations

NASA Astrophysics Data System (ADS)

Chandrasekaran, Srinivas Niranj

Computational treatments of protein conformational changes tend to focus on the trajectories themselves, despite the fact that it is the transition state structures that contain information about the barriers that impose multi-state behavior. PATH is an algorithm that computes a transition pathway between two protein crystal structures, along with the transition state structure, by minimizing the Onsager-Machlup action functional. It is rapid but depends on several unknown input parameters whose range of different values can potentially generate different transition-state structures. Transition-state structures arising from different input parameters cannot be uniquely compared with those generated by other methods. I outline modifications that I have made to the PATH algorithm that estimates these input parameters in a manner that circumvents these difficulties, and describe two complementary tests that validate the transition-state structures found by the PATH algorithm. First, I show that although the PATH algorithm and two other approaches to computing transition pathways produce different low-energy structures connecting the initial and final ground-states with the transition state, all three methods agree closely on the configurations of their transition states. Second, I show that the PATH transition states are close to the saddle points of free-energy surfaces connecting initial and final states generated by replica-exchange Discrete Molecular Dynamics simulations. I show that aromatic side-chain rearrangements create similar potential energy barriers in the transition-state structures identified by PATH for a signaling protein, a contractile protein, and an enzyme. Finally, I observed, but cannot account for, the fact that trajectories obtained for all-atom and Calpha-only simulations identify transition state structures in which the Calpha atoms are in essentially the same positions. The consistency between transition-state structures derived by different algorithms for unrelated protein systems argues that although functionally important protein conformational change trajectories are to a degree stochastic, they nonetheless pass through a well-defined transition state whose detailed structural properties can rapidly be identified using PATH. In the end, I outline the strategies that could enhance the efficiency and applicability of PATH.

Online sequential Monte Carlo smoother for partially observed diffusion processes

NASA Astrophysics Data System (ADS)

Gloaguen, Pierre; Étienne, Marie-Pierre; Le Corff, Sylvain

2018-12-01

This paper introduces a new algorithm to approximate smoothed additive functionals of partially observed diffusion processes. This method relies on a new sequential Monte Carlo method which allows to compute such approximations online, i.e., as the observations are received, and with a computational complexity growing linearly with the number of Monte Carlo samples. The original algorithm cannot be used in the case of partially observed stochastic differential equations since the transition density of the latent data is usually unknown. We prove that it may be extended to partially observed continuous processes by replacing this unknown quantity by an unbiased estimator obtained for instance using general Poisson estimators. This estimator is proved to be consistent and its performance are illustrated using data from two models.

Single-server blind quantum computation with quantum circuit model

NASA Astrophysics Data System (ADS)

Zhang, Xiaoqian; Weng, Jian; Li, Xiaochun; Luo, Weiqi; Tan, Xiaoqing; Song, Tingting

2018-06-01

Blind quantum computation (BQC) enables the client, who has few quantum technologies, to delegate her quantum computation to a server, who has strong quantum computabilities and learns nothing about the client's quantum inputs, outputs and algorithms. In this article, we propose a single-server BQC protocol with quantum circuit model by replacing any quantum gate with the combination of rotation operators. The trap quantum circuits are introduced, together with the combination of rotation operators, such that the server is unknown about quantum algorithms. The client only needs to perform operations X and Z, while the server honestly performs rotation operators.

Automatic ground control point recognition with parallel associative memory

NASA Technical Reports Server (NTRS)

Al-Tahir, Raid; Toth, Charles K.; Schenck, Anton F.

1990-01-01

The basic principle of the associative memory is to match the unknown input pattern against a stored training set, and responding with the 'closest match' and the corresponding label. Generally, an associative memory system requires two preparatory steps: selecting attributes of the pattern class, and training the system by associating patterns with labels. Experimental results gained from using Parallel Associative Memory are presented. The primary concern is an automatic search for ground control points in aerial photographs. Synthetic patterns are tested followed by real data. The results are encouraging as a relatively high level of correct matches is reached.

Multiphoton Scattering Tomography with Coherent States.

PubMed

Ramos, Tomás; García-Ripoll, Juan José

2017-10-13

In this work we develop an experimental procedure to interrogate the single- and multiphoton scattering matrices of an unknown quantum system interacting with propagating photons. Our proposal requires coherent state laser or microwave inputs and homodyne detection at the scatterer's output, and provides simultaneous information about multiple-elastic and inelastic-segments of the scattering matrix. The method is resilient to detector noise and its errors can be made arbitrarily small by combining experiments at various laser powers. Finally, we show that the tomography of scattering has to be performed using pulsed lasers to efficiently gather information about the nonlinear processes in the scatterer.

Adjustment technique without explicit formation of normal equations /conjugate gradient method/

NASA Technical Reports Server (NTRS)

Saxena, N. K.

1974-01-01

For a simultaneous adjustment of a large geodetic triangulation system, a semiiterative technique is modified and used successfully. In this semiiterative technique, known as the conjugate gradient (CG) method, original observation equations are used, and thus the explicit formation of normal equations is avoided, 'huge' computer storage space being saved in the case of triangulation systems. This method is suitable even for very poorly conditioned systems where solution is obtained only after more iterations. A detailed study of the CG method for its application to large geodetic triangulation systems was done that also considered constraint equations with observation equations. It was programmed and tested on systems as small as two unknowns and three equations up to those as large as 804 unknowns and 1397 equations. When real data (573 unknowns, 965 equations) from a 1858-km-long triangulation system were used, a solution vector accurate to four decimal places was obtained in 2.96 min after 1171 iterations (i.e., 2.0 times the number of unknowns).

Improved mapping of radio sources from VLBI data by least-square fit

NASA Technical Reports Server (NTRS)

Rodemich, E. R.

1985-01-01

A method is described for producing improved mapping of radio sources from Very Long Base Interferometry (VLBI) data. The method described is more direct than existing Fourier methods, is often more accurate, and runs at least as fast. The visibility data is modeled here, as in existing methods, as a function of the unknown brightness distribution and the unknown antenna gains and phases. These unknowns are chosen so that the resulting function values are as near as possible to the observed values. If researchers use the radio mapping source deviation to measure the closeness of this fit to the observed values, they are led to the problem of minimizing a certain function of all the unknown parameters. This minimization problem cannot be solved directly, but it can be attacked by iterative methods which we show converge automatically to the minimum with no user intervention. The resulting brightness distribution will furnish the best fit to the data among all brightness distributions of given resolution.

A Particle Smoother with Sequential Importance Resampling for soil hydraulic parameter estimation: A lysimeter experiment

NASA Astrophysics Data System (ADS)

Montzka, Carsten; Hendricks Franssen, Harrie-Jan; Moradkhani, Hamid; Pütz, Thomas; Han, Xujun; Vereecken, Harry

2013-04-01

An adequate description of soil hydraulic properties is essential for a good performance of hydrological forecasts. So far, several studies showed that data assimilation could reduce the parameter uncertainty by considering soil moisture observations. However, these observations and also the model forcings were recorded with a specific measurement error. It seems a logical step to base state updating and parameter estimation on observations made at multiple time steps, in order to reduce the influence of outliers at single time steps given measurement errors and unknown model forcings. Such outliers could result in erroneous state estimation as well as inadequate parameters. This has been one of the reasons to use a smoothing technique as implemented for Bayesian data assimilation methods such as the Ensemble Kalman Filter (i.e. Ensemble Kalman Smoother). Recently, an ensemble-based smoother has been developed for state update with a SIR particle filter. However, this method has not been used for dual state-parameter estimation. In this contribution we present a Particle Smoother with sequentially smoothing of particle weights for state and parameter resampling within a time window as opposed to the single time step data assimilation used in filtering techniques. This can be seen as an intermediate variant between a parameter estimation technique using global optimization with estimation of single parameter sets valid for the whole period, and sequential Monte Carlo techniques with estimation of parameter sets evolving from one time step to another. The aims are i) to improve the forecast of evaporation and groundwater recharge by estimating hydraulic parameters, and ii) to reduce the impact of single erroneous model inputs/observations by a smoothing method. In order to validate the performance of the proposed method in a real world application, the experiment is conducted in a lysimeter environment.

Step-control of electromechanical systems

DOEpatents

Lewis, Robert N.

1979-01-01

The response of an automatic control system to a general input signal is improved by applying a test input signal, observing the response to the test input signal and determining correctional constants necessary to provide a modified input signal to be added to the input to the system. A method is disclosed for determining correctional constants. The modified input signal, when applied in conjunction with an operating signal, provides a total system output exhibiting an improved response. This method is applicable to open-loop or closed-loop control systems. The method is also applicable to unstable systems, thus allowing controlled shut-down before dangerous or destructive response is achieved and to systems whose characteristics vary with time, thus resulting in improved adaptive systems.

Frequency and function in the basal ganglia: the origins of beta and gamma band activity.

