Experimental Demonstration of Adaptive Quantum State Estimation
NASA Astrophysics Data System (ADS)
Okamoto, Ryo; Iefuji, Minako; Oyama, Satoshi; Yamagata, Koichi; Imai, Hiroshi; Fujiwara, Akio; Takeuchi, Shigeki
2012-09-01
The first experimental demonstration of an adaptive quantum state estimation (AQSE) is reported. The strong consistency and asymptotic efficiency of AQSE have been mathematically proven [A. Fujiwara, J. Phys. A 39, 12489 (2006)JPHAC50305-447010.1088/0305-4470/39/40/014]. In this Letter, the angle of linear polarization of single photons, the phase parameter between the right and the left circularly polarization, is estimated using AQSE, and the strong consistency and asymptotic efficiency are experimentally verified. AQSE will provide a general useful method in both quantum information processing and metrology.
Adaptive state estimation for control of flexible structures
NASA Technical Reports Server (NTRS)
Chen, Chung-Wen; Huang, Jen-Kuang
1990-01-01
This paper proposes a new approach of obtaining adaptive state estimation of a system in the presence of unknown system disturbances and measurement noise. In the beginning, a non-optimal Kalman filter with arbitrary initial guess for the process and measurement noises is implemented. At the same time, an adaptive transversal predictor (ATP) based on the recursive least-squares (RLS) algorithm is used to yield optimal one- to p- step-ahead output predictions using the previous input/output data. Referring to these optimal predictions the Kalman filter gain is updated and the performance of the state estimation is thus improved. If forgetting factor is implemented in the recursive least-squares algorithm, this method is also capable of dealing with the situation when the noise statistics are slowly time-varying. This feature makes this new approach especially suitable for the control of flexible structures. A numerical example demonstrates the feasibility of this real time adaptive state estimation method.
On-line, adaptive state estimator for active noise control
NASA Technical Reports Server (NTRS)
Lim, Tae W.
1994-01-01
Dynamic characteristics of airframe structures are expected to vary as aircraft flight conditions change. Accurate knowledge of the changing dynamic characteristics is crucial to enhancing the performance of the active noise control system using feedback control. This research investigates the development of an adaptive, on-line state estimator using a neural network concept to conduct active noise control. In this research, an algorithm has been developed that can be used to estimate displacement and velocity responses at any locations on the structure from a limited number of acceleration measurements and input force information. The algorithm employs band-pass filters to extract from the measurement signal the frequency contents corresponding to a desired mode. The filtered signal is then used to train a neural network which consists of a linear neuron with three weights. The structure of the neural network is designed as simple as possible to increase the sampling frequency as much as possible. The weights obtained through neural network training are then used to construct the transfer function of a mode in z-domain and to identify modal properties of each mode. By using the identified transfer function and interpolating the mode shape obtained at sensor locations, the displacement and velocity responses are estimated with reasonable accuracy at any locations on the structure. The accuracy of the response estimates depends on the number of modes incorporated in the estimates and the number of sensors employed to conduct mode shape interpolation. Computer simulation demonstrates that the algorithm is capable of adapting to the varying dynamic characteristics of structural properties. Experimental implementation of the algorithm on a DSP (digital signal processing) board for a plate structure is underway. The algorithm is expected to reach the sampling frequency range of about 10 kHz to 20 kHz which needs to be maintained for a typical active noise control
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.
NASA Astrophysics Data System (ADS)
Malek, Obaidul; Venetsanopoulos, Anastasios; Anpalagan, Alagan
2010-08-01
Space Time Adaptive Processing (STAP) is a multi-dimensional adaptive signal processing technique, which processes the signal in spatial and Doppler domains for which a target detection hypothesis is to be formed. It is a sample based technique and based on the assumption of adequate number of Independent and Identically Distributed (i.i.d.) training data set in the surrounding environment. The principal challenge of the radar processing lies when it violates these underlying assumptions due to severe dynamic heterogeneous clutter (hot clutter) and jammer effects. This in turn degrades the Signal to Interference-plus-Noise Ratio (SINR), hence signal detection performance. Classical Wiener filtering theory is inadequate to deal with nonlinear and nonstationary interferences, however Wiener filtering approach is optimal for stationary and linear systems. But, these challenges can be overcome by Adaptive Sequential State Estimation (ASSE) filtering technique.
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.
NASA Astrophysics Data System (ADS)
Lubey, D.; Scheeres, D.
Tracking objects in Earth orbit is fraught with complications. This is due to the large population of orbiting spacecraft and debris that continues to grow, passive (i.e. no direct communication) and data-sparse observations, and the presence of maneuvers and dynamics mismodeling. Accurate orbit determination in this environment requires an algorithm to capture both a system's state and its state dynamics in order to account for mismodelings. Previous studies by the authors yielded an algorithm called the Optimal Control Based Estimator (OCBE) - an algorithm that simultaneously estimates a system's state and optimal control policies that represent dynamic mismodeling in the system for an arbitrary orbit-observer setup. The stochastic properties of these estimated controls are then used to determine the presence of mismodelings (maneuver detection), as well as characterize and reconstruct the mismodelings. The purpose of this paper is to develop the OCBE into an accurate real-time orbit tracking and maneuver detection algorithm by automating the algorithm and removing its linear assumptions. This results in a nonlinear adaptive estimator. In its original form the OCBE had a parameter called the assumed dynamic uncertainty, which is selected by the user with each new measurement to reflect the level of dynamic mismodeling in the system. This human-in-the-loop approach precludes real-time application to orbit tracking problems due to their complexity. This paper focuses on the Adaptive OCBE, a version of the estimator where the assumed dynamic uncertainty is chosen automatically with each new measurement using maneuver detection results to ensure that state uncertainties are properly adjusted to account for all dynamic mismodelings. The paper also focuses on a nonlinear implementation of the estimator. Originally, the OCBE was derived from a nonlinear cost function then linearized about a nominal trajectory, which is assumed to be ballistic (i.e. the nominal optimal
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)
Zhang, Weige; Shi, Wei; Ma, Zeyu
2015-09-01
Accurate estimations of battery energy and available power capability are of great of importance for realizing an efficient and reliable operation of electric vehicles. To improve the estimation accuracy and reliability for battery state of energy and power capability, a novel model-based joint estimation approach has been proposed against uncertain external operating conditions and internal degradation status of battery cells. Firstly, it proposes a three-dimensional response surface open circuit voltage model to calibrate the estimation inaccuracies of battery state of energy. Secondly, the adaptive unscented Kalman filter (AUKF) is employed to develop a novel model-based joint state estimator for battery state of energy and power capability. The AUKF algorithm utilizes the well-known features of the Kalman filter but employs the method of unscented transform (UT) and adaptive error covariance matching technology to improve the state estimation accuracy. Thirdly, the proposed joint estimator has been verified by a LiFePO4 lithium-ion battery cell under different operating temperatures and aging levels. The result indicates that the estimation errors of battery voltage and state-of-energy are less than 2% even if given a large erroneous initial value, which makes the state of available power capability predict more accurate and reliable for the electric vehicles application.
Adaptive spectral doppler estimation.
Gran, Fredrik; Jakobsson, Andreas; Jensen, Jørgen Arendt
2009-04-01
In this paper, 2 adaptive spectral estimation techniques are analyzed for spectral Doppler ultrasound. The purpose is to minimize the observation window needed to estimate the spectrogram to provide a better temporal resolution and gain more flexibility when designing the data acquisition sequence. The methods can also provide better quality of the estimated power spectral density (PSD) of the blood signal. Adaptive spectral estimation techniques are known to provide good spectral resolution and contrast even when the observation window is very short. The 2 adaptive techniques are tested and compared with the averaged periodogram (Welch's method). The blood power spectral capon (BPC) method is based on a standard minimum variance technique adapted to account for both averaging over slow-time and depth. The blood amplitude and phase estimation technique (BAPES) is based on finding a set of matched filters (one for each velocity component of interest) and filtering the blood process over slow-time and averaging over depth to find the PSD. The methods are tested using various experiments and simulations. First, controlled flow-rig experiments with steady laminar flow are carried out. Simulations in Field II for pulsating flow resembling the femoral artery are also analyzed. The simulations are followed by in vivo measurement on the common carotid artery. In all simulations and experiments it was concluded that the adaptive methods display superior performance for short observation windows compared with the averaged periodogram. Computational costs and implementation details are also discussed.
NASA Astrophysics Data System (ADS)
Wei, Zhongbao; Tseng, King Jet; Wai, Nyunt; Lim, Tuti Mariana; Skyllas-Kazacos, Maria
2016-11-01
Reliable state estimate depends largely on an accurate battery model. However, the parameters of battery model are time varying with operating condition variation and battery aging. The existing co-estimation methods address the model uncertainty by integrating the online model identification with state estimate and have shown improved accuracy. However, the cross interference may arise from the integrated framework to compromise numerical stability and accuracy. Thus this paper proposes the decoupling of model identification and state estimate to eliminate the possibility of cross interference. The model parameters are online adapted with the recursive least squares (RLS) method, based on which a novel joint estimator based on extended Kalman Filter (EKF) is formulated to estimate the state of charge (SOC) and capacity concurrently. The proposed joint estimator effectively compresses the filter order which leads to substantial improvement in the computational efficiency and numerical stability. Lab scale experiment on vanadium redox flow battery shows that the proposed method is highly authentic with good robustness to varying operating conditions and battery aging. The proposed method is further compared with some existing methods and shown to be superior in terms of accuracy, convergence speed, and computational cost.
Robust, Adaptive Radar Detection and Estimation
2015-07-21
AFRL-OSR-VA-TR-2015-0208 Robust, Adaptive Radar Detection and Estimation Vishal Monga PENNSYLVANIA STATE UNIVERSITY THE Final Report 07/21/2015...Robust, Adaptive Radar Detection and Estimation 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-12-1-0333 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Monga...we develop robust estimators that can adapt to imperfect knowledge of physical constraints using an expected likelihood (EL) approach. We analyze
NASA Astrophysics Data System (ADS)
Chen, Xiaopeng; Shen, Weixiang; Cao, Zhenwei; Kapoor, Ajay
2014-01-01
In this paper, a novel approach for battery state of charge (SOC) estimation in electric vehicles (EVs) based on an adaptive switching gain sliding mode observer (ASGSMO) has been presented. To design the ASGSMO for the SOC estimation, the state equations based on a battery equivalent circuit model (BECM) are derived to represent dynamic behaviours of a battery. Comparing with a conventional sliding mode observer, the ASGSMO has a capability of minimising chattering levels in the SOC estimation by using the self-adjusted switching gain while maintaining the characteristics of being able to compensate modelling errors caused by the parameter variations of the BECM. Lyapunov stability theory is adopted to prove the error convergence of the ASGSMO for the SOC estimation. The lithium-polymer battery (LiPB) is utilised to conduct experiments for determining the parameters of the BECM and verifying the effectiveness of the proposed ASGSMO in various discharge current profiles including EV driving conditions in both city and suburban.
Operator State Estimation for Adaptive Aiding in Uninhabited Combat Air Vehicles
2005-09-01
Michael, Eiselt, and Gert Griessbach. “Common Optimization of Adaptive Preprocessing Units and a Neural Network during the Learning Period...2000). Guger, Christop, Alois Schlögl, Christa Neuper, Dirk Walterspacher, Thomas Strein, and Gert Pfurtscheller. “Rapid Prototyping of an EEG...North_Holland, (1988). Haselsteiner, Ernst and Gert Pfurtscheller. “Using Time-Dependent Neural Networks for EEG Classification,” IEEE Transactions on
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)
Gharamti, M. E.; Valstar, J.; Hoteit, I.
