Applications of adaptive state estimation theory
NASA Technical Reports Server (NTRS)
Moose, R. L.; Vanlandingham, H. F.; Mccabe, D. H.
1980-01-01
Two main areas of application of adaptive state estimation theory are presented. Following a review of the basic estimation approach, its application to both the control of nonlinear plants and to the problem of tracking maneuvering targets is presented. Results are brought together from these two areas of investigation to provide insight into the wide range of possible applications of the general estimation method.
Navigating sensory conflict in dynamic environments using adaptive state estimation.
Klein, Theresa J; Jeka, John; Kiemel, Tim; Lewis, M Anthony
2011-12-01
Most conventional robots rely on controlling the location of the center of pressure to maintain balance, relying mainly on foot pressure sensors for information. By contrast,humans rely on sensory data from multiple sources, including proprioceptive, visual, and vestibular sources. Several models have been developed to explain how humans reconcile information from disparate sources to form a stable sense of balance. These models may be useful for developing robots that are able to maintain dynamic balance more readily using multiple sensory sources. Since these information sources may conflict, reliance by the nervous system on any one channel can lead to ambiguity in the system state. In humans, experiments that create conflicts between different sensory channels by moving the visual field or the support surface indicate that sensory information is adaptively reweighted. Unreliable information is rapidly down-weighted,then gradually up-weighted when it becomes valid again.Human balance can also be studied by building robots that model features of human bodies and testing them under similar experimental conditions. We implement a sensory reweighting model based on an adaptive Kalman filter in abipedal robot, and subject it to sensory tests similar to those used on human subjects. Unlike other implementations of sensory reweighting in robots, our implementation includes vision, by using optic flow to calculate forward rotation using a camera (visual modality), as well as a three-axis gyro to represent the vestibular system (non-visual modality), and foot pressure sensors (proprioceptive modality). Our model estimates measurement noise in real time, which is then used to recompute the Kalman gain on each iteration, improving the ability of the robot to dynamically balance. We observe that we can duplicate many important features of postural sw ay in humans, including automatic sensory reweighting,effects, constant phase with respect to amplitude, and a temporal
NASA Astrophysics Data System (ADS)
D'Amato, Anthony M.
Input reconstruction is the process of using the output of a system to estimate its input. In some cases, input reconstruction can be accomplished by determining the output of the inverse of a model of the system whose input is the output of the original system. Inversion, however, requires an exact and fully known analytical model, and is limited by instabilities arising from nonminimum-phase zeros. The main contribution of this work is a novel technique for input reconstruction that does not require model inversion. This technique is based on a retrospective cost, which requires a limited number of Markov parameters. Retrospective cost input reconstruction (RCIR) does not require knowledge of nonminimum-phase zero locations or an analytical model of the system. RCIR provides a technique that can be used for model refinement, state estimation, and adaptive control. In the model refinement application, data are used to refine or improve a model of a system. It is assumed that the difference between the model output and the data is due to an unmodeled subsystem whose interconnection with the modeled system is inaccessible, that is, the interconnection signals cannot be measured and thus standard system identification techniques cannot be used. Using input reconstruction, these inaccessible signals can be estimated, and the inaccessible subsystem can be fitted. We demonstrate input reconstruction in a model refinement framework by identifying unknown physics in a space weather model and by estimating an unknown film growth in a lithium ion battery. The same technique can be used to obtain estimates of states that cannot be directly measured. Adaptive control can be formulated as a model-refinement problem, where the unknown subsystem is the idealized controller that minimizes a measured performance variable. Minimal modeling input reconstruction for adaptive control is useful for applications where modeling information may be difficult to obtain. We demonstrate
NASA Technical Reports Server (NTRS)
Balas, Mark J.; Thapa Magar, Kaman S.; Frost, Susan A.
2013-01-01
A theory called Adaptive Disturbance Tracking Control (ADTC) is introduced and used to track the Tip Speed Ratio (TSR) of 5 MW Horizontal Axis Wind Turbine (HAWT). Since ADTC theory requires wind speed information, a wind disturbance generator model is combined with lower order plant model to estimate the wind speed as well as partial states of the wind turbine. In this paper, we present a proof of stability and convergence of ADTC theory with lower order estimator and show that the state feedback can be adaptive.
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively. PMID:27438835
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix ‘R’ and the system noise V-C matrix ‘Q’. Then, the global filter uses R to calculate the information allocation factor ‘β’ for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively. PMID:27438835
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-07-16
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively.
Adaptive nonlinear observer for state and unknown parameter estimation in noisy systems
NASA Astrophysics Data System (ADS)
Vijayaraghavan, Krishna; Valibeygi, Amir
2016-01-01
This paper proposes a novel adaptive observer for Lipschitz nonlinear systems and dissipative nonlinear systems in the presence of disturbances and sensor noise. The observer is based on an H∞ observer that can estimate both the system states and unknown parameters by minimising a cost function consisting of the sum of the square integrals of the estimation errors in the states and unknown parameters. The paper presents necessary and sufficient conditions for the existence of the observer, and the equations for determining observer gains are formulated as linear matrix inequalities (LMIs) that can be solved offline using commercially available LMI solvers. The observer design has also been extended to the case of time-varying unknown parameters. The use of the observer is demonstrated through illustrative examples and the performance is compared with extended Kalman filtering. Compared to previous results on nonlinear observers, the proposed observer is more computationally efficient, and guarantees state and parameter estimation for two very broad classes of nonlinear systems (Lipschitz and dissipative nonlinear systems) in the presence of input disturbances and sensor noise. In addition, the proposed observer does not require online computation of the observer gain.
NASA Astrophysics Data System (ADS)
Mariño, Inés P.; Míguez, Joaquín; Meucci, Riccardo
2009-05-01
We propose a Monte Carlo methodology for the joint estimation of unobserved dynamic variables and unknown static parameters in chaotic systems. The technique is sequential, i.e., it updates the variable and parameter estimates recursively as new observations become available, and, hence, suitable for online implementation. We demonstrate the validity of the method by way of two examples. In the first one, we tackle the estimation of all the dynamic variables and one unknown parameter of a five-dimensional nonlinear model using a time series of scalar observations experimentally collected from a chaotic CO2 laser. In the second example, we address the estimation of the two dynamic variables and the phase parameter of a numerical model commonly employed to represent the dynamics of optoelectronic feedback loops designed for chaotic communications over fiber-optic links.
NASA Astrophysics Data System (ADS)
Shankar, Praveen
The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. This algorithm is implemented on a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Self-Organizing Radial Basis Function Network (SORBFN) to minimize the effect of the inversion error which may occur due to imperfect modeling, approximate inversion or sudden changes in aircraft dynamics. The dynamic inversion controller is simulated for different situations including control surface failures, modeling errors and external disturbances with and without the adaptive network. A performance measure of maximum tracking error is specified for both the controllers a priori. Excellent tracking error minimization to a pre-specified level using the adaptive approximation based controller was achieved while the baseline dynamic inversion controller failed to meet this performance specification. The performance of the SORBFN based controller is also compared to a fixed RBF network
NASA Astrophysics Data System (ADS)
Bewley, Thomas
2015-11-01
Accurate long-term forecasts of the path and intensity of hurricanes are imperative to protect property and save lives. Accurate estimations and forecasts of the spread of large-scale contaminant plumes, such as those from Deepwater Horizon, Fukushima, and recent volcanic eruptions in Iceland, are essential for assessing environment impact, coordinating remediation efforts, and in certain cases moving folks out of harm's way. The challenges in estimating and forecasting such systems include: (a) environmental flow modeling, (b) high-performance real-time computing, (c) assimilating measured data into numerical simulations, and (d) acquiring in-situ data, beyond what can be measured from satellites, that is maximally relevant for reducing forecast uncertainty. This talk will focus on new techniques for addressing (c) and (d), namely, data assimilation and adaptive observation, in both hurricanes and large-scale environmental plumes. In particular, we will present a new technique for the energy-efficient coordination of swarms of sensor-laden balloons for persistent, in-situ, distributed, real-time measurement of developing hurricanes, leveraging buoyancy control only (coupled with the predictable and strongly stratified flowfield within the hurricane). Animations of these results are available at http://flowcontrol.ucsd.edu/3dhurricane.mp4 and http://flowcontrol.ucsd.edu/katrina.mp4. We also will survey our unique hybridization of the venerable Ensemble Kalman and Variational approaches to large-scale data assimilation in environmental flow systems, and how essentially the dual of this hybrid approach may be used to solve the adaptive observation problem in a uniquely effective and rigorous fashion.
Mühler, Roland; Mentzel, Katrin; Verhey, Jesko
2012-01-01
This paper describes the estimation of hearing thresholds in normal-hearing and hearing-impaired subjects on the basis of multiple-frequency auditory steady-state responses (ASSRs). The ASSR was measured using two new techniques: (i) adaptive stimulus patterns and (ii) narrow-band chirp stimuli. ASSR thresholds in 16 normal-hearing and 16 hearing-impaired adults were obtained simultaneously at both ears at 500, 1000, 2000, and 4000 Hz, using a multiple-frequency stimulus built up of four one-octave-wide narrow-band chirps with a repetition rate of 40 Hz. A statistical test in the frequency domain was used to detect the response. The recording of the steady-state responses was controlled in eight independent recording channels with an adaptive, semiautomatic algorithm. The average differences between the behavioural hearing thresholds and the ASSR threshold estimate were 10, 8, 13, and 15 dB for test frequencies of 500, 1000, 2000, and 4000 Hz, respectively. The average overall test duration of 18.6 minutes for the threshold estimations at the four frequencies and both ears demonstrates the benefit of an adaptive recording algorithm and the efficiency of optimised narrow-band chirp stimuli. PMID:22619622
Adaptive link selection algorithms for distributed estimation
NASA Astrophysics Data System (ADS)
Xu, Songcen; de Lamare, Rodrigo C.; Poor, H. Vincent
2015-12-01
This paper presents adaptive link selection algorithms for distributed estimation and considers their application to wireless sensor networks and smart grids. In particular, exhaustive search-based least mean squares (LMS) / recursive least squares (RLS) link selection algorithms and sparsity-inspired LMS / RLS link selection algorithms that can exploit the topology of networks with poor-quality links are considered. The proposed link selection algorithms are then analyzed in terms of their stability, steady-state, and tracking performance and computational complexity. In comparison with the existing centralized or distributed estimation strategies, the key features of the proposed algorithms are as follows: (1) more accurate estimates and faster convergence speed can be obtained and (2) the network is equipped with the ability of link selection that can circumvent link failures and improve the estimation performance. The performance of the proposed algorithms for distributed estimation is illustrated via simulations in applications of wireless sensor networks and smart grids.
NASA Astrophysics Data System (ADS)
Duong, Van-Huan; Bastawrous, Hany Ayad; Lim, KaiChin; See, Khay Wai; Zhang, Peng; Dou, Shi Xue
2015-11-01
This paper deals with the contradiction between simplicity and accuracy of the LiFePO4 battery states estimation in the electric vehicles (EVs) battery management system (BMS). State of charge (SOC) and state of health (SOH) are normally obtained from estimating the open circuit voltage (OCV) and the internal resistance of the equivalent electrical circuit model of the battery, respectively. The difficulties of the parameters estimation arise from their complicated variations and different dynamics which require sophisticated algorithms to simultaneously estimate multiple parameters. This, however, demands heavy computation resources. In this paper, we propose a novel technique which employs a simplified model and multiple adaptive forgetting factors recursive least-squares (MAFF-RLS) estimation to provide capability to accurately capture the real-time variations and the different dynamics of the parameters whilst the simplicity in computation is still retained. The validity of the proposed method is verified through two standard driving cycles, namely Urban Dynamometer Driving Schedule and the New European Driving Cycle. The proposed method yields experimental results that not only estimated the SOC with an absolute error of less than 2.8% but also characterized the battery model parameters accurately.
Adaptable state based control system
NASA Technical Reports Server (NTRS)
Rasmussen, Robert D. (Inventor); Dvorak, Daniel L. (Inventor); Gostelow, Kim P. (Inventor); Starbird, Thomas W. (Inventor); Gat, Erann (Inventor); Chien, Steve Ankuo (Inventor); Keller, Robert M. (Inventor)
2004-01-01
An autonomous controller, comprised of a state knowledge manager, a control executor, hardware proxies and a statistical estimator collaborates with a goal elaborator, with which it shares common models of the behavior of the system and the controller. The elaborator uses the common models to generate from temporally indeterminate sets of goals, executable goals to be executed by the controller. The controller may be updated to operate in a different system or environment than that for which it was originally designed by the replacement of shared statistical models and by the instantiation of a new set of state variable objects derived from a state variable class. The adaptation of the controller does not require substantial modification of the goal elaborator for its application to the new system or environment.
Seidel, David Ulrich; Flemming, Tobias Angelo; Park, Jonas Jae-Hyun; Remmert, Stephan
2015-01-01
Objective hearing threshold estimation by auditory steady-state responses (ASSR) can be accelerated by the use of narrow-band chirps and adaptive stimulus patterns. This modification has been examined in only a few clinical studies. In this study, clinical data is validated and extended, and the applicability of the method in audiological diagnostics routine is examined. In 60 patients (normal hearing and hearing impaired), ASSR and pure tone audiometry (PTA) thresholds were compared. ASSR were evoked by binaural multi-frequent narrow-band chirps with adaptive stimulus patterns. The precision and required testing time for hearing threshold estimation were determined. The average differences between ASSR and PTA thresholds were 18, 12, 17 and 19 dB for normal hearing (PTA ≤ 20 dB) and 5, 9, 9 and 11 dB for hearing impaired (PTA > 20 dB) at the frequencies of 500, 1,000, 2,000 and 4,000 Hz, respectively, and the differences were significant in all frequencies with the exception of 1 kHz. Correlation coefficients between ASSR and PTA thresholds were 0.36, 0.47, 0.54 and 0.51 for normal hearing and 0.73, 0.74, 0.72 and 0.71 for hearing impaired at 500, 1,000, 2,000 and 4,000 Hz, respectively. Mean ASSR testing time was 33 ± 8 min. In conclusion, auditory steady-state responses with narrow-band-chirps and adaptive stimulus patterns is an efficient method for objective frequency-specific hearing threshold estimation. Precision of threshold estimation is most limited for slighter hearing loss at 500 Hz. The required testing time is acceptable for the application in everyday clinical routine. PMID:24305781
Estimator reduction and convergence of adaptive BEM.
Aurada, Markus; Ferraz-Leite, Samuel; Praetorius, Dirk
2012-06-01
A posteriori error estimation and related adaptive mesh-refining algorithms have themselves proven to be powerful tools in nowadays scientific computing. Contrary to adaptive finite element methods, convergence of adaptive boundary element schemes is, however, widely open. We propose a relaxed notion of convergence of adaptive boundary element schemes. Instead of asking for convergence of the error to zero, we only aim to prove estimator convergence in the sense that the adaptive algorithm drives the underlying error estimator to zero. We observe that certain error estimators satisfy an estimator reduction property which is sufficient for estimator convergence. The elementary analysis is only based on Dörfler marking and inverse estimates, but not on reliability and efficiency of the error estimator at hand. In particular, our approach gives a first mathematical justification for the proposed steering of anisotropic mesh-refinements, which is mandatory for optimal convergence behavior in 3D boundary element computations.
Estimator reduction and convergence of adaptive BEM
Aurada, Markus; Ferraz-Leite, Samuel; Praetorius, Dirk
2012-01-01
A posteriori error estimation and related adaptive mesh-refining algorithms have themselves proven to be powerful tools in nowadays scientific computing. Contrary to adaptive finite element methods, convergence of adaptive boundary element schemes is, however, widely open. We propose a relaxed notion of convergence of adaptive boundary element schemes. Instead of asking for convergence of the error to zero, we only aim to prove estimator convergence in the sense that the adaptive algorithm drives the underlying error estimator to zero. We observe that certain error estimators satisfy an estimator reduction property which is sufficient for estimator convergence. The elementary analysis is only based on Dörfler marking and inverse estimates, but not on reliability and efficiency of the error estimator at hand. In particular, our approach gives a first mathematical justification for the proposed steering of anisotropic mesh-refinements, which is mandatory for optimal convergence behavior in 3D boundary element computations. PMID:23482248
Adaptive Quantum State Tomography Improves Accuracy Quadratically
NASA Astrophysics Data System (ADS)
Mahler, D. H.; Rozema, Lee A.; Darabi, Ardavan; Ferrie, Christopher; Blume-Kohout, Robin; Steinberg, A. M.
2013-11-01
We introduce a simple protocol for adaptive quantum state tomography, which reduces the worst-case infidelity [1-F(ρ^,ρ)] between the estimate and the true state from O(1/N) to O(1/N). It uses a single adaptation step and just one extra measurement setting. In a linear optical qubit experiment, we demonstrate a full order of magnitude reduction in infidelity (from 0.1% to 0.01%) for a modest number of samples (N≈3×104).
Adaptive vehicle motion estimation and prediction
NASA Astrophysics Data System (ADS)
Zhao, Liang; Thorpe, Chuck E.
1999-01-01
Accurate motion estimation and reliable maneuver prediction enable an automated car to react quickly and correctly to the rapid maneuvers of the other vehicles, and so allow safe and efficient navigation. In this paper, we present a car tracking system which provides motion estimation, maneuver prediction and detection of the tracked car. The three strategies employed - adaptive motion modeling, adaptive data sampling, and adaptive model switching probabilities - result in an adaptive interacting multiple model algorithm (AIMM). The experimental results on simulated and real data demonstrate that our tracking system is reliable, flexible, and robust. The adaptive tracking makes the system intelligent and useful in various autonomous driving tasks.
Some Reliability Estimates for Computerized Adaptive Tests.
ERIC Educational Resources Information Center
Nicewander, W. Alan; Thomasson, Gary L.
1999-01-01
Derives three reliability estimates for the Bayes modal estimate (BME) and the maximum-likelihood estimate (MLE) of theta in computerized adaptive tests (CATs). Computes the three reliability estimates and the true reliabilities of both BME and MLE for seven simulated CATs. Results show the true reliabilities for BME and MLE to be nearly identical…
Adaptive density estimator for galaxy surveys
NASA Astrophysics Data System (ADS)
Saar, Enn
2016-10-01
Galaxy number or luminosity density serves as a basis for many structure classification algorithms. Several methods are used to estimate this density. Among them kernel methods have probably the best statistical properties and allow also to estimate the local sample errors of the estimate. We introduce a kernel density estimator with an adaptive data-driven anisotropic kernel, describe its properties and demonstrate the wealth of additional information it gives us about the local properties of the galaxy distribution.
Accurate Biomass Estimation via Bayesian Adaptive Sampling
NASA Technical Reports Server (NTRS)
Wheeler, Kevin R.; Knuth, Kevin H.; Castle, Joseph P.; Lvov, Nikolay
2005-01-01
The following concepts were introduced: a) Bayesian adaptive sampling for solving biomass estimation; b) Characterization of MISR Rahman model parameters conditioned upon MODIS landcover. c) Rigorous non-parametric Bayesian approach to analytic mixture model determination. d) Unique U.S. asset for science product validation and verification.
Parameter adaptive estimation of random processes
NASA Technical Reports Server (NTRS)
Caglayan, A. K.; Vanlandingham, H. F.
1975-01-01
This paper is concerned with the parameter adaptive least squares estimation of random processes. The main result is a general representation theorem for the conditional expectation of a random variable on a product probability space. Using this theorem along with the general likelihood ratio expression, the least squares estimate of the process is found in terms of the parameter conditioned estimates. The stochastic differential for the a posteriori probability and the stochastic differential equation for the a posteriori density are found by using simple stochastic calculus on the representations obtained. The results are specialized to the case when the parameter has a discrete distribution. The results can be used to construct an implementable recursive estimator for certain types of nonlinear filtering problems. This is illustrated by some simple examples.
2014-10-09
This software code is designed to track generator state variables in real time using the Ensemble Kalman Filter method with the aid of PMU measurements. This code can also be used to calibrate dynamic model parameters by augmenting parameters in the state variable vector.
Nonlinear and adaptive estimation in reentry.
NASA Technical Reports Server (NTRS)
Jazwinski, A. H.
1972-01-01
The problem of real-time estimation of a lifting reentry vehicle trajectory of the shuttle orbiter type is considered. Simulations feature large position and velocity uncertainties at radar acquisition and realistic model errors in lift, drag and other model parameters. Radar tracking and accelerometer data are simulated. Significant nonlinearities are found to exist on spacecraft acquisition. An iterated nonlinear filter is shown to perform optimally during the radar acquisition phase. An adaptive filter is shown to track time-varying model errors, such as errors in the lift and drag coefficients, down to the noise level. Such real-time model tracking (identification) is frequently required for guidance and control implementation.
Fast adaptive estimation of multidimensional psychometric functions.
DiMattina, Christopher
2015-01-01
Recently in vision science there has been great interest in understanding the perceptual representations of complex multidimensional stimuli. Therefore, it is becoming very important to develop methods for performing psychophysical experiments with multidimensional stimuli and efficiently estimating psychometric models that have multiple free parameters. In this methodological study, I analyze three efficient implementations of the popular Ψ method for adaptive data collection, two of which are novel approaches to psychophysical experiments. Although the standard implementation of the Ψ procedure is intractable in higher dimensions, I demonstrate that my implementations generalize well to complex psychometric models defined in multidimensional stimulus spaces and can be implemented very efficiently on standard laboratory computers. I show that my implementations may be of particular use for experiments studying how subjects combine multiple cues to estimate sensory quantities. I discuss strategies for speeding up experiments and suggest directions for future research in this rapidly growing area at the intersection of cognitive science, neuroscience, and machine learning.
Fast adaptive estimation of multidimensional psychometric functions.
DiMattina, Christopher
2015-01-01
Recently in vision science there has been great interest in understanding the perceptual representations of complex multidimensional stimuli. Therefore, it is becoming very important to develop methods for performing psychophysical experiments with multidimensional stimuli and efficiently estimating psychometric models that have multiple free parameters. In this methodological study, I analyze three efficient implementations of the popular Ψ method for adaptive data collection, two of which are novel approaches to psychophysical experiments. Although the standard implementation of the Ψ procedure is intractable in higher dimensions, I demonstrate that my implementations generalize well to complex psychometric models defined in multidimensional stimulus spaces and can be implemented very efficiently on standard laboratory computers. I show that my implementations may be of particular use for experiments studying how subjects combine multiple cues to estimate sensory quantities. I discuss strategies for speeding up experiments and suggest directions for future research in this rapidly growing area at the intersection of cognitive science, neuroscience, and machine learning. PMID:26200886
Adaptive Estimation with Partially Overlapping Models
Shin, Sunyoung; Fine, Jason; Liu, Yufeng
2015-01-01
In many problems, one has several models of interest that capture key parameters describing the distribution of the data. Partially overlapping models are taken as models in which at least one covariate effect is common to the models. A priori knowledge of such structure enables efficient estimation of all model parameters. However, in practice, this structure may be unknown. We propose adaptive composite M-estimation (ACME) for partially overlapping models using a composite loss function, which is a linear combination of loss functions defining the individual models. Penalization is applied to pairwise differences of parameters across models, resulting in data driven identification of the overlap structure. Further penalization is imposed on the individual parameters, enabling sparse estimation in the regression setting. The recovery of the overlap structure enables more efficient parameter estimation. An oracle result is established. Simulation studies illustrate the advantages of ACME over existing methods that fit individual models separately or make strong a priori assumption about the overlap structure. PMID:26917931
Online Parameter Estimation and Adaptive Control of Magnetic Wire Actuators
NASA Astrophysics Data System (ADS)
Karve, Harshwardhan
Cantilevered magnetic wires and fibers can be used as actuators in microfluidic applications. The actuator may be unstable in some range of displacements. Precise position control is required for actuation. The goal of this work is to develop position controllers for cantilevered magnetic wires. A simple exact model knowledge (EMK) controller can be used for position control, but the actuator needs to be modeled accurately for the EMK controller to work. Continuum models have been proposed for magnetic wires in literature. Reduced order models have also been proposed. A one degree of freedom model sufficiently describes the dynamics of a cantilevered wire in the field of one magnet over small displacements. This reduced order model is used to develop the EMK controller here. The EMK controller assumes that model parameters are known accurately. Some model parameters depend on the magnetic field. However, the effect of the magnetic field on the wire is difficult to measure in practice. Stability analysis shows that an inaccurate estimate of the magnetic field introduces parametric perturbations in the closed loop system. This makes the system less robust to disturbances. Therefore, the model parameters need to be estimated accurately for the EMK controller to work. An adaptive observer that can estimate system parameters on-line and reduce parametric perturbations is designed here. The adaptive observer only works if the system is stable. The EMK controller is not guaranteed to stabilize the system under perturbations. Precise tuning of parameters is required to stabilize the system using the EMK controller. Therefore, a controller that stabilizes the system using imprecise model parameters is required for the observer to work as intended. The adaptive observer estimates system states and parameters. These states and parameters are used here to implement an indirect adaptive controller. This indirect controller can stabilize the system using imprecise initial
Parameter Estimation Analysis for Hybrid Adaptive Fault Tolerant Control
NASA Astrophysics Data System (ADS)
Eshak, Peter B.
Research efforts have increased in recent years toward the development of intelligent fault tolerant control laws, which are capable of helping the pilot to safely maintain aircraft control at post failure conditions. Researchers at West Virginia University (WVU) have been actively involved in the development of fault tolerant adaptive control laws in all three major categories: direct, indirect, and hybrid. The first implemented design to provide adaptation was a direct adaptive controller, which used artificial neural networks to generate augmentation commands in order to reduce the modeling error. Indirect adaptive laws were implemented in another controller, which utilized online PID to estimate and update the controller parameter. Finally, a new controller design was introduced, which integrated both direct and indirect control laws. This controller is known as hybrid adaptive controller. This last control design outperformed the two earlier designs in terms of less NNs effort and better tracking quality. The performance of online PID has an important role in the quality of the hybrid controller; therefore, the quality of the estimation will be of a great importance. Unfortunately, PID is not perfect and the online estimation process has some inherited issues; the online PID estimates are primarily affected by delays and biases. In order to ensure updating reliable estimates to the controller, the estimator consumes some time to converge. Moreover, the estimator will often converge to a biased value. This thesis conducts a sensitivity analysis for the estimation issues, delay and bias, and their effect on the tracking quality. In addition, the performance of the hybrid controller as compared to direct adaptive controller is explored. In order to serve this purpose, a simulation environment in MATLAB/SIMULINK has been created. The simulation environment is customized to provide the user with the flexibility to add different combinations of biases and delays to
State Estimation for Tensegrity Robots
NASA Technical Reports Server (NTRS)
Caluwaerts, Ken; Bruce, Jonathan; Friesen, Jeffrey M.; Sunspiral, Vytas
2016-01-01
Tensegrity robots are a class of compliant robots that have many desirable traits when designing mass efficient systems that must interact with uncertain environments. Various promising control approaches have been proposed for tensegrity systems in simulation. Unfortunately, state estimation methods for tensegrity robots have not yet been thoroughly studied. In this paper, we present the design and evaluation of a state estimator for tensegrity robots. This state estimator will enable existing and future control algorithms to transfer from simulation to hardware. Our approach is based on the unscented Kalman filter (UKF) and combines inertial measurements, ultra wideband time-of-flight ranging measurements, and actuator state information. We evaluate the effectiveness of our method on the SUPERball, a tensegrity based planetary exploration robotic prototype. In particular, we conduct tests for evaluating both the robot's success in estimating global position in relation to fixed ranging base stations during rolling maneuvers as well as local behavior due to small-amplitude deformations induced by cable actuation.
Runtime Verification with State Estimation
NASA Technical Reports Server (NTRS)
Stoller, Scott D.; Bartocci, Ezio; Seyster, Justin; Grosu, Radu; Havelund, Klaus; Smolka, Scott A.; Zadok, Erez
2011-01-01
We introduce the concept of Runtime Verification with State Estimation and show how this concept can be applied to estimate theprobability that a temporal property is satisfied by a run of a program when monitoring overhead is reduced by sampling. In such situations, there may be gaps in the observed program executions, thus making accurate estimation challenging. To deal with the effects of sampling on runtime verification, we view event sequences as observation sequences of a Hidden Markov Model (HMM), use an HMM model of the monitored program to "fill in" sampling-induced gaps in observation sequences, and extend the classic forward algorithm for HMM state estimation (which determines the probability of a state sequence, given an observation sequence) to compute the probability that the property is satisfied by an execution of the program. To validate our approach, we present a case study based on the mission software for a Mars rover. The results of our case study demonstrate high prediction accuracy for the probabilities computed by our algorithm. They also show that our technique is much more accurate than simply evaluating the temporal property on the given observation sequences, ignoring the gaps.
Sub-Second Parallel State Estimation
Chen, Yousu; Rice, Mark J.; Glaesemann, Kurt R.; Wang, Shaobu; Huang, Zhenyu
2014-10-31
This report describes the performance of Pacific Northwest National Laboratory (PNNL) sub-second parallel state estimation (PSE) tool using the utility data from the Bonneville Power Administrative (BPA) and discusses the benefits of the fast computational speed for power system applications. The test data were provided by BPA. They are two-days’ worth of hourly snapshots that include power system data and measurement sets in a commercial tool format. These data are extracted out from the commercial tool box and fed into the PSE tool. With the help of advanced solvers, the PSE tool is able to solve each BPA hourly state estimation problem within one second, which is more than 10 times faster than today’s commercial tool. This improved computational performance can help increase the reliability value of state estimation in many aspects: (1) the shorter the time required for execution of state estimation, the more time remains for operators to take appropriate actions, and/or to apply automatic or manual corrective control actions. This increases the chances of arresting or mitigating the impact of cascading failures; (2) the SE can be executed multiple times within time allowance. Therefore, the robustness of SE can be enhanced by repeating the execution of the SE with adaptive adjustments, including removing bad data and/or adjusting different initial conditions to compute a better estimate within the same time as a traditional state estimator’s single estimate. There are other benefits with the sub-second SE, such as that the PSE results can potentially be used in local and/or wide-area automatic corrective control actions that are currently dependent on raw measurements to minimize the impact of bad measurements, and provides opportunities to enhance the power grid reliability and efficiency. PSE also can enable other advanced tools that rely on SE outputs and could be used to further improve operators’ actions and automated controls to mitigate effects
Common Core State Standards and Adaptive Teaching
ERIC Educational Resources Information Center
Kamil, Michael L.
2016-01-01
This article examines the issues of how Common Core State Standards (CCSS) will impact adaptive teaching. It focuses on 2 of the major differences between conventional standards and CCSS: the increased complexity of text and the addition of disciplinary literacy standards to reading instruction. The article argues that adaptive teaching under CCSS…
Application of Sequential Interval Estimation to Adaptive Mastery Testing
ERIC Educational Resources Information Center
Chang, Yuan-chin Ivan
2005-01-01
In this paper, we apply sequential one-sided confidence interval estimation procedures with beta-protection to adaptive mastery testing. The procedures of fixed-width and fixed proportional accuracy confidence interval estimation can be viewed as extensions of one-sided confidence interval procedures. It can be shown that the adaptive mastery…
Adaptive distributed video coding with correlation estimation using expectation propagation
NASA Astrophysics Data System (ADS)
Cui, Lijuan; Wang, Shuang; Jiang, Xiaoqian; Cheng, Samuel
2012-10-01
Distributed video coding (DVC) is rapidly increasing in popularity by the way of shifting the complexity from encoder to decoder, whereas no compression performance degrades, at least in theory. In contrast with conventional video codecs, the inter-frame correlation in DVC is explored at decoder based on the received syndromes of Wyner-Ziv (WZ) frame and side information (SI) frame generated from other frames available only at decoder. However, the ultimate decoding performances of DVC are based on the assumption that the perfect knowledge of correlation statistic between WZ and SI frames should be available at decoder. Therefore, the ability of obtaining a good statistical correlation estimate is becoming increasingly important in practical DVC implementations. Generally, the existing correlation estimation methods in DVC can be classified into two main types: pre-estimation where estimation starts before decoding and on-the-fly (OTF) estimation where estimation can be refined iteratively during decoding. As potential changes between frames might be unpredictable or dynamical, OTF estimation methods usually outperforms pre-estimation techniques with the cost of increased decoding complexity (e.g., sampling methods). In this paper, we propose a low complexity adaptive DVC scheme using expectation propagation (EP), where correlation estimation is performed OTF as it is carried out jointly with decoding of the factor graph-based DVC code. Among different approximate inference methods, EP generally offers better tradeoff between accuracy and complexity. Experimental results show that our proposed scheme outperforms the benchmark state-of-the-art DISCOVER codec and other cases without correlation tracking, and achieves comparable decoding performance but with significantly low complexity comparing with sampling method.
Adaptive Distributed Video Coding with Correlation Estimation using Expectation Propagation.
Cui, Lijuan; Wang, Shuang; Jiang, Xiaoqian; Cheng, Samuel
2012-10-15
Distributed video coding (DVC) is rapidly increasing in popularity by the way of shifting the complexity from encoder to decoder, whereas no compression performance degrades, at least in theory. In contrast with conventional video codecs, the inter-frame correlation in DVC is explored at decoder based on the received syndromes of Wyner-Ziv (WZ) frame and side information (SI) frame generated from other frames available only at decoder. However, the ultimate decoding performances of DVC are based on the assumption that the perfect knowledge of correlation statistic between WZ and SI frames should be available at decoder. Therefore, the ability of obtaining a good statistical correlation estimate is becoming increasingly important in practical DVC implementations. Generally, the existing correlation estimation methods in DVC can be classified into two main types: pre-estimation where estimation starts before decoding and on-the-fly (OTF) estimation where estimation can be refined iteratively during decoding. As potential changes between frames might be unpredictable or dynamical, OTF estimation methods usually outperforms pre-estimation techniques with the cost of increased decoding complexity (e.g., sampling methods). In this paper, we propose a low complexity adaptive DVC scheme using expectation propagation (EP), where correlation estimation is performed OTF as it is carried out jointly with decoding of the factor graph-based DVC code. Among different approximate inference methods, EP generally offers better tradeoff between accuracy and complexity. Experimental results show that our proposed scheme outperforms the benchmark state-of-the-art DISCOVER codec and other cases without correlation tracking, and achieves comparable decoding performance but with significantly low complexity comparing with sampling method.
Model reference adaptive control, estimation and identification using only input and output signals
NASA Technical Reports Server (NTRS)
Carroll, R. L.; Monopoli, R. V.
1975-01-01
Significant recent advances in the application of stability theory to the adaptive control and identification of systems, and adaptive state estimation, are considered. Emphasis is on those methods which utilize only input and output measurements of the system, and do not require derivatives of the output signal.
NASA Astrophysics Data System (ADS)
Jina, A.; Hsiang, S. M.; Kopp, R. E., III; Rasmussen, D.; Rising, J.
2014-12-01
The American Climate Prospectus (ACP), the technical analysis underlying the Risky Business project, quantitatively assessed the climate risks posed to the United States' economy in a number of economic sectors [1]. The main analysis presents projections of climate impacts with an assumption of "no adaptation". Yet, historically, when the climate imposed an economic cost upon society, adaptive responses were taken to minimise these costs. These adaptive behaviours, both autonomous and planned, can be expected to occur as climate impacts increase in the future. To understand the extent to which adaptation might decrease some of the worst impacts of climate change, we empirically estimate adaptive responses. We do this in three sectors considered in the analysis - crop yield, crime, and mortality - and estimate adaptive capacity in two steps. First, looking at changes in climate impacts through time, we identify a historical rate of adaptation. Second, spatial differences in climate impacts are then used to stratify regions into more adapted or less adapted based on climate averages. As these averages change across counties in the US, we allow each to become more adapted at the rate identified in step one. We are then able to estimate the residual damages, assuming that only the historical adaptive behaviours have taken place (fig 1). Importantly, we are unable to estimate any costs associated with these adaptations, nor are we able to estimate more novel (for example, new technological discoveries) or more disruptive (for example, migration) adaptive behaviours. However, an important insight is that historical adaptive behaviours may not be capable of reducing the worst impacts of climate change. The persistence of impacts in even the most exposed areas indicates that there are non-trivial costs associated with adaptation that will need to be met from other sources or through novel behavioural changes. References: [1] T. Houser et al. (2014), American Climate
Rath, J J; Veluvolu, K C; Defoort, M
2014-01-01
The estimation of road excitation profile is important for evaluation of vehicle stability and vehicle suspension performance for autonomous vehicle control systems. In this work, the nonlinear dynamics of the active automotive system that is excited by the unknown road excitation profile are considered for modeling. To address the issue of estimation of road profile, we develop an adaptive supertwisting observer for state and unknown road profile estimation. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is proven. Simulation results with Ford Fiesta MK2 demonstrate the effectiveness of the proposed observer for state and unknown input estimation for nonlinear active suspension system. PMID:24683321
Rath, J J; Veluvolu, K C; Defoort, M
2014-01-01
The estimation of road excitation profile is important for evaluation of vehicle stability and vehicle suspension performance for autonomous vehicle control systems. In this work, the nonlinear dynamics of the active automotive system that is excited by the unknown road excitation profile are considered for modeling. To address the issue of estimation of road profile, we develop an adaptive supertwisting observer for state and unknown road profile estimation. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is proven. Simulation results with Ford Fiesta MK2 demonstrate the effectiveness of the proposed observer for state and unknown input estimation for nonlinear active suspension system.
Rath, J. J.; Veluvolu, K. C.; Defoort, M.
2014-01-01
The estimation of road excitation profile is important for evaluation of vehicle stability and vehicle suspension performance for autonomous vehicle control systems. In this work, the nonlinear dynamics of the active automotive system that is excited by the unknown road excitation profile are considered for modeling. To address the issue of estimation of road profile, we develop an adaptive supertwisting observer for state and unknown road profile estimation. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is proven. Simulation results with Ford Fiesta MK2 demonstrate the effectiveness of the proposed observer for state and unknown input estimation for nonlinear active suspension system. PMID:24683321
Adaptive position estimation for an automated guided vehicle
NASA Astrophysics Data System (ADS)
Lapin, Brett D.
1993-05-01
In a mobile robotic system, complexities in positioning arise due to the motion. An adaptive position estimation scheme has been developed for an automated guide vehicle (AGV) to overcome these complexities. The scheme's purpose is to minimize the position error--the difference between the estimated position and the actual position. The method to achieve this is to adapt the system model by incorporating a parameter vector and using a maximum likelihood algorithm to estimate the parameters after an accurate position determination is made. A simulation of the vehicle's guidance system was developed and the estimator tested on an oval-shaped path. Upon injecting biases into the system, initial position errors were 10 centimeters or more. After the estimator converged, the maximum final errors were on the order of 1 to 2 centimeters (prior to measurement update). After each measurement update, after the estimator had converged, errors were on the order of 1 to 2 millimeters.
State energy data report 1994: Consumption estimates
1996-10-01
This document provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), operated by EIA. SEDS provides State energy consumption estimates to members of Congress, Federal and State agencies, and the general public, and provides the historical series needed for EIA`s energy models. Division is made for each energy type and end use sector. Nuclear electric power is included.
Encoding attentional states during visuomotor adaptation
Im, Hee Yeon; Bédard, Patrick; Song, Joo-Hyun
2015-01-01
We recently showed that visuomotor adaptation acquired under attentional distraction is better recalled under a similar level of distraction compared to no distraction. This paradoxical effect suggests that attentional state (e.g., divided or undivided) is encoded as an internal context during visuomotor learning and should be reinstated for successful recall (Song & Bédard, 2015). To investigate if there is a critical temporal window for encoding attentional state in visuomotor memory, we manipulated whether participants performed the secondary attention-demanding task concurrently in the early or late phase of visuomotor learning. Recall performance was enhanced when the attentional states between recall and the early phase of visuomotor learning were consistent. However, it reverted to untrained levels when tested under the attentional state of the late-phase learning. This suggests that attentional state is primarily encoded during the early phase of learning before motor errors decrease and reach an asymptote. Furthermore, we demonstrate that when divided and undivided attentional states were mixed during visuomotor adaptation, only divided attention was encoded as an internal cue for memory retrieval. Therefore, a single attentional state appears to be primarily integrated with visuomotor memory while motor error reduction is in progress during learning. PMID:26114683
Robust time and frequency domain estimation methods in adaptive control
NASA Technical Reports Server (NTRS)
Lamaire, Richard Orville
1987-01-01
A robust identification method was developed for use in an adaptive control system. The type of estimator is called the robust estimator, since it is robust to the effects of both unmodeled dynamics and an unmeasurable disturbance. The development of the robust estimator was motivated by a need to provide guarantees in the identification part of an adaptive controller. To enable the design of a robust control system, a nominal model as well as a frequency-domain bounding function on the modeling uncertainty associated with this nominal model must be provided. Two estimation methods are presented for finding parameter estimates, and, hence, a nominal model. One of these methods is based on the well developed field of time-domain parameter estimation. In a second method of finding parameter estimates, a type of weighted least-squares fitting to a frequency-domain estimated model is used. The frequency-domain estimator is shown to perform better, in general, than the time-domain parameter estimator. In addition, a methodology for finding a frequency-domain bounding function on the disturbance is used to compute a frequency-domain bounding function on the additive modeling error due to the effects of the disturbance and the use of finite-length data. The performance of the robust estimator in both open-loop and closed-loop situations is examined through the use of simulations.
Adaptive Error Estimation in Linearized Ocean General Circulation Models
NASA Technical Reports Server (NTRS)
Chechelnitsky, Michael Y.
1999-01-01
Data assimilation methods are routinely used in oceanography. The statistics of the model and measurement errors need to be specified a priori. This study addresses the problem of estimating model and measurement error statistics from observations. We start by testing innovation based methods of adaptive error estimation with low-dimensional models in the North Pacific (5-60 deg N, 132-252 deg E) to TOPEX/POSEIDON (TIP) sea level anomaly data, acoustic tomography data from the ATOC project, and the MIT General Circulation Model (GCM). A reduced state linear model that describes large scale internal (baroclinic) error dynamics is used. The methods are shown to be sensitive to the initial guess for the error statistics and the type of observations. A new off-line approach is developed, the covariance matching approach (CMA), where covariance matrices of model-data residuals are "matched" to their theoretical expectations using familiar least squares methods. This method uses observations directly instead of the innovations sequence and is shown to be related to the MT method and the method of Fu et al. (1993). Twin experiments using the same linearized MIT GCM suggest that altimetric data are ill-suited to the estimation of internal GCM errors, but that such estimates can in theory be obtained using acoustic data. The CMA is then applied to T/P sea level anomaly data and a linearization of a global GFDL GCM which uses two vertical modes. We show that the CMA method can be used with a global model and a global data set, and that the estimates of the error statistics are robust. We show that the fraction of the GCM-T/P residual variance explained by the model error is larger than that derived in Fukumori et al.(1999) with the method of Fu et al.(1993). Most of the model error is explained by the barotropic mode. However, we find that impact of the change in the error statistics on the data assimilation estimates is very small. This is explained by the large
State energy data report 1993: Consumption estimates
1995-07-01
The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public; and (2) to provide the historical series necessary for EIA`s energy models.
Stability and error estimation for Component Adaptive Grid methods
NASA Technical Reports Server (NTRS)
Oliger, Joseph; Zhu, Xiaolei
1994-01-01
Component adaptive grid (CAG) methods for solving hyperbolic partial differential equations (PDE's) are discussed in this paper. Applying recent stability results for a class of numerical methods on uniform grids. The convergence of these methods for linear problems on component adaptive grids is established here. Furthermore, the computational error can be estimated on CAG's using the stability results. Using these estimates, the error can be controlled on CAG's. Thus, the solution can be computed efficiently on CAG's within a given error tolerance. Computational results for time dependent linear problems in one and two space dimensions are presented.
Adaptive Estimation of Intravascular Shear Rate Based on Parameter Optimization
NASA Astrophysics Data System (ADS)
Nitta, Naotaka; Takeda, Naoto
2008-05-01
The relationships between the intravascular wall shear stress, controlled by flow dynamics, and the progress of arteriosclerosis plaque have been clarified by various studies. Since the shear stress is determined by the viscosity coefficient and shear rate, both factors must be estimated accurately. In this paper, an adaptive method for improving the accuracy of quantitative shear rate estimation was investigated. First, the parameter dependence of the estimated shear rate was investigated in terms of the differential window width and the number of averaged velocity profiles based on simulation and experimental data, and then the shear rate calculation was optimized. The optimized result revealed that the proposed adaptive method of shear rate estimation was effective for improving the accuracy of shear rate calculation.
Distributed estimation for adaptive sensor selection in wireless sensor networks
NASA Astrophysics Data System (ADS)
Mahmoud, Magdi S.; Hassan Hamid, Matasm M.
2014-05-01
Wireless sensor networks (WSNs) are usually deployed for monitoring systems with the distributed detection and estimation of sensors. Sensor selection in WSNs is considered for target tracking. A distributed estimation scenario is considered based on the extended information filter. A cost function using the geometrical dilution of precision measure is derived for active sensor selection. A consensus-based estimation method is proposed in this paper for heterogeneous WSNs with two types of sensors. The convergence properties of the proposed estimators are analyzed under time-varying inputs. Accordingly, a new adaptive sensor selection (ASS) algorithm is presented in which the number of active sensors is adaptively determined based on the absolute local innovations vector. Simulation results show that the tracking accuracy of the ASS is comparable to that of the other algorithms.
State energy data report 1995 - consumption estimates
1997-12-01
The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sectors. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public, and (2) to provide the historical series necessary for EIA`s energy models.
State Energy Data Report, 1991: Consumption estimates
Not Available
1993-05-01
The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to the Government, policy makers, and the public; and (2) to provide the historical series necessary for EIA`s energy models.
On Using Exponential Parameter Estimators with an Adaptive Controller
NASA Technical Reports Server (NTRS)
Patre, Parag; Joshi, Suresh M.
2011-01-01
Typical adaptive controllers are restricted to using a specific update law to generate parameter estimates. This paper investigates the possibility of using any exponential parameter estimator with an adaptive controller such that the system tracks a desired trajectory. The goal is to provide flexibility in choosing any update law suitable for a given application. The development relies on a previously developed concept of controller/update law modularity in the adaptive control literature, and the use of a converse Lyapunov-like theorem. Stability analysis is presented to derive gain conditions under which this is possible, and inferences are made about the tracking error performance. The development is based on a class of Euler-Lagrange systems that are used to model various engineering systems including space robots and manipulators.
NASA Technical Reports Server (NTRS)
Xu, Ru-Gang; Koga, Dennis (Technical Monitor)
2001-01-01
The goal of 'Estimate' is to take advantage of attitude information to produce better pose while staying flexible and robust. Currently there are several instruments that are used for attitude: gyros, inclinometers, and compasses. However, precise and useful attitude information cannot come from one instrument. Integration of rotational rates, from gyro data for example, would result in drift. Therefore, although gyros are accurate in the short-term, accuracy in the long term is unlikely. Using absolute instruments such as compasses and inclinometers can result in an accurate measurement of attitude in the long term. However, in the short term, the physical nature of compasses and inclinometers, and the dynamic nature of a mobile platform result in highly volatile and therefore useless data. The solution then is to use both absolute and relative data. Kalman Filtering is known to be able to combine gyro and compass/inclinometer data to produce stable and accurate attitude information. Since the model of motion is linear and the data comes in as discrete samples, a Discrete Kalman Filter was selected as the core of the new estimator. Therefore, 'Estimate' can be divided into two parts: the Discrete Kalman Filter and the code framework.
The Role of Parametric Assumptions in Adaptive Bayesian Estimation
ERIC Educational Resources Information Center
Alcala-Quintana, Rocio; Garcia-Perez, Miguel A.
2004-01-01
Variants of adaptive Bayesian procedures for estimating the 5% point on a psychometric function were studied by simulation. Bias and standard error were the criteria to evaluate performance. The results indicated a superiority of (a) uniform priors, (b) model likelihood functions that are odd symmetric about threshold and that have parameter…
Adaptive Channel Estimation for MIMO-Constant Envelope Modulation
NASA Astrophysics Data System (ADS)
Mahmoud Mohamed, Ehab; Muta, Osamu; Furukawa, Hiroshi
The authors have proposed Multi-Input Multi-Output (MIMO)-Constant Envelope Modulation, (MIMO-CEM), as a power and complexity efficient alternative to MIMO-OFDM, suitable for wireless backhaul networks in which relay nodes are fixed in their positions. One of the major problems hindering the real application of MIMO-CEM is to estimate MIMO channel characteristics. MIMO-CEM is based upon two contrary schemes; one is nonlinear equalization such as maximum likelihood sequence estimator, which needs accurate channel information to replicate the received signal passing through it. The other is a low resolution analog-to-digital converter (ADC), e.g., 1-bit in the default operation that removes the received signal amplitude fluctuation. In this paper, as a solution to the channel estimation problem in MIMO-CEM with low resolution ADC receiver, we propose an adaptive MIMO-CEM channel estimation scheme where iterative adaptive channel estimation is carried out to minimize the error between the received preamble signal and the replicated one. We also prove that Code Division Multiplexing (CDM) preamble transmission is effective in estimating MIMO channel parameters in the presence of large quantization noise. Computer simulation results show that MIMO-CEM with the proposed channel estimator using CDM preambles achieves identical BER performance to that with the ideal channel estimation even in presence of severe quantization noise caused by a low resolution ADC.
Frequency tracking and parameter estimation for robust quantum state estimation
Ralph, Jason F.; Jacobs, Kurt; Hill, Charles D.
2011-11-15
In this paper we consider the problem of tracking the state of a quantum system via a continuous weak measurement. If the system Hamiltonian is known precisely, this merely requires integrating the appropriate stochastic master equation. However, even a small error in the assumed Hamiltonian can render this approach useless. The natural answer to this problem is to include the parameters of the Hamiltonian as part of the estimation problem, and the full Bayesian solution to this task provides a state estimate that is robust against uncertainties. However, this approach requires considerable computational overhead. Here we consider a single qubit in which the Hamiltonian contains a single unknown parameter. We show that classical frequency estimation techniques greatly reduce the computational overhead associated with Bayesian estimation and provide accurate estimates for the qubit frequency.
Methodologies for Adaptive Flight Envelope Estimation and Protection
NASA Technical Reports Server (NTRS)
Tang, Liang; Roemer, Michael; Ge, Jianhua; Crassidis, Agamemnon; Prasad, J. V. R.; Belcastro, Christine
2009-01-01
This paper reports the latest development of several techniques for adaptive flight envelope estimation and protection system for aircraft under damage upset conditions. Through the integration of advanced fault detection algorithms, real-time system identification of the damage/faulted aircraft and flight envelop estimation, real-time decision support can be executed autonomously for improving damage tolerance and flight recoverability. Particularly, a bank of adaptive nonlinear fault detection and isolation estimators were developed for flight control actuator faults; a real-time system identification method was developed for assessing the dynamics and performance limitation of impaired aircraft; online learning neural networks were used to approximate selected aircraft dynamics which were then inverted to estimate command margins. As off-line training of network weights is not required, the method has the advantage of adapting to varying flight conditions and different vehicle configurations. The key benefit of the envelope estimation and protection system is that it allows the aircraft to fly close to its limit boundary by constantly updating the controller command limits during flight. The developed techniques were demonstrated on NASA s Generic Transport Model (GTM) simulation environments with simulated actuator faults. Simulation results and remarks on future work are presented.
Vehicle lateral state estimation based on measured tyre forces.
Tuononen, Ari J
2009-01-01
Future active safety systems need more accurate information about the state of vehicles. This article proposes a method to evaluate the lateral state of a vehicle based on measured tyre forces. The tyre forces of two tyres are estimated from optically measured tyre carcass deflections and transmitted wirelessly to the vehicle body. The two remaining tyres are so-called virtual tyre sensors, the forces of which are calculated from the real tyre sensor estimates. The Kalman filter estimator for lateral vehicle state based on measured tyre forces is presented, together with a simple method to define adaptive measurement error covariance depending on the driving condition of the vehicle. The estimated yaw rate and lateral velocity are compared with the validation sensor measurements. PMID:22291535
Vehicle Lateral State Estimation Based on Measured Tyre Forces
Tuononen, Ari J.
2009-01-01
Future active safety systems need more accurate information about the state of vehicles. This article proposes a method to evaluate the lateral state of a vehicle based on measured tyre forces. The tyre forces of two tyres are estimated from optically measured tyre carcass deflections and transmitted wirelessly to the vehicle body. The two remaining tyres are so-called virtual tyre sensors, the forces of which are calculated from the real tyre sensor estimates. The Kalman filter estimator for lateral vehicle state based on measured tyre forces is presented, together with a simple method to define adaptive measurement error covariance depending on the driving condition of the vehicle. The estimated yaw rate and lateral velocity are compared with the validation sensor measurements. PMID:22291535
Estimating Skin Cancer Risk: Evaluating Mobile Computer-Adaptive Testing
Djaja, Ngadiman; Janda, Monika; Olsen, Catherine M; Whiteman, David C
2016-01-01
Background Response burden is a major detriment to questionnaire completion rates. Computer adaptive testing may offer advantages over non-adaptive testing, including reduction of numbers of items required for precise measurement. Objective Our aim was to compare the efficiency of non-adaptive (NAT) and computer adaptive testing (CAT) facilitated by Partial Credit Model (PCM)-derived calibration to estimate skin cancer risk. Methods We used a random sample from a population-based Australian cohort study of skin cancer risk (N=43,794). All 30 items of the skin cancer risk scale were calibrated with the Rasch PCM. A total of 1000 cases generated following a normal distribution (mean [SD] 0 [1]) were simulated using three Rasch models with three fixed-item (dichotomous, rating scale, and partial credit) scenarios, respectively. We calculated the comparative efficiency and precision of CAT and NAT (shortening of questionnaire length and the count difference number ratio less than 5% using independent t tests). Results We found that use of CAT led to smaller person standard error of the estimated measure than NAT, with substantially higher efficiency but no loss of precision, reducing response burden by 48%, 66%, and 66% for dichotomous, Rating Scale Model, and PCM models, respectively. Conclusions CAT-based administrations of the skin cancer risk scale could substantially reduce participant burden without compromising measurement precision. A mobile computer adaptive test was developed to help people efficiently assess their skin cancer risk. PMID:26800642
Estimated spectrum adaptive postfilter and the iterative prepost filtering algirighms
NASA Technical Reports Server (NTRS)
Linares, Irving (Inventor)
2004-01-01
The invention presents The Estimated Spectrum Adaptive Postfilter (ESAP) and the Iterative Prepost Filter (IPF) algorithms. These algorithms model a number of image-adaptive post-filtering and pre-post filtering methods. They are designed to minimize Discrete Cosine Transform (DCT) blocking distortion caused when images are highly compressed with the Joint Photographic Expert Group (JPEG) standard. The ESAP and the IPF techniques of the present invention minimize the mean square error (MSE) to improve the objective and subjective quality of low-bit-rate JPEG gray-scale images while simultaneously enhancing perceptual visual quality with respect to baseline JPEG images.
Optimal, reliable estimation of quantum states
NASA Astrophysics Data System (ADS)
Blume-Kohout, Robin
2010-04-01
Accurately inferring the state of a quantum device from the results of measurements is a crucial task in building quantum information processing hardware. The predominant state estimation procedure, maximum likelihood estimation (MLE), generally reports an estimate with zero eigenvalues. These cannot be justified. Furthermore, the MLE estimate is incompatible with error bars, so conclusions drawn from it are suspect. I propose an alternative procedure, Bayesian mean estimation (BME). BME never yields zero eigenvalues, its eigenvalues provide a bound on their own uncertainties, and under certain circumstances it is provably the most accurate procedure possible. I show how to implement BME numerically, and how to obtain natural error bars that are compatible with the estimate. Finally, I briefly discuss the differences between Bayesian and frequentist estimation techniques.
Linear Covariance Analysis and Epoch State Estimators
NASA Technical Reports Server (NTRS)
Markley, F. Landis; Carpenter, J. Russell
2014-01-01
This paper extends in two directions the results of prior work on generalized linear covariance analysis of both batch least-squares and sequential estimators. The first is an improved treatment of process noise in the batch, or epoch state, estimator with an epoch time that may be later than some or all of the measurements in the batch. The second is to account for process noise in specifying the gains in the epoch state estimator. We establish the conditions under which the latter estimator is equivalent to the Kalman filter.
Linear Covariance Analysis and Epoch State Estimators
NASA Technical Reports Server (NTRS)
Markley, F. Landis; Carpenter, J. Russell
2012-01-01
This paper extends in two directions the results of prior work on generalized linear covariance analysis of both batch least-squares and sequential estimators. The first is an improved treatment of process noise in the batch, or epoch state, estimator with an epoch time that may be later than some or all of the measurements in the batch. The second is to account for process noise in specifying the gains in the epoch state estimator. We establish the conditions under which the latter estimator is equivalent to the Kalman filter.
Parallel State Estimation Assessment with Practical Data
Chen, Yousu; Jin, Shuangshuang; Rice, Mark J.; Huang, Zhenyu
2014-10-31
This paper presents a full-cycle parallel state estimation (PSE) implementation using a preconditioned conjugate gradient algorithm. The developed code is able to solve large-size power system state estimation within 5 seconds using real-world data, comparable to the Supervisory Control And Data Acquisition (SCADA) rate. This achievement allows the operators to know the system status much faster to help improve grid reliability. Case study results of the Bonneville Power Administration (BPA) system with real measurements are presented. The benefits of fast state estimation are also discussed.
Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO
NASA Astrophysics Data System (ADS)
Gao, Zhen; Dai, Linglong; Wang, Zhaocheng; Chen, Sheng
2015-12-01
This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a non-orthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. Additionally, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the non-orthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramer-Rao lower bound of the proposed scheme, which enlightens us to design the non-orthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.
State energy data report 1996: Consumption estimates
1999-02-01
The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sectors. The estimates are developed in the Combined State Energy Data System (CSEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining CSEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. CSEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public and (2) to provide the historical series necessary for EIA`s energy models. To the degree possible, energy consumption has been assigned to five sectors: residential, commercial, industrial, transportation, and electric utility sectors. Fuels covered are coal, natural gas, petroleum, nuclear electric power, hydroelectric power, biomass, and other, defined as electric power generated from geothermal, wind, photovoltaic, and solar thermal energy. 322 tabs.
Adaptive whitening of the electromyogram to improve amplitude estimation.
Clancy, E A; Farry, K A
2000-06-01
Previous research showed that whitening the surface electromyogram (EMG) can improve EMG amplitude estimation (where EMG amplitude is defined as the time-varying standard deviation of the EMG). However, conventional whitening via a linear filter seems to fail at low EMG amplitude levels, perhaps due to additive background noise in the measured EMG. This paper describes an adaptive whitening technique that overcomes this problem by cascading a nonadaptive whitening filter, an adaptive Wiener filter, and an adaptive gain correction. These stages can be calibrated from two, five second duration, constant-angle, constant-force contractions, one at a reference level [e.g., 50% maximum voluntary contraction (MVC)] and one at 0% MVC. In experimental studies, subjects used real-time EMG amplitude estimates to track a uniform-density, band-limited random target. With a 0.25-Hz bandwidth target, either adaptive whitening or multiple-channel processing reduced the tracking error roughly half-way to the error achieved using the dynamometer signal as the feedback. At the 1.00-Hz bandwidth, all of the EMG processors had errors equivalent to that of the dynamometer signal, reflecting that errors in this task were dominated by subjects' inability to track targets at this bandwidth. Increases in the additive noise level, smoothing window length, and tracking bandwidth diminish the advantages of whitening. PMID:10833845
Bayesian Adaptive Estimation of Psychometric Functions in Noisy Environments
NASA Astrophysics Data System (ADS)
Aihara, Takatsugu; Kitajo, Keiichi; Nozaki, Daichi; Yamamoto, Yoshiharu
2007-07-01
We propose a new psychometric model incorporating noise as well as stimulus effects, based on recent findings that noise can improve human perception via a mechanism of stochastic resonance (SR). This model assumes that the psychometric function can be regarded as a bivariate function of noise and stimulus intensities. The algorithm of the Ψ Bayesian adaptive estimation method is modified so that it is applicable to our new model. In computer simulations, our new procedure successfully estimates the bivariate psychometric function within a few hundred trials. We also demonstrate several examples in which the procedure is applied to actual psychophysical experiments.
State estimation for spacecraft power systems
NASA Technical Reports Server (NTRS)
Williamson, Susan H.; Sheble, Gerald B.
1990-01-01
A state estimator appropriate for spacecraft power systems is presented. Phasor voltage and current measurements are used to determine the system state. A weighted least squares algorithm with a multireference transmission cable model is used. Bad data are identified and resolved. Once the bad data have been identified, they are removed from the measurement set and the system state can be estimated from the remaining data. An observability analysis is performed on the remaining measurements to determine if the system state can be found from the reduced measurement set. An example of the algorithm for a sample spacecraft power system is presented.
An Adaptive Motion Estimation Scheme for Video Coding
Gao, Yuan; Jia, Kebin
2014-01-01
The unsymmetrical-cross multihexagon-grid search (UMHexagonS) is one of the best fast Motion Estimation (ME) algorithms in video encoding software. It achieves an excellent coding performance by using hybrid block matching search pattern and multiple initial search point predictors at the cost of the computational complexity of ME increased. Reducing time consuming of ME is one of the key factors to improve video coding efficiency. In this paper, we propose an adaptive motion estimation scheme to further reduce the calculation redundancy of UMHexagonS. Firstly, new motion estimation search patterns have been designed according to the statistical results of motion vector (MV) distribution information. Then, design a MV distribution prediction method, including prediction of the size of MV and the direction of MV. At last, according to the MV distribution prediction results, achieve self-adaptive subregional searching by the new estimation search patterns. Experimental results show that more than 50% of total search points are dramatically reduced compared to the UMHexagonS algorithm in JM 18.4 of H.264/AVC. As a result, the proposed algorithm scheme can save the ME time up to 20.86% while the rate-distortion performance is not compromised. PMID:24672313
An adaptive motion estimation scheme for video coding.
Liu, Pengyu; Gao, Yuan; Jia, Kebin
2014-01-01
The unsymmetrical-cross multihexagon-grid search (UMHexagonS) is one of the best fast Motion Estimation (ME) algorithms in video encoding software. It achieves an excellent coding performance by using hybrid block matching search pattern and multiple initial search point predictors at the cost of the computational complexity of ME increased. Reducing time consuming of ME is one of the key factors to improve video coding efficiency. In this paper, we propose an adaptive motion estimation scheme to further reduce the calculation redundancy of UMHexagonS. Firstly, new motion estimation search patterns have been designed according to the statistical results of motion vector (MV) distribution information. Then, design a MV distribution prediction method, including prediction of the size of MV and the direction of MV. At last, according to the MV distribution prediction results, achieve self-adaptive subregional searching by the new estimation search patterns. Experimental results show that more than 50% of total search points are dramatically reduced compared to the UMHexagonS algorithm in JM 18.4 of H.264/AVC. As a result, the proposed algorithm scheme can save the ME time up to 20.86% while the rate-distortion performance is not compromised.
Adaptive distributed Kalman filtering with wind estimation for astronomical adaptive optics.
Massioni, Paolo; Gilles, Luc; Ellerbroek, Brent
2015-12-01
In the framework of adaptive optics (AO) for astronomy, it is a common assumption to consider the atmospheric turbulent layers as "frozen flows" sliding according to the wind velocity profile. For this reason, having knowledge of such a velocity profile is beneficial in terms of AO control system performance. In this paper we show that it is possible to exploit the phase estimate from a Kalman filter running on an AO system in order to estimate wind velocity. This allows the update of the Kalman filter itself with such knowledge, making it adaptive. We have implemented such an adaptive controller based on the distributed version of the Kalman filter, for a realistic simulation of a multi-conjugate AO system with laser guide stars on a 30 m telescope. Simulation results show that this approach is effective and promising and the additional computational cost with respect to the distributed filter is negligible. Comparisons with a previously published slope detection and ranging wind profiler are made and the impact of turbulence profile quantization is assessed. One of the main findings of the paper is that all flavors of the adaptive distributed Kalman filter are impacted more significantly by turbulence profile quantization than the static minimum mean square estimator which does not incorporate wind profile information.
NASA Astrophysics Data System (ADS)
Fleischer, Christian; Waag, Wladislaw; Heyn, Hans-Martin; Sauer, Dirk Uwe
2014-08-01
Lithium-ion battery systems employed in high power demanding systems such as electric vehicles require a sophisticated monitoring system to ensure safe and reliable operation. Three major states of the battery are of special interest and need to be constantly monitored, these include: battery state of charge (SoC), battery state of health (capcity fade determination, SoH), and state of function (power fade determination, SoF). In a series of two papers, we propose a system of algorithms based on a weighted recursive least quadratic squares parameter estimator, that is able to determine the battery impedance and diffusion parameters for accurate state estimation. The functionality was proven on different battery chemistries with different aging conditions. The first paper investigates the general requirements on BMS for HEV/EV applications. In parallel, the commonly used methods for battery monitoring are reviewed to elaborate their strength and weaknesses in terms of the identified requirements for on-line applications. Special emphasis will be placed on real-time capability and memory optimized code for cost-sensitive industrial or automotive applications in which low-cost microcontrollers must be used. Therefore, a battery model is presented which includes the influence of the Butler-Volmer kinetics on the charge-transfer process. Lastly, the mass transport process inside the battery is modeled in a novel state-space representation.
State energy data report 1992: Consumption estimates
Not Available
1994-05-01
This is a report of energy consumption by state for the years 1960 to 1992. The report contains summaries of energy consumption for the US and by state, consumption by source, comparisons to other energy use reports, consumption by energy use sector, and describes the estimation methodologies used in the preparation of the report. Some years are not listed specifically although they are included in the summary of data.
Adaptive spectral estimators for fast flow-profile detection.
Ricci, Stefano
2013-02-01
In multigate spectral Doppler (MSD) analysis, hundreds of small sample volumes (SVs) aligned along a pulse wave-line can be simultaneously investigated. The so-called spectral profile, reporting the scatterers' velocity distribution in a vessel, is obtained by estimating the frequency content of the echoes detected from each SV. The preferred frequency estimator is the Welch method, which is robust and fast, but requires an observation window (OW) of at least 64 to 128 samples to guarantee adequate spectral resolution. The blood amplitude and phase estimator (BAPES) and the blood iterative adaptive approach (BIAA) are alternative methods which were recently proven to be capable of producing good spectrograms from one SV using shorter OWs. This paper shows that BAPES and BIAA can be successfully applied to MSD estimations. The use of short OWs can be exploited to produce spectral profiles with high temporal resolution and/or to perform simultaneous investigations at multiple sites. Two in vivo examples of application are reported: in the first, the blood velocity distribution during the fast systolic acceleration in a carotid artery is detailed with high temporal resolution; in the second, four spectral profiles are simultaneously detected at different sites of the carotid bifurcation.
Estimating the potential for adaptation of corals to climate warming.
Császár, Nikolaus B M; Ralph, Peter J; Frankham, Richard; Berkelmans, Ray; van Oppen, Madeleine J H
2010-03-18
The persistence of tropical coral reefs is threatened by rapidly increasing climate warming, causing a functional breakdown of the obligate symbiosis between corals and their algal photosymbionts (Symbiodinium) through a process known as coral bleaching. Yet the potential of the coral-algal symbiosis to genetically adapt in an evolutionary sense to warming oceans is unknown. Using a quantitative genetics approach, we estimated the proportion of the variance in thermal tolerance traits that has a genetic basis (i.e. heritability) as a proxy for their adaptive potential in the widespread Indo-Pacific reef-building coral Acropora millepora. We chose two physiologically different populations that associate respectively with one thermo-tolerant (Symbiodinium clade D) and one less tolerant symbiont type (Symbiodinium C2). In both symbiont types, pulse amplitude modulated (PAM) fluorometry and high performance liquid chromatography (HPLC) analysis revealed significant heritabilities for traits related to both photosynthesis and photoprotective pigment profile. However, quantitative real-time polymerase chain reaction (qRT-PCR) assays showed a lack of heritability in both coral host populations for their own expression of fundamental stress genes. Coral colony growth, contributed to by both symbiotic partners, displayed heritability. High heritabilities for functional key traits of algal symbionts, along with their short clonal generation time and high population sizes allow for their rapid thermal adaptation. However, the low overall heritability of coral host traits, along with the corals' long generation time, raise concern about the timely adaptation of the coral-algal symbiosis in the face of continued rapid climate warming.
Motorcycle state estimation for lateral dynamics
NASA Astrophysics Data System (ADS)
Teerhuis, A. P.; Jansen, S. T. H.
2012-08-01
The motorcycle lean (or roll) angle development is one of the main characteristics of motorcycle lateral dynamics. Control of motorcycle motions requires an accurate assessment of this quantity and for safety applications also the risk of sliding needs to be considered. Direct measurement of the roll angle and tyre slip is not available; therefore, a method of model-based estimation is developed to estimate the state of a motorcycle. This paper investigates the feasibility of such a motorcycle state estimator (MCSE). A simplified analytic model of a motorcycle is developed by comparison to an extended multi-body model of the motorcycle, designed in Matlab/SimMechanics. The analytic model is used inside an extended Kalman filter. Experimental results of an instrumented Yamaha FJR1300 motorcycle show that the MCSE is a feasible concept for obtaining signals related to the lateral dynamics of the motorcycle.
Adaptive on-line estimation and control of overlay tool bias
NASA Astrophysics Data System (ADS)
Martinez, Victor M.; Finn, Karen; Edgar, Thomas F.
2003-06-01
Modern lithographic manufacturing processes rely on various types of exposure tools, used in a mix-and-match fashion. The motivation to use older tools alongside state-of-the-art tools is lower cost and one of the tradeoffs is a degradation in overlay performance. While average prices of semiconductor products continue to fall, the cost of manufacturing equipment rises with every product generation. Lithography processing, including the cost of ownership for tools, accounts for roughly 30% of the wafer processing costs, thus the importance of mix-and-match strategies. Exponentially Weighted Moving Average (EWMA) run-by-run controllers are widely used in the semiconductor manufacturing industry. This type of controller has been implemented successfully in volume manufacturing, improving Cpk values dramatically in processes like photolithography and chemical mechanical planarization. This simple, but powerful control scheme is well suited for adding corrections to compensate for Overlay Tool Bias (OTB). We have developed an adaptive estimation technique to compensate for overlay variability due to differences in the processing tools. The OTB can be dynamically calculated for each tool, based on the most recent measurements available, and used to correct the control variables. One approach to tracking the effect of different tools is adaptive modeling and control. The basic premise of an adaptive system is to change or adapt the controller as the operating conditions of the system change. Using closed-loop data, the adaptive control algorithm estimates the controller parameters using a recursive estimation technique. Once an updated model of the system is available, modelbased control becomes feasible. In the simplest scenario, the control law can be reformulated to include the current state of the tool (or its estimate) to compensate dynamically for OTB. We have performed simulation studies to predict the impact of deploying this strategy in production. The results
Energy Detection Based Estimation of Channel Occupancy Rate with Adaptive Noise Estimation
NASA Astrophysics Data System (ADS)
Lehtomäki, Janne J.; Vuohtoniemi, Risto; Umebayashi, Kenta; Mäkelä, Juha-Pekka
Recently, there has been growing interest in opportunistically utilizing the 2.4GHz ISM-band. Numerous spectrum occupancy measurements covering the ISM-band have been performed to analyze the spectrum usage. However, in these campaigns the verification of the correctness of the obtained occupancy values for the highly dynamic ISM-band has not been presented. In this paper, we propose and verify channel occupancy rate (COR) estimation utilizing energy detection mechanism with a novel adaptive energy detection threshold setting method. The results are compared with the true reference COR values. Several different types of verification measurements showed that our setup can estimate the COR values of 802.11 traffic well, with negligible overestimation. The results from real-time real-life measurements also confirm that the proposed adaptive threshold setting method enables accurate thresholds even in the situations where multiple interferers are present in the received signal.
Zorgani, Youssef Agrebi; Koubaa, Yassine; Boussak, Mohamed
2016-03-01
This paper presents a novel method for estimating the load torque of a sensorless indirect stator flux oriented controlled (ISFOC) induction motor drive based on the model reference adaptive system (MRAS) scheme. As a matter of fact, this method is meant to inter-connect a speed estimator with the load torque observer. For this purpose, a MRAS has been applied to estimate the rotor speed with tuned load torque in order to obtain a high performance ISFOC induction motor drive. The reference and adjustable models, developed in the stationary stator reference frame, are used in the MRAS scheme in an attempt to estimate the speed of the measured terminal voltages and currents. The load torque is estimated by means of a Luenberger observer defined throughout the mechanical equation. Every observer state matrix depends on the mechanical characteristics of the machine taking into account the vicious friction coefficient and inertia moment. Accordingly, some simulation results are presented to validate the proposed method and to highlight the influence of the variation of the inertia moment and the friction coefficient on the speed and the estimated load torque. The experimental results, concerning to the sensorless speed with a load torque estimation, are elaborated in order to validate the effectiveness of the proposed method. The complete sensorless ISFOC with load torque estimation is successfully implemented in real time using a digital signal processor board DSpace DS1104 for a laboratory 3 kW induction motor. PMID:26775088
Estimated Water Flows in 2005: United States
Smith, C A; Belles, R D; Simon, A J
2011-03-16
Flow charts depicting water use in the United States have been constructed from publicly available data and estimates of water use patterns. Approximately 410,500 million gallons per day of water are managed throughout the United States for use in farming, power production, residential, commercial, and industrial applications. Water is obtained from four major resource classes: fresh surface-water, saline (ocean) surface-water, fresh groundwater and saline (brackish) groundwater. Water that is not consumed or evaporated during its use is returned to surface bodies of water. The flow patterns are represented in a compact 'visual atlas' of 52 state-level (all 50 states in addition to Puerto Rico and the Virgin Islands) and one national water flow chart representing a comprehensive systems view of national water resources, use, and disposition.
An Empirical State Error Covariance Matrix for Batch State Estimation
NASA Technical Reports Server (NTRS)
Frisbee, Joseph H., Jr.
2011-01-01
State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. Consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. It then follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully account for the error in the state estimate. By way of a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm, it is possible to arrive at an appropriate, and formally correct, empirical state error covariance matrix. The first specific step of the method is to use the average form of the weighted measurement residual variance performance index rather than its usual total weighted residual form. Next it is helpful to interpret the solution to the normal equations as the average of a collection of sample vectors drawn from a hypothetical parent population. From here, using a standard statistical analysis approach, it directly follows as to how to determine the standard empirical state error covariance matrix. This matrix will contain the total uncertainty in the
Parallel State Estimation Assessment with Practical Data
Chen, Yousu; Jin, Shuangshuang; Rice, Mark J.; Huang, Zhenyu
2013-07-31
This paper presents a parallel state estimation (PSE) implementation using a preconditioned gradient algorithm and an orthogonal decomposition-based algorithm. The preliminary tests against a commercial Energy Management System (EMS) State Estimation (SE) tool using real-world data are performed. The results show that while the precondition gradient algorithm can solve the SE problem quicker with the help of parallel computing techniques, it might not be good for real-world data due to the large condition number of gain matrix introduced by the wide range of measurement weights. With the help of PETSc package and considering one iteration of the SE process, the orthogonal decomposition-based PSE algorithm can achieve 5-20 times speedup comparing against the commercial EMS tool. It is very promising that the developed PSE can solve the SE problem for large power systems at the SCADA rate, to improve grid reliability.
Estimated United States Transportation Energy Use 2005
Smith, C A; Simon, A J; Belles, R D
2011-11-09
A flow chart depicting energy flow in the transportation sector of the United States economy in 2005 has been constructed from publicly available data and estimates of national energy use patterns. Approximately 31,000 trillion British Thermal Units (trBTUs) of energy were used throughout the United States in transportation activities. Vehicles used in these activities include automobiles, motorcycles, trucks, buses, airplanes, rail, and ships. The transportation sector is powered primarily by petroleum-derived fuels (gasoline, diesel and jet fuel). Biomass-derived fuels, electricity and natural gas-derived fuels are also used. The flow patterns represent a comprehensive systems view of energy used within the transportation sector.
Robustness enhancement of neurocontroller and state estimator
NASA Technical Reports Server (NTRS)
Troudet, Terry
1993-01-01
The feasibility of enhancing neurocontrol robustness, through training of the neurocontroller and state estimator in the presence of system uncertainties, is investigated on the example of a multivariable aircraft control problem. The performance and robustness of the newly trained neurocontroller are compared to those for an existing neurocontrol design scheme. The newly designed dynamic neurocontroller exhibits a better trade-off between phase and gain stability margins, and it is significantly more robust to degradations of the plant dynamics.
Occupancy estimation and modeling with multiple states and state uncertainty
Nichols, J.D.; Hines, J.E.; MacKenzie, D.I.; Seamans, M.E.; Gutierrez, R.J.
2007-01-01
The distribution of a species over space is of central interest in ecology, but species occurrence does not provide all of the information needed to characterize either the well-being of a population or the suitability of occupied habitat. Recent methodological development has focused on drawing inferences about species occurrence in the face of imperfect detection. Here we extend those methods by characterizing occupied locations by some additional state variable ( e. g., as producing young or not). Our modeling approach deals with both detection probabilities,1 and uncertainty in state classification. We then use the approach with occupancy and reproductive rate data from California Spotted Owls (Strix occidentalis occidentalis) collected in the central Sierra Nevada during the breeding season of 2004 to illustrate the utility of the modeling approach. Estimates of owl reproductive rate were larger than naive estimates, indicating the importance of appropriately accounting for uncertainty in detection and state classification.
Occupancy estimation and modeling with multiple states and state uncertainty.
Nichols, James D; Hines, A James E; Mackenzie, Darryl I; Seamans, Mark E; Gutiérrez, R J
2007-06-01
The distribution of a species over space is of central interest in ecology, but species occurrence does not provide all of the information needed to characterize either the well-being of a population or the suitability of occupied habitat. Recent methodological development has focused on drawing inferences about species occurrence in the face of imperfect detection. Here we extend those methods by characterizing occupied locations by some additional state variable (e.g., as producing young or not). Our modeling approach deals with both detection probabilities <1 and uncertainty in state classification. We then use the approach with occupancy and reproductive rate data from California Spotted Owls (Strix occidentalis occidentalis) collected in the central Sierra Nevada during the breeding season of 2004 to illustrate the utility of the modeling approach. Estimates of owl reproductive rate were larger than naïve estimates, indicating the importance of appropriately accounting for uncertainty in detection and state classification. PMID:17601132
Spin State Estimation of Tumbling Small Bodies
NASA Astrophysics Data System (ADS)
Olson, Corwin; Russell, Ryan P.; Bhaskaran, Shyam
2016-06-01
It is expected that a non-trivial percentage of small bodies that future missions may visit are in non-principal axis rotation (i.e. "tumbling"). The primary contribution of this paper is the application of the Extended Kalman Filter (EKF) Simultaneous Localization and Mapping (SLAM) method to estimate the small body spin state, mass, and moments of inertia; the spacecraft position and velocity; and the surface landmark locations. The method uses optical landmark measurements, and an example scenario based on the Rosetta mission is used. The SLAM method proves effective, with order of magnitude decreases in the spacecraft and small body spin state errors after less than a quarter of the comet characterization phase. The SLAM method converges nicely for initial small body angular velocity errors several times larger than the true rates (effectively having no a priori knowledge of the angular velocity). Surface landmark generation and identification are not treated in this work, but significant errors in the initial body-fixed landmark positions are effectively estimated. The algorithm remains effective for a range of different truth spin states, masses, and center of mass offsets that correspond to expected tumbling small bodies throughout the solar system.
Incremental state aggregation for value function estimation in reinforcement learning.
Mori, Takeshi; Ishii, Shin
2011-10-01
In reinforcement learning, large state and action spaces make the estimation of value functions impractical, so a value function is often represented as a linear combination of basis functions whose linear coefficients constitute parameters to be estimated. However, preparing basis functions requires a certain amount of prior knowledge and is, in general, a difficult task. To overcome this difficulty, an adaptive basis function construction technique has been proposed by Keller recently, but it requires excessive computational cost. We propose an efficient approach to this difficulty, in which the problem of approximating the value function is decomposed into a number of subproblems, each of which can be solved with small computational cost. Computer experiments show that the CPU time needed by our method is much smaller than that by the existing method.
Estimating instantaneous energetic cost during non-steady-state gait.
Selinger, Jessica C; Donelan, J Maxwell
2014-12-01
Respiratory measures of oxygen and carbon dioxide are routinely used to estimate the body's steady-state metabolic energy use. However, slow mitochondrial dynamics, long transit times, complex respiratory control mechanisms, and high breath-by-breath variability obscure the relationship between the body's instantaneous energy demands (instantaneous energetic cost) and that measured from respiratory gases (measured energetic cost). The purpose of this study was to expand on traditional methods of assessing metabolic cost by estimating instantaneous energetic cost during non-steady-state conditions. To accomplish this goal, we first imposed known changes in energy use (input), while measuring the breath-by-breath response (output). We used these input/output relationships to model the body as a dynamic system that maps instantaneous to measured energetic cost. We found that a first-order linear differential equation well approximates transient energetic cost responses during gait. Across all subjects, model fits were parameterized by an average time constant (τ) of 42 ± 12 s with an average R(2) of 0.94 ± 0.05 (mean ± SD). Armed with this input/output model, we next tested whether we could use it to reliably estimate instantaneous energetic cost from breath-by-breath measures under conditions that simulated dynamically changing gait. A comparison of the imposed energetic cost profiles and our estimated instantaneous cost demonstrated a close correspondence, supporting the use of our methodology to study the role of energetics during locomotor adaptation and learning.
Adaptive neuro-fuzzy estimation of optimal lens system parameters
NASA Astrophysics Data System (ADS)
Petković, Dalibor; Pavlović, Nenad T.; Shamshirband, Shahaboddin; Mat Kiah, Miss Laiha; Badrul Anuar, Nor; Idna Idris, Mohd Yamani
2014-04-01
Due to the popularization of digital technology, the demand for high-quality digital products has become critical. The quantitative assessment of image quality is an important consideration in any type of imaging system. Therefore, developing a design that combines the requirements of good image quality is desirable. Lens system design represents a crucial factor for good image quality. Optimization procedure is the main part of the lens system design methodology. Lens system optimization is a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. Therefore lens system design provides ideal problems for intelligent optimization algorithms. There are many tools which can be used to measure optical performance. One very useful tool is the spot diagram. The spot diagram gives an indication of the image of a point object. In this paper, one optimization criterion for lens system, the spot size radius, is considered. This paper presents new lens optimization methods based on adaptive neuro-fuzzy inference strategy (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated.
Adaptive optimal stochastic state feedback control of resistive wall modes in tokamaks
Sun, Z.; Sen, A.K.; Longman, R.W.
2006-01-15
An adaptive optimal stochastic state feedback control is developed to stabilize the resistive wall mode (RWM) instability in tokamaks. The extended least-square method with exponential forgetting factor and covariance resetting is used to identify (experimentally determine) the time-varying stochastic system model. A Kalman filter is used to estimate the system states. The estimated system states are passed on to an optimal state feedback controller to construct control inputs. The Kalman filter and the optimal state feedback controller are periodically redesigned online based on the identified system model. This adaptive controller can stabilize the time-dependent RWM in a slowly evolving tokamak discharge. This is accomplished within a time delay of roughly four times the inverse of the growth rate for the time-invariant model used.
The state of climate change adaptation in the Arctic
NASA Astrophysics Data System (ADS)
Ford, James D.; McDowell, Graham; Jones, Julie
2014-10-01
The Arctic climate is rapidly changing, with wide ranging impacts on natural and social systems. A variety of adaptation policies, programs and practices have been adopted to this end, yet our understanding of if, how, and where adaptation is occurring is limited. In response, this paper develops a systematic approach to characterize the current state of adaptation in the Arctic. Using reported adaptations in the English language peer reviewed literature as our data source, we document 157 discrete adaptation initiatives between 2003 and 2013. Results indicate large variations in adaptation by region and sector, dominated by reporting from North America, particularly with regards to subsistence harvesting by Inuit communities. Few adaptations were documented in the European and Russian Arctic, or have a focus on the business and economy, or infrastructure sectors. Adaptations are being motivated primarily by the combination of climatic and non-climatic factors, have a strong emphasis on reducing current vulnerability involving incremental changes to existing risk management processes, and are primarily initiated and led at the individual/community level. There is limited evidence of trans-boundary adaptations or initiatives considering potential cross-scale/sector impacts.
Adaptive importance sampling of random walks on continuous state spaces
Baggerly, K.; Cox, D.; Picard, R.
1998-11-01
The authors consider adaptive importance sampling for a random walk with scoring in a general state space. Conditions under which exponential convergence occurs to the zero-variance solution are reviewed. These results generalize previous work for finite, discrete state spaces in Kollman (1993) and in Kollman, Baggerly, Cox, and Picard (1996). This paper is intended for nonstatisticians and includes considerable explanatory material.
Hellander, Andreas; Lawson, Michael J; Drawert, Brian; Petzold, Linda
2015-01-01
The efficiency of exact simulation methods for the reaction-diffusion master equation (RDME) is severely limited by the large number of diffusion events if the mesh is fine or if diffusion constants are large. Furthermore, inherent properties of exact kinetic-Monte Carlo simulation methods limit the efficiency of parallel implementations. Several approximate and hybrid methods have appeared that enable more efficient simulation of the RDME. A common feature to most of them is that they rely on splitting the system into its reaction and diffusion parts and updating them sequentially over a discrete timestep. This use of operator splitting enables more efficient simulation but it comes at the price of a temporal discretization error that depends on the size of the timestep. So far, existing methods have not attempted to estimate or control this error in a systematic manner. This makes the solvers hard to use for practitioners since they must guess an appropriate timestep. It also makes the solvers potentially less efficient than if the timesteps are adapted to control the error. Here, we derive estimates of the local error and propose a strategy to adaptively select the timestep when the RDME is simulated via a first order operator splitting. While the strategy is general and applicable to a wide range of approximate and hybrid methods, we exemplify it here by extending a previously published approximate method, the Diffusive Finite-State Projection (DFSP) method, to incorporate temporal adaptivity. PMID:26865735
NASA Astrophysics Data System (ADS)
Hellander, Andreas; Lawson, Michael J.; Drawert, Brian; Petzold, Linda
2014-06-01
The efficiency of exact simulation methods for the reaction-diffusion master equation (RDME) is severely limited by the large number of diffusion events if the mesh is fine or if diffusion constants are large. Furthermore, inherent properties of exact kinetic-Monte Carlo simulation methods limit the efficiency of parallel implementations. Several approximate and hybrid methods have appeared that enable more efficient simulation of the RDME. A common feature to most of them is that they rely on splitting the system into its reaction and diffusion parts and updating them sequentially over a discrete timestep. This use of operator splitting enables more efficient simulation but it comes at the price of a temporal discretization error that depends on the size of the timestep. So far, existing methods have not attempted to estimate or control this error in a systematic manner. This makes the solvers hard to use for practitioners since they must guess an appropriate timestep. It also makes the solvers potentially less efficient than if the timesteps were adapted to control the error. Here, we derive estimates of the local error and propose a strategy to adaptively select the timestep when the RDME is simulated via a first order operator splitting. While the strategy is general and applicable to a wide range of approximate and hybrid methods, we exemplify it here by extending a previously published approximate method, the diffusive finite-state projection (DFSP) method, to incorporate temporal adaptivity.
Reliability Value of Fast State Estimation on Power Systems
Elizondo, Marcelo A.; Chen, Yousu; Huang, Zhenyu
2012-05-07
Monitoring the state of a power system under stress is key to achieving reliable operation. State estimation and timely measurements become more important when applying and designing corrective control actions (manual and automatic) to arrest or mitigate cascading blackouts. The execution time of each process, including state estimation, should be as short as possible to allow for timely action. In this paper, we provide a methodology for estimating one of the components of value of faster and more frequent state estimation: the reliability value of state estimation to assist corrective control actions for arresting or mitigating cascading blackouts. We present a new algorithm for estimating the time between successive line trips in a cascading failure. The algorithm combines power flow calculations with characteristics of the protection system to estimate the time between successive equipment trips. Using this algorithm, we illustrate the value of fast state estimation by calculating the time remaining for automatic or manual corrective actions after state estimation is finalized.
ZZ-Type a posteriori error estimators for adaptive boundary element methods on a curve.
Feischl, Michael; Führer, Thomas; Karkulik, Michael; Praetorius, Dirk
2014-01-01
In the context of the adaptive finite element method (FEM), ZZ-error estimators named after Zienkiewicz and Zhu (1987) [52] are mathematically well-established and widely used in practice. In this work, we propose and analyze ZZ-type error estimators for the adaptive boundary element method (BEM). We consider weakly singular and hyper-singular integral equations and prove, in particular, convergence of the related adaptive mesh-refining algorithms. Throughout, the theoretical findings are underlined by numerical experiments.
Eldred, Michael Scott; Subia, Samuel Ramirez; Neckels, David; Hopkins, Matthew Morgan; Notz, Patrick K.; Adams, Brian M.; Carnes, Brian; Wittwer, Jonathan W.; Bichon, Barron J.; Copps, Kevin D.
2006-10-01
This report documents the results for an FY06 ASC Algorithms Level 2 milestone combining error estimation and adaptivity, uncertainty quantification, and probabilistic design capabilities applied to the analysis and design of bistable MEMS. Through the use of error estimation and adaptive mesh refinement, solution verification can be performed in an automated and parameter-adaptive manner. The resulting uncertainty analysis and probabilistic design studies are shown to be more accurate, efficient, reliable, and convenient.
Estimating Power System Dynamic States Using Extended Kalman Filter
Huang, Zhenyu; Schneider, Kevin P.; Nieplocha, Jaroslaw; Zhou, Ning
2014-10-31
Abstract—The state estimation tools which are currently deployed in power system control rooms are based on a steady state assumption. As a result, the suite of operational tools that rely on state estimation results as inputs do not have dynamic information available and their accuracy is compromised. This paper investigates the application of Extended Kalman Filtering techniques for estimating dynamic states in the state estimation process. The new formulated “dynamic state estimation” includes true system dynamics reflected in differential equations, not like previously proposed “dynamic state estimation” which only considers the time-variant snapshots based on steady state modeling. This new dynamic state estimation using Extended Kalman Filter has been successfully tested on a multi-machine system. Sensitivity studies with respect to noise levels, sampling rates, model errors, and parameter errors are presented as well to illustrate the robust performance of the developed dynamic state estimation process.
Nonparametric estimation of quantum states, processes and measurements
NASA Astrophysics Data System (ADS)
Lougovski, Pavel; Bennink, Ryan
Quantum state, process, and measurement estimation methods traditionally use parametric models, in which the number and role of relevant parameters is assumed to be known. When such an assumption cannot be justified, a common approach in many disciplines is to fit the experimental data to multiple models with different sets of parameters and utilize an information criterion to select the best fitting model. However, it is not always possible to assume a model with a finite (countable) number of parameters. This typically happens when there are unobserved variables that stem from hidden correlations that can only be unveiled after collecting experimental data. How does one perform quantum characterization in this situation? We present a novel nonparametric method of experimental quantum system characterization based on the Dirichlet Process (DP) that addresses this problem. Using DP as a prior in conjunction with Bayesian estimation methods allows us to increase model complexity (number of parameters) adaptively as the number of experimental observations grows. We illustrate our approach for the one-qubit case and show how a probability density function for an unknown quantum process can be estimated.
Estimating State IQ: Measurement Challenges and Preliminary Correlates
ERIC Educational Resources Information Center
McDaniel, Michael A.
2006-01-01
The purpose of this study is threefold. First, an estimate of state IQ is derived and its strengths and limitations are considered. To that end, an indicator of downward bias in estimating state IQ is provided. Two preliminary causal models are offered that predict state IQ. These models were found to be highly predictive of state IQ, yielding…
NASA Astrophysics Data System (ADS)
Li, Judith Yue; Kokkinaki, Amalia; Ghorbanidehno, Hojat; Darve, Eric F.; Kitanidis, Peter K.
2015-12-01
Reservoir monitoring aims to provide snapshots of reservoir conditions and their uncertainties to assist operation management and risk analysis. These snapshots may contain millions of state variables, e.g., pressures and saturations, which can be estimated by assimilating data in real time using the Kalman filter (KF). However, the KF has a computational cost that scales quadratically with the number of unknowns, m, due to the cost of computing and storing the covariance and Jacobian matrices, along with their products. The compressed state Kalman filter (CSKF) adapts the KF for solving large-scale monitoring problems. The CSKF uses N preselected orthogonal bases to compute an accurate rank-N approximation of the covariance that is close to the optimal spectral approximation given by SVD. The CSKF has a computational cost that scales linearly in m and uses an efficient matrix-free approach that propagates uncertainties using N + 1 forward model evaluations, where N≪m. Here we present a generalized CSKF algorithm for nonlinear state estimation problems such as CO2 monitoring. For simultaneous estimation of multiple types of state variables, the algorithm allows selecting bases that represent the variability of each state type. Through synthetic numerical experiments of CO2 monitoring, we show that the CSKF can reproduce the Kalman gain accurately even for large compression ratios (m/N). For a given computational cost, the CSKF uses a robust and flexible compression scheme that gives more reliable uncertainty estimates than the ensemble Kalman filter, which may display loss of ensemble variability leading to suboptimal uncertainty estimates.
Parameter and state estimation for articulated heavy vehicles
NASA Astrophysics Data System (ADS)
Cheng, Caizhen; Cebon, David
2011-02-01
This article discusses algorithms to estimate parameters and states of articulated heavy vehicles. First, 3- and 5-degrees-of-freedom linear vehicle models of a tractor semitrailer are presented. Vehicle parameter estimation methods based on the dual extended Kalman filter and state estimation based on the Kalman filter are presented. A program of experimental tests on an instrumental heavy goods vehicle is described. Simulation and experimental results showed that the algorithms generate accurate estimates of vehicle parameters and states under most circumstances.
Lesmes, Luis A.; Lu, Zhong-Lin; Baek, Jongsoo; Tran, Nina; Dosher, Barbara A.; Albright, Thomas D.
2015-01-01
Motivated by Signal Detection Theory (SDT), we developed a family of novel adaptive methods that estimate the sensitivity threshold—the signal intensity corresponding to a pre-defined sensitivity level (d′ = 1)—in Yes-No (YN) and Forced-Choice (FC) detection tasks. Rather than focus stimulus sampling to estimate a single level of %Yes or %Correct, the current methods sample psychometric functions more broadly, to concurrently estimate sensitivity and decision factors, and thereby estimate thresholds that are independent of decision confounds. Developed for four tasks—(1) simple YN detection, (2) cued YN detection, which cues the observer's response state before each trial, (3) rated YN detection, which incorporates a Not Sure response, and (4) FC detection—the qYN and qFC methods yield sensitivity thresholds that are independent of the task's decision structure (YN or FC) and/or the observer's subjective response state. Results from simulation and psychophysics suggest that 25 trials (and sometimes less) are sufficient to estimate YN thresholds with reasonable precision (s.d. = 0.10–0.15 decimal log units), but more trials are needed for FC thresholds. When the same subjects were tested across tasks of simple, cued, rated, and FC detection, adaptive threshold estimates exhibited excellent agreement with the method of constant stimuli (MCS), and with each other. These YN adaptive methods deliver criterion-free thresholds that have previously been exclusive to FC methods. PMID:26300798
Lesmes, Luis A; Lu, Zhong-Lin; Baek, Jongsoo; Tran, Nina; Dosher, Barbara A; Albright, Thomas D
2015-01-01
Motivated by Signal Detection Theory (SDT), we developed a family of novel adaptive methods that estimate the sensitivity threshold-the signal intensity corresponding to a pre-defined sensitivity level (d' = 1)-in Yes-No (YN) and Forced-Choice (FC) detection tasks. Rather than focus stimulus sampling to estimate a single level of %Yes or %Correct, the current methods sample psychometric functions more broadly, to concurrently estimate sensitivity and decision factors, and thereby estimate thresholds that are independent of decision confounds. Developed for four tasks-(1) simple YN detection, (2) cued YN detection, which cues the observer's response state before each trial, (3) rated YN detection, which incorporates a Not Sure response, and (4) FC detection-the qYN and qFC methods yield sensitivity thresholds that are independent of the task's decision structure (YN or FC) and/or the observer's subjective response state. Results from simulation and psychophysics suggest that 25 trials (and sometimes less) are sufficient to estimate YN thresholds with reasonable precision (s.d. = 0.10-0.15 decimal log units), but more trials are needed for FC thresholds. When the same subjects were tested across tasks of simple, cued, rated, and FC detection, adaptive threshold estimates exhibited excellent agreement with the method of constant stimuli (MCS), and with each other. These YN adaptive methods deliver criterion-free thresholds that have previously been exclusive to FC methods.
NASA Astrophysics Data System (ADS)
Zhang, T.; Lin, X.; Yang, X.
2014-12-01
More serious drought has been projected due to the climate change in the Kansas State of the U.S., which might threaten the local agriculture and thus require effective adaptation responses to drought, e.g. better irrigation. But the irrigation adaptation on drought at the current technology-level is poorly quantified, therefore challenges to figure out how much additional efforts are required under more aridity of climate. Here, we collect the irrigation application data for maize, soybean, sorghum and wheat in Kansas, and establish a two-stage model to quantify the crop-specific irrigation application responses to changes in climatic drivers, and further estimate the existing effectiveness of the irrigation to adapt future drought based on the IPCC AR5 ensemble PDSI prediction under RCP4.5 scenario. We find that the three summer season crops (maize, soybean and sorghum) would experience 0 - 20% yield losses depending on county due to more serious drought since 2030s, even though increased irrigation application as the response of drought had saved 0 - 10% yields. At the state level, maize receives most benefits from irrigation, whereas the beneficial effects are least for sorghum among the three crops. To wheat, irrigation adaptation is very weak since irrigation water applied is much less than the above three crops. But wheat yields were projected to have a slight increase in central and eastern regions because climate would become more moisture over the growing season of winter wheat in future. Our results highlight that the existing beneficial effects from irrigation would be surpassed by the negative impact of drought in future, which would cause overall yield reduction in Kansas especially for those summer season crops.
Functional error estimators for the adaptive discretization of inverse problems
NASA Astrophysics Data System (ADS)
Clason, Christian; Kaltenbacher, Barbara; Wachsmuth, Daniel
2016-10-01
So-called functional error estimators provide a valuable tool for reliably estimating the discretization error for a sum of two convex functions. We apply this concept to Tikhonov regularization for the solution of inverse problems for partial differential equations, not only for quadratic Hilbert space regularization terms but also for nonsmooth Banach space penalties. Examples include the measure-space norm (i.e., sparsity regularization) or the indicator function of an {L}∞ ball (i.e., Ivanov regularization). The error estimators can be written in terms of residuals in the optimality system that can then be estimated by conventional techniques, thus leading to explicit estimators. This is illustrated by means of an elliptic inverse source problem with the above-mentioned penalties, and numerical results are provided for the case of sparsity regularization.
State estimation for large-scale wastewater treatment plants.
Busch, Jan; Elixmann, David; Kühl, Peter; Gerkens, Carine; Schlöder, Johannes P; Bock, Hans G; Marquardt, Wolfgang
2013-09-01
Many relevant process states in wastewater treatment are not measurable, or their measurements are subject to considerable uncertainty. This poses a serious problem for process monitoring and control. Model-based state estimation can provide estimates of the unknown states and increase the reliability of measurements. In this paper, an integrated approach is presented for the optimization-based sensor network design and the estimation problem. Using the ASM1 model in the reference scenario BSM1, a cost-optimal sensor network is designed and the prominent estimators EKF and MHE are evaluated. Very good estimation results for the system comprising 78 states are found requiring sensor networks of only moderate complexity.
Predictive control and estimation - State space approach
NASA Technical Reports Server (NTRS)
Gawronski, W.
1991-01-01
A modified output prediction procedure and a new controller design based on the predictive control law are presented. A new predictive estimator enhances system performance. The predictive controller was designed and applied to the tracking control of the NASA/JPL 70-m antenna. Simulation results show significant improvement in tracking performance over the linear quadratic controller and estimator presently in use.
An adaptive displacement estimation algorithm for improved reconstruction of thermal strain.
Ding, Xuan; Dutta, Debaditya; Mahmoud, Ahmed M; Tillman, Bryan; Leers, Steven A; Kim, Kang
2015-01-01
Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas' estimator and time-shift estimators such as normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas' estimator is limited by phase-wrapping and NXcorr performs poorly when the SNR is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas' estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas' estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas' estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI showed that the adaptive displacement estimator was less biased than either Loupas' estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7 to 350% and the spatial accuracy by 1.2 to 23.0% (P < 0.001). An ex vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and resulted in improved strain reconstruction.
An Adaptive Displacement Estimation Algorithm for Improved Reconstruction of Thermal Strain
Ding, Xuan; Dutta, Debaditya; Mahmoud, Ahmed M.; Tillman, Bryan; Leers, Steven A.; Kim, Kang
2014-01-01
Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas’ estimator and time-shift estimators like normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas’ estimator is limited by phase-wrapping and NXcorr performs poorly when the signal-to-noise ratio (SNR) is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas’ estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex-vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas’ estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas’ estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI using Field II showed that the adaptive displacement estimator was less biased than either Loupas’ estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7–350% and the spatial accuracy by 1.2–23.0% (p < 0.001). An ex-vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and results in improved strain reconstruction. PMID:25585398
Quantum state and process tomography via adaptive measurements
NASA Astrophysics Data System (ADS)
Wang, HengYan; Zheng, WenQiang; Yu, NengKun; Li, KeRen; Lu, DaWei; Xin, Tao; Li, Carson; Ji, ZhengFeng; Kribs, David; Zeng, Bei; Peng, XinHua; Du, JiangFeng
2016-10-01
We investigate quantum state tomography (QST) for pure states and quantum process tomography (QPT) for unitary channels via adaptive measurements. For a quantum system with a d-dimensional Hilbert space, we first propose an adaptive protocol where only 2 d - 1 measurement outcomes are used to accomplish the QST for all pure states. This idea is then extended to study QPT for unitary channels, where an adaptive unitary process tomography (AUPT) protocol of d 2+ d-1 measurement outcomes is constructed for any unitary channel. We experimentally implement the AUPT protocol in a 2-qubit nuclear magnetic resonance system. We examine the performance of the AUPT protocol when applied to Hadamard gate, T gate ( π/8 phase gate), and controlled-NOT gate, respectively, as these gates form the universal gate set for quantum information processing purpose. As a comparison, standard QPT is also implemented for each gate. Our experimental results show that the AUPT protocol that reconstructing unitary channels via adaptive measurements significantly reduce the number of experiments required by standard QPT without considerable loss of fidelity.
Magnitude Estimation with Noisy Integrators Linked by an Adaptive Reference
Thurley, Kay
2016-01-01
Judgments of physical stimuli show characteristic biases; relatively small stimuli are overestimated whereas relatively large stimuli are underestimated (regression effect). Such biases likely result from a strategy that seeks to minimize errors given noisy estimates about stimuli that itself are drawn from a distribution, i.e., the statistics of the environment. While being conceptually well described, it is unclear how such a strategy could be implemented neurally. The present paper aims toward answering this question. A theoretical approach is introduced that describes magnitude estimation as two successive stages of noisy (neural) integration. Both stages are linked by a reference memory that is updated with every new stimulus. The model reproduces the behavioral characteristics of magnitude estimation and makes several experimentally testable predictions. Moreover, the model identifies the regression effect as a means of minimizing estimation errors and explains how this optimality strategy depends on the subject's discrimination abilities and on the stimulus statistics. The latter influence predicts another property of magnitude estimation, the so-called range effect. Beyond being successful in describing decision-making, the present work suggests that noisy integration may also be important in processing magnitudes. PMID:26909028
Qin, J; Choi, K S; Ho, Simon S M; Heng, P A
2008-01-01
A force prediction algorithm is proposed to facilitate virtual-reality (VR) based collaborative surgical simulation by reducing the effect of network latencies. State regeneration is used to correct the estimated prediction. This algorithm is incorporated into an adaptive transmission protocol in which auxiliary features such as view synchronization and coupling control are equipped to ensure the system consistency. We implemented this protocol using multi-threaded technique on a cluster-based network architecture. PMID:18391327
Guo, Qing; Sun, Ping; Yin, Jing-Min; Yu, Tian; Jiang, Dan
2016-05-01
Some unknown parameter estimation of electro-hydraulic system (EHS) should be considered in hydraulic controller design due to many parameter uncertainties in practice. In this study, a parametric adaptive backstepping control method is proposed to improve the dynamic behavior of EHS under parametric uncertainties and unknown disturbance (i.e., hydraulic parameters and external load). The unknown parameters of EHS model are estimated by the parametric adaptive estimation law. Then the recursive backstepping controller is designed by Lyapunov technique to realize the displacement control of EHS. To avoid explosion of virtual control in traditional backstepping, a decayed memory filter is presented to re-estimate the virtual control and the dynamic external load. The effectiveness of the proposed controller has been demonstrated by comparison with the controller without adaptive and filter estimation. The comparative experimental results in critical working conditions indicate the proposed approach can achieve better dynamic performance on the motion control of Two-DOF robotic arm. PMID:26920086
Guo, Qing; Sun, Ping; Yin, Jing-Min; Yu, Tian; Jiang, Dan
2016-05-01
Some unknown parameter estimation of electro-hydraulic system (EHS) should be considered in hydraulic controller design due to many parameter uncertainties in practice. In this study, a parametric adaptive backstepping control method is proposed to improve the dynamic behavior of EHS under parametric uncertainties and unknown disturbance (i.e., hydraulic parameters and external load). The unknown parameters of EHS model are estimated by the parametric adaptive estimation law. Then the recursive backstepping controller is designed by Lyapunov technique to realize the displacement control of EHS. To avoid explosion of virtual control in traditional backstepping, a decayed memory filter is presented to re-estimate the virtual control and the dynamic external load. The effectiveness of the proposed controller has been demonstrated by comparison with the controller without adaptive and filter estimation. The comparative experimental results in critical working conditions indicate the proposed approach can achieve better dynamic performance on the motion control of Two-DOF robotic arm.
An hp-adaptivity and error estimation for hyperbolic conservation laws
NASA Technical Reports Server (NTRS)
Bey, Kim S.
1995-01-01
This paper presents an hp-adaptive discontinuous Galerkin method for linear hyperbolic conservation laws. A priori and a posteriori error estimates are derived in mesh-dependent norms which reflect the dependence of the approximate solution on the element size (h) and the degree (p) of the local polynomial approximation. The a posteriori error estimate, based on the element residual method, provides bounds on the actual global error in the approximate solution. The adaptive strategy is designed to deliver an approximate solution with the specified level of error in three steps. The a posteriori estimate is used to assess the accuracy of a given approximate solution and the a priori estimate is used to predict the mesh refinements and polynomial enrichment needed to deliver the desired solution. Numerical examples demonstrate the reliability of the a posteriori error estimates and the effectiveness of the hp-adaptive strategy.
Adaptive beat-to-beat heart rate estimation in ballistocardiograms.
Brüser, Christoph; Stadlthanner, Kurt; de Waele, Stijn; Leonhardt, Steffen
2011-09-01
A ballistocardiograph records the mechanical activity of the heart. We present a novel algorithm for the detection of individual heart beats and beat-to-beat interval lengths in ballistocardiograms (BCGs) from healthy subjects. An automatic training step based on unsupervised learning techniques is used to extract the shape of a single heart beat from the BCG. Using the learned parameters, the occurrence of individual heart beats in the signal is detected. A final refinement step improves the accuracy of the estimated beat-to-beat interval lengths. Compared to many existing algorithms, the new approach offers heart rate estimates on a beat-to-beat basis. The agreement of the proposed algorithm with an ECG reference has been evaluated. A relative beat-to-beat interval error of 1.79% with a coverage of 95.94% was achieved on recordings from 16 subjects.
Adaptive noise estimation and suppression for improving microseismic event detection
NASA Astrophysics Data System (ADS)
Mousavi, S. Mostafa; Langston, Charles A.
2016-09-01
Microseismic data recorded by surface arrays are often strongly contaminated by unwanted noise. This background noise makes the detection of small magnitude events difficult. A noise level estimation and noise reduction algorithm is presented for microseismic data analysis based upon minimally controlled recursive averaging and neighborhood shrinkage estimators. The method might not be compared with more sophisticated and computationally expensive denoising algorithm in terms of preserving detailed features of seismic signal. However, it is fast and data-driven and can be applied in real-time processing of continuous data for event detection purposes. Results from application of this algorithm to synthetic and real seismic data show that it holds a great promise for improving microseismic event detection.
ZZ-Type a posteriori error estimators for adaptive boundary element methods on a curve☆
Feischl, Michael; Führer, Thomas; Karkulik, Michael; Praetorius, Dirk
2014-01-01
In the context of the adaptive finite element method (FEM), ZZ-error estimators named after Zienkiewicz and Zhu (1987) [52] are mathematically well-established and widely used in practice. In this work, we propose and analyze ZZ-type error estimators for the adaptive boundary element method (BEM). We consider weakly singular and hyper-singular integral equations and prove, in particular, convergence of the related adaptive mesh-refining algorithms. Throughout, the theoretical findings are underlined by numerical experiments. PMID:24748725
Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.
Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu
2015-08-01
This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm. PMID:25265622
Bounded Linear Stability Analysis - A Time Delay Margin Estimation Approach for Adaptive Control
NASA Technical Reports Server (NTRS)
Nguyen, Nhan T.; Ishihara, Abraham K.; Krishnakumar, Kalmanje Srinlvas; Bakhtiari-Nejad, Maryam
2009-01-01
This paper presents a method for estimating time delay margin for model-reference adaptive control of systems with almost linear structured uncertainty. The bounded linear stability analysis method seeks to represent the conventional model-reference adaptive law by a locally bounded linear approximation within a small time window using the comparison lemma. The locally bounded linear approximation of the combined adaptive system is cast in a form of an input-time-delay differential equation over a small time window. The time delay margin of this system represents a local stability measure and is computed analytically by a matrix measure method, which provides a simple analytical technique for estimating an upper bound of time delay margin. Based on simulation results for a scalar model-reference adaptive control system, both the bounded linear stability method and the matrix measure method are seen to provide a reasonably accurate and yet not too conservative time delay margin estimation.
Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.
Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu
2015-08-01
This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm.
Improving Quantum State Estimation with Mutually Unbiased Bases
NASA Astrophysics Data System (ADS)
Adamson, R. B. A.; Steinberg, A. M.
2010-07-01
When used in quantum state estimation, projections onto mutually unbiased bases have the ability to maximize information extraction per measurement and to minimize redundancy. We present the first experimental demonstration of quantum state tomography of two-qubit polarization states to take advantage of mutually unbiased bases. We demonstrate improved state estimation as compared to standard measurement strategies and discuss how this can be understood from the structure of the measurements we use. We experimentally compared our method to the standard state estimation method for three different states and observe that the infidelity was up to 1.84±0.06 times lower by using our technique than it was by using standard state estimation methods.
Systems identification and the adaptive management of waterfowl in the United States
Williams, B.K.; Nichols, J.D.
2001-01-01
Waterfowl management in the United States is one of the more visible conservation success stories in the United States. It is authorized and supported by appropriate legislative authorities, based on large-scale monitoring programs, and widely accepted by the public. The process is one of only a limited number of large-scale examples of effective collaboration between research and management, integrating scientific information with management in a coherent framework for regulatory decision-making. However, harvest management continues to face some serious technical problems, many of which focus on sequential identification of the resource system in a context of optimal decision-making. The objective of this paper is to provide a theoretical foundation of adaptive harvest management, the approach currently in use in the United States for regulatory decision-making. We lay out the legal and institutional framework for adaptive harvest management and provide a formal description of regulatory decision-making in terms of adaptive optimization. We discuss some technical and institutional challenges in applying adaptive harvest management and focus specifically on methods of estimating resource states for linear resource systems.
Adaptive error covariances estimation methods for ensemble Kalman filters
Zhen, Yicun; Harlim, John
2015-08-01
This paper presents a computationally fast algorithm for estimating, both, the system and observation noise covariances of nonlinear dynamics, that can be used in an ensemble Kalman filtering framework. The new method is a modification of Belanger's recursive method, to avoid an expensive computational cost in inverting error covariance matrices of product of innovation processes of different lags when the number of observations becomes large. When we use only product of innovation processes up to one-lag, the computational cost is indeed comparable to a recently proposed method by Berry–Sauer's. However, our method is more flexible since it allows for using information from product of innovation processes of more than one-lag. Extensive numerical comparisons between the proposed method and both the original Belanger's and Berry–Sauer's schemes are shown in various examples, ranging from low-dimensional linear and nonlinear systems of SDEs and 40-dimensional stochastically forced Lorenz-96 model. Our numerical results suggest that the proposed scheme is as accurate as the original Belanger's scheme on low-dimensional problems and has a wider range of more accurate estimates compared to Berry–Sauer's method on L-96 example.
NASA Technical Reports Server (NTRS)
Canfield, Stephen
1999-01-01
This work will demonstrate the integration of sensor and system dynamic data and their appropriate models using an optimal filter to create a robust, adaptable, easily reconfigurable state (motion) estimation system. This state estimation system will clearly show the application of fundamental modeling and filtering techniques. These techniques are presented at a general, first principles level, that can easily be adapted to specific applications. An example of such an application is demonstrated through the development of an integrated GPS/INS navigation system. This system acquires both global position data and inertial body data, to provide optimal estimates of current position and attitude states. The optimal states are estimated using a Kalman filter. The state estimation system will include appropriate error models for the measurement hardware. The results of this work will lead to the development of a "black-box" state estimation system that supplies current motion information (position and attitude states) that can be used to carry out guidance and control strategies. This black-box state estimation system is developed independent of the vehicle dynamics and therefore is directly applicable to a variety of vehicles. Issues in system modeling and application of Kalman filtering techniques are investigated and presented. These issues include linearized models of equations of state, models of the measurement sensors, and appropriate application and parameter setting (tuning) of the Kalman filter. The general model and subsequent algorithm is developed in Matlab for numerical testing. The results of this system are demonstrated through application to data from the X-33 Michael's 9A8 mission and are presented in plots and simple animations.
The Problem of Bias in Person Parameter Estimation in Adaptive Testing
ERIC Educational Resources Information Center
Doebler, Anna
2012-01-01
It is shown that deviations of estimated from true values of item difficulty parameters, caused for example by item calibration errors, the neglect of randomness of item difficulty parameters, testlet effects, or rule-based item generation, can lead to systematic bias in point estimation of person parameters in the context of adaptive testing.…
Estimating unbiased phenological trends by adapting site-occupancy models.