PubMed

Blenkinsop, Alexander; Anderson, Sean; Gurney, Kevin

2017-07-01

Neuronal oscillations in the basal ganglia have been observed to correlate with behaviours, although the causal mechanisms and functional significance of these oscillations remain unknown. We present a novel computational model of the healthy basal ganglia, constrained by single unit recordings from non-human primates. When the model is run using inputs that might be expected during performance of a motor task, the network shows emergent phenomena: it functions as a selection mechanism and shows spectral properties that match those seen in vivo. Beta frequency oscillations are shown to require pallido-striatal feedback, and occur with behaviourally relevant cortical input. Gamma oscillations arise in the subthalamic-globus pallidus feedback loop, and occur during movement. The model provides a coherent framework for the study of spectral, temporal and functional analyses of the basal ganglia and lays the foundation for an integrated approach to study basal ganglia pathologies such as Parkinson's disease in silico. Neural oscillations in the basal ganglia (BG) are well studied yet remain poorly understood. Behavioural correlates of spectral activity are well described, yet a quantitative hypothesis linking time domain dynamics and spectral properties to BG function has been lacking. We show, for the first time, that a unified description is possible by interpreting previously ignored structure in data describing globus pallidus interna responses to cortical stimulation. These data were used to expose a pair of distinctive neuronal responses to the stimulation. This observation formed the basis for a new mathematical model of the BG, quantitatively fitted to the data, which describes the dynamics in the data, and is validated against other stimulus protocol experiments. A key new result is that when the model is run using inputs hypothesised to occur during the performance of a motor task, beta and gamma frequency oscillations emerge naturally during static-force and movement, respectively, consistent with experimental local field potentials. This new model predicts that the pallido-striatum connection has a key role in the generation of beta band activity, and that the gamma band activity associated with motor task performance has its origins in the pallido-subthalamic feedback loop. The network's functionality as a selection mechanism also occurs as an emergent property, and closer fits to the data gave better selection properties. The model provides a coherent framework for the study of spectral, temporal and functional analyses of the BG and therefore lays the foundation for an integrated approach to study BG pathologies such as Parkinson's disease in silico. © 2017 The Authors. The Journal of Physiology © 2017 The Physiological Society.

Observability of Boolean multiplex control networks

NASA Astrophysics Data System (ADS)

Wu, Yuhu; Xu, Jingxue; Sun, Xi-Ming; Wang, Wei

2017-04-01

Boolean multiplex (multilevel) networks (BMNs) are currently receiving considerable attention as theoretical arguments for modeling of biological systems and system level analysis. Studying control-related problems in BMNs may not only provide new views into the intrinsic control in complex biological systems, but also enable us to develop a method for manipulating biological systems using exogenous inputs. In this article, the observability of the Boolean multiplex control networks (BMCNs) are studied. First, the dynamical model and structure of BMCNs with control inputs and outputs are constructed. By using of Semi-Tensor Product (STP) approach, the logical dynamics of BMCNs is converted into an equivalent algebraic representation. Then, the observability of the BMCNs with two different kinds of control inputs is investigated by giving necessary and sufficient conditions. Finally, examples are given to illustrate the efficiency of the obtained theoretical results.

Fuzzy Neuron: Method and Hardware Realization

NASA Technical Reports Server (NTRS)

Krasowski, Michael J.; Prokop, Norman F.

2014-01-01

This innovation represents a method by which single-to-multi-input, single-to-many-output system transfer functions can be estimated from input/output data sets. This innovation can be run in the background while a system is operating under other means (e.g., through human operator effort), or may be utilized offline using data sets created from observations of the estimated system. It utilizes a set of fuzzy membership functions spanning the input space for each input variable. Linear combiners associated with combinations of input membership functions are used to create the output(s) of the estimator. Coefficients are adjusted online through the use of learning algorithms.

Modification of persistent responses in medial prefrontal cortex during learning in trace eyeblink conditioning

PubMed Central

2014-01-01

Persistent spiking in response to a discrete stimulus is considered to reflect the active maintenance of a memory for that stimulus until a behavioral response is made. This response pattern has been reported in learning paradigms that impose a temporal gap between stimulus presentation and behavioral response, including trace eyeblink conditioning. However, it is unknown whether persistent responses are acquired as a function of learning or simply represent an already existing category of response type. This fundamental question was addressed by recording single-unit activity in the medial prefrontal cortex (mPFC) of rabbits during the initial learning phase of trace eyeblink conditioning. Persistent responses to the tone conditioned stimulus were observed in the mPFC during the very first training sessions. Further analysis revealed that most cells with persistent responses showed this pattern during the very first training trial, before animals had experienced paired training. However, persistent cells showed reliable decreases in response magnitude over the first training session, which were not observed on the second day of training or for sessions in which learning criterion was met. This modification of response magnitude was specific to persistent responses and was not observed for cells showing phasic tone-evoked responses. The data suggest that persistent responses to discrete stimuli do not require learning but that the ongoing robustness of such responses over the course of training is modified as a result of experience. Putative mechanisms for this modification are discussed, including changes in cellular or network properties, neuromodulatory tone, and/or the synaptic efficacy of tone-associated inputs. PMID:25080570

Inverse modelling for real-time estimation of radiological consequences in the early stage of an accidental radioactivity release.

PubMed

Pecha, Petr; Šmídl, Václav

2016-11-01

A stepwise sequential assimilation algorithm is proposed based on an optimisation approach for recursive parameter estimation and tracking of radioactive plume propagation in the early stage of a radiation accident. Predictions of the radiological situation in each time step of the plume propagation are driven by an existing short-term meteorological forecast and the assimilation procedure manipulates the model parameters to match the observations incoming concurrently from the terrain. Mathematically, the task is a typical ill-posed inverse problem of estimating the parameters of the release. The proposed method is designated as a stepwise re-estimation of the source term release dynamics and an improvement of several input model parameters. It results in a more precise determination of the adversely affected areas in the terrain. The nonlinear least-squares regression methodology is applied for estimation of the unknowns. The fast and adequately accurate segmented Gaussian plume model (SGPM) is used in the first stage of direct (forward) modelling. The subsequent inverse procedure infers (re-estimates) the values of important model parameters from the actual observations. Accuracy and sensitivity of the proposed method for real-time forecasting of the accident propagation is studied. First, a twin experiment generating noiseless simulated "artificial" observations is studied to verify the minimisation algorithm. Second, the impact of the measurement noise on the re-estimated source release rate is examined. In addition, the presented method can be used as a proposal for more advanced statistical techniques using, e.g., importance sampling. Copyright © 2016 Elsevier Ltd. All rights reserved.

Fusion of pan-tropical biomass maps using weighted averaging and regional calibration data

NASA Astrophysics Data System (ADS)

Ge, Yong; Avitabile, Valerio; Heuvelink, Gerard B. M.; Wang, Jianghao; Herold, Martin

2014-09-01

Biomass is a key environmental variable that influences many biosphere-atmosphere interactions. Recently, a number of biomass maps at national, regional and global scales have been produced using different approaches with a variety of input data, such as from field observations, remotely sensed imagery and other spatial datasets. However, the accuracy of these maps varies regionally and is largely unknown. This research proposes a fusion method to increase the accuracy of regional biomass estimates by using higher-quality calibration data. In this fusion method, the biases in the source maps were first adjusted to correct for over- and underestimation by comparison with the calibration data. Next, the biomass maps were combined linearly using weights derived from the variance-covariance matrix associated with the accuracies of the source maps. Because each map may have different biases and accuracies for different land use types, the biases and fusion weights were computed for each of the main land cover types separately. The conceptual arguments are substantiated by a case study conducted in East Africa. Evaluation analysis shows that fusing multiple source biomass maps may produce a more accurate map than when only one biomass map or unweighted averaging is used.

Reinforcement learning controller design for affine nonlinear discrete-time systems using online approximators.

PubMed

Yang, Qinmin; Jagannathan, Sarangapani

2012-04-01

In this paper, reinforcement learning state- and output-feedback-based adaptive critic controller designs are proposed by using the online approximators (OLAs) for a general multi-input and multioutput affine unknown nonlinear discretetime systems in the presence of bounded disturbances. The proposed controller design has two entities, an action network that is designed to produce optimal signal and a critic network that evaluates the performance of the action network. The critic estimates the cost-to-go function which is tuned online using recursive equations derived from heuristic dynamic programming. Here, neural networks (NNs) are used both for the action and critic whereas any OLAs, such as radial basis functions, splines, fuzzy logic, etc., can be utilized. For the output-feedback counterpart, an additional NN is designated as the observer to estimate the unavailable system states, and thus, separation principle is not required. The NN weight tuning laws for the controller schemes are also derived while ensuring uniform ultimate boundedness of the closed-loop system using Lyapunov theory. Finally, the effectiveness of the two controllers is tested in simulation on a pendulum balancing system and a two-link robotic arm system.

Ultrahigh-energy Cosmic Rays from Fanaroff Riley class II radio galaxies

NASA Astrophysics Data System (ADS)

Rachen, Joerg; Biermann, Peter L.

1992-08-01

The hot spots of very powerful radio galaxies (Fanaroff Riley class II) are argued to be the sources of the ultrahigh energy component in Cosmic Rays. We present calculations of Cosmic Ray transport in an evolving universe, taking the losses against the microwave background properly into account. As input we use the models for the cosmological radio source evolution derived by radioastronomers (mainly Peacock 1985). The model we adopt for the acceleration in the radio hot spots has been introduced by Biermann and Strittmatter (1987), and Meisenheimer et al. (1989) and is based on first order Fermi theory of particle acceleration at shocks (see, e.g., Drury 1983). As an unknown the actual proportion of energy density in protons enters, which together with structural uncertainties in the hot spots should introduce no more than one order of magnitude in uncertainty: We easily reproduce the observed spectra of high energy cosmic rays. It follows that scattering of charged energetic particles in intergalactic space must be sufficiently small in order to obtain contributions from sources as far away as even the nearest Fanaroff Riley class II radio galaxies. This implies a strong constraint on the turbulent magnetic field in intergalactic space.

16S rRNA Gene-Based Metagenomic Analysis of Ozark Cave Bacteria

PubMed Central

Oliveira, Cássia; Gunderman, Lauren; Coles, Cathryn A.; Lochmann, Jason; Parks, Megan; Ballard, Ethan; Glazko, Galina; Rahmatallah, Yasir; Tackett, Alan J.; Thomas, David J.