2014-09-01
Reactive contaminant transport models are used by hydrologists to simulate and study the migration and fate of industrial waste in subsurface aquifers. Accurate transport modeling of such waste requires clear understanding of the system’s parameters, such as sorption and biodegradation. In this study, we present an efficient sequential data assimilation scheme that computes accurate estimates of aquifer contamination and spatially variable sorption coefficients. This assimilation scheme is based on a hybrid formulation of the ensemble Kalman filter (EnKF) and optimal interpolation (OI) in which solute concentration measurements are assimilated via a recursive dual estimation of sorption coefficients and contaminant state variables. This hybrid EnKF-OI scheme is used to mitigate background covariance limitations due to ensemble under-sampling and neglected model errors. Numerical experiments are conducted with a two-dimensional synthetic aquifer in which cobalt-60, a radioactive contaminant, is leached in a saturated heterogeneous clayey sandstone zone. Assimilation experiments are investigated under different settings and sources of model and observational errors. Simulation results demonstrate that the proposed hybrid EnKF-OI scheme successfully recovers both the contaminant and the sorption rate and reduces their uncertainties. Sensitivity analyses also suggest that the adaptive hybrid scheme remains effective with small ensembles, allowing to reduce the ensemble size by up to 80% with respect to the standard EnKF scheme.
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.
Stochastic Adaptive Control and Estimation Enhancement
1990-02-01
ilM(k-S)1.izt-) (p. 1 and then the time after which the jump n’-* ’ takes place (i.e.. the sojourn time) is chosen 11 flp~ij) gil "’(n s~i,.k 39...Asilmar ant. pp 61-5. 184.Control or High Performance Aircraft using Adaptive ( Gil N.H. Ghalson and R.L. Moose. "Maneuverirng Target Aerstim ati nd...N It Dec. 1988. [ Gil N.H. Gholson and R.L. Moose, "Maneuveringl1(k.1) Is known, thus Target Tracking Using Adaptive State Estimation.- IEEE
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.
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.
Parameter estimating state reconstruction
NASA Technical Reports Server (NTRS)
George, E. B.
1976-01-01
Parameter estimation is considered for systems whose entire state cannot be measured. Linear observers are designed to recover the unmeasured states to a sufficient accuracy to permit the estimation process. There are three distinct dynamics that must be accommodated in the system design: the dynamics of the plant, the dynamics of the observer, and the system updating of the parameter estimation. The latter two are designed to minimize interaction of the involved systems. These techniques are extended to weakly nonlinear systems. The application to a simulation of a space shuttle POGO system test is of particular interest. A nonlinear simulation of the system is developed, observers designed, and the parameters estimated.
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.
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.
NASA Astrophysics Data System (ADS)
El Gharamti, Mohamad; Valstar, Johan; Hoteit, Ibrahim
2014-05-01
Reactive contaminant transport models are used by hydrologists to simulate and study the migration and fate of industrial waste in subsurface aquifers. Accurate transport modeling of such waste requires clear understanding of the system's parameters, such as sorption and biodegradation. In this study, we present an efficient sequential data assimilation scheme that computes accurate estimates of aquifer contamination and spatially variable sorption coefficients. This assimilation scheme is based on a hybrid formulation of the ensemble Kalman filter (EnKF) and optimal interpolation (OI) in which solute concentration measurements are assimilated via a recursive dual estimation of sorption coefficients and contaminant state variables. This hybrid EnKF-OI scheme is used to mitigate background covariance limitations due to ensemble under-sampling and neglected model errors. Numerical experiments are conducted with a two-dimensional synthetic aquifer in which cobalt-60, a radioactive contaminant, is leached in a saturated heterogeneous clayey sandstone zone. Assimilation experiments are investigated under different settings and sources of model and observational errors. Our results suggest that the proposed scheme allows a reduction of around 80% of the ensemble size as compared to the standard EnKF scheme.
J-adaptive estimation with estimated noise statistics
NASA Technical Reports Server (NTRS)
Jazwinski, A. H.; Hipkins, C.
1973-01-01
The J-adaptive sequential estimator is extended to include simultaneous estimation of the noise statistics in a model for system dynamics. This extension completely automates the estimator, eliminating the requirement of an analyst in the loop. Simulations in satellite orbit determination demonstrate the efficacy of the sequential estimation algorithm.
Adaptive filtering with correlated state noise
NASA Technical Reports Server (NTRS)
Argentiero, P.
1972-01-01
An adaptive filter which uses a minimum variance criteria to estimate state noise covariance is presented. It is not necessary to assume white state noise in order to implement the filter. Simulation results are given which demonstrate that the filter tracks a satellite in the presence of modeling errors better than a conventional minimum variance filter with state noise. It is also shown that the propagated convariance matrix of the filter is an accurate indicator of the filter's performance.
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.
State Estimation for Humanoid Robots
2015-07-01
natural for a controller to produce force commands to the robot using inverse dynamics. Model based control and state estimation relies on the accuracy of...produce force commands to the robot using inverse dynamics. Model based control and state estimation relies on the accuracy of the model. We address the...natural for a controller to produce force commands to the robot using inverse dynamics. Model based control and state estimation relies on the accuracy of
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.
Adaptive arrival cost update for improving Moving Horizon Estimation performance.
Sánchez, G; Murillo, M; Giovanini, L
2017-03-01
Moving horizon estimation is an efficient technique to estimate states and parameters of constrained dynamical systems. It relies on the solution of a finite horizon optimization problem to compute the estimates, providing a natural framework to handle bounds and constraints on estimates, noises and parameters. However, the approximation of the arrival cost and its updating mechanism are an active research topic. The arrival cost is very important because it provides a mean to incorporate information from previous measurements to the current estimates and it is difficult to estimate its true value. In this work, we exploit the features of adaptive estimation methods to update the parameters of the arrival cost. We show that, having a better approximation of the arrival cost, the size of the optimization problem can be significantly reduced guaranteeing the stability and convergence of the estimates. These properties are illustrated through simulation studies.
Accurate Biomass Estimation via Bayesian Adaptive Sampling
NASA Astrophysics Data System (ADS)
Wheeler, K.; Knuth, K.; Castle, P.
2005-12-01
Typical estimates of standing wood derived from remote sensing sources take advantage of aggregate measurements of canopy heights (e.g. LIDAR) and canopy diameters (segmentation of IKONOS imagery) to obtain a wood volume estimate by assuming homogeneous species and a fixed function that returns volume. The validation of such techniques use manually measured diameter at breast height records (DBH). Our goal is to improve the accuracy and applicability of biomass estimation methods to heterogeneous forests and transitional areas. We are developing estimates with quantifiable uncertainty using a new form of estimation function, active sampling, and volumetric reconstruction image rendering for species specific mass truth. Initially we are developing a Bayesian adaptive sampling method for BRDF associated with the MISR Rahman model with respect to categorical biomes. This involves characterizing the probability distributions of the 3 free parameters of the Rahman model for the 6 categories of biomes used by MISR. Subsequently, these distributions can be used to determine the optimal sampling methodology to distinguish biomes during acquisition. We have a remotely controlled semi-autonomous helicopter that has stereo imaging, lidar, differential GPS, and spectrometers covering wavelengths from visible to NIR. We intend to automatically vary the way points of the flight path via the Bayesian adaptive sampling method. The second critical part of this work is in automating the validation of biomass estimates via using machine vision techniques. This involves taking 2-D pictures of trees of known species, and then via Bayesian techniques, reconstructing 3-D models of the trees to estimate the distribution moments associated with wood volume. Similar techniques have been developed by the medical imaging community. This then provides probability distributions conditional upon species. The final part of this work is in relating the BRDF actively sampled measurements to species
Ultimate Precision of Adaptive Noise Estimation
NASA Astrophysics Data System (ADS)
Pirandola, Stefano; Lupo, Cosmo
2017-03-01
We consider the estimation of noise parameters in a quantum channel, assuming the most general strategy allowed by quantum mechanics. This is based on the exploitation of unlimited entanglement and arbitrary quantum operations, so that the channel inputs may be interactively updated. In this general scenario, we draw a novel connection between quantum metrology and teleportation. In fact, for any teleportation-covariant channel (e.g., Pauli, erasure, or Gaussian channel), we find that adaptive noise estimation cannot beat the standard quantum limit, with the quantum Fisher information being determined by the channel's Choi matrix. As an example, we establish the ultimate precision for estimating excess noise in a thermal-loss channel, which is crucial for quantum cryptography. Because our general methodology applies to any functional that is monotonic under trace-preserving maps, it can be applied to simplify other adaptive protocols, including those for quantum channel discrimination. Setting the ultimate limits for noise estimation and discrimination paves the way for exploring the boundaries of quantum sensing, imaging, and tomography.
Ultimate Precision of Adaptive Noise Estimation.
Pirandola, Stefano; Lupo, Cosmo
2017-03-10
We consider the estimation of noise parameters in a quantum channel, assuming the most general strategy allowed by quantum mechanics. This is based on the exploitation of unlimited entanglement and arbitrary quantum operations, so that the channel inputs may be interactively updated. In this general scenario, we draw a novel connection between quantum metrology and teleportation. In fact, for any teleportation-covariant channel (e.g., Pauli, erasure, or Gaussian channel), we find that adaptive noise estimation cannot beat the standard quantum limit, with the quantum Fisher information being determined by the channel's Choi matrix. As an example, we establish the ultimate precision for estimating excess noise in a thermal-loss channel, which is crucial for quantum cryptography. Because our general methodology applies to any functional that is monotonic under trace-preserving maps, it can be applied to simplify other adaptive protocols, including those for quantum channel discrimination. Setting the ultimate limits for noise estimation and discrimination paves the way for exploring the boundaries of quantum sensing, imaging, and tomography.
Adaptive Spectral Envelope Estimation for Doppler Ultrasound.
Kathpalia, Aditi; Karabiyik, Yucel; Eik-Nes, Sturla; Tegnander, Eva; Ekroll, Ingvild; Kiss, Gabriel; Torp, Hans
2016-07-07
Estimation of accurate maximum velocities and spectral envelope in ultrasound Doppler blood flow spectrograms are both essential for clinical diagnostic purposes. However, obtaining accurate maximum velocity is not straightforward due to intrinsic spectral broadening and variance in the power spectrum estimate. The method proposed in this work for maximum velocity point detection has been developed by modifying an existing method - Signal Noise Slope Intersection (SNSI), incorporating in it steps from an altered version of another method called Geometric Method (GM). Adaptive noise estimation from the spectrogram ensures that a smooth spectral envelope is obtained post detection of these maximum velocity points. The method has been tested on simulated Doppler signal with scatterers possessing a parabolic flow velocity profile constant in time, steady and pulsatile string phantom recordings as well as in vivo recordings from uterine, umbilical, carotid and subclavian arteries. Results from simulation experiments indicate a bias of less than 2.5% in maximum velocities when estimated for a range of peak velocities, Doppler angles and SNR levels. Standard deviation in the envelope is low - less than 2% in case of experiments done by varying the peak velocity and Doppler angle for steady phantom and simulated flow; and also less than 2% in case of experiments done by varying SNR but keeping constant flow conditions for in vivo and simulated flow. Low variability in the envelope makes the prospect of using the envelope for automated blood flow measurements possible and is illustrated for the case of Pulsatility Index estimation in uterine and umbilical arteries.
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.
Adapted polarization state contrast image.
Richert, Michael; Orlik, Xavier; De Martino, Antonello
2009-08-03
We propose a general method to maximize the polarimetric contrast between an object and its background using a predetermined illumination polarization state. After a first estimation of the polarimetric properties of the scene by classical Mueller imaging, we evaluate the incident polarized field that induces scattered polarization states by the object and background, as opposite as possible on the Poincar e sphere. With a detection method optimized for a 2-channel imaging system, Monte Carlo simulations of low flux coherent imaging are performed with various objects and backgrounds having different properties of retardance, dichroism and depolarization. With respect to classical Mueller imaging, possibly associated to the polar decomposition, our results show a noticeable increase in the Bhattacharyya distance used as our contrast parameter.
Adaptive Spectral Envelope Estimation for Doppler Ultrasound.