Roth, Tobias; Strebel, Nicolas; Amrhein, Valentin
2014-08-01
As a response to climate warming, many animals and plants have been found to shift phenologies, such as appearance in spring or timing of reproduction. However, traditional measures for shifts in phenology that are based on observational data likely are biased due to a large influence of population size, observational effort, starting date of a survey, or other causes that may affect the probability of detecting a species. Understanding phenological responses of species to climate change, however, requires a robust measure that could be compared among studies and study years. Here, we developed a new method for estimating arrival and departure dates based on site-occupancy models. Using simulated data, we show that our method provided virtually unbiased estimates of phenological events even if detection probability or the number of sites occupied by the species is changing over time. To illustrate the flexibility of our method, we analyzed spring arrival of two long-distance migrant songbirds and the length of the flight period of two butterfly species, using data from a long-term biodiversity monitoring program in Switzerland. In contrast to many birds that migrate short distances, the two long-distance migrant songbirds tended to postpone average spring arrival by -0.5 days per year between 1995 and 2012. Furthermore, the flight period of the short-distance-flying butterfly species apparently became even shorter over the study period, while the flight period of the longer-distance-flying butterfly species remained relatively stable. Our method could be applied to temporally and spatially extensive data from a wide range of monitoring programs and citizen science projects, to help unravel how species and communities respond to global warming.
Language Model Combination and Adaptation Using Weighted Finite State Transducers
NASA Technical Reports Server (NTRS)
Liu, X.; Gales, M. J. F.; Hieronymus, J. L.; Woodland, P. C.
2010-01-01
In speech recognition systems language model (LMs) are often constructed by training and combining multiple n-gram models. They can be either used to represent different genres or tasks found in diverse text sources, or capture stochastic properties of different linguistic symbol sequences, for example, syllables and words. Unsupervised LM adaption may also be used to further improve robustness to varying styles or tasks. When using these techniques, extensive software changes are often required. In this paper an alternative and more general approach based on weighted finite state transducers (WFSTs) is investigated for LM combination and adaptation. As it is entirely based on well-defined WFST operations, minimum change to decoding tools is needed. A wide range of LM combination configurations can be flexibly supported. An efficient on-the-fly WFST decoding algorithm is also proposed. Significant error rate gains of 7.3% relative were obtained on a state-of-the-art broadcast audio recognition task using a history dependently adapted multi-level LM modelling both syllable and word sequences
A model of cerebellar computations for dynamical state estimation
NASA Technical Reports Server (NTRS)
Paulin, M. G.; Hoffman, L. F.; Assad, C.
2001-01-01
The cerebellum is a neural structure that is essential for agility in vertebrate movements. Its contribution to motor control appears to be due to a fundamental role in dynamical state estimation, which also underlies its role in various non-motor tasks. Single spikes in vestibular sensory neurons carry information about head state. We show how computations for optimal dynamical state estimation may be accomplished when signals are encoded in spikes. This provides a novel way to design dynamical state estimators, and a novel way to interpret the structure and function of the cerebellum.
Dynamic State Estimation Utilizing High Performance Computing Methods
Schneider, Kevin P.; Huang, Zhenyu; Yang, Bo; Hauer, Matthew L.; Nieplocha, Jaroslaw
2009-03-18
The state estimation tools which are currently deployed in power system control rooms are based on a quasi-steady-state assumption. As a result, the suite of operational tools that rely on state estimation results as inputs do not have dynamic information available and their accuracy is compromised. This paper presents an overview of the Kalman Filtering process and then focuses on the implementation of the predication component on multiple processors.
Preliminary Exploration of Adaptive State Predictor Based Human Operator Modeling
NASA Technical Reports Server (NTRS)
Trujillo, Anna C.; Gregory, Irene M.
2012-01-01
Control-theoretic modeling of the human operator dynamic behavior in manual control tasks has a long and rich history. In the last two decades, there has been a renewed interest in modeling the human operator. There has also been significant work on techniques used to identify the pilot model of a given structure. The purpose of this research is to attempt to go beyond pilot identification based on collected experimental data and to develop a predictor of pilot behavior. An experiment was conducted to quantify the effects of changing aircraft dynamics on an operator s ability to track a signal in order to eventually model a pilot adapting to changing aircraft dynamics. A gradient descent estimator and a least squares estimator with exponential forgetting used these data to predict pilot stick input. The results indicate that individual pilot characteristics and vehicle dynamics did not affect the accuracy of either estimator method to estimate pilot stick input. These methods also were able to predict pilot stick input during changing aircraft dynamics and they may have the capability to detect a change in a subject due to workload, engagement, etc., or the effects of changes in vehicle dynamics on the pilot.
Sakaguchi, Yutaka; Takano, Mitsuo
2004-09-01
This article proposes an adaptive action-selection method for a model-free reinforcement learning system, based on the concept of the 'reliability of internal prediction/estimation'. This concept is realized using an internal variable, called the Reliability Index (RI), which estimates the accuracy of the internal estimator. We define this index for a value function of a temporal difference learning system and substitute it for the temperature parameter of the Boltzmann action-selection rule. Accordingly, the weight of exploratory actions adaptively changes depending on the uncertainty of the prediction. We use this idea for tabular and weighted-sum type value functions. Moreover, we use the RI to adjust the learning coefficient in addition to the temperature parameter, meaning that the reliability becomes a general basis for meta-learning. Numerical experiments were performed to examine the behavior of the proposed method. The RI-based Q-learning system demonstrated its features when the adaptive learning coefficient and large RI-discount rate (which indicate how the RI values of future states are reflected in the RI value of the current state) were introduced. Statistical tests confirmed that the algorithm spent more time exploring in the initial phase of learning, but accelerated learning from the midpoint of learning. It is also shown that the proposed method does not work well with the actor-critic models. The limitations of the proposed method and its relationship to relevant research are discussed. PMID:15312837
State estimation for autopilot control of small unmanned aerial vehicles in windy conditions
NASA Astrophysics Data System (ADS)
Poorman, David Paul
The use of small unmanned aerial vehicles (UAVs) both in the military and civil realms is growing. This is largely due to the proliferation of inexpensive sensors and the increase in capability of small computers that has stemmed from the personal electronic device market. Methods for performing accurate state estimation for large scale aircraft have been well known and understood for decades, which usually involve a complex array of expensive high accuracy sensors. Performing accurate state estimation for small unmanned aircraft is a newer area of study and often involves adapting known state estimation methods to small UAVs. State estimation for small UAVs can be more difficult than state estimation for larger UAVs due to small UAVs employing limited sensor suites due to cost, and the fact that small UAVs are more susceptible to wind than large aircraft. The purpose of this research is to evaluate the ability of existing methods of state estimation for small UAVs to accurately capture the states of the aircraft that are necessary for autopilot control of the aircraft in a Dryden wind field. The research begins by showing which aircraft states are necessary for autopilot control in Dryden wind. Then two state estimation methods that employ only accelerometer, gyro, and GPS measurements are introduced. The first method uses assumptions on aircraft motion to directly solve for attitude information and smooth GPS data, while the second method integrates sensor data to propagate estimates between GPS measurements and then corrects those estimates with GPS information. The performance of both methods is analyzed with and without Dryden wind, in straight and level flight, in a coordinated turn, and in a wings level ascent. It is shown that in zero wind, the first method produces significant steady state attitude errors in both a coordinated turn and in a wings level ascent. In Dryden wind, it produces large noise on the estimates for its attitude states, and has a non
Error estimation and adaptive order nodal method for solving multidimensional transport problems
Zamonsky, O.M.; Gho, C.J.; Azmy, Y.Y.
1998-01-01
The authors propose a modification of the Arbitrarily High Order Transport Nodal method whereby they solve each node and each direction using different expansion order. With this feature and a previously proposed a posteriori error estimator they develop an adaptive order scheme to automatically improve the accuracy of the solution of the transport equation. They implemented the modified nodal method, the error estimator and the adaptive order scheme into a discrete-ordinates code for solving monoenergetic, fixed source, isotropic scattering problems in two-dimensional Cartesian geometry. They solve two test problems with large homogeneous regions to test the adaptive order scheme. The results show that using the adaptive process the storage requirements are reduced while preserving the accuracy of the results.
Estimated use of water in the United States in 1995
Solley, Wayne B.; Pierce, Robert R.; Perlman, Howard A.
1998-01-01
The purpose of this report is to present consistent and current water-use estimates by state and water-resources region for the United States, Puerto Rico, the U.S. Virgin Islands, and the District of Columbia. Estimates of water withdrawn from surface- and ground-water sources, estimates of consumptive use, and estimates of instream use and wastewater releases during 1995 are presented in this report. This report discusses eight categories of offstream water use--public supply, domestic, commercial, irrigation, livestock, industrial, mining, and thermoelectric power--and one category of instream use: hydroelectric power.
Power system state estimation for a spacecraft power system
NASA Technical Reports Server (NTRS)
Berry, F. C.; Benitez, N. L.; Cox, M. D.
1990-01-01
An application of the maximum likelihood state estimator to a space-based power system is presented. The state estimator uses current and voltage measurements to generate estimates of node voltages for an electrical power distribution system for the Space Shuttle. Preliminary results on the effect of noisy measurements on estimated parameters are reported. The software used in generating these results is part of an overall package being developed at Louisiana Tech University. Intended applications of this package include the analysis of power systems and real-time parallel processing on the Space Shuttle.
Nonlinear State Estimation and Modeling of a Helicopter UAV
NASA Astrophysics Data System (ADS)
Barczyk, Martin
Experimentally-validated nonlinear flight control of a helicopter UAV has two necessary conditions: an estimate of the vehicle’s states from noisy multirate output measurements, and a nonlinear dynamics model with minimum complexity, physically controllable inputs and experimentally identified parameter values. This thesis addresses both these objectives for the Applied Nonlinear Controls Lab (ANCL)'s helicopter UAV project. A magnetometer-plus-GPS aided Inertial Navigation System (INS) for outdoor flight as well as an Attitude and Heading Reference System (AHRS) for indoor testing are designed, implemented and experimentally validated employing an Extended Kalman Filter (EKF), using a novel calibration technique for the magnetometer aiding sensor added to remove the limitations of an earlier GPS-only aiding design. Next the recently-developed nonlinear observer design methodology of invariant observers is adapted to the aided INS and AHRS examples, employing a rotation matrix representation for the state manifold to obtain designs amenable to global stability analysis, obtaining a direct nonlinear design for gains of the AHRS observer, modifying the previously-proposed Invariant EKF systematic method for computing gains, and culminating in simulation and experimental validation of the observers. Lastly a nonlinear control-oriented model of the helicopter UAV is derived from first principles, using a rigid-body dynamics formulation augmented with models of the on-board subsystems: main rotor forces and blade flapping dynamics, the Bell-Hiller system and flybar flapping dynamics, tail rotor forces, tail gyro unit, engine and rotor speed, servo operation, fuselage drag, and tail stabilizer forces. The parameter values in the resulting models are identified experimentally. Using these the model is further simplified to be tractable for model-based control design.
Mode estimation and adaptive feedforward control for stabilization of a flexible gun tube
NASA Astrophysics Data System (ADS)
Vandegrift, Mark W.; DiRenzo, Michael T.
1998-07-01
In this paper we describe an approach for designing a pointing and stabilization system for an unbalanced, flexible gun. Our approach is based upon classical control techniques as well as system identification and adaptive feedforward techniques. Adaptive algorithms identify the flexible modes of the system and estimate the dynamics unbalance. This information is used to update the control law in order to improve the stabilization accuracy of the system.
Distributed Dynamic State Estimation with Extended Kalman Filter
Du, Pengwei; Huang, Zhenyu; Sun, Yannan; Diao, Ruisheng; Kalsi, Karanjit; Anderson, Kevin K.; Li, Yulan; Lee, Barry
2011-08-04
Increasing complexity associated with large-scale renewable resources and novel smart-grid technologies necessitates real-time monitoring and control. Our previous work applied the extended Kalman filter (EKF) with the use of phasor measurement data (PMU) for dynamic state estimation. However, high computation complexity creates significant challenges for real-time applications. In this paper, the problem of distributed dynamic state estimation is investigated. One domain decomposition method is proposed to utilize decentralized computing resources. The performance of distributed dynamic state estimation is tested on a 16-machine, 68-bus test system.
NASA Astrophysics Data System (ADS)
Dat, Tran Huy; Takeda, Kazuya; Itakura, Fumitada
We present a multichannel speech enhancement method based on MAP speech spectral magnitude estimation using a generalized gamma model of speech prior distribution, where the model parameters are adapted from actual noisy speech in a frame-by-frame manner. The utilization of a more general prior distribution with its online adaptive estimation is shown to be effective for speech spectral estimation in noisy environments. Furthermore, the multi-channel information in terms of cross-channel statistics are shown to be useful to better adapt the prior distribution parameters to the actual observation, resulting in better performance of speech enhancement algorithm. We tested the proposed algorithm in an in-car speech database and obtained significant improvements of the speech recognition performance, particularly under non-stationary noise conditions such as music, air-conditioner and open window.
NASA Astrophysics Data System (ADS)
Hu, Chaofang; Gao, Zhifei; Ren, Yanli; Liu, Yunbing
2016-11-01
In this paper, a reusable launch vehicle (RLV) attitude control problem with actuator faults is addressed via the robust adaptive nonlinear fault-tolerant control (FTC) with norm estimation. Firstly, the accurate tracking task of attitude angles in the presence of parameter uncertainties and external disturbances is considered. A fault-free controller is proposed using dynamic surface control (DSC) combined with fuzzy adaptive approach. Furthermore, the minimal learning parameter strategy via norm estimation technique is introduced to reduce the multi-parameter adaptive computation burden of fuzzy approximation of the lump uncertainties. Secondly, a compensation controller is designed to handle the partial loss fault of actuator effectiveness. The unknown maximum eigenvalue of actuator efficiency loss factors is estimated online. Moreover, stability analysis guarantees that all signals of the closed-loop control system are semi-global uniformly ultimately bounded. Finally, illustrative simulations show the effectiveness of the proposed method.
Masud, Muhammad Mehedi; Junsheng, Ha; Akhtar, Rulia; Al-Amin, Abul Quasem; Kari, Fatimah Binti
2015-02-01
This paper estimates Malaysian farmers' willingness to pay (WTP) for a planned adaptation programme for addressing climate issues in the Malaysian agricultural sector. We used the contingent valuation method (CVM) for a monetary valuation of farmers' preferences for a planned adaptation programme by ascertaining the value attached to address climatic issues in the Malaysian agricultural sector. Structured questionnaires were distributed among the sampled farmers. The study found that 74 % of respondents were willing to pay for a planned adaptation programme and that several socioeconomic and motivation factors have greater influence on their WTP. This paper clearly specifies the steps needed for all institutional bodies to better address issues in climate change. The outcomes of this paper will support policy makers to better design an efficient adaptation framework for adapting to the adverse impacts of climate change. PMID:25632900
Masud, Muhammad Mehedi; Junsheng, Ha; Akhtar, Rulia; Al-Amin, Abul Quasem; Kari, Fatimah Binti
2015-02-01
This paper estimates Malaysian farmers' willingness to pay (WTP) for a planned adaptation programme for addressing climate issues in the Malaysian agricultural sector. We used the contingent valuation method (CVM) for a monetary valuation of farmers' preferences for a planned adaptation programme by ascertaining the value attached to address climatic issues in the Malaysian agricultural sector. Structured questionnaires were distributed among the sampled farmers. The study found that 74 % of respondents were willing to pay for a planned adaptation programme and that several socioeconomic and motivation factors have greater influence on their WTP. This paper clearly specifies the steps needed for all institutional bodies to better address issues in climate change. The outcomes of this paper will support policy makers to better design an efficient adaptation framework for adapting to the adverse impacts of climate change.
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-01-01
Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback-Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. PMID:27249002
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-01-01
Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback–Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. PMID:27249002
Multiscale video compression using adaptive finite-state vector quantization
NASA Astrophysics Data System (ADS)
Kwon, Heesung; Venkatraman, Mahesh; Nasrabadi, Nasser M.
1998-10-01
We investigate the use of vector quantizers (VQs) with memory to encode image sequences. A multiscale video coding technique using adaptive finite-state vector quantization (FSVQ) is presented.In this technique, a small codebook (subcodebook) is generated for each input vector from a much larger codebook (supercodebook) by the selection (through a reordering procedure) of a set of appropriate codevectors that is the best representative of the input vector. Therefore, the subcodebook dynamically adapts to the characteristics of the motion-compensated frame difference signal. Several reordering procedures are introduced, and their performance is evaluated. In adaptive FSVQ, two different methods, predefined thresholding and rate- distortion cost optimization, are used to decide between the supercodebook and subcodebook for encoding a given input vector. A cache-based vector quantizer, a form of adaptive FSVQ, is also presented for very-low-bit-rate video coding. An efficient bit-allocation strategy using quadtree decomposition is used with the cache-based VQ to compress the video signal. The proposed video codec outperforms H.263 in terms of the peak signal-to-noise ratio and perceptual quality at very low bit rates, ranging from 5 to 20 kbps. The picture quality of the proposed video codec is a significant improvement over previous codecs, in terms of annoying distortions (blocking artifacts and mosquito noises), and is comparable to that of recently developed wavelet-based video codecs. This similarity in picture quality can be explained by the fact that the proposed video codex uses multiscale segmentation and subsequent variable- rate coding, which are conceptually similar to wavelet-based coding techniques. The simplicity of the encoder and decoder of the proposed codec makes it more suitable than wavelet- based coding for real-time, very-low-bit rate video applications.
Empirical State Error Covariance Matrix for Batch Estimation
NASA Technical Reports Server (NTRS)
Frisbee, Joe
2015-01-01
State estimation techniques effectively provide mean state estimates. However, the theoretical state error covariance matrices provided as part of these techniques often suffer from a lack of confidence in their ability to describe the uncertainty in the estimated states. By a reinterpretation of the equations involved in the weighted batch least squares algorithm, it is possible to directly arrive at an empirical state error covariance matrix. The proposed empirical state error covariance matrix will contain the effect of all error sources, known or not. This empirical error covariance matrix may be calculated as a side computation for each unique batch solution. Results based on the proposed technique will be presented for a simple, two observer and measurement error only problem.
National intelligence estimates and the Failed State Index.
Voracek, Martin
2013-10-01
Across 177 countries around the world, the Failed State Index, a measure of state vulnerability, was reliably negatively associated with the estimates of national intelligence. Psychometric analysis of the Failed State Index, compounded of 12 social, economic, and political indicators, suggested factorial unidimensionality of this index. The observed correspondence of higher national intelligence figures to lower state vulnerability might arise through these two macro-level variables possibly being proxies of even more pervasive historical and societal background variables that affect both.
Adaptive algorithm for cloud cover estimation from all-sky images over the sea
NASA Astrophysics Data System (ADS)
Krinitskiy, M. A.; Sinitsyn, A. V.
2016-05-01
A new algorithm for cloud cover estimation has been formulated and developed based on the synthetic control index, called the grayness rate index, and an additional algorithm step of adaptive filtering of the Mie scattering contribution. A setup for automated cloud cover estimation has been designed, assembled, and tested under field conditions. The results shows a significant advantage of the new algorithm over currently commonly used procedures.
NASA Astrophysics Data System (ADS)
Bo, Yizhou; Shifa, Naima
2013-09-01
An estimator for finding the abundance of a rare, clustered and mobile population has been introduced. This model is based on adaptive cluster sampling (ACS) to identify the location of the population and negative binomial distribution to estimate the total in each site. To identify the location of the population we consider both sampling with replacement (WR) and sampling without replacement (WOR). Some mathematical properties of the model are also developed.
LACIE large area acreage estimation. [United States of America
NASA Technical Reports Server (NTRS)
Chhikara, R. S.; Feiveson, A. H. (Principal Investigator)
1979-01-01
A sample wheat acreage for a large area is obtained by multiplying its small grains acreage estimate as computed by the classification and mensuration subsystem by the best available ratio of wheat to small grains acreages obtained from historical data. In the United States, as in other countries with detailed historical data, an additional level of aggregation was required because sample allocation was made at the substratum level. The essential features of the estimation procedure for LACIE countries are included along with procedures for estimating wheat acreage in the United States.
A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops
NASA Astrophysics Data System (ADS)
Lu, Hai-liang; Wen, Xi-shan; Lan, Lei; An, Yun-zhu; Li, Xiao-ping
2015-01-01
A self-adaptive genetic algorithm for estimating Jiles-Atherton (JA) magnetic hysteresis model parameters is presented. The fitness function is established based on the distances between equidistant key points of normalized hysteresis loops. Linearity function and logarithm function are both adopted to code the five parameters of JA model. Roulette wheel selection is used and the selection pressure is adjusted adaptively by deducting a proportional which depends on current generation common value. The Crossover operator is established by combining arithmetic crossover and multipoint crossover. Nonuniform mutation is improved by adjusting the mutation ratio adaptively. The algorithm is used to estimate the parameters of one kind of silicon-steel sheet's hysteresis loops, and the results are in good agreement with published data.
Contributions to Adaptive Educational Hypermedia Systems via On-Line Learning Style Estimation
ERIC Educational Resources Information Center
Botsios, Sotiris; Georgiou, Demetrius; Safouris, Nikolaos
2008-01-01
In order to establish an online diagnostic system for Learning Style Estimation that contributes to the adaptation of learning objects, we propose an easily applicable expert system founded on Bayesian Networks. The proposed system makes use of Learning Style theories and associated diagnostic techniques, simultaneously avoiding certain error…
ERIC Educational Resources Information Center
Sahin, Alper; Weiss, David J.
2015-01-01
This study aimed to investigate the effects of calibration sample size and item bank size on examinee ability estimation in computerized adaptive testing (CAT). For this purpose, a 500-item bank pre-calibrated using the three-parameter logistic model with 10,000 examinees was simulated. Calibration samples of varying sizes (150, 250, 350, 500,…
NASA Astrophysics Data System (ADS)
Xia, Xiang-Gen; Wang, Genyuan; Chen, Victor C.
2001-03-01
This paper first reviews some basic properties of the discrete chirp-Fourier transform and then present an adaptive chirp- Fourier transform, a generalization of the amplitude and phase estimation of sinusoids (APES) algorithm proposed by Li and Stoica for sinusoidal signals. We finally applied it to the ISAR imaging of maneuvering targets.
Efforts To Improve Estimates of State and Local Unemployment
ERIC Educational Resources Information Center
Ziegler, Martin
1977-01-01
Describes how local area unemployment statistics are developed by state employment security agencies to provide the Bureau of Labor Statistics with data on the insured unemployed by county of residence. Includes a discussion of the handbook method, a consistent and uniform method of estimating total unemployment for states and areas. (Editor/TA)
A Simplified Estimation of Latent State--Trait Parameters
ERIC Educational Resources Information Center
Hagemann, Dirk; Meyerhoff, David
2008-01-01
The latent state-trait (LST) theory is an extension of the classical test theory that allows one to decompose a test score into a true trait, a true state residual, and an error component. For practical applications, the variances of these latent variables may be estimated with standard methods of structural equation modeling (SEM). These…
Relationships between mood state, time estimation, and selected movement speed.
Naruse, Kumi
2004-10-01
We investigated whether different aspects of mood state influence sense of time estimation and movement speed. Mood states were measured on the Multiple Mood Scale for 142 female undergraduate students, who were then asked to estimate the interval of time elapsed between the words "start" and "stop" spoken by a tester. Next, the same subjects were told to draw circles inside 1-cm squares printed on an A4 size sheet of paper in succession at their freely elected comfortable speed. Scores on Concentration (r=-.22, p<.01) and Being Startled (r=-.26, p<.01) each correlated significantly and negatively with time estimation, while scores on Boredom (r =.17, p <.05) had a significant positive correlation with movement speed. These results suggest that different aspects of mood state have some association with time estimation and selected movement speed. Values account for small common variance. PMID:15560352
NASA Technical Reports Server (NTRS)
Beyon, Jeffrey Y.; Koch, Grady J.
2006-01-01
The signal processing aspect of a 2-m wavelength coherent Doppler lidar system under development at NASA Langley Research Center in Virginia is investigated in this paper. The lidar system is named VALIDAR (validation lidar) and its signal processing program estimates and displays various wind parameters in real-time as data acquisition occurs. The goal is to improve the quality of the current estimates such as power, Doppler shift, wind speed, and wind direction, especially in low signal-to-noise-ratio (SNR) regime. A novel Nonlinear Adaptive Doppler Shift Estimation Technique (NADSET) is developed on such behalf and its performance is analyzed using the wind data acquired over a long period of time by VALIDAR. The quality of Doppler shift and power estimations by conventional Fourier-transform-based spectrum estimation methods deteriorates rapidly as SNR decreases. NADSET compensates such deterioration in the quality of wind parameter estimates by adaptively utilizing the statistics of Doppler shift estimate in a strong SNR range and identifying sporadic range bins where good Doppler shift estimates are found. The authenticity of NADSET is established by comparing the trend of wind parameters with and without NADSET applied to the long-period lidar return data.
NASA Astrophysics Data System (ADS)
Alvey, Brendan; Zare, Alina; Cook, Matthew; Ho, Dominic K. C.
2016-05-01
The adaptive coherence estimator (ACE) estimates the squared cosine of the angle between a known target vector and a sample vector in a transformed coordinate space. The space is transformed according to an estimation of the background statistics, which directly effects the performance of the statistic as a target detector. In this paper, the ACE detection statistic is used to detect buried explosive hazards with data from a Wideband Electromagnetic Induction (WEMI) sensor. Target signatures are based on a dictionary defined using a Discrete Spectrum of Relaxation Frequencies (DSRF) model. Results are summarized as a receiver operator curve (ROC) and compared to other leading methods.
F-8C adaptive flight control extensions. [for maximum likelihood estimation
NASA Technical Reports Server (NTRS)
Stein, G.; Hartmann, G. L.
1977-01-01
An adaptive concept which combines gain-scheduled control laws with explicit maximum likelihood estimation (MLE) identification to provide the scheduling values is described. The MLE algorithm was improved by incorporating attitude data, estimating gust statistics for setting filter gains, and improving parameter tracking during changing flight conditions. A lateral MLE algorithm was designed to improve true air speed and angle of attack estimates during lateral maneuvers. Relationships between the pitch axis sensors inherent in the MLE design were examined and used for sensor failure detection. Design details and simulation performance are presented for each of the three areas investigated.
Quantum phase estimation using path-symmetric entangled states
Lee, Su-Yong; Lee, Chang-Woo; Lee, Jaehak; Nha, Hyunchul
2016-01-01
We study the sensitivity of phase estimation using a generic class of path-symmetric entangled states |φ〉|0〉 + |0〉|φ〉, where an arbitrary state |φ〉 occupies one of two modes in quantum superposition. With this generalization, we identify the fundamental limit of phase estimation under energy constraint that is characterized by the photon statistics of the component state |φ〉. We show that quantum Cramer-Rao bound (QCRB) can be indefinitely lowered with super-Poissonianity of the state |φ〉. For possible measurement schemes, we demonstrate that a full photon-counting employing the path-symmetric entangled states achieves the QCRB over the entire range [0, 2π] of unknown phase shift ϕ whereas a parity measurement does so in a certain confined range of ϕ. By introducing a component state of the form , we particularly show that an arbitrarily small QCRB can be achieved even with a finite energy in an ideal situation. This component state also provides the most robust resource against photon loss among considered entangled states over the range of the average input energy Nav > 1. Finally we propose experimental schemes to generate these path-symmetric entangled states for phase estimation. PMID:27457267
Kwon, Oh-Sang; Knill, David C
2013-03-12
Because of uncertainty and noise, the brain should use accurate internal models of the statistics of objects in scenes to interpret sensory signals. Moreover, the brain should adapt its internal models to the statistics within local stimulus contexts. Consider the problem of hitting a baseball. The impoverished nature of the visual information available makes it imperative that batters use knowledge of the temporal statistics and history of previous pitches to accurately estimate pitch speed. Using a laboratory analog of hitting a baseball, we tested the hypothesis that the brain uses adaptive internal models of the statistics of object speeds to plan hand movements to intercept moving objects. We fit Bayesian observer models to subjects' performance to estimate the statistical environments in which subjects' performance would be ideal and compared the estimated statistics with the true statistics of stimuli in an experiment. A first experiment showed that subjects accurately estimated and used the variance of object speeds in a stimulus set to time hitting behavior but also showed serial biases that are suboptimal for stimuli that were uncorrelated over time. A second experiment showed that the strength of the serial biases depended on the temporal correlations within a stimulus set, even when the biases were estimated from uncorrelated stimulus pairs subsampled from the larger set. Taken together, the results show that subjects adapted their internal models of the variance and covariance of object speeds within a stimulus set to plan interceptive movements but retained a bias to positive correlations.
Estimating Model Parameters of Adaptive Software Systems in Real-Time
NASA Astrophysics Data System (ADS)
Kumar, Dinesh; Tantawi, Asser; Zhang, Li
Adaptive software systems have the ability to adapt to changes in workload and execution environment. In order to perform resource management through model based control in such systems, an accurate mechanism for estimating the software system's model parameters is required. This paper deals with real-time estimation of a performance model for adaptive software systems that process multiple classes of transactional workload. First, insights in to the static performance model estimation problem are provided. Then an Extended Kalman Filter (EKF) design is combined with an open queueing network model to dynamically estimate the model parameters in real-time. Specific problems that are encountered in the case of multiple classes of workload are analyzed. These problems arise mainly due to the under-deterministic nature of the estimation problem. This motivates us to propose a modified design of the filter. Insights for choosing tuning parameters of the modified design, i.e., number of constraints and sampling intervals are provided. The modified filter design is shown to effectively tackle problems with multiple classes of workload through experiments.
Kwon, Oh-Sang; Knill, David C
2013-03-12
Because of uncertainty and noise, the brain should use accurate internal models of the statistics of objects in scenes to interpret sensory signals. Moreover, the brain should adapt its internal models to the statistics within local stimulus contexts. Consider the problem of hitting a baseball. The impoverished nature of the visual information available makes it imperative that batters use knowledge of the temporal statistics and history of previous pitches to accurately estimate pitch speed. Using a laboratory analog of hitting a baseball, we tested the hypothesis that the brain uses adaptive internal models of the statistics of object speeds to plan hand movements to intercept moving objects. We fit Bayesian observer models to subjects' performance to estimate the statistical environments in which subjects' performance would be ideal and compared the estimated statistics with the true statistics of stimuli in an experiment. A first experiment showed that subjects accurately estimated and used the variance of object speeds in a stimulus set to time hitting behavior but also showed serial biases that are suboptimal for stimuli that were uncorrelated over time. A second experiment showed that the strength of the serial biases depended on the temporal correlations within a stimulus set, even when the biases were estimated from uncorrelated stimulus pairs subsampled from the larger set. Taken together, the results show that subjects adapted their internal models of the variance and covariance of object speeds within a stimulus set to plan interceptive movements but retained a bias to positive correlations. PMID:23440185
Estimating annualized earthquake losses for the conterminous United States
Jaiswal, Kishor S.; Bausch, Douglas; Chen, Rui; Bouabid, Jawhar; Seligson, Hope
2015-01-01
We make use of the most recent National Seismic Hazard Maps (the years 2008 and 2014 cycles), updated census data on population, and economic exposure estimates of general building stock to quantify annualized earthquake loss (AEL) for the conterminous United States. The AEL analyses were performed using the Federal Emergency Management Agency's (FEMA) Hazus software, which facilitated a systematic comparison of the influence of the 2014 National Seismic Hazard Maps in terms of annualized loss estimates in different parts of the country. The losses from an individual earthquake could easily exceed many tens of billions of dollars, and the long-term averaged value of losses from all earthquakes within the conterminous U.S. has been estimated to be a few billion dollars per year. This study estimated nationwide losses to be approximately $4.5 billion per year (in 2012$), roughly 80% of which can be attributed to the States of California, Oregon and Washington. We document the change in estimated AELs arising solely from the change in the assumed hazard map. The change from the 2008 map to the 2014 map results in a 10 to 20% reduction in AELs for the highly seismic States of the Western United States, whereas the reduction is even more significant for Central and Eastern United States.
Estimation of State Transition Probabilities: A Neural Network Model
NASA Astrophysics Data System (ADS)
Saito, Hiroshi; Takiyama, Ken; Okada, Masato
2015-12-01
Humans and animals can predict future states on the basis of acquired knowledge. This prediction of the state transition is important for choosing the best action, and the prediction is only possible if the state transition probability has already been learned. However, how our brains learn the state transition probability is unknown. Here, we propose a simple algorithm for estimating the state transition probability by utilizing the state prediction error. We analytically and numerically confirmed that our algorithm is able to learn the probability completely with an appropriate learning rate. Furthermore, our learning rule reproduced experimentally reported psychometric functions and neural activities in the lateral intraparietal area in a decision-making task. Thus, our algorithm might describe the manner in which our brains learn state transition probabilities and predict future states.
Rankings & Estimates: Rankings of the States 2010 and Estimates of School Statistics 2011
ERIC Educational Resources Information Center
National Education Association Research Department, 2010
2010-01-01
The data presented in this combined report--"Rankings & Estimates"--provide facts about the extent to which local, state, and national governments commit resources to public education. As one might expect in a nation as diverse as the United States--with respect to economics, geography, and politics--the level of commitment to education varies on…
Development of the One-Sided Nonlinear Adaptive Doppler Shift Estimation
NASA Technical Reports Server (NTRS)
Beyon, Jeffrey Y.; Koch, Grady J.; Singh, Upendra N.; Kavaya, Michael J.; Serror, Judith A.
2009-01-01
The new development of a one-sided nonlinear adaptive shift estimation technique (NADSET) is introduced. The background of the algorithm and a brief overview of NADSET are presented. The new technique is applied to the wind parameter estimates from a 2-micron wavelength coherent Doppler lidar system called VALIDAR located in NASA Langley Research Center in Virginia. The new technique enhances wind parameters such as Doppler shift and power estimates in low Signal-To-Noise-Ratio (SNR) regimes using the estimates in high SNR regimes as the algorithm scans the range bins from low to high altitude. The original NADSET utilizes the statistics in both the lower and the higher range bins to refine the wind parameter estimates in between. The results of the two different approaches of NADSET are compared.
Quantum state tomography and fidelity estimation via Phaselift
Lu, Yiping; Liu, Huan; Zhao, Qing
2015-09-15
Experiments of multi-photon entanglement have been performed by several groups. Obviously, an increase on the photon number for fidelity estimation and quantum state tomography causes a dramatic increase in the elements of the positive operator valued measures (POVMs), which results in a great consumption of time in measurements. In practice, we wish to obtain a good estimation of fidelity and quantum states through as few measurements as possible for multi-photon entanglement. Phaselift provides such a chance to estimate fidelity for entangling states based on less data. In this paper, we would like to show how the Phaselift works for six qubits in comparison to the data given by Pan’s group, i.e., we use a fraction of the data as input to estimate the rest of the data through the obtained density matrix, and thus goes beyond the simple fidelity analysis. The fidelity bound is also provided for general Schrödinger Cat state. Based on the fidelity bound, we propose an optimal measurement approach which could both reduce the copies and keep the fidelity bound gap small. The results demonstrate that the Phaselift can help decrease the measured elements of POVMs for six qubits. Our conclusion is based on the prior knowledge that a pure state is the target state prepared by experiments.
Gu, Yuanyuan; Norman, Richard; Viney, Rosalie
2014-09-01
Using discrete choice experiments (DCEs) to estimate health state utility values has become an important alternative to the conventional methods of Time Trade-Off and Standard Gamble. Studies using DCEs have typically used the conditional logit to estimate the underlying utility function. The conditional logit is known for several limitations. In this paper, we propose two types of models based on the mixed logit: one using preference space and the other using quality-adjusted life year (QALY) space, a concept adapted from the willingness-to-pay literature. These methods are applied to a dataset collected using the EQ-5D. The results showcase the advantages of using QALY space and demonstrate that the preferred QALY space model provides lower estimates of the utility values than the conditional logit, with the divergence increasing with worsening health states.
A Testbed for Deploying Distributed State Estimation in Power Grid
Jin, Shuangshuang; Chen, Yousu; Rice, Mark J.; Liu, Yan; Gorton, Ian
2012-07-22
Abstract—With the increasing demand, scale and data information of power systems, fast distributed applications are becoming more important in power system operation and control. This paper proposes a testbed for evaluating power system distributed applications, considering data exchange among distributed areas. A high-performance computing (HPC) version of distributed state estimation is implemented and used as a distributed application example. The IEEE 118-bus system is used to deploy the parallel distributed state estimation, and the MeDICi middleware is used for data communication. The performance of the testbed demonstrates its capability to evaluate parallel distributed state estimation by leveraging the HPC paradigm. This testbed can also be applied to evaluate other distributed applications.
Robust state estimation for neural networks with discontinuous activations.
Liu, Xiaoyang; Cao, Jinde
2010-12-01
Discontinuous dynamical systems, particularly neural networks with discontinuous activation functions, arise in a number of applications and have received considerable research attention in recent years. In this paper, the robust state estimation problem is investigated for uncertain neural networks with discontinuous activations and time-varying delays, where the neuron-dependent nonlinear disturbance on the network outputs are only assumed to satisfy the local Lipschitz condition. Based on the theory of differential inclusions and nonsmooth analysis, several criteria are presented to guarantee the existence of the desired robust state estimator for the discontinuous neural networks. It is shown that the design of the state estimator for such networks can be achieved by solving some linear matrix inequalities, which are dependent on the size of the time derivative of the time-varying delays. Finally, numerical examples are given to illustrate the theoretical results.
Using State Estimation Residuals to Detect Abnormal SCADA Data
Ma, Jian; Chen, Yousu; Huang, Zhenyu; Wong, Pak C.
2010-04-30
Detection of abnormal supervisory control and data acquisition (SCADA) data is critically important for safe and secure operation of modern power systems. In this paper, a methodology of abnormal SCADA data detection based on state estimation residuals is presented. Preceded with a brief overview of outlier detection methods and bad SCADA data detection for state estimation, the framework of the proposed methodology is described. Instead of using original SCADA measurements as the bad data sources, the residuals calculated based on the results of the state estimator are used as the input for the outlier detection algorithm. The BACON algorithm is applied to the outlier detection task. The IEEE 118-bus system is used as a test base to evaluate the effectiveness of the proposed methodology. The accuracy of the BACON method is compared with that of the 3-σ method for the simulated SCADA measurements and residuals.
Using State Estimation Residuals to Detect Abnormal SCADA Data
Ma, Jian; Chen, Yousu; Huang, Zhenyu; Wong, Pak C.
2010-06-14
Detection of manipulated supervisory control and data acquisition (SCADA) data is critically important for the safe and secure operation of modern power systems. In this paper, a methodology of detecting manipulated SCADA data based on state estimation residuals is presented. A framework of the proposed methodology is described. Instead of using original SCADA measurements as the bad data sources, the residuals calculated based on the results of the state estimator are used as the input for the outlier detection process. The BACON algorithm is applied to detect outliers in the state estimation residuals. The IEEE 118-bus system is used as a test case to evaluate the effectiveness of the proposed methodology. The accuracy of the BACON method is compared with that of the 3-σ method for the simulated SCADA measurements and residuals.
2013-01-01
Background Elective patient admission and assignment planning is an important task of the strategic and operational management of a hospital and early on became a central topic of clinical operations research. The management of hospital beds is an important subtask. Various approaches have been proposed, involving the computation of efficient assignments with regard to the patients’ condition, the necessity of the treatment, and the patients’ preferences. However, these approaches are mostly based on static, unadaptable estimates of the length of stay and, thus, do not take into account the uncertainty of the patient’s recovery. Furthermore, the effect of aggregated bed capacities have not been investigated in this context. Computer supported bed management, combining an adaptable length of stay estimation with the treatment of shared resources (aggregated bed capacities) has not yet been sufficiently investigated. The aim of our work is: 1) to define a cost function for patient admission taking into account adaptable length of stay estimations and aggregated resources, 2) to define a mathematical program formally modeling the assignment problem and an architecture for decision support, 3) to investigate four algorithmic methodologies addressing the assignment problem and one base-line approach, and 4) to evaluate these methodologies w.r.t. cost outcome, performance, and dismissal ratio. Methods The expected free ward capacity is calculated based on individual length of stay estimates, introducing Bernoulli distributed random variables for the ward occupation states and approximating the probability densities. The assignment problem is represented as a binary integer program. Four strategies for solving the problem are applied and compared: an exact approach, using the mixed integer programming solver SCIP; and three heuristic strategies, namely the longest expected processing time, the shortest expected processing time, and random choice. A baseline approach
State estimation for anaerobic digesters using the ADM1.
Gaida, D; Wolf, C; Meyer, C; Stuhlsatz, A; Lippel, J; Bäck, T; Bongards, M; McLoone, S
2012-01-01
The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative control and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal control and operating decisions according to the actual state of a plant. In this paper such a state estimator is developed using a calibrated simulation model of a full-scale biogas plant, which is based on the Anaerobic Digestion Model No.1. The use of advanced pattern recognition methods shows that model states can be predicted from basic online measurements such as biogas production, CH4 and CO2 content in the biogas, pH value and substrate feed volume of known substrates. The machine learning methods used are trained and evaluated using synthetic data created with the biogas plant model simulating over a wide range of possible plant operating regions. Results show that the operating state vector of the modelled anaerobic digestion process can be predicted with an overall accuracy of about 90%. This facilitates the application of state-based optimization and control algorithms on full-scale biogas plants and therefore fosters the production of eco-friendly energy from biomass.
Support vector based battery state of charge estimator
NASA Astrophysics Data System (ADS)
Hansen, Terry; Wang, Chia-Jiu
This paper investigates the use of a support vector machine (SVM) to estimate the state-of-charge (SOC) of a large-scale lithium-ion-polymer (LiP) battery pack. The SOC of a battery cannot be measured directly and must be estimated from measurable battery parameters such as current and voltage. The coulomb counting SOC estimator has been used in many applications but it has many drawbacks [S. Piller, M. Perrin, Methods for state-of-charge determination and their application, J. Power Sources 96 (2001) 113-120]. The proposed SVM based solution not only removes the drawbacks of the coulomb counting SOC estimator but also produces accurate SOC estimates, using industry standard US06 [V.H. Johnson, A.A. Pesaran, T. Sack, Temperature-dependent battery models for high-power lithium-ion batteries, in: Presented at the 17th Annual Electric Vehicle Symposium Montreal, Canada, October 15-18, 2000. The paper is downloadable at website http://www.nrel.gov/docs/fy01osti/28716.pdf] aggressive driving cycle test procedures. The proposed SOC estimator extracts support vectors from a battery operation history then uses only these support vectors to estimate SOC, resulting in minimal computation load and suitable for real-time embedded system applications.
ERIC Educational Resources Information Center
Yang, Xiangdong; Poggio, John C.; Glasnapp, Douglas R.
2006-01-01
The effects of five ability estimators, that is, maximum likelihood estimator, weighted likelihood estimator, maximum a posteriori, expected a posteriori, and Owen's sequential estimator, on the performances of the item response theory-based adaptive classification procedure on multiple categories were studied via simulations. The following…
Demographic estimation methods for plants with unobservable life-states
Kery, M.; Gregg, K.B.; Schaub, M.
2005-01-01
Demographic estimation of vital parameters in plants with an unobservable dormant state is complicated, because time of death is not known. Conventional methods assume that death occurs at a particular time after a plant has last been seen aboveground but the consequences of assuming a particular duration of dormancy have never been tested. Capture-recapture methods do not make assumptions about time of death; however, problems with parameter estimability have not yet been resolved. To date, a critical comparative assessment of these methods is lacking. We analysed data from a 10 year study of Cleistes bifaria, a terrestrial orchid with frequent dormancy, and compared demographic estimates obtained by five varieties of the conventional methods, and two capture-recapture methods. All conventional methods produced spurious unity survival estimates for some years or for some states, and estimates of demographic rates sensitive to the time of death assumption. In contrast, capture-recapture methods are more parsimonious in terms of assumptions, are based on well founded theory and did not produce spurious estimates. In Cleistes, dormant episodes lasted for 1-4 years (mean 1.4, SD 0.74). The capture-recapture models estimated ramet survival rate at 0.86 (SE~ 0.01), ranging from 0.77-0.94 (SEs # 0.1) in anyone year. The average fraction dormant was estimated at 30% (SE 1.5), ranging 16 -47% (SEs # 5.1) in anyone year. Multistate capture-recapture models showed that survival rates were positively related to precipitation in the current year, but transition rates were more strongly related to precipitation in the previous than in the current year, with more ramets going dormant following dry years. Not all capture-recapture models of interest have estimable parameters; for instance, without excavating plants in years when they do not appear aboveground, it is not possible to obtain independent timespecific survival estimates for dormant plants. We introduce rigorous
Regularized estimate of the weight vector of an adaptive antenna array
NASA Astrophysics Data System (ADS)
Ermolayev, V. T.; Flaksman, A. G.; Sorokin, I. S.
2013-02-01
We consider an adaptive antenna array (AAA) with the maximum signal-to-noise ratio (SNR) at the output. The antenna configuration is assumed to be arbitrary. A rigorous analytical solution for the optimal weight vector of the AAA is obtained if the input process is defined by the noise correlation matrix and the useful-signal vector. On the basis of this solution, the regularized estimate of the weight vector is derived by using a limited number of input noise samples, which can be either greater or smaller than the number of array elements. Computer simulation results of adaptive signal processing indicate small losses in the SNR compared with the optimal SNR value. It is shown that the computing complexity of the proposed estimate is proportional to the number of noise samples, the number of external noise sources, and the squared number of array elements.
Adaptation strategies for high order discontinuous Galerkin methods based on Tau-estimation
NASA Astrophysics Data System (ADS)
Kompenhans, Moritz; Rubio, Gonzalo; Ferrer, Esteban; Valero, Eusebio
2016-02-01
In this paper three p-adaptation strategies based on the minimization of the truncation error are presented for high order discontinuous Galerkin methods. The truncation error is approximated by means of a τ-estimation procedure and enables the identification of mesh regions that require adaptation. Three adaptation strategies are developed and termed a posteriori, quasi-a priori and quasi-a priori corrected. All strategies require fine solutions, which are obtained by enriching the polynomial order, but while the former needs time converged solutions, the last two rely on non-converged solutions, which lead to faster computations. In addition, the high order method permits the spatial decoupling for the estimated errors and enables anisotropic p-adaptation. These strategies are verified and compared in terms of accuracy and computational cost for the Euler and the compressible Navier-Stokes equations. It is shown that the two quasi-a priori methods achieve a significant reduction in computational cost when compared to a uniform polynomial enrichment. Namely, for a viscous boundary layer flow, we obtain a speedup of 6.6 and 7.6 for the quasi-a priori and quasi-a priori corrected approaches, respectively.
NASA Technical Reports Server (NTRS)
Linares, Irving; Mersereau, Russell M.; Smith, Mark J. T.
1994-01-01
Two representative sample images of Band 4 of the Landsat Thematic Mapper are compressed with the JPEG algorithm at 8:1, 16:1 and 24:1 Compression Ratios for experimental browsing purposes. We then apply the Optimal PSNR Estimated Spectra Adaptive Postfiltering (ESAP) algorithm to reduce the DCT blocking distortion. ESAP reduces the blocking distortion while preserving most of the image's edge information by adaptively postfiltering the decoded image using the block's spectral information already obtainable from each block's DCT coefficients. The algorithm iteratively applied a one dimensional log-sigmoid weighting function to the separable interpolated local block estimated spectra of the decoded image until it converges to the optimal PSNR with respect to the original using a 2-D steepest ascent search. Convergence is obtained in a few iterations for integer parameters. The optimal logsig parameters are transmitted to the decoder as a negligible byte of overhead data. A unique maxima is guaranteed due to the 2-D asymptotic exponential overshoot shape of the surface generated by the algorithm. ESAP is based on a DFT analysis of the DCT basis functions. It is implemented with pixel-by-pixel spatially adaptive separable FIR postfilters. PSNR objective improvements between 0.4 to 0.8 dB are shown together with their corresponding optimal PSNR adaptive postfiltered images.
Adaptive feedforward of estimated ripple improves the closed loop system performance significantly
Kwon, S.; Regan, A.; Wang, Y.M.; Rohlev, T.
1998-12-31
The Low Energy Demonstration Accelerator (LEDA) being constructed at Los Alamos National Laboratory will serve as the prototype for the low energy section of Acceleration Production of Tritium (APT) accelerator. This paper addresses the problem of LLRF control system for LEDA. The authors propose an estimator of the ripple and its time derivative and a control law which is based on PID control and adaptive feedforward of estimated ripple. The control law reduces the effect of the deterministic cathode ripple that is due to high voltage power supply and achieves tracking of desired set points.
NASA Astrophysics Data System (ADS)
Hanachi, Houman; Liu, Jie; Banerjee, Avisekh; Chen, Ying
2016-05-01
Health state estimation of inaccessible components in complex systems necessitates effective state estimation techniques using the observable variables of the system. The task becomes much complicated when the system is nonlinear/non-Gaussian and it receives stochastic input. In this work, a novel sequential state estimation framework is developed based on particle filtering (PF) scheme for state estimation of general class of nonlinear dynamical systems with stochastic input. Performance of the developed framework is then validated with simulation on a Bivariate Non-stationary Growth Model (BNGM) as a benchmark. In the next step, three-year operating data of an industrial gas turbine engine (GTE) are utilized to verify the effectiveness of the developed framework. A comprehensive thermodynamic model for the GTE is therefore developed to formulate the relation of the observable parameters and the dominant degradation symptoms of the turbine, namely, loss of isentropic efficiency and increase of the mass flow. The results confirm the effectiveness of the developed framework for simultaneous estimation of multiple degradation symptoms in complex systems with noisy measured inputs.
Rescue of endemic states in interconnected networks with adaptive coupling
NASA Astrophysics Data System (ADS)
Vazquez, F.; Serrano, M. Ángeles; Miguel, M. San
2016-07-01
We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network -and therefore on the interconnected system- the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime.
Rescue of endemic states in interconnected networks with adaptive coupling
NASA Astrophysics Data System (ADS)
Vazquez, F.; Serrano, M. Ángeles; Miguel, M. San
2016-07-01
We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network –and therefore on the interconnected system– the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime.
Rescue of endemic states in interconnected networks with adaptive coupling
Vazquez, F.; Serrano, M. Ángeles; Miguel, M. San
2016-01-01
We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network –and therefore on the interconnected system– the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime. PMID:27380771
Rescue of endemic states in interconnected networks with adaptive coupling.
Vazquez, F; Serrano, M Ángeles; Miguel, M San
2016-07-06
We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network -and therefore on the interconnected system- the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime.
Optimal PMU Placement Evaluation for Power System Dynamic State Estimation
Zhang, Jinghe; Welch, Greg; Bishop, Gary; Huang, Zhenyu
2010-10-10
Abstract - The synchronized phaor measurements unit (PMU), developed in the 1980s, is concidered to be one of the most important devices in the future of power systems. The recent development of PMU technology provides high-speed, precisely synchronized sensor data, which has been found to be usefule for dynamic, state estimation of power the power grid.
Fault detection in electromagnetic suspension systems with state estimation methods
Sinha, P.K.; Zhou, F.B.; Kutiyal, R.S. . Dept. of Engineering)
1993-11-01
High-speed maglev vehicles need a high level of safety that depends on the whole vehicle system's reliability. There are many ways of attaining high reliability for the system. Conventional method uses redundant hardware with majority vote logic circuits. Hardware redundancy costs more, weigh more and occupy more space than that of analytically redundant methods. Analytically redundant systems use parameter identification and state estimation methods based on the system models to detect and isolate the fault of instruments (sensors), actuator and components. In this paper the authors use the Luenberger observer to estimate three state variables of the electromagnetic suspension system: position (airgap), vehicle velocity, and vertical acceleration. These estimates are compared with the corresponding sensor outputs for fault detection. In this paper, they consider FDI of the accelerometer, the sensor which provides the ride quality.
Multilevel Error Estimation and Adaptive h-Refinement for Cartesian Meshes with Embedded Boundaries
NASA Technical Reports Server (NTRS)
Aftosmis, M. J.; Berger, M. J.; Kwak, Dochan (Technical Monitor)
2002-01-01
This paper presents the development of a mesh adaptation module for a multilevel Cartesian solver. While the module allows mesh refinement to be driven by a variety of different refinement parameters, a central feature in its design is the incorporation of a multilevel error estimator based upon direct estimates of the local truncation error using tau-extrapolation. This error indicator exploits the fact that in regions of uniform Cartesian mesh, the spatial operator is exactly the same on the fine and coarse grids, and local truncation error estimates can be constructed by evaluating the residual on the coarse grid of the restricted solution from the fine grid. A new strategy for adaptive h-refinement is also developed to prevent errors in smooth regions of the flow from being masked by shocks and other discontinuous features. For certain classes of error histograms, this strategy is optimal for achieving equidistribution of the refinement parameters on hierarchical meshes, and therefore ensures grid converged solutions will be achieved for appropriately chosen refinement parameters. The robustness and accuracy of the adaptation module is demonstrated using both simple model problems and complex three dimensional examples using meshes with from 10(exp 6), to 10(exp 7) cells.
Trapping phenomenon of the parameter estimation in asymptotic quantum states
NASA Astrophysics Data System (ADS)
Berrada, K.
2016-09-01
In this paper, we study in detail the behavior of the precision of the parameter estimation in open quantum systems using the quantum Fisher information (QFI). In particular, we study the sensitivity of the estimation on a two-qubit system evolving under Kossakowski-type quantum dynamical semigroups of completely positive maps. In such an environment, the precision of the estimation can even persist asymptotically for different effects of the initial parameters. We find that the QFI can be resistant to the action of the environment with respect to the initial asymptotic states, and it can persist even in the asymptotic long-time regime. In addition, our results provide further evidence that the initial pure and separable mixed states of the input state may enhance quantum metrology. These features make quantum states in this kind of environment a good candidate for the implementation of different schemes of quantum optics and information with high precision. Finally, we show that this quantity may be proposed to detect the amount of the total quantum information that the whole state contains with respect to projective measurements.
Estimation of beryllium ground state energy by Monte Carlo simulation
Kabir, K. M. Ariful; Halder, Amal
2015-05-15
Quantum Monte Carlo method represent a powerful and broadly applicable computational tool for finding very accurate solution of the stationary Schrödinger equation for atoms, molecules, solids and a variety of model systems. Using variational Monte Carlo method we have calculated the ground state energy of the Beryllium atom. Our calculation are based on using a modified four parameters trial wave function which leads to good result comparing with the few parameters trial wave functions presented before. Based on random Numbers we can generate a large sample of electron locations to estimate the ground state energy of Beryllium. Our calculation gives good estimation for the ground state energy of the Beryllium atom comparing with the corresponding exact data.
On-Board Event-Based State Estimation for Trajectory Approaching and Tracking of a Vehicle
Martínez-Rey, Miguel; Espinosa, Felipe; Gardel, Alfredo; Santos, Carlos
2015-01-01
For the problem of pose estimation of an autonomous vehicle using networked external sensors, the processing capacity and battery consumption of these sensors, as well as the communication channel load should be optimized. Here, we report an event-based state estimator (EBSE) consisting of an unscented Kalman filter that uses a triggering mechanism based on the estimation error covariance matrix to request measurements from the external sensors. This EBSE generates the events of the estimator module on-board the vehicle and, thus, allows the sensors to remain in stand-by mode until an event is generated. The proposed algorithm requests a measurement every time the estimation distance root mean squared error (DRMS) value, obtained from the estimator's covariance matrix, exceeds a threshold value. This triggering threshold can be adapted to the vehicle's working conditions rendering the estimator even more efficient. An example of the use of the proposed EBSE is given, where the autonomous vehicle must approach and follow a reference trajectory. By making the threshold a function of the distance to the reference location, the estimator can halve the use of the sensors with a negligible deterioration in the performance of the approaching maneuver. PMID:26102489
On-Board Event-Based State Estimation for Trajectory Approaching and Tracking of a Vehicle.
Martínez-Rey, Miguel; Espinosa, Felipe; Gardel, Alfredo; Santos, Carlos
2015-01-01
For the problem of pose estimation of an autonomous vehicle using networked external sensors, the processing capacity and battery consumption of these sensors, as well as the communication channel load should be optimized. Here, we report an event-based state estimator (EBSE) consisting of an unscented Kalman filter that uses a triggering mechanism based on the estimation error covariance matrix to request measurements from the external sensors. This EBSE generates the events of the estimator module on-board the vehicle and, thus, allows the sensors to remain in stand-by mode until an event is generated. The proposed algorithm requests a measurement every time the estimation distance root mean squared error (DRMS) value, obtained from the estimator's covariance matrix, exceeds a threshold value. This triggering threshold can be adapted to the vehicle's working conditions rendering the estimator even more efficient. An example of the use of the proposed EBSE is given, where the autonomous vehicle must approach and follow a reference trajectory. By making the threshold a function of the distance to the reference location, the estimator can halve the use of the sensors with a negligible deterioration in the performance of the approaching maneuver. PMID:26102489
NASA Astrophysics Data System (ADS)
Moore, F.; Burke, M.
2015-12-01
A wide range of studies using a variety of methods strongly suggest that climate change will have a negative impact on agricultural production in many areas. Farmers though should be able to learn about a changing climate and to adjust what they grow and how they grow it in order to reduce these negative impacts. However, it remains unclear how effective these private (autonomous) adaptations will be, or how quickly they will be adopted. Constraining the uncertainty on this adaptation is important for understanding the impacts of climate change on agriculture. Here we review a number of empirical methods that have been proposed for understanding the rate and effectiveness of private adaptation to climate change. We compare these methods using data on agricultural yields in the United States and western Europe.