2018-01-01

The microbial diversity within cave ecosystems is largely unknown. Ozark caves maintain a year-round stable temperature (12–14 °C), but most parts of the caves experience complete darkness. The lack of sunlight and geological isolation from surface-energy inputs generate nutrient-poor conditions that may limit species diversity in such environments. Although microorganisms play a crucial role in sustaining life on Earth and impacting human health, little is known about their diversity, ecology, and evolution in community structures. We used five Ozark region caves as test sites for exploring bacterial diversity and monitoring long-term biodiversity. Illumina MiSeq sequencing of five cave soil samples and a control sample revealed a total of 49 bacterial phyla, with seven major phyla: Proteobacteria, Acidobacteria, Actinobacteria, Firmicutes, Chloroflexi, Bacteroidetes, and Nitrospirae. Variation in bacterial composition was observed among the five caves studied. Sandtown Cave had the lowest richness and most divergent community composition. 16S rRNA gene-based metagenomic analysis of cave-dwelling microbial communities in the Ozark caves revealed that species abundance and diversity are vast and included ecologically, agriculturally, and economically relevant taxa. PMID:29551950

Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

PubMed Central

Nessler, Bernhard; Pfeiffer, Michael; Buesing, Lars; Maass, Wolfgang

2013-01-01

The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex. PMID:23633941

Multiple approaches to microbial source tracking in tropical northern Australia

PubMed Central

Neave, Matthew; Luter, Heidi; Padovan, Anna; Townsend, Simon; Schobben, Xavier; Gibb, Karen

2014-01-01

Microbial source tracking is an area of research in which multiple approaches are used to identify the sources of elevated bacterial concentrations in recreational lakes and beaches. At our study location in Darwin, northern Australia, water quality in the harbor is generally good, however dry-season beach closures due to elevated Escherichia coli and enterococci counts are a cause for concern. The sources of these high bacteria counts are currently unknown. To address this, we sampled sewage outfalls, other potential inputs, such as urban rivers and drains, and surrounding beaches, and used genetic fingerprints from E. coli and enterococci communities, fecal markers and 454 pyrosequencing to track contamination sources. A sewage effluent outfall (Larrakeyah discharge) was a source of bacteria, including fecal bacteria that impacted nearby beaches. Two other treated effluent discharges did not appear to influence sites other than those directly adjacent. Several beaches contained fecal indicator bacteria that likely originated from urban rivers and creeks within the catchment. Generally, connectivity between the sites was observed within distinct geographical locations and it appeared that most of the bacterial contamination on Darwin beaches was confined to local sources. PMID:25224738

Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model

PubMed Central

Gönner, Lorenz; Vitay, Julien; Hamker, Fred H.

2017-01-01

Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, we propose a model of spatial learning and sequence generation as interdependent processes, integrating cortical contextual coding, synaptic plasticity and neuromodulatory mechanisms into a map-based approach. Following goal learning, sequential activity emerges from continuous attractor network dynamics biased by goal memory inputs. We apply Bayesian decoding on the resulting spike trains, allowing a direct comparison with experimental data. Simulations show that this model (1) explains the generation of never-experienced sequence trajectories in familiar environments, without requiring virtual self-motion signals, (2) accounts for the bias in place-cell sequences toward goal locations, (3) highlights their utility in flexible route planning, and (4) provides specific testable predictions. PMID:29075187

Black Carbon in Estuarine (Coastal) High-molecular-weight Dissolved Organic Matter

NASA Technical Reports Server (NTRS)

Mannino, Antonio; Harvey, H. Rodger

2003-01-01

Dissolved organic matter (DOM) in the ocean constitutes one of the largest pools of organic carbon in the biosphere, yet much of its composition is uncharacterized. Observations of black carbon (BC) particles (by-products of fossil fuel combustion and biomass burning) in the atmosphere, ice, rivers, soils and marine sediments suggest that this material is ubiquitous, yet the contribution of BC to the ocean s DOM pool remains unknown. Analysis of high-molecular-weight DOM isolated from surface waters of two estuaries in the northwest Atlantic Ocean finds that BC is a significant component of DOM, suggesting that river-estuary systems are important exporters of BC to the ocean through DOM. We show that BC comprises 4-7% of the dissolved organic carbon (DOC) at coastal ocean sites, which supports the hypothesis that the DOC pool is the intermediate reservoir in which BC ages prior to sedimentary deposition. Flux calculations suggest that BC could be as important as vascular plant-derived lignin in terms of carbon inputs to the ocean. Production of BC sequesters fossil fuel- and biomass-derived carbon into a refractory carbon pool. Hence, BC may represent a significant sink for carbon to the ocean.

Nondegenerate parametric oscillations in a tunable superconducting resonator

NASA Astrophysics Data System (ADS)

Bengtsson, Andreas; Krantz, Philip; Simoen, Michaël; Svensson, Ida-Maria; Schneider, Ben; Shumeiko, Vitaly; Delsing, Per; Bylander, Jonas

2018-04-01

We investigate nondegenerate parametric oscillations in a superconducting microwave multimode resonator that is terminated by a superconducting quantum interference device (SQUID). The parametric effect is achieved by modulating magnetic flux through the SQUID at a frequency close to the sum of two resonator-mode frequencies. For modulation amplitudes exceeding an instability threshold, self-sustained oscillations are observed in both modes. The amplitudes of these oscillations show good quantitative agreement with a theoretical model. The oscillation phases are found to be correlated and exhibit strong fluctuations which broaden the oscillation spectral linewidths. These linewidths are significantly reduced by applying a weak on-resonant tone, which also suppresses the phase fluctuations. When the weak tone is detuned, we observe synchronization of the oscillation frequency with the frequency of the input. For the detuned input, we also observe an emergence of three idlers in the output. This observation is in agreement with theory indicating four-mode amplification and squeezing of a coherent input.

River flow prediction using hybrid models of support vector regression with the wavelet transform, singular spectrum analysis and chaotic approach

NASA Astrophysics Data System (ADS)

Baydaroğlu, Özlem; Koçak, Kasım; Duran, Kemal

2018-06-01

Prediction of water amount that will enter the reservoirs in the following month is of vital importance especially for semi-arid countries like Turkey. Climate projections emphasize that water scarcity will be one of the serious problems in the future. This study presents a methodology for predicting river flow for the subsequent month based on the time series of observed monthly river flow with hybrid models of support vector regression (SVR). Monthly river flow over the period 1940-2012 observed for the Kızılırmak River in Turkey has been used for training the method, which then has been applied for predictions over a period of 3 years. SVR is a specific implementation of support vector machines (SVMs), which transforms the observed input data time series into a high-dimensional feature space (input matrix) by way of a kernel function and performs a linear regression in this space. SVR requires a special input matrix. The input matrix was produced by wavelet transforms (WT), singular spectrum analysis (SSA), and a chaotic approach (CA) applied to the input time series. WT convolutes the original time series into a series of wavelets, and SSA decomposes the time series into a trend, an oscillatory and a noise component by singular value decomposition. CA uses a phase space formed by trajectories, which represent the dynamics producing the time series. These three methods for producing the input matrix for the SVR proved successful, while the SVR-WT combination resulted in the highest coefficient of determination and the lowest mean absolute error.

The rapid distraction of attentional resources toward the source of incongruent stimulus input during multisensory conflict.

PubMed

Donohue, Sarah E; Todisco, Alexandra E; Woldorff, Marty G

2013-04-01

Neuroimaging work on multisensory conflict suggests that the relevant modality receives enhanced processing in the face of incongruency. However, the degree of stimulus processing in the irrelevant modality and the temporal cascade of the attentional modulations in either the relevant or irrelevant modalities are unknown. Here, we employed an audiovisual conflict paradigm with a sensory probe in the task-irrelevant modality (vision) to gauge the attentional allocation to that modality. ERPs were recorded as participants attended to and discriminated spoken auditory letters while ignoring simultaneous bilateral visual letter stimuli that were either fully congruent, fully incongruent, or partially incongruent (one side incongruent, one congruent) with the auditory stimulation. Half of the audiovisual letter stimuli were followed 500-700 msec later by a bilateral visual probe stimulus. As expected, ERPs to the audiovisual stimuli showed an incongruency ERP effect (fully incongruent versus fully congruent) of an enhanced, centrally distributed, negative-polarity wave starting ∼250 msec. More critically here, the sensory ERP components to the visual probes were larger when they followed fully incongruent versus fully congruent multisensory stimuli, with these enhancements greatest on fully incongruent trials with the slowest RTs. In addition, on the slowest-response partially incongruent trials, the P2 sensory component to the visual probes was larger contralateral to the preceding incongruent visual stimulus. These data suggest that, in response to conflicting multisensory stimulus input, the initial cognitive effect is a capture of attention by the incongruent irrelevant-modality input, pulling neural processing resources toward that modality, resulting in rapid enhancement, rather than rapid suppression, of that input.

Occurrence of chloromethane in tropical terrestrial and marine areas

NASA Astrophysics Data System (ADS)

Laturnus, F.; Kolusu, S.; Grawe, D.; Mehlig, U.; Asp, N.; Schlünzen, K. H.; Seifert, R.

2011-12-01

The discussion of a possible global climate change induced by human activities brought sources into focus not yet considered to be important in global climate changes. One source is the natural emission of chloromethane, a compound which is known to participate in atmospheric processes affecting the global climate, such as stratospheric ozone destruction and warming of the troposphere. Especially natural emissions of chloromethane have been under scrutiny recently as the part of the natural contribution is still unknown and may be influenced by human activities. A comparison between global atmospheric occurrence of chloromethane and their input from so far known industrial and natural sources revealed a gap of 40-50% in missing input. Recently, it has been suggested that tropical areas may be the missing link in filling the gap of the atmospheric input of chloromethane. In our studies, we investigated tropical oceanic areas and mangrove forests regarding their occurrence and emission of chloromethane. For the oceanic areas, ambient air concentrations and stable carbon rations were taken. Together with backward air mass trajectory analysis the results revealed a coastal influence on the occurrence of chloromethane in the tropical ocean. For the mangrove forest areas, ambient air concentrations and stable carbon rations were taken at upwind and downwind position at the coast of Brazil. The results showed a considerable natural emission of chloromethane suggesting mangroves as an important source for the atmospheric input of chloromethane. With the help of a mesoscale atmospheric model meteorological conditions were simulated and the fluxes of chloromethane from mangrove forest were estimated.