Kathpalia, Aditi; Karabiyik, Yucel; Eik-Nes, Sturla H; Tegnander, Eva; Ekroll, Ingvild Kinn; Kiss, Gabriel; Torp, Hans
2016-11-01
Estimation of accurate maximum velocities and spectral envelope in ultrasound Doppler blood flow spectrograms are both essential for clinical diagnostic purposes. However, obtaining accurate maximum velocity is not straightforward due to intrinsic spectral broadening and variance in the power spectrum estimate. The method proposed in this paper for maximum velocity point detection has been developed by modifying an existing method-signal noise slope intersection, incorporating in it steps from an altered version of another method called geometric method. Adaptive noise estimation from the spectrogram ensures that a smooth spectral envelope is obtained postdetection of these maximum velocity points. The method has been tested on simulated Doppler signal with scatterers possessing a parabolic flow velocity profile constant in time, steady and pulsatile string phantom recordings, as well as in vivo recordings from uterine, umbilical, carotid, and subclavian arteries. The results from simulation experiments indicate a bias of less than 2.5% in maximum velocities when estimated for a range of peak velocities, Doppler angles, and SNR levels. Standard deviation in the envelope is low-less than 2% in the case of experiments done by varying the peak velocity and Doppler angle for steady phantom and simulated flow, and also less than 2% in the case of experiments done by varying SNR but keeping constant flow conditions for in vivo and simulated flow. Low variability in the envelope makes the prospect of using the envelope for automated blood flow measurements possible and is illustrated for the case of pulsatility index estimation in uterine and umbilical arteries.
Bayesian adaptive estimation of the auditory filter.
Shen, Yi; Richards, Virginia M
2013-08-01
A Bayesian adaptive procedure for estimating the auditory-filter shape was proposed and evaluated using young, normal-hearing listeners at moderate stimulus levels. The resulting quick-auditory-filter (qAF) procedure assumed the power spectrum model of masking with the auditory-filter shape being modeled using a spectrally symmetric, two-parameter rounded-exponential (roex) function. During data collection using the qAF procedure, listeners detected the presence of a pure-tone signal presented in the spectral notch of a noise masker. Dependent on the listener's response on each trial, the posterior probability distributions of the model parameters were updated, and the resulting parameter estimates were then used to optimize the choice of stimulus parameters for the subsequent trials. Results showed that the qAF procedure gave similar parameter estimates to the traditional threshold-based procedure in many cases and was able to reasonably predict the masked signal thresholds. Additional measurements suggested that occasional failures of the qAF procedure to reliably converge could be a consequence of incorrect responses early in a qAF track. The addition of a parameter describing lapses of attention reduced the likelihood of such failures.
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.
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
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
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…
Zakharova, N O; Iakovlev, O G; Treneva, E V
2014-01-01
The article presents some aspects of the health status of the veterans of the Samara region. Intercommunication is marked between the level of social adaptation, quality of life and rate aging combatants. The study shows the effect of chronic posttraumatic stress disorder on the occurrence of psychosomatic pathology and development of senescence combat veterans suffering from arterial hypertension.
Estimating the Global Solar Magnetic Field Distribution Using ADAPT
NASA Astrophysics Data System (ADS)
Arge, C. N.; Henney, C. J.; Toussaint, W. A.; Godinez, H. C.; Hickmann, K. S.
2014-12-01
Estimation of the global solar photospheric magnetic field distribution is currently difficult, since only approximately half of the solar surface is magnetically observed at any given time. With the solar rotational period relative to Earth at approximately 27 days, these global maps include observed data that are more than 13 days old. Data assimilation between old and new observations can result in spatial polarity discontinuities that result in significant monopole signals. To help minimize these large discontinuities and to specify the global state of the photospheric magnetic flux distribution as accurately as possible, we have developed the ADAPT (Air Force Data Assimilative Photospheric flux Transport) model, which is comprised of a photospheric magnetic flux transport model that makes use of data assimilation methods. The ADAPT transport model evolves the solar magnetic flux for an ensemble of realizations using different model parameter values, e.g., for rotational, meridional, and super-granular diffusive transport processes. In this presentation, the ADAPT model and the data assimilative methods used within it will be reviewed. Coronal, solar wind, F10.7, and EUV model predictions based on ADAPT global photospheric magnetic field maps as input will be discussed.
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…
Simultaneous parameter and state estimation of shear buildings
NASA Astrophysics Data System (ADS)
Concha, Antonio; Alvarez-Icaza, Luis; Garrido, Rubén
2016-03-01
This paper proposes an adaptive observer that simultaneously estimates the damping/mass and stiffness/mass ratios, and the state of a seismically excited building. The adaptive observer uses only acceleration measurements of the ground and floors for both parameter and state estimation; it identifies all the parameter ratios, velocities and displacements of the structure if all the floors are instrumented; and it also estimates the state and the damping/mass and stiffness/mass ratios of a reduced model of the building if only some floors are equipped with accelerometers. This observer does not resort to any particular canonical form and employs the Least Squares (LS) algorithm and a Luenberger state estimator. The LS method is combined with a smooth parameter projection technique that provides only positive estimates, which are employed by the state estimator. Boundedness of the estimate produced by the LS algorithm does not depend on the boundedness of the state estimates. Moreover, the LS method uses a parametrization based on Linear Integral Filters that eliminate offsets in the acceleration measurements in finite time and attenuate high-frequency measurement noise. Experimental results obtained using a reduced-scale five-story confirm the effectiveness of the proposed adaptive observer.
Adaptive Distributed Video Coding with Correlation Estimation using Expectation Propagation
Cui, Lijuan; Wang, Shuang; Jiang, Xiaoqian; Cheng, Samuel
2013-01-01
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. PMID:23750314
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.
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
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.
Stopping rules in Bayesian adaptive threshold estimation.
Alcalá-Quintana, Rocío; García-Pérez, Miguel A
2005-01-01
Threshold estimation with sequential procedures is justifiable on the surmise that the index used in the so-called dynamic stopping rule has diagnostic value for identifying when an accurate estimate has been obtained. The performance of five types of Bayesian sequential procedure was compared here to that of an analogous fixed-length procedure. Indices for use in sequential procedures were: (1) the width of the Bayesian probability interval, (2) the posterior standard deviation, (3) the absolute change, (4) the average change, and (5) the number of sign fluctuations. A simulation study was carried out to evaluate which index renders estimates with less bias and smaller standard error at lower cost (i.e. lower average number of trials to completion), in both yes-no and two-alternative forced-choice (2AFC) tasks. We also considered the effect of the form and parameters of the psychometric function and its similarity with the model function assumed in the procedure. Our results show that sequential procedures do not outperform fixed-length procedures in yes-no tasks. However, in 2AFC tasks, sequential procedures not based on sign fluctuations all yield minimally better estimates than fixed-length procedures, although most of the improvement occurs with short runs that render undependable estimates and the differences vanish when the procedures run for a number of trials (around 70) that ensures dependability. Thus, none of the indices considered here (some of which are widespread) has the diagnostic value that would justify its use. In addition, difficulties of implementation make sequential procedures unfit as alternatives to fixed-length procedures.
Stochastic Adaptive Control and Estimation Enhancement.
1985-03-19
minima behave as the terminal state weighting changes . This is illustrated in Fig. ,..ith terminal state weighting Q(2) and control %,eighting 5. For...been shown that the various cost components lea-rng changes the present behavior of the (’L controller, can vary drastically with changes in the...abrupt change in the damping and frequencies of wing structural modes. The structural and aerodynamic models used z(k) = hkx(k)J + w(k), k = ,.,-1 in
Stochastic Adaptive Control and Estimation Enhancement
1989-09-01
total Zu(N-J)’Gj’Q(N)FxIN-1)ou (N-I)I’[ R (N- 1) ’(N I Gil probability theorem to (4.3) yields J*(k.k 3 - min ( Ejx(kl 0(k)x(k) - u(k)’R(klu(k) trQ(N)VI m...Is Independent of Mil), I-k*2 .... N If Dec. 1988. [ Gil N.H. Gholson and R.L. Moose, "ManeuveringM(k.1J Is known, thus Target Tracking Using Adaptive...Control and A(t) =_ J1N X(i,t) is uniformly bounded. Quasi-Variational Inequalities, Gauthier- Villars , . (t9. tER4 , exits 0’ at most a countable
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
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.
Power system operations: State estimation distributed processing
NASA Astrophysics Data System (ADS)
Ebrahimian, Mohammad Reza
We present an application of a robust and fast parallel algorithm to power system state estimation with minimal amount of modifications to existing state estimators presently in place using the Auxiliary Problem Principle. We demonstrate its effectiveness on IEEE test systems, the Electric Reliability Counsel of Texas (ERCOT), and the Southwest Power Pool (SPP) systems. Since state estimation formulation may lead to an ill-conditioned system, we provide analytical explanations of the effects of mixtures of measurements on the condition of the state estimation information matrix. We demonstrate the closeness of the analytical equations to condition of several test case systems including IEEE RTS-96 and IEEE 118 bus systems. The research on the condition of the state estimation problem covers the centralized as well as distributed state estimation.
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.
A posteriori error estimation for hp -adaptivity for fourth-order equations
NASA Astrophysics Data System (ADS)
Moore, Peter K.; Rangelova, Marina
2010-04-01
A posteriori error estimates developed to drive hp-adaptivity for second-order reaction-diffusion equations are extended to fourth-order equations. A C^1 hierarchical finite element basis is constructed from Hermite-Lobatto polynomials. A priori estimates of the error in several norms for both the interpolant and finite element solution are derived. In the latter case this requires a generalization of the well-known Aubin-Nitsche technique to time-dependent fourth-order equations. We show that the finite element solution and corresponding Hermite-Lobatto interpolant are asymptotically equivalent. A posteriori error estimators based on this equivalence for solutions at two orders are presented. Both are shown to be asymptotically exact on grids of uniform order. These estimators can be used to control various adaptive strategies. Computational results for linear steady-state and time-dependent equations corroborate the theory and demonstrate the effectiveness of the estimators in adaptive settings.
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
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.
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.
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.
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
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.
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.
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…
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.
Adaptive Quantum State Detection through Repetitive Mapping
NASA Astrophysics Data System (ADS)
Hume, David; Rosenband, Till; Wineland, David; Bergquist, Jim
2007-06-01
State detection plays an important role in quantum information processing and quantum-limited metrology. In some quantum systems direct detection is impossible or inefficient. This can be overcome by coupling the primary quantum system to an ancillary system used for measurement [1]. The measurement process consists of mapping the primary state to the ancilla followed by ancilla detection. If the measurement does not affect the projected populations of the primary system, it may be repeated yielding higher fidelity. Using two trapped ion species (^27Al^+ and ^9Be^+) as the primary and ancillary systems, we demonstrate high-fidelity measurement despite imperfect information transfer and ancilla detection. An adaptive measurement strategy allows for multiple qubit state discrimination with one ancilla. This opens the way for several applications in quantum information processing and advances our optical clock effort. [1] P.O. Schmidt, et. al. Science 309 749 (2005)
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.
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.
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.
Cardiorespiratory synchronism in estimation of regulatory and adaptive organism status.
Pokrovskii, Vladimir M; Polischuk, Lily V
2016-03-01
The proposed method of quantitative estimation of regulatory and adaptive status (RAS) of human organism is based on complex responses of two major vegetative functions - breath and heart rates under organism exposure to a number of factors and diseases. It has been evidenced that during the follicular menstruation stage and during optimum readiness of female organism for childbirth RAS increases, however, stress impact can also cause RAS set off to decrease. Likewise, the possibility of quantitative organism stress resistance estimation is also presented. Under some pathological conditions (myocardial infarction, hypo-and hyperthyroidism, diabetes type 2), RAS goes down, and the degree of its restoration depends on the attained therapy effect. It is shown that RAS dynamics provides an innovative methodological approach to medication efficiency estimation based on its influence not only on the body organ or target function, but also on adaptive abilities of the organism.
Quantifying uncertainty in state and parameter estimation.
Parlitz, Ulrich; Schumann-Bischoff, Jan; Luther, Stefan
2014-05-01
Observability of state variables and parameters of a dynamical system from an observed time series is analyzed and quantified by means of the Jacobian matrix of the delay coordinates map. For each state variable and each parameter to be estimated, a measure of uncertainty is introduced depending on the current state and parameter values, which allows us to identify regions in state and parameter space where the specific unknown quantity can(not) be estimated from a given time series. The method is demonstrated using the Ikeda map and the Hindmarsh-Rose model.
Estimating meme fitness in adaptive memetic algorithms for combinatorial problems.