Battery state-of-charge estimation using approximate least squares
NASA Astrophysics Data System (ADS)
Unterrieder, C.; Zhang, C.; Lunglmayr, M.; Priewasser, R.; Marsili, S.; Huemer, M.
2015-03-01
In recent years, much effort has been spent to extend the runtime of battery-powered electronic applications. In order to improve the utilization of the available cell capacity, high precision estimation approaches for battery-specific parameters are needed. In this work, an approximate least squares estimation scheme is proposed for the estimation of the battery state-of-charge (SoC). The SoC is determined based on the prediction of the battery's electromotive force. The proposed approach allows for an improved re-initialization of the Coulomb counting (CC) based SoC estimation method. Experimental results for an implementation of the estimation scheme on a fuel gauge system on chip are illustrated. Implementation details and design guidelines are presented. The performance of the presented concept is evaluated for realistic operating conditions (temperature effects, aging, standby current, etc.). For the considered test case of a GSM/UMTS load current pattern of a mobile phone, the proposed method is able to re-initialize the CC-method with a high accuracy, while state-of-the-art methods fail to perform a re-initialization.
Attention control learning in the decision space using state estimation
NASA Astrophysics Data System (ADS)
Gharaee, Zahra; Fatehi, Alireza; Mirian, Maryam S.; Nili Ahmadabadi, Majid
2016-05-01
The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information.
NASA Astrophysics Data System (ADS)
Pedretti, Daniele; Fernàndez-Garcia, Daniel
2013-09-01
Particle tracking methods to simulate solute transport deal with the issue of having to reconstruct smooth concentrations from a limited number of particles. This is an error-prone process that typically leads to large fluctuations in the determined late-time behavior of breakthrough curves (BTCs). Kernel density estimators (KDE) can be used to automatically reconstruct smooth BTCs from a small number of particles. The kernel approach incorporates the uncertainty associated with subsampling a large population by equipping each particle with a probability density function. Two broad classes of KDE methods can be distinguished depending on the parametrization of this function: global and adaptive methods. This paper shows that each method is likely to estimate a specific portion of the BTCs. Although global methods offer a valid approach to estimate early-time behavior and peak of BTCs, they exhibit important fluctuations at the tails where fewer particles exist. In contrast, locally adaptive methods improve tail estimation while oversmoothing both early-time and peak concentrations. Therefore a new method is proposed combining the strength of both KDE approaches. The proposed approach is universal and only needs one parameter (α) which slightly depends on the shape of the BTCs. Results show that, for the tested cases, heavily-tailed BTCs are properly reconstructed with α ≈ 0.5 .
Forward Models and State Estimation in Compensatory Eye Movements
Frens, Maarten A.; Donchin, Opher
2009-01-01
The compensatory eye movement (CEM) system maintains a stable retinal image, integrating information from different sensory modalities to compensate for head movements. Inspired by recent models of the physiology of limb movements, we suggest that CEM can be modeled as a control system with three essential building blocks: a forward model that predicts the effects of motor commands; a state estimator that integrates sensory feedback into this prediction; and, a feedback controller that translates a state estimate into motor commands. We propose a specific mapping of nuclei within the CEM system onto these control functions. Specifically, we suggest that the Flocculus is responsible for generating the forward model prediction and that the Vestibular Nuclei integrate sensory feedback to generate an estimate of current state. Finally, the brainstem motor nuclei – in the case of horizontal compensation this means the Abducens Nucleus and the Nucleus Prepositus Hypoglossi – implement a feedback controller, translating state into motor commands. While these efforts to understand the physiological control system as a feedback control system are in their infancy, there is the intriguing possibility that CEM and targeted voluntary movements use the same cerebellar circuitry in fundamentally different ways. PMID:19956563
Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation
Liu, Xi; Qu, Hua; Zhao, Jihong; Yue, Pengcheng; Wang, Meng
2016-01-01
A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF) provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC), the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT) is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm. PMID:27657069
NASA Astrophysics Data System (ADS)
Shi, Lei; Wang, Z. J.
2015-08-01
Adjoint-based mesh adaptive methods are capable of distributing computational resources to areas which are important for predicting an engineering output. In this paper, we develop an adjoint-based h-adaptation approach based on the high-order correction procedure via reconstruction formulation (CPR) to minimize the output or functional error. A dual-consistent CPR formulation of hyperbolic conservation laws is developed and its dual consistency is analyzed. Super-convergent functional and error estimate for the output with the CPR method are obtained. Factors affecting the dual consistency, such as the solution point distribution, correction functions, boundary conditions and the discretization approach for the non-linear flux divergence term, are studied. The presented method is then used to perform simulations for the 2D Euler and Navier-Stokes equations with mesh adaptation driven by the adjoint-based error estimate. Several numerical examples demonstrate the ability of the presented method to dramatically reduce the computational cost comparing with uniform grid refinement.
The State of Literacy in America: Estimates at the Local, State, and National Levels.
ERIC Educational Resources Information Center
National Inst. for Literacy, Washington, DC.
This document presents synthesized estimates of the rates of level 1 literacy by congressional district in the 50 states and District of Columbia. The estimates are extrapolations of the National Adult Literacy Survey (NALS) that were based on the findings of approximately 26,000 interviews. The document begins with an introduction containing the…
NASA Astrophysics Data System (ADS)
Farmann, Alexander; Waag, Wladislaw; Sauer, Dirk Uwe
2015-12-01
Robust algorithms using reduced order equivalent circuit model (ECM) for an accurate and reliable estimation of battery states in various applications become more popular. In this study, a novel adaptive, self-learning heuristic algorithm for on-board impedance parameters and voltage estimation of lithium-ion batteries (LIBs) in electric vehicles is introduced. The presented approach is verified using LIBs with different composition of chemistries (NMC/C, NMC/LTO, LFP/C) at different aging states. An impedance-based reduced order ECM incorporating ohmic resistance and a combination of a constant phase element and a resistance (so-called ZARC-element) is employed. Existing algorithms in vehicles are much more limited in the complexity of the ECMs. The algorithm is validated using seven day real vehicle data with high temperature variation including very low temperatures (from -20 °C to +30 °C) at different Depth-of-Discharges (DoDs). Two possibilities to approximate both ZARC-elements with finite number of RC-elements on-board are shown and the results of the voltage estimation are compared. Moreover, the current dependence of the charge-transfer resistance is considered by employing Butler-Volmer equation. Achieved results indicate that both models yield almost the same grade of accuracy.
NASA Astrophysics Data System (ADS)
Egorova, Tatiana; Gatsonis, Nikolaos A.; Demetriou, Michael A.
2013-11-01
In this work the process of gas release into the atmosphere by a moving aerial source is simulated and estimated using a sensing aerial vehicle (SAV). The process is modeled with atmospheric advection diffusion equation, which is solved by the finite volume method (FVM). Advective fluxes are constrained using total variation diminishing (TVD) approach. The estimator provides on-line estimates of concentration field and proximity of the source. The guidance of the SAV is dictated by the performance of the estimator. To further improve the estimation algorithm from the computational prospective, the grid is adapted dynamically through local refinement and coarsening. The adaptation algorithm uses the current sensor position as a center of refinement, with the areas further away from the SAV being covered by a coarse grid. This leads to the time varying state matrix of the estimator and the variation depends on the SAV motion. Advantages of the adaptive FVM-TVD implementation are illustrated on the examples of estimator performance for different source trajectories.
Estimating ecosystem carbon stocks at Redwood National and State Parks
van Mantgem, Phillip J.; Madej, Mary Ann; Seney, Joseph; Deshais, Janelle
2013-01-01
Accounting for ecosystem carbon is increasingly important for park managers. In this case study we present our efforts to estimate carbon stocks and the effects of management on carbon stocks for Redwood National and State Parks in northern California. Using currently available information, we estimate that on average these parks’ soils contain approximately 89 tons of carbon per acre (200 Mg C per ha), while vegetation contains about 130 tons C per acre (300 Mg C per ha). estoration activities at the parks (logging-road removal, second-growth forest management) were shown to initially reduce ecosystem carbon, but may provide for enhanced ecosystem carbon storage over the long term. We highlight currently available tools that could be used to estimate ecosystem carbon at other units of the National Park System.
Parameter estimation of qubit states with unknown phase parameter
NASA Astrophysics Data System (ADS)
Suzuki, Jun
2015-02-01
We discuss a problem of parameter estimation for quantum two-level system, qubit system, in presence of unknown phase parameter. We analyze trade-off relations for mean square errors (MSEs) when estimating relevant parameters with separable measurements based on known precision bounds; the symmetric logarithmic derivative (SLD) Cramér-Rao (CR) bound and Hayashi-Gill-Massar (HGM) bound. We investigate the optimal measurement which attains the HGM bound and discuss its properties. We show that the HGM bound for relevant parameters can be attained asymptotically by using some fraction of given n quantum states to estimate the phase parameter. We also discuss the Holevo bound which can be attained asymptotically by a collective measurement.
Estimation of death rates in US states with small subpopulations.
Voulgaraki, Anastasia; Wei, Rong; Kedem, Benjamin
2015-05-20
In US states with small subpopulations, the observed mortality rates are often zero, particularly among young ages. Because in life tables, death rates are reported mostly on a log scale, zero mortality rates are problematic. To overcome the observed zero death rates problem, appropriate probability models are used. Using these models, observed zero mortality rates are replaced by the corresponding expected values. This enables logarithmic transformations and, in some cases, the fitting of the eight-parameter Heligman-Pollard model to produce mortality estimates for ages 0-130 years, a procedure illustrated in terms of mortality data from several states.
NASA Astrophysics Data System (ADS)
Zhang, Xiangwen; Xu, Yong; Pan, Ming; Ren, Fenghua
2014-04-01
A sliding-mode observer is designed to estimate the vehicle velocity with the measured vehicle acceleration, the wheel speeds and the braking torques. Based on the Burckhardt tyre model, the extended Kalman filter is designed to estimate the parameters of the Burckhardt model with the estimated vehicle velocity, the measured wheel speeds and the vehicle acceleration. According to the estimated parameters of the Burckhardt tyre model, the tyre/road friction coefficients and the optimal slip ratios are calculated. A vehicle adaptive sliding-mode control (SMC) algorithm is presented with the estimated vehicle velocity, the tyre/road friction coefficients and the optimal slip ratios. And the adjustment method of the sliding-mode gain factors is discussed. Based on the adaptive SMC algorithm, a vehicle's antilock braking system (ABS) control system model is built with the Simulink Toolbox. Under the single-road condition as well as the different road conditions, the performance of the vehicle ABS system is simulated with the vehicle velocity observer, the tyre/road friction coefficient estimator and the adaptive SMC algorithm. The results indicate that the estimated errors of the vehicle velocity and the tyre/road friction coefficients are acceptable and the vehicle ABS adaptive SMC algorithm is effective. So the proposed adaptive SMC algorithm can be used to control the vehicle ABS without the information of the vehicle velocity and the road conditions.
Support vector machines for nuclear reactor state estimation
Zavaljevski, N.; Gross, K. C.
2000-02-14
Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformed into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear power reactors. In particular, they implemented and tested kernels developed at Argonne National Laboratory for the Multivariate State Estimation Technique (MSET), a nonlinear, nonparametric estimation technique with a wide range of applications in nuclear reactors. The methodology has been applied to three data sets from experimental and commercial nuclear power reactor applications. The results are promising. The combination of MSET kernels with the SVM method has better noise reduction and generalization properties than the standard MSET algorithm.
Adaptive RBF network for parameter estimation and stable air-fuel ratio control.
Wang, Shiwei; Yu, D L
2008-01-01
In the application of variable structure control to engine air-fuel ratio, the ratio is subjected to chattering due to system uncertainty, such as unknown parameters or time varying dynamics. This paper proposes an adaptive neural network method to estimate two immeasurable physical parameters on-line and to compensate for the model uncertainty and engine time varying dynamics, so that the chattering is substantially reduced and the air-fuel ratio is regulated within the desired range of the stoichiometric value. The adaptive law of the neural network is derived using the Lyapunov method, so that the stability of the whole system and the convergence of the networks are guaranteed. Computer simulations based on a mean value engine model demonstrate the effectiveness of the technique. PMID:18166378
Koofigar, Hamid Reza
2016-01-01
The problem of maximum power point tracking (MPPT) in photovoltaic (PV) systems, despite the model uncertainties and the variations in environmental circumstances, is addressed. Introducing a mathematical description, an adaptive sliding mode control (ASMC) algorithm is first developed. Unlike many previous investigations, the output voltage is not required to be sensed and the upper bound of system uncertainties and the variations of irradiance and temperature are not required to be known. Estimating the output voltage by an update law, an adaptive-based H∞ tracking algorithm is then developed for the case the perturbations are energy-bounded. The stability analysis is presented for the proposed tracking control schemes, based on the Lyapunov stability theorem. From a comparison viewpoint, some numerical and experimental studies are also presented and discussed. PMID:26606851
Real-time Adaptive Kinematic Model Estimation of Concentric Tube Robots
Kim, Chunwoo; Ryu, Seok Chang; Dupont, Pierre E.
2016-01-01
Kinematic models of concentric tube robots have matured from considering only tube bending to considering tube twisting as well as external loading. While these models have been demonstrated to approximate actual behavior, modeling error can be significant for medical applications that often call for positioning accuracy of 1–2mm. As an alternative to moving to more complex models, this paper proposes using sensing to adaptively update model parameters during robot operation. Advantages of this method are that the model is constantly tuning itself to provide high accuracy in the region of the workspace where it is currently operating. It also adapts automatically to changes in robot shape and compliance associated with the insertion and removal of tools through its lumen. As an initial exploration of this approach, a recursive on-line estimator is proposed and evaluated experimentally. PMID:27175307
Adaptive Pre-FFT Equalizer with High-Precision Channel Estimator for ISI Channels
NASA Astrophysics Data System (ADS)
Yoshida, Makoto
We present an attractive approach for OFDM transmission using an adaptive pre-FFT equalizer, which can select ICI reduction mode according to channel condition, and a degenerated-inverse-matrix-based channel estimator (DIME), which uses a cyclic sinc-function matrix uniquely determined by transmitted subcarriers. In addition to simulation results, the proposed system with an adaptive pre-FFT equalizer and DIME has been laboratory tested by using a software defined radio (SDR)-based test bed. The simulation and experimental results demonstrated that the system at a rate of more than 100Mbps can provide a bit error rate of less than 10-3 for a fast multi-path fading channel that has a moving velocity of more than 200km/h with a delay spread of 1.9µs (a maximum delay path of 7.3µs) in the 5-GHz band.
Koofigar, Hamid Reza
2016-01-01
The problem of maximum power point tracking (MPPT) in photovoltaic (PV) systems, despite the model uncertainties and the variations in environmental circumstances, is addressed. Introducing a mathematical description, an adaptive sliding mode control (ASMC) algorithm is first developed. Unlike many previous investigations, the output voltage is not required to be sensed and the upper bound of system uncertainties and the variations of irradiance and temperature are not required to be known. Estimating the output voltage by an update law, an adaptive-based H∞ tracking algorithm is then developed for the case the perturbations are energy-bounded. The stability analysis is presented for the proposed tracking control schemes, based on the Lyapunov stability theorem. From a comparison viewpoint, some numerical and experimental studies are also presented and discussed.
Estimated use of water in the New England States, 1990
Korzendorfer, B.A.; Horn, M.A.
1995-01-01
Data on freshwater withdrawals in 1990 were compiled for the New England States. An estimated 4,160 Mgal/d (million gallons per day) of freshwater was withdrawn in 1990 in the six States. Of this total, 1,430 Mgal/d was withdrawn by public suppliers and delivered to users, and 2,720 Mgal/d was withdrawn by domestic, commercial, industrial, agricultural, mining, and thermoelectric power-generation users. More than 83 percent of the freshwater was from surface-water sources. Massachusetts, with the largest population, had the largest withdrawals of water. Data on saline water withdraw, and instream flow at hydroelectric plants were also compiled. An estimated 9, 170 Mgal/d of saline water was used for thermoelectric-power generation and industrial use in Connecticut, Maine, Massachusetts, New Hampshire, and Rhode Island. Return flow fro public wastewater-treatment plants totaled 1,750 Mgal/d; more than half (55 percent) of this return flow was in Massachusetts. In addition, about 178,000 Mgal/d was used for instream hydroelectric power generation; the largest users were Maine (about 83,000 Mgal/d) and New Hampshire (46,000 Mgal/d). These data, some of which were based on site-specific water-use information and some based on estimation techniques, were compiled through joint efforts by the U.S. Geological Survey and State cooperators for the 1990 national water-use compilation.
Uncertainty Quantification in State Estimation using the Probabilistic Collocation Method
Lin, Guang; Zhou, Ning; Ferryman, Thomas A.; Tuffner, Francis K.
2011-03-23
In this study, a new efficient uncertainty quantification technique, probabilistic collocation method (PCM) on sparse grid points is employed to enable the evaluation of uncertainty in state estimation. The PCM allows us to use just a small number of ensembles to quantify the uncertainty in estimating the state variables of power systems. By sparse grid points, the PCM approach can handle large number of uncertain parameters in power systems with relatively lower computational cost, when comparing with classic Monte Carlo (MC) simulations. The algorithm and procedure is outlined and we demonstrate the capability and illustrate the application of PCM on sparse grid points approach on uncertainty quantification in state estimation of the IEEE 14 bus model as an example. MC simulations have also been conducted to verify accuracy of the PCM approach. By comparing the results obtained from MC simulations with PCM results for mean and standard deviation of uncertain parameters, it is evident that the PCM approach is computationally more efficient than MC simulations.
Optimization of an adaptive SPECT system with the scanning linear estimator
NASA Astrophysics Data System (ADS)
Ghanbari, Nasrin; Clarkson, Eric; Kupinski, Matthew A.; Li, Xin
2015-08-01
The adaptive single-photon emission computed tomography (SPECT) system studied here acquires an initial scout image to obtain preliminary information about the object. Then the configuration is adjusted by selecting the size of the pinhole and the magnification that optimize system performance on an ensemble of virtual objects generated to be consistent with the scout data. In this study the object is a lumpy background that contains a Gaussian signal with a variable width and amplitude. The virtual objects in the ensemble are imaged by all of the available configurations and the subsequent images are evaluated with the scanning linear estimator to obtain an estimate of the signal width and amplitude. The ensemble mean squared error (EMSE) on the virtual ensemble between the estimated and the true parameters serves as the performance figure of merit for selecting the optimum configuration. The results indicate that variability in the original object background, noise and signal parameters leads to a specific optimum configuration in each case. A statistical study carried out for a number of objects show that the adaptive system on average performs better than its nonadaptive counterpart.
Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology
NASA Astrophysics Data System (ADS)
Petković, Dalibor; Shamshirband, Shahaboddin; Pavlović, Nenad T.; Anuar, Nor Badrul; Kiah, Miss Laiha Mat
2014-07-01
The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.
Johansson, A Torbjorn; White, Paul R
2011-08-01
This paper proposes an adaptive filter-based method for detection and frequency estimation of whistle calls, such as the calls of birds and marine mammals, which are typically analyzed in the time-frequency domain using a spectrogram. The approach taken here is based on adaptive notch filtering, which is an established technique for frequency tracking. For application to automatic whistle processing, methods for detection and improved frequency tracking through frequency crossings as well as interfering transients are developed and coupled to the frequency tracker. Background noise estimation and compensation is accomplished using order statistics and pre-whitening. Using simulated signals as well as recorded calls of marine mammals and a human whistled speech utterance, it is shown that the proposed method can detect more simultaneous whistles than two competing spectrogram-based methods while not reporting any false alarms on the example datasets. In one example, it extracts complete 1.4 and 1.8 s bottlenose dolphin whistles successfully through frequency crossings. The method performs detection and estimates frequency tracks even at high sweep rates. The algorithm is also shown to be effective on human whistled utterances. PMID:21877804
Estimated Carbon Dioxide Emissions in 2008: United States
Smith, C A; Simon, A J; Belles, R D
2011-04-01
Flow charts depicting carbon dioxide emissions in the United States have been constructed from publicly available data and estimates of state-level energy use patterns. Approximately 5,800 million metric tons of carbon dioxide were emitted throughout the United States for use in power production, residential, commercial, industrial, and transportation applications in 2008. Carbon dioxide is emitted from the use of three major energy resources: natural gas, coal, and petroleum. The flow patterns are represented in a compact 'visual atlas' of 52 state-level (all 50 states, the District of Columbia, and one national) carbon dioxide flow charts representing a comprehensive systems view of national CO{sub 2} emissions. Lawrence Livermore National Lab (LLNL) has published flow charts (also referred to as 'Sankey Diagrams') of important national commodities since the early 1970s. The most widely recognized of these charts is the U.S. energy flow chart (http://flowcharts.llnl.gov). LLNL has also published charts depicting carbon (or carbon dioxide potential) flow and water flow at the national level as well as energy, carbon, and water flows at the international, state, municipal, and organizational (i.e. United States Air Force) level. Flow charts are valuable as single-page references that contain quantitative data about resource, commodity, and byproduct flows in a graphical form that also convey structural information about the system that manages those flows. Data on carbon dioxide emissions from the energy sector are reported on a national level. Because carbon dioxide emissions are not reported for individual states, the carbon dioxide emissions are estimated using published energy use information. Data on energy use is compiled by the U.S. Department of Energy's Energy Information Administration (U.S. EIA) in the State Energy Data System (SEDS). SEDS is updated annually and reports data from 2 years prior to the year of the update. SEDS contains data on primary
Improved Battery State Estimation Using Novel Sensing Techniques
NASA Astrophysics Data System (ADS)
Abdul Samad, Nassim
Lithium-ion batteries have been considered a great complement or substitute for gasoline engines due to their high energy and power density capabilities among other advantages. However, these types of energy storage devices are still yet not widespread, mainly because of their relatively high cost and safety issues, especially at elevated temperatures. This thesis extends existing methods of estimating critical battery states using model-based techniques augmented by real-time measurements from novel temperature and force sensors. Typically, temperature sensors are located near the edge of the battery, and away from the hottest core cell regions, which leads to slower response times and increased errors in the prediction of core temperatures. New sensor technology allows for flexible sensor placement at the cell surface between cells in a pack. This raises questions about the optimal locations of these sensors for best observability and temperature estimation. Using a validated model, which is developed and verified using experiments in laboratory fixtures that replicate vehicle pack conditions, it is shown that optimal sensor placement can lead to better and faster temperature estimation. Another equally important state is the state of health or the capacity fading of the cell. This thesis introduces a novel method of using force measurements for capacity fade estimation. Monitoring capacity is important for defining the range of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs). Current capacity estimation techniques require a full discharge to monitor capacity. The proposed method can complement or replace current methods because it only requires a shallow discharge, which is especially useful in EVs and PHEVs. Using the accurate state estimation accomplished earlier, a method for downsizing a battery pack is shown to effectively reduce the number of cells in a pack without compromising safety. The influence on the battery performance (e
Speech Enhancement, Gain, and Noise Spectrum Adaptation Using Approximate Bayesian Estimation.
Hao, Jiucang; Attias, Hagai; Nagarajan, Srikantan; Lee, Te-Won; Sejnowski, Terrence J
2009-01-01
This paper presents a new approximate Bayesian estimator for enhancing a noisy speech signal. The speech model is assumed to be a Gaussian mixture model (GMM) in the log-spectral domain. This is in contrast to most current models in frequency domain. Exact signal estimation is a computationally intractable problem. We derive three approximations to enhance the efficiency of signal estimation. The Gaussian approximation transforms the log-spectral domain GMM into the frequency domain using minimal Kullback-Leiber (KL)-divergency criterion. The frequency domain Laplace method computes the maximum a posteriori (MAP) estimator for the spectral amplitude. Correspondingly, the log-spectral domain Laplace method computes the MAP estimator for the log-spectral amplitude. Further, the gain and noise spectrum adaptation are implemented using the expectation-maximization (EM) algorithm within the GMM under Gaussian approximation. The proposed algorithms are evaluated by applying them to enhance the speeches corrupted by the speech-shaped noise (SSN). The experimental results demonstrate that the proposed algorithms offer improved signal-to-noise ratio, lower word recognition error rate, and less spectral distortion. PMID:20428253
Epidemic processes over adaptive state-dependent networks
NASA Astrophysics Data System (ADS)
Ogura, Masaki; Preciado, Victor M.
2016-06-01
In this paper we study the dynamics of epidemic processes taking place in adaptive networks of arbitrary topology. We focus our study on the adaptive susceptible-infected-susceptible (ASIS) model, where healthy individuals are allowed to temporarily cut edges connecting them to infected nodes in order to prevent the spread of the infection. In this paper we derive a closed-form expression for a lower bound on the epidemic threshold of the ASIS model in arbitrary networks with heterogeneous node and edge dynamics. For networks with homogeneous node and edge dynamics, we show that the resulting lower bound is proportional to the epidemic threshold of the standard SIS model over static networks, with a proportionality constant that depends on the adaptation rates. Furthermore, based on our results, we propose an efficient algorithm to optimally tune the adaptation rates in order to eradicate epidemic outbreaks in arbitrary networks. We confirm the tightness of the proposed lower bounds with several numerical simulations and compare our optimal adaptation rates with popular centrality measures.
An Adaptive Nonlinear Aircraft Maneuvering Envelope Estimation Approach for Online Applications
NASA Technical Reports Server (NTRS)
Schuet, Stefan R.; Lombaerts, Thomas Jan; Acosta, Diana; Wheeler, Kevin; Kaneshige, John
2014-01-01
A nonlinear aircraft model is presented and used to develop an overall unified robust and adaptive approach to passive trim and maneuverability envelope estimation with uncertainty quantification. The concept of time scale separation makes this method suitable for the online characterization of altered safe maneuvering limitations after impairment. The results can be used to provide pilot feedback and/or be combined with flight planning, trajectory generation, and guidance algorithms to help maintain safe aircraft operations in both nominal and off-nominal scenarios.
Fundamental Bounds in Measurements for Estimating Quantum States
NASA Astrophysics Data System (ADS)
Lim, Hyang-Tag; Ra, Young-Sik; Hong, Kang-Hee; Lee, Seung-Woo; Kim, Yoon-Ho
2014-07-01
Quantum measurement unavoidably disturbs the state of a quantum system if any information about the system is extracted. Recently, the concept of reversing quantum measurement has been introduced and has attracted much attention. Numerous efforts have thus been devoted to understanding the fundamental relation of the amount of information obtained by measurement to either state disturbance or reversibility. Here, we experimentally prove the trade-off relations in quantum measurement with respect to both state disturbance and reversibility. By demonstrating the quantitative bound of the trade-off relations, we realize an optimal measurement for estimating quantum systems with minimum disturbance and maximum reversibility. Our results offer fundamental insights on quantum measurement and practical guidelines for implementing various quantum information protocols.
Performance Bounds on Micro-Doppler Estimation and Adaptive Waveform Design Using OFDM Signals
Sen, Satyabrata; Barhen, Jacob; Glover, Charles Wayne
2014-01-01
We analyze the performance of a wideband orthogonal frequency division multiplexing (OFDM) signal in estimating the micro-Doppler frequency of a target having multiple rotating scatterers (e.g., rotor blades of a helicopter, propellers of a submarine). The presence of rotating scatterers introduces Doppler frequency modulation in the received signal by generating sidebands about the transmitted frequencies. This is called the micro-Doppler effects. The use of a frequency-diverse OFDM signal in this context enables us to independently analyze the micro-Doppler characteristics with respect to a set of orthogonal subcarrier frequencies. Therefore, to characterize the accuracy of micro-Doppler frequency estimation, we compute the Cram er-Rao Bound (CRB) on the angular-velocity estimate of the target while considering the scatterer responses as deterministic but unknown nuisance parameters. Additionally, to improve the accuracy of the estimation procedure, we formulate and solve an optimization problem by minimizing the CRB on the angular-velocity estimate with respect to the transmitting OFDM spectral coefficients. We present several numerical examples to demonstrate the CRB variations at different values of the signal-to-noise ratio (SNR) and the number of OFDM subcarriers. The CRB values not only decrease with the increase in the SNR values, but also reduce as we increase the number of subcarriers implying the significance of frequency-diverse OFDM waveforms. The improvement in estimation accuracy due to the adaptive waveform design is also numerically analyzed. Interestingly, we find that the relative decrease in the CRBs on the angular-velocity estimate is more pronounced for larger number of OFDM subcarriers.
Learning to Estimate Dynamical State with Probabilistic Population Codes
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 UAV attitude estimation employing unscented Kalman Filter, FOAM and low-cost MEMS sensors.
de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos
2012-01-01
Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance.
Hensen, Ulf; Grubmüller, Helmut; Lange, Oliver F
2009-07-01
The quasiharmonic approximation is the most widely used estimate for the configurational entropy of macromolecules from configurational ensembles generated from atomistic simulations. This method, however, rests on two assumptions that severely limit its applicability, (i) that a principal component analysis yields sufficiently uncorrelated modes and (ii) that configurational densities can be well approximated by Gaussian functions. In this paper we introduce a nonparametric density estimation method which rests on adaptive anisotropic kernels. It is shown that this method provides accurate configurational entropies for up to 45 dimensions thus improving on the quasiharmonic approximation. When embedded in the minimally coupled subspace framework, large macromolecules of biological interest become accessible, as demonstrated for the 67-residue coldshock protein. PMID:19658735
Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors
de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos
2012-01-01
Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance. PMID:23012559
Motion Estimation Based on Mutual Information and Adaptive Multi-Scale Thresholding.
Xu, Rui; Taubman, David; Naman, Aous Thabit
2016-03-01
This paper proposes a new method of calculating a matching metric for motion estimation. The proposed method splits the information in the source images into multiple scale and orientation subbands, reduces the subband values to a binary representation via an adaptive thresholding algorithm, and uses mutual information to model the similarity of corresponding square windows in each image. A moving window strategy is applied to recover a dense estimated motion field whose properties are explored. The proposed matching metric is a sum of mutual information scores across space, scale, and orientation. This facilitates the exploitation of information diversity in the source images. Experimental comparisons are performed amongst several related approaches, revealing that the proposed matching metric is better able to exploit information diversity, generating more accurate motion fields.
[Estimated mammogram coverage in Goiás State, Brazil].
Corrêa, Rosangela da Silveira; Freitas-Júnior, Ruffo; Peixoto, João Emílio; Rodrigues, Danielle Cristina Netto; Lemos, Maria Eugênia da Fonseca; Marins, Lucy Aparecida Parreira; Silveira, Erika Aparecida da
2011-09-01
This cross-sectional study aimed to estimate mammogram coverage in the State of Goiás, Brazil, describing the supply, demand, and variations in different age groups, evaluating 98 mammography services as observational units. We estimated the mammogram rates by age group and type of health service, as well as the number of tests required to cover 70% and 100% of the target population. We assessed the association between mammograms, geographical distribution of mammography machines, type of service, and age group. Full coverage estimates, considering 100% of women in the 40-69 and 50-69-year age brackets, were 61% and 66%, of which the Brazilian Unified National Health System provided 13% and 14%, respectively. To achieve 70% coverage, 43,424 additional mammograms would be needed. All the associations showed statistically significant differences (p < 0.001). We conclude that mammogram coverage is unevenly distributed in the State of Goiás and that fewer tests are performed than required. PMID:21986603
NASA Astrophysics Data System (ADS)
Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua
2014-03-01
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua
2014-03-01
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua
2014-03-01
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm. PMID:24697395
Sheng, Zheng; Wang, Jun; Zhou, Bihua; Zhou, Shudao
2014-03-15
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
Hierarchical state-space estimation of leatherback turtle navigation ability.
Mills Flemming, Joanna; Jonsen, Ian D; Myers, Ransom A; Field, Christopher A
2010-01-01
Remotely sensed tracking technology has revealed remarkable migration patterns that were previously unknown; however, models to optimally use such data have developed more slowly. Here, we present a hierarchical Bayes state-space framework that allows us to combine tracking data from a collection of animals and make inferences at both individual and broader levels. We formulate models that allow the navigation ability of animals to be estimated and demonstrate how information can be combined over many animals to allow improved estimation. We also show how formal hypothesis testing regarding navigation ability can easily be accomplished in this framework. Using Argos satellite tracking data from 14 leatherback turtles, 7 males and 7 females, during their southward migration from Nova Scotia, Canada, we find that the circle of confusion (the radius around an animal's location within which it is unable to determine its location precisely) is approximately 96 km. This estimate suggests that the turtles' navigation does not need to be highly accurate, especially if they are able to use more reliable cues as they near their destination. Moreover, for the 14 turtles examined, there is little evidence to suggest that male and female navigation abilities differ. Because of the minimal assumptions made about the movement process, our approach can be used to estimate and compare navigation ability for many migratory species that are able to carry electronic tracking devices. PMID:21203382
Hierarchical state-space estimation of leatherback turtle navigation ability.
Mills Flemming, Joanna; Jonsen, Ian D; Myers, Ransom A; Field, Christopher A
2010-12-28
Remotely sensed tracking technology has revealed remarkable migration patterns that were previously unknown; however, models to optimally use such data have developed more slowly. Here, we present a hierarchical Bayes state-space framework that allows us to combine tracking data from a collection of animals and make inferences at both individual and broader levels. We formulate models that allow the navigation ability of animals to be estimated and demonstrate how information can be combined over many animals to allow improved estimation. We also show how formal hypothesis testing regarding navigation ability can easily be accomplished in this framework. Using Argos satellite tracking data from 14 leatherback turtles, 7 males and 7 females, during their southward migration from Nova Scotia, Canada, we find that the circle of confusion (the radius around an animal's location within which it is unable to determine its location precisely) is approximately 96 km. This estimate suggests that the turtles' navigation does not need to be highly accurate, especially if they are able to use more reliable cues as they near their destination. Moreover, for the 14 turtles examined, there is little evidence to suggest that male and female navigation abilities differ. Because of the minimal assumptions made about the movement process, our approach can be used to estimate and compare navigation ability for many migratory species that are able to carry electronic tracking devices.
Farooqui, Habib; Jit, Mark; Heymann, David L.; Zodpey, Sanjay
2015-01-01
The burden of severe pneumonia in terms of morbidity and mortality is unknown in India especially at sub-national level. In this context, we aimed to estimate the number of severe pneumonia episodes, pneumococcal pneumonia episodes and pneumonia deaths in children younger than 5 years in 2010. We adapted and parameterized a mathematical model based on the epidemiological concept of potential impact fraction developed CHERG for this analysis. The key parameters that determine the distribution of severe pneumonia episode across Indian states were state-specific under-5 population, state-specific prevalence of selected definite pneumonia risk factors and meta-estimates of relative risks for each of these risk factors. We applied the incidence estimates and attributable fraction of risk factors to population estimates for 2010 of each Indian state. We then estimated the number of pneumococcal pneumonia cases by applying the vaccine probe methodology to an existing trial. We estimated mortality due to severe pneumonia and pneumococcal pneumonia by combining incidence estimates with case fatality ratios from multi-centric hospital-based studies. Our results suggest that in 2010, 3.6 million (3.3–3.9 million) episodes of severe pneumonia and 0.35 million (0.31–0.40 million) all cause pneumonia deaths occurred in children younger than 5 years in India. The states that merit special mention include Uttar Pradesh where 18.1% children reside but contribute 24% of pneumonia cases and 26% pneumonia deaths, Bihar (11.3% children, 16% cases, 22% deaths) Madhya Pradesh (6.6% children, 9% cases, 12% deaths), and Rajasthan (6.6% children, 8% cases, 11% deaths). Further, we estimated that 0.56 million (0.49–0.64 million) severe episodes of pneumococcal pneumonia and 105 thousand (92–119 thousand) pneumococcal deaths occurred in India. The top contributors to India’s pneumococcal pneumonia burden were Uttar Pradesh, Bihar, Madhya Pradesh and Rajasthan in that order. Our
Farooqui, Habib; Jit, Mark; Heymann, David L; Zodpey, Sanjay
2015-01-01
The burden of severe pneumonia in terms of morbidity and mortality is unknown in India especially at sub-national level. In this context, we aimed to estimate the number of severe pneumonia episodes, pneumococcal pneumonia episodes and pneumonia deaths in children younger than 5 years in 2010. We adapted and parameterized a mathematical model based on the epidemiological concept of potential impact fraction developed CHERG for this analysis. The key parameters that determine the distribution of severe pneumonia episode across Indian states were state-specific under-5 population, state-specific prevalence of selected definite pneumonia risk factors and meta-estimates of relative risks for each of these risk factors. We applied the incidence estimates and attributable fraction of risk factors to population estimates for 2010 of each Indian state. We then estimated the number of pneumococcal pneumonia cases by applying the vaccine probe methodology to an existing trial. We estimated mortality due to severe pneumonia and pneumococcal pneumonia by combining incidence estimates with case fatality ratios from multi-centric hospital-based studies. Our results suggest that in 2010, 3.6 million (3.3-3.9 million) episodes of severe pneumonia and 0.35 million (0.31-0.40 million) all cause pneumonia deaths occurred in children younger than 5 years in India. The states that merit special mention include Uttar Pradesh where 18.1% children reside but contribute 24% of pneumonia cases and 26% pneumonia deaths, Bihar (11.3% children, 16% cases, 22% deaths) Madhya Pradesh (6.6% children, 9% cases, 12% deaths), and Rajasthan (6.6% children, 8% cases, 11% deaths). Further, we estimated that 0.56 million (0.49-0.64 million) severe episodes of pneumococcal pneumonia and 105 thousand (92-119 thousand) pneumococcal deaths occurred in India. The top contributors to India's pneumococcal pneumonia burden were Uttar Pradesh, Bihar, Madhya Pradesh and Rajasthan in that order. Our results
Improving our legacy: Incorporation of adaptive management into state wildlife action plans
Fontaine, J.J.
2011-01-01
The loss of biodiversity is a mounting concern, but despite numerous attempts there are few large scale conservation efforts that have proven successful in reversing current declines. Given the challenge of biodiversity conservation, there is a need to develop strategic conservation plans that address species declines even with the inherent uncertainty in managing multiple species in complex environments. In 2002, the State Wildlife Grant program was initiated to fulfill this need, and while not explicitly outlined by Congress follows the fundamental premise of adaptive management, 'Learning by doing'. When action is necessary, but basic biological information and an understanding of appropriate management strategies are lacking, adaptive management enables managers to be proactive in spite of uncertainty. However, regardless of the strengths of adaptive management, the development of an effective adaptive management framework is challenging. In a review of 53 State Wildlife Action Plans, I found a keen awareness by planners that adaptive management was an effective method for addressing biodiversity conservation, but the development and incorporation of explicit adaptive management approaches within each plan remained elusive. Only ???25% of the plans included a framework for how adaptive management would be implemented at the project level within their state. There was, however, considerable support across plans for further development and implementation of adaptive management. By furthering the incorporation of adaptive management principles in conservation plans and explicitly outlining the decision making process, states will be poised to meet the pending challenges to biodiversity conservation. ?? 2010 .
Kang, Jin Ah; Kim, Hong Kook
2011-01-01
An adaptive redundant speech transmission (ARST) approach to improve the perceived speech quality (PSQ) of speech streaming applications over wireless multimedia sensor networks (WMSNs) is proposed in this paper. The proposed approach estimates the PSQ as well as the packet loss rate (PLR) from the received speech data. Subsequently, it decides whether the transmission of redundant speech data (RSD) is required in order to assist a speech decoder to reconstruct lost speech signals for high PLRs. According to the decision, the proposed ARST approach controls the RSD transmission, then it optimizes the bitrate of speech coding to encode the current speech data (CSD) and RSD bitstream in order to maintain the speech quality under packet loss conditions. The effectiveness of the proposed ARST approach is then demonstrated using the adaptive multirate-narrowband (AMR-NB) speech codec and ITU-T Recommendation P.563 as a scalable speech codec and the PSQ estimation, respectively. It is shown from the experiments that a speech streaming application employing the proposed ARST approach significantly improves speech quality under packet loss conditions in WMSNs. PMID:22164086
Error estimation and adaptive mesh refinement for parallel analysis of shell structures
NASA Technical Reports Server (NTRS)
Keating, Scott C.; Felippa, Carlos A.; Park, K. C.
1994-01-01
The formulation and application of element-level, element-independent error indicators is investigated. This research culminates in the development of an error indicator formulation which is derived based on the projection of element deformation onto the intrinsic element displacement modes. The qualifier 'element-level' means that no information from adjacent elements is used for error estimation. This property is ideally suited for obtaining error values and driving adaptive mesh refinements on parallel computers where access to neighboring elements residing on different processors may incur significant overhead. In addition such estimators are insensitive to the presence of physical interfaces and junctures. An error indicator qualifies as 'element-independent' when only visible quantities such as element stiffness and nodal displacements are used to quantify error. Error evaluation at the element level and element independence for the error indicator are highly desired properties for computing error in production-level finite element codes. Four element-level error indicators have been constructed. Two of the indicators are based on variational formulation of the element stiffness and are element-dependent. Their derivations are retained for developmental purposes. The second two indicators mimic and exceed the first two in performance but require no special formulation of the element stiffness mesh refinement which we demonstrate for two dimensional plane stress problems. The parallelizing of substructures and adaptive mesh refinement is discussed and the final error indicator using two-dimensional plane-stress and three-dimensional shell problems is demonstrated.
Error estimation and adaptive mesh refinement for parallel analysis of shell structures
NASA Astrophysics Data System (ADS)
Keating, Scott C.; Felippa, Carlos A.; Park, K. C.
1994-11-01
The formulation and application of element-level, element-independent error indicators is investigated. This research culminates in the development of an error indicator formulation which is derived based on the projection of element deformation onto the intrinsic element displacement modes. The qualifier 'element-level' means that no information from adjacent elements is used for error estimation. This property is ideally suited for obtaining error values and driving adaptive mesh refinements on parallel computers where access to neighboring elements residing on different processors may incur significant overhead. In addition such estimators are insensitive to the presence of physical interfaces and junctures. An error indicator qualifies as 'element-independent' when only visible quantities such as element stiffness and nodal displacements are used to quantify error. Error evaluation at the element level and element independence for the error indicator are highly desired properties for computing error in production-level finite element codes. Four element-level error indicators have been constructed. Two of the indicators are based on variational formulation of the element stiffness and are element-dependent. Their derivations are retained for developmental purposes. The second two indicators mimic and exceed the first two in performance but require no special formulation of the element stiffness mesh refinement which we demonstrate for two dimensional plane stress problems. The parallelizing of substructures and adaptive mesh refinement is discussed and the final error indicator using two-dimensional plane-stress and three-dimensional shell problems is demonstrated.
Bayesian adaptive estimation of the contrast sensitivity function: The quick CSF method
Lesmes, Luis Andres; Lu, Zhong-Lin; Baek, Jongsoo; Albright, Thomas D.
2015-01-01
The contrast sensitivity function (CSF) predicts functional vision better than acuity, but long testing times prevent its psychophysical assessment in clinical and practical applications. This study presents the quick CSF (qCSF) method, a Bayesian adaptive procedure that applies a strategy developed to estimate multiple parameters of the psychometric function (A. B. Cobo-Lewis, 1996; L. L. Kontsevich & C. W. Tyler, 1999). Before each trial, a one-step-ahead search finds the grating stimulus (defined by frequency and contrast) that maximizes the expected information gain (J. V. Kujala & T. J. Lukka, 2006; L. A. Lesmes et al., 2006), about four CSF parameters. By directly estimating CSF parameters, data collected at one spatial frequency improves sensitivity estimates across all frequencies. A psychophysical study validated that CSFs obtained with 100 qCSF trials (~10 min) exhibited good precision across spatial frequencies (SD < 2–3 dB) and excellent agreement with CSFs obtained independently (mean RMSE = 0.86 dB). To estimate the broad sensitivity metric provided by the area under the log CSF (AULCSF), only 25 trials were needed to achieve a coefficient of variation of 15–20%. The current study demonstrates the method’s value for basic and clinical investigations. Further studies, applying the qCSF to measure wider ranges of normal and abnormal vision, will determine how its efficiency translates to clinical assessment. PMID:20377294
Lee, Jae Hoon; Joshi, Amit; Sevick-Muraca, Eva M
2008-01-01
A variety of biomedical imaging techniques such as optical and fluorescence tomography, electrical impedance tomography, and ultrasound imaging can be cast as inverse problems, wherein image reconstruction involves the estimation of spatially distributed parameter(s) of the PDE system describing the physics of the imaging process. Finite element discretization of imaged domain with tetrahedral elements is a popular way of solving the forward and inverse imaging problems on complicated geometries. A dual-adaptive mesh-based approach wherein, one mesh is used for solving the forward imaging problem and the other mesh used for iteratively estimating the unknown distributed parameter, can result in high resolution image reconstruction at minimum computation effort, if both the meshes are allowed to adapt independently. Till date, no efficient method has been reported to identify and resolve intersection between tetrahedrons in independently refined or coarsened dual meshes. Herein, we report a fast and robust algorithm to identify and resolve intersection of tetrahedrons within nested dual meshes generated by 8-similar subtetrahedron subdivision scheme. The algorithm exploits finite element weight functions and gives rise to a set of weight functions on each vertex of disjoint tetrahedron pieces that completely cover up the intersection region of two tetrahedrons. The procedure enables fully adaptive tetrahedral finite elements by supporting independent refinement and coarsening of each individual mesh while preserving fast identification and resolution of intersection. The computational efficiency of the algorithm is demonstrated by diffuse photon density wave solutions obtained from a single- and a dual-mesh, and by reconstructing a fluorescent inclusion in simulated phantom from boundary frequency domain fluorescence measurements.
Estimating irrigation water use in the humid eastern United States
Levin, Sara B.; Zarriello, Phillip J.
2013-01-01
Accurate accounting of irrigation water use is an important part of the U.S. Geological Survey National Water-Use Information Program and the WaterSMART initiative to help maintain sustainable water resources in the Nation. Irrigation water use in the humid eastern United States is not well characterized because of inadequate reporting and wide variability associated with climate, soils, crops, and farming practices. To better understand irrigation water use in the eastern United States, two types of predictive models were developed and compared by using metered irrigation water-use data for corn, cotton, peanut, and soybean crops in Georgia and turf farms in Rhode Island. Reliable metered irrigation data were limited to these areas. The first predictive model that was developed uses logistic regression to predict the occurrence of irrigation on the basis of antecedent climate conditions. Logistic regression equations were developed for corn, cotton, peanut, and soybean crops by using weekly irrigation water-use data from 36 metered sites in Georgia in 2009 and 2010 and turf farms in Rhode Island from 2000 to 2004. For the weeks when irrigation was predicted to take place, the irrigation water-use volume was estimated by multiplying the average metered irrigation application rate by the irrigated acreage for a given crop. The second predictive model that was developed is a crop-water-demand model that uses a daily soil water balance to estimate the water needs of a crop on a given day based on climate, soil, and plant properties. Crop-water-demand models were developed independently of reported irrigation water-use practices and relied on knowledge of plant properties that are available in the literature. Both modeling approaches require accurate accounting of irrigated area and crop type to estimate total irrigation water use. Water-use estimates from both modeling methods were compared to the metered irrigation data from Rhode Island and Georgia that were used to
Uncertainty Quantification in Ocean State Estimation using Hessian Information
NASA Astrophysics Data System (ADS)
Kalmikov, A.; Heimbach, P.
2012-12-01
We present a second derivative-based (Hessian) method for Uncertainty Quantification (UQ) in large-scale Ocean State Estimation. Matrix-free Hessian-times-vector code of the MIT General Circulation Model (MITgcm) is generated by means of algorithmic differentiation (AD). Lanczos-type numerical algebra tools are then applied for extracting leading rank eigenvectors and eigenvalues used in the UQ algorithm. Computational complexity is reduced by tangent linear (forward-mode) differentiation of the adjoint code, which preserves the efficiency of the checkpointing schemes. The inverse and forward uncertainty propagation algorithm is designed for assimilating observation and control variable uncertainties, and for projecting these uncertainties onto oceanographically relevant target quantities of interest. The algorithm evaluates both reduction of a priori-assumed uncertainty as well as prior-independent information gain. The inverse propagation maps prior and data uncertainties onto posterior uncertainties in each component of the high-dimensional control space. The forward propagation of the posteriors provides a measure of uncertainties of the target quantity. The time-resolving analysis of uncertainty propagation in the ocean model reveals transient and steady state uncertainty regimes. The system is applied to quantifying uncertainties in Drake Passage transport in a global barotropic configuration of the MITgcm. The model is constrained by synthetic observations of sea surface height and velocities. The control space consists of two-dimensional maps of initial conditions (velocities and sea surface height), surface boundary conditions (wind stress), and model parameters (bottom drag), amounting to a 10^5-dimensional space of uncertain variables. It is demonstrated how the choice of observations and their geographic coverage determines the reduction in uncertainties of the estimated transport. The system also yields information on how well control parameters are
Estimated United States Residential Energy Use in 2005
Smith, C A; Johnson, D M; Simon, A J; Belles, R D
2011-12-12
A flow chart depicting energy flow in the residential sector of the United States economy in 2005 has been constructed from publicly available data and estimates of national energy use patterns. Approximately 11,000 trillion British Thermal Units (trBTUs) of electricity and fuels were used throughout the United States residential sector in lighting, electronics, air conditioning, space heating, water heating, washing appliances, cooking appliances, refrigerators, and other appliances. The residential sector is powered mainly by electricity and natural gas. Other fuels used include petroleum products (fuel oil, liquefied petroleum gas and kerosene), biomass (wood), and on-premises solar, wind, and geothermal energy. The flow patterns represent a comprehensive systems view of energy used within the residential sector.
State Estimation and Forecasting of the Ski-Slope Model Using an Improved Shadowing Filter
NASA Astrophysics Data System (ADS)
Mat Daud, Auni Aslah
In this paper, we present the application of the gradient descent of indeterminism (GDI) shadowing filter to a chaotic system, that is the ski-slope model. The paper focuses on the quality of the estimated states and their usability for forecasting. One main problem is that the existing GDI shadowing filter fails to provide stability to the convergence of the root mean square error and the last point error of the ski-slope model. Furthermore, there are unexpected cases in which the better state estimates give worse forecasts than the worse state estimates. We investigate these unexpected cases in particular and show how the presence of the humps contributes to them. However, the results show that the GDI shadowing filter can successfully be applied to the ski-slope model with only slight modification, that is, by introducing the adaptive step-size to ensure the convergence of indeterminism. We investigate its advantages over fixed step-size and how it can improve the performance of our shadowing filter.
Estimated use of water in the United States in 1990
Solley, Wayne B.; Pierce, Robert R.; Perlman, Howard A.
1993-01-01
Water withdrawals in the United States during 1990 were estimated to average 408,000 million gallons per day (Mgal/d) of freshwater and saline water for offstream uses--2 percent more than the 1985 estimate. Total freshwater withdrawals were an estimated 339,000 Mgal/d during 1990, about the same as during 1985. Average per-capita use for all offstream uses was 1,620 gallons per day (gal/d) of freshwater and saline water combined and 1,340 gal/d of freshwater. Offstream water-use categories are classified in this report as public supply, domestic, commercial, irrigation, livestock, industrial, mining, and thermoelectric power. During 1990, public-supply withdrawals were an estimated 38,500 Mgal/d, and self-supplied withdrawals were estimated as follows: domestic, 3,390 Mgal/d; commercial, 2,390 Mgal/d; irrigation, 137,000 Mgal/d; livestock, 4,500 Mgal/d; industrial, 22,600 Mgal/d, of which 3,270 Mgal/d was saline water; mining, 4,960 Mgal/d, of which 1,650 Mgal/d was saline; and thermoelectric power, 195,000 Mgal/d, of which 64,500 Mgal/d was saline. Water use for hydroelectric power generation, the only instream use compiled in this report, was estimated to be 3,290,000 Mgal/d during 1990, or 8 percent more than during 1985 and about the same as estimated for 1975 and 1980. Estimates of withdrawals by source indicate that during 1990, total surface-water withdrawals were 327,000 Mgal/d, or 1 percent more than during 1985, and total ground-water withdrawals were 80,600 Mgal/d, or 9 percent more than during 1985. Total saline-water withdrawals during 1990 were 69,400 Mgal/d, or 15 percent more than during 1985, most of which was saline surface water. Reclaimed wastewater averaged about 750 Mgal/d during 1990, or 30 percent more than during 1985. Total freshwater consumptive use was an estimated 94,000 Mgal/d during 1990, or 2 percent more than during 1985. Consumptive use by irrigation accounted for the largest part of total consumptive use, and was an estimated 76
Estimated Use of Water in the United States in 1980
Solley, Wayne B.; Chase, Edith B.; Mann, William B.
1983-01-01
Water use in the United States in 1980 was estimated to be an average of 450 bgd (billion gallons per day) of fresh and saline water for offstream uses- an 8-percent increase from the 1975 estimate and a 22-percent increase from the 1970 estimate. Average per capita use for all offstream uses was 2,000 gpd (gallons per day) of fresh and saline water, and 1,600 gpd of fresh water; this represents a slight increase since 1975. Offstream uses include (1) public supply (domestic, public, commercial, and industrial uses), (2) rural (domestic and livestock uses), (3) irrigation, and (4) self-supplied industrial uses (including thermoelectric power). From 1975 to 1980, public supply use increased 15 percent to 34 bgd, rural use increased 14 percent to 5.6 bgd, irrigation use increased 7 percent to 150 bgd, and self-supplied industrial use increased 8 percent to 260 bgd. Within the industrial category, thermoelectric power generation increased 9 percent to 210 bgd, whereas other self-supplied industrial uses remained approximately constant at 45 bgd. Total fresh water consumed- that part of water withdrawn that is no longer available for subsequent use- by these offstream uses increased 7 percent to 100 bgd, with irrigation accounting for the largest part of water consumed, estimated at 83 bgd. Estimates of withdrawals by source indicate that from 1975 to 1980, total groundwater withdrawals increased 7 percent to 89 bgd, and total surface-water withdrawals increased 9 percent to 360 bgd. Total saline-water withdrawals increased by about 2 bgd to 72 bgd, of which 71 bgd was saline surface water. Reclaimed sewage amounted to about 0.5 bgd in 1980, an 11-percent decrease from 1975. Water used for hydroelectric power generation, an instream use, remained unchanged from 1975 at 3,300 bgd. This is in contrast to the increasing trend from 1950 to 1975. Although 1980 estimates of water use were higher than the 1975 estimates for all offstream categories, trends establishing during
Estimating irrigation water use in the humid eastern United States
Levin, Sara B.; Zarriello, Phillip J.
2013-01-01
Accurate accounting of irrigation water use is an important part of the U.S. Geological Survey National Water-Use Information Program and the WaterSMART initiative to help maintain sustainable water resources in the Nation. Irrigation water use in the humid eastern United States is not well characterized because of inadequate reporting and wide variability associated with climate, soils, crops, and farming practices. To better understand irrigation water use in the eastern United States, two types of predictive models were developed and compared by using metered irrigation water-use data for corn, cotton, peanut, and soybean crops in Georgia and turf farms in Rhode Island. Reliable metered irrigation data were limited to these areas. The first predictive model that was developed uses logistic regression to predict the occurrence of irrigation on the basis of antecedent climate conditions. Logistic regression equations were developed for corn, cotton, peanut, and soybean crops by using weekly irrigation water-use data from 36 metered sites in Georgia in 2009 and 2010 and turf farms in Rhode Island from 2000 to 2004. For the weeks when irrigation was predicted to take place, the irrigation water-use volume was estimated by multiplying the average metered irrigation application rate by the irrigated acreage for a given crop. The second predictive model that was developed is a crop-water-demand model that uses a daily soil water balance to estimate the water needs of a crop on a given day based on climate, soil, and plant properties. Crop-water-demand models were developed independently of reported irrigation water-use practices and relied on knowledge of plant properties that are available in the literature. Both modeling approaches require accurate accounting of irrigated area and crop type to estimate total irrigation water use. Water-use estimates from both modeling methods were compared to the metered irrigation data from Rhode Island and Georgia that were used to
Estimation of HIV infection and incubation via state space models.
Tan, W Y; Ye, Z
2000-09-01
By using the state space model (Kalman filter model) of the HIV epidemic, in this paper we have developed a general Bayesian procedure to estimate simultaneously the HIV infection distribution, the HIV incubation distribution, the numbers of susceptible people, infective people and AIDS cases. The basic approach is to use the Gibbs sampling method combined with the weighted bootstrap method. We have applied this method to the San Francisco AIDS incidence data from January 1981 to December 1992. The results show clearly that both the probability density function of the HIV infection and the probability density function of the HIV incubation are curves with two peaks. The results of the HIV infection distribution are clearly consistent with the finding by Tan et al. [W.Y. Tan, S.C. Tang, S.R. Lee, Estimation of HIV seroconversion and effects of age in San Francisco homosexual populations, J. Appl. Stat. 25 (1998) 85]. The results of HIV incubation distribution seem to confirm the staged model used by Satten and Longini [G. Satten, I. Longini, Markov chain with measurement error: estimating the 'true' course of marker of the progression of human immunodeficiency virus disease, Appl. Stat. 45 (1996) 275]. PMID:10942785
Estimated use of water in the United States in 1975
Murray, Charles Richard; Reeves, E. Bodette
1977-01-01
Estimates of water use in the United States in 1975 indicate that an average of about 420 bgd (billion gallons per day) about 1,900 gallons per capita per day was withdrawn for the four principal off-channel uses which are (1) publicsupply (for domestic, commercial, and industrial uses), (2) rural (domestic and livestock), (3) irrigation, and (4) self-supplied industrial (including thermoelectric power). In 1975, withdrawals for these uses exceeded by 11.7 percent the 370 bgd estimated for 1970. Increases in the various categories of off-channel water use since 1970 were: approximately 12.8 percent for self-supplied industry (mainly in electric-utility thermoelectric plants), 7.9 percent for public supplies, 10.3 percent for rural supplies, and 10.9 percent for irrigation. Industrial water withdrawals included 70 bgd of saline water, a 30 percent increase in 5 years. The fifth principal withdrawal use, hydroelectric power (an in-channel use), amounted to 3,300 bgd, a 5-year increase of 20.7 percent. In computing total withdrawals, recycling within a plant (reuse) is not counted, but withdrawal of the same water by a downstream user (cumulative withdrawals) is counted. The quantity of freshwater consumed that is, water made unavailable for further possible withdrawal because of evaporation, incorporation in crops and manufactured products, and other causes was estimated to average 96 bgd for 1975, an increase of about 10 percent since 1970.