Impacts of uncertainties in weather and streamflow observations in calibration and evaluation of an elevation distributed HBV-model

NASA Astrophysics Data System (ADS)

Engeland, K.; Steinsland, I.; Petersen-Øverleir, A.; Johansen, S.

2012-04-01

The aim of this study is to assess the uncertainties in streamflow simulations when uncertainties in both observed inputs (precipitation and temperature) and streamflow observations used in the calibration of the hydrological model are explicitly accounted for. To achieve this goal we applied the elevation distributed HBV model operating on daily time steps to a small catchment in high elevation in Southern Norway where the seasonal snow cover is important. The uncertainties in precipitation inputs were quantified using conditional simulation. This procedure accounts for the uncertainty related to the density of the precipitation network, but neglects uncertainties related to measurement bias/errors and eventual elevation gradients in precipitation. The uncertainties in temperature inputs were quantified using a Bayesian temperature interpolation procedure where the temperature lapse rate is re-estimated every day. The uncertainty in the lapse rate was accounted for whereas the sampling uncertainty related to network density was neglected. For every day a random sample of precipitation and temperature inputs were drawn to be applied as inputs to the hydrologic model. The uncertainties in observed streamflow were assessed based on the uncertainties in the rating curve model. A Bayesian procedure was applied to estimate the probability for rating curve models with 1 to 3 segments and the uncertainties in their parameters. This method neglects uncertainties related to errors in observed water levels. Note that one rating curve was drawn to make one realisation of a whole time series of streamflow, thus the rating curve errors lead to a systematic bias in the streamflow observations. All these uncertainty sources were linked together in both calibration and evaluation of the hydrologic model using a DREAM based MCMC routine. Effects of having less information (e.g. missing one streamflow measurement for defining the rating curve or missing one precipitation station) was also investigated.

Mean-square state and parameter estimation for stochastic linear systems with Gaussian and Poisson noises

NASA Astrophysics Data System (ADS)

Basin, M.; Maldonado, J. J.; Zendejo, O.

2016-07-01

This paper proposes new mean-square filter and parameter estimator design for linear stochastic systems with unknown parameters over linear observations, where unknown parameters are considered as combinations of Gaussian and Poisson white noises. The problem is treated by reducing the original problem to a filtering problem for an extended state vector that includes parameters as additional states, modelled as combinations of independent Gaussian and Poisson processes. The solution to this filtering problem is based on the mean-square filtering equations for incompletely polynomial states confused with Gaussian and Poisson noises over linear observations. The resulting mean-square filter serves as an identifier for the unknown parameters. Finally, a simulation example shows effectiveness of the proposed mean-square filter and parameter estimator.

Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data

NASA Astrophysics Data System (ADS)

Jothiprakash, V.; Magar, R. B.

2012-07-01

SummaryIn this study, artificial intelligent (AI) techniques such as artificial neural network (ANN), Adaptive neuro-fuzzy inference system (ANFIS) and Linear genetic programming (LGP) are used to predict daily and hourly multi-time-step ahead intermittent reservoir inflow. To illustrate the applicability of AI techniques, intermittent Koyna river watershed in Maharashtra, India is chosen as a case study. Based on the observed daily and hourly rainfall and reservoir inflow various types of time-series, cause-effect and combined models are developed with lumped and distributed input data. Further, the model performance was evaluated using various performance criteria. From the results, it is found that the performances of LGP models are found to be superior to ANN and ANFIS models especially in predicting the peak inflows for both daily and hourly time-step. A detailed comparison of the overall performance indicated that the combined input model (combination of rainfall and inflow) performed better in both lumped and distributed input data modelling. It was observed that the lumped input data models performed slightly better because; apart from reducing the noise in the data, the better techniques and their training approach, appropriate selection of network architecture, required inputs, and also training-testing ratios of the data set. The slight poor performance of distributed data is due to large variations and lesser number of observed values.

A validity study of self-reported daily texting frequency, cell phone characteristics, and texting styles among young adults.

PubMed

Gold, Judith E; Rauscher, Kimberly J; Zhu, Motao

2015-04-02

Texting is associated with adverse health effects including musculoskeletal disorders, sleep disturbances, and traffic crashes. Many studies have relied on self-reported texting frequency, yet the validity of self-reports is unknown. Our objective was to provide some of the first data on the validity of self-reported texting frequency, cell phone characteristics including input device (e.g. touchscreen), key configuration (e.g., QWERTY), and texting styles including phone orientation (e.g., horizontal) and hands holding the phone while texting. Data were collected using a self-administered questionnaire and observation of a texting task among college students ages 18 to 24. To gauge agreement between self-reported and phone bill-derived categorical number of daily text messages sent, we calculated percent of agreement, Spearman correlation coefficient, and a linear weighted kappa statistic. For agreement between self-reported and observed cell phone characteristics and texting styles we calculated percentages of agreement. We used chi-square tests to detect significant differences (α = 0.05) by gender and study protocol. There were 106 participants; 87 of which had complete data for texting frequency analyses. Among these 87, there was 26% (95% CI: 21-31) agreement between self-reported and phone bill-derived number of daily text messages sent with a Spearman's rho of 0.48 and a weighted kappa of 0.17 (95% CI: 0.06-0.27). Among those who did not accurately report the number of daily texts sent, 81% overestimated this number. Among the full sample (n = 106), there was high agreement between self-reported and observed texting input device (96%, 95% CI: 91-99), key configuration (89%, 95% CI: 81-94), and phone orientation while texting (93%, 95% CI: 86-97). No differences were found by gender or study protocol among any items. While young adults correctly reported their cell phone's characteristics and phone orientation while texting, most incorrectly estimated the number of daily text messages they sent. This suggests that while self-reported texting frequency may be useful for studies where relative ordering is adequate, it should not be used in epidemiologic studies to identify a risk threshold. For these studies, it is recommended that a less biased measure, such as a cell phone bill, be utilized.

Motor excitability during visual perception of known and unknown spoken languages.

PubMed

Swaminathan, Swathi; MacSweeney, Mairéad; Boyles, Rowan; Waters, Dafydd; Watkins, Kate E; Möttönen, Riikka

2013-07-01

It is possible to comprehend speech and discriminate languages by viewing a speaker's articulatory movements. Transcranial magnetic stimulation studies have shown that viewing speech enhances excitability in the articulatory motor cortex. Here, we investigated the specificity of this enhanced motor excitability in native and non-native speakers of English. Both groups were able to discriminate between speech movements related to a known (i.e., English) and unknown (i.e., Hebrew) language. The motor excitability was higher during observation of a known language than an unknown language or non-speech mouth movements, suggesting that motor resonance is enhanced specifically during observation of mouth movements that convey linguistic information. Surprisingly, however, the excitability was equally high during observation of a static face. Moreover, the motor excitability did not differ between native and non-native speakers. These findings suggest that the articulatory motor cortex processes several kinds of visual cues during speech communication. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

Numerical reconstruction of unknown Robin inclusions inside a heat conductor by a non-iterative method

NASA Astrophysics Data System (ADS)

Nakamura, Gen; Wang, Haibing

2017-05-01

Consider the problem of reconstructing unknown Robin inclusions inside a heat conductor from boundary measurements. This problem arises from active thermography and is formulated as an inverse boundary value problem for the heat equation. In our previous works, we proposed a sampling-type method for reconstructing the boundary of the Robin inclusion and gave its rigorous mathematical justification. This method is non-iterative and based on the characterization of the solution to the so-called Neumann- to-Dirichlet map gap equation. In this paper, we give a further investigation of the reconstruction method from both the theoretical and numerical points of view. First, we clarify the solvability of the Neumann-to-Dirichlet map gap equation and establish a relation of its solution to the Green function associated with an initial-boundary value problem for the heat equation inside the Robin inclusion. This naturally provides a way of computing this Green function from the Neumann-to-Dirichlet map and explains what is the input for the linear sampling method. Assuming that the Neumann-to-Dirichlet map gap equation has a unique solution, we also show the convergence of our method for noisy measurements. Second, we give the numerical implementation of the reconstruction method for two-dimensional spatial domains. The measurements for our inverse problem are simulated by solving the forward problem via the boundary integral equation method. Numerical results are presented to illustrate the efficiency and stability of the proposed method. By using a finite sequence of transient input over a time interval, we propose a new sampling method over the time interval by single measurement which is most likely to be practical.

Identifying quantum phase transitions with adversarial neural networks

NASA Astrophysics Data System (ADS)

Huembeli, Patrick; Dauphin, Alexandre; Wittek, Peter

2018-04-01

The identification of phases of matter is a challenging task, especially in quantum mechanics, where the complexity of the ground state appears to grow exponentially with the size of the system. Traditionally, physicists have to identify the relevant order parameters for the classification of the different phases. We here follow a radically different approach: we address this problem with a state-of-the-art deep learning technique, adversarial domain adaptation. We derive the phase diagram of the whole parameter space starting from a fixed and known subspace using unsupervised learning. This method has the advantage that the input of the algorithm can be directly the ground state without any ad hoc feature engineering. Furthermore, the dimension of the parameter space is unrestricted. More specifically, the input data set contains both labeled and unlabeled data instances. The first kind is a system that admits an accurate analytical or numerical solution, and one can recover its phase diagram. The second type is the physical system with an unknown phase diagram. Adversarial domain adaptation uses both types of data to create invariant feature extracting layers in a deep learning architecture. Once these layers are trained, we can attach an unsupervised learner to the network to find phase transitions. We show the success of this technique by applying it on several paradigmatic models: the Ising model with different temperatures, the Bose-Hubbard model, and the Su-Schrieffer-Heeger model with disorder. The method finds unknown transitions successfully and predicts transition points in close agreement with standard methods. This study opens the door to the classification of physical systems where the phase boundaries are complex such as the many-body localization problem or the Bose glass phase.