Smith, J E
2012-01-01
Among the most promising and active research areas in heuristic optimisation is the field of adaptive memetic algorithms (AMAs). These gain much of their reported robustness by adapting the probability with which each of a set of local improvement operators is applied, according to an estimate of their current value to the search process. This paper addresses the issue of how the current value should be estimated. Assuming the estimate occurs over several applications of a meme, we consider whether the extreme or mean improvements should be used, and whether this aggregation should be global, or local to some part of the solution space. To investigate these issues, we use the well-established COMA framework that coevolves the specification of a population of memes (representing different local search algorithms) alongside a population of candidate solutions to the problem at hand. Two very different memetic algorithms are considered: the first using adaptive operator pursuit to adjust the probabilities of applying a fixed set of memes, and a second which applies genetic operators to dynamically adapt and create memes and their functional definitions. For the latter, especially on combinatorial problems, credit assignment mechanisms based on historical records, or on notions of landscape locality, will have limited application, and it is necessary to estimate the value of a meme via some form of sampling. The results on a set of binary encoded combinatorial problems show that both methods are very effective, and that for some problems it is necessary to use thousands of variables in order to tease apart the differences between different reward schemes. However, for both memetic algorithms, a significant pattern emerges that reward based on mean improvement is better than that based on extreme improvement. This contradicts recent findings from adapting the parameters of operators involved in global evolutionary search. The results also show that local reward schemes
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.
State estimation of the power system using robust estimator
NASA Astrophysics Data System (ADS)
Khan, Zahid; Razali, Radzuan B.; Daud, Hanita; Nor, Nursyarizal Mohd; Firuzabad, Mahmud Fotuhi
2016-11-01
The presence of gross errors in the process data for the power system state estimation (PSSE) algorithm is very crucial as they may severely degrade its results. The conventionally used state estimator is based on the method of the weighted least squares (WLS) which is not robust against the bad measurements that results in larger deviation in output estimates. In this study, a new robust algorithm based on the quasi weighted least squares (QWLS) estimator is presented. The robustness of the QWLS approach is achieved by reducing the impact of bad measurements on the objective function. In the existence of gross errors, the proposed algorithm provides estimates as good as those that are achieved by the conventional method of the WLS when no gross error exists in the process data. The implementation of the proposed algorithm has been illustrated for the case studies on the 6-bus and IEEE 14-bus power networks. The numerical results validate the performance of the proposed estimator in the PSSE algorithm.
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.
Structured estimation - Sample size reduction for adaptive pattern classification
NASA Technical Reports Server (NTRS)
Morgera, S.; Cooper, D. B.
1977-01-01
The Gaussian two-category classification problem with known category mean value vectors and identical but unknown category covariance matrices is considered. The weight vector depends on the unknown common covariance matrix, so the procedure is to estimate the covariance matrix in order to obtain an estimate of the optimum weight vector. The measure of performance for the adapted classifier is the output signal-to-interference noise ratio (SIR). A simple approximation for the expected SIR is gained by using the general sample covariance matrix estimator; this performance is both signal and true covariance matrix independent. An approximation is also found for the expected SIR obtained by using a Toeplitz form covariance matrix estimator; this performance is found to be dependent on both the signal and the true covariance matrix.
Noninvasive fetal ECG estimation using adaptive comb filter.
Wei, Zheng; Xueyun, Wei; Jian jian, Zhong; Hongxing, Liu
2013-10-01
This paper describes a robust and simple algorithm for fetal electrocardiogram (FECG) estimation from abdominal signal using adaptive comb filter (ACF). The ACF can adjust itself to the temporal variations in fundamental frequency, which makes it qualified for the estimation of quasi-periodic component from physiologic signal, such as ECG. The validity and performance of the described method are confirmed through experiments on real fetal ECG data. A comparison with the well-known independent component analysis (ICA) method has also been presented.
Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters.
Mathew, B; Bauer, A M; Koistinen, P; Reetz, T C; Léon, J; Sillanpää, M J
2012-10-01
Accurate and fast estimation of genetic parameters that underlie quantitative traits using mixed linear models with additive and dominance effects is of great importance in both natural and breeding populations. Here, we propose a new fast adaptive Markov chain Monte Carlo (MCMC) sampling algorithm for the estimation of genetic parameters in the linear mixed model with several random effects. In the learning phase of our algorithm, we use the hybrid Gibbs sampler to learn the covariance structure of the variance components. In the second phase of the algorithm, we use this covariance structure to formulate an effective proposal distribution for a Metropolis-Hastings algorithm, which uses a likelihood function in which the random effects have been integrated out. Compared with the hybrid Gibbs sampler, the new algorithm had better mixing properties and was approximately twice as fast to run. Our new algorithm was able to detect different modes in the posterior distribution. In addition, the posterior mode estimates from the adaptive MCMC method were close to the REML (residual maximum likelihood) estimates. Moreover, our exponential prior for inverse variance components was vague and enabled the estimated mode of the posterior variance to be practically zero, which was in agreement with the support from the likelihood (in the case of no dominance). The method performance is illustrated using simulated data sets with replicates and field data in barley.
Adaptive Detection and Parameter Estimation for Multidimensional Signal Models
1989-04-19
expected value of the non-adaptive parameter array estimator directly from Equation (5-1), using the fact that .zP = dppH = d We obtain EbI = (e-H E eI 1...depend only on the dimensional parameters of tlc problem. We will caerive these properties shcrLly, but first we wish to express the conditional pdf
Adaptive estimation and control with application to vision-based autonomous formation flight
NASA Astrophysics Data System (ADS)
Sattigeri, Ramachandra
2007-05-01
Modern Unmanned Aerial Vehicles (UAVs) are equipped with vision sensors because of their light-weight, low-cost characteristics and also their ability to provide a rich variety of information of the environment in which the UAVs are navigating in. The problem of vision based autonomous flight is very difficult and challenging since it requires bringing together concepts from image processing and computer vision, target tracking and state estimation, and flight guidance and control. This thesis focuses on the adaptive state estimation, guidance and control problems involved in vision-based formation flight. Specifically, the thesis presents a composite adaptation approach to the partial state estimation of a class of nonlinear systems with unmodeled dynamics. In this approach, a linear time-varying Kalman filter is the nominal state estimator which is augmented by the output of an adaptive neural network (NN) that is trained with two error signals. The benefit of the proposed approach is in its faster and more accurate adaptation to the modeling errors over a conventional approach. The thesis also presents two approaches to the design of adaptive guidance and control (G&C) laws for line-of-sight formation flight. In the first approach, the guidance and autopilot systems are designed separately and then combined together by assuming time-scale separation. The second approach is based on integrating the guidance and autopilot design process. The developed G&C laws using both approaches are adaptive to unmodeled leader aircraft acceleration and to own aircraft aerodynamic uncertainties. The thesis also presents theoretical justification based on Lyapunov-like stability analysis for integrating the adaptive state estimation and adaptive G&C designs. All the developed designs are validated in nonlinear, 6DOF fixed-wing aircraft simulations. Finally, the thesis presents a decentralized coordination strategy for vision-based multiple-aircraft formation control. In this
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
Error estimation in the direct state tomography
NASA Astrophysics Data System (ADS)
Sainz, I.; Klimov, A. B.
2016-10-01
We show that reformulating the Direct State Tomography (DST) protocol in terms of projections into a set of non-orthogonal bases one can perform an accuracy analysis of DST in a similar way as in the standard projection-based reconstruction schemes, i.e., in terms of the Hilbert-Schmidt distance between estimated and true states. This allows us to determine the estimation error for any measurement strength, including the weak measurement case, and to obtain an explicit analytic form for the average minimum square errors.
Optimal full estimation of qubit mixed states
Bagan, E.; Monras, A.; Munoz-Tapia, R.; Ballester, M. A.; Gill, R. D.
2006-03-15
We obtain the optimal scheme for estimating unknown qubit mixed states when an arbitrary number N of identically prepared copies is available. We discuss the case of states in the whole Bloch sphere as well as the restricted situation where these states are known to lie on the equatorial plane. For the former case we obtain that the optimal measurement does not depend on the prior probability distribution provided it is isotropic. Although the equatorial-plane case does not have this property for arbitrary N, we give a prior-independent scheme which becomes optimal in the asymptotic limit of large N. We compute the maximum mean fidelity in this asymptotic regime for the two cases. We show that within the pointwise estimation approach these limits can be obtained in a rather easy and rapid way. This derivation is based on heuristic arguments that are made rigorous by using van Trees inequalities. The interrelation between the estimation of the purity and the direction of the state is also discussed. In the general case we show that they correspond to independent estimations whereas for the equatorial-plane states this is only true asymptotically.
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.
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.
Error estimation and adaptivity for transport problems with uncertain parameters
NASA Astrophysics Data System (ADS)
Sahni, Onkar; Li, Jason; Oberai, Assad
2016-11-01
Stochastic partial differential equations (PDEs) with uncertain parameters and source terms arise in many transport problems. In this study, we develop and apply an adaptive approach based on the variational multiscale (VMS) formulation for discretizing stochastic PDEs. In this approach we employ finite elements in the physical domain and generalize polynomial chaos based spectral basis in the stochastic domain. We demonstrate our approach on non-trivial transport problems where the uncertain parameters are such that the advective and diffusive regimes are spanned in the stochastic domain. We show that the proposed method is effective as a local error estimator in quantifying the element-wise error and in driving adaptivity in the physical and stochastic domains. We will also indicate how this approach may be extended to the Navier-Stokes equations. NSF Award 1350454 (CAREER).
Digital control of high performance aircraft using adaptive estimation techniques
NASA Technical Reports Server (NTRS)
Van Landingham, H. F.; Moose, R. L.
1977-01-01
In this paper, an adaptive signal processing algorithm is joined with gain-scheduling for controlling the dynamics of high performance aircraft. A technique is presented for a reduced-order model (the longitudinal dynamics) of a high performance STOL aircraft. The actual controller views the nonlinear behavior of the aircraft as equivalent to a randomly switching sequence of linear models taken from a preliminary piecewise-linear fit of the system nonlinearities. The adaptive nature of the estimator is necessary to select the proper sequence of linear models along the flight trajectory. Nonlinear behavior is approximated by effective switching of the linear models at random times, with durations reflecting aircraft motion in response to pilot commands.
Estimation of ground state pentaquark masses
NASA Astrophysics Data System (ADS)
Xu, K.; Ritjoho, N.; Srisuphaphon, S.; Yan, Y.
2014-04-01
Permutation groups are applied to analyze the symmetries of multiquark systems and wave functions of pentaquark states are constructed systematically in the language of Yamanouchi basis. We estimate the mass of baryons in the constituent quark model with one-gluon-exchange interaction, assuming that baryons consist of the q3 component as well as the q4/line q pentaquark component.
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.
Zhang, Jian-Hua; Böhme, Johann F
2007-11-01
In this paper we report an adaptive regularization network (ARN) approach to realizing fast blind separation of cerebral evoked potentials (EPs) from background electroencephalogram (EEG) activity with no need to make any explicit assumption on the statistical (or deterministic) signal model. The ARNs are proposed to construct nonlinear EEG and EP signal models. A novel adaptive regularization training (ART) algorithm is proposed to improve the generalization performance of the ARN. Two adaptive neural modeling methods based on the ARN are developed and their implementation and performance analysis are also presented. The computer experiments using simulated and measured visual evoked potential (VEP) data have shown that the proposed ARN modeling paradigm yields computationally efficient and more accurate VEP signal estimation owing to its intrinsic model-free and nonlinear processing characteristics.
NASA Astrophysics Data System (ADS)
Shen, Ting-ao; Li, Hua-nan; Zhang, Qi-xin; Li, Ming
2017-02-01
The convergence rate and the continuous tracking precision are two main problems of the existing adaptive notch filter (ANF) for frequency tracking. To solve the problems, the frequency is detected by interpolation FFT at first, which aims to overcome the convergence rate of the ANF. Then, referring to the idea of negative feedback, an evaluation factor is designed to monitor the ANF parameters and realize continuously high frequency tracking accuracy. According to the principle, a novel adaptive frequency estimation algorithm based on interpolation FFT and improved ANF is put forward. Its basic idea, specific measures and implementation steps are described in detail. The proposed algorithm obtains a fast estimation of the signal frequency, higher accuracy and better universality qualities. Simulation results verified the superiority and validity of the proposed algorithm when compared with original algorithms.