Stability and adaptability of popcorn genotypes in the State of Rio de Janeiro, Brazil.
Pena, G F; do Amaral, A T; Gonçalves, L S A; Candido, L S; Vittorazzi, C; Ribeiro, R M; Freitas, S P
2012-01-01
This study aimed to obtain estimates of stability and adaptability of phase launched materials and materials recommended in the country, for the northern and northwestern regions of Rio de Janeiro State, Brazil, and made a comparative analysis of different methods to evaluate stability and adaptability of grain yield and popping expansion. To this end, 10 genotypes were evaluated (UNB2U-C3, UNB2U-C4, BRS Angela, Viçosa, Beija-Flor, IAC 112, IAC 125, Zélia, Jade, and UFVM2 Barão de Viçosa) in five environments. The Yates and Cochran method revealed that genotypes UFV2M Barão de Viçosa, BRS Angela and UNB2U-C3 were the most stable for grain yield. This method also indicated superiority of genotypes UNB2U-C3, UNB2U-C4, BRS Angela, Viçosa, IAC 125, and Zélia for popping expansion. The Plaisted and Peterson and Wricke methods demonstrated that genotypes Zélia and UNB2U-C4 were the most productive and stable. These methods indicated genotypes UNB2U-C3 and BRS Angela as the most stable for popping expansion. The Kang and Phan ranking system uses methods based on analysis of variance and classified population UNB2U-C4 as the genotype with the highest stability of grain production and confirmed cultivar BRS Angela as the most stable for popping expansion. Genotypes IAC 112 and UNB2U-C4 were the most stable and adapted for grain yield, according to the Lin and Binns method. The P(i) statistics also ranked UNB2U-C3 and UNB2U-C4 as the genotypes with the best predictability and capacity for popping expansion. PMID:23007981
Stability and adaptability of popcorn genotypes in the State of Rio de Janeiro, Brazil.
Pena, G F; do Amaral, A T; Gonçalves, L S A; Candido, L S; Vittorazzi, C; Ribeiro, R M; Freitas, S P
2012-08-31
This study aimed to obtain estimates of stability and adaptability of phase launched materials and materials recommended in the country, for the northern and northwestern regions of Rio de Janeiro State, Brazil, and made a comparative analysis of different methods to evaluate stability and adaptability of grain yield and popping expansion. To this end, 10 genotypes were evaluated (UNB2U-C3, UNB2U-C4, BRS Angela, Viçosa, Beija-Flor, IAC 112, IAC 125, Zélia, Jade, and UFVM2 Barão de Viçosa) in five environments. The Yates and Cochran method revealed that genotypes UFV2M Barão de Viçosa, BRS Angela and UNB2U-C3 were the most stable for grain yield. This method also indicated superiority of genotypes UNB2U-C3, UNB2U-C4, BRS Angela, Viçosa, IAC 125, and Zélia for popping expansion. The Plaisted and Peterson and Wricke methods demonstrated that genotypes Zélia and UNB2U-C4 were the most productive and stable. These methods indicated genotypes UNB2U-C3 and BRS Angela as the most stable for popping expansion. The Kang and Phan ranking system uses methods based on analysis of variance and classified population UNB2U-C4 as the genotype with the highest stability of grain production and confirmed cultivar BRS Angela as the most stable for popping expansion. Genotypes IAC 112 and UNB2U-C4 were the most stable and adapted for grain yield, according to the Lin and Binns method. The P(i) statistics also ranked UNB2U-C3 and UNB2U-C4 as the genotypes with the best predictability and capacity for popping expansion.
Influence of adaptation state and stimulus luminance on peri-saccadic localization.
Georg, Katharina; Hamker, Fred H; Lappe, Markus
2008-01-01
Spatial localization of flashed stimuli across saccades shows transient distortions of perceived position: Stimuli appear shifted in saccade direction and compressed towards the saccade target. The strength and spatial pattern of this mislocalization is influenced by contrast, duration, and spatial and temporal arrangement of stimuli and background. Because mislocalization of stimuli on a background depends on contrast, we asked whether mislocalization of stimuli in darkness depends on luminance. Since dark adaptation changes luminance thresholds, we compared mislocalization in dark-adapted and light-adapted states. Peri-saccadic mislocalization was measured with near-threshold stimuli and above-threshold stimuli in dark-adapted and light-adapted subjects. In both adaptation states, near-threshold stimuli gave much larger mislocalization than above-threshold stimuli. Furthermore, when the stimulus was presented near-threshold, the perceived positions of the stimuli clustered closer together. Stimulus luminance that produced strong mislocalization in the light-adapted state produced very little mislocalization in the dark-adapted state because it was now well above threshold. We conclude that the strength of peri-saccadic mislocalization depends on the strength of the stimulus: stimuli with near-threshold luminance, and hence low visibility, are more mis-localized than clearly visible stimuli with high luminance.
ERIC Educational Resources Information Center
Blais, Jean-Guy; Raiche, Gilles
This paper examines some characteristics of the statistics associated with the sampling distribution of the proficiency level estimate when the Rasch model is used. These characteristics allow the judgment of the meaning to be given to the proficiency level estimate obtained in adaptive testing, and as a consequence, they can illustrate the…
ERIC Educational Resources Information Center
Kim, Sooyeon; Moses, Tim; Yoo, Hanwook Henry
2015-01-01
The purpose of this inquiry was to investigate the effectiveness of item response theory (IRT) proficiency estimators in terms of estimation bias and error under multistage testing (MST). We chose a 2-stage MST design in which 1 adaptation to the examinees' ability levels takes place. It includes 4 modules (1 at Stage 1, 3 at Stage 2) and 3 paths…
Conroy, M.J.; Runge, J.P.; Barker, R.J.; Schofield, M.R.; Fonnesbeck, C.J.
2008-01-01
Many organisms are patchily distributed, with some patches occupied at high density, others at lower densities, and others not occupied. Estimation of overall abundance can be difficult and is inefficient via intensive approaches such as capture-mark-recapture (CMR) or distance sampling. We propose a two-phase sampling scheme and model in a Bayesian framework to estimate abundance for patchily distributed populations. In the first phase, occupancy is estimated by binomial detection samples taken on all selected sites, where selection may be of all sites available, or a random sample of sites. Detection can be by visual surveys, detection of sign, physical captures, or other approach. At the second phase, if a detection threshold is achieved, CMR or other intensive sampling is conducted via standard procedures (grids or webs) to estimate abundance. Detection and CMR data are then used in a joint likelihood to model probability of detection in the occupancy sample via an abundance-detection model. CMR modeling is used to estimate abundance for the abundance-detection relationship, which in turn is used to predict abundance at the remaining sites, where only detection data are collected. We present a full Bayesian modeling treatment of this problem, in which posterior inference on abundance and other parameters (detection, capture probability) is obtained under a variety of assumptions about spatial and individual sources of heterogeneity. We apply the approach to abundance estimation for two species of voles (Microtus spp.) in Montana, USA. We also use a simulation study to evaluate the frequentist properties of our procedure given known patterns in abundance and detection among sites as well as design criteria. For most population characteristics and designs considered, bias and mean-square error (MSE) were low, and coverage of true parameter values by Bayesian credibility intervals was near nominal. Our two-phase, adaptive approach allows efficient estimation of
NASA Astrophysics Data System (ADS)
Durán-Barroso, Pablo; González, Javier; Valdés, Juan B.
2016-04-01
Rainfall-runoff quantification is one of the most important tasks in both engineering and watershed management as it allows to identify, forecast and explain watershed response. For that purpose, the Natural Resources Conservation Service Curve Number method (NRCS CN) is the conceptual lumped model more recognized in the field of rainfall-runoff estimation. Furthermore, there is still an ongoing discussion about the procedure to determine the portion of rainfall retained in the watershed before runoff is generated, called as initial abstractions. This concept is computed as a ratio (λ) of the soil potential maximum retention S of the watershed. Initially, this ratio was assumed to be 0.2, but later it has been proposed to be modified to 0.05. However, the actual procedures to convert NRCS CN model parameters obtained under a different hypothesis about λ do not incorporate any adaptation of climatic conditions of each watershed. By this reason, we propose a new simple method for computing model parameters which is adapted to local conditions taking into account regional patterns of climate conditions. After checking the goodness of this procedure against the actual ones in 34 different watersheds located in Ohio and Texas (United States), we concluded that this novel methodology represents the most accurate and efficient alternative to refit the initial abstraction ratio.
Kolus, Ahmet; Dubé, Philippe-Antoine; Imbeau, Daniel; Labib, Richard; Dubeau, Denise
2014-11-01
In new approaches based on adaptive neuro-fuzzy systems (ANFIS) and analytical method, heart rate (HR) measurements were used to estimate oxygen consumption (VO2). Thirty-five participants performed Meyer and Flenghi's step-test (eight of which performed regeneration release work), during which heart rate and oxygen consumption were measured. Two individualized models and a General ANFIS model that does not require individual calibration were developed. Results indicated the superior precision achieved with individualized ANFIS modelling (RMSE = 1.0 and 2.8 ml/kg min in laboratory and field, respectively). The analytical model outperformed the traditional linear calibration and Flex-HR methods with field data. The General ANFIS model's estimates of VO2 were not significantly different from actual field VO2 measurements (RMSE = 3.5 ml/kg min). With its ease of use and low implementation cost, the General ANFIS model shows potential to replace any of the traditional individualized methods for VO2 estimation from HR data collected in the field. PMID:24793823
Meliopoulos, Sakis; Cokkinides, George; Fardanesh, Bruce; Hedrington, Clinton
2013-12-31
This is the final report for this project that was performed in the period: October1, 2009 to June 30, 2013. In this project, a fully distributed high-fidelity dynamic state estimator (DSE) that continuously tracks the real time dynamic model of a wide area system with update rates better than 60 times per second is achieved. The proposed technology is based on GPS-synchronized measurements but also utilizes data from all available Intelligent Electronic Devices in the system (numerical relays, digital fault recorders, digital meters, etc.). The distributed state estimator provides the real time model of the system not only the voltage phasors. The proposed system provides the infrastructure for a variety of applications and two very important applications (a) a high fidelity generating unit parameters estimation and (b) an energy function based transient stability monitoring of a wide area electric power system with predictive capability. Also the dynamic distributed state estimation results are stored (the storage scheme includes data and coincidental model) enabling an automatic reconstruction and “play back” of a system wide disturbance. This approach enables complete play back capability with fidelity equal to that of real time with the advantage of “playing back” at a user selected speed. The proposed technologies were developed and tested in the lab during the first 18 months of the project and then demonstrated on two actual systems, the USVI Water and Power Administration system and the New York Power Authority’s Blenheim-Gilboa pumped hydro plant in the last 18 months of the project. The four main thrusts of this project, mentioned above, are extremely important to the industry. The DSE with the achieved update rates (more than 60 times per second) provides a superior solution to the “grid visibility” question. The generator parameter identification method fills an important and practical need of the industry. The “energy function” based
Kendall, W.L.; Nichols, J.D.
2002-01-01
Temporary emigration was identified some time ago as causing potential problems in capture-recapture studies, and in the last five years approaches have been developed for dealing with special cases of this general problem. Temporary emigration can be viewed more generally as involving transitions to and from an unobservable state, and frequently the state itself is one of biological interest (e.g., 'nonbreeder'). Development of models that permit estimation of relevant parameters in the presence of an unobservable state requires either extra information (e.g., as supplied by Pollock's robust design) or the following classes of model constraints: reducing the order of Markovian transition probabilities, imposing a degree of determinism on transition probabilities, removing state specificity of survival probabilities, and imposing temporal constancy of parameters. The objective of the work described in this paper is to investigate estimability of model parameters under a variety of models that include an unobservable state. Beginning with a very general model and no extra information, we used numerical methods to systematically investigate the use of ancillary information and constraints to yield models that are useful for estimation. The result is a catalog of models for which estimation is possible. An example analysis of sea turtle capture-recapture data under two different models showed similar point estimates but increased precision for the model that incorporated ancillary data (the robust design) when compared to the model with deterministic transitions only. This comparison and the results of our numerical investigation of model structures lead to design suggestions for capture-recapture studies in the presence of an unobservable state.
Adaptive time-delayed stabilization of steady states and periodic orbits.
Selivanov, Anton; Lehnert, Judith; Fradkov, Alexander; Schöll, Eckehard
2015-01-01
We derive adaptive time-delayed feedback controllers that stabilize fixed points and periodic orbits. First, we develop an adaptive controller for stabilization of a steady state by applying the speed-gradient method to an appropriate goal function and prove global asymptotic stability of the resulting system. For an example we show that the advantage of the adaptive controller over the nonadaptive one is in a smaller controller gain. Second, we propose adaptive time-delayed algorithms for stabilization of periodic orbits. Their efficiency is confirmed by local stability analysis. Numerical examples demonstrate the applicability of the proposed controllers.
Estimated Use of Water in the United States in 1985
Solley, Wayne B.; Merk, Charles F.; Pierce, Robert R.
1988-01-01
Water withdrawals in the United States during 1985 were estimated to average 399,000 million gallons per day (Mgal/d) of freshwater and saline water for offstream uses--10 percent less than the 1980 estimate. Average per-capita use for all offstream uses was 1,650 gallons per day (gal/d) of freshwater and saline water combined and 1,400 gal/d of freshwater alone. Offstream water-use categories are classified in this report as public supply, domestic, commercial, irrigation, livestock, industrial, mining, and thermoelectric power. During 1985, public-supply withdrawals were estimated to be 36,500 Mgal/d, and self-supplied withdrawals were estimated as follows: domestic, 3,320 Mgal/d: commercial, 1,230 Mgal/d; irrigation, 137,000 Mgal/d: livestock, 4,470 Mgal/d; industrial, 25,800 Mgal/d; mining, 3,440 Mgal/d; and thermoelectric power, 187,000 Mgal/d. Water use for hydroelectric power generation, the only instream use compiled in this report, was estimated to be 3,050,000 Mgal/d during 1985, or 7 percent less than during 1980. This is in contrast to an increasing trend that persisted from 1950 to 1980. Estimates of withdrawals by source indicate that, during 1985, total surface-water withdrawals were 325,000 Mgal/d, or 10 percent less than during 1980, and total ground-water withdrawals were 74,000 Mgal/d, or 12 percent less than during 1980. Total saline-water withdrawals during 1985 were 60,300 Mgal/d, or 16 percent less than during 1980; most was saline surface water. Reclaimed sewage averaged about 579 Mgal/d during 1985, or 22 percent more than during 1980. Total freshwater consumptive use was estimated to be 92,300 Mgal/d during 1985, or 9 percent less than during 1980. Consumptive use by irrigation accounted for the largest part of consumptive use during 1985 and was estimated to be 73,800 Mgal/d. A comparison of total withdrawals (fresh and saline) by State indicates that 37 States and Puerto Rico had less water withdrawn for offstream uses during 1985 than
Modeling, State Estimation and Control of Unmanned Helicopters
NASA Astrophysics Data System (ADS)
Lau, Tak Kit
Unmanned helicopters hold both tremendous potential and challenges. Without risking the lives of human pilots, these vehicles exhibit agile movement and the ability to hover and hence open up a wide range of applications in the hazardous situations. Sparing human lives, however, comes at a stiff price for technology. Some of the key difficulties that arise in these challenges are: (i) There are unexplained cross-coupled responses between the control axes on the hingeless helicopters that have puzzled researchers for years. (ii) Most, if not all, navigation on the unmanned helicopters relies on Global Navigation Satellite Systems (GNSSs), which are susceptible to jamming. (iii) It is often necessary to accommodate the re-configurations of the payload or the actuators on the helicopters by repeatedly tuning an autopilot, and that requires intensive human supervision and/or system identification. For the dynamics modeling and analysis, we present a comprehensive review on the helicopter actuation and dynamics, and contributes toward a more complete understanding on the on-axis and off-axis dynamical responses on the helicopter. We focus on a commonly used modeling technique, namely the phase-lag treatment, and employ a first-principles modeling method to justify that (i) why that phase-lag technique is inaccurate, (ii) how we can analyze the helicopter actuation and dynamics more accurately. Moreover, these dynamics modeling and analysis reveal the hard-to-measure but crucial parameters on a helicopter model that require the constant identifications, and hence convey the reasoning of seeking a model-implicit method to solve the state estimation and control problems on the unmanned helicopters. For the state estimation, we present a robust localization method for the unmanned helicopter against the GNSS outage. This method infers position from the acceleration measurement from an inertial measurement unit (IMU). In the core of our method are techniques of the sensor
Wavelet-Based Speech Enhancement Using Time-Adapted Noise Estimation
NASA Astrophysics Data System (ADS)
Lei, Sheau-Fang; Tung, Ying-Kai
Spectral subtraction is commonly used for speech enhancement in a single channel system because of the simplicity of its implementation. However, this algorithm introduces perceptually musical noise while suppressing the background noise. We propose a wavelet-based approach in this paper for suppressing the background noise for speech enhancement in a single channel system. The wavelet packet transform, which emulates the human auditory system, is used to decompose the noisy signal into critical bands. Wavelet thresholding is then temporally adjusted with the noise power by time-adapted noise estimation. The proposed algorithm can efficiently suppress the noise while reducing speech distortion. Experimental results, including several objective measurements, show that the proposed wavelet-based algorithm outperforms spectral subtraction and other wavelet-based denoising approaches for speech enhancement for nonstationary noise environments.
Improved Estimation of Earth Rotation Parameters Using the Adaptive Ridge Regression
NASA Astrophysics Data System (ADS)
Huang, Chengli; Jin, Wenjing
1998-05-01
The multicollinearity among regression variables is a common phenomenon in the reduction of astronomical data. The phenomenon of multicollinearity and the diagnostic factors are introduced first. As a remedy, a new method, called adaptive ridge regression (ARR), which is an improved method of choosing the departure constant θ in ridge regression, is suggested and applied in a case that the Earth orientation parameters (EOP) are determined by lunar laser ranging (LLR). It is pointed out, via a diagnosis, the variance inflation factors (VIFs), that there exists serious multicollinearity among the regression variables. It is shown that the ARR method is effective in reducing the multicollinearity and makes the regression coefficients more stable than that of using ordinary least squares estimation (LS), especially when there is serious multicollinearity.
Estimated use of water in the United States in 1970
Murray, Charles Richard; Reeves, E. Bodette
1972-01-01
The average annual streamflow--simplified measure of the total available water supply--is approximately 1,200 bgd in the conterminous United States. Total water withdrawn in 1970 for off-channel uses (withdrawals other than for hydroelectric power) amounted to about 30 percent of the average annual streamflow: 7 percent of the 1,200 bgd basic supply was consumed. However, comparisons of Water Resources Council regions indicate that the rate of withdrawal was higher than the locally dependable supply in the Middle Atlantic, Texas-Gulf, Rio Grande, Lower Colorado, and California-South Pacific regions. Consumption amounted to nearly 25 percent of withdrawals in the conterminous United States; however, fresh-water consumption amounted to only 14 percent of off-channel withdrawals in the 31 Eastern States and ranged from 30 percent to nearly 70 percent of off-channel withdrawals in the Water Resources Council regions in the West. In the Rio Grande and Lower Colorado regions, fresh-water consumption in 1970 exceeded the estimated dependable supply of fresh water.
Halloran, Jason P.; Erdemir, Ahmet
2011-01-01
Simulation-based prediction of specimen-specific biomechanical behavior commonly requires inverse analysis using geometrically consistent finite element (FE) models. Optimization drives such analyses but previous studies have highlighted a large computational cost dictated by iterative use of nonlinear FE models. The goal of this study was to evaluate the performance of a local regression-based adaptive surrogate modeling approach to decrease computational cost for both global and local optimization approaches using an inverse FE application. Nonlinear elastic material parameters for patient-specific heel-pad tissue were found, both with and without the surrogate model. Surrogate prediction replaced a FE simulation using local regression of previous simulations when the corresponding error estimate was less than a given tolerance. Performance depended on optimization type and tolerance value. The surrogate reduced local optimization expense up to 68%, but achieved accurate results for only 1 of 20 initial conditions. Conversely, up to a tolerance value of 20 N2, global optimization with the surrogate yielded consistent parameter predictions with a concurrent decrease in computational cost (up to 77%). However, the local optimization method without the surrogate, although sensitive to the initial conditions, was still on average seven times faster than the global approach. Our results help establish guide-lines for setting acceptable tolerance values while using an adaptive surrogate model for inverse FE analysis. Most important, the study demonstrates the benefits of a surrogate modeling approach for intensive FE-based iterative analysis. PMID:21544674
NASA Astrophysics Data System (ADS)
Bu, Guochao; Wang, Pei
2016-04-01
Terrestrial laser scanning (TLS) has been used to extract accurate forest biophysical parameters for inventory purposes. The diameter at breast height (DBH) is a key parameter for individual trees because it has the potential for modeling the height, volume, biomass, and carbon sequestration potential of the tree based on empirical allometric scaling equations. In order to extract the DBH from the single-scan data of TLS automatically and accurately within a certain range, we proposed an adaptive circle-ellipse fitting method based on the point cloud transect. This proposed method can correct the error caused by the simple circle fitting method when a tree is slanted. A slanted tree was detected by the circle-ellipse fitting analysis, then the corresponding slant angle was found based on the ellipse fitting result. With this information, the DBH of the trees could be recalculated based on reslicing the point cloud data at breast height. Artificial stem data simulated by a cylindrical model of leaning trees and the scanning data acquired with the RIEGL VZ-400 were used to test the proposed adaptive fitting method. The results shown that the proposed method can detect the trees and accurately estimate the DBH for leaning trees.
Adaptive estimation of hand movement trajectory in an EEG based brain-computer interface system
NASA Astrophysics Data System (ADS)
Robinson, Neethu; Guan, Cuntai; Vinod, A. P.
2015-12-01
Objective. The various parameters that define a hand movement such as its trajectory, speed, etc, are encoded in distinct brain activities. Decoding this information from neurophysiological recordings is a less explored area of brain-computer interface (BCI) research. Applying non-invasive recordings such as electroencephalography (EEG) for decoding makes the problem more challenging, as the encoding is assumed to be deep within the brain and not easily accessible by scalp recordings. Approach. EEG based BCI systems can be developed to identify the neural features underlying movement parameters that can be further utilized to provide a detailed and well defined control command set to a BCI output device. A real-time continuous control is better suited for practical BCI systems, and can be achieved by continuous adaptive reconstruction of movement trajectory than discrete brain activity classifications. In this work, we adaptively reconstruct/estimate the parameters of two-dimensional hand movement trajectory, namely movement speed and position, from multi-channel EEG recordings. The data for analysis is collected by performing an experiment that involved center-out right-hand movement tasks in four different directions at two different speeds in random order. We estimate movement trajectory using a Kalman filter that models the relation between brain activity and recorded parameters based on a set of defined predictors. We propose a method to define these predictor variables that includes spatial, spectral and temporally localized neural information and to select optimally informative variables. Main results. The proposed method yielded correlation of (0.60 ± 0.07) between recorded and estimated data. Further, incorporating the proposed predictor subset selection, the correlation achieved is (0.57 ± 0.07, p {\\lt }0.004) with significant gain in stability of the system, as well as dramatic reduction in number of predictors (76%) for the savings of computational
Lombardo, Marco; Serrao, Sebastiano; Lombardo, Giuseppe
2014-01-01
Purpose To investigate the influence of various technical factors on the variation of cone packing density estimates in adaptive optics flood illuminated retinal images. Methods Adaptive optics images of the photoreceptor mosaic were obtained in fifteen healthy subjects. The cone density and Voronoi diagrams were assessed in sampling windows of 320×320 µm, 160×160 µm and 64×64 µm at 1.5 degree temporal and superior eccentricity from the preferred locus of fixation (PRL). The technical factors that have been analyzed included the sampling window size, the corrected retinal magnification factor (RMFcorr), the conversion from radial to linear distance from the PRL, the displacement between the PRL and foveal center and the manual checking of cone identification algorithm. Bland-Altman analysis was used to assess the agreement between cone density estimated within the different sampling window conditions. Results The cone density declined with decreasing sampling area and data between areas of different size showed low agreement. A high agreement was found between sampling areas of the same size when comparing density calculated with or without using individual RMFcorr. The agreement between cone density measured at radial and linear distances from the PRL and between data referred to the PRL or the foveal center was moderate. The percentage of Voronoi tiles with hexagonal packing arrangement was comparable between sampling areas of different size. The boundary effect, presence of any retinal vessels, and the manual selection of cones missed by the automated identification algorithm were identified as the factors influencing variation of cone packing arrangements in Voronoi diagrams. Conclusions The sampling window size is the main technical factor that influences variation of cone density. Clear identification of each cone in the image and the use of a large buffer zone are necessary to minimize factors influencing variation of Voronoi diagrams of the cone
Adaptive data-driven models for estimating carbon fluxes in the Northern Great Plains
Wylie, B.K.; Fosnight, E.A.; Gilmanov, T.G.; Frank, A.B.; Morgan, J.A.; Haferkamp, Marshall R.; Meyers, T.P.
2007-01-01
Rangeland carbon fluxes are highly variable in both space and time. Given the expansive areas of rangelands, how rangelands respond to climatic variation, management, and soil potential is important to understanding carbon dynamics. Rangeland carbon fluxes associated with Net Ecosystem Exchange (NEE) were measured from multiple year data sets at five flux tower locations in the Northern Great Plains. These flux tower measurements were combined with 1-km2 spatial data sets of Photosynthetically Active Radiation (PAR), Normalized Difference Vegetation Index (NDVI), temperature, precipitation, seasonal NDVI metrics, and soil characteristics. Flux tower measurements were used to train and select variables for a rule-based piece-wise regression model. The accuracy and stability of the model were assessed through random cross-validation and cross-validation by site and year. Estimates of NEE were produced for each 10-day period during each growing season from 1998 to 2001. Growing season carbon flux estimates were combined with winter flux estimates to derive and map annual estimates of NEE. The rule-based piece-wise regression model is a dynamic, adaptive model that captures the relationships of the spatial data to NEE as conditions evolve throughout the growing season. The carbon dynamics in the Northern Great Plains proved to be in near equilibrium, serving as a small carbon sink in 1999 and as a small carbon source in 1998, 2000, and 2001. Patterns of carbon sinks and sources are very complex, with the carbon dynamics tilting toward sources in the drier west and toward sinks in the east and near the mountains in the extreme west. Significant local variability exists, which initial investigations suggest are likely related to local climate variability, soil properties, and management.
NASA Astrophysics Data System (ADS)
Elmi, Omid; Javad Tourian, Mohammad; Sneeuw, Nico
2015-04-01
The importance of river discharge monitoring is critical for e.g., water resource planning, climate change, hazard monitoring. River discharge has been measured at in situ gauges for more than a century. Despite various attempts, some basins are still ungauged. Moreover, a reduction in the number of worldwide gauging stations increases the interest to employ remote sensing data for river discharge monitoring. Finding an empirical relationship between simultaneous in situ measurements of discharge and river widths derived from satellite imagery has been introduced as a straightforward remote sensing alternative. Classifying water and land in an image is the primary task for defining the river width. Water appears dark in the near infrared and infrared bands in satellite images. As a result low values in the histogram usually represent the water content. In this way, applying a threshold on the image histogram and separating into two different classes is one of the most efficient techniques to build a water mask. Beside its simple definition, finding the appropriate threshold value in each image is the most critical issue. The threshold is variable due to changes in the water level, river extent, atmosphere, sunlight radiation, onboard calibration of the satellite over time. These complexities in water body classification are the main source of error in river width estimation. In this study, we are looking for the most efficient adaptive threshold algorithm to estimate the river discharge. To do this, all cloud free MODIS images coincident with the in situ measurement are collected. Next a number of automatic threshold selection techniques are employed to generate different dynamic water masks. Then, for each of them a separate empirical relationship between river widths and discharge measurements are determined. Through these empirical relationships, we estimate river discharge at the gauge and then validate our results against in situ measurements and also
Estimating the costs of landslide damage in the United States
Fleming, Robert W.; Taylor, Fred A.
1980-01-01
Landslide damages are one of the most costly natural disasters in the United States. A recent estimate of the total annual cost of landslide damage is in excess of $1 billion {Schuster, 1978}. The damages can be significantly reduced, however, through the combined action of technical experts, government, and the public. Before they can be expected to take action, local governments need to have an appreciation of costs of damage in their areas of responsibility and of the reductions in losses that can be achieved. Where studies of cost of landslide damages have been conducted, it is apparent that {1} costs to the public and private sectors of our economy due to landslide damage are much larger than anticipated; {2} taxpayers and public officials generally are unaware of the magnitude of the cost, owing perhaps to the lack of any centralization of data; and {3} incomplete records and unavailability of records result in lower reported costs than actually were incurred. The U.S. Geological Survey has developed a method to estimate the cost of landslide damages in regional and local areas and has applied the method in three urban areas and one rural area. Costs are for different periods and are unadjusted for inflation; therefore, strict comparisons of data from different years should be avoided. Estimates of the average annual cost of landslide damage for the urban areas studied are $5,900,000 in the San Francisco Bay area; $4,000,000 in Allegheny County, Pa.; and $5,170,000 in Hamilton County, Ohio. Adjusting these figures for the population of each area, the annual cost of damages per capita are $1.30 in the nine-county San Francisco Bay region; $2.50 in Allegheny County, Pa.; and $5.80 in Hamilton County, Ohio. On the basis of data from other sources, the estimated annual damages on a per capita basis for the City of Los Angeles, Calif., are about $1.60. If the costs were available for the damages from landslides in Los Angeles in 1977-78 and 1979-80, the annual per
Estimated Use of Water in the United States in 2000
Hutson, Susan S.; Barber, Nancy L.; Kenny, Joan F.; Linsey, Kristin S.; Lumia, Deborah S.; Maupin, Molly A.
2004-01-01
Estimates of water use in the United States indicate that about 408 billion gallons per day (one thousand million gallons per day, abbreviated Bgal/d) were withdrawn for all uses during 2000. This total has varied less than 3 percent since 1985 as withdrawals have stabilized for the two largest uses?thermoelectric power and irrigation. Fresh ground-water withdrawals (83.3 Bgal/d) during 2000 were 14 percent more than during 1985. Fresh surface-water withdrawals for 2000 were 262 Bgal/d, varying less than 2 percent since 1985. About 195 Bgal/d, or 48 percent of all freshwater and saline-water withdrawals for 2000, were used for thermoelectric power. Most of this water was derived from surface water and used for once-through cooling at power plants. About 52 percent of fresh surface-water withdrawals and about 96 percent of saline-water withdrawals were for thermoelectric-power use. Withdrawals for thermoelectric power have been relatively stable since 1985. Irrigation remained the largest use of freshwater in the United States and totaled 137 Bgal/d for 2000. Since 1950, irrigation has accounted for about 65 percent of total water withdrawals, excluding those for thermoelectric power. Historically, more surface water than ground water has been used for irrigation. However, the percentage of total irrigation withdrawals from ground water has continued to increase, from 23 percent in 1950 to 42 percent in 2000. Total irrigation withdrawals were 2 percent more for 2000 than for 1995, because of a 16-percent increase in ground-water withdrawals and a small decrease in surface-water withdrawals. Irrigated acreage more than doubled between 1950 and 1980, then remained constant before increasing nearly 7 percent between 1995 and 2000. The number of acres irrigated with sprinkler and microirrigation systems has continued to increase and now comprises more than one-half the total irrigated acreage. Public-supply withdrawals were more than 43 Bgal/d for 2000. Public
State-Estimation Algorithm Based on Computer Vision
NASA Technical Reports Server (NTRS)
Bayard, David; Brugarolas, Paul
2007-01-01
An algorithm and software to implement the algorithm are being developed as means to estimate the state (that is, the position and velocity) of an autonomous vehicle, relative to a visible nearby target object, to provide guidance for maneuvering the vehicle. In the original intended application, the autonomous vehicle would be a spacecraft and the nearby object would be a small astronomical body (typically, a comet or asteroid) to be explored by the spacecraft. The algorithm could also be used on Earth in analogous applications -- for example, for guiding underwater robots near such objects of interest as sunken ships, mineral deposits, or submerged mines. It is assumed that the robot would be equipped with a vision system that would include one or more electronic cameras, image-digitizing circuitry, and an imagedata- processing computer that would generate feature-recognition data products.
Using Anchors to Estimate Clinical State without Labeled Data
Halpern, Yoni; Choi, Youngduck; Horng, Steven; Sontag, David
2014-01-01
We present a novel framework for learning to estimate and predict clinical state variables without labeled data. The resulting models can used for electronic phenotyping, triggering clinical decision support, and cohort selection. The framework relies on key observations which we characterize and term “anchor variables”. By specifying anchor variables, an expert encodes a certain amount of domain knowledge about the problem while the rest of learning proceeds in an unsupervised manner. The ability to build anchors upon standardized ontologies and the framework’s ability to learn from unlabeled data promote generalizability across institutions. We additionally develop a user interface to enable experts to choose anchor variables in an informed manner. The framework is applied to electronic medical record-based phenotyping to enable real-time decision support in the emergency department. We validate the learned models using a prospectively gathered set of gold-standard responses from emergency physicians for nine clinically relevant variables. PMID:25954366
Chen Lin; Zhu Huangjun; Wei, Tzu-Chieh
2011-01-15
We study the geometric measure of entanglement (GM) of pure symmetric states related to rank 1 positive-operator-valued measures (POVMs) and establish a general connection with quantum state estimation theory, especially the maximum likelihood principle. Based on this connection, we provide a method for computing the GM of these states and demonstrate its additivity property under certain conditions. In particular, we prove the additivity of the GM of pure symmetric multiqubit states whose Majorana points under Majorana representation are distributed within a half sphere, including all pure symmetric three-qubit states. We then introduce a family of symmetric states that are generated from mutually unbiased bases and derive an analytical formula for their GM. These states include Dicke states as special cases, which have already been realized in experiments. We also derive the GM of symmetric states generated from symmetric informationally complete POVMs (SIC POVMs) and use it to characterize all inequivalent SIC POVMs in three-dimensional Hilbert space that are covariant with respect to the Heisenberg-Weyl group. Finally, we describe an experimental scheme for creating the symmetric multiqubit states studied in this article and a possible scheme for measuring the permanence of the related Gram matrix.
NASA Technical Reports Server (NTRS)
Lee-Rausch, E. M.; Park, M. A.; Jones, W. T.; Hammond, D. P.; Nielsen, E. J.
2005-01-01
This paper demonstrates the extension of error estimation and adaptation methods to parallel computations enabling larger, more realistic aerospace applications and the quantification of discretization errors for complex 3-D solutions. Results were shown for an inviscid sonic-boom prediction about a double-cone configuration and a wing/body segmented leading edge (SLE) configuration where the output function of the adjoint was pressure integrated over a part of the cylinder in the near field. After multiple cycles of error estimation and surface/field adaptation, a significant improvement in the inviscid solution for the sonic boom signature of the double cone was observed. Although the double-cone adaptation was initiated from a very coarse mesh, the near-field pressure signature from the final adapted mesh compared very well with the wind-tunnel data which illustrates that the adjoint-based error estimation and adaptation process requires no a priori refinement of the mesh. Similarly, the near-field pressure signature for the SLE wing/body sonic boom configuration showed a significant improvement from the initial coarse mesh to the final adapted mesh in comparison with the wind tunnel results. Error estimation and field adaptation results were also presented for the viscous transonic drag prediction of the DLR-F6 wing/body configuration, and results were compared to a series of globally refined meshes. Two of these globally refined meshes were used as a starting point for the error estimation and field-adaptation process where the output function for the adjoint was the total drag. The field-adapted results showed an improvement in the prediction of the drag in comparison with the finest globally refined mesh and a reduction in the estimate of the remaining drag error. The adjoint-based adaptation parameter showed a need for increased resolution in the surface of the wing/body as well as a need for wake resolution downstream of the fuselage and wing trailing edge
Censoring distributed nonlinear state estimates in radar networks
NASA Astrophysics Data System (ADS)
Conte, Armond S.; Niu, Ruixin
2016-05-01
In a distributed radar track fusion system, it is desired to limit the communication rate between the sensors and the central node to only the most relevant information available. One way to do this is to use some metric that judges quantity of new information available, in comparison to that which has already been provided. The J-Divergence is a symmetric metric, derived from the Kullback-Liebler divergence, which performs a comparison of the statistical distance between two probability distributions. For the comparison between new and old data, a large J-Divergence can represent the existence of new information, while a small J-Divergence represents the lack of new information. Previous work included an application where the J-Divergence was used to limit data for scenarios in which the primary state estimator was an Extended Kalman Filter and used only Gaussian approximations at the local sensors. This paper expands the range of estimators to particle filters in order to account for situations where censoring is desired to be applied to non-linear/non-Gaussian environments. A derivation of the J-Divergence between probability density functions (PDFs) which are approximated by particles is provided for use in a non-feedback fusion case. An example application is given involving a 2D radar tracking scenario using the J-Divergences of a particle filter with the Gaussian approximation and a particle filter with the approximated discrete prior/posterior PDFs.
NASA Astrophysics Data System (ADS)
Klein, R.; Gordon, E.
2010-12-01
Scholars and policy analysts often contend that an effective climate adaptation strategy must entail "mainstreaming," or incorporating responses to possible climate impacts into existing planning and management decision frameworks. Such an approach, however, makes it difficult to assess the degree to which decisionmaking entities are engaging in adaptive activities that may or may not be explicitly framed around a changing climate. For example, a drought management plan may not explicitly address climate change, but the activities and strategies outlined in it may reduce vulnerabilities posed by a variable and changing climate. Consequently, to generate a strategic climate adaptation plan requires identifying the entire suite of activities that are implicitly linked to climate and may affect adaptive capacity within the system. Here we outline a novel, two-pronged approach, leveraging social science methods, to understanding adaptation throughout state government in Colorado. First, we conducted a series of interviews with key actors in state and federal government agencies, non-governmental organizations, universities, and other entities engaged in state issues. The purpose of these interviews was to elicit information about current activities that may affect the state’s adaptive capacity and to identify future climate-related needs across the state. Second, we have developed an interactive database cataloging organizations, products, projects, and people actively engaged in adaptive planning and policymaking that are relevant to the state of Colorado. The database includes a wiki interface, helping create a dynamic component that will enable frequent updating as climate-relevant information emerges. The results of this project are intended to paint a clear picture of sectors and agencies with higher and lower levels of adaptation awareness and to provide a roadmap for the next gubernatorial administration to pursue a more sophisticated climate adaptation agenda
Vidaurre, C; Schlögl, A; Cabeza, R; Scherer, R; Pfurtscheller, G
2005-11-01
We present the result of on-line feedback Brain Computer Interface experiments using adaptive and non-adaptive feature extraction methods with an on-line adaptive classifier based on Quadratic Discriminant Analysis. Experiments were performed with 12 naïve subjects, feedback was provided from the first moment and no training sessions were needed. Experiments run in three different days with each subject. Six of them received feedback with Adaptive Autoregressive parameters and the rest with logarithmic Band Power estimates. The study was done using single trial analysis of each of the sessions and the value of the Error Rate and the Mutual Information of the classification were used to discuss the results. Finally, it was shown that even subjects starting with a low performance were able to control the system in a few hours: and contrary to previous results no differences between AAR and BP estimates were found.
Tuna, E. Erdem; Franke, Timothy J.; Bebek, Özkan; Shiose, Akira; Fukamachi, Kiyotaka; Çavuşoğlu, M. Cenk
2013-01-01
Robotic assisted beating heart surgery aims to allow surgeons to operate on a beating heart without stabilizers as if the heart is stationary. The robot actively cancels heart motion by closely following a point of interest (POI) on the heart surface—a process called Active Relative Motion Canceling (ARMC). Due to the high bandwidth of the POI motion, it is necessary to supply the controller with an estimate of the immediate future of the POI motion over a prediction horizon in order to achieve sufficient tracking accuracy. In this paper, two least-square based prediction algorithms, using an adaptive filter to generate future position estimates, are implemented and studied. The first method assumes a linear system relation between the consecutive samples in the prediction horizon. On the contrary, the second method performs this parametrization independently for each point over the whole the horizon. The effects of predictor parameters and variations in heart rate on tracking performance are studied with constant and varying heart rate data. The predictors are evaluated using a 3 degrees of freedom test-bed and prerecorded in-vivo motion data. Then, the one-step prediction and tracking performances of the presented approaches are compared with an Extended Kalman Filter predictor. Finally, the essential features of the proposed prediction algorithms are summarized. PMID:23976889
Federal Register 2010, 2011, 2012, 2013, 2014
2011-07-15
... HUMAN SERVICES Administration for Children and Families Estimated Federal Allotments to State... Families (ACF), Department of Health and Human Services (HHS). ACTION: Notification of Estimated Fiscal... Advocacy Systems Formula Grant Programs. SUMMARY: This notice sets forth estimated FY 2012...
International Students' Psychological and Sociocultural Adaptation in the United States
ERIC Educational Resources Information Center
Sumer, Seda
2009-01-01
International students constitute an important cohort in the United States (U.S.) colleges and universities. In order for the U.S. colleges and universities to better accommodate the significant number of international students and to recruit them in the future, it is critical to identify factors that influence these students' acculturation and…
A Two-Stage Kalman Filter Approach for Robust and Real-Time Power System State Estimation
Zhang, Jinghe; Welch, Greg; Bishop, Gary; Huang, Zhenyu
2014-04-01
As electricity demand continues to grow and renewable energy increases its penetration in the power grid, realtime state estimation becomes essential for system monitoring and control. Recent development in phasor technology makes it possible with high-speed time-synchronized data provided by Phasor Measurement Units (PMU). In this paper we present a two-stage Kalman filter approach to estimate the static state of voltage magnitudes and phase angles, as well as the dynamic state of generator rotor angles and speeds. Kalman filters achieve optimal performance only when the system noise characteristics have known statistical properties (zero-mean, Gaussian, and spectrally white). However in practice the process and measurement noise models are usually difficult to obtain. Thus we have developed the Adaptive Kalman Filter with Inflatable Noise Variances (AKF with InNoVa), an algorithm that can efficiently identify and reduce the impact of incorrect system modeling and/or erroneous measurements. In stage one, we estimate the static state from raw PMU measurements using the AKF with InNoVa; then in stage two, the estimated static state is fed into an extended Kalman filter to estimate the dynamic state. Simulations demonstrate its robustness to sudden changes of system dynamics and erroneous measurements.
Using support vector machines in the multivariate state estimation technique
Zavaljevski, N.; Gross, K.C.
1999-07-01
One approach to validate nuclear power plant (NPP) signals makes use of pattern recognition techniques. This approach often assumes that there is a set of signal prototypes that are continuously compared with the actual sensor signals. These signal prototypes are often computed based on empirical models with little or no knowledge about physical processes. A common problem of all data-based models is their limited ability to make predictions on the basis of available training data. Another problem is related to suboptimal training algorithms. Both of these potential shortcomings with conventional approaches to signal validation and sensor operability validation are successfully resolved by adopting a recently proposed learning paradigm called the support vector machine (SVM). The work presented here is a novel application of SVM for data-based modeling of system state variables in an NPP, integrated with a nonlinear, nonparametric technique called the multivariate state estimation technique (MSET), an algorithm developed at Argonne National Laboratory for a wide range of nuclear plant applications.
Goodness-of-Fit Tests and Nonparametric Adaptive Estimation for Spike Train Analysis
2014-01-01
When dealing with classical spike train analysis, the practitioner often performs goodness-of-fit tests to test whether the observed process is a Poisson process, for instance, or if it obeys another type of probabilistic model (Yana et al. in Biophys. J. 46(3):323–330, 1984; Brown et al. in Neural Comput. 14(2):325–346, 2002; Pouzat and Chaffiol in Technical report, http://arxiv.org/abs/arXiv:0909.2785, 2009). In doing so, there is a fundamental plug-in step, where the parameters of the supposed underlying model are estimated. The aim of this article is to show that plug-in has sometimes very undesirable effects. We propose a new method based on subsampling to deal with those plug-in issues in the case of the Kolmogorov–Smirnov test of uniformity. The method relies on the plug-in of good estimates of the underlying model that have to be consistent with a controlled rate of convergence. Some nonparametric estimates satisfying those constraints in the Poisson or in the Hawkes framework are highlighted. Moreover, they share adaptive properties that are useful from a practical point of view. We show the performance of those methods on simulated data. We also provide a complete analysis with these tools on single unit activity recorded on a monkey during a sensory-motor task. Electronic Supplementary Material The online version of this article (doi:10.1186/2190-8567-4-3) contains supplementary material. PMID:24742008
Transitional states in marine fisheries: adapting to predicted global change.
MacNeil, M Aaron; Graham, Nicholas A J; Cinner, Joshua E; Dulvy, Nicholas K; Loring, Philip A; Jennings, Simon; Polunin, Nicholas V C; Fisk, Aaron T; McClanahan, Tim R
2010-11-27
Global climate change has the potential to substantially alter the production and community structure of marine fisheries and modify the ongoing impacts of fishing. Fish community composition is already changing in some tropical, temperate and polar ecosystems, where local combinations of warming trends and higher environmental variation anticipate the changes likely to occur more widely over coming decades. Using case studies from the Western Indian Ocean, the North Sea and the Bering Sea, we contextualize the direct and indirect effects of climate change on production and biodiversity and, in turn, on the social and economic aspects of marine fisheries. Climate warming is expected to lead to (i) yield and species losses in tropical reef fisheries, driven primarily by habitat loss; (ii) community turnover in temperate fisheries, owing to the arrival and increasing dominance of warm-water species as well as the reduced dominance and departure of cold-water species; and (iii) increased diversity and yield in Arctic fisheries, arising from invasions of southern species and increased primary production resulting from ice-free summer conditions. How societies deal with such changes will depend largely on their capacity to adapt--to plan and implement effective responses to change--a process heavily influenced by social, economic, political and cultural conditions.
On the estimation algorithm used in adaptive performance optimization of turbofan engines
NASA Technical Reports Server (NTRS)
Espana, Martin D.; Gilyard, Glenn B.
1993-01-01
The performance seeking control algorithm is designed to continuously optimize the performance of propulsion systems. The performance seeking control algorithm uses a nominal model of the propulsion system and estimates, in flight, the engine deviation parameters characterizing the engine deviations with respect to nominal conditions. In practice, because of measurement biases and/or model uncertainties, the estimated engine deviation parameters may not reflect the engine's actual off-nominal condition. This factor has a necessary impact on the overall performance seeking control scheme exacerbated by the open-loop character of the algorithm. The effects produced by unknown measurement biases over the estimation algorithm are evaluated. This evaluation allows for identification of the most critical measurements for application of the performance seeking control algorithm to an F100 engine. An equivalence relation between the biases and engine deviation parameters stems from an observability study; therefore, it is undecided whether the estimated engine deviation parameters represent the actual engine deviation or whether they simply reflect the measurement biases. A new algorithm, based on the engine's (steady-state) optimization model, is proposed and tested with flight data. When compared with previous Kalman filter schemes, based on local engine dynamic models, the new algorithm is easier to design and tune and it reduces the computational burden of the onboard computer.
Linking Federal, State, and Local Adaptation Strategies in New York (Invited)
NASA Astrophysics Data System (ADS)
Rosenzweig, C.
2010-12-01
New York City and New York State are leaders in adaptation in the U.S. In 2008 Mayor Bloomberg convened the NYC Climate Change Adaptation Task Force and the New York City Panel on Climate Change (NPCC). Also in 2008, the New York State Energy Research and Development Authority (NYSERDA) initiated the Integrated Assessment for Effective Climate Change Adaptation Strategies (ClimAID), to provide New York State decision-makers with cutting-edge information on its vulnerability to climate change and to facilitate the development of adaptation strategies informed by both local experience and scientific knowledge. The two efforts are working together to develop effective adaptation strategies across multiple jurisdictions. The New York Task Force consists of approximate 40 city, state, and federal agencies, regional public authorities, and private companies that operate, maintain, or regulate critical infrastructure in the region. The NPCC consisted of climate change and impacts scientists, and legal, insurance, and risk-management experts and served as the technical advisory body for the Mayor and the Task Force on issues related to climate change, impacts, and adaptation. In its 2010 report, the NPCC recommended adoption of a risk-based approach to climate change; creation of a monitoring program to track and analyze key climate change factors, impacts, and adaptation indicators; review of relevant standards and codes; inclusion of multiple layers of government and a wide range of public and private stakeholder experts to build buy-in; and formation of crucial partnerships for development of coordinated adaptation strategies. The task now is for these partnerships to create pilot programs that move adaptation from the planning phase to implementation; urban areas can provide critical ‘test-beds’ for such efforts.
NASA Astrophysics Data System (ADS)
Bargatze, L. F.
2015-12-01
Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted
Sub-second State Estimation Implementation and its Evaluation with Real Data
Chen, Yousu; Rice, Mark J.; Glaesemann, Kurt R.; Huang, Zhenyu
2015-07-31
This paper describes the performance of a parallel state estimation (PSE) tool implemented using advanced computing techniques. The developed code is able to solve state estimation of large-size, practical power systems within one second. Benchmark tests against a commercial tool shows that the computational speed is 10 times faster. Benefits brought by the sub-second state estimation are also discussed.
The estimation error covariance matrix for the ideal state reconstructor with measurement noise
NASA Technical Reports Server (NTRS)
Polites, Michael E.
1988-01-01
A general expression is derived for the state estimation error covariance matrix for the Ideal State Reconstructor when the input measurements are corrupted by measurement noise. An example is presented which shows that the more measurements used in estimating the state at a given time, the better the estimator.
Piaggi, Paolo; Menicucci, Danilo; Gentili, Claudio; Handjaras, Giacomo; Gemignani, Angelo; Landi, Alberto
2014-05-01
Sources of noise in resting-state fMRI experiments include instrumental and physiological noises, which need to be filtered before a functional connectivity analysis of brain regions is performed. These noisy components show autocorrelated and nonstationary properties that limit the efficacy of standard techniques (i.e. time filtering and general linear model). Herein we describe a novel approach based on the combination of singular spectrum analysis and adaptive filtering, which allows a greater noise reduction and yields better connectivity estimates between regions at rest, providing a new feasible procedure to analyze fMRI data.
Rainfall Estimation over the Nile Basin using an Adapted Version of the SCaMPR Algorithm
NASA Astrophysics Data System (ADS)
Habib, E. H.; Kuligowski, R. J.; Elshamy, M. E.; Ali, M. A.; Haile, A.; Amin, D.; Eldin, A.
2011-12-01
Management of Egypt's Aswan High Dam is critical not only for flood control on the Nile but also for ensuring adequate water supplies for most of Egypt since rainfall is scarce over the vast majority of its land area. However, reservoir inflow is driven by rainfall over Sudan, Ethiopia, Uganda, and several other countries from which routine rain gauge data are sparse. Satellite-derived estimates of rainfall offer a much more detailed and timely set of data to form a basis for decisions on the operation of the dam. A single-channel infrared algorithm is currently in operational use at the Egyptian Nile Forecast Center (NFC). This study reports on the adaptation of a multi-spectral, multi-instrument satellite rainfall estimation algorithm (Self-Calibrating Multivariate Precipitation Retrieval, SCaMPR) for operational application over the Nile Basin. The algorithm uses a set of rainfall predictors from multi-spectral Infrared cloud top observations and self-calibrates them to a set of predictands from Microwave (MW) rain rate estimates. For application over the Nile Basin, the SCaMPR algorithm uses multiple satellite IR channels recently available to NFC from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Microwave rain rates are acquired from multiple sources such as SSM/I, SSMIS, AMSU, AMSR-E, and TMI. The algorithm has two main steps: rain/no-rain separation using discriminant analysis, and rain rate estimation using stepwise linear regression. We test two modes of algorithm calibration: real-time calibration with continuous updates of coefficients with newly coming MW rain rates, and calibration using static coefficients that are derived from IR-MW data from past observations. We also compare the SCaMPR algorithm to other global-scale satellite rainfall algorithms (e.g., 'Tropical Rainfall Measuring Mission (TRMM) and other sources' (TRMM-3B42) product, and the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA
Reflectivity Reconstruction Based on Adaptive Speed of Sound Estimation from Reflection Information
NASA Astrophysics Data System (ADS)
Yuan, Weiquan
The reflectivity image reconstructed by the synthetic focus algorithm has been verified to be one of the highest in resolution of any ultrasound imaging systems. However present imaging systems with the synthetic focus algorithm suffer from signal registration problems due to diffraction and refraction effects. Recent research efforts on refraction correction for reflection imaging are based on speed of sound estimation. One approach is to determine the speed of the sound map from transmission information. In this technique, both reflection and transmission data are required. Unfortunately, it is difficult to acquire both transmission and reflection in many practical medical environments. In this dissertation, a new reflectivity reconstruction technique is investigated in which the speed of sound estimation is determined only from the reflection data. This method of speed of sound estimation is based on an optimization approach. The speed of sound of the imaging region is updated iterately based one optimization. The criterion employed is the minimization of an objective functional. In particular, the alignment error functional, correlation functional or brightness functional are defined and then investigated. These objective functionals are all dependent on the speed of sound. During the process of minimizing the objective functional with respect to the speed of sound, an approximation to the true speed of sound is determined adaptively. To minimize these specific objective functionals, the conjugate gradient method with line search and with quadratic approximation is investigated. For such a large optimization problem, an undesired local minimum is often found. The penalty function technique is suggested to overcome this local minimum problem. Due to the limitation of available computer capability, all the methods developed are first investigated by a 8 x 8 grid reconstruction of speed of sound. Finally, the simulation results for a lager 64 x 64 reflectivity
Tsanas, Athanasios; Zañartu, Matías; Little, Max A.; Fox, Cynthia; Ramig, Lorraine O.; Clifford, Gari D.
2014-01-01
There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F0) of speech signals. This study examines ten F0 estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F0 in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F0 estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F0 estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F0 estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F0 estimation is required. PMID:24815269
Tsanas, Athanasios; Zañartu, Matías; Little, Max A; Fox, Cynthia; Ramig, Lorraine O; Clifford, Gari D
2014-05-01
There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F(0)) of speech signals. This study examines ten F(0) estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F(0) in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F(0) estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F(0) estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F(0) estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F(0) estimation is required. PMID:24815269
Todorov, Emanuel
2005-05-01
Optimality principles of biological movement are conceptually appealing and straightforward to formulate. Testing them empirically, however, requires the solution to stochastic optimal control and estimation problems for reasonably realistic models of the motor task and the sensorimotor periphery. Recent studies have highlighted the importance of incorporating biologically plausible noise into such models. Here we extend the linear-quadratic-gaussian framework--currently the only framework where such problems can be solved efficiently--to include control-dependent, state-dependent, and internal noise. Under this extended noise model, we derive a coordinate-descent algorithm guaranteed to converge to a feedback control law and a nonadaptive linear estimator optimal with respect to each other. Numerical simulations indicate that convergence is exponential, local minima do not exist, and the restriction to nonadaptive linear estimators has negligible effects in the control problems of interest. The application of the algorithm is illustrated in the context of reaching movements. A Matlab implementation is available at www.cogsci.ucsd.edu/~todorov.
CDC 2011 Estimates of Foodborne Illness in the United States
... Total number of foodborne illnesses each year CDC estimated the number of illnesses, hospitalizations, and deaths caused by both known and unspecified agents. CDC estimated what proportion of each were foodborne. The first ...
Topographic form stress in the Southern Ocean State Estimate
NASA Astrophysics Data System (ADS)
Masich, Jessica; Chereskin, Teresa K.; Mazloff, Matthew R.
2015-12-01
We diagnose the Southern Ocean momentum balance in a 6 year, eddy-permitting state estimate of the Southern Ocean. We find that 95% of the zonal momentum input via wind stress at the surface is balanced by topographic form stress across ocean ridges, while the remaining 5% is balanced via bottom friction and momentum flux divergences at the northern and southern boundaries of the analysis domain. While the time-mean zonal wind stress field exhibits a relatively uniform spatial distribution, time-mean topographic form stress concentrates at shallow ridges and across the continents that lie within the Antarctic Circumpolar Current (ACC) latitudes; nearly 40% of topographic form stress occurs across South America, while the remaining 60% occurs across the major submerged ridges that underlie the ACC. Topographic form stress can be divided into shallow and deep regimes: the shallow regime contributes most of the westward form stress that serves as a momentum sink for the ACC system, while the deep regime consists of strong eastward and westward form stresses that largely cancel in the zonal integral. The time-varying form stress signal, integrated longitudinally and over the ACC latitudes, tracks closely with the wind stress signal integrated over the same domain; at zero lag, 88% of the variance in the 6 year form stress time series can be explained by the wind stress signal, suggesting that changes in the integrated wind stress signal are communicated via rapid barotropic response down to the level of bottom topography.