When causality does not imply correlation: more spadework at the foundations of scientific psychology.

PubMed

Marken, Richard S; Horth, Brittany

2011-06-01

Experimental research in psychology is based on an open-loop causal model which assumes that sensory input causes behavioral output. This model was tested in a tracking experiment where participants were asked to control a cursor, keeping it aligned with a target by moving a mouse to compensate for disturbances of differing difficulty. Since cursor movements (inputs) are the only observable cause of mouse movements (outputs), the open-loop model predicts that there will be a correlation between input and output that increases as tracking performance improves. In fact, the correlation between sensory input and motor output is very low regardless of the quality of tracking performance; causality, in terms of the effect of input on output, does not seem to imply correlation in this situation. This surprising result can be explained by a closed-loop model which assumes that input is causing output while output is causing input.

Language input and acquisition in a Mayan village: how important is directed speech?

PubMed

Shneidman, Laura A; Goldin-Meadow, Susan

2012-09-01

Theories of language acquisition have highlighted the importance of adult speakers as active participants in children's language learning. However, in many communities children are reported to be directly engaged by their caregivers only rarely (Lieven, 1994). This observation raises the possibility that these children learn language from observing, rather than participating in, communicative exchanges. In this paper, we quantify naturally occurring language input in one community where directed interaction with children has been reported to be rare (Yucatec Mayan). We compare this input to the input heard by children growing up in large families in the United States, and we consider how directed and overheard input relate to Mayan children's later vocabulary. In Study 1, we demonstrate that 1-year-old Mayan children do indeed hear a smaller proportion of total input in directed speech than children from the US. In Study 2, we show that for Mayan (but not US) children, there are great increases in the proportion of directed input that children receive between 13 and 35 months. In Study 3, we explore the validity of using videotaped data in a Mayan village. In Study 4, we demonstrate that word types directed to Mayan children from adults at 24 months (but not word types overheard by children or word types directed from other children) predict later vocabulary. These findings suggest that adult talk directed to children is important for early word learning, even in communities where much of children's early language input comes from overheard speech. © 2012 Blackwell Publishing Ltd.

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

NASA Astrophysics Data System (ADS)

Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

2013-09-01

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

Historical trace metal accumulation in the sediments of an urbanized region of the Lake Champlain watershed, Burlington, Vermont

USGS Publications Warehouse

Mecray, E.L.; King, J.W.; Appleby, P.G.; Hunt, A.S.

2001-01-01

This study documents the history of pollution inputs in the Burlington region of Lake Champlain, Vermont using measurements of anthropogenic metals (Cu, Zn, Cr, Pb, Cd, and Ag) in four age-dated sediment cores. Sediments record a history of contamination in a region and can be used to assess the changing threat to biota over time and to evaluate the effectiveness of discharge regulations on anthropogenic inputs. Grain size, magnetic susceptibility, radiometric dating and pollen stratigraphy were combined with trace metal data to provide an assessment of the history of contamination over the last 350 yr in the Burlington region of Lake Champlain. Magnetic susceptibility was initially used to identify land-use history for each site because it is a proxy indicator of soil erosion. Historical trends in metal inputs in the Burlington region from the seventeenth through the twentieth centuries are reflected in downcore variations in metal concentrations and accumulation rates. Metal concentrations increase above background values in the early to mid nineteenth century. The metal input rate to the sediments increases around 1920 and maximum concentrations and accumulation rates are observed in the late 1960s. Decreases in concentration and accumulation rate between 1970 and the present are observed, for most metals. The observed trends are primarily a function of variations in anthropogenic inputs and not variations in sediment grain size. Grain size data were used to remove texture variations from the metal profiles and results show trends in the anthropogenic metal signals remain. Radiometric dating and pollen stratigraphy provide well-constrained dates for the sediments thereby allowing the metal profiles to be interpreted in terms of land-use history.This study documents the history of pollution inputs in the Burlington region of Lake Champlain, Vermont using measurements of anthropogenic metals (Cu, Zn, Cr, Pb, Cd, and Ag) in four age-dated sediment cores. Sediments record a history of contamination in a region and can be used to assess the changing threat to biota over time and to evaluate the effectiveness of discharge regulations on anthropogenic inputs. Grain size, magnetic susceptibility, radiometric dating and pollen stratigraphy were combined with trace metal data to provide an assessment of the history of contamination over the last 350 yr in the Burlington region of Lake Champlain. Magnetic susceptibility was initially used to identify land-use history for each site because it is a proxy indicator of soil erosion. Historical trends in metal inputs in the Burlington region from the seventeenth through the twentieth centuries are reflected in downcore variations in metal concentrations and accumulation rates. Metal concentrations increase above background values in the early to mid nineteenth century. The metal input rate to the sediments increases around 1920 and maximum concentrations and accumulation rates are observed in the late 1960s. Decreases in concentration and accumulation rate between 1970 and the present are observed for most metals. The observed trends are primarily a function of variations in anthropogenic inputs and not variations in sediment grain size. Grain size data were used to remove texture variations from the metal profiles and results show trends in the anthropogenic metal signals remain. Radiometric dating and pollen stratigraphy provide well-constrained dates for the sediments thereby allowing the metal profiles to be interpreted in terms of land-use history.

Derivation of Aerosol Profiles for MC3E Convection Studies and Use in Simulations of the 20 May Squall Line Case

NASA Technical Reports Server (NTRS)

Fridlind, Ann M.; Li, Xiaowen; Wu, Di; van Lier-Walqui, Marcus; Ackerman, Andrew S.; Tao, Wei-Kuo; McFarquhar, Greg M.; Wu, Wei; Dong, Xiquan; Wang, Jingyu;

Orbit Determination and Maneuver Detection Using Event Representation with Thrust-Fourier-Coefficients

NASA Astrophysics Data System (ADS)

Lubey, D.; Ko, H.; Scheeres, D.

The classical orbit determination (OD) method of dealing with unknown maneuvers is to restart the OD process with post-maneuver observations. However, it is also possible to continue the OD process through such unknown maneuvers by representing those unknown maneuvers with an appropriate event representation. It has been shown in previous work (Ko & Scheeres, JGCD 2014) that any maneuver performed by a satellite transitioning between two arbitrary orbital states can be represented as an equivalent maneuver connecting those two states using Thrust-Fourier-Coefficients (TFCs). Event representation using TFCs rigorously provides a unique control law that can generate the desired secular behavior for a given unknown maneuver. This paper presents applications of this representation approach to orbit prediction and maneuver detection problem across unknown maneuvers. The TFCs are appended to a sequential filter as an adjoint state to compensate unknown perturbing accelerations and the modified filter estimates the satellite state and thrust coefficients by processing OD across the time of an unknown maneuver. This modified sequential filter with TFCs is capable of fitting tracking data and maintaining an OD solution in the presence of unknown maneuvers. Also, the modified filter is found effective in detecting a sudden change in TFC values which indicates a maneuver. In order to illustrate that the event representation approach with TFCs is robust and sufficiently general to be easily adjustable, different types of measurement data are processed with the filter in a realistic LEO setting. Further, cases with mis-modeling of non-gravitational force are included in our study to verify the versatility and efficiency of our presented algorithm. Simulation results show that the modified sequential filter with TFCs can detect and estimate the orbit and thrust parameters in the presence of unknown maneuvers with or without measurement data during maneuvers. With no measurement data during maneuvers, the modified filter with TFCs uses an existing pre-maneuver orbit solution to compute a post-maneuver orbit solution by forcing TFCs to compensate for an unknown maneuver. With observation data available during maneuvers, maneuver start time and stop time is determined

Early thawing after snow removal and no straw mulching accelerates organic carbon cycling in a paddy soil in Northeast China.

PubMed

Zhang, Hao; Tang, Jie; Liang, Shuang; Li, Zhaoyang; Wang, Jingjing; Wang, Sining

2018-03-01

Variations in soil organic carbon (SOC) have implications for atmospheric CO 2 concentrations and the greenhouse effect. However, the effects of snow cover and straw mulching on the variations in SOC fractions across winter remain largely unknown. In this study, soil samples were collected during different stages of winter from an in situ experiment comprising three treatments: 1) snow removal with no straw mulching (Sn-SM-); 2) snow cover with no straw mulching (SC), and; 3) snow cover with straw mulching (SC + SM+). Results showed that labile organic carbon, semi-labile organic carbon, recalcitrant organic carbon (ROC), the light fraction of organic carbon (LFOC), and easily oxidized organic carbon (EOC) contents did not vary significantly (P > .05) during the unfrozen to hard frost stages. Compared to the unfrozen stage, microbial biomass carbon (MBC) contents decreased by 519.03 mg kg -1 , 325.21 mg kg -1 , and 244.09 mg kg -1 and dissolved organic carbon (DOC) contents increased by 473.36 mg kg -1 , 348.10 mg kg -1 , and 258.89 mg kg -1  at the hard frost stage in Sn-SM-, SC, and SC + SM + treatments, respectively. Throughout all thawing stages, > 61% and 59% of SOC and ROC accumulation, respectively in the three treatments were observed in thawing stage II, indicating that higher temperatures and microbial activities in thawing stage II accelerated the inputs of SOC and ROC. ROC accumulation accounted for >65% of the SOC accumulation and the proportions of ROC in SOC increased in the three treatments during the thawing stages. SC + SM + treatment maintained lower EOC contents during thawing stages than other treatments. The observation of lowest SOC and LFOC accumulation and contents in the SC + SM + treatment during thawing stages showed that SC + SM + experienced the least inputs of SOC in the soil. Copyright © 2018 Elsevier Ltd. All rights reserved.