Information criteria for efficient quantum state estimation
Yin, J. O. S.; Enk, S. J. van
2011-06-15
Recently several more efficient versions of quantum state tomography have been proposed, with the purpose of making tomography feasible even for many-qubit states. The number of state parameters to be estimated is reduced by tentatively introducing certain simplifying assumptions on the form of the quantum state, and subsequently using the data to rigorously verify these assumptions. The simplifying assumptions considered so far were (i) the state can be well approximated to be of low rank, or (ii) the state can be well approximated as a matrix product state, or (iii) only the permutationally invariant part of the density matrix is determined. We add one more method in that same spirit: We allow in principle any model for the state, using any (small) number of parameters (which can, e.g., be chosen to have a clear physical meaning), and the data are used to verify the model. The proof that this method is valid cannot be as strict as in the above-mentioned cases, but is based on well-established statistical methods that go under the name of ''information criteria.'' We exploit here, in particular, the Akaike information criterion. We illustrate the method by simulating experiments on (noisy) Dicke states.
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.
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
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.
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.
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.
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
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.
Adaptive stiffness estimation for compliant robotic manipulation using stochastic disturbance models
NASA Astrophysics Data System (ADS)
Coutinho, Fernanda; Cortesão, Rui
2011-08-01
To achieve haptic telepresence and proper contact behaviour, the control action of a robotic manipulator must be designed with respect to contact parameters. Unfortunately, it is hard to know these parameters exactly in unknown or partly known environments. In this case, contact instability and poor dynamic accuracy can arise due to the presence of modelling errors in the control design. To overcome these problems, online estimation of the relevant contact parameters can be performed, with corresponding adaptation of control laws. This article presents an algorithm for online stiffness estimation for compliant robotic manipulation based on the extended state-space representation of the system and force signals. No position or velocity measurements are required. The algorithm, supported by theoretical analysis, uses offline data concerning several stiffness mismatch scenarios and, through a least square error analysis, computes an estimate of the stiffness value. Simulation results are presented, with fast and accurate estimation even in the presence of noise, highlighting the merits of the method.
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.
New Algorithms for Moving-Bank Multiple Model Adaptive Estimation
2007-11-02
Detection 12 2.1.1 Voting Method 12 2.1.2 Chi-Square Test and the Kaiman Filter 13 2.1.3 Sequential Probability Ratio Test (SPRT) 16 2.1.4 Generalized...Numerical Integration 120 3.2.3 Discretization Method , 122 3.2.3.1 Parameter Value Search Routine 126 3.2.3.2 Implementation Issues 132 3.2.4...Measure exRMS - State 3 297 xvin Abstract The focus of this research is to provide methods for generating precise parameter estimates in the face
Hoogi, Assaf; Subramaniam, Arjun; Veerapaneni, Rishi; Rubin, Daniel
2016-11-11
In this paper, we propose a generalization of the level set segmentation approach by supplying a novel method for adaptive estimation of active contour parameters. The presented segmentation method is fully automatic once the lesion has been detected. First, the location of the level set contour relative to the lesion is estimated using a convolutional neural network (CNN). The CNN has two convolutional layers for feature extraction, which lead into dense layers for classification. Second, the output CNN probabilities are then used to adaptively calculate the parameters of the active contour functional during the segmentation process. Finally, the adaptive window size surrounding each contour point is re-estimated by an iterative process that considers lesion size and spatial texture. We demonstrate the capabilities of our method on a dataset of 164 MRI and 112 CT images of liver lesions that includes low contrast and heterogeneous lesions as well as noisy images. To illustrate the strength of our method, we evaluated it against state of the art CNNbased and active contour techniques. For all cases, our method, as assessed by Dice similarity coefficients, performed significantly better than currently available methods. An average Dice improvement of 0.27 was found across the entire dataset over all comparisons. We also analyzed two challenging subsets of lesions and obtained a significant Dice improvement of ����.�������� with our method (p < 0.001, Wilcoxon).
Hoogi, Assaf; Subramaniam, Arjun; Veerapaneni, Rishi; Rubin, Daniel
2016-11-11
In this paper, we propose a generalization of the level set segmentation approach by supplying a novel method for adaptive estimation of active contour parameters. The presented segmentation method is fully automatic once the lesion has been detected. First, the location of the level set contour relative to the lesion is estimated using a convolutional neural network (CNN). The CNN has two convolutional layers for feature extraction, which lead into dense layers for classification. Second, the output CNN probabilities are then used to adaptively calculate the parameters of the active contour functional during the segmentation process. Finally, the adaptive window size surrounding each contour point is re-estimated by an iterative process that considers lesion size and spatial texture. We demonstrate the capabilities of our method on a dataset of 164 MRI and 112 CT images of liver lesions that includes low contrast and heterogeneous lesions as well as noisy images. To illustrate the strength of our method, we evaluated it against state of the art CNNbased and active contour techniques. For all cases, our method, as assessed by Dice similarity coefficients, performed significantly better than currently available methods. An average Dice improvement of 0.27 was found across the entire dataset over all comparisons. We also analyzed two challenging subsets of lesions and obtained a significant Dice improvement of 0.24 with our method (p < 0.001, Wilcoxon).
Neural network payload estimation for adaptive robot control.
Leahy, M R; Johnson, M A; Rogers, S K
1991-01-01
A concept is proposed for utilizing artificial neural networks to enhance the high-speed tracking accuracy of robotic manipulators. Tracking accuracy is a function of the controller's ability to compensate for disturbances produced by dynamical interactions between the links. A model-based control algorithm uses a nominal model of those dynamical interactions to reduce the disturbances. The problem is how to provide accurate dynamics information to the controller in the presence of payload uncertainty and modeling error. Neural network payload estimation uses a series of artificial neural networks to recognize the payload variation associated with a degradation in tracking performance. The network outputs are combined with a knowledge of nominal dynamics to produce a computationally efficient direct form of adaptive control. The concept is validated through experimentation and analysis on the first three links of a PUMA-560 manipulator. A multilayer perceptron architecture with two hidden layers is used. Integration of the principles of neural network pattern recognition and model-based control produces a tracking algorithm with enhanced robustness to incomplete dynamic information. Tracking efficacy and applicability to robust control algorithms are discussed.
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.
Experimental demonstration of real-time adaptive one-qubit quantum-state tomography
NASA Astrophysics Data System (ADS)
Yin, Qi; Li, Li; Xiang, Xiao; Xiang, Guo-Yong; Li, Chuang-Feng; Guo, Guang-Can
2017-01-01
Quantum-state tomography plays a pivotal role in quantum computation and information processing. To improve the accuracy in estimating an unknown state, carefully designed measurement schemes, such as adopting an adaptive strategy, are necessarily needed, which have gained great interest recently. In this work, based on the proposal of Sugiyama et al. [Phys. Rev. A 85, 052107 (2012)], 10.1103/PhysRevA.85.052107, we experimentally realize an adaptive quantum-state tomography for one qubit in an optical system. Since this scheme gives an analytical solution to the optimal measurement basis problem, our experiment is updated in real time and the infidelity between the real state and the estimated state is tracked with the detected photons. We observe an almost 1 /N scaling rule of averaged infidelity against the overall number of photons, N , in our experiment, which outperforms 1 /√{N } of nonadaptive schemes.
State Water Agency Practices for Climate Adaptation
This webpage contains short descriptions of innovative practices that state water agencies are currently implementing to reduce their vulnerability to climate-related impacts and to build resilience to climate change.
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.
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.
Maximal adaptive-decision speedups in quantum-state readout
NASA Astrophysics Data System (ADS)
D'Anjou, Benjamin; Kuret, Loutfi; Childress, Lilian; Coish, William A.
The average time T required for high-fidelity readout of quantum states can be significantly reduced via a real-time adaptive decision rule. An adaptive decision rule stops the readout as soon as a desired level of confidence has been achieved, as opposed to setting a fixed readout time tf. The performance of the adaptive decision is characterized by the ``adaptive-decision speedup'', tf / T . In this work, we reformulate this readout problem in terms of the first-passage time of a particle undergoing stochastic motion. This formalism allows us to theoretically establish the maximum achievable adaptive-decision speedups for several physical two-state readout implementations. We show that for two common readout schemes (the Gaussian latching readout and a readout relying on state-dependent decay), the speedup is bounded by 4 and 2, respectively, in the limit of high single-shot readout fidelity. We experimentally study the achievable speedup in a real-world scenario by applying the adaptive decision rule to a readout of the nitrogen-vacancy-center (NV-center) charge state. We find a speedup of ~ 2 with our experimental parameters. Our results should lead to immediate improvements in nano-scale magnetometry based on spin-to-charge conversion of the NV-center spin. We acknowledge support from NSERC, INTRIQ, CIFAR and the Walter C. Sumner Foundation.
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 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.
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…
Modeling Speed-Accuracy Tradeoff in Adaptive System for Practicing Estimation
ERIC Educational Resources Information Center
Nižnan, Juraj
2015-01-01
Estimation is useful in situations where an exact answer is not as important as a quick answer that is good enough. A web-based adaptive system for practicing estimates is currently being developed. We propose a simple model for estimating student's latent skill of estimation. This model combines a continuous measure of correctness and response…
An algorithmic approach to adaptive state filtering using recurrent neural networks.
Parlos, A G; Menon, S K; Atiya, A
2001-01-01
Practical algorithms are presented for adaptive state filtering in nonlinear dynamic systems when the state equations are unknown. The state equations are constructively approximated using neural networks. The algorithms presented are based on the two-step prediction-update approach of the Kalman filter. The proposed algorithms make minimal assumptions regarding the underlying nonlinear dynamics and their noise statistics. Non-adaptive and adaptive state filtering algorithms are presented with both off-line and online learning stages. The algorithms are implemented using feedforward and recurrent neural network and comparisons are presented. Furthermore, extended Kalman filters (EKFs) are developed and compared to the filter algorithms proposed. For one of the case studies, the EKF converges but results in higher state estimation errors that the equivalent neural filters. For another, more complex case study with unknown system dynamics and noise statistics, the developed EKFs do not converge. The off-line trained neural state filters converge quite rapidly and exhibit acceptable performance. Online training further enhances the estimation accuracy of the developed adaptive filters, effectively decoupling the eventual filter accuracy from the accuracy of the process model.
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.
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.
NASA Astrophysics Data System (ADS)
Wei, Zhongbao; Meng, Shujuan; Tseng, King Jet; Lim, Tuti Mariana; Soong, Boon Hee; Skyllas-Kazacos, Maria
2017-03-01
An accurate battery model is the prerequisite for reliable state estimate of vanadium redox battery (VRB). As the battery model parameters are time varying with operating condition variation and battery aging, the common methods where model parameters are empirical or prescribed offline lacks accuracy and robustness. To address this issue, this paper proposes to use an online adaptive battery model to reproduce the VRB dynamics accurately. The model parameters are online identified with both the recursive least squares (RLS) and the extended Kalman filter (EKF). Performance comparison shows that the RLS is superior with respect to the modeling accuracy, convergence property, and computational complexity. Based on the online identified battery model, an adaptive peak power estimator which incorporates the constraints of voltage limit, SOC limit and design limit of current is proposed to fully exploit the potential of the VRB. Experiments are conducted on a lab-scale VRB system and the proposed peak power estimator is verified with a specifically designed "two-step verification" method. It is shown that different constraints dominate the allowable peak power at different stages of cycling. The influence of prediction time horizon selection on the peak power is also analyzed.
Scaling multiconjugate adaptive optics performance estimates to extremely large telescopes
NASA Astrophysics Data System (ADS)
Ellerbroek, Brent L.; Rigaut, Francois J.