Methods for estimating the amount of vernal pool habitat in the northeastern United States
Van Meter, R.; Bailey, L.L.; Grant, E.H.C.
2008-01-01
The loss of small, seasonal wetlands is a major concern for a variety of state, local, and federal organizations in the northeastern U.S. Identifying and estimating the number of vernal pools within a given region is critical to developing long-term conservation and management strategies for these unique habitats and their faunal communities. We use three probabilistic sampling methods (simple random sampling, adaptive cluster sampling, and the dual frame method) to estimate the number of vernal pools on protected, forested lands. Overall, these methods yielded similar values of vernal pool abundance for each study area, and suggest that photographic interpretation alone may grossly underestimate the number of vernal pools in forested habitats. We compare the relative efficiency of each method and discuss ways of improving precision. Acknowledging that the objectives of a study or monitoring program ultimately determine which sampling designs are most appropriate, we recommend that some type of probabilistic sampling method be applied. We view the dual-frame method as an especially useful way of combining incomplete remote sensing methods, such as aerial photograph interpretation, with a probabilistic sample of the entire area of interest to provide more robust estimates of the number of vernal pools and a more representative sample of existing vernal pool habitats.
Climate adaptation wedges: a case study of premium wine in the western United States
Diffenbaugh, Noah; White, Michael A; Jones, Gregory V; Ashfaq, Moetasim
2011-01-01
Design and implementation of effective climate change adaptation activities requires quantitative assessment of the impacts that are likely to occur without adaptation, as well as the fraction of impact that can be avoided through each activity. Here we present a quantitative framework inspired by the greenhouse gas stabilization wedges of Pacala and Socolow. In our proposed framework, the damage avoided by each adaptation activity creates an 'adaptation wedge' relative to the loss that would occur without that adaptation activity. We use premium winegrape suitability in the western United States as an illustrative case study, focusing on the near-term period that covers the years 2000 39. We find that the projected warming over this period results in the loss of suitable winegrape area throughout much of California, including most counties in the high-value North Coast and Central Coast regions. However, in quantifying adaptation wedges for individual high-value counties, we find that a large adaptation wedge can be captured by increasing the severe heat tolerance, including elimination of the 50% loss projected by the end of the 2030 9 period in the North Coast region, and reduction of the projected loss in the Central Coast region from 30% to less than 15%. Increased severe heat tolerance can capture an even larger adaptation wedge in the Pacific Northwest, including conversion of a projected loss of more than 30% in the Columbia Valley region of Washington to a projected gain of more than 150%. We also find that warming projected over the near-term decades has the potential to alter the quality of winegrapes produced in the western US, and we discuss potential actions that could create adaptation wedges given these potential changes in quality. While the present effort represents an initial exploration of one aspect of one industry, the climate adaptation wedge framework could be used to quantitatively evaluate the opportunities and limits of climate adaptation
NASA Astrophysics Data System (ADS)
Vadivel, R.; Bhaskaran, V. Murali
2010-10-01
The main reason for packet loss in ad hoc networks is the link failure or node failure. In order to increase the path stability, it is essential to distinguish and moderate the failures. By knowing individual link stability along a path, path stability can be identified. In this paper, we develop an adaptive reliable routing protocol using combined link stability estimation for mobile ad hoc networks. The main objective of this protocol is to determine a Quality of Service (QoS) path along with prolonging the network life time and to reduce the packet loss. We calculate a combined metric for a path based on the parameters Link Expiration Time, Node Remaining Energy and Node Velocity and received signal strength to predict the link stability or lifetime. Then, a bypass route is established to retransmit the lost data, when a link failure occurs. By simulation results, we show that the proposed reliable routing protocol achieves high delivery ratio with reduced delay and packet drop.
NASA Astrophysics Data System (ADS)
Teal, Paul D.; Eccles, Craig
2015-04-01
The two most successful methods of estimating the distribution of nuclear magnetic resonance relaxation times from two dimensional data are data compression followed by application of the Butler-Reeds-Dawson algorithm, and a primal-dual interior point method using preconditioned conjugate gradient. Both of these methods have previously been presented using a truncated singular value decomposition of matrices representing the exponential kernel. In this paper it is shown that other matrix factorizations are applicable to each of these algorithms, and that these illustrate the different fundamental principles behind the operation of the algorithms. These are the rank-revealing QR (RRQR) factorization and the LDL factorization with diagonal pivoting, also known as the Bunch-Kaufman-Parlett factorization. It is shown that both algorithms can be improved by adaptation of the truncation as the optimization process progresses, improving the accuracy as the optimal value is approached. A variation on the interior method viz, the use of barrier function instead of the primal-dual approach, is found to offer considerable improvement in terms of speed and reliability. A third type of algorithm, related to the algorithm known as Fast iterative shrinkage-thresholding algorithm, is applied to the problem. This method can be efficiently formulated without the use of a matrix decomposition.
Im, Subin; Min, Soonhong
2013-04-01
Exploratory factor analyses of the Kirton Adaption-Innovation Inventory (KAI), which serves to measure individual cognitive styles, generally indicate three factors: sufficiency of originality, efficiency, and rule/group conformity. In contrast, a 2005 study by Im and Hu using confirmatory factor analysis supported a four-factor structure, dividing the sufficiency of originality dimension into two subdimensions, idea generation and preference for change. This study extends Im and Hu's (2005) study of a derived version of the KAI by providing additional evidence of the four-factor structure. Specifically, the authors test the robustness of the parameter estimates to the violation of normality assumptions in the sample using bootstrap methods. A bias-corrected confidence interval bootstrapping procedure conducted among a sample of 356 participants--members of the Arkansas Household Research Panel, with middle SES and average age of 55.6 yr. (SD = 13.9)--showed that the four-factor model with two subdimensions of sufficiency of originality fits the data significantly better than the three-factor model in non-normality conditions. PMID:23833873
Image adaptive point-spread function estimation and deconvolution for in vivo confocal microscopy.
Von Tiedemann, M; Fridberger, A; Ulfendahl, M; Tomo, I; Boutet de Monvel, J; De Monvel, J Boutet
2006-01-01
Visualizing deep inside the tissue of a thick biological sample often poses severe constraints on image conditions. Standard restoration techniques (denoising and deconvolution) can then be very useful, allowing one to increase the signal-to-noise ratio and the resolution of the images. In this paper, we consider the problem of obtaining a good determination of the point-spread function (PSF) of a confocal microscope, a prerequisite for applying deconvolution to three-dimensional image stacks acquired with this system. Because of scattering and optical distortion induced by the sample, the PSF has to be acquired anew for each experiment. To tackle this problem, we used a screening approach to estimate the PSF adaptively and automatically from the images. Small PSF-like structures were detected in the images, and a theoretical PSF model reshaped to match the geometric characteristics of these structures. We used numerical experiments to quantify the sensitivity of our detection method, and we demonstrated its usefulness by deconvolving images of the hearing organ acquired in vitro and in vivo.
Host adaption to the bacteriophage carrier state of Campylobacter jejuni.
Brathwaite, Kelly J; Siringan, Patcharin; Connerton, Phillippa L; Connerton, Ian F
2015-01-01
The carrier state of the foodborne pathogen Campylobacter jejuni represents an alternative life cycle whereby virulent bacteriophages can persist in association with host bacteria without commitment to lysogeny. Host bacteria exhibit significant phenotypic changes that improve their ability to survive extra-intestinal environments, but exhibit growth-phase-dependent impairment in motility. We demonstrate that early exponential phase cultures become synchronised with respect to the non-motile phenotype, which corresponds with a reduction in their ability to adhere to and invade intestinal epithelial cells. Comparative transcriptome analyses (RNA-seq) identify changes in gene expression that account for the observed phenotypes: downregulation of stress response genes hrcA, hspR and per and downregulation of the major flagellin flaA with the chemotactic response signalling genes cheV, cheA and cheW. These changes present mechanisms by which the host and bacteriophage can remain associated without lysis, and the cultures survive extra-intestinal transit. These data provide a basis for understanding a critical link in the ecology of the Campylobacter bacteriophage.
Host adaption to the bacteriophage carrier state of Campylobacter jejuni
Brathwaite, Kelly J.; Siringan, Patcharin; Connerton, Phillippa L.; Connerton, Ian F.
2015-01-01
The carrier state of the foodborne pathogen Campylobacter jejuni represents an alternative life cycle whereby virulent bacteriophages can persist in association with host bacteria without commitment to lysogeny. Host bacteria exhibit significant phenotypic changes that improve their ability to survive extra-intestinal environments, but exhibit growth-phase-dependent impairment in motility. We demonstrate that early exponential phase cultures become synchronised with respect to the non-motile phenotype, which corresponds with a reduction in their ability to adhere to and invade intestinal epithelial cells. Comparative transcriptome analyses (RNA-seq) identify changes in gene expression that account for the observed phenotypes: downregulation of stress response genes hrcA, hspR and per and downregulation of the major flagellin flaA with the chemotactic response signalling genes cheV, cheA and cheW. These changes present mechanisms by which the host and bacteriophage can remain associated without lysis, and the cultures survive extra-intestinal transit. These data provide a basis for understanding a critical link in the ecology of the Campylobacter bacteriophage. PMID:26004283
Societal Adaptation to Decadal Climate Variability in the United States
NASA Astrophysics Data System (ADS)
Rosenberg, Norman J.; Mehta, Vikram M.; Olsen, J. Rolf; von Storch, Hans; Varady, Robert G.; Hayes, Michael J.; Wilhite, Donald
2007-10-01
CRCES Workshop on Societal Impacts of Decadal Climate Variability in the United States, 26-28 April 2007, Waikoloa, Hawaii The search for evidence of decadal climatic variability (DCV) has a very long history. In the past decade, a research community has coalesced around a series of roughly biennial workshops that have emphasized description of past DCV events; their causes and their ``teleconnections'' responsible for droughts, floods, and warm and cold spells around the world; and recently, the predictability of DCV events. Researchers studying climate change put great emphasis on prospective impacts, but the DCV community has yet to do so. To begin rectifying this deficiency, a short but ambitious workshop was convened in Waikoloa, near Kona, Hawaii, from 26-28 April 2007. This workshop, sponsored by the Center for Research on the Changing Earth System (CRCES), NOAA, the U.S. Geological Survey, and the U.S. Army Corps of Engineers, brought together climatologists and sectoral specialists representing agriculture, water resources, economics, the insurance industry, and developing country interests.
Jonsen, Ian
2016-01-01
State-space models provide a powerful way to scale up inference of movement behaviours from individuals to populations when the inference is made across multiple individuals. Here, I show how a joint estimation approach that assumes individuals share identical movement parameters can lead to improved inference of behavioural states associated with different movement processes. I use simulated movement paths with known behavioural states to compare estimation error between nonhierarchical and joint estimation formulations of an otherwise identical state-space model. Behavioural state estimation error was strongly affected by the degree of similarity between movement patterns characterising the behavioural states, with less error when movements were strongly dissimilar between states. The joint estimation model improved behavioural state estimation relative to the nonhierarchical model for simulated data with heavy-tailed Argos location errors. When applied to Argos telemetry datasets from 10 Weddell seals, the nonhierarchical model estimated highly uncertain behavioural state switching probabilities for most individuals whereas the joint estimation model yielded substantially less uncertainty. The joint estimation model better resolved the behavioural state sequences across all seals. Hierarchical or joint estimation models should be the preferred choice for estimating behavioural states from animal movement data, especially when location data are error-prone. PMID:26853261
Feasibility Studies of Applying Kalman Filter Techniques to Power System Dynamic State Estimation
Huang, Zhenyu; Schneider, Kevin P.; Nieplocha, Jarek
2007-08-01
Abstract—Lack of dynamic information in power system operations mainly attributes to the static modeling of traditional state estimation, as state estimation is the basis driving many other operations functions. This paper investigates the feasibility of applying Kalman filter techniques to enable the inclusion of dynamic modeling in the state estimation process and the estimation of power system dynamic states. The proposed Kalman-filter-based dynamic state estimation is tested on a multi-machine system with both large and small disturbances. Sensitivity studies of the dynamic state estimation performance with respect to measurement characteristics – sampling rate and noise level – are presented as well. The study results show that there is a promising path forward to implementation the Kalman-filter-based dynamic state estimation with the emerging phasor measurement technologies.
Design of reduced-order state estimators for linear time-varying multivariable systems
NASA Technical Reports Server (NTRS)
Nguyen, Charles C.
1987-01-01
The design of reduced-order state estimators for linear time-varying multivariable systems is considered. Employing the concepts of matrix operators and the method of canonical transformations, this paper shows that there exists a reduced-order state estimator for linear time-varying systems that are 'lexicography-fixedly observable'. In addition, the eigenvalues of the estimator can be arbitrarily assigned. A simple algorithm is proposed for the design of the state estimator.
ERIC Educational Resources Information Center
Wang, Shudong; Jiao, Hong; He, Wei
2011-01-01
The ability estimation procedure is one of the most important components in a computerized adaptive testing (CAT) system. Currently, all CATs that provide K-12 student scores are based on the item response theory (IRT) model(s); while such application directly violates the assumption of independent sample of a person in IRT models because ability…
ERIC Educational Resources Information Center
Klinkenberg, S.; Straatemeier, M.; van der Maas, H. L. J.
2011-01-01
In this paper we present a model for computerized adaptive practice and monitoring. This model is used in the Maths Garden, a web-based monitoring system, which includes a challenging web environment for children to practice arithmetic. Using a new item response model based on the Elo (1978) rating system and an explicit scoring rule, estimates of…
Steady-state evoked potentials possibilities for mental-state estimation
NASA Technical Reports Server (NTRS)
Junker, Andrew M.; Schnurer, John H.; Ingle, David F.; Downey, Craig W.
1988-01-01
The use of the human steady-state evoked potential (SSEP) as a possible measure of mental-state estimation is explored. A method for evoking a visual response to a sum-of-ten sine waves is presented. This approach provides simultaneous multiple frequency measurements of the human EEG to the evoking stimulus in terms of describing functions (gain and phase) and remnant spectra. Ways in which these quantities vary with the addition of performance tasks (manual tracking, grammatical reasoning, and decision making) are presented. Models of the describing function measures can be formulated using systems engineering technology. Relationships between model parameters and performance scores during manual tracking are discussed. Problems of unresponsiveness and lack of repeatability of subject responses are addressed in terms of a need for loop closure of the SSEP. A technique to achieve loop closure using a lock-in amplifier approach is presented. Results of a study designed to test the effectiveness of using feedback to consciously connect humans to their evoked response are presented. Findings indicate that conscious control of EEG is possible. Implications of these results in terms of secondary tasks for mental-state estimation and brain actuated control are addressed.
Predictive Sea State Estimation for Automated Ride Control and Handling - PSSEARCH
NASA Technical Reports Server (NTRS)
Huntsberger, Terrance L.; Howard, Andrew B.; Aghazarian, Hrand; Rankin, Arturo L.
2012-01-01
PSSEARCH provides predictive sea state estimation, coupled with closed-loop feedback control for automated ride control. It enables a manned or unmanned watercraft to determine the 3D map and sea state conditions in its vicinity in real time. Adaptive path-planning/ replanning software and a control surface management system will then use this information to choose the best settings and heading relative to the seas for the watercraft. PSSEARCH looks ahead and anticipates potential impact of waves on the boat and is used in a tight control loop to adjust trim tabs, course, and throttle settings. The software uses sensory inputs including IMU (Inertial Measurement Unit), stereo, radar, etc. to determine the sea state and wave conditions (wave height, frequency, wave direction) in the vicinity of a rapidly moving boat. This information can then be used to plot a safe path through the oncoming waves. The main issues in determining a safe path for sea surface navigation are: (1) deriving a 3D map of the surrounding environment, (2) extracting hazards and sea state surface state from the imaging sensors/map, and (3) planning a path and control surface settings that avoid the hazards, accomplish the mission navigation goals, and mitigate crew injuries from excessive heave, pitch, and roll accelerations while taking into account the dynamics of the sea surface state. The first part is solved using a wide baseline stereo system, where 3D structure is determined from two calibrated pairs of visual imagers. Once the 3D map is derived, anything above the sea surface is classified as a potential hazard and a surface analysis gives a static snapshot of the waves. Dynamics of the wave features are obtained from a frequency analysis of motion vectors derived from the orientation of the waves during a sequence of inputs. Fusion of the dynamic wave patterns with the 3D maps and the IMU outputs is used for efficient safe path planning.
Jakeman, J.D. Wildey, T.
2015-01-01
In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the physical discretization error and the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity of the sparse grid. Utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchical surplus based strategies. Throughout this paper we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation.
Jakeman, J. D.; Wildey, T.
2015-01-01
In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity. We show that utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchical surplus based strategies. Throughout this papermore » we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation.« less
Jakeman, J. D.; Wildey, T.
2015-01-01
In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity. We show that utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchical surplus based strategies. Throughout this paper we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation.
JUN dependency in distinct early and late BRAF inhibition adaptation states of melanoma.
Titz, Bjoern; Lomova, Anastasia; Le, Allison; Hugo, Willy; Kong, Xiangju; Ten Hoeve, Johanna; Friedman, Michael; Shi, Hubing; Moriceau, Gatien; Song, Chunying; Hong, Aayoung; Atefi, Mohammad; Li, Richard; Komisopoulou, Evangelia; Ribas, Antoni; Lo, Roger S; Graeber, Thomas G
2016-01-01
A prominent mechanism of acquired resistance to BRAF inhibitors in BRAF (V600) -mutant melanoma is associated with the upregulation of receptor tyrosine kinases. Evidences suggested that this resistance mechanism is part of a more complex cellular adaptation process. Using an integrative strategy, we found this mechanism to invoke extensive transcriptomic, (phospho-) proteomic and phenotypic alterations that accompany a cellular transition to a de-differentiated, mesenchymal and invasive state. Even short-term BRAF-inhibitor exposure leads to an early adaptive, differentiation state change-characterized by a slow-cycling, persistent state. The early persistent state is distinct from the late proliferative, resistant state. However, both differentiation states share common signaling alterations including JUN upregulation. Motivated by the similarities, we found that co-targeting of BRAF and JUN is synergistic in killing fully resistant cells; and when used up-front, co-targeting substantially impairs the formation of the persistent subpopulation. We confirmed that JUN upregulation is a common response to BRAF inhibitor treatment in clinically treated patient tumors. Our findings demonstrate that events shared between early- and late-adaptation states provide candidate up-front co-treatment targets. PMID:27648299
JUN dependency in distinct early and late BRAF inhibition adaptation states of melanoma
Titz, Bjoern; Lomova, Anastasia; Le, Allison; Hugo, Willy; Kong, Xiangju; ten Hoeve, Johanna; Friedman, Michael; Shi, Hubing; Moriceau, Gatien; Song, Chunying; Hong, Aayoung; Atefi, Mohammad; Li, Richard; Komisopoulou, Evangelia; Ribas, Antoni; Lo, Roger S; Graeber, Thomas G
2016-01-01
A prominent mechanism of acquired resistance to BRAF inhibitors in BRAFV600-mutant melanoma is associated with the upregulation of receptor tyrosine kinases. Evidences suggested that this resistance mechanism is part of a more complex cellular adaptation process. Using an integrative strategy, we found this mechanism to invoke extensive transcriptomic, (phospho-) proteomic and phenotypic alterations that accompany a cellular transition to a de-differentiated, mesenchymal and invasive state. Even short-term BRAF-inhibitor exposure leads to an early adaptive, differentiation state change—characterized by a slow-cycling, persistent state. The early persistent state is distinct from the late proliferative, resistant state. However, both differentiation states share common signaling alterations including JUN upregulation. Motivated by the similarities, we found that co-targeting of BRAF and JUN is synergistic in killing fully resistant cells; and when used up-front, co-targeting substantially impairs the formation of the persistent subpopulation. We confirmed that JUN upregulation is a common response to BRAF inhibitor treatment in clinically treated patient tumors. Our findings demonstrate that events shared between early- and late-adaptation states provide candidate up-front co-treatment targets. PMID:27648299
NASA Astrophysics Data System (ADS)
Picallo, Clara B.; Riecke, Hermann
2011-03-01
Motivated by recent observations in neuronal systems we investigate all-to-all networks of nonidentical oscillators with adaptive coupling. The adaptation models spike-timing-dependent plasticity in which the sum of the weights of all incoming links is conserved. We find multiple phase-locked states that fall into two classes: near-synchronized states and splay states. Among the near-synchronized states are states that oscillate with a frequency that depends only very weakly on the coupling strength and is essentially given by the frequency of one of the oscillators, which is, however, neither the fastest nor the slowest oscillator. In sufficiently large networks the adaptive coupling is found to develop effective network topologies dominated by one or two loops. This results in a multitude of stable splay states, which differ in their firing sequences. With increasing coupling strength their frequency increases linearly and the oscillators become less synchronized. The essential features of the two classes of states are captured analytically in perturbation analyses of the extended Kuramoto model used in the simulations.
JUN dependency in distinct early and late BRAF inhibition adaptation states of melanoma
Titz, Bjoern; Lomova, Anastasia; Le, Allison; Hugo, Willy; Kong, Xiangju; ten Hoeve, Johanna; Friedman, Michael; Shi, Hubing; Moriceau, Gatien; Song, Chunying; Hong, Aayoung; Atefi, Mohammad; Li, Richard; Komisopoulou, Evangelia; Ribas, Antoni; Lo, Roger S; Graeber, Thomas G
2016-01-01
A prominent mechanism of acquired resistance to BRAF inhibitors in BRAFV600-mutant melanoma is associated with the upregulation of receptor tyrosine kinases. Evidences suggested that this resistance mechanism is part of a more complex cellular adaptation process. Using an integrative strategy, we found this mechanism to invoke extensive transcriptomic, (phospho-) proteomic and phenotypic alterations that accompany a cellular transition to a de-differentiated, mesenchymal and invasive state. Even short-term BRAF-inhibitor exposure leads to an early adaptive, differentiation state change—characterized by a slow-cycling, persistent state. The early persistent state is distinct from the late proliferative, resistant state. However, both differentiation states share common signaling alterations including JUN upregulation. Motivated by the similarities, we found that co-targeting of BRAF and JUN is synergistic in killing fully resistant cells; and when used up-front, co-targeting substantially impairs the formation of the persistent subpopulation. We confirmed that JUN upregulation is a common response to BRAF inhibitor treatment in clinically treated patient tumors. Our findings demonstrate that events shared between early- and late-adaptation states provide candidate up-front co-treatment targets.
JUN dependency in distinct early and late BRAF inhibition adaptation states of melanoma.
Titz, Bjoern; Lomova, Anastasia; Le, Allison; Hugo, Willy; Kong, Xiangju; Ten Hoeve, Johanna; Friedman, Michael; Shi, Hubing; Moriceau, Gatien; Song, Chunying; Hong, Aayoung; Atefi, Mohammad; Li, Richard; Komisopoulou, Evangelia; Ribas, Antoni; Lo, Roger S; Graeber, Thomas G
2016-01-01
A prominent mechanism of acquired resistance to BRAF inhibitors in BRAF (V600) -mutant melanoma is associated with the upregulation of receptor tyrosine kinases. Evidences suggested that this resistance mechanism is part of a more complex cellular adaptation process. Using an integrative strategy, we found this mechanism to invoke extensive transcriptomic, (phospho-) proteomic and phenotypic alterations that accompany a cellular transition to a de-differentiated, mesenchymal and invasive state. Even short-term BRAF-inhibitor exposure leads to an early adaptive, differentiation state change-characterized by a slow-cycling, persistent state. The early persistent state is distinct from the late proliferative, resistant state. However, both differentiation states share common signaling alterations including JUN upregulation. Motivated by the similarities, we found that co-targeting of BRAF and JUN is synergistic in killing fully resistant cells; and when used up-front, co-targeting substantially impairs the formation of the persistent subpopulation. We confirmed that JUN upregulation is a common response to BRAF inhibitor treatment in clinically treated patient tumors. Our findings demonstrate that events shared between early- and late-adaptation states provide candidate up-front co-treatment targets.
NASA Astrophysics Data System (ADS)
Kalawoun, Jana; Biletska, Krystyna; Suard, Frédéric; Montaru, Maxime
2015-04-01
An efficient estimation of the State of Charge (SoC) of an electrical battery in a real-time context is essential for the development of an intelligent management of the battery energy. The main performance limitations of a SoC estimator originate in limited Battery Management System hardware resources as well as in the battery behavior cross-dependence on the battery chemistry and its cycling conditions. This paper presents a review of methods and models used for SoC estimation and discusses their concept, adaptability and performances in real-time applications. It introduces a novel classification of SoC estimation methods to facilitate the identification of aspects to be improved to create an ideal SoC model. An ideal model is defined as the model that provides a reliable SoC for any battery type and cycling condition, online. The benefits of the machine learning methods in providing an online adaptive SoC estimator are thoroughly detailed. Remaining challenges are specified, through which the characteristics of an ideal model can emerge.
Adaptive quarter-pel motion estimation and motion vector coding algorithm for the H.264/AVC standard
NASA Astrophysics Data System (ADS)
Jung, Seung-Won; Park, Chun-Su; Ha, Le Thanh; Ko, Sung-Jea
2009-11-01
We present an adaptive quarter-pel (Qpel) motion estimation (ME) method for H.264/AVC. Instead of applying Qpel ME to all macroblocks (MBs), the proposed method selectively performs Qpel ME in an MB level. In order to reduce the bit rate, we also propose a motion vector (MV) encoding technique that adaptively selects a different variable length coding (VLC) table according to the accuracy of the MV. Experimental results show that the proposed method can achieve about 3% average bit rate reduction.
Estimation of Ability Level by Using Only Observable Quantities in Adaptive Testing.
ERIC Educational Resources Information Center
Kirisci, Levent; Hsu, Tse-Chi
A predictive adaptive testing (PAT) strategy was developed based on statistical predictive analysis, and its feasibility was studied by comparing PAT performance to those of the Flexilevel, Bayesian modal, and expected a posteriori (EAP) strategies in a simulated environment. The proposed adaptive test is based on the idea of using item difficulty…
NASA Technical Reports Server (NTRS)
Balas, Mark; Frost, Susan
2012-01-01
Flexible structures containing a large number of modes can benefit from adaptive control techniques which are well suited to applications that have unknown modeling parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend our adaptive control theory to accommodate troublesome modal subsystems of a plant that might inhibit the adaptive controller. In some cases the plant does not satisfy the requirements of Almost Strict Positive Realness. Instead, there maybe be a modal subsystem that inhibits this property. This section will present new results for our adaptive control theory. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for the troublesome modal subsystem, or the Q modes. Here we present the theory for adaptive controllers modified by RMFs, with attention to the issue of disturbances propagating through the Q modes. We apply the theoretical results to a flexible structure example to illustrate the behavior with and without the residual mode filter.
ERIC Educational Resources Information Center
Aggarwal, Raj
1987-01-01
The adaptation of a typical corporate strategic planning process to a midwestern state university is discussed. The university found that an ongoing planning process with a yearly cycle offers several advantages not available with prior approaches, and that strategic planning is a culture that takes time before it becomes pervasive in any…
Technology Transfer Automated Retrieval System (TEKTRAN)
Genetic variation for potentially adaptive traits of the key restoration species Sandberg bluegrass (Poa secunda J. Presl) was assessed over the intermountain western United States in relation to source climate. Common gardens were established at two intermountain west sites with progeny from two m...
[The problem of health state assessment from the point of view of adaptational reactions theory].
Radchenko, O M
2004-01-01
Human health condition can be estimated only using adaptive responses theory. We include overactivation responses and incomplete adaptation in addition to stress reactions in the distress-group. Quiet and raised activation reactions were included into the eustress-group. All health spectrum should be divided in three groups. For health persons: 1) physiologic standard = safe health level = eustress reactions of high reactivity level 2) prenosological conditions = eustress reactions of low reactivity level, orientation, 3) premorbid conditions = adaptation mechanism breaking = distress reaction. For patients: 1) auspicious course of the disease = complete remission = eustress reactions of high reactivity level, 2) indefinite course of the disease = partial remission = eustress reactions of low reactivity level, orientation, 3) severe course of the disease = absence of remission = unfavorable prognosis = distress reaction.
Wang, Han; Du, Wencai; Xu, Lingwei
2016-01-01
The conventional channel estimation methods based on a preamble for filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) systems in mobile-to-mobile sensor networks are inefficient. By utilizing the intrinsicsparsity of wireless channels, channel estimation is researched as a compressive sensing (CS) problem to improve the estimation performance. In this paper, an AdaptiveRegularized Compressive Sampling Matching Pursuit (ARCoSaMP) algorithm is proposed. Unlike anterior greedy algorithms, the new algorithm can achieve the accuracy of reconstruction by choosing the support set adaptively, and exploiting the regularization process, which realizes the second selecting of atoms in the support set although the sparsity of the channel is unknown. Simulation results show that CS-based methods obtain significant channel estimation performance improvement compared to that of conventional preamble-based methods. The proposed ARCoSaMP algorithm outperforms the conventional sparse adaptive matching pursuit (SAMP) algorithm. ARCoSaMP provides even more interesting results than the mostadvanced greedy compressive sampling matching pursuit (CoSaMP) algorithm without a prior sparse knowledge of the channel. PMID:27347967
Wang, Han; Du, Wencai; Xu, Lingwei
2016-01-01
The conventional channel estimation methods based on a preamble for filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) systems in mobile-to-mobile sensor networks are inefficient. By utilizing the intrinsicsparsity of wireless channels, channel estimation is researched as a compressive sensing (CS) problem to improve the estimation performance. In this paper, an AdaptiveRegularized Compressive Sampling Matching Pursuit (ARCoSaMP) algorithm is proposed. Unlike anterior greedy algorithms, the new algorithm can achieve the accuracy of reconstruction by choosing the support set adaptively, and exploiting the regularization process, which realizes the second selecting of atoms in the support set although the sparsity of the channel is unknown. Simulation results show that CS-based methods obtain significant channel estimation performance improvement compared to that of conventional preamble-based methods. The proposed ARCoSaMP algorithm outperforms the conventional sparse adaptive matching pursuit (SAMP) algorithm. ARCoSaMP provides even more interesting results than the mostadvanced greedy compressive sampling matching pursuit (CoSaMP) algorithm without a prior sparse knowledge of the channel. PMID:27347967
Wang, Han; Du, Wencai; Xu, Lingwei
2016-06-24
The conventional channel estimation methods based on a preamble for filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) systems in mobile-to-mobile sensor networks are inefficient. By utilizing the intrinsicsparsity of wireless channels, channel estimation is researched as a compressive sensing (CS) problem to improve the estimation performance. In this paper, an AdaptiveRegularized Compressive Sampling Matching Pursuit (ARCoSaMP) algorithm is proposed. Unlike anterior greedy algorithms, the new algorithm can achieve the accuracy of reconstruction by choosing the support set adaptively, and exploiting the regularization process, which realizes the second selecting of atoms in the support set although the sparsity of the channel is unknown. Simulation results show that CS-based methods obtain significant channel estimation performance improvement compared to that of conventional preamble-based methods. The proposed ARCoSaMP algorithm outperforms the conventional sparse adaptive matching pursuit (SAMP) algorithm. ARCoSaMP provides even more interesting results than the mostadvanced greedy compressive sampling matching pursuit (CoSaMP) algorithm without a prior sparse knowledge of the channel.
Estimated use of water in the United States in 2010
Maupin, Molly A.; Kenny, Joan F.; Hutson, Susan S.; Lovelace, John K.; Barber, Nancy L.; Linsey, Kristin S.
2014-01-01
In 2010, more than 50 percent of the total withdrawals in the United States were accounted for by 12 States. California accounted for about 11 percent of the total withdrawals and 10 percent of freshwater withdrawals in the United States, predominantly for irrigation. Texas accounted for about 7 percent of total withdrawals, predominantly for thermoelectric power, irrigation, and public supply. Florida accounted for 18 percent of the total saline-water withdrawals in the United States, mostly from surface-water sources for thermoelectric power. Oklahoma and Texas accounted for about 70 percent of the total saline groundwater withdrawals in the United States, mostly for mining.
An Empirical State Error Covariance Matrix for the Weighted Least Squares Estimation Method
NASA Technical Reports Server (NTRS)
Frisbee, Joseph H., Jr.
2011-01-01
State estimation techniques effectively provide mean state estimates. However, the theoretical state error covariance matrices provided as part of these techniques often suffer from a lack of confidence in their ability to describe the un-certainty in the estimated states. By a reinterpretation of the equations involved in the weighted least squares algorithm, it is possible to directly arrive at an empirical state error covariance matrix. This proposed empirical state error covariance matrix will contain the effect of all error sources, known or not. Results based on the proposed technique will be presented for a simple, two observer, measurement error only problem.
Feischl, Michael; Gantner, Gregor; Praetorius, Dirk
2015-01-01
We consider the Galerkin boundary element method (BEM) for weakly-singular integral equations of the first-kind in 2D. We analyze some residual-type a posteriori error estimator which provides a lower as well as an upper bound for the unknown Galerkin BEM error. The required assumptions are weak and allow for piecewise smooth parametrizations of the boundary, local mesh-refinement, and related standard piecewise polynomials as well as NURBS. In particular, our analysis gives a first contribution to adaptive BEM in the frame of isogeometric analysis (IGABEM), for which we formulate an adaptive algorithm which steers the local mesh-refinement and the multiplicity of the knots. Numerical experiments underline the theoretical findings and show that the proposed adaptive strategy leads to optimal convergence. PMID:26085698
NASA Technical Reports Server (NTRS)
Whitmore, S. A.
1985-01-01
The dynamics model and data sources used to perform air-data reconstruction are discussed, as well as the Kalman filter. The need for adaptive determination of the noise statistics of the process is indicated. The filter innovations are presented as a means of developing the adaptive criterion, which is based on the true mean and covariance of the filter innovations. A method for the numerical approximation of the mean and covariance of the filter innovations is presented. The algorithm as developed is applied to air-data reconstruction for the space shuttle, and data obtained from the third landing are presented. To verify the performance of the adaptive algorithm, the reconstruction is also performed using a constant covariance Kalman filter. The results of the reconstructions are compared, and the adaptive algorithm exhibits better performance.
Multi-Sensor Consensus Estimation of State, Sensor Biases and Unknown Input.
Zhou, Jie; Liang, Yan; Yang, Feng; Xu, Linfeng; Pan, Quan
2016-09-01
This paper addresses the problem of the joint estimation of system state and generalized sensor bias (GSB) under a common unknown input (UI) in the case of bias evolution in a heterogeneous sensor network. First, the equivalent UI-free GSB dynamic model is derived and the local optimal estimates of system state and sensor bias are obtained in each sensor node; Second, based on the state and bias estimates obtained by each node from its neighbors, the UI is estimated via the least-squares method, and then the state estimates are fused via consensus processing; Finally, the multi-sensor bias estimates are further refined based on the consensus estimate of the UI. A numerical example of distributed multi-sensor target tracking is presented to illustrate the proposed filter.
Multi-Sensor Consensus Estimation of State, Sensor Biases and Unknown Input
Zhou, Jie; Liang, Yan; Yang, Feng; Xu, Linfeng; Pan, Quan
2016-01-01
This paper addresses the problem of the joint estimation of system state and generalized sensor bias (GSB) under a common unknown input (UI) in the case of bias evolution in a heterogeneous sensor network. First, the equivalent UI-free GSB dynamic model is derived and the local optimal estimates of system state and sensor bias are obtained in each sensor node; Second, based on the state and bias estimates obtained by each node from its neighbors, the UI is estimated via the least-squares method, and then the state estimates are fused via consensus processing; Finally, the multi-sensor bias estimates are further refined based on the consensus estimate of the UI. A numerical example of distributed multi-sensor target tracking is presented to illustrate the proposed filter. PMID:27598156
Multi-Sensor Consensus Estimation of State, Sensor Biases and Unknown Input.
Zhou, Jie; Liang, Yan; Yang, Feng; Xu, Linfeng; Pan, Quan
2016-01-01
This paper addresses the problem of the joint estimation of system state and generalized sensor bias (GSB) under a common unknown input (UI) in the case of bias evolution in a heterogeneous sensor network. First, the equivalent UI-free GSB dynamic model is derived and the local optimal estimates of system state and sensor bias are obtained in each sensor node; Second, based on the state and bias estimates obtained by each node from its neighbors, the UI is estimated via the least-squares method, and then the state estimates are fused via consensus processing; Finally, the multi-sensor bias estimates are further refined based on the consensus estimate of the UI. A numerical example of distributed multi-sensor target tracking is presented to illustrate the proposed filter. PMID:27598156
ESTIMATING AND PROJECTING IMPERVIOUS COVER IN THE SOUTHEASTERN UNITED STATES
Urban/suburban land use constitutes the fastest growing land use class in the Southeastern United States. Predominant development practices increase impervious surface--areas preventing infiltration of water into the underlying soil. Uncontrolled increase of impervious areas (ro...
ESTIMATING AND PROJECTING IMPERVIOUS COVER IN THE SOUTHEASTERN UNITED STATES
Urban/suburban land constitutes the fastest growing land use class in the Southeastern United States. Predominant development practices increase impervious surface--areas preventing infiltration of water into the underlying soil. Uncontrolled increase of impervious areas (roads,...
Estimated use of water in the United States in 2005
Kenny, Joan F.; Barber, Nancy L.; Hutson, Susan S.; Linsey, Kristin S.; Lovelace, John K.; Maupin, Molly A.
2009-01-01
About 67 percent of fresh groundwater withdrawals in 2005 were for irrigation, and 18 percent were for public supply. More than half of fresh groundwater withdrawals in the United States in 2005 occurred in six States. In California, Texas, Nebraska, Arkansas, and Idaho, most of the fresh groundwater withdrawals were for irrigation. In Florida, 52 percent of all fresh groundwater withdrawals were for public supply, and 34 percent were for irrigation.
Macroscopic description of complex adaptive networks coevolving with dynamic node states.
Wiedermann, Marc; Donges, Jonathan F; Heitzig, Jobst; Lucht, Wolfgang; Kurths, Jürgen
2015-05-01
In many real-world complex systems, the time evolution of the network's structure and the dynamic state of its nodes are closely entangled. Here we study opinion formation and imitation on an adaptive complex network which is dependent on the individual dynamic state of each node and vice versa to model the coevolution of renewable resources with the dynamics of harvesting agents on a social network. The adaptive voter model is coupled to a set of identical logistic growth models and we mainly find that, in such systems, the rate of interactions between nodes as well as the adaptive rewiring probability are crucial parameters for controlling the sustainability of the system's equilibrium state. We derive a macroscopic description of the system in terms of ordinary differential equations which provides a general framework to model and quantify the influence of single node dynamics on the macroscopic state of the network. The thus obtained framework is applicable to many fields of study, such as epidemic spreading, opinion formation, or socioecological modeling. PMID:26066206
Gui, Guan; Chen, Zhang-xin; Xu, Li; Wan, Qun; Huang, Jiyan; Adachi, Fumiyuki
2014-01-01
Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics. PMID:25089286
Fu, Zening; Chan, Shing-Chow; Di, Xin; Biswal, Bharat; Zhang, Zhiguo
2014-04-01
Time-varying covariance is an important metric to measure the statistical dependence between non-stationary biological processes. Time-varying covariance is conventionally estimated from short-time data segments within a window having a certain bandwidth, but it is difficult to choose an appropriate bandwidth to estimate covariance with different degrees of non-stationarity. This paper introduces a local polynomial regression (LPR) method to estimate time-varying covariance and performs an asymptotic analysis of the LPR covariance estimator to show that both the estimation bias and variance are functions of the bandwidth and there exists an optimal bandwidth to minimize the mean square error (MSE) locally. A data-driven variable bandwidth selection method, namely the intersection of confidence intervals (ICI), is adopted in LPR for adaptively determining the local optimal bandwidth that minimizes the MSE. Experimental results on simulated signals show that the LPR-ICI method can achieve robust and reliable performance in estimating time-varying covariance with different degrees of variations and under different noise scenarios, making it a powerful tool to study the dynamic relationship between non-stationary biomedical signals. Further, we apply the LPR-ICI method to estimate time-varying covariance of functional magnetic resonance imaging (fMRI) signals in a visual task for the inference of dynamic functional brain connectivity. The results show that the LPR-ICI method can effectively capture the transient connectivity patterns from fMRI.
Gui, Guan; Chen, Zhang-xin; Xu, Li; Wan, Qun; Huang, Jiyan; Adachi, Fumiyuki
2014-01-01
Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics.
Gui, Guan; Chen, Zhang-xin; Xu, Li; Wan, Qun; Huang, Jiyan; Adachi, Fumiyuki
2014-01-01
Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics. PMID:25089286
Adaptive control for space debris removal with uncertain kinematics, dynamics and states
NASA Astrophysics Data System (ADS)
Huang, Panfeng; Zhang, Fan; Meng, Zhongjie; Liu, Zhengxiong
2016-11-01
As the Tethered Space Robot is considered to be a promising solution for the Active Debris Removal, a lot of problems arise in the approaching, capturing and removing phases. Particularly, kinematics and dynamics parameters of the debris are unknown, and parts of the states are unmeasurable according to the specifics of tether, which is a tough problem for the target retrieval/de-orbiting. This work proposes a full adaptive control strategy for the space debris removal via a Tethered Space Robot with unknown kinematics, dynamics and part of the states. First we derive a dynamics model for the retrieval by treating the base satellite (chaser) and the unknown space debris (target) as rigid bodies in the presence of offsets, and involving the flexibility and elasticity of tether. Then, a full adaptive controller is presented including a control law, a dynamic adaption law, and a kinematic adaption law. A modified controller is also presented according to the peculiarities of this system. Finally, simulation results are presented to illustrate the performance of two proposed controllers.
Unequal State Air Pollution: Adopting and Adapting to State Clean Air Policy
NASA Astrophysics Data System (ADS)
Glasgow, Derek John
This dissertation looks at the relationship between American subnational governments and clean air policy in three different cases. I investigate the impact of state reduction policies on the emission of Greenhouse emissions, the subnational adoption of Greenhouse Gas tracking and reduction policies, and the impact of Clean Air Act standards on the siting of coal-fired power plants. The major finding is that in both the adoption and business response to these policies, a state's political context can limit its ability to regulate air pollution. These factors contribute to the unequal protection of air quality across the United States.
The use of series-solutions for batch and sequential estimation. [of nonlinear spacecraft state
NASA Technical Reports Server (NTRS)
Feagin, T.; Mikkilineni, R. P.
1975-01-01
Iterative methods for the approximate solution of the nonlinear state estimation problem are investigated in which the solution is retained in the form of a finite series of Chebyshev polynomials. Algorithms are presented which allow the state to be estimated from observational data in either the batch or the sequential form. The advantages of these techniques are discussed.
Adaptive strategies for graph-state growth in the presence of monitored errors
NASA Astrophysics Data System (ADS)
Campbell, Earl T.; Fitzsimons, Joseph; Benjamin, Simon C.; Kok, Pieter
2007-04-01
Graph states (or cluster states) are the entanglement resource that enables one-way quantum computing. They can be grown by projective measurements on the component qubits. Such measurements typically carry a significant failure probability. Moreover, they may generate imperfect entanglement. Here we describe strategies to adapt growth operations in order to cancel incurred errors. Nascent states that initially deviate from the ideal graph states evolve toward the desired high fidelity resource without impractical overheads. Our analysis extends the diagrammatic language of graph states to include characteristics such as tilted vertices, weighted edges, and partial fusion, which arise from experimental imperfections. The strategies we present are relevant to parity projection schemes such as optical path erasure with distributed matter qubits.
Vasile, Gabriel; Trouvé, Emmanuel; Ciuc, Mihai; Buzuloiu, Vasile
2004-08-01
A new method for filtering the coherence map issued from synthetic aperture radar (SAR) interferometric data is presented. For each pixel of the interferogram, an adaptive neighborhood is determined by a region-growing technique driven by the information provided by the amplitude images. Then pixels in the derived adaptive neighborhood are complex averaged to yield the filtered value of the coherence, after a phase-compensation step is performed. An extension of the algorithm is proposed for polarimetric interferometric SAR images. The proposed method has been applied to both European Remote Sensing (ERS) satellite SAR images and airborne high-resolution polarimetric interferometric SAR images. Both subjective and objective performance analysis, including coherence edge detection, shows that the proposed method provides better results than the standard phase-compensated fixed multilook filter and the Lee adaptive coherence filter.
Large scale state estimation algorithms for DSN tracking station location determination
NASA Technical Reports Server (NTRS)
Ellis, J.
1979-01-01
Estimation of precise tracking station locations for deep space navigation is based on combining state estimates derived from a multitude of planetary encounter missions with planet direction information provided by the planetary ephemeris. Procedures for reducing the dimensionality of the station location estimation problem and for analytically correcting estimates for ephemeris updates have been developed. Using Householder transforms the large scale state estimation problem is decomposed into a sequence of dynamically uncoupled problems of lower dimension. The effect of an ephemeris update is shown to be adequately approximated by Brouwer-Clemence Set III perturbations for the earth-moon barycenter and the target planet for each mission.
Device-independent state estimation based on Bell's inequalities
Bardyn, C.-E.; Liew, T. C. H.; Massar, S.; McKague, M.; Scarani, V.
2009-12-15
The only information available about an alleged source of entangled quantum states is the amount S by which the Clauser-Horne-Shimony-Holt inequality is violated: nothing is known about the nature of the system or the measurements that are performed. We discuss how the quality of the source can be assessed in this black-box scenario, as compared to an ideal source that would produce maximally entangled states (more precisely, any state for which S=2sq root(2)). To this end, we present several inequivalent notions of fidelity, each one related to the use one can make of the source after having assessed it, and we derive quantitative bounds for each of them in terms of the violation S. We also derive a lower bound on the entanglement of the source as a function of S only.
State Wildlife Action Plans as Tools for Adapting to a Continuously Changing Climate
NASA Astrophysics Data System (ADS)
Metivier, D. W.; Yocum, H.; Ray, A. J.
2015-12-01
Public land management plans are potentially powerful policies for building sustainability and adaptive capacity. Land managers are recognizing the need to respond to numerous climate change impacts on natural and human systems. For the first time, in 2015, the federal government required each state to incorporate climate change into their State Wildlife Action Plans (SWAP) as a condition for funding. As important land management tools, SWAPs have the potential to guide state agencies in shaping and implementing practices for climate change adaptation. Intended to be revised every ten years, SWAPs can change as conditions and understanding of climate change evolves. This study asks what practices are states using to integrate climate change, and how does this vary between states? To answer this question, we conducted a broad analysis among seven states (CO, MT, NE, ND, SD, UT, WY) and a more in-depth analysis of four states (CO, ND, SD, WY). We use seven key factors that represent best practices for incorporating climate change identified in the literature. These best practices are species prioritization, key habitats, threats, monitoring, partnerships and participation, identification of management options, and implementation of management options. The in-depth analysis focuses on how states are using climate change information for specific habitats addressed in the plans. We find that states are integrating climate change in many different ways, showing varying degrees of sophistication and preparedness. We summarize different practices and highlight opportunities to improve the effectiveness of plans through: communication tools across state lines and stakeholders, explicit targeting of key habitats, enforcement and monitoring progress and success, and conducting vulnerability analyses that incorporate topics beyond climate and include other drivers, trajectories, and implications of historic and future land-use change.
Adult Cigarette Smoking in the United States: Current Estimates
... Report 2015;64(44):1233â€“40 [accessed 2016 Mar 14]. U.S. Department of Health and Human Services. ... Office on Smoking and Health, 2014 [accessed 2016 Mar 14]. Centers for Disease Control and Prevention . State ...
Exponential H∞ synchronization and state estimation for chaotic systems via a unified model.
Liu, Meiqin; Zhang, Senlin; Fan, Zhen; Zheng, Shiyou; Sheng, Weihua
2013-07-01
In this paper, H∞ synchronization and state estimation problems are considered for different types of chaotic systems. A unified model consisting of a linear dynamic system and a bounded static nonlinear operator is employed to describe these chaotic systems, such as Hopfield neural networks, cellular neural networks, Chua's circuits, unified chaotic systems, Qi systems, chaotic recurrent multilayer perceptrons, etc. Based on the H∞ performance analysis of this unified model using the linear matrix inequality approach, novel state feedback controllers are established not only to guarantee exponentially stable synchronization between two unified models with different initial conditions but also to reduce the effect of external disturbance on the synchronization error to a minimal H∞ norm constraint. The state estimation problem is then studied for the same unified model, where the purpose is to design a state estimator to estimate its states through available output measurements so that the exponential stability of the estimation error dynamic systems is guaranteed and the influence of noise on the estimation error is limited to the lowest level. The parameters of these controllers and filters are obtained by solving the eigenvalue problem. Most chaotic systems can be transformed into this unified model, and H∞ synchronization controllers and state estimators for these systems are designed in a unified way. Three numerical examples are provided to show the usefulness of the proposed H∞ synchronization and state estimation conditions.
NASA Astrophysics Data System (ADS)
Aslan, Serdar; Taylan Cemgil, Ali; Akın, Ata
2016-08-01
Objective. In this paper, we aimed for the robust estimation of the parameters and states of the hemodynamic model by using blood oxygen level dependent signal. Approach. In the fMRI literature, there are only a few successful methods that are able to make a joint estimation of the states and parameters of the hemodynamic model. In this paper, we implemented a maximum likelihood based method called the particle smoother expectation maximization (PSEM) algorithm for the joint state and parameter estimation. Main results. Former sequential Monte Carlo methods were only reliable in the hemodynamic state estimates. They were claimed to outperform the local linearization (LL) filter and the extended Kalman filter (EKF). The PSEM algorithm is compared with the most successful method called square-root cubature Kalman smoother (SCKS) for both state and parameter estimation. SCKS was found to be better than the dynamic expectation maximization (DEM) algorithm, which was shown to be a better estimator than EKF, LL and particle filters. Significance. PSEM was more accurate than SCKS for both the state and the parameter estimation. Hence, PSEM seems to be the most accurate method for the system identification and state estimation for the hemodynamic model inversion literature. This paper do not compare its results with Tikhonov-regularized Newton—CKF (TNF-CKF), a recent robust method which works in filtering sense.
NASA Technical Reports Server (NTRS)
Miles, Jeffrey Hilton
2015-01-01
A cross-power spectrum phase based adaptive technique is discussed which iteratively determines the time delay between two digitized signals that are coherent. The adaptive delay algorithm belongs to a class of algorithms that identifies a minimum of a pattern matching function. The algorithm uses a gradient technique to find the value of the adaptive delay that minimizes a cost function based in part on the slope of a linear function that fits the measured cross power spectrum phase and in part on the standard error of the curve fit. This procedure is applied to data from a Honeywell TECH977 static-engine test. Data was obtained using a combustor probe, two turbine exit probes, and far-field microphones. Signals from this instrumentation are used estimate the post-combustion residence time in the combustor. Comparison with previous studies of the post-combustion residence time validates this approach. In addition, the procedure removes the bias due to misalignment of signals in the calculation of coherence which is a first step in applying array processing methods to the magnitude squared coherence data. The procedure also provides an estimate of the cross-spectrum phase-offset.
Paul, P.; Bhattacharyya, D.; Turton, R.; Zitney, S.
2012-01-01
An accurate estimation of process state variables not only can increase the effectiveness and reliability of process measurement technology, but can also enhance plant efficiency, improve control system performance, and increase plant availability. Future integrated gasification combined cycle (IGCC) power plants with CO2 capture will have to satisfy stricter operational and environmental constraints. To operate the IGCC plant without violating stringent environmental emission standards requires accurate estimation of the relevant process state variables, outputs, and disturbances. Unfortunately, a number of these process variables cannot be measured at all, while some of them can be measured, but with low precision, low reliability, or low signal-to-noise ratio. As a result, accurate estimation of the process variables is of great importance to avoid the inherent difficulties associated with the inaccuracy of the data. Motivated by this, the current paper focuses on the state estimation of an acid gas removal (AGR) process as part of an IGCC plant with CO2 capture. This process has extensive heat and mass integration and therefore is very suitable for testing the efficiency of the designed estimators in the presence of complex interactions between process variables. The traditional Kalman filter (KF) (Kalman, 1960) algorithm has been used as a state estimator which resembles that of a predictor-corrector algorithm for solving numerical problems. In traditional KF implementation, good guesses for the process noise covariance matrix (Q) and the measurement noise covariance matrix (R) are required to obtain satisfactory filter performance. However, in the real world, these matrices are unknown and it is difficult to generate good guesses for them. In this paper, use of an adaptive KF will be presented that adapts Q and R at every time step of the algorithm. Results show that very accurate estimations of the desired process states, outputs or disturbances can be
Line broadening estimate from averaged energy differences of coupled states
NASA Astrophysics Data System (ADS)
Lavrentieva, Nina N.; Dudaryonok, Anna S.; Ma, Qiancheng
2014-11-01
The method to the calculation of rotation-vibrational line half-width of asymmetric top molecules is proposed. The influence of the buffer gas on the internal state of the absorbing molecule is emphasized in this method. The basic expressions of present approach are given. The averaged energy differences method was used for the calculation of H2O and HDO lines broadening. Comparisons of the calculated line shape parameters with the experimental values in different absorption bands are made.
Plitt, Mark; Barnes, Kelly Anne; Wallace, Gregory L; Kenworthy, Lauren; Martin, Alex
2015-12-01
Although typically identified in early childhood, the social communication symptoms and adaptive behavior deficits that are characteristic of autism spectrum disorder (ASD) persist throughout the lifespan. Despite this persistence, even individuals without cooccurring intellectual disability show substantial heterogeneity in outcomes. Previous studies have found various behavioral assessments [such as intelligence quotient (IQ), early language ability, and baseline autistic traits and adaptive behavior scores] to be predictive of outcome, but most of the variance in functioning remains unexplained by such factors. In this study, we investigated to what extent functional brain connectivity measures obtained from resting-state functional connectivity MRI (rs-fcMRI) could predict the variance left unexplained by age and behavior (follow-up latency and baseline autistic traits and adaptive behavior scores) in two measures of outcome--adaptive behaviors and autistic traits at least 1 y postscan (mean follow-up latency = 2 y, 10 mo). We found that connectivity involving the so-called salience network (SN), default-mode network (DMN), and frontoparietal task control network (FPTCN) was highly predictive of future autistic traits and the change in autistic traits and adaptive behavior over the same time period. Furthermore, functional connectivity involving the SN, which is predominantly composed of the anterior insula and the dorsal anterior cingulate, predicted reliable improvement in adaptive behaviors with 100% sensitivity and 70.59% precision. From rs-fcMRI data, our study successfully predicted heterogeneity in outcomes for individuals with ASD that was unaccounted for by simple behavioral metrics and provides unique evidence for networks underlying long-term symptom abatement.
Elenchezhiyan, M; Prakash, J
2015-09-01
In this work, state estimation schemes for non-linear hybrid dynamic systems subjected to stochastic state disturbances and random errors in measurements using interacting multiple-model (IMM) algorithms are formulated. In order to compute both discrete modes and continuous state estimates of a hybrid dynamic system either an IMM extended Kalman filter (IMM-EKF) or an IMM based derivative-free Kalman filters is proposed in this study. The efficacy of the proposed IMM based state estimation schemes is demonstrated by conducting Monte-Carlo simulation studies on the two-tank hybrid system and switched non-isothermal continuous stirred tank reactor system. Extensive simulation studies reveal that the proposed IMM based state estimation schemes are able to generate fairly accurate continuous state estimates and discrete modes. In the presence and absence of sensor bias, the simulation studies reveal that the proposed IMM unscented Kalman filter (IMM-UKF) based simultaneous state and parameter estimation scheme outperforms multiple-model UKF (MM-UKF) based simultaneous state and parameter estimation scheme.
Load Modeling and State Estimation Methods for Power Distribution Systems: Final Report
Tom McDermott
2010-05-07
The project objective was to provide robust state estimation for distribution systems, comparable to what has been available on transmission systems for decades. This project used an algorithm called Branch Current State Estimation (BCSE), which is more effective than classical methods because it decouples the three phases of a distribution system, and uses branch current instead of node voltage as a state variable, which is a better match to current measurement.
Aircraft sensor validation monitor and state estimator using artificial intelligence
NASA Astrophysics Data System (ADS)
Kwak, Seung-Keon; Yoon, Hwan-Sik
2006-03-01
A new Sensor Validity Monitoring, Verification, and Accommodation (SVMVA) technique based on an artificial neural network is developed for a self-repairing Flight Control System (FCS). For the proposed system, the Learning Vector Quantization (LVQ) method is employed as the on-line, real time learning, monitoring, and estimation tool. In order to conduct a feasibility study, we applied the developed algorithm to a flight vehicle simulator. The simulation results show that the proposed SVMVA with LVQ can instantly detect the failure of physical sensors and accommodate them for more than 30 minutes. By employing this type of analytical sensor redundancy, a flight vehicle can save power, weight, and space, which are required for installing redundant physical sensors.
The de-adhesive activity of matricellular proteins: is intermediate cell adhesion an adaptive state?