Comparative study of methods for recognition of an unknown person's action from a video sequence

NASA Astrophysics Data System (ADS)

Hori, Takayuki; Ohya, Jun; Kurumisawa, Jun

2009-02-01

This paper proposes a Tensor Decomposition Based method that can recognize an unknown person's action from a video sequence, where the unknown person is not included in the database (tensor) used for the recognition. The tensor consists of persons, actions and time-series image features. For the observed unknown person's action, one of the actions stored in the tensor is assumed. Using the motion signature obtained from the assumption, the unknown person's actions are synthesized. The actions of one of the persons in the tensor are replaced by the synthesized actions. Then, the core tensor for the replaced tensor is computed. This process is repeated for the actions and persons. For each iteration, the difference between the replaced and original core tensors is computed. The assumption that gives the minimal difference is the action recognition result. For the time-series image features to be stored in the tensor and to be extracted from the observed video sequence, the human body silhouette's contour shape based feature is used. To show the validity of our proposed method, our proposed method is experimentally compared with Nearest Neighbor rule and Principal Component analysis based method. Experiments using 33 persons' seven kinds of action show that our proposed method achieves better recognition accuracies for the seven actions than the other methods.

Extraordinary optical transmission inside a waveguide: spatial mode dependence.

PubMed

Reichel, Kimberly S; Lu, Peter Y; Backus, Sterling; Mendis, Rajind; Mittleman, Daniel M

2016-12-12

We study the influence of the input spatial mode on the extraordinary optical transmission (EOT) effect. By placing a metal screen with a 1D array of subwavelength holes inside a terahertz (THz) parallel-plate waveguide (PPWG), we can directly compare the transmission spectra with different input waveguide modes. We observe that the transmitted spectrum depends strongly on the input mode. A conventional description of EOT based on the excitation of surface plasmons is not predictive in all cases. Instead, we utilize a formalism based on impedance matching, which accurately predicts the spectral resonances for both TEM and non-TEM input modes.

A combined ANN-GA and experimental based technique for the estimation of the unknown heat flux for a conjugate heat transfer problem

NASA Astrophysics Data System (ADS)

M K, Harsha Kumar; P S, Vishweshwara; N, Gnanasekaran; C, Balaji

2018-05-01

The major objectives in the design of thermal systems are obtaining the information about thermophysical, transport and boundary properties. The main purpose of this paper is to estimate the unknown heat flux at the surface of a solid body. A constant area mild steel fin is considered and the base is subjected to constant heat flux. During heating, natural convection heat transfer occurs from the fin to ambient. The direct solution, which is the forward problem, is developed as a conjugate heat transfer problem from the fin and the steady state temperature distribution is recorded for any assumed heat flux. In order to model the natural convection heat transfer from the fin, an extended domain is created near the fin geometry and air is specified as a fluid medium and Navier Stokes equation is solved by incorporating the Boussinesq approximation. The computational time involved in executing the forward model is then reduced by developing a neural network (NN) between heat flux values and temperatures based on back propagation algorithm. The conjugate heat transfer NN model is now coupled with Genetic algorithm (GA) for the solution of the inverse problem. Initially, GA is applied to the pure surrogate data, the results are then used as input to the Levenberg- Marquardt method and such hybridization is proven to result in accurate estimation of the unknown heat flux. The hybrid method is then applied for the experimental temperature to estimate the unknown heat flux. A satisfactory agreement between the estimated and actual heat flux is achieved by incorporating the hybrid method.

Social and Linguistic Input in Low-Income African American Mother-Child Dyads from 1 Month through 2 Years: Relations to Vocabulary Development

ERIC Educational Resources Information Center

Shimpi, Priya M.; Fedewa, Alicia; Hans, Sydney

2012-01-01

The relation of social and linguistic input measures to early vocabulary development was examined in 30 low-income African American mother-infant pairs. Observations were conducted when the child was 0 years, 1 month (0;1), 0;4, 0;8, 1;0, 1;6, and 2;0. Maternal input was coded for word types and tokens, contingent responsiveness, and…

Red Pine Pocket Mortality - Unknown Cause (Pest Alert)

Treesearch

USDA Forest Service

1985-01-01

Continuing mortality of red pine from an unknown cause has been observed in 30 to 40 year old plantations in southern and west central Wisconsin. A single tree or small group of trees die, followed by mortality of adjacent trees. These circular pockets of dead trees expand up to 0.3 acre per year.

Alpha1 LASSO data bundles Lamont, OK

DOE Data Explorer

Gustafson, William Jr; Vogelmann, Andrew; Endo, Satoshi; Toto, Tami; Xiao, Heng; Li, Zhijin; Cheng, Xiaoping; Krishna, Bhargavi (ORCID:000000018828528X)

2016-08-03

A data bundle is a unified package consisting of LASSO LES input and output, observations, evaluation diagnostics, and model skill scores. LES input includes model configuration information and forcing data. LES output includes profile statistics and full domain fields of cloud and environmental variables. Model evaluation data consists of LES output and ARM observations co-registered on the same grid and sampling frequency. Model performance is quantified by skill scores and diagnostics in terms of cloud and environmental variables.

Effect of random phase mask on input plane in photorefractive authentic memory with two-wave encryption method

NASA Astrophysics Data System (ADS)

Mita, Akifumi; Okamoto, Atsushi; Funakoshi, Hisatoshi

2004-06-01

We have proposed an all-optical authentic memory with the two-wave encryption method. In the recording process, the image data are encrypted to a white noise by the random phase masks added on the input beam with the image data and the reference beam. Only reading beam with the phase-conjugated distribution of the reference beam can decrypt the encrypted data. If the encrypted data are read out with an incorrect phase distribution, the output data are transformed into a white noise. Moreover, during read out, reconstructions of the encrypted data interfere destructively resulting in zero intensity. Therefore our memory has a merit that we can detect unlawful accesses easily by measuring the output beam intensity. In our encryption method, the random phase mask on the input plane plays important roles in transforming the input image into a white noise and prohibiting to decrypt a white noise to the input image by the blind deconvolution method. Without this mask, when unauthorized users observe the output beam by using CCD in the readout with the plane wave, the completely same intensity distribution as that of Fourier transform of the input image is obtained. Therefore the encrypted image will be decrypted easily by using the blind deconvolution method. However in using this mask, even if unauthorized users observe the output beam using the same method, the encrypted image cannot be decrypted because the observed intensity distribution is dispersed at random by this mask. Thus it can be said the robustness is increased by this mask. In this report, we compare two correlation coefficients, which represents the degree of a white noise of the output image, between the output image and the input image in using this mask or not. We show that the robustness of this encryption method is increased as the correlation coefficient is improved from 0.3 to 0.1 by using this mask.

A single footshock causes long-lasting hypoactivity in unknown environments that is dependent on the development of contextual fear conditioning.

PubMed

Daviu, Núria; Fuentes, Silvia; Nadal, Roser; Armario, Antonio

2010-09-01

Exposure to a single session of footshocks induces long-lasting inhibition of activity in unknown environments that markedly differ from the shock context. Interestingly, these effects are not necessarily associated to an enhanced anxiety and interpretation of this hypoactivity remains unclear. In the present experiment we further studied this phenomenon in male Sprague-Dawley rats. In a first experiment, a session of three shocks resulted in hypoactivity during exposure, 6-12days later, to three different unknown environments. This altered behaviour was not accompanied by a greater hypothalamic-pituitary-adrenal (HPA) activation, although greater HPA activation paralleling higher levels of freezing was observed in the shock context. In a second experiment we used a single shock and two procedures, one with pre-exposure to the context before the shock and another with immediate shock that did not induce contextual fear conditioning. Hypoactivity and a certain level of generalization of fear (freezing) to the unknown environments only appeared in the group that developed fear conditioning, but no evidence for enhanced anxiety in the elevated plus-maze was found in any group. The results suggest that if animals are able to associate an aversive experience with a distinct unknown environment, they would display more cautious behaviour in any unknown environment and such strategy persists despite repeated experience with different environments. This long-lasting cautious behaviour was not associated to greater HPA response to the unknown environment that was however observed in the shock context. The present findings raised some concerns about interpretation of long-lasting behavioural changes caused by brief stressors. Copyright 2010 Elsevier Inc. All rights reserved.

How model and input uncertainty impact maize yield simulations in West Africa

NASA Astrophysics Data System (ADS)

Waha, Katharina; Huth, Neil; Carberry, Peter; Wang, Enli

2015-02-01

Crop models are common tools for simulating crop yields and crop production in studies on food security and global change. Various uncertainties however exist, not only in the model design and model parameters, but also and maybe even more important in soil, climate and management input data. We analyze the performance of the point-scale crop model APSIM and the global scale crop model LPJmL with different climate and soil conditions under different agricultural management in the low-input maize-growing areas of Burkina Faso, West Africa. We test the models’ response to different levels of input information from little to detailed information on soil, climate (1961-2000) and agricultural management and compare the models’ ability to represent the observed spatial (between locations) and temporal variability (between years) in crop yields. We found that the resolution of different soil, climate and management information influences the simulated crop yields in both models. However, the difference between models is larger than between input data and larger between simulations with different climate and management information than between simulations with different soil information. The observed spatial variability can be represented well from both models even with little information on soils and management but APSIM simulates a higher variation between single locations than LPJmL. The agreement of simulated and observed temporal variability is lower due to non-climatic factors e.g. investment in agricultural research and development between 1987 and 1991 in Burkina Faso which resulted in a doubling of maize yields. The findings of our study highlight the importance of scale and model choice and show that the most detailed input data does not necessarily improve model performance.

Importance of terrestrial arthropods as subsidies in lowland Neotropical rain forest stream ecosystems

USGS Publications Warehouse

Small, Gaston E.; Torres, Pedro J.; Schwizer, Lauren M.; Duff, John H.; Pringle, Catherine M.