2000-07-01
Multi-conjugate adaptive optics (MCAO) is a key technology for extremely large, ground-based telescopes (ELT's) because it enables near-uniform atmospheric turbulence compensation over fields-of-view considerably larger than can be corrected with more conventional AO systems. Quantitative performance evaluation using detailed analytical or simulation models is difficult, however, due to the very large number of deformable mirror (DM) actuators, wave front sensors (WFS) subapertures, and guide stars which might comprise an MCAO system for an ELT. This paper employs more restricted minimal variance estimation methods to evaluate the fundamental performance limits imposed by anisoplanatism alone upon MCAO performance for a range of sample cases. Each case is defined by a atmospheric turbulence profile, telescope aperture diameter, field-of-view, guide star constellation, and set of DM conjugate ranges. For a Kolmogorov turbulence spectrum with an infinite outer scale, MCAO performance for a whole range of aperture diameters and proportional fields-of-view can be computed at once using a scaling law analogous to the (D/dO)5/3 formula for the cone effect. For 30 meter telescopes, useful levels of performance are possible across a 1.0 - 2.0 arc minute square field-of-view using 5 laser guide stars (LGS's) and 3 DM's, and somewhat larger fields can be corrected using 9 guide stars and 4 mirrors. 3 or more tip/tilt natural guide stars (NGS's) are necessary to detect modes of tilt anisoplanatism which cannot be detected using LGS's, however. LGS MCAO performance is a quite weak function of aperture diameter for a fixed field-of-view, and it is tempting to scale these results to larger apertures. NGS MCAO performance is moderately superior to LGS MCAO if the NGS constellation is within the compensated field-of-view, but degrades rapidly as the guide stars move away from the field. The penalty relaxes slowly with increasing aperture diameter, but how to extrapolate this trend
Parameter Estimation for a Hybrid Adaptive Flight Controller
NASA Technical Reports Server (NTRS)
Campbell, Stefan F.; Nguyen, Nhan T.; Kaneshige, John; Krishnakumar, Kalmanje
2009-01-01
This paper expands on the hybrid control architecture developed at the NASA Ames Research Center by addressing issues related to indirect adaptation using the recursive least squares (RLS) algorithm. Specifically, the hybrid control architecture is an adaptive flight controller that features both direct and indirect adaptation techniques. This paper will focus almost exclusively on the modifications necessary to achieve quality indirect adaptive control. Additionally this paper will present results that, using a full non -linear aircraft model, demonstrate the effectiveness of the hybrid control architecture given drastic changes in an aircraft s dynamics. Throughout the development of this topic, a thorough discussion of the RLS algorithm as a system identification technique will be provided along with results from seven well-known modifications to the popular RLS algorithm.
Constrained model predictive control, state estimation and coordination
NASA Astrophysics Data System (ADS)
Yan, Jun
In this dissertation, we study the interaction between the control performance and the quality of the state estimation in a constrained Model Predictive Control (MPC) framework for systems with stochastic disturbances. This consists of three parts: (i) the development of a constrained MPC formulation that adapts to the quality of the state estimation via constraints; (ii) the application of such a control law in a multi-vehicle formation coordinated control problem in which each vehicle operates subject to a no-collision constraint posed by others' imperfect prediction computed from finite bit-rate, communicated data; (iii) the design of the predictors and the communication resource assignment problem that satisfy the performance requirement from Part (ii). Model Predictive Control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. MPC is normally posed as a full-state feedback control and is implemented in a certainty-equivalence fashion with best estimates of the states being used in place of the exact state. However, if the state constraints were handled in the same certainty-equivalence fashion, the resulting control law could drive the real state to violate the constraints frequently. Part (i) focuses on exploring the inclusion of state estimates into the constraints. It does this by applying constrained MPC to a system with stochastic disturbances. The stochastic nature of the problem requires re-posing the constraints in a probabilistic form. In Part (ii), we consider applying constrained MPC as a local control law in a coordinated control problem of a group of distributed autonomous systems. Interactions between the systems are captured via constraints. First, we inspect the application of constrained MPC to a completely deterministic case. Formation stability theorems are derived for the subsystems and conditions on the local constraint set are derived in order to
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.
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
Mood states influence cognitive control: the case of conflict adaptation.
Schuch, Stefanie; Koch, Iring
2015-09-01
Conflict adaptation can be measured by the "congruency sequence effect", denoting the reduction of congruency effects after incongruent trials (where response conflict occurs) relative to congruent trials (without response conflict). Recently, it has been reported that conflict adaptation is larger in negative mood than in positive mood (van Steenbergen et al., Psychological Science 21:1629-1634, 2010). We conducted two experiments further investigating this important finding. Two different interference paradigms were applied to measure conflict adaptation: Experiment 1 was a Flanker task, Experiment 2 was a Stroop-like task. To get as pure a measure of conflict adaptation as possible, we minimized the influence of trial-to-trial priming effects by excluding all kinds of stimulus repetitions. Mood states were induced by presenting film clips with emotional content prior to the interference task. Three mood states were manipulated between subjects: amused, anxious, and sad. Across both interference paradigms, we consistently found conflict adaptation in negative, but not in positive mood. Taken together with van Steenbergen et al. (Psychological Science 21:1629-1634, 2010) findings, the results suggest that the negative-mood-triggered increase in conflict adaptation is a general phenomenon that occurs independently of the particular mood-induction procedure and interference paradigm involved.
Load flow and state estimation algorithms for three-phase unbalanced power distribution systems
NASA Astrophysics Data System (ADS)
Madvesh, Chiranjeevi
Distribution load flow and state estimation are two important functions in distribution energy management systems (DEMS) and advanced distribution automation (ADA) systems. Distribution load flow analysis is a tool which helps to analyze the status of a power distribution system under steady-state operating conditions. In this research, an effective and comprehensive load flow algorithm is developed to extensively incorporate the distribution system components. Distribution system state estimation is a mathematical procedure which aims to estimate the operating states of a power distribution system by utilizing the information collected from available measurement devices in real-time. An efficient and computationally effective state estimation algorithm adapting the weighted-least-squares (WLS) method has been developed in this research. Both the developed algorithms are tested on different IEEE test-feeders and the results obtained are justified.
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.
Least absolute value state estimation with equality and inequality constraints
Abur, A. ); Celik, M.K. )
1993-05-01
A least absolute value (LAV) state estimator, which can handle both equality and inequality constraints on measurements, is developed. It is shown that, the use of equality constraints will actually reduce the number of Simplex iterations and thus the overall cpu time. The constraints can be used to enhance the reliability of the state estimator without affecting the computational efficiency of the estimator. The developed estimation program is tested using 14 through 1,000 bus power systems.
Maximal Adaptive-Decision Speedups in Quantum-State Readout
NASA Astrophysics Data System (ADS)
D'Anjou, B.; Kuret, L.; Childress, L.; Coish, W. A.
2016-01-01
The average time T required for high-fidelity readout of quantum states can be significantly reduced via a real-time adaptive decision rule. An adaptive decision rule stops the readout as soon as a desired level of confidence has been achieved, as opposed to setting a fixed readout time tf . The performance of the adaptive decision is characterized by the "adaptive-decision speedup," tf/T . In this work, we reformulate this readout problem in terms of the first-passage time of a particle undergoing stochastic motion. This formalism allows us to theoretically establish the maximum achievable adaptive-decision speedups for several physical two-state readout implementations. We show that for two common readout schemes (the Gaussian latching readout and a readout relying on state-dependent decay), the speedup is bounded by 4 and 2, respectively, in the limit of high single-shot readout fidelity. We experimentally study the achievable speedup in a real-world scenario by applying the adaptive decision rule to a readout of the nitrogen-vacancy-center (NV-center) charge state. We find a speedup of ≈2 with our experimental parameters. In addition, we propose a simple readout scheme for which the speedup can, in principle, be increased without bound as the fidelity is increased. Our results should lead to immediate improvements in nanoscale magnetometry based on spin-to-charge conversion of the NV-center spin, and provide a theoretical framework for further optimization of the bandwidth of quantum measurements.
Psychosocial Adaptation of the Taiwanese Immigrants in the United States.
ERIC Educational Resources Information Center
Lin, Josh C. H.
The unique characteristics of immigrants from Taiwan are explored, and the strategies they use in adapting to life in the United States are described. Recent Taiwanese immigrants have been characterized by their diversity in background, their high economic and social status, a variety of reasons for immigration, and sequential migration patterns.…
State-space self-tuner for on-line adaptive control
NASA Technical Reports Server (NTRS)
Shieh, L. S.
1994-01-01
Dynamic systems, such as flight vehicles, satellites and space stations, operating in real environments, constantly face parameter and/or structural variations owing to nonlinear behavior of actuators, failure of sensors, changes in operating conditions, disturbances acting on the system, etc. In the past three decades, adaptive control has been shown to be effective in dealing with dynamic systems in the presence of parameter uncertainties, structural perturbations, random disturbances and environmental variations. Among the existing adaptive control methodologies, the state-space self-tuning control methods, initially proposed by us, are shown to be effective in designing advanced adaptive controllers for multivariable systems. In our approaches, we have embedded the standard Kalman state-estimation algorithm into an online parameter estimation algorithm. Thus, the advanced state-feedback controllers can be easily established for digital adaptive control of continuous-time stochastic multivariable systems. A state-space self-tuner for a general multivariable stochastic system has been developed and successfully applied to the space station for on-line adaptive control. Also, a technique for multistage design of an optimal momentum management controller for the space station has been developed and reported in. Moreover, we have successfully developed various digital redesign techniques which can convert a continuous-time controller to an equivalent digital controller. As a result, the expensive and unreliable continuous-time controller can be implemented using low-cost and high performance microprocessors. Recently, we have developed a new hybrid state-space self tuner using a new dual-rate sampling scheme for on-line adaptive control of continuous-time uncertain systems.
Li, Dongming; Sun, Changming; Yang, Jinhua; Liu, Huan; Peng, Jiaqi; Zhang, Lijuan
2017-04-06
An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image restoration algorithm via maximum likelihood estimation. Our proposed algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed algorithm. Experimental results show that our algorithm produces accurate AO image restoration results and outperforms the current state-of-the-art blind deconvolution methods.
Estimates of software size from state machine designs
NASA Technical Reports Server (NTRS)
Britcher, R. N.; Gaffney, J. E.
1982-01-01
The length, or size (in number of Source Lines of Code) of programs represented as state machines, it is demonstrated, can be reliably estimated in terms of the number of internal state machine variables.
Adaptive output feedback control for a class of nonlinear systems with full-state constraints
NASA Astrophysics Data System (ADS)
Liu, Yan-Jun; Li, Dong-Juan; Tong, Shaocheng
2014-02-01
This paper studies an adaptive output-feedback control for a class of nonlinear single-input and single-output (SISO) systems with the full-state constraints. A state observer is designed to estimate those unmeasured states. At present, all the results in the output-feedback area ignore the effects of the full-state constraints. The presence of these constraints results in a complicated procedure and the major difficulties in the design. The barrier Lyapunov function (BLF) and a novel design procedure are given to overcome these difficulties. The adaptation law and the controllers are obtained based on the backstepping design procedure. In addition, only one adjustable parameter needs to be updated, and thus, the online computation burden is alleviated. The stability of the closed-loop system is proven by using the Lyapunov theorem. A simulation example is given to verify the effectiveness of the approach.
A Kalman filter approach to adaptive estimation of multispectral signatures
NASA Technical Reports Server (NTRS)
Crane, R. B.
1973-01-01
The signatures of remote sensing data from agricultural crops exhibit significant non-stationarity, so that the performance of fixed parameter classifiers degenerates with time and distance from the initial training data. A class of adaptive decision-directed classifiers are being developed, based on Kalman filter theory. Limited results to date on two data sets indicate approximately a 25 to 40% reduction in rates of misclassification.
A fast algorithm for control and estimation using a polynomial state-space structure
NASA Technical Reports Server (NTRS)
Shults, James R.; Brubaker, Thomas; Lee, Gordon K. F.
1991-01-01
One of the major problems associated with the control of flexible structures is the estimation of system states. Since the parameters of the structures are not constant under varying loads and conditions, conventional fixed parameter state estimators can not be used to effectively estimate the states of the system. One alternative is to use a state estimator which adapts to the condition of the system. One such estimator is the Kalman filter. This filter is a time varying recursive digital filter which is based upon a model of the system being measured. This filter adapts the model according to the output of the system. Previously, the Kalman filter has only been used in an off-line capacity due to the computational time required for implementation. With recent advances in computer technology, it is becoming a viable tool for use in the on-line environment. A distributed Kalman filter implementation is described for fast estimation of the state of a flexible arm. A key issue, is the sensor structure and initial work on a distributed sensor that could be used with the Kalman filter is presented.
Estimating equations for biomarker based exposure estimation under non-steady-state conditions.