Murphy-Ullrich, J E
2001-04-01
The process of cellular de-adhesion is potentially important for the ability of a cell to participate in morphogenesis and to respond to injurious stimuli. Cellular de-adhesion is induced by the highly regulated matricellular proteins TSP1 and 2, tenascin-C, and SPARC. These proteins induce a rapid transition to an intermediate state of adhesiveness characterized by loss of actin-containing stress fibers and restructuring of the focal adhesion plaque that includes loss of vinculin and alpha-actinin, but not of talin or integrin. This process involves intracellular signaling mediators, which are engaged in response to matrix protein-receptor interactions. Each of these proteins employs different receptors and signaling pathways to achieve this common morphologic endpoint. What is the function of this intermediate adhesive state and what is the physiologic significance of this action of the matricellular proteins? Given that matricellular proteins are expressed in response to injury and during development, one can speculate that the intermediate adhesive state is an adaptive condition that facilitates expression of specific genes that are involved in repair and adaptation. Since cell shape is maintained in weakly adherent cells, this state might induce survival signals to prevent apoptosis due to loss of strong cell adhesion, but yet allow for cell locomotion. The three matricellular proteins considered here might each preferentially facilitate one or more aspects of this adaptive response rather than all of these equally. Currently, we have only preliminary data to support the specific ideas proposed in this article. It will be interesting in the next several years to continue to elucidate the biological roles of the intermediate adhesive state induced by these matricellular proteins. and focal adhesions in a cell that nevertheless maintains a spread, extended morphology and integrin clustering. TSP1, tenascin-C, and SPARC induce the intermediate adhesive state, as
Orbit sequential estimation using the unified state model
NASA Astrophysics Data System (ADS)
Neto, Ernesto Vieira
1994-02-01
The hodographic theory, developed first by Hamilton/Mobius in the middle of the nineteenth century and reintroduced by Altman in the 1960's, is presented in this work as the basis for the orbital unified state model in the orbit determination of artificial satellites. The full model defines the trajectory and attitude dynamics of an orbital spacecraft and enables efficient and rapid machine computation for mission analysis, orbit determination, and prediction. In this work, the orbital part of the model, together with the Kalman filter, is implemented for the orbit determination problem and the results are compared with conventional formulations.
Distributing Power Grid State Estimation on HPC Clusters A System Architecture Prototype
Liu, Yan; Jiang, Wei; Jin, Shuangshuang; Rice, Mark J.; Chen, Yousu
2012-08-20
The future power grid is expected to further expand with highly distributed energy sources and smart loads. The increased size and complexity lead to increased burden on existing computational resources in energy control centers. Thus the need to perform real-time assessment on such systems entails efficient means to distribute centralized functions such as state estimation in the power system. In this paper, we present our early prototype of a system architecture that connects distributed state estimators individually running parallel programs to solve non-linear estimation procedure. The prototype consists of a middleware and data processing toolkits that allows data exchange in the distributed state estimation. We build a test case based on the IEEE 118 bus system and partition the state estimation of the whole system model to available HPC clusters. The measurement from the testbed demonstrates the low overhead of our solution.
Climate change adaptation: a panacea for food security in Ondo State, Nigeria
NASA Astrophysics Data System (ADS)
Fatuase, A. I.
2016-05-01
This paper examines the likely perceived causes of climate change, adaptation strategies employed and technical inefficiency of arable crop farmers in Ondo State, Nigeria. Data were obtained from primary sources using a set of structured questionnaire assisted with interview schedule. Multistage sampling technique was used. Data were analyzed using the following: descriptive statistics and the stochastic frontier production function. The findings showed that majority of the respondents (59.1 %) still believed that climate change is a natural phenomenon that is beyond man's power to abate while industrial release, improper sewage disposal, fossil fuel use, deforestation and bush burning were perceived as the most human factors that influence climate change by the category that chose human activities (40.9 %) as the main causes of climate change. The main employed adaptation strategies by the farmers were mixed cropping, planting early matured crop, planting of resistant crops and use of agrochemicals. The arable crop farmers were relatively technically efficient with about 53 % of them having technical efficiency above the average of 0.784 for the study area. The study observed that education, adaptation, perception, climate information and farming experience were statistically significant in decreasing inefficiency of arable crop production. Therefore, advocacy on climate change and its adaptation strategies should be intensified in the study area.
Song, Sang-Hun; Madsen, Dorte; van der Steen, Jeroen B; Pullman, Robert; Freer, Lucy H; Hellingwerf, Klaas J; Larsen, Delmar S
2013-11-12
The primary (100 fs to 10 ns) and secondary (10 ns to 100 μs) photodynamics in the type II light-oxygen-voltage (LOV) domain from the blue light YtvA photoreceptor extracted from Bacillus subtilis were explored with transient absorption spectroscopy. The photodynamics of full-length YtvA were characterized after femtosecond 400 nm excitation of both the dark-adapted D447 state and the light-adapted S390 state. The S390 state relaxes on a 43 min time scale at room temperature back into D447, which is weakly accelerated by the introduction of imidazole. This is ascribed to an obstructed cavity in YtvA that hinders access to the embedded FMN chromophore and is more open in type I LOV domains. The primary photochemistry of dark-adapted YtvA is qualitatively similar to that of the type I LOV domains, including AsLOV2 from Avena sativa, but exhibits an appreciably higher (60% greater) terminal triplet yield, estimated near the maximal ΦISC value of ≈78%; the other 22% decays via non-triplet-generating fluorescence. The subsequent secondary dynamics are inhomogeneous, with three triplet populations co-evolving: the faster-decaying (I)T* population (38% occupancy) with a 200 ns decay time is nonproductive in generating the S390 adduct state, a slower (II)T* population (57% occupancy) exhibits a high yield (Φadduct ≈ 100%) in generating S390 and a third (5%) (III)T*population persists (>100 μs) with unresolved photoactivity. The ultrafast photoswitching dynamics of the S390 state appreciably differ from those previously resolved for the type I AcLOV2 domain from Adiantum capillus-veneris [Kennis, J. T., et al. (2004) J. Am. Chem. Soc. 126, 4512], with a low-yield dissociation (Φdis ≈ 2.5%) reaction, which is due to an ultrafast recombination reaction, following photodissociation, and is absent in AcLOV2, which results in the increased photoswitching activity of the latter domain.
Tommasi, C.; May, C.
2010-09-30
The DKL-optimality criterion has been recently proposed for the dual problem of model discrimination and parameter estimation, for the case of two rival models. A sequential version of the DKL-optimality criterion is herein proposed in order to discriminate and efficiently estimate more than two nested non-linear models. Our sequential method is inspired by the procedure of Biswas and Chaudhuri (2002), which is however useful only in the set up of nested linear models.
Kazemipoor, Mahnaz; Hajifaraji, Majid; Radzi, Che Wan Jasimah Bt Wan Mohamed; Shamshirband, Shahaboddin; Petković, Dalibor; Mat Kiah, Miss Laiha
2015-01-01
This research examines the precision of an adaptive neuro-fuzzy computing technique in estimating the anti-obesity property of a potent medicinal plant in a clinical dietary intervention. Even though a number of mathematical functions such as SPSS analysis have been proposed for modeling the anti-obesity properties estimation in terms of reduction in body mass index (BMI), body fat percentage, and body weight loss, there are still disadvantages of the models like very demanding in terms of calculation time. Since it is a very crucial problem, in this paper a process was constructed which simulates the anti-obesity activities of caraway (Carum carvi) a traditional medicine on obese women with adaptive neuro-fuzzy inference (ANFIS) method. The ANFIS results are compared with the support vector regression (SVR) results using root-mean-square error (RMSE) and coefficient of determination (R(2)). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The following statistical characteristics are obtained for BMI loss estimation: RMSE=0.032118 and R(2)=0.9964 in ANFIS testing and RMSE=0.47287 and R(2)=0.361 in SVR testing. For fat loss estimation: RMSE=0.23787 and R(2)=0.8599 in ANFIS testing and RMSE=0.32822 and R(2)=0.7814 in SVR testing. For weight loss estimation: RMSE=0.00000035601 and R(2)=1 in ANFIS testing and RMSE=0.17192 and R(2)=0.6607 in SVR testing. Because of that, it can be applied for practical purposes.
Kazemipoor, Mahnaz; Hajifaraji, Majid; Radzi, Che Wan Jasimah Bt Wan Mohamed; Shamshirband, Shahaboddin; Petković, Dalibor; Mat Kiah, Miss Laiha
2015-01-01
This research examines the precision of an adaptive neuro-fuzzy computing technique in estimating the anti-obesity property of a potent medicinal plant in a clinical dietary intervention. Even though a number of mathematical functions such as SPSS analysis have been proposed for modeling the anti-obesity properties estimation in terms of reduction in body mass index (BMI), body fat percentage, and body weight loss, there are still disadvantages of the models like very demanding in terms of calculation time. Since it is a very crucial problem, in this paper a process was constructed which simulates the anti-obesity activities of caraway (Carum carvi) a traditional medicine on obese women with adaptive neuro-fuzzy inference (ANFIS) method. The ANFIS results are compared with the support vector regression (SVR) results using root-mean-square error (RMSE) and coefficient of determination (R(2)). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The following statistical characteristics are obtained for BMI loss estimation: RMSE=0.032118 and R(2)=0.9964 in ANFIS testing and RMSE=0.47287 and R(2)=0.361 in SVR testing. For fat loss estimation: RMSE=0.23787 and R(2)=0.8599 in ANFIS testing and RMSE=0.32822 and R(2)=0.7814 in SVR testing. For weight loss estimation: RMSE=0.00000035601 and R(2)=1 in ANFIS testing and RMSE=0.17192 and R(2)=0.6607 in SVR testing. Because of that, it can be applied for practical purposes. PMID:25453384
Online state of health estimation on NMC cells based on predictive analytics
NASA Astrophysics Data System (ADS)
Berecibar, Maitane; Devriendt, Floris; Dubarry, Matthieu; Villarreal, Igor; Omar, Noshin; Verbeke, Wouter; Van Mierlo, Joeri
2016-07-01
Accurate on board state of health estimation is a key battery management system function to provide optimal management of the battery system under control. In this regard, this paper presents an extensive study and comparison of three of commonly used supervised learning methods for state of health estimation in Graphite/Nickel Manganese Cobalt oxide cells. The three methods were based from the study of both incremental capacity and differential voltage curves. According to the ageing evolution of both curves, features were extracted and used as inputs for the estimation techniques. Ordinary Least Squares, Multilayer Perceptron and Support Vector Machine were used as the estimation techniques and accurate results were obtained while requiring a low computational effort. Moreover, this work allows a deep comparison of the different estimation techniques in terms of accuracy, online estimation and BMS applicability. In addition, estimation can be developed by partial charging and/or partial discharging, reducing the required maintenance time.
NASA Astrophysics Data System (ADS)
Jones, Reese E.; Mandadapu, Kranthi K.
2012-04-01
We present a rigorous Green-Kubo methodology for calculating transport coefficients based on on-the-fly estimates of: (a) statistical stationarity of the relevant process, and (b) error in the resulting coefficient. The methodology uses time samples efficiently across an ensemble of parallel replicas to yield accurate estimates, which is particularly useful for estimating the thermal conductivity of semi-conductors near their Debye temperatures where the characteristic decay times of the heat flux correlation functions are large. Employing and extending the error analysis of Zwanzig and Ailawadi [Phys. Rev. 182, 280 (1969)], 10.1103/PhysRev.182.280 and Frenkel [in Proceedings of the International School of Physics "Enrico Fermi", Course LXXV (North-Holland Publishing Company, Amsterdam, 1980)] to the integral of correlation, we are able to provide tight theoretical bounds for the error in the estimate of the transport coefficient. To demonstrate the performance of the method, four test cases of increasing computational cost and complexity are presented: the viscosity of Ar and water, and the thermal conductivity of Si and GaN. In addition to producing accurate estimates of the transport coefficients for these materials, this work demonstrates precise agreement of the computed variances in the estimates of the correlation and the transport coefficient with the extended theory based on the assumption that fluctuations follow a Gaussian process. The proposed algorithm in conjunction with the extended theory enables the calculation of transport coefficients with the Green-Kubo method accurately and efficiently.
Jones, Reese E; Mandadapu, Kranthi K
2012-04-21
We present a rigorous Green-Kubo methodology for calculating transport coefficients based on on-the-fly estimates of: (a) statistical stationarity of the relevant process, and (b) error in the resulting coefficient. The methodology uses time samples efficiently across an ensemble of parallel replicas to yield accurate estimates, which is particularly useful for estimating the thermal conductivity of semi-conductors near their Debye temperatures where the characteristic decay times of the heat flux correlation functions are large. Employing and extending the error analysis of Zwanzig and Ailawadi [Phys. Rev. 182, 280 (1969)] and Frenkel [in Proceedings of the International School of Physics "Enrico Fermi", Course LXXV (North-Holland Publishing Company, Amsterdam, 1980)] to the integral of correlation, we are able to provide tight theoretical bounds for the error in the estimate of the transport coefficient. To demonstrate the performance of the method, four test cases of increasing computational cost and complexity are presented: the viscosity of Ar and water, and the thermal conductivity of Si and GaN. In addition to producing accurate estimates of the transport coefficients for these materials, this work demonstrates precise agreement of the computed variances in the estimates of the correlation and the transport coefficient with the extended theory based on the assumption that fluctuations follow a Gaussian process. The proposed algorithm in conjunction with the extended theory enables the calculation of transport coefficients with the Green-Kubo method accurately and efficiently.
State Estimation for a Class of Non-Uniform Sampling Systems with Missing Measurements
Lin, Honglei; Sun, Shuli
2016-01-01
This paper is concerned with the state estimation problem for a class of non-uniform sampling systems with missing measurements where the state is updated uniformly and the measurements are sampled randomly. A new state model is developed to depict the dynamics at the measurement sampling points within a state update period. A non-augmented state estimator dependent on the missing rate is presented by applying an innovation analysis approach. It can provide the state estimates at the state update points and at the measurement sampling points within a state update period. Compared with the augmented method, the proposed algorithm can reduce the computational burden with the increase of the number of measurement samples within a state update period. It can deal with the optimal estimation problem for single and multi-sensor systems in a unified way. To improve the reliability, a distributed suboptimal fusion estimator at the state update points is also given for multi-sensor systems by using the covariance intersection fusion algorithm. The simulation research verifies the effectiveness of the proposed algorithms. PMID:27455282
A Value-Added Estimate of Higher Education Quality of US States
ERIC Educational Resources Information Center
Zhang, Lei
2009-01-01
States differ substantially in higher education policies. Little is known about the effects of state policies on the performance of public colleges and universities, largely because no clear measures of college quality exist. In this paper, I estimate the average quality of public colleges of US states based on the value-added to individuals'…
Using National Data to Estimate Average Cost Effectiveness of EFNEP Outcomes by State/Territory
ERIC Educational Resources Information Center
Baral, Ranju; Davis, George C.; Blake, Stephanie; You, Wen; Serrano, Elena
2013-01-01
This report demonstrates how existing national data can be used to first calculate upper limits on the average cost per participant and per outcome per state/territory for the Expanded Food and Nutrition Education Program (EFNEP). These upper limits can then be used by state EFNEP administrators to obtain more precise estimates for their states,…
Least-squares sequential parameter and state estimation for large space structures
NASA Technical Reports Server (NTRS)
Thau, F. E.; Eliazov, T.; Montgomery, R. C.
1982-01-01
This paper presents the formulation of simultaneous state and parameter estimation problems for flexible structures in terms of least-squares minimization problems. The approach combines an on-line order determination algorithm, with least-squares algorithms for finding estimates of modal approximation functions, modal amplitudes, and modal parameters. The approach combines previous results on separable nonlinear least squares estimation with a regression analysis formulation of the state estimation problem. The technique makes use of sequential Householder transformations. This allows for sequential accumulation of matrices required during the identification process. The technique is used to identify the modal prameters of a flexible beam.
Franca, A.S.; Haghighi, K.
1996-06-01
This is the second of two articles concerning error estimation and adaptive refinement techniques applied to convective heat transfer problems. In the first article (Part 1), the development of the proposed methodology was presented. This article (Part 2) concerns the validation of the formulation. Examples dealing with heat and momentum transfer were used to verify the efficiency and accuracy of this technique. Applications include sterilization of food products and pasteurization of liquids contained in bottles. The desired accuracy level was always attained. Refined meshes agreed with the physical aspects of the problems. Results show significant improvements when compared with the conventional finite element approach.
An adaptive optics system for solid-state laser systems used in inertial confinement fusion
Salmon, J.T.; Bliss, E.S.; Byrd, J.L.; Feldman, M.; Kartz, M.A.; Toeppen, J.S.; Wonterghem, B. Van; Winters, S.E.
1995-09-17
Using adaptive optics the authors have obtained nearly diffraction-limited 5 kJ, 3 nsec output pulses at 1.053 {micro}m from the Beamlet demonstration system for the National Ignition Facility (NIF). The peak Strehl ratio was improved from 0.009 to 0.50, as estimated from measured wavefront errors. They have also measured the relaxation of the thermally induced aberrations in the main beam line over a period of 4.5 hours. Peak-to-valley aberrations range from 6.8 waves at 1.053 {micro}m within 30 minutes after a full system shot to 3.9 waves after 4.5 hours. The adaptive optics system must have enough range to correct accumulated thermal aberrations from several shots in addition to the immediate shot-induced error. Accumulated wavefront errors in the beam line will affect both the design of the adaptive optics system for NIF and the performance of that system.
Ferguson, Gail M.; Bornstein, Marc H.; Pottinger, Audrey M.
2011-01-01
A bidimensional acculturation framework cannot account for multiple destination cultures within contemporary settlement societies. We propose and test a tridimensional model among Jamaican adolescent-mother dyads in the United States compared with Jamaican Islander, European American, African American, and other Black and non-Black U.S. immigrant dyads (473 dyads, M adolescent age = 14 years). Jamaican immigrants evidence tridimensional acculturation, orienting toward Jamaican, African American, and European American cultures. Integration is favored (70%), particularly tricultural integration; moreover, Jamaican and other Black U.S. immigrants are more oriented toward African American than European American culture. Jamaican immigrant youth adapt at least as well as non-immigrant Jamaican and U.S. peers, although assimilated adolescents, particularly first generation, have worse sociocultural adaptation than integrated and separated adolescents. PMID:22966917
NASA Astrophysics Data System (ADS)
Plett, Gregory L.
Battery management systems in hybrid-electric-vehicle battery packs must estimate values descriptive of the pack's present operating condition. These include: battery state-of-charge, power fade, capacity fade, and instantaneous available power. The estimation mechanism must adapt to changing cell characteristics as cells age and therefore provide accurate estimates over the lifetime of the pack. In a series of three papers, we propose methods, based on extended Kalman filtering (EKF), that are able to accomplish these goals for a lithium ion polymer battery pack. We expect that they will also work well on other battery chemistries. These papers cover the required mathematical background, cell modeling and system identification requirements, and the final solution, together with results. This third paper concludes the series by presenting five additional applications where either an EKF or results from EKF may be used in typical BMS algorithms: initializing state estimates after the vehicle has been idle for some time; estimating state-of-charge with dynamic error bounds on the estimate; estimating pack available dis/charge power; tracking changing pack parameters (including power fade and capacity fade) as the pack ages, and therefore providing a quantitative estimate of state-of-health; and determining which cells must be equalized. Results from pack tests are presented.
Real-Time State Estimation in a Flight Simulator Using fNIRS
Gateau, Thibault; Durantin, Gautier; Lancelot, Francois; Scannella, Sebastien; Dehais, Frederic
2015-01-01
Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot’s instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot’s mental state matched significantly better than chance with the pilot’s real state (62% global accuracy, 58% specificity, and 72% sensitivity). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development. PMID:25816347
Real-time state estimation in a flight simulator using fNIRS.
Gateau, Thibault; Durantin, Gautier; Lancelot, Francois; Scannella, Sebastien; Dehais, Frederic
2015-01-01
Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot's instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot's mental state matched significantly better than chance with the pilot's real state (62% global accuracy, 58% specificity, and 72% sensitivity). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development.
Systematic variational method for statistical nonlinear state and parameter estimation.
Ye, Jingxin; Rey, Daniel; Kadakia, Nirag; Eldridge, Michael; Morone, Uriel I; Rozdeba, Paul; Abarbanel, Henry D I; Quinn, John C
2015-11-01
In statistical data assimilation one evaluates the conditional expected values, conditioned on measurements, of interesting quantities on the path of a model through observation and prediction windows. This often requires working with very high dimensional integrals in the discrete time descriptions of the observations and model dynamics, which become functional integrals in the continuous-time limit. Two familiar methods for performing these integrals include (1) Monte Carlo calculations and (2) variational approximations using the method of Laplace plus perturbative corrections to the dominant contributions. We attend here to aspects of the Laplace approximation and develop an annealing method for locating the variational path satisfying the Euler-Lagrange equations that comprises the major contribution to the integrals. This begins with the identification of the minimum action path starting with a situation where the model dynamics is totally unresolved in state space, and the consistent minimum of the variational problem is known. We then proceed to slowly increase the model resolution, seeking to remain in the basin of the minimum action path, until a path that gives the dominant contribution to the integral is identified. After a discussion of some general issues, we give examples of the assimilation process for some simple, instructive models from the geophysical literature. Then we explore a slightly richer model of the same type with two distinct time scales. This is followed by a model characterizing the biophysics of individual neurons. PMID:26651756
An adaptive segment method for smoothing lidar signal based on noise estimation
NASA Astrophysics Data System (ADS)
Wang, Yuzhao; Luo, Pingping
2014-10-01
An adaptive segmentation smoothing method (ASSM) is introduced in the paper to smooth the signal and suppress the noise. In the ASSM, the noise is defined as the 3σ of the background signal. An integer number N is defined for finding the changing positions in the signal curve. If the difference of adjacent two points is greater than 3Nσ, the position is recorded as an end point of the smoothing segment. All the end points detected as above are recorded and the curves between them will be smoothed separately. In the traditional method, the end points of the smoothing windows in the signals are fixed. The ASSM creates changing end points in different signals and the smoothing windows could be set adaptively. The windows are always set as the half of the segmentations and then the average smoothing method will be applied in the segmentations. The Iterative process is required for reducing the end-point aberration effect in the average smoothing method and two or three times are enough. In ASSM, the signals are smoothed in the spacial area nor frequent area, that means the frequent disturbance will be avoided. A lidar echo was simulated in the experimental work. The echo was supposed to be created by a space-born lidar (e.g. CALIOP). And white Gaussian noise was added to the echo to act as the random noise resulted from environment and the detector. The novel method, ASSM, was applied to the noisy echo to filter the noise. In the test, N was set to 3 and the Iteration time is two. The results show that, the signal could be smoothed adaptively by the ASSM, but the N and the Iteration time might be optimized when the ASSM is applied in a different lidar.
Adaptive State Predictor Based Human Operator Modeling on Longitudinal and Lateral Control
NASA Technical Reports Server (NTRS)
Trujillo, Anna C.; Gregory, Irene M.; Hempley, Lucas E.
2015-01-01
Control-theoretic modeling of the human operator dynamic behavior in manual control tasks has a long and rich history. In the last two decades, there has been a renewed interest in modeling the human operator. There has also been significant work on techniques used to identify the pilot model of a given structure. The purpose of this research is to attempt to go beyond pilot identification based on collected experimental data and to develop a predictor of pilot behavior. An experiment was conducted to categorize these interactions of the pilot with an adaptive controller compensating during control surface failures. A general linear in-parameter model structure is used to represent a pilot. Three different estimation methods are explored. A gradient descent estimator (GDE), a least squares estimator with exponential forgetting (LSEEF), and a least squares estimator with bounded gain forgetting (LSEBGF) used the experiment data to predict pilot stick input. Previous results have found that the GDE and LSEEF methods are fairly accurate in predicting longitudinal stick input from commanded pitch. This paper discusses the accuracy of each of the three methods - GDE, LSEEF, and LSEBGF - to predict both pilot longitudinal and lateral stick input from the flight director's commanded pitch and bank attitudes.
Redox state, reactive oxygen species and adaptive growth in colonial hydroids.
Blackstone, N W
2001-06-01
Colonial metazoans often encrust surfaces over which the food supply varies in time or space. In such an environment, adaptive colony development entails adjusting the timing and spacing of feeding structures and gastrovascular connections to correspond to this variable food supply. To investigate the possibility of such adaptive growth, within-colony differential feeding experiments were carried out using the hydroid Podocoryna carnea. Indeed, such colonies strongly exhibited adaptive growth, developing dense arrays of polyps (feeding structures) and gastrovascular connections in areas that were fed relative to areas that were starved, and this effect became more consistent over time. To investigate mechanisms of signaling between the food supply and colony development, measurements were taken of metabolic parameters that have been implicated in signal transduction in other systems, particularly redox state and levels of reactive oxygen species. Utilizing fluorescence microscopy of P. carnea cells in vivo, simultaneous measurements of redox state [using NAD(P)H] and hydrogen peroxide (using 2',7'-dichlorofluorescin diacetate) were taken. Both measures focused on polyp epitheliomuscular cells, since these exhibit the greatest metabolic activity. Colonies 3-5h after feeding were relatively oxidized, with low levels of peroxide, while colonies 24h after feeding were relatively reduced, with high levels of peroxide. The functional role of polyps in feeding and generating gastrovascular flow probably produced this dichotomy. Polyps 3-5h after feeding contract maximally, and this metabolic demand probably shifts the redox state in the direction of oxidation and diminishes levels of reactive oxygen species. In contrast, 24h after feeding, polyps are quiescent, and this lack of metabolic demand probably shifts the redox state in the direction of reduction and increases levels of reactive oxygen species. Within-colony differential feeding experiments were carried out on
NASA Astrophysics Data System (ADS)
Estrada, Antonio; Efimov, Denis; Perruquetti, Wilfrid
2016-09-01
The present work focuses on the problem of velocity and position estimation. A solution is presented for a class of oscillating systems in which position, velocity and acceleration are zero mean signals. The proposed scheme considers that the dynamic model of the system is unknown. Only noisy acceleration measurements, that may be contaminated by zero mean noise and constant bias, are considered to be available. The proposal uses the periodic nature of the signals obtaining finite-time estimations while tackling integration drift accumulation.
NASA Technical Reports Server (NTRS)
Boland, J. S., III
1975-01-01
A general simulation program is presented (GSP) involving nonlinear state estimation for space vehicle flight navigation systems. A complete explanation of the iterative guidance mode guidance law, derivation of the dynamics, coordinate frames, and state estimation routines are given so as to fully clarify the assumptions and approximations involved so that simulation results can be placed in their proper perspective. A complete set of computer acronyms and their definitions as well as explanations of the subroutines used in the GSP simulator are included. To facilitate input/output, a complete set of compatable numbers, with units, are included to aid in data development. Format specifications, output data phrase meanings and purposes, and computer card data input are clearly spelled out. A large number of simulation and analytical studies were used to determine the validity of the simulator itself as well as various data runs.
Ward, Zachary J.; Long, Michael W.; Resch, Stephen C.; Gortmaker, Steven L.; Cradock, Angie L.; Giles, Catherine; Hsiao, Amber; Wang, Y. Claire
2016-01-01
Background State-level estimates from the Centers for Disease Control and Prevention (CDC) underestimate the obesity epidemic because they use self-reported height and weight. We describe a novel bias-correction method and produce corrected state-level estimates of obesity and severe obesity. Methods Using non-parametric statistical matching, we adjusted self-reported data from the Behavioral Risk Factor Surveillance System (BRFSS) 2013 (n = 386,795) using measured data from the National Health and Nutrition Examination Survey (NHANES) (n = 16,924). We validated our national estimates against NHANES and estimated bias-corrected state-specific prevalence of obesity (BMI≥30) and severe obesity (BMI≥35). We compared these results with previous adjustment methods. Results Compared to NHANES, self-reported BRFSS data underestimated national prevalence of obesity by 16% (28.67% vs 34.01%), and severe obesity by 23% (11.03% vs 14.26%). Our method was not significantly different from NHANES for obesity or severe obesity, while previous methods underestimated both. Only four states had a corrected obesity prevalence below 30%, with four exceeding 40%–in contrast, most states were below 30% in CDC maps. Conclusions Twelve million adults with obesity (including 6.7 million with severe obesity) were misclassified by CDC state-level estimates. Previous bias-correction methods also resulted in underestimates. Accurate state-level estimates are necessary to plan for resources to address the obesity epidemic. PMID:26954566
Sargent, B. Pierre; Maupin, Molly A.; Hinkle, Stephen R.
2008-01-01
The U.S. Geological Survey National Water Use Information Program compiles estimates of fresh ground-water withdrawals in the United States on a 5-year interval. In the year-2000 compilation, withdrawals were reported from principal aquifers and aquifer systems including two general aquifers - Alluvial and Other aquifers. Withdrawals from a widespread aquifer group - stream-valley aquifers - were not specifically identified in the year-2000 compilation, but they are important sources of ground water. Stream-valley aquifers are alluvial aquifers located in the valley of major streams and rivers. Stream-valley aquifers are long but narrow aquifers that are in direct hydraulic connection with associated streams and limited in extent compared to most principal aquifers. Based in large part on information published in U.S. Geological Survey reports, preliminary analysis of withdrawal data and hydrogeologic and surface-water information indicated areas in the United States where possible stream-valley aquifers were located. Further assessment focused on 24 states and the Commonwealth of Puerto Rico. Withdrawals reported from Alluvial aquifers in 16 states and withdrawals reported from Other aquifers in 6 states and the Commonwealth of Puerto Rico were investigated. Two additional States - Arkansas and New Jersey - were investigated because withdrawals reported from other principal aquifers in these two States may be from stream-valley aquifers. Withdrawals from stream-valley aquifers were identified in 20 States and were about 1,560 Mgal/d (million gallons per day), a rate comparable to withdrawals from the 10 most productive principal aquifers in the United States. Of the 1,560 Mgal/d of withdrawals attributed to stream-valley aquifers, 1,240 Mgal/d were disaggregated from Alluvial aquifers, 150 Mgal/d from glacial sand and gravel aquifers, 116 Mgal/d from Other aquifers, 28.1 Mgal/d from Pennsylvanian aquifers, and 24.9 Mgal/d from the Mississippi River Valley alluvial
A Comparison of IRT Proficiency Estimation Methods under Adaptive Multistage Testing
ERIC Educational Resources Information Center
Kim, Sooyeon; Moses, Tim; Yoo, Hanwook
2015-01-01
This inquiry is an investigation of item response theory (IRT) proficiency estimators' accuracy under multistage testing (MST). We chose a two-stage MST design that includes four modules (one at Stage 1, three at Stage 2) and three difficulty paths (low, middle, high). We assembled various two-stage MST panels (i.e., forms) by manipulating two…
NASA Astrophysics Data System (ADS)
Ding, Derui; Shen, Yuxuan; Song, Yan; Wang, Yongxiong
2016-07-01
This paper is concerned with the state estimation problem for a class of discrete time-varying stochastic nonlinear systems with randomly occurring deception attacks. The stochastic nonlinearity described by statistical means which covers several classes of well-studied nonlinearities as special cases is taken into discussion. The randomly occurring deception attacks are modelled by a set of random variables obeying Bernoulli distributions with given probabilities. The purpose of the addressed state estimation problem is to design an estimator with hope to minimize the upper bound for estimation error covariance at each sampling instant. Such an upper bound is minimized by properly designing the estimator gain. The proposed estimation scheme in the form of two Riccati-like difference equations is of a recursive form. Finally, a simulation example is exploited to demonstrate the effectiveness of the proposed scheme.
NASA Astrophysics Data System (ADS)
Liu, Hongjian; Wang, Zidong; Shen, Bo; Alsaadi, Fuad E.
2016-07-01
This paper deals with the robust H∞ state estimation problem for a class of memristive recurrent neural networks with stochastic time-delays. The stochastic time-delays under consideration are governed by a Bernoulli-distributed stochastic sequence. The purpose of the addressed problem is to design the robust state estimator such that the dynamics of the estimation error is exponentially stable in the mean square, and the prescribed ? performance constraint is met. By utilizing the difference inclusion theory and choosing a proper Lyapunov-Krasovskii functional, the existence condition of the desired estimator is derived. Based on it, the explicit expression of the estimator gain is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is employed to demonstrate the effectiveness and applicability of the proposed estimation approach.
An Automated Technique for Estimating Daily Precipitation over the State of Virginia
NASA Technical Reports Server (NTRS)
Follansbee, W. A.; Chamberlain, L. W., III
1981-01-01
Digital IR and visible imagery obtained from a geostationary satellite located over the equator at 75 deg west latitude were provided by NASA and used to obtain a linear relationship between cloud top temperature and hourly precipitation. Two computer programs written in FORTRAN were used. The first program computes the satellite estimate field from the hourly digital IR imagery. The second program computes the final estimate for the entire state area by comparing five preliminary estimates of 24 hour precipitation with control raingage readings and determining which of the five methods gives the best estimate for the day. The final estimate is then produced by incorporating control gage readings into the winning method. In presenting reliable precipitation estimates for every cell in Virginia in near real time on a daily on going basis, the techniques require on the order of 125 to 150 daily gage readings by dependable, highly motivated observers distributed as uniformly as feasible across the state.
Baker, Nancy T.
2015-10-05
Thelin, G.P., and Stone, W.W., 2013, Estimation of annual agricultural pesticide use for counties of the conterminous United States, 1992–2009: U.S. Geological Survey Scientific Investigations Report 2013–5009, 54 p.
State Estimates of Adolescent Cigarette Use and Perceptions of Risk of Smoking: 2012 and 2013
... 2015 STATE ESTIMATES OF ADOLESCENT CIGARETTE USE AND PERCEPTIONS OF RISK OF SMOKING: 2012 AND 2013 AUTHORS ... with an inverse association between use and risk perceptions (i.e., the prevalence of use is lower ...
NASA Astrophysics Data System (ADS)
Zha, Guofeng; Wang, Hongqiang; Yang, Zhaocheng; Cheng, Yongqiang; Qin, Yuliang
2016-04-01
As a complementary imaging technology, coincidence imaging radar (CIR) achieves high resolution for stationary or low-speed targets under the assumption of ignoring the influence of the original position mismatching. As to high-speed moving targets moving from the original imaging cell to other imaging cells during imaging, it is inaccurate to reconstruct the target using the previous imaging plane. We focus on the recovery problem for high-speed moving targets in the CIR system based on the intrapulse frequency random modulation signal in a single pulse. The effects induced by the motion on the imaging performance are analyzed. Because the basis matrix in the CIR imaging equation is determined by the unknown velocity parameter of the moving target, both the target images and basis matrix should be estimated jointly. We propose an adaptive joint parametric estimation recovery algorithm based on the Tikhonov regularization method to update the target velocity and basis matrix adaptively and recover the target images synchronously. Finally, the target velocity and target images are obtained in an iterative manner. Simulation results are presented to demonstrate the efficiency of the proposed algorithm.
Mean-square-error bounds for reduced-order linear state estimators
NASA Technical Reports Server (NTRS)
Baram, Y.; Kalit, G.
1987-01-01
The mean-square error of reduced-order linear state estimators for continuous-time linear systems is investigated. Lower and upper bounds on the minimal mean-square error are presented. The bounds are readily computable at each time-point and at steady state from the solutions to the Ricatti and the Liapunov equations. The usefulness of the error bounds for the analysis and design of reduced-order estimators is illustrated by a practical numerical example.
ERIC Educational Resources Information Center
Passel, Jeffrey S.; Woodrow, Karen A.
1984-01-01
Compares the 1980 census count of aliens with estimates of the legally resident alien population based on data collected by the Immigration and Naturalization Service in January 1980. Provides estimates for each of the states for selected countries of birth and for age, sex, and period of entry categories. (RDN)
The Use of Collateral Information in Proficiency Estimation for the Trial State Assessment.
ERIC Educational Resources Information Center
Mazzeo, John; And Others
The adequacy of several approaches to estimation of proficiency distributions for the Trial State Assessment (TSA) in eighth grade mathematics of the National Assessment of Educational Progress was examined. These approaches are more restrictive than the estimation procedures originally used, with the same kind of plausible-values approach that…
Insect-Inspired Self-Motion Estimation with Dense Flow Fields—An Adaptive Matched Filter Approach
Strübbe, Simon; Stürzl, Wolfgang; Egelhaaf, Martin
2015-01-01
The control of self-motion is a basic, but complex task for both technical and biological systems. Various algorithms have been proposed that allow the estimation of self-motion from the optic flow on the eyes. We show that two apparently very different approaches to solve this task, one technically and one biologically inspired, can be transformed into each other under certain conditions. One estimator of self-motion is based on a matched filter approach; it has been developed to describe the function of motion sensitive cells in the fly brain. The other estimator, the Koenderink and van Doorn (KvD) algorithm, was derived analytically with a technical background. If the distances to the objects in the environment can be assumed to be known, the two estimators are linear and equivalent, but are expressed in different mathematical forms. However, for most situations it is unrealistic to assume that the distances are known. Therefore, the depth structure of the environment needs to be determined in parallel to the self-motion parameters and leads to a non-linear problem. It is shown that the standard least mean square approach that is used by the KvD algorithm leads to a biased estimator. We derive a modification of this algorithm in order to remove the bias and demonstrate its improved performance by means of numerical simulations. For self-motion estimation it is beneficial to have a spherical visual field, similar to many flying insects. We show that in this case the representation of the depth structure of the environment derived from the optic flow can be simplified. Based on this result, we develop an adaptive matched filter approach for systems with a nearly spherical visual field. Then only eight parameters about the environment have to be memorized and updated during self-motion. PMID:26308839
Insect-Inspired Self-Motion Estimation with Dense Flow Fields--An Adaptive Matched Filter Approach.
Strübbe, Simon; Stürzl, Wolfgang; Egelhaaf, Martin
2015-01-01
The control of self-motion is a basic, but complex task for both technical and biological systems. Various algorithms have been proposed that allow the estimation of self-motion from the optic flow on the eyes. We show that two apparently very different approaches to solve this task, one technically and one biologically inspired, can be transformed into each other under certain conditions. One estimator of self-motion is based on a matched filter approach; it has been developed to describe the function of motion sensitive cells in the fly brain. The other estimator, the Koenderink and van Doorn (KvD) algorithm, was derived analytically with a technical background. If the distances to the objects in the environment can be assumed to be known, the two estimators are linear and equivalent, but are expressed in different mathematical forms. However, for most situations it is unrealistic to assume that the distances are known. Therefore, the depth structure of the environment needs to be determined in parallel to the self-motion parameters and leads to a non-linear problem. It is shown that the standard least mean square approach that is used by the KvD algorithm leads to a biased estimator. We derive a modification of this algorithm in order to remove the bias and demonstrate its improved performance by means of numerical simulations. For self-motion estimation it is beneficial to have a spherical visual field, similar to many flying insects. We show that in this case the representation of the depth structure of the environment derived from the optic flow can be simplified. Based on this result, we develop an adaptive matched filter approach for systems with a nearly spherical visual field. Then only eight parameters about the environment have to be memorized and updated during self-motion.
Performance of the JPEG Estimated Spectrum Adaptive Postfilter (JPEG-ESAP) for Low Bit Rates
NASA Technical Reports Server (NTRS)
Linares, Irving (Inventor)
2016-01-01
Frequency-based, pixel-adaptive filtering using the JPEG-ESAP algorithm for low bit rate JPEG formatted color images may allow for more compressed images while maintaining equivalent quality at a smaller file size or bitrate. For RGB, an image is decomposed into three color bands--red, green, and blue. The JPEG-ESAP algorithm is then applied to each band (e.g., once for red, once for green, and once for blue) and the output of each application of the algorithm is rebuilt as a single color image. The ESAP algorithm may be repeatedly applied to MPEG-2 video frames to reduce their bit rate by a factor of 2 or 3, while maintaining equivalent video quality, both perceptually, and objectively, as recorded in the computed PSNR values.
Antier, P; Minjares, A; Roussos, S; Viniegra-González, G
1993-01-01
The aim of this paper is to review and study a new approach for improving strains of Aspergillus niger specially adapted to produce pectinases by Solid State Fermentation (SSF) with materials having low levels of water activity (a(w)), i.e., coffee pulp. Special emphasis is placed on the use of two antimetabolic compounds: 2-deoxy-glucose (DG) and 2,4-dinitro-phenol (DNP) combined with a water depressant (ethylene glycol = EG) in order to put strong selection pressures on UV treated spores from parental strain C28B25 isolated from a coffee plantation. Such a strain was found to be DG sensitive. Results suggested the existence of a reciprocal relation between adaptation of isolated strains to SSF or to Submerged Fermentation (SmF) systems. Preliminary physiological analysis of isolated strains showed that at least some few initially DG resistant mutants could revert to DG sensitive phenotype but conserving increased pectinase production. Also it was found that phenotype for DNP resistance could be associated to changes of DG resistance. Finally, it was found that low levels of a(w) produced by adding 15% EG to agar plates, were a significant selection factor for strains well adapted to SSF system.
Development of advanced techniques for rotorcraft state estimation and parameter identification
NASA Technical Reports Server (NTRS)
Hall, W. E., Jr.; Bohn, J. G.; Vincent, J. H.
1980-01-01
An integrated methodology for rotorcraft system identification consists of rotorcraft mathematical modeling, three distinct data processing steps, and a technique for designing inputs to improve the identifiability of the data. These elements are as follows: (1) a Kalman filter smoother algorithm which estimates states and sensor errors from error corrupted data. Gust time histories and statistics may also be estimated; (2) a model structure estimation algorithm for isolating a model which adequately explains the data; (3) a maximum likelihood algorithm for estimating the parameters and estimates for the variance of these estimates; and (4) an input design algorithm, based on a maximum likelihood approach, which provides inputs to improve the accuracy of parameter estimates. Each step is discussed with examples to both flight and simulated data cases.
Survey of State-Level Cost and Benefit Estimates of Renewable Portfolio Standards
Heeter, J.; Barbose, G.; Bird, L.; Weaver, S.; Flores-Espino, F.; Kuskova-Burns, K.; Wiser, R.
2014-05-01
Most renewable portfolio standards (RPS) have five or more years of implementation experience, enabling an assessment of their costs and benefits. Understanding RPS costs and benefits is essential for policymakers evaluating existing RPS policies, assessing the need for modifications, and considering new policies. This study provides an overview of methods used to estimate RPS compliance costs and benefits, based on available data and estimates issued by utilities and regulators. Over the 2010-2012 period, average incremental RPS compliance costs in the United States were equivalent to 0.8% of retail electricity rates, although substantial variation exists around this average, both from year-to-year and across states. The methods used by utilities and regulators to estimate incremental compliance costs vary considerably from state to state and a number of states are currently engaged in processes to refine and standardize their approaches to RPS cost calculation. The report finds that state assessments of RPS benefits have most commonly attempted to quantitatively assess avoided emissions and human health benefits, economic development impacts, and wholesale electricity price savings. Compared to the summary of RPS costs, the summary of RPS benefits is more limited, as relatively few states have undertaken detailed benefits estimates, and then only for a few types of potential policy impacts. In some cases, the same impacts may be captured in the assessment of incremental costs. For these reasons, and because methodologies and level of rigor vary widely, direct comparisons between the estimates of benefits and costs are challenging.
A hybrid EKF/KF state estimator for a skid-steered ATV
NASA Astrophysics Data System (ADS)
Javed, Mohammad Azam; Owen, William; Biglarbegian, Mohammad; Melek, William
2014-01-01
This article presents a novel state estimation structure developed for a skid-steered, six-wheeled, ARGO® all-terrain vehicle (ATV). The ARGO ATV is a teleoperated unmanned ground vehicle custom fitted with an inertial measurement unit, wheel encoders and a Geographic Positioning System (GPS). This paper offers the following contributions: (1) a hybrid extended Kalman filter/Kalman filter state estimation technique that estimates the motion, orientation and wheel slips for an ARGO and (2) a virtual GPS point generation algorithm that can be used to adjust position estimates using a GPS sensor. Our field experiments reveal that the proposed estimation structure is able to estimate the position, velocity, orientation and longitudinal slip of the ARGO with a reasonable amount of accuracy. In addition, the proposed estimation structure is well suited for online applications and can also incorporate offline virtual GPS data to further improve the accuracy of the position estimates. The proposed estimation structure is also capable of estimating the longitudinal slip for every wheel of the ARGO.
NASA Astrophysics Data System (ADS)
Dalkilic, Turkan Erbay; Apaydin, Aysen
2009-11-01
In a regression analysis, it is assumed that the observations come from a single class in a data cluster and the simple functional relationship between the dependent and independent variables can be expressed using the general model; Y=f(X)+[epsilon]. However; a data cluster may consist of a combination of observations that have different distributions that are derived from different clusters. When faced with issues of estimating a regression model for fuzzy inputs that have been derived from different distributions, this regression model has been termed the [`]switching regression model' and it is expressed with . Here li indicates the class number of each independent variable and p is indicative of the number of independent variables [J.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transaction on Systems, Man and Cybernetics 23 (3) (1993) 665-685; M. Michel, Fuzzy clustering and switching regression models using ambiguity and distance rejects, Fuzzy Sets and Systems 122 (2001) 363-399; E.Q. Richard, A new approach to estimating switching regressions, Journal of the American Statistical Association 67 (338) (1972) 306-310]. In this study, adaptive networks have been used to construct a model that has been formed by gathering obtained models. There are methods that suggest the class numbers of independent variables heuristically. Alternatively, in defining the optimal class number of independent variables, the use of suggested validity criterion for fuzzy clustering has been aimed. In the case that independent variables have an exponential distribution, an algorithm has been suggested for defining the unknown parameter of the switching regression model and for obtaining the estimated values after obtaining an optimal membership function, which is suitable for exponential distribution.
Communication: Spin-free quantum computational simulations and symmetry adapted states.
Whitfield, James Daniel
2013-07-14
The ideas of digital simulation of quantum systems using a quantum computer parallel the original ideas of numerical simulation using a classical computer. In order for quantum computational simulations to advance to a competitive point, many techniques from classical simulations must be imported into the quantum domain. In this article, we consider the applications of symmetry in the context of quantum simulation. Building upon well established machinery, we propose a form of first quantized simulation that only requires the spatial part of the wave function, thereby allowing spin-free quantum computational simulations. We go further and discuss the preparation of N-body states with specified symmetries based on projection techniques. We consider two simple examples, molecular hydrogen and cyclopropenyl cation, to illustrate the ideas. The methods here are the first to explicitly deal with preparing N-body symmetry-adapted states and open the door for future investigations into group theory, chemistry, and quantum simulation. PMID:23862919
Radiographic image sequence coding using adaptive finite-state vector quantization
NASA Astrophysics Data System (ADS)
Joo, Chang-Hee; Choi, Jong S.
1990-11-01
Vector quantization is an effective spatial domain image coding technique at under 1 . 0 bits per pixel. To achieve the quality at lower rates it is necessary to exploit spatial redundancy over a larger region of pixels than is possible with memoryless VQ. A fmite state vector quant. izer can achieve the same performance as memoryless VQ at lower rates. This paper describes an athptive finite state vector quantization for radiographic image sequence coding. Simulation experiment has been carried out with 4*4 blocks of pixels from a sequence of cardiac angiogram consisting of 40 frames of size 256*256pixels each. At 0. 45 bpp the resulting adaptive FSVQ encoder achieves performance comparable to earlier memoryless VQs at 0. 8 bpp.
Communication: Spin-free quantum computational simulations and symmetry adapted states.
Whitfield, James Daniel
2013-07-14
The ideas of digital simulation of quantum systems using a quantum computer parallel the original ideas of numerical simulation using a classical computer. In order for quantum computational simulations to advance to a competitive point, many techniques from classical simulations must be imported into the quantum domain. In this article, we consider the applications of symmetry in the context of quantum simulation. Building upon well established machinery, we propose a form of first quantized simulation that only requires the spatial part of the wave function, thereby allowing spin-free quantum computational simulations. We go further and discuss the preparation of N-body states with specified symmetries based on projection techniques. We consider two simple examples, molecular hydrogen and cyclopropenyl cation, to illustrate the ideas. The methods here are the first to explicitly deal with preparing N-body symmetry-adapted states and open the door for future investigations into group theory, chemistry, and quantum simulation.
Symmetry-adapted excited states for the T1u⊗hg Jahn-Teller system
NASA Astrophysics Data System (ADS)
Qiu, Q. C.; Dunn, J. L.; Bates, C. A.
2001-08-01
Jahn-Teller (JT) systems typically contain a set of equivalent-energy wells in the lowest adiabatic potential-energy surface (APES). Quantum-mechanical tunneling between these wells (the dynamic JT effect) must be allowed for by taking appropriate symmetrized combinations of oscillator-type states associated with the wells. It is important to be able to describe the excited states of such systems for a number of reasons. One particular reason is that they are required for the calculation of second-order vibronic reduction factors, which in turn are useful for modeling experimental data using effective Hamiltonians. In this paper, projection-operator techniques are used to obtain general expressions for the symmetry-adapted excited states of the icosahedral T1u⊗hg JT system for the case of D5d minima in the APES. Analytical expressions for the states and their energies for one-phonon excitation are given explicitly. The energies of a selection of states with two-phonon excitations are also obtained and plotted. The results obtained in this paper are applicable to the C-60 molecule.
NASA Astrophysics Data System (ADS)
Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi
2014-03-01
A reliable and precise classification of schizophrenia is significant for its diagnosis and treatment of schizophrenia. Functional magnetic resonance imaging (fMRI) is a novel tool increasingly used in schizophrenia research. Recent advances in statistical learning theory have led to applying pattern classification algorithms to access the diagnostic value of functional brain networks, discovered from resting state fMRI data. The aim of this study was to propose an adaptive learning algorithm to distinguish schizophrenia patients from normal controls using resting-state functional language network. Furthermore, here the classification of schizophrenia was regarded as a sample selection problem where a sparse subset of samples was chosen from the labeled training set. Using these selected samples, which we call informative vectors, a classifier for the clinic diagnosis of schizophrenia was established. We experimentally demonstrated that the proposed algorithm incorporating resting-state functional language network achieved 83.6% leaveone- out accuracy on resting-state fMRI data of 27 schizophrenia patients and 28 normal controls. In contrast with KNearest- Neighbor (KNN), Support Vector Machine (SVM) and l1-norm, our method yielded better classification performance. Moreover, our results suggested that a dysfunction of resting-state functional language network plays an important role in the clinic diagnosis of schizophrenia.
Adaptive Partial Response Maximum Likelihood Detection with Tilt Estimation Using Sync Pattern
NASA Astrophysics Data System (ADS)
Lee, Kyusuk; Lee, Joohyun; Lee, Jaejin
2006-02-01
We propose an improved detection method that concurrently adjusts the coefficients of equalizer and reference branch values in Viterbi detector. For the estimation of asymmetric channel characteristics, we exploit sync patterns in each data frame. Because of using the read-only memory (ROM) table to renew the coefficients of equalizer and reference values of branches, the complexity of the hardware is reduced. The proposed partial response maximum likelihood (PRML) detector has been designed and verified by VerilogHDL and synthesized by Synopsys Design Compiler with Hynix 0.35 μm standard cell library.
Lipnikov, Konstantin; Agouzal, Abdellatif; Vassilevski, Yuri
2009-01-01
We present a new technology for generating meshes minimizing the interpolation and discretization errors or their gradients. The key element of this methodology is construction of a space metric from edge-based error estimates. For a mesh with N{sub h} triangles, the error is proportional to N{sub h}{sup -1} and the gradient of error is proportional to N{sub h}{sup -1/2} which are optimal asymptotics. The methodology is verified with numerical experiments.
... 2014 estimates to 2012–2013 estimates). However, youth perceptions of great risk of harm from monthly marijuana ... change. State Estimates of Adolescent Marijuana Use and Perceptions of Risk of Harm From Marijuana Use: 2013 ...
Molecular adaptation of photoprotection: triplet states in light-harvesting proteins.
Gall, Andrew; Berera, Rudi; Alexandre, Maxime T A; Pascal, Andrew A; Bordes, Luc; Mendes-Pinto, Maria M; Andrianambinintsoa, Sandra; Stoitchkova, Katerina V; Marin, Alessandro; Valkunas, Leonas; Horton, Peter; Kennis, John T M; van Grondelle, Rienk; Ruban, Alexander; Robert, Bruno
2011-08-17
The photosynthetic light-harvesting systems of purple bacteria and plants both utilize specific carotenoids as quenchers of the harmful (bacterio)chlorophyll triplet states via triplet-triplet energy transfer. Here, we explore how the binding of carotenoids to the different types of light-harvesting proteins found in plants and purple bacteria provides adaptation in this vital photoprotective function. We show that the creation of the carotenoid triplet states in the light-harvesting complexes may occur without detectable conformational changes, in contrast to that found for carotenoids in solution. However, in plant light-harvesting complexes, the triplet wavefunction is shared between the carotenoids and their adjacent chlorophylls. This is not observed for the antenna proteins of purple bacteria, where the triplet is virtually fully located on the carotenoid molecule. These results explain the faster triplet-triplet transfer times in plant light-harvesting complexes. We show that this molecular mechanism, which spreads the location of the triplet wavefunction through the pigments of plant light-harvesting complexes, results in the absence of any detectable chlorophyll triplet in these complexes upon excitation, and we propose that it emerged as a photoprotective adaptation during the evolution of oxygenic photosynthesis.
Motion-adapted pulse sequences for oriented sample (OS) solid-state NMR of biopolymers.
Lu, George J; Opella, Stanley J
2013-08-28
One of the main applications of solid-state NMR is to study the structure and dynamics of biopolymers, such as membrane proteins, under physiological conditions where the polypeptides undergo global motions as they do in biological membranes. The effects of NMR radiofrequency irradiations on nuclear spins are strongly influenced by these motions. For example, we previously showed that the MSHOT-Pi4 pulse sequence yields spectra with resonance line widths about half of those observed using the conventional pulse sequence when applied to membrane proteins undergoing rapid uniaxial rotational diffusion in phospholipid bilayers. In contrast, the line widths were not changed in microcrystalline samples where the molecules did not undergo global motions. Here, we demonstrate experimentally and describe analytically how some Hamiltonian terms are susceptible to sample motions, and it is their removal through the critical π/2 Z-rotational symmetry that confers the "motion adapted" property to the MSHOT-Pi4 pulse sequence. This leads to the design of separated local field pulse sequence "Motion-adapted SAMPI4" and is generalized to an approach for the design of decoupling sequences whose performance is superior in the presence of molecular motions. It works by cancelling the spin interaction by explicitly averaging the reduced Wigner matrix to zero, rather than utilizing the 2π nutation to average spin interactions. This approach is applicable to both stationary and magic angle spinning solid-state NMR experiments.
Estimate of underflow in the Niobrara River Basin across the Wyoming-Nebraska state line
Babcock, H.M.; Keech, Charles F.
1957-01-01
The purpose of this report is to estimate the amount of ground water flowing across the Wyoming-Nebraska State line within the Niobrara Rive basin and to evaluate the accuracy of that estimate. The approximate effort involed in obtaining additional data to determine the underflow more accurately also is discussed. This report was prepared by the U.S. Geological Survey in cooperation with the Wyoming State Engineer and Director of the Conservation and Survey Division of the University of Nebraska, at the request of the Niobrara River Compact Commission. The following paragraph requesting the work is quoted from the report of the Engineering Subcommittee to the Niobrara River Compact Commission, Ainsworth, Nebr., October 29, 1956: Need for additional data under this item is confined to ground-water data since surface-water data discussions are covered under item 1. It is recommended that the Commission request the Geological Survey in cooperation with each of the three states to develop estimates of ground-water flows across state lines, together with ground-water contour maps extending adequate distanced into each state, such estimates and maps to be based on existing data and qualified by their evaluation of resultant percentage degree of accuracy. In addition they should be requested to furnish an estimate of cost to obtain additional data necessary to bring the estimate to within a more acceptable degree of accuracy as may be desired by the Commission.
Estimates of Resident Indian Population by State and Reservation: March 1972.
ERIC Educational Resources Information Center
Bureau of Indian Affairs (Dept. of Interior), Washington, DC.
The document gives estimates of resident American Indian population by state and Indian Reservation for March 1972. The term resident Indian means Indians enrolled in a tribe recognized by the United States Government living on or near reservations. It also includes Indians living in former reservation areas of Oklahoma, and all Alaskan Indians…
Accurate state estimation for a hydraulic actuator via a SDRE nonlinear filter
NASA Astrophysics Data System (ADS)
Strano, Salvatore; Terzo, Mario
2016-06-01
The state estimation in hydraulic actuators is a fundamental tool for the detection of faults or a valid alternative to the installation of sensors. Due to the hard nonlinearities that characterize the hydraulic actuators, the performances of the linear/linearization based techniques for the state estimation are strongly limited. In order to overcome these limits, this paper focuses on an alternative nonlinear estimation method based on the State-Dependent-Riccati-Equation (SDRE). The technique is able to fully take into account the system nonlinearities and the measurement noise. A fifth order nonlinear model is derived and employed for the synthesis of the estimator. Simulations and experimental tests have been conducted and comparisons with the largely used Extended Kalman Filter (EKF) are illustrated. The results show the effectiveness of the SDRE based technique for applications characterized by not negligible nonlinearities such as dead zone and frictions.
State estimation applications in aircraft flight-data analysis: A user's manual for SMACK
NASA Technical Reports Server (NTRS)
Bach, Ralph E., Jr.
1991-01-01
The evolution in the use of state estimation is traced for the analysis of aircraft flight data. A unifying mathematical framework for state estimation is reviewed, and several examples are presented that illustrate a general approach for checking instrument accuracy and data consistency, and for estimating variables that are difficult to measure. Recent applications associated with research aircraft flight tests and airline turbulence upsets are described. A computer program for aircraft state estimation is discussed in some detail. This document is intended to serve as a user's manual for the program called SMACK (SMoothing for AirCraft Kinematics). The diversity of the applications described emphasizes the potential advantages in using SMACK for flight-data analysis.