2013-01-01

The importance of terrestrial arthropods has been documented in temperate stream ecosystems, but little is known about the magnitude of these inputs in tropical streams. Terrestrial arthropods falling from the canopy of tropical forests may be an important subsidy to tropical stream food webs and could also represent an important flux of nitrogen (N) and phosphorus (P) in nutrient-poor headwater streams. We quantified input rates of terrestrial insects in eight streams draining lowland tropical wet forest in Costa Rica. In two focal headwater streams, we also measured capture efficiency by the fish assemblage and quantified terrestrially derived N- and P-excretion relative to stream nutrient uptake rates. Average input rates of terrestrial insects ranged from 5 to 41 mg dry mass/m2/d, exceeding previous measurements of aquatic invertebrate secondary production in these study streams, and were relatively consistent year-round, in contrast to values reported in temperate streams. Terrestrial insects accounted for half of the diet of the dominant fish species, Priapicthys annectens. Although terrestrially derived fish excretion was found to be a small flux relative to measured nutrient uptake rates in the focal streams, the efficient capture and processing of terrestrial arthropods by fish made these nutrients available to the local stream ecosystem. This aquatic-terrestrial linkage is likely being decoupled by deforestation in many tropical regions, with largely unknown but potentially important ecological consequences.

Relationship between early and late stages of information processing: an event-related potential study

PubMed Central

Portella, Claudio; Machado, Sergio; Arias-Carrión, Oscar; Sack, Alexander T.; Silva, Julio Guilherme; Orsini, Marco; Leite, Marco Antonio Araujo; Silva, Adriana Cardoso; Nardi, Antonio E.; Cagy, Mauricio; Piedade, Roberto; Ribeiro, Pedro

2012-01-01

The brain is capable of elaborating and executing different stages of information processing. However, exactly how these stages are processed in the brain remains largely unknown. This study aimed to analyze the possible correlation between early and late stages of information processing by assessing the latency to, and amplitude of, early and late event-related potential (ERP) components, including P200, N200, premotor potential (PMP) and P300, in healthy participants in the context of a visual oddball paradigm. We found a moderate positive correlation among the latency of P200 (electrode O2), N200 (electrode O2), PMP (electrode C3), P300 (electrode PZ) and the reaction time (RT). In addition, moderate negative correlation between the amplitude of P200 and the latencies of N200 (electrode O2), PMP (electrode C3), P300 (electrode PZ) was found. Therefore, we propose that if the secondary processing of visual input (P200 latency) occurs faster, the following will also happen sooner: discrimination and classification process of this input (N200 latency), motor response processing (PMP latency), reorganization of attention and working memory update (P300 latency), and RT. N200, PMP, and P300 latencies are also anticipated when higher activation level of occipital areas involved in the secondary processing of visual input rise (P200 amplitude). PMID:23355929

Economic Value Biases Uncertain Perceptual Choices in the Parietal and Prefrontal Cortices

PubMed Central

Summerfield, Christopher; Koechlin, Etienne

2010-01-01

An observer detecting a noisy sensory signal is biased by the costs and benefits associated with its presence or absence. When these costs and benefits are asymmetric, sensory, and economic information must be integrated to inform the final choice. However, it remains unknown how this information is combined at the neural or computational levels. To address this question, we asked healthy human observers to judge the presence or absence of a noisy sensory signal under economic conditions that favored yes responses (liberal blocks), no responses (conservative blocks), or neither response (neutral blocks). Economic information biased fast choices more than slow choices, suggesting that value and sensory information are integrated early in the decision epoch. More formal simulation analyses using an Ornstein–Uhlenbeck process demonstrated that the influence of economic information was best captured by shifting the origin of evidence accumulation toward the more valuable bound. We then used the computational model to generate trial-by-trial estimates of decision-related evidence that were based on combined sensory and economic information (the decision variable or DV), and regressed these against fMRI activity recorded whilst participants performed the task. Extrastriate visual regions responded to the level of sensory input (momentary evidence), but fMRI signals in the parietal and prefrontal cortices responded to the decision variable. These findings support recent single-neuron data suggesting that economic information biases decision-related signals in higher cortical regions. PMID:21267421

DAISY: a new software tool to test global identifiability of biological and physiological systems.

PubMed

Bellu, Giuseppina; Saccomani, Maria Pia; Audoly, Stefania; D'Angiò, Leontina

2007-10-01

A priori global identifiability is a structural property of biological and physiological models. It is considered a prerequisite for well-posed estimation, since it concerns the possibility of recovering uniquely the unknown model parameters from measured input-output data, under ideal conditions (noise-free observations and error-free model structure). Of course, determining if the parameters can be uniquely recovered from observed data is essential before investing resources, time and effort in performing actual biomedical experiments. Many interesting biological models are nonlinear but identifiability analysis for nonlinear system turns out to be a difficult mathematical problem. Different methods have been proposed in the literature to test identifiability of nonlinear models but, to the best of our knowledge, so far no software tools have been proposed for automatically checking identifiability of nonlinear models. In this paper, we describe a software tool implementing a differential algebra algorithm to perform parameter identifiability analysis for (linear and) nonlinear dynamic models described by polynomial or rational equations. Our goal is to provide the biological investigator a completely automatized software, requiring minimum prior knowledge of mathematical modelling and no in-depth understanding of the mathematical tools. The DAISY (Differential Algebra for Identifiability of SYstems) software will potentially be useful in biological modelling studies, especially in physiology and clinical medicine, where research experiments are particularly expensive and/or difficult to perform. Practical examples of use of the software tool DAISY are presented. DAISY is available at the web site http://www.dei.unipd.it/~pia/.

Final Report, DOE Early Career Award: Predictive modeling of complex physical systems: new tools for statistical inference, uncertainty quantification, and experimental design

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

Marzouk, Youssef

Predictive simulation of complex physical systems increasingly rests on the interplay of experimental observations with computational models. Key inputs, parameters, or structural aspects of models may be incomplete or unknown, and must be developed from indirect and limited observations. At the same time, quantified uncertainties are needed to qualify computational predictions in the support of design and decision-making. In this context, Bayesian statistics provides a foundation for inference from noisy and limited data, but at prohibitive computional expense. This project intends to make rigorous predictive modeling *feasible* in complex physical systems, via accelerated and scalable tools for uncertainty quantification, Bayesianmore » inference, and experimental design. Specific objectives are as follows: 1. Develop adaptive posterior approximations and dimensionality reduction approaches for Bayesian inference in high-dimensional nonlinear systems. 2. Extend accelerated Bayesian methodologies to large-scale {\\em sequential} data assimilation, fully treating nonlinear models and non-Gaussian state and parameter distributions. 3. Devise efficient surrogate-based methods for Bayesian model selection and the learning of model structure. 4. Develop scalable simulation/optimization approaches to nonlinear Bayesian experimental design, for both parameter inference and model selection. 5. Demonstrate these inferential tools on chemical kinetic models in reacting flow, constructing and refining thermochemical and electrochemical models from limited data. Demonstrate Bayesian filtering on canonical stochastic PDEs and in the dynamic estimation of inhomogeneous subsurface properties and flow fields.« less

Adaptive robust control of a class of non-affine variable-speed variable-pitch wind turbines with unmodeled dynamics.

PubMed

Bagheri, Pedram; Sun, Qiao

2016-07-01

In this paper, a novel synthesis of Nussbaum-type functions, and an adaptive radial-basis function neural network is proposed to design controllers for variable-speed, variable-pitch wind turbines. Dynamic equations of the wind turbine are highly nonlinear, uncertain, and affected by unknown disturbance sources. Furthermore, the dynamic equations are non-affine with respect to the pitch angle, which is a control input. To address these problems, a Nussbaum-type function, along with a dynamic control law are adopted to resolve the non-affine nature of the equations. Moreover, an adaptive radial-basis function neural network is designed to approximate non-parametric uncertainties. Further, the closed-loop system is made robust to unknown disturbance sources, where no prior knowledge of disturbance bound is assumed in advance. Finally, the Lyapunov stability analysis is conducted to show the stability of the entire closed-loop system. In order to verify analytical results, a simulation is presented and the results are compared to both a PI and an existing adaptive controllers. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm.

PubMed

Jafari, Masoumeh; Salimifard, Maryam; Dehghani, Maryam

2014-07-01

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

National Dam Inspection Program. Sharpe’s Pond Dam (NDI I.D. PA-0888 DER I.D. 066-009) Susquehanna River Basin, Little Mehoopany Creek, Wyoming County, Pennsylvania. Phase I Inspection Report.

DTIC Science & Technology

1981-01-01

quadrangle. a. Drainage Area 0.99 square mile b. Discharge at Dam Site ( cfs ) Maximum known flood at dam site Unknown Outlet conduit at maximum pool...located near the left abutment. The capacity of the spillway was determined to be 35 cfs , based on the available 1.1-foot freeboard relative to the lov...peak flows of 3014 and 1507 cfs for full and 50 percent of PMF, respectively. Computer input and summary of computer output are also included in

An adaptive controller for enhancing operator performance during teleoperation

NASA Technical Reports Server (NTRS)

Carignan, Craig R.; Tarrant, Janice M.; Mosier, Gary E.

1989-01-01

An adaptive controller is developed for adjusting robot arm parameters while manipulating payloads of unknown mass and inertia. The controller is tested experimentally in a master/slave configuration where the adaptive slave arm is commanded via human operator inputs from a master. Kinematically similar six-joint master and slave arms are used with the last three joints locked for simplification. After a brief initial adaptation period for the unloaded arm, the slave arm retrieves different size payloads and maneuvers them about the workspace. Comparisons are then drawn with similar tasks where the adaptation is turned off. Several simplifications of the controller dynamics are also addressed and experimentally verified.