Bartell, Scott M; Johnson, Wesley O
2011-06-13
Unrealistic steady-state assumptions are often used to estimate toxicant exposure rates from biomarkers. A biomarker may instead be modeled as a weighted sum of historical time-varying exposures. Estimating equations are derived for a zero-inflated gamma distribution for daily exposures with a known exposure frequency. Simulation studies suggest that the estimating equations can provide accurate estimates of exposure magnitude at any reasonable sample size, and reasonable estimates of the exposure variance at larger sample sizes.
Improving quantum state estimation with mutually unbiased bases.
Adamson, R B A; Steinberg, A M
2010-07-16
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.
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.
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.
A model of cerebellar computations for dynamical state estimation.
Paulin, M G; Hoffman, L F; Assad, C
2001-11-01
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.
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.
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.
Estimation and prediction of noise power based on variational Bayesian and adaptive ARMA time series
NASA Astrophysics Data System (ADS)
Zhang, Jingyi; Li, Yonggui; Zhu, Yonggang; Li, Binwu
2014-04-01
Estimation and prediction of noise power are very important for communication anti-jamming and efficient allocation of spectrum resources in adaptive wireless communication and cognitive radio. In order to estimate and predict the time-varying noise power caused by natural factors and jamming in the high frequency channel, Variational Bayesian algorithm and adaptive ARMA time series are proposed. Through establishing the time-varying noise power model, which controlled by the noise variance rate, the noise power can be estimated with Variational Bayesian algorithm, and the results show that the estimation error is related to observation interval. What's more, through the analysis of the correlation characteristics of the estimation power, noise power can be predicted based on adaptive ARMA time series, and the results show that it will be available to predict the noise power in next 5 intervals with the proportional error less than 0.2.
Remaining lifetime modeling using State-of-Health estimation
NASA Astrophysics Data System (ADS)
Beganovic, Nejra; Söffker, Dirk
2017-08-01
Technical systems and system's components undergo gradual degradation over time. Continuous degradation occurred in system is reflected in decreased system's reliability and unavoidably lead to a system failure. Therefore, continuous evaluation of State-of-Health (SoH) is inevitable to provide at least predefined lifetime of the system defined by manufacturer, or even better, to extend the lifetime given by manufacturer. However, precondition for lifetime extension is accurate estimation of SoH as well as the estimation and prediction of Remaining Useful Lifetime (RUL). For this purpose, lifetime models describing the relation between system/component degradation and consumed lifetime have to be established. In this contribution modeling and selection of suitable lifetime models from database based on current SoH conditions are discussed. Main contribution of this paper is the development of new modeling strategies capable to describe complex relations between measurable system variables, related system degradation, and RUL. Two approaches with accompanying advantages and disadvantages are introduced and compared. Both approaches are capable to model stochastic aging processes of a system by simultaneous adaption of RUL models to current SoH. The first approach requires a priori knowledge about aging processes in the system and accurate estimation of SoH. An estimation of SoH here is conditioned by tracking actual accumulated damage into the system, so that particular model parameters are defined according to a priori known assumptions about system's aging. Prediction accuracy in this case is highly dependent on accurate estimation of SoH but includes high number of degrees of freedom. The second approach in this contribution does not require a priori knowledge about system's aging as particular model parameters are defined in accordance to multi-objective optimization procedure. Prediction accuracy of this model does not highly depend on estimated SoH. This model
Broom, Donald M
2006-01-01
The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and
Surface estimation methods with phased-arrays for adaptive ultrasonic imaging in complex components
NASA Astrophysics Data System (ADS)
Robert, S.; Calmon, P.; Calvo, M.; Le Jeune, L.; Iakovleva, E.
2015-03-01
Immersion ultrasonic testing of structures with complex geometries may be significantly improved by using phased-arrays and specific adaptive algorithms that allow to image flaws under a complex and unknown interface. In this context, this paper presents a comparative study of different Surface Estimation Methods (SEM) available in the CIVA software and used for adaptive imaging. These methods are based either on time-of-flight measurements or on image processing. We also introduce a generalized adaptive method where flaws may be fully imaged with half-skip modes. In this method, both the surface and the back-wall of a complex structure are estimated before imaging flaws.
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
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.
Adapting Autonomous Behavior Using an Inverse Trust Estimation
2014-07-01
to achieve team goals. Trustworthy behavior is not something that can be programmed into an agent in advance since how humans measure trust may be task...Inverse Trust Estimation 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK...wheeled unmanned ground vehicle (UGV) and uses eBotwork’s built-in natural language processing (for interpreting user commands), locomotion, and path
On Time Delay Margin Estimation for Adaptive Control and Optimal Control Modification
NASA Technical Reports Server (NTRS)
Nguyen, Nhan T.
2011-01-01
This paper presents methods for estimating time delay margin for adaptive control of input delay systems with almost linear structured uncertainty. The bounded linear stability analysis method seeks to represent an adaptive law by a locally bounded linear approximation within a small time window. The time delay margin of this input delay system represents a local stability measure and is computed analytically by three methods: Pade approximation, Lyapunov-Krasovskii method, and the matrix measure method. These methods are applied to the standard model-reference adaptive control, s-modification adaptive law, and optimal control modification adaptive law. The windowing analysis results in non-unique estimates of the time delay margin since it is dependent on the length of a time window and parameters which vary from one time window to the next. The optimal control modification adaptive law overcomes this limitation in that, as the adaptive gain tends to infinity and if the matched uncertainty is linear, then the closed-loop input delay system tends to a LTI system. A lower bound of the time delay margin of this system can then be estimated uniquely without the need for the windowing analysis. Simulation results demonstrates the feasibility of the bounded linear stability method for time delay margin estimation.
Space Object Classification and Characterization Via Multiple Model Adaptive Estimation
2014-07-14
mathematical models of the same physical process or of the same model but dependent upon different constants or model parameters . The classification approach...Rodrigues Parameters (GRPs) resulting in a minimum parameter representation for the attitude state error.8 To within first order, the state error...i)|| 2 f2 + ||χδpk (i)|| 2 (10b) δq−k (i) = [ δ̺−k (i) δq−4k(i) ] (10c) where a is a parameter from 0 to 1 and f is a scale factor, which is often
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
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.
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.
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.…
Xing, Li; Hang, Yijun; Xiong, Zhi; Liu, Jianye; Wan, Zhong
2016-01-01
This paper describes a disturbance acceleration adaptive estimate and correction approach for an attitude reference system (ARS) so as to improve the attitude estimate precision under vehicle movement conditions. The proposed approach depends on a Kalman filter, where the attitude error, the gyroscope zero offset error and the disturbance acceleration error are estimated. By switching the filter decay coefficient of the disturbance acceleration model in different acceleration modes, the disturbance acceleration is adaptively estimated and corrected, and then the attitude estimate precision is improved. The filter was tested in three different disturbance acceleration modes (non-acceleration, vibration-acceleration and sustained-acceleration mode, respectively) by digital simulation. Moreover, the proposed approach was tested in a kinematic vehicle experiment as well. Using the designed simulations and kinematic vehicle experiments, it has been shown that the disturbance acceleration of each mode can be accurately estimated and corrected. Moreover, compared with the complementary filter, the experimental results have explicitly demonstrated the proposed approach further improves the attitude estimate precision under vehicle movement conditions. PMID:27754469
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.
[Adaptive moving averaging based estimation of single event-related potentials].
Qi, C; Liang, D; Jiang, X
2001-03-01
Event-related potentials (ERP) is pertinent to medical research and clinical diagnosis. Estimation of single event-related potentials (sERP) is the objective of ERP processing. A new technique, adaptive moving averaging based method for estimation of sERP, is presented. After analysis of the properties of background noise by crossing zero, the window length of moving averaging is adaptively set according to the maximum width of the impulse noise for each recorded raw data. The experiments are made with real recorded data and the results demonstrate that the performance of sERP estimation is excellent. So the method proposed is suitable to sERP processing.
Adaptive estimation of the log fluctuating conductivity from tracer data at the Cape Cod Site
Deng, F.W.; Cushman, J.H.; Delleur, J.W.
1993-01-01
An adaptive estimation scheme is used to obtain the integral scale and variance of the log-fluctuating conductivity at the Cape Cod site based on the fast Fourier transform/stochastic model of Deng et al. (1993) and a Kalmanlike filter. The filter incorporates prior estimates of the unknown parameters with tracer moment data to adaptively obtain improved estimates as the tracer evolves. The results show that significant improvement in the prior estimates of the conductivity can lead to substantial improvement in the ability to predict plume movement. The structure of the covariance function of the log-fluctuating conductivity can be identified from the robustness of the estimation. Both the longitudinal and transverse spatial moment data are important to the estimation.
NASA Astrophysics Data System (ADS)
Ran, Qiwen; Yang, Zhonghua; Ma, Jing; Tan, Liying; Liao, Huixi; Liu, Qingfeng
2013-02-01
In this paper, a weighted adaptive threshold estimating method is proposed to deal with long and deep channel fades in Satellite-to-Ground optical communications. During the channel correlation interval where there are sufficient correlations in adjacent signal samples, the correlations in its change rates are described by weighted equations in the form of Toeplitz matrix. As vital inputs to the proposed adaptive threshold estimator, the optimal values of the change rates can be obtained by solving the weighted equation systems. The effect of channel fades and aberrant samples can be mitigated by joint use of weighted equation systems and Kalman estimation. Based on the channel information data from star observation trails, simulations are made and the numerical results show that the proposed method have better anti-fade performances than the D-value adaptive threshold estimating method in both weak and strong turbulence conditions.
An adaptive technique for estimating the atmospheric density profile during the AE mission
NASA Technical Reports Server (NTRS)
Argentiero, P.
1973-01-01
A technique is presented for processing accelerometer data obtained during the AE missions in order to estimate the atmospheric density profile. A minimum variance, adaptive filter is utilized. The trajectory of the probe and probe parameters are in a consider mode where their estimates are unimproved but their associated uncertainties are permitted an impact on filter behavior. Simulations indicate that the technique is effective in estimating a density profile to within a few percentage points.
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
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.
Optical Flow-Based State Estimation for Guided Projectiles
2015-06-01
ARL-TR-7321 ● JUNE 2015 US Army Research Laboratory Optical Flow-Based State Estimation for Guided Projectiles by Moshe... Guided Projectiles by Moshe Hamaoui Weapons and Materials Research Directorate, ARL Approved for public release...
Complex lung motion estimation via adaptive bilateral filtering of the deformation field.
Papiez, Bartlomiej W; Heinrich, Mattias Paul; Risser, Laurent; Schnabel, Julia A
2013-01-01
Estimation of physiologically plausible deformations is critical for several medical applications. For example, lung cancer diagnosis and treatment requires accurate image registration which preserves sliding motion in the pleural cavity, and the rigidity of chest bones. This paper addresses these challenges by introducing a novel approach for regularisation of non-linear transformations derived from a bilateral filter. For this purpose, the classic Gaussian kernel is replaced by a new kernel that smoothes the estimated deformation field with respect to the spatial position, intensity and deformation dissimilarity. The proposed regularisation is a spatially adaptive filter that is able to preserve discontinuity between the lungs and the pleura and reduces any rigid structures deformations in volumes. Moreover, the presented framework is fully automatic and no prior knowledge of the underlying anatomy is required. The performance of our novel regularisation technique is demonstrated on phantom data for a proof of concept as well as 3D inhale and exhale pairs of clinical CT lung volumes. The results of the quantitative evaluation exhibit a significant improvement when compared to the corresponding state-of-the-art method using classic Gaussian smoothing.
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.
An Alternative Consideration on Singular Linear State Estimation,
1980-01-01
SMC-8, pp.849-854, Dec. 1978. [9] J.. Meditch , Stochastic 0ptimal A reduced-order optimal state esti- Linear Estimation an Control. mator for a...singular case; thus the computational efficiency is improved. This estimation procedure can be applied similarly to smoothing and predicting problems or
NASA Astrophysics Data System (ADS)
Li, Yiming; Gao, Chao; Liang, Haodong; Miao, Maoke; Li, Xiaofeng
2017-04-01
This paper investigates an adaptive phase estimator for coherent free-space optical (FSO) communication systems. Closed-form solutions for variance of phase errors are derived when the optical beam is subjected to Gamma-Gamma distributed turbulence. The adaptive phase estimator has improved upon the phase error performance in comparison to conventional phase estimators. We also demonstrate notable improvement in BER performance when applying our adaptive phase estimator to coherent FSO communication systems.