Goto, Ryosuke; Kumakura, Hiroo
2013-05-01
In this study we compared the power arm lengths and mechanical advantages attributed to 12 lower leg muscles across three prosimian species. The origins and insertions of the lower leg muscles in Garnett's galago, the ring-tailed lemur, and the slow loris were quantified and correlated with positional behaviour. The ankle joint of the galago has a speed-oriented mechanical system, in contrast to that of the slow loris, which exhibits more power-oriented mechanics. The lemur ankle joint exhibited intermediate power arm lengths and an intermediate mechanical advantage relative to the other primates. This result suggests that the mechanical differences in the ankle between the galago and the lemur, taxa that exhibit similar locomotory repertoires, reflect a difference in the kinematics and kinetics of leaping (i.e. generalised vs. specialised leapers). In contrast to leaping primates, lorises have developed a more power-oriented mechanical system as a foot adaptation for positional behaviours such as bridging or cantilevering in their arboreal habitat. PMID:23489408
Goto, Ryosuke; Kumakura, Hiroo
2013-05-01
In this study we compared the power arm lengths and mechanical advantages attributed to 12 lower leg muscles across three prosimian species. The origins and insertions of the lower leg muscles in Garnett's galago, the ring-tailed lemur, and the slow loris were quantified and correlated with positional behaviour. The ankle joint of the galago has a speed-oriented mechanical system, in contrast to that of the slow loris, which exhibits more power-oriented mechanics. The lemur ankle joint exhibited intermediate power arm lengths and an intermediate mechanical advantage relative to the other primates. This result suggests that the mechanical differences in the ankle between the galago and the lemur, taxa that exhibit similar locomotory repertoires, reflect a difference in the kinematics and kinetics of leaping (i.e. generalised vs. specialised leapers). In contrast to leaping primates, lorises have developed a more power-oriented mechanical system as a foot adaptation for positional behaviours such as bridging or cantilevering in their arboreal habitat.
Guidelines for preparation of state water-use estimates for 2000
Kenny, Joan F.
2004-01-01
This report describes the water-use categories and data elements required for the 2000 national water-use compilation conducted by the U.S. Geological Survey (USGS) as part of its National Water Use Information Program. It identifies sources of water-use information, guidelines for estimating water use, and required documentation for preparation of the national compilation by State for the United States, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands. The data are published in USGS Circular 1268, Estimated Use of Water in the United States in 2000. USGS has published circulars on estimated use of water in the United States at 5-year intervals since 1950. As part of this USGS program to document water use on a national scale for the year 2000, all States prepare estimates of water withdrawals for public supply, industrial, irrigation, and thermoelectric power generation water uses at the county level. All States prepare estimates of domestifc use and population served by public supply at least at the State level. All States provide estimates of irrigated acres by irrigation system type (sprinkler, surface, or microirrigation) at the county level. County-level estimates of withdrawals for mining, livestock, and aquaculture uses are compiled by selected States that comprised the largest percentage of national use in 1995 for these categories, and are optional for other States. Ground-water withdrawals for public-supply, industrial, and irrigation use are aggregated by principal aquifer or aquifer system, as identified by the USGS Office of Ground Water. Some categories and data elements that were mandatory in previous compilations are optional for the 2000 compilation, in response to budget considerations at the State level. Optional categories are commercial, hydroelectric, and wastewater treatment. Estimation of deliveries from public supply to domestic, commercial, industrial, and thermoelectric uses, consumptive use for any category, and
Siddlingeshwar, B; Hanagodimath, S M
2009-04-01
The ground state (micro(g)) and the excited state (micro(e)) dipole moments of three substituted anthraquinones, namely 1-aminoanthracene-9,10-dione (AAQ), 1-(methylamino)anthracence-9,10-dione (MAQ) and 1,5-diaminoanthracene-9,10-dione (DAQ) were estimated in various solvents. The dipole moments (micro(g) and micro(e)) were estimated from Lippert, Bakhshiev, Kawski-Chamma-Viallet, McRae and Suppan equations by using the variation of Stokes shift with the solvent dielectric constant and refractive index. The excited state dipole moments were also calculated by using the variation of Stokes shift with microscopic solvent polarity parameter (Epsilon(T)(N)). It was observed that dipole moment values of excited states (micro(e)) were higher than corresponding ground state values (micro(g)), indicating a substantial redistribution of the pi-electron densities in a more polar excited state for all the molecules investigated.
1980-06-01
This study reviewed 1979 energy savings reports provided by states for conservation measures in four major categories of State Energy Conservation Program services, namely: (1) industrial, commercial, and institutional; (2) residential; (3) thermal and lighting; and (4) transportation. Conservation measures in these categories constitute a major portion of the total estimated 1980 savings for the State Energy Conservation Program. This study only addressed measures in these categories for which usable documentation had been submitted by states. Based on a review of measures supported by available documentation, the study estimates that energy savings associated with the conservation measures reviewed were 108 TBtu's for the calendar year 1979. These estimated energy savings for 1979 were converted into 540 million dollars for 1979 and 2.8 billion dollars over the projected life of the conservation measures.
Particle and Kalman filtering for state estimation and control of DC motors.
Rigatos, Gerasimos G
2009-01-01
State estimation is a major problem in industrial systems. To this end, Gaussian and nonparametric filters have been developed. In this paper the Kalman Filter, which assumes Gaussian measurement noise, is compared to the Particle Filter, which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a DC motor is used. The reconstructed state vector is used in a feedback control loop to generate the control input of the DC motor. In simulation tests it was observed that for a large number of particles the Particle Filter could succeed in accurately estimating the motor's state vector, but at the same time it required higher computational effort.
Dynamic State Estimation and Parameter Calibration of DFIG based on Ensemble Kalman Filter
Fan, Rui; Huang, Zhenyu; Wang, Shaobu; Diao, Ruisheng; Meng, Da
2015-07-30
With the growing interest in the application of wind energy, doubly fed induction generator (DFIG) plays an essential role in the industry nowadays. To deal with the increasing stochastic variations introduced by intermittent wind resource and responsive loads, dynamic state estimation (DSE) are introduced in any power system associated with DFIGs. However, sometimes this dynamic analysis canould not work because the parameters of DFIGs are not accurate enough. To solve the problem, an ensemble Kalman filter (EnKF) method is proposed for the state estimation and parameter calibration tasks. In this paper, a DFIG is modeled and implemented with the EnKF method. Sensitivity analysis is demonstrated regarding the measurement noise, initial state errors and parameter errors. The results indicate this EnKF method has a robust performance on the state estimation and parameter calibration of DFIGs.
Stenmark, Matthew H.; Cao, Yue; Wang, Hesheng; Jackson, Andrew; Ben-Josef, Edgar; Ten Haken, Randall K.; Lawrence, Theodore S.; Feng, Mary
2014-01-01
Purpose To estimate the limit of functional liver reserve for safe application of hepatic irradiation using changes in indocyanine green, an established assay of liver function. Materials and Methods From 2005–2011, 60 patients undergoing hepatic irradiation were enrolled in a prospective study assessing the plasma retention fraction of indocyanine green at 15-min (ICG-R15) prior to, during (at 60% of planned dose), and after radiotherapy (RT). The limit of functional liver reserve was estimated from the damage fraction of functional liver (DFL) post-RT [1−(ICG-R15pre-RT/ICG-R15post-RT)] where no toxicity was observed using a beta distribution function. Results Of 48 evaluable patients, 3 (6%) developed RILD, all within 2.5 months of completing RT. The mean ICG-R15 for non-RILD patients pre-RT, during-RT and 1-month post-RT was 20.3%(SE 2.6), 22.0%(3.0), and 27.5%(2.8), and for RILD patients was 6.3%(4.3), 10.8%(2.7), and 47.6%(8.8). RILD was observed at post-RT damage fractions of ≥78%. Both DFL assessed by during-RT ICG and MLD predicted for DFL post-RT (p<0.0001). Limiting the post-RT DFL to 50%, predicted a 99% probability of a true complication rate <15%. Conclusion The DFL as assessed by changes in ICG during treatment serves as an early indicator of a patient’s tolerance to hepatic irradiation. PMID:24813090
Recursive Bayesian filtering framework for lithium-ion cell state estimation
NASA Astrophysics Data System (ADS)
Tagade, Piyush; Hariharan, Krishnan S.; Gambhire, Priya; Kolake, Subramanya Mayya; Song, Taewon; Oh, Dukjin; Yeo, Taejung; Doo, Seokgwang
2016-02-01
Robust battery management system is critical for a safe and reliable electric vehicle operation. One of the most important functions of the battery management system is to accurately estimate the battery state using minimal on-board instrumentation. This paper presents a recursive Bayesian filtering framework for on-board battery state estimation by assimilating measurables like cell voltage, current and temperature with physics-based reduced order model (ROM) predictions. The paper proposes an improved Particle filtering algorithm for implementation of the framework, and compares its performance against the unscented Kalman filter. Functionality of the proposed framework is demonstrated for a commercial NCA/C cell state estimation at different operating conditions including constant current discharge at room and low temperatures, hybrid power pulse characterization (HPPC) and urban driving schedule (UDDS) protocols. In addition to accurate voltage prediction, the electrochemical nature of ROM enables drawing of physical insights into the cell behavior. Advantages of using electrode concentrations over conventional Coulomb counting for accessible capacity estimation are discussed. In addition to the mean state estimation, the framework also provides estimation of the associated confidence bounds that are used to establish predictive capability of the proposed framework.
Estimation of the number of wild pigs found in the United States
Mayer, J.
2014-08-01
Based on a compilation of three estimation approaches, the total nationwide population of wild pigs in the United States numbers approximately 6.3 million animals, with that total estimate ranging from 4.4 up to 11.3 million animals. The majority of these numbers (99 percent), which were encompassed by ten states (i.e., Alabama, Arkansas, California, Florida, Georgia, Louisiana, Mississippi, Oklahoma, South Carolina and Texas), were based on defined estimation methodologies (e.g., density estimates correlated to the total potential suitable wild pig habitat statewide, statewide harvest percentages, statewide agency surveys regarding wild pig distribution and numbers). In contrast to the pre-1990 estimates, none of these more recent efforts, collectively encompassing 99 percent of the total, were based solely on anecdotal information or speculation. To that end, one can defensibly state that the wild pigs found in the United States number in the millions of animals, with the nationwide population estimated to arguably vary from about four million up to about eleven million individuals.
NASA Astrophysics Data System (ADS)
Karwowski, Damian; Domański, Marek
2016-01-01
An improved context-based adaptive binary arithmetic coding (CABAC) is presented. The idea for the improvement is to use a more accurate mechanism for estimation of symbol probabilities in the standard CABAC algorithm. The authors' proposal of such a mechanism is based on the context-tree weighting technique. In the framework of a high-efficiency video coding (HEVC) video encoder, the improved CABAC allows 0.7% to 4.5% bitrate saving compared to the original CABAC algorithm. The application of the proposed algorithm marginally affects the complexity of HEVC video encoder, but the complexity of video decoder increases by 32% to 38%. In order to decrease the complexity of video decoding, a new tool has been proposed for the improved CABAC that enables scaling of the decoder complexity. Experiments show that this tool gives 5% to 7.5% reduction of the decoding time while still maintaining high efficiency in the data compression.
Sigron, Netta; Tselniker, Igor; Nazarathy, Moshe
2012-01-30
The MSDD carrier phase estimation technique is derived here for optically coherent QPSK transmission, introducing the principle of operation while providing intuitive insight in terms of a multi-symbol extension of naïve delay-detection. We derive here for the first time Wiener-optimized and LMS-adapted versions of MSDD, introduce simplified hardware realizations, and evaluate complexity and numerical performance tradeoffs of this highly robust and low-complexity carrier phase recovery method. A multiplier-free carrier phase recovery version of the MSDD provides nearly optimal performance for linewidths up to ~0.5 MHz, whereas for wider linewidths, the Wiener or LMS versions provide optimal performance at about 9 taps, using 1 or 2 complex multipliers per tap.
An Estimate of Avian Mortality at Communication Towers in the United States and Canada
Longcore, Travis; Rich, Catherine; Mineau, Pierre; MacDonald, Beau; Bert, Daniel G.; Sullivan, Lauren M.; Mutrie, Erin; Gauthreaux, Sidney A.; Avery, Michael L.; Crawford, Robert L.; Manville, Albert M.; Travis, Emilie R.; Drake, David
2012-01-01
Avian mortality at communication towers in the continental United States and Canada is an issue of pressing conservation concern. Previous estimates of this mortality have been based on limited data and have not included Canada. We compiled a database of communication towers in the continental United States and Canada and estimated avian mortality by tower with a regression relating avian mortality to tower height. This equation was derived from 38 tower studies for which mortality data were available and corrected for sampling effort, search efficiency, and scavenging where appropriate. Although most studies document mortality at guyed towers with steady-burning lights, we accounted for lower mortality at towers without guy wires or steady-burning lights by adjusting estimates based on published studies. The resulting estimate of mortality at towers is 6.8 million birds per year in the United States and Canada. Bootstrapped subsampling indicated that the regression was robust to the choice of studies included and a comparison of multiple regression models showed that incorporating sampling, scavenging, and search efficiency adjustments improved model fit. Estimating total avian mortality is only a first step in developing an assessment of the biological significance of mortality at communication towers for individual species or groups of species. Nevertheless, our estimate can be used to evaluate this source of mortality, develop subsequent per-species mortality estimates, and motivate policy action. PMID:22558082
An estimate of avian mortality at communication towers in the United States and Canada.
Longcore, Travis; Rich, Catherine; Mineau, Pierre; MacDonald, Beau; Bert, Daniel G; Sullivan, Lauren M; Mutrie, Erin; Gauthreaux, Sidney A; Avery, Michael L; Crawford, Robert L; Manville, Albert M; Travis, Emilie R; Drake, David
2012-01-01
Avian mortality at communication towers in the continental United States and Canada is an issue of pressing conservation concern. Previous estimates of this mortality have been based on limited data and have not included Canada. We compiled a database of communication towers in the continental United States and Canada and estimated avian mortality by tower with a regression relating avian mortality to tower height. This equation was derived from 38 tower studies for which mortality data were available and corrected for sampling effort, search efficiency, and scavenging where appropriate. Although most studies document mortality at guyed towers with steady-burning lights, we accounted for lower mortality at towers without guy wires or steady-burning lights by adjusting estimates based on published studies. The resulting estimate of mortality at towers is 6.8 million birds per year in the United States and Canada. Bootstrapped subsampling indicated that the regression was robust to the choice of studies included and a comparison of multiple regression models showed that incorporating sampling, scavenging, and search efficiency adjustments improved model fit. Estimating total avian mortality is only a first step in developing an assessment of the biological significance of mortality at communication towers for individual species or groups of species. Nevertheless, our estimate can be used to evaluate this source of mortality, develop subsequent per-species mortality estimates, and motivate policy action. PMID:22558082
Doppler shift estimation for GNSS reflectometry using a land topography adapted reflection model
NASA Astrophysics Data System (ADS)
Semmling, Maximilian; Peraza, Luis; Falck, Carsten; Gerland, Sebastian; Wickert, Jens
2016-04-01
A GNSS setup with a receiver capable for reflectometry is operated by GFZ at Kongsfjorden (Spitsbergen), 78°54'14''N, 11°52'37''E, 512 m above ellipsoid (WGS-84). This permanent station at the Zeppelin mountain outpost, operated by the Norwegian Polar Institute (NPI), accumulates data since Summer 2013 observing reflections over the fjord and the adjacent land surface. Especially the presence of sea ice over the fjord and snow cover over land are of interest for reflectometry to investigate altimetry and remote sensing applications. The setup contains a GORS (GNSS Occultation Reflectometry Scatterometry) two-frontend receiver, which is based on commercial JAVAD hardware. The receiver is connected to one up-looking and one horizon-looking patch antenna with right-handed and left-handed circular polarization, respectively. Both antennas are installed on the same mount approximately 475 m above the fjord mean sea level. Reflections are observed at low transmitter elevation angles (between 10 and 2°). For these geometries the relative Doppler shift (sea surface reflected relative to direct signal) is almost constant 0.5 to 0.6 Hz and can be calculated with an established reflection model. Rather easily, sea surface reflections are identified in the data and the corresponding reflection points are located. About 55 daily recurring reflection events over the fjord are observed. They form a fan-shaped swath with 3 to 13 km distance around the receiver, corresponding to elevations of 10° to 2°. Also signatures of potential land reflections are found in the data. About 13 daily recurring events extend mainly over land. The potential land signatures have a rather variable Doppler shift between 0.2 to 1Hz. The significant topography of the mountainous surrounding, which varies between sea level and 900 m altitude, prevents the use of established reflection models. A topography adapted reflection model, which considers sloped surface facets, is developed. It incorporates
NASA Astrophysics Data System (ADS)
Lin, Tsungpo
Performance engineers face the major challenge in modeling and simulation for the after-market power system due to system degradation and measurement errors. Currently, the majority in power generation industries utilizes the deterministic data matching method to calibrate the model and cascade system degradation, which causes significant calibration uncertainty and also the risk of providing performance guarantees. In this research work, a maximum-likelihood based simultaneous data reconciliation and model calibration (SDRMC) is used for power system modeling and simulation. By replacing the current deterministic data matching with SDRMC one can reduce the calibration uncertainty and mitigate the error propagation to the performance simulation. A modeling and simulation environment for a complex power system with certain degradation has been developed. In this environment multiple data sets are imported when carrying out simultaneous data reconciliation and model calibration. Calibration uncertainties are estimated through error analyses and populated to performance simulation by using principle of error propagation. System degradation is then quantified by performance comparison between the calibrated model and its expected new & clean status. To mitigate smearing effects caused by gross errors, gross error detection (GED) is carried out in two stages. The first stage is a screening stage, in which serious gross errors are eliminated in advance. The GED techniques used in the screening stage are based on multivariate data analysis (MDA), including multivariate data visualization and principal component analysis (PCA). Subtle gross errors are treated at the second stage, in which the serial bias compensation or robust M-estimator is engaged. To achieve a better efficiency in the combined scheme of the least squares based data reconciliation and the GED technique based on hypotheses testing, the Levenberg-Marquardt (LM) algorithm is utilized as the optimizer. To
NASA Astrophysics Data System (ADS)
Garnier, Romain; Odunlami, Marc; Le Bris, Vincent; Bégué, Didier; Baraille, Isabelle; Coulaud, Olivier
2016-05-01
A new variational algorithm called adaptive vibrational configuration interaction (A-VCI) intended for the resolution of the vibrational Schrödinger equation was developed. The main advantage of this approach is to efficiently reduce the dimension of the active space generated into the configuration interaction (CI) process. Here, we assume that the Hamiltonian writes as a sum of products of operators. This adaptive algorithm was developed with the use of three correlated conditions, i.e., a suitable starting space, a criterion for convergence, and a procedure to expand the approximate space. The velocity of the algorithm was increased with the use of a posteriori error estimator (residue) to select the most relevant direction to increase the space. Two examples have been selected for benchmark. In the case of H2CO, we mainly study the performance of A-VCI algorithm: comparison with the variation-perturbation method, choice of the initial space, and residual contributions. For CH3CN, we compare the A-VCI results with a computed reference spectrum using the same potential energy surface and for an active space reduced by about 90%.
Garnier, Romain; Odunlami, Marc; Le Bris, Vincent; Bégué, Didier; Baraille, Isabelle; Coulaud, Olivier
2016-05-28
A new variational algorithm called adaptive vibrational configuration interaction (A-VCI) intended for the resolution of the vibrational Schrödinger equation was developed. The main advantage of this approach is to efficiently reduce the dimension of the active space generated into the configuration interaction (CI) process. Here, we assume that the Hamiltonian writes as a sum of products of operators. This adaptive algorithm was developed with the use of three correlated conditions, i.e., a suitable starting space, a criterion for convergence, and a procedure to expand the approximate space. The velocity of the algorithm was increased with the use of a posteriori error estimator (residue) to select the most relevant direction to increase the space. Two examples have been selected for benchmark. In the case of H2CO, we mainly study the performance of A-VCI algorithm: comparison with the variation-perturbation method, choice of the initial space, and residual contributions. For CH3CN, we compare the A-VCI results with a computed reference spectrum using the same potential energy surface and for an active space reduced by about 90%. PMID:27250295
NOAA Atlas 14: Updated Precipitation Frequency Estimates for the United States
NASA Astrophysics Data System (ADS)
Pavlovic, S.; Perica, S.; Martin, D.; Roy, I.; StLaurent, M.; Trypaluk, C.; Unruh, D.; Yekta, M.; Bonnin, G. M.
2013-12-01
NOAA Atlas 14 precipitation frequency estimates, developed by the National Weather Service's Hydrometeorological Design Studies Center, serve as the de-facto standards for a wide variety of design and planning activities under federal, state, and local regulations. Precipitation frequency estimates are used in the design of drainage for highways, culverts, bridges, parking lots, as well as in sizing sewer and stormwater infrastructure. Water resources engineers use them to estimate the amount of runoff, to estimate the volume of detention basins and size detention-basin outlet structures, and to estimate the volume of sediment or the amount of erosion. They are also used by floodplain managers to delineate floodplains and regulate the development in floodplains, which is crucial for all communities in the National Flood Insurance Program. Hydrometeorological Design Studies Center now provides more than 35,000 downloads per month to its Precipitation Frequency Data Server. Precipitation frequency estimates are often used in engineering design without any understanding how these estimates have been developed or without any understanding of the uncertainties associated with these estimates. This presentation will describe novel tools and techniques that have being developed in the last years to determine precipitation frequency estimates in NOAA Atlas 14. Particular attention will be given to the regional frequency analysis approach based on L-moment statistics calculated from annual maximum series, selected statistics obtained in determining and parameterizing the probability distribution functions, and the potential implication for engineering design of recently published estimates.
NOAA Atlas 14: Updated Precipitation Frequency Estimates for the United States
NASA Astrophysics Data System (ADS)
Pavlovic, S.; Perica, S.; Martin, D.; Roy, I.; StLaurent, M.; Trypaluk, C.; Unruh, D.; Yekta, M.; Bonnin, G. M.
2011-12-01
NOAA Atlas 14 precipitation frequency estimates, developed by the National Weather Service's Hydrometeorological Design Studies Center, serve as the de-facto standards for a wide variety of design and planning activities under federal, state, and local regulations. Precipitation frequency estimates are used in the design of drainage for highways, culverts, bridges, parking lots, as well as in sizing sewer and stormwater infrastructure. Water resources engineers use them to estimate the amount of runoff, to estimate the volume of detention basins and size detention-basin outlet structures, and to estimate the volume of sediment or the amount of erosion. They are also used by floodplain managers to delineate floodplains and regulate the development in floodplains, which is crucial for all communities in the National Flood Insurance Program. Hydrometeorological Design Studies Center now provides more than 35,000 downloads per month to its Precipitation Frequency Data Server. Precipitation frequency estimates are often used in engineering design without any understanding how these estimates have been developed or without any understanding of the uncertainties associated with these estimates. This presentation will describe novel tools and techniques that have being developed in the last years to determine precipitation frequency estimates in NOAA Atlas 14. Particular attention will be given to the regional frequency analysis approach based on L-moment statistics calculated from annual maximum series, selected statistics obtained in determining and parameterizing the probability distribution functions, and the potential implication for engineering design of recently published estimates.
One size does not fit all: Adapting mark-recapture and occupancy models for state uncertainty
Kendall, W.L.; Thomson, David L.; Cooch, Evan G.; Conroy, Michael J.
2009-01-01
Multistate capture?recapture models continue to be employed with greater frequency to test hypotheses about metapopulation dynamics and life history, and more recently disease dynamics. In recent years efforts have begun to adjust these models for cases where there is uncertainty about an animal?s state upon capture. These efforts can be categorized into models that permit misclassification between two states to occur in either direction or one direction, where state is certain for a subset of individuals or is always uncertain, and where estimation is based on one sampling occasion per period of interest or multiple sampling occasions per period. State uncertainty also arises in modeling patch occupancy dynamics. I consider several case studies involving bird and marine mammal studies that illustrate how misclassified states can arise, and outline model structures for properly utilizing the data that are produced. In each case misclassification occurs in only one direction (thus there is a subset of individuals or patches where state is known with certainty), and there are multiple sampling occasions per period of interest. For the cases involving capture?recapture data I allude to a general model structure that could include each example as a special case. However, this collection of cases also illustrates how difficult it is to develop a model structure that can be directly useful for answering every ecological question of interest and account for every type of data from the field.
Forecasting aftershock activity: 1. Adaptive estimates based on the Omori and Gutenberg-Richter laws
NASA Astrophysics Data System (ADS)
Baranov, S. V.; Shebalin, P. N.
2016-05-01
The method for forecasting the intensity of the aftershock processes after strong earthquakes in different magnitude intervals is considered. The method is based on the joint use of the time model of the aftershock process and the Gutenberg-Richter law. The time model serves for estimating the intensity of the aftershock flow with a magnitude larger than or equal to the magnitude of completeness. The Gutenberg-Richter law is used for magnitude scaling. The suggested approach implements successive refinement of the parameters of both components of the method, which is the main novelty distinguishing it from the previous ones. This approach, to a significant extent, takes into account the variations in the parameters of the frequency-magnitude distribution, which often show themselves by the decreasing fraction of stronger aftershocks with time. Testing the method on eight aftershock sequences in the regions with different patterns of seismicity demonstrates the high probability of successful forecasts. The suggested technique can be employed in seismological monitoring centers for forecasting the aftershock activity of a strong earthquake based on the results of operational processing.
Scale parameter-estimating method for adaptive fingerprint pore extraction model
NASA Astrophysics Data System (ADS)
Yi, Yao; Cao, Liangcai; Guo, Wei; Luo, Yaping; He, Qingsheng; Jin, Guofan
2011-11-01
Sweat pores and other level 3 features have been proven to provide more discriminatory information about fingerprint characteristics, which is useful for personal identification especially in law enforcement applications. With the advent of high resolution (>=1000 ppi) fingerprint scanning equipment, sweat pores are attracting increasing attention in automatic fingerprint identification system (AFIS), where the extraction of pores is a critical step. This paper presents a scale parameter-estimating method in filtering-based pore extraction procedure. Pores are manually extracted from a 1000 ppi grey-level fingerprint image. The size and orientation of each detected pore are extracted together with local ridge width and orientation. The quantitative relation between the pore parameters (size and orientation) and local image parameters (ridge width and orientation) is statistically obtained. The pores are extracted by filtering fingerprint image with the new pore model, whose parameters are determined by local image parameters and the statistically established relation. Experiments conducted on high resolution fingerprints indicate that the new pore model gives good performance in pore extraction.
NASA Astrophysics Data System (ADS)
Ait-El-Fquih, Boujemaa; El Gharamti, Mohamad; Hoteit, Ibrahim
2016-08-01
Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface groundwater models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model's state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKFOSA. Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25 % more accurate state and parameter estimations than the joint and dual approaches.
Methods for Estimating Water Withdrawals for Aquaculture in the United States, 2005
Lovelace, John K.
2009-01-01
Aquaculture water use is associated with raising organisms that live in water - such as finfish and shellfish - for food, restoration, conservation, or sport. Aquaculture production occurs under controlled feeding, sanitation, and harvesting procedures primarily in ponds, flow-through raceways, and, to a lesser extent, cages, net pens, and tanks. Aquaculture ponds, raceways, and tanks usually require the withdrawal or diversion of water from a ground or surface source. Most water withdrawn or diverted for aquaculture production is used to maintain pond levels and/or water quality. Water typically is added for maintenance of levels, oxygenation, temperature control, and flushing of wastes. This report documents methods used to estimate withdrawals of fresh ground water and surface water for aqua-culture in 2005 for each county and county-equivalent in the United States, Puerto Rico, and the U.S. Virgin Islands by using aquaculture statistics and estimated water-use coefficients and water-replacement rates. County-level data for commercial and noncommercial operations compiled for the 2005 Census of Aquaculture were obtained from the National Agricultural Statistics Service. Withdrawals of water used at commercial and noncommercial operations for aquaculture ponds, raceways, tanks, egg incubators, and pens and cages for alligators were estimated and totaled by ground-water or surface-water source for each county and county equivalent. Use of the methods described in this report, when measured or reported data are unavailable, could result in more consistent water-withdrawal estimates for aquaculture that can be used by water managers and planners to determine water needs and trends across the United States. The results of this study were distributed to U.S. Geological Survey water-use personnel in each State during 2007. Water-use personnel are required to submit estimated withdrawals for all categories of use in their State to the U.S. Geological Survey National
ERIC Educational Resources Information Center
Iowa State Dept. of Natural Resources, Des Moines.
This document summarizes materials on aquatic education used by state programs. Emphasis is on materials developed by, or adapted for use with, programs in various states and territories. The 234 entries are categorized as activity books, brochures, newsletters, posters, videos, and other materials. Major subjects include fishing, boating and…
Johnson, Richard C; Horning, Matthew E; Espeland, Erin K; Vance-Borland, Ken
2015-01-01
Genetic variation for potentially adaptive traits of the key restoration species Sandberg bluegrass (Poa secunda J. Presl) was assessed over the intermountain western United States in relation to source population climate. Common gardens were established at two intermountain west sites with progeny from two maternal parents from each of 130 wild populations. Data were collected over 2 years at each site on fifteen plant traits associated with production, phenology, and morphology. Analyses of variance revealed strong population differences for all plant traits (P < 0.0001), indicating genetic variation. Both the canonical correlation and linear correlation established associations between source populations and climate variability. Populations from warmer, more arid climates had generally lower dry weight, earlier phenology, and smaller, narrower leaves than those from cooler, moister climates. The first three canonical variates were regressed with climate variables resulting in significant models (P < 0.0001) used to map 12 seed zones. Of the 700 981 km2 mapped, four seed zones represented 92% of the area in typically semi-arid and arid regions. The association of genetic variation with source climates in the intermountain west suggested climate driven natural selection and evolution. We recommend seed transfer zones and population movement guidelines to enhance adaptation and diversity for large-scale restoration projects. PMID:25685192
On the transient and steady-state estimates of interval genetic regulatory networks.
Li, Ping; Lam, James; Shu, Zhan
2010-04-01
This paper is concerned with the transient and steady-state estimates of a class of genetic regulatory networks (GRNs). Some sufficient conditions, which do not only present the transient estimate but also provide the estimates of decay rate and decay coefficient of the GRN with interval parameter uncertainties (interval GRN), are established by means of linear matrix inequality (LMI) and Lyapunov-Krasovskii functional. Moreover, the steady-state estimate of the proposed GRN model is also investigated. Furthermore, it is well known that gene regulation is an intrinsically noisy process due to intracellular and extracellular noise perturbations and environmental fluctuations. Then, by utilizing stochastic differential equation theory, the obtained results are extended to the case with noise perturbations due to natural random fluctuations. All the conditions are expressed within the framework of LMIs, which can easily be computed by using standard numerical software. A three-gene network is provided to illustrate the effectiveness of the theoretical results.
Methods for Estimating Water Withdrawals for Mining in the United States, 2005
Lovelace, John K.
2009-01-01
The mining water-use category includes groundwater and surface water that is withdrawn and used for nonfuels and fuels mining. Nonfuels mining includes the extraction of ores, stone, sand, and gravel. Fuels mining includes the extraction of coal, petroleum, and natural gas. Water is used for mineral extraction, quarrying, milling, and other operations directly associated with mining activities. For petroleum and natural gas extraction, water often is injected for secondary oil or gas recovery. Estimates of water withdrawals for mining are needed for water planning and management. This report documents methods used to estimate withdrawals of fresh and saline groundwater and surface water for mining during 2005 for each county and county equivalent in the United States, Puerto Rico, and the U.S. Virgin Islands. Fresh and saline groundwater and surface-water withdrawals during 2005 for nonfuels- and coal-mining operations in each county or county equivalent in the United States, Puerto Rico, and the U.S. Virgin Islands were estimated. Fresh and saline groundwater withdrawals for oil and gas operations in counties of six states also were estimated. Water withdrawals for nonfuels and coal mining were estimated by using mine-production data and water-use coefficients. Production data for nonfuels mining included the mine location and weight (in metric tons) of crude ore, rock, or mineral produced at each mine in the United States, Puerto Rico, and the U.S. Virgin Islands during 2004. Production data for coal mining included the weight, in metric tons, of coal produced in each county or county equivalent during 2004. Water-use coefficients for mined commodities were compiled from various sources including published reports and written communications from U.S. Geological Survey National Water-use Information Program (NWUIP) personnel in several states. Water withdrawals for oil and gas extraction were estimated for six States including California, Colorado, Louisiana, New
Guo, Penghong; Rivera, Daniel E.; Downs, Danielle S.; Savage, Jennifer S.
2016-01-01
Excessive gestational weight gain (i.e., weight gain during pregnancy) is a significant public health concern, and has been the recent focus of novel, control systems-based interventions. This paper develops a control-oriented dynamical systems model based on a first-principles energy balance model from the literature, which is evaluated against participant data from a study targeted to obese and overweight pregnant women. The results indicate significant under-reporting of energy intake among the participant population. A series of approaches based on system identification and state estimation are developed in the paper to better understand and characterize the extent of under-reporting; these range from back-calculating energy intake from a closed-form of the energy balance model, to a constrained semi-physical identification approach that estimates the extent of systematic under-reporting in the presence of noise and possibly missing data. Additionally, we describe an adaptive algorithm based on Kalman filtering to estimate energy intake in real-time. The approaches are illustrated with data from both simulated and actual intervention participants. PMID:27570366
Method of Enhancing On-Board State Estimation Using Communication Signals
NASA Technical Reports Server (NTRS)
Anzalone, Evan J. (Inventor); Chuang, Jason C. H. (Inventor)
2015-01-01
A method of enhancing on-board state estimation for a spacecraft utilizes a network of assets to include planetary-based assets and space-based assets. Communication signals transmitted from each of the assets into space are defined by a common protocol. Data is embedded in each communication signal transmitted by the assets. The data includes a time-of-transmission for a corresponding one of the communication signals and a position of a corresponding one of the assets at the time-of-transmission. A spacecraft is equipped to receive the communication signals, has a clock synchronized to the space-wide time reference frame, and has a processor programmed to generate state estimates of the spacecraft. Using its processor, the spacecraft determines a one-dimensional range from itself to at least one of the assets and then updates its state estimates using each one-dimensional range.
NASA Technical Reports Server (NTRS)
Sullivan, Michael J.
2005-01-01
This thesis develops a state estimation algorithm for the Centrifuge Rotor (CR) system where only relative measurements are available with limited knowledge of both rotor imbalance disturbances and International Space Station (ISS) thruster disturbances. A Kalman filter is applied to a plant model augmented with sinusoidal disturbance states used to model both the effect of the rotor imbalance and the 155 thrusters on the CR relative motion measurement. The sinusoidal disturbance states compensate for the lack of the availability of plant inputs for use in the Kalman filter. Testing confirms that complete disturbance modeling is necessary to ensure reliable estimation. Further testing goes on to show that increased estimator operational bandwidth can be achieved through the expansion of the disturbance model within the filter dynamics. In addition, Monte Carlo analysis shows the varying levels of robustness against defined plant/filter uncertainty variations.
Online state-of-health estimation of lithium-ion batteries using Dynamic Bayesian Networks
NASA Astrophysics Data System (ADS)
He, Zhiwei; Gao, Mingyu; Ma, Guojin; Liu, Yuanyuan; Chen, Sanxin
2014-12-01
Li-ion batteries are widely used in energy storage systems, electric vehicles, communication systems, etc. The State of Health (SOH) of batteries is of great importance to the safety of these systems. This paper presents a novel online method for the estimation of the SOH of Lithium (Li)-ion batteries based on Dynamic Bayesian Networks (DBNs). The structure of the DBN model is built according to the experience of experts, with the state of charges used as hidden states and the terminal voltages used as observations in the DBN. Parameters of the DBN model are learned based on training data collected through Li-ion battery aging experiments. A forward algorithm is applied for the inference of the DBN model in order to estimate the SOH in real-time. Experimental results show that the proposed method is effective and efficient in estimating the SOH of Li-ion batteries.
Adaptive behaviour and multiple equilibrium states in a predator-prey model.
Pimenov, Alexander; Kelly, Thomas C; Korobeinikov, Andrei; O'Callaghan, Michael J A; Rachinskii, Dmitrii
2015-05-01
There is evidence that multiple stable equilibrium states are possible in real-life ecological systems. Phenomenological mathematical models which exhibit such properties can be constructed rather straightforwardly. For instance, for a predator-prey system this result can be achieved through the use of non-monotonic functional response for the predator. However, while formal formulation of such a model is not a problem, the biological justification for such functional responses and models is usually inconclusive. In this note, we explore a conjecture that a multitude of equilibrium states can be caused by an adaptation of animal behaviour to changes of environmental conditions. In order to verify this hypothesis, we consider a simple predator-prey model, which is a straightforward extension of the classic Lotka-Volterra predator-prey model. In this model, we made an intuitively transparent assumption that the prey can change a mode of behaviour in response to the pressure of predation, choosing either "safe" of "risky" (or "business as usual") behaviour. In order to avoid a situation where one of the modes gives an absolute advantage, we introduce the concept of the "cost of a policy" into the model. A simple conceptual two-dimensional predator-prey model, which is minimal with this property, and is not relying on odd functional responses, higher dimensionality or behaviour change for the predator, exhibits two stable co-existing equilibrium states with basins of attraction separated by a separatrix of a saddle point.
Adaptive behaviour and multiple equilibrium states in a predator-prey model.
Pimenov, Alexander; Kelly, Thomas C; Korobeinikov, Andrei; O'Callaghan, Michael J A; Rachinskii, Dmitrii
2015-05-01
There is evidence that multiple stable equilibrium states are possible in real-life ecological systems. Phenomenological mathematical models which exhibit such properties can be constructed rather straightforwardly. For instance, for a predator-prey system this result can be achieved through the use of non-monotonic functional response for the predator. However, while formal formulation of such a model is not a problem, the biological justification for such functional responses and models is usually inconclusive. In this note, we explore a conjecture that a multitude of equilibrium states can be caused by an adaptation of animal behaviour to changes of environmental conditions. In order to verify this hypothesis, we consider a simple predator-prey model, which is a straightforward extension of the classic Lotka-Volterra predator-prey model. In this model, we made an intuitively transparent assumption that the prey can change a mode of behaviour in response to the pressure of predation, choosing either "safe" of "risky" (or "business as usual") behaviour. In order to avoid a situation where one of the modes gives an absolute advantage, we introduce the concept of the "cost of a policy" into the model. A simple conceptual two-dimensional predator-prey model, which is minimal with this property, and is not relying on odd functional responses, higher dimensionality or behaviour change for the predator, exhibits two stable co-existing equilibrium states with basins of attraction separated by a separatrix of a saddle point. PMID:25732186
Operator functional state estimation based on EEG-data-driven fuzzy model.
Zhang, Jianhua; Yin, Zhong; Yang, Shaozeng; Wang, Rubin
2016-10-01
This paper proposed a max-min-entropy-based fuzzy partition method for fuzzy model based estimation of human operator functional state (OFS). The optimal number of fuzzy partitions for each I/O variable of fuzzy model is determined by using the entropy criterion. The fuzzy models were constructed by using Wang-Mendel method. The OFS estimation results showed the practical usefulness of the proposed fuzzy modeling approach. PMID:27668017
Robust Second Order Sliding mode Observer for the Estimation of the Vehicle States
NASA Astrophysics Data System (ADS)
Chaibet, A.; Nouveliere, L.; Hima, S.; Mammar, S.
2008-06-01
This paper is dedicated to the observation of non measurable variables for automotive systems. A non linear observer, based on a sliding mode approach, is presented for the estimation of the dynamic states of the vehicle. The considered technique is applied to the estimation problem for an automated vehicle following. Both the simulation and the experimental results are addressed to demonstrate the effectiveness of the sliding mode observer for different maneuvers, in terms of performances and robustness.
Optimal quantum state estimation with use of the no-signaling principle
Han, Yeong-Deok; Bae, Joonwoo; Wang Xiangbin; Hwang, Won-Young
2010-12-15
A simple derivation of the optimal state estimation of a quantum bit was obtained by using the no-signaling principle. In particular, the no-signaling principle determines a unique form of the guessing probability independent of figures of merit, such as the fidelity or information gain. This proves that the optimal estimation for a quantum bit can be achieved by the same measurement for almost all figures of merit.
Operator functional state estimation based on EEG-data-driven fuzzy model.
Zhang, Jianhua; Yin, Zhong; Yang, Shaozeng; Wang, Rubin
2016-10-01
This paper proposed a max-min-entropy-based fuzzy partition method for fuzzy model based estimation of human operator functional state (OFS). The optimal number of fuzzy partitions for each I/O variable of fuzzy model is determined by using the entropy criterion. The fuzzy models were constructed by using Wang-Mendel method. The OFS estimation results showed the practical usefulness of the proposed fuzzy modeling approach.
[Adolescents and young adults with cancer between adaptation and addiction: state of the question].
Grégoire, Solène; Flahault, Cécile; Laurence, Valérie; Levy, Dominique; Dolbeault, Sylvie
2015-05-01
The purpose of this literature review is to make a point on the state of health of adolescents and young adults (15-25 years) suffering from cancer. The adaptation strategies and the impact of the announcement of cancer will be discussed. In addition, we are going to consider the characteristics of teenagers and young adults, given the fact that development is still in progress. This period is especially punctuated by various experiments and the emergence of some clinical signs. Also, we have identified various studies concerning the use of licit and illicit substances. Furthermore, we have taken interest in behavioral addictions, particularly cyber addiction. While trying to cross these variables with a population of teenagers and young adults in the context of somatic diseases, it occurred that this population was not well known and studied. The interest of this synthesis is to underline the importance to make future researches in these perspectives.
AgRP Neural Circuits Mediate Adaptive Behaviors in the Starved State
Padilla, Stephanie L.; Qiu, Jian; Soden, Marta E.; Sanz, Elisenda; Nestor, Casey C; Barker, Forrest D.; Quintana, Albert; Zweifel, Larry S.; Rønnekleiv, Oline K.; Kelly, Martin J.; Palmiter, Richard D.
2016-01-01
In the face of starvation animals will engage in high-risk behaviors that would normally be considered maladaptive. Starving rodents for example will forage in areas that are more susceptible to predators and will also modulate aggressive behavior within a territory of limited or depleted nutrients. The neural basis of these adaptive behaviors likely involves circuits that link innate feeding, aggression, and fear. Hypothalamic AgRP neurons are critically important for driving feeding and project axons to brain regions implicated in aggression and fear. Using circuit-mapping techniques, we define a disynaptic network originating from a subset of AgRP neurons that project to the medial nucleus of the amygdala and then to the principle bed nucleus of the stria terminalis, which plays a role in suppressing territorial aggression and reducing contextual fear. We propose that AgRP neurons serve as a master switch capable of coordinating behavioral decisions relative to internal state and environmental cues. PMID:27019015
Intracavity adaptive correction of a 10 kW, solid-state, heat-capacity laser
LaFortune, K N; Hurd, R L; Brase, J M; Yamamoto, R M
2004-05-13
The Solid-State, Heat-Capacity Laser (SSHCL), under development at Lawrence Livermore National Laboratory (LLNL) is a large aperture (100 cm{sup 2}), confocal, unstable resonator requiring near-diffraction-limited beam quality. There are two primary sources of the aberrations in the system: residual, static aberrations from the fabrication of the optical components and predictable, time-dependent, thermally-induced index gradients within the gain medium. A deformable mirror placed within the cavity is used to correct the aberrations that are sensed externally with a Shack-Hartmann wavefront sensor. Although the complexity of intracavity adaptive correction is greater than that of external correction, it enables control of the mode growth within the resonator, resulting in the ability to correct a more aberrated system longer. The overall system design, measurement techniques and correction algorithms are discussed. Experimental results from initial correction of the static aberrations and dynamic correction of the time-dependent aberrations are presented.
Space Weather and a State of Cardiovascular System of Human Being with a Weakened Adaptation System
NASA Astrophysics Data System (ADS)
Samsonov, S. N.
As has been shown in [Samsonov et al., 2013] even at the considerable disturbances of space weather parameters a healthy human being did not undergo painful symptoms although measurements of objective physiological indices showed their changes. At the same time the state of health of people with the weakened adaptation system under the same conditions can considerably be deteriorated up to fatal outcome. The analysis of results of the project "Heliomed" and the number of calls for the emergency medical care (EMC) around Yakutsk as to cardiovascular diseases (CVD) has shown:- the total number of calls for EMC concerning myocardial infarction (MI) per year near the geomagnetic disturbance maximum (1992) exceeds the number of calls per year near the geomagnetic activity minimum (1998) by a factor of 1,5 and concerning to strokes - by a factor of 1,8.- maxima of MI are observed during spring and autumn periods coinciding with maxima of geophysical disturbance;- the coincidence of 30-32 daily periods in a power spectrum of MI with the same periods in power spectra of space weather parameters (speeds and density of the solar wind, interplanetary magnetic field, geophysical disturbance);- the existence of 3 maxima of the number of calls for EMC: a) at the moment of disturbance on the Sun; during a geophysical disturbance (in 2-4 days after a disturbance on the Sun); in 2-4 days after a geophysical disturbance;- the availability of coincidence of insignificant disturbances of space weather parameters with changes of the functional state of cardiovascular system of a human being with the weakened adaptation system and the occurrence of MI and strokes at considerable values of such disturbances is explained by a quasi-logarithmic dependence of the response of human being organisms to the environment disturbance intensity.
2011-12-01
Climate change is already beginning to affect New York State, and these impacts are projected to grow. At the same time, the state has the ability to develop adaptation strategies to prepare for and respond to climate risks now and in the future. The ClimAID assessment provides information on climate change impacts and adaptation for eight sectors in New York State: water resources, coastal zones, ecosystems, agriculture, energy, transportation,telecommunications, and public health. Observed climate trends and future climate projections were developed for seven regions across the state. Within each of the sectors, climate risks, vulnerabilities, and adaptation strategies are identified. Integrating themes across all of the sectors are equity and environmental justice and economics.Case studies are used to examine specific vulnerabilities and potential adaptation strategies in each of the eight sectors. These case studies also illustrate the linkages among climate vulnerabilities, risks, and adaptation, and demonstrate specific monitoring needs. Stakeholder participation was critical to the ClimAID assessment process to ensure relevance to decision makers across the state.
Ho, Ngoc-Huynh; Truong, Phuc Huu; Jeong, Gu-Min
2016-01-01
We propose a walking distance estimation method based on an adaptive step-length estimator at various walking speeds using a smartphone. First, we apply a fast Fourier transform (FFT)-based smoother on the acceleration data collected by the smartphone to remove the interference signals. Then, we analyze these data using a set of step-detection rules in order to detect walking steps. Using an adaptive estimator, which is based on a model of average step speed, we accurately obtain the walking step length. To evaluate the accuracy of the proposed method, we examine the distance estimation for four different distances and three speed levels. The experimental results show that the proposed method significantly outperforms conventional estimation methods in terms of accuracy. PMID:27598171
Ho, Ngoc-Huynh; Truong, Phuc Huu; Jeong, Gu-Min
2016-01-01
We propose a walking distance estimation method based on an adaptive step-length estimator at various walking speeds using a smartphone. First, we apply a fast Fourier transform (FFT)-based smoother on the acceleration data collected by the smartphone to remove the interference signals. Then, we analyze these data using a set of step-detection rules in order to detect walking steps. Using an adaptive estimator, which is based on a model of average step speed, we accurately obtain the walking step length. To evaluate the accuracy of the proposed method, we examine the distance estimation for four different distances and three speed levels. The experimental results show that the proposed method significantly outperforms conventional estimation methods in terms of accuracy. PMID:27598171
State-space model with deep learning for functional dynamics estimation in resting-state fMRI.
Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang
2016-04-01
Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. PMID:26774612
State-space model with deep learning for functional dynamics estimation in resting-state fMRI.
Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang
2016-04-01
Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach.
State-Dependent Pseudo-Linear Filter for Spacecraft Attitude and Rate Estimation
NASA Technical Reports Server (NTRS)
Bar-Itzhack, Itzhack Y.; Harman, Richard R.
2001-01-01
This paper presents the development and performance of a special algorithm for estimating the attitude and angular rate of a spacecraft. The algorithm is a pseudo-linear Kalman filter, which is an ordinary linear Kalman filter that operates on a linear model whose matrices are current state estimate dependent. The nonlinear rotational dynamics equation of the spacecraft is presented in the state space as a state-dependent linear system. Two types of measurements are considered. One type is a measurement of the quaternion of rotation, which is obtained from a newly introduced star tracker based apparatus. The other type of measurement is that of vectors, which permits the use of a variety of vector measuring sensors like sun sensors and magnetometers. While quaternion measurements are related linearly to the state vector, vector measurements constitute a nonlinear function of the state vector. Therefore, in this paper, a state-dependent linear measurement equation is developed for the vector measurement case. The state-dependent pseudo linear filter is applied to simulated spacecraft rotations and adequate estimates of the spacecraft attitude and rate are obtained for the case of quaternion measurements as well as of vector measurements.
A Review on the Computational Methods for Emotional State Estimation from the Human EEG
Kim, Min-Ki; Kim, Miyoung; Oh, Eunmi
2013-01-01
A growing number of affective computing researches recently developed a computer system that can recognize an emotional state of the human user to establish affective human-computer interactions. Various measures have been used to estimate emotional states, including self-report, startle response, behavioral response, autonomic measurement, and neurophysiologic measurement. Among them, inferring emotional states from electroencephalography (EEG) has received considerable attention as EEG could directly reflect emotional states with relatively low costs and simplicity. Yet, EEG-based emotional state estimation requires well-designed computational methods to extract information from complex and noisy multichannel EEG data. In this paper, we review the computational methods that have been developed to deduct EEG indices of emotion, to extract emotion-related features, or to classify EEG signals into one of many emotional states. We also propose using sequential Bayesian inference to estimate the continuous emotional state in real time. We present current challenges for building an EEG-based emotion recognition system and suggest some future directions. PMID:23634176
Iterative methods for the WLS state estimation on RISC, vector, and parallel computers
Nieplocha, J.; Carroll, C.C.
1993-10-01
We investigate the suitability and effectiveness of iterative methods for solving the weighted-least-square (WLS) state estimation problem on RISC, vector, and parallel processors. Several of the most popular iterative methods are tested and evaluated. The best performing preconditioned conjugate gradient (PCG) is very well suited for vector and parallel processing as is demonstrated for the WLS state estimation of the IEEE standard test systems. A new sparse matrix format for the gain matrix improves vector performance of the PCG algorithm and makes it competitive to the direct solver. Internal parallelism in RISC processors, used in current multiprocessor systems, can be taken advantage of in an implementation of this algorithm.
Guidelines for preparation of State water-use estimates for 2005
Hutson, Susan S.
2007-01-01
The U.S. Geological Survey (USGS) has estimated the use of water in the United States at 5-year intervals since 1950. This report describes the water-use categories and data elements required for the 2005 national water-use compilation conducted as part of the USGS National Water Use Information Program. The report identifies sources of water-use information, provides standard methods and techniques for estimating water use at the county level, and outlines steps for preparing documentation for the United States, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands. As part of this USGS program to document water use on a national scale for the year 2005, estimates of water withdrawals for the categories of public supply, self-supplied domestic, industrial, irrigation, and thermoelectric power at the county level are prepared for each State using the guidelines in this report. Estimates of water withdrawals for aquaculture, livestock, and mining are prepared for each State using a county-based national model, although study chiefs in each State have the option of producing independent county estimates of water withdrawals for these categories. Estimates of deliveries of water from public supplies for domestic use by county also will be prepared for each State for 2005. As a result, domestic water use can be determined for each State by combining self-supplied domestic withdrawals and publicly supplied domestic deliveries. Fresh ground-water and surfacewater estimates will be prepared for all categories of use; and saline ground-water and surface-water estimates by county will be prepared for the categories of public supply, industrial, and thermoelectric power. Power production for thermoelectric power will be compiled for 2005. If data are available, reclaimed wastewater use will be compiled for the industrial and irrigation categories. Optional water-use categories are commercial, hydroelectric power, and wastewater treatment. Optional data elements
State of health estimation in composite electrode lithium-ion cells
NASA Astrophysics Data System (ADS)
Bartlett, Alexander; Marcicki, James; Rhodes, Kevin; Rizzoni, Giorgio
2015-06-01
Electrochemical models of lithium-ion batteries have been increasingly considered for online state of health estimation. These models can more accurately predict cell performance than traditional circuit models and can better relate physical degradation mechanisms to changes in model parameters. However, examples of state of health estimation algorithms that are validated with experimental data are scarce in the literature, particularly for cells with a composite electrode. The individual electrode active materials in a composite electrode may degrade at different rates and according to different physical mechanisms, and online estimation of this degradation facilitates more robust knowledge of how battery performance changes over its life. In this paper we use a reduced-order electrochemical model for a composite LiMn2O4-LiNi1/3Mn1/3 Co1/3O2 (LMO-NMC) electrode cell for online estimation of active material loss. Experimental data collected from composite electrode half cells that were aged under constant current cycling are used in an extended Kalman filter to estimate model parameters associated with loss of each active material. The capacity loss predicted by the online estimates agrees well with the measured capacity loss. Additionally, a differential capacity analysis demonstrates that active materials lose capacity at a similar rate, the same conclusion obtained from the online estimation algorithm.
Validity time domain estimation for the transient two terms and steady state infiltration equations
NASA Astrophysics Data System (ADS)
Lassabatere, L.; Angulo-Jaramillo, R.; Haverkamp, R.
2009-04-01
Modeling of water fluxes in the vadose zone that links surface water with groundwater is important with regards to understanding hydrological cycle and transfer of water-transported contaminants. Such modeling is usually based on the description of water flow using models such as Richards' equation, unless preferential flow is involved. Several analytical solutions have been proposed to provide either approximate or exact solutions for 1D water infiltration. Based on the analytical model for 1D ponded cumulative infiltration and its extension to 3D for a surface disc source, Haverkamp et al. (1994) proposed a set of analytical equations that were adapted for a constant water pressure head at surface (hsurf) and an initial uniform water pressure head profile, ho(z). This equation system includes a quasi-exact implicit formulation and the related direct approximations. Even if the quasi-exact formulation may be much more precise than the related approximations, it is much more difficult to employ, which has lead to a more common use of the direct approximations. Yet, it can be demonstrated that in certain cases, the error can be no longer acceptable, suggesting that the use of related approximations should be restricted to the domain in which these are consistent approximations of the quasi-exact formulation. This study focuses then on the definition for such validity domains for the two-term and steady state approximations. A specific analytical procedure is developed. First, we will scale the equations proposed by Haverkamp et al. (1994) to derive the scaled (dimensionless) one-dimensional cumulative infiltration and the scaled difference between three- and one-dimensional (3D-1D) cumulative infiltration. Then the 1D scaled cumulative infiltration is analytically studied to derive the expressions for the two-term and steady state approximations and also their accuracy to reproduce the quasi-exact formulation. This last part leads to the definition for validity
A genetic resampling particle filter for freeway traffic-state estimation
NASA Astrophysics Data System (ADS)
Bi, Jun; Guan, Wei; Qi, Long-Tao
2012-06-01
On-line estimation of the state of traffic based on data sampled by electronic detectors is important for intelligent traffic management and control. Because a nonlinear feature exists in the traffic state, and because particle filters have good characteristics when it comes to solving the nonlinear problem, a genetic resampling particle filter is proposed to estimate the state of freeway traffic. In this paper, a freeway section of the northern third ring road in the city of Beijing in China is considered as the experimental object. By analysing the traffic-state characteristics of the freeway, the traffic is modeled based on the second-order validated macroscopic traffic flow model. In order to solve the particle degeneration issue in the performance of the particle filter, a genetic mechanism is introduced into the resampling process. The realization of a genetic particle filter for freeway traffic-state estimation is discussed in detail, and the filter estimation performance is validated and evaluated by the achieved experimental data.
ERIC Educational Resources Information Center
Franchak, Stephen J.; And Others
The document is the report of a research project designed to provide occupational employment projections for the State of Pennsylvania through the development and use of a computer-based system. Section 1 of the report (11 pages) discusses the three projection methods used: two recommended by the Bureau of Labor Statistics, one an econometric…
NASA Astrophysics Data System (ADS)
Emamgolizadeh, S.; Bateni, S. M.; Shahsavani, D.; Ashrafi, T.; Ghorbani, H.
2015-10-01
The soil cation exchange capacity (CEC) is one of the main soil chemical properties, which is required in various fields such as environmental and agricultural engineering as well as soil science. In situ measurement of CEC is time consuming and costly. Hence, numerous studies have used traditional regression-based techniques to estimate CEC from more easily measurable soil parameters (e.g., soil texture, organic matter (OM), and pH). However, these models may not be able to adequately capture the complex and highly nonlinear relationship between CEC and its influential soil variables. In this study, Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) were employed to estimate CEC from more readily measurable soil physical and chemical variables (e.g., OM, clay, and pH) by developing functional relations. The GEP- and MARS-based functional relations were tested at two field sites in Iran. Results showed that GEP and MARS can provide reliable estimates of CEC. Also, it was found that the MARS model (with root-mean-square-error (RMSE) of 0.318 Cmol+ kg-1 and correlation coefficient (R2) of 0.864) generated slightly better results than the GEP model (with RMSE of 0.270 Cmol+ kg-1 and R2 of 0.807). The performance of GEP and MARS models was compared with two existing approaches, namely artificial neural network (ANN) and multiple linear regression (MLR). The comparison indicated that MARS and GEP outperformed the MLP model, but they did not perform as good as ANN. Finally, a sensitivity analysis was conducted to determine the most and the least influential variables affecting CEC. It was found that OM and pH have the most and least significant effect on CEC, respectively.