Polynomial approximation of non-Gaussian unitaries by counting one photon at a time

NASA Astrophysics Data System (ADS)

Arzani, Francesco; Treps, Nicolas; Ferrini, Giulia

2017-05-01

In quantum computation with continuous-variable systems, quantum advantage can only be achieved if some non-Gaussian resource is available. Yet, non-Gaussian unitary evolutions and measurements suited for computation are challenging to realize in the laboratory. We propose and analyze two methods to apply a polynomial approximation of any unitary operator diagonal in the amplitude quadrature representation, including non-Gaussian operators, to an unknown input state. Our protocols use as a primary non-Gaussian resource a single-photon counter. We use the fidelity of the transformation with the target one on Fock and coherent states to assess the quality of the approximate gate.

Manifold traversing as a model for learning control of autonomous robots

NASA Technical Reports Server (NTRS)

Szakaly, Zoltan F.; Schenker, Paul S.

1992-01-01

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

Earth observing system. Output data products and input requirements, version 2.0. Volume 1: Instrument data product characteristics

NASA Technical Reports Server (NTRS)

Lu, Yun-Chi; Chang, Hyo Duck; Krupp, Brian; Kumar, Ravindra; Swaroop, Anand

1992-01-01

Information on Earth Observing System (EOS) output data products and input data requirements that has been compiled by the Science Processing Support Office (SPSO) at GSFC is presented. Since Version 1.0 of the SPSO Report was released in August 1991, there have been significant changes in the EOS program. In anticipation of a likely budget cut for the EOS Project, NASA HQ restructured the EOS program. An initial program consisting of two large platforms was replaced by plans for multiple, smaller platforms, and some EOS instruments were either deselected or descoped. Updated payload information reflecting the restructured EOS program superseding the August 1991 version of the SPSO report is included. This report has been expanded to cover information on non-EOS data products, and consists of three volumes (Volumes 1, 2, and 3). Volume 1 provides information on instrument outputs and input requirements. Volume 2 is devoted to Interdisciplinary Science (IDS) outputs and input requirements, including the 'best' and 'alternative' match analysis. Volume 3 provides information about retrieval algorithms, non-EOS input requirements of instrument teams and IDS investigators, and availability of non-EOS data products at seven primary Distributed Active Archive Centers (DAAC's).

Sensitivity of a Bayesian atmospheric-transport inversion model to spatio-temporal sensor resolution applied to the 2006 North Korean nuclear test

NASA Astrophysics Data System (ADS)

Lundquist, K. A.; Jensen, D. D.; Lucas, D. D.

2017-12-01

Atmospheric source reconstruction allows for the probabilistic estimate of source characteristics of an atmospheric release using observations of the release. Performance of the inversion depends partially on the temporal frequency and spatial scale of the observations. The objective of this study is to quantify the sensitivity of the source reconstruction method to sparse spatial and temporal observations. To this end, simulations of atmospheric transport of noble gasses are created for the 2006 nuclear test at the Punggye-ri nuclear test site. Synthetic observations are collected from the simulation, and are taken as "ground truth". Data denial techniques are used to progressively coarsen the temporal and spatial resolution of the synthetic observations, while the source reconstruction model seeks to recover the true input parameters from the synthetic observations. Reconstructed parameters considered here are source location, source timing and source quantity. Reconstruction is achieved by running an ensemble of thousands of dispersion model runs that sample from a uniform distribution of the input parameters. Machine learning is used to train a computationally-efficient surrogate model from the ensemble simulations. Monte Carlo sampling and Bayesian inversion are then used in conjunction with the surrogate model to quantify the posterior probability density functions of source input parameters. This research seeks to inform decision makers of the tradeoffs between more expensive, high frequency observations and less expensive, low frequency observations.

A new algorithm for reliable and general NMR resonance assignment.

PubMed

Schmidt, Elena; Güntert, Peter

2012-08-01

The new FLYA automated resonance assignment algorithm determines NMR chemical shift assignments on the basis of peak lists from any combination of multidimensional through-bond or through-space NMR experiments for proteins. Backbone and side-chain assignments can be determined. All experimental data are used simultaneously, thereby exploiting optimally the redundancy present in the input peak lists and circumventing potential pitfalls of assignment strategies in which results obtained in a given step remain fixed input data for subsequent steps. Instead of prescribing a specific assignment strategy, the FLYA resonance assignment algorithm requires only experimental peak lists and the primary structure of the protein, from which the peaks expected in a given spectrum can be generated by applying a set of rules, defined in a straightforward way by specifying through-bond or through-space magnetization transfer pathways. The algorithm determines the resonance assignment by finding an optimal mapping between the set of expected peaks that are assigned by definition but have unknown positions and the set of measured peaks in the input peak lists that are initially unassigned but have a known position in the spectrum. Using peak lists obtained by purely automated peak picking from the experimental spectra of three proteins, FLYA assigned correctly 96-99% of the backbone and 90-91% of all resonances that could be assigned manually. Systematic studies quantified the impact of various factors on the assignment accuracy, namely the extent of missing real peaks and the amount of additional artifact peaks in the input peak lists, as well as the accuracy of the peak positions. Comparing the resonance assignments from FLYA with those obtained from two other existing algorithms showed that using identical experimental input data these other algorithms yielded significantly (40-142%) more erroneous assignments than FLYA. The FLYA resonance assignment algorithm thus has the reliability and flexibility to replace most manual and semi-automatic assignment procedures for NMR studies of proteins.

Identification of an internal combustion engine model by nonlinear multi-input multi-output system identification. Ph.D. Thesis

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

Luh, G.C.

1994-01-01

This thesis presents the application of advanced modeling techniques to construct nonlinear forward and inverse models of internal combustion engines for the detection and isolation of incipient faults. The NARMAX (Nonlinear Auto-Regressive Moving Average modeling with eXogenous inputs) technique of system identification proposed by Leontaritis and Billings was used to derive the nonlinear model of a internal combustion engine, over operating conditions corresponding to the I/M240 cycle. The I/M240 cycle is a standard proposed by the United States Environmental Protection Agency to measure tailpipe emissions in inspection and maintenance programs and consists of a driving schedule developed for the purposemore » of testing compliance with federal vehicle emission standards for carbon monoxide, unburned hydrocarbons, and nitrogen oxides. The experimental work for model identification and validation was performed on a 3.0 liter V6 engine installed in an engine test cell at the Center for Automotive Research at The Ohio State University. In this thesis, different types of model structures were proposed to obtain multi-input multi-output (MIMO) nonlinear NARX models. A modification of the algorithm proposed by He and Asada was used to estimate the robust orders of the derived MIMO nonlinear models. A methodology for the analysis of inverse NARX model was developed. Two methods were proposed to derive the inverse NARX model: (1) inversion from the forward NARX model; and (2) direct identification of inverse model from the output-input data set. In this thesis, invertibility, minimum-phase characteristic of zero dynamics, and stability analysis of NARX forward model are also discussed. Stability in the sense of Lyapunov is also investigated to check the stability of the identified forward and inverse models. This application of inverse problem leads to the estimation of unknown inputs and to actuator fault diagnosis.« less

Laminar- and Target-Specific Amygdalar Inputs in Rat Primary Gustatory Cortex.

PubMed

Haley, Melissa S; Fontanini, Alfredo; Maffei, Arianna

2016-03-02

The primary gustatory cortex (GC) receives projections from the basolateral nucleus of the amygdala (BLA). Behavioral and electrophysiological studies demonstrated that this projection is involved in encoding the hedonic value of taste and is a source of anticipatory activity in GC. Anatomically, this projection is largest in the agranular portion of GC; however, its synaptic targets and synaptic properties are currently unknown. In vivo electrophysiological recordings report conflicting evidence about BLA afferents either selectively activating excitatory neurons or driving a compound response consistent with the activation of inhibitory circuits. Here we demonstrate that BLA afferents directly activate excitatory neurons and two distinct populations of inhibitory neurons in both superficial and deep layers of rat GC. BLA afferents recruit different proportions of excitatory and inhibitory neurons and show distinct patterns of circuit activation in the superficial and deep layers of GC. These results provide the first circuit-level analysis of BLA inputs to a sensory area. Laminar- and target-specific differences of BLA inputs likely explain the complexity of amygdalocortical interactions during sensory processing. Projections from the basolateral nucleus of the amygdala (BLA) to the cortex convey information about the emotional value and the expectation of a sensory stimulus. Although much work has been done to establish the behavioral role of BLA inputs to sensory cortices, very little is known about the circuit organization of BLA projections. Here we provide the first in-depth analysis of connectivity and synaptic properties of the BLA input to the gustatory cortex. We show that BLA afferents activate excitatory and inhibitory circuits in a layer-specific and pattern-specific manner. Our results provide important new information about how neural circuits establishing the hedonic value of sensory stimuli and driving anticipatory behaviors are organized at the synaptic level. Copyright © 2016 the authors 0270-6474/16/362623-15$15.00/0.

A particle filter for ammonia coverage ratio and input simultaneous estimations in Diesel-engine SCR system.

PubMed

Sun, Kangfeng; Ji, Fenzhu; Yan, Xiaoyu; Jiang, Kai; Yang, Shichun

2018-01-01

As NOx emissions legislation for Diesel-engines is becoming more stringent than ever before, an aftertreatment system has been widely used in many countries. Specifically, to reduce the NOx emissions, a selective catalytic reduction(SCR) system has become one of the most promising techniques for Diesel-engine vehicle applications. In the SCR system, input ammonia concentration and ammonia coverage ratio are regarded as essential states in the control-oriental model. Currently, an ammonia sensor placed before the SCR Can is a good strategy for the input ammonia concentration value. However, physical sensor would increase the SCR system cost and the ammonia coverage ratio information cannot be directly measured by physical sensor. Aiming to tackle this problem, an observer based on particle filter(PF) is investigated to estimate the input ammonia concentration and ammonia coverage ratio. Simulation results through the experimentally-validated full vehicle simulator cX-Emission show that the performance of observer based on PF is outstanding, and the estimation error is very small.