Nonlocal transform-domain denoising of volumetric data with groupwise adaptive variance estimation
NASA Astrophysics Data System (ADS)
Maggioni, Matteo; Foi, Alessandro
2012-03-01
We propose an extension of the BM4D volumetric filter to the denoising of data corrupted by spatially nonuniform noise. BM4D implements the grouping and collaborative filtering paradigm, where similar cubes of voxels are stacked into a four-dimensional "group". Each group undergoes a sparsifying four-dimensional transform, that exploits the local correlation among voxels in each cube and the nonlocal correlation between corresponding voxels of different cubes. Thus, signal and noise are effectively separated in transform domain. In this work we take advantage of the sparsity induced by the four-dimensional transform to provide a spatially adaptive estimation of the local noise variance by applying a robust median estimator of the absolute deviation to the spectrum of each filtered group. The adaptive variance estimates are then used during coefficients shrinkage. Finally, the inverse four-dimensional transform is applied to the filtered group, and each individual cube estimate is adaptively aggregated at its original location. Experiments on medical data corrupted by spatially varying Gaussian and Rician noise demonstrate the efficacy of the proposed approach in volumetric data denoising. In case of magnetic resonance signals, the adaptive variance estimate can be also used to compensate the estimation bias due to the non-zero-mean errors of the Rician-distributed data.
Adapting Autonomous Behavior Based on an Estimate of an Operator’s Trust
2014-11-01
teammate’s trust and adapt to it, and also pro- vide directions for this work. 2 Inverse Trust and Behavior Adaptation Traditional trust metrics measure how...looking at an inverse trust metric where an agent (the robot) estimates how much trust another agent has in it. One option would be to get direct feedback...trustworthy behaviors from previous adap- tations (Floyd, Drinkwater, and Aha 2014b). 3 Discussion Our work has focused on an approach for inverse trust
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.
Risk, resources and state-dependent adaptive behavioural syndromes.
Luttbeg, Barney; Sih, Andrew
2010-12-27
Many animals exhibit behavioural syndromes-consistent individual differences in behaviour across two or more contexts or situations. Here, we present adaptive, state-dependent mathematical models for analysing issues about behavioural syndromes. We find that asset protection (where individuals with more 'assets' tend be more cautious) and starvation avoidance, two state-dependent mechanisms, can explain short-term behavioural consistency, but not long-term stable behavioural types (BTs). These negative-feedback mechanisms tend to produce convergence in state and behaviour over time. In contrast, a positive-feedback mechanism, state-dependent safety (where individuals with higher energy reserves, size, condition or vigour are better at coping with predators), can explain stable differences in personality over the long term. The relative importance of negative- and positive-feedback mechanisms in governing behavioural consistency depends on environmental conditions (predation risk and resource availability). Behavioural syndromes emerge more readily in conditions of intermediate ecological favourability (e.g. medium risk and medium resources, or high risk and resources, or low risk and resources). Under these conditions, individuals with higher initial state maintain a tendency to be bolder than individuals that start with low initial state; i.e. later BT is determined by state during an early 'developmental window'. In contrast, when conditions are highly favourable (low risk, high resources) or highly unfavourable (high risk, low resources), individuals converge to be all relatively bold or all relatively cautious, respectively. In those circumstances, initial differences in BT are not maintained over the long term, and there is no early developmental window where initial state governs later BT. The exact range of ecological conditions favouring behavioural syndromes depends also on the strength of state-dependent safety.
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.
Sequential Monte Carlo hydraulic state estimation of an irrigation canal
NASA Astrophysics Data System (ADS)
Sau, Jacques; Malaterre, Pierre-Olivier; Baume, Jean-Pierre
2010-04-01
The estimation in real time of the hydraulic state of irrigation canals is becoming one of the major concerns of network managers. With this end in view, this Note presents a new approach based on the combination of a numerical solution of the open channel Saint-Venant PDE with a sequential Monte Carlo state-space estimation. We shall show that discharges and elevations along the canal are successfully estimated, and also that, concurrently, model parameters identification, such as the Manning-Strickler friction coefficient, can be performed.
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-05-30
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.
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…
Estimating the Burden of Chagas Disease in the United States
Manne-Goehler, Jennifer; Umeh, Chukwuemeka A.; Montgomery, Susan P.; Wirtz, Veronika J.
2016-01-01
Background In recent years, there has been growing awareness of the significant burden of Chagas disease in the United States (US). However, epidemiological data on both prevalence and access to care for this disease are limited. The objective of this study is to provide an updated national estimate of Chagas disease prevalence, the first state-level estimates of cases of T. cruzi infection in the US and to analyze these estimates in the context of data on confirmed cases of infection in the US blood supply. Methods In this study, we calculated estimates of the state and national prevalence of Chagas disease. The number of residents originally from Chagas disease endemic countries were computed using data on Foreign-Born Hispanic populations from the American Community Survey, along with recent prevalence estimates for Chagas disease in Latin America from the World Health Organization that were published in 2006 and updated in 2015. We then describe the distribution of estimated cases in each state in relation to the number of infections identified in the donated blood supply per data from the AABB (formerly American Association of Blood Banks). Findings The results of this analysis offer an updated national estimate of 238,091 cases of T. cruzi infection in the United States as of 2012, using the same method as was used by Bern and Montgomery to estimate cases in 2005. This estimate indicates that there are 62,070 cases less than the most recent prior estimate, though it does not include undocumented immigrants who may account for as many as 109,000 additional cases. The state level results show that four states (California, Texas, Florida and New York) have over 10,000 cases and an additional seven states have over 5,000 cases. Moreover, since 2007, the AABB has reported 1,908 confirmed cases of T. cruzi infection identified through screening of blood donations. Conclusions This study demonstrates a substantial burden of Chagas disease in the US, with state
NASA Astrophysics Data System (ADS)
Yang, Yuxiao; Shanechi, Maryam M.
2016-12-01
Objective. Design of closed-loop anesthetic delivery (CLAD) systems is an important topic, particularly for medically induced coma, which needs to be maintained for long periods. Current CLADs for medically induced coma require a separate offline experiment for model parameter estimation, which causes interruption in treatment and is difficult to perform. Also, CLADs may exhibit bias due to inherent time-variation and non-stationarity, and may have large infusion rate variations at steady state. Finally, current CLADs lack theoretical performance guarantees. We develop the first adaptive CLAD for medically induced coma, which addresses these limitations. Further, we extend our adaptive system to be generalizable to other states of anesthesia. Approach. We designed general parametric pharmacodynamic, pharmacokinetic and neural observation models with associated guidelines, and derived a novel adaptive controller. We further penalized large steady-state drug infusion rate variations in the controller. We derived theoretical guarantees that the adaptive system has zero steady-state bias. Using simulations that resembled real time-varying and noisy environments, we tested the closed-loop system for control of two different anesthetic states, burst suppression in medically induced coma and unconsciousness in general anesthesia. Main results. In 1200 simulations, the adaptive system achieved precise control of both anesthetic states despite non-stationarity, time-variation, noise, and no initial parameter knowledge. In both cases, the adaptive system performed close to a baseline system that knew the parameters exactly. In contrast, a non-adaptive system resulted in large steady-state bias and error. The adaptive system also resulted in significantly smaller steady-state infusion rate variations compared to prior systems. Significance. These results have significant implications for clinically viable CLAD design for a wide range of anesthetic states, with potential cost
Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation.
Liu, Xi; Qu, Hua; Zhao, Jihong; Yue, Pengcheng; Wang, Meng
2016-09-20
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.
Analytic Steady-State Accuracy of a Spacecraft Attitude Estimator
NASA Technical Reports Server (NTRS)
Markley, F. Landis
2000-01-01
This paper extends Farrenkopf's analysis of a single-axis spacecraft attitude estimator using gyro and angle sensor data to include the angle output white noise of a rate-integrating gyro. Analytic expressions are derived for the steady-state pre-update and post-update angle and drift bias variances and for the state update equations. It is shown that only part of the state update resulting from the angle sensor measurement is propagated to future times.
Quantum phase estimation using path-symmetric entangled states
NASA Astrophysics Data System (ADS)
Lee, Su-Yong; Lee, Chang-Woo; Lee, Jaehak; Nha, Hyunchul
2016-07-01
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.
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
Validation of a Bayesian Adaptive Estimation Technique in the Stop-Signal Task
Livesey, Evan J.; Livesey, David J.
2016-01-01
The Stop Signal Task (SST), a commonly used measure of response inhibition, uses standard psychophysical methods to gain an estimate of the time needed to withhold a prepotent response. Under some circumstances, conventional forms of the SST are impractical to use because of the large number of trials necessary to gain a reliable estimate of the speed of inhibition. Here we applied to the SST an adaptive method for estimating psychometric parameters that can find reliable threshold estimates over a relatively small number of trials. The Ψ adaptive staircase, which uses a Bayesian algorithm to find the most likely parameters of a psychophysical function, was used to estimate the critical stop signal delay at which the probability of successful response inhibition equals 0.5. Using computational modeling and adult participants, estimates of stop signal reaction time (SSRT) based on the Ψ staircase were compared to estimates using the method of constant stimuli and a standard staircase method of adjustment. Results demonstrate that a reliable estimate of SSRT can be gained very quickly (20–30 stop trials), making the method very useful for testing populations that cannot maintain concentration for long periods or for rapidly obtaining multiple SSRT estimates from healthy adult participants. PMID:27880815
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.
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.
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.
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…
Rankings & Estimates: Rankings of the States 2015 and Estimates of School Statistics 2016
ERIC Educational Resources Information Center
National Education Association, 2016
2016-01-01
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…
NASA Astrophysics Data System (ADS)
Zeng, X.
2015-12-01
A large number of model executions are required to obtain alternative conceptual models' predictions and their posterior probabilities in Bayesian model averaging (BMA). The posterior model probability is estimated through models' marginal likelihood and prior probability. The heavy computation burden hinders the implementation of BMA prediction, especially for the elaborated marginal likelihood estimator. For overcoming the computation burden of BMA, an adaptive sparse grid (SG) stochastic collocation method is used to build surrogates for alternative conceptual models through the numerical experiment of a synthetical groundwater model. BMA predictions depend on model posterior weights (or marginal likelihoods), and this study also evaluated four marginal likelihood estimators, including arithmetic mean estimator (AME), harmonic mean estimator (HME), stabilized harmonic mean estimator (SHME), and thermodynamic integration estimator (TIE). The results demonstrate that TIE is accurate in estimating conceptual models' marginal likelihoods. The BMA-TIE has better predictive performance than other BMA predictions. TIE has high stability for estimating conceptual model's marginal likelihood. The repeated estimated conceptual model's marginal likelihoods by TIE have significant less variability than that estimated by other estimators. In addition, the SG surrogates are efficient to facilitate BMA predictions, especially for BMA-TIE. The number of model executions needed for building surrogates is 4.13%, 6.89%, 3.44%, and 0.43% of the required model executions of BMA-AME, BMA-HME, BMA-SHME, and BMA-TIE, respectively.
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.
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)
Rahimi, Afshin; Kumar, Krishna Dev; Alighanbari, Hekmat
2017-05-01
Reaction wheels, as one of the most commonly used actuators in satellite attitude control systems, are prone to malfunction which could lead to catastrophic failures. Such malfunctions can be detected and addressed in time if proper analytical redundancy algorithms such as parameter estimation and control reconfiguration are employed. Major challenges in parameter estimation include speed and accuracy of the employed algorithm. This paper presents a new approach for improving parameter estimation with adaptive unscented Kalman filter. The enhancement in tracking speed of unscented Kalman filter is achieved by systematically adapting the covariance matrix to the faulty estimates using innovation and residual sequences combined with an adaptive fault annunciation scheme. The proposed approach provides the filter with the advantage of tracking sudden changes in the system non-measurable parameters accurately. Results showed successful detection of reaction wheel malfunctions without requiring a priori knowledge about system performance in the presence of abrupt, transient, intermittent, and incipient faults. Furthermore, the proposed approach resulted in superior filter performance with less mean squared errors for residuals compared to generic and adaptive unscented Kalman filters, and thus, it can be a promising method for the development of fail-safe satellites.
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…
The Use of Unidimensional Item Parameter Estimates of Multidimensional Items in Adaptive Testing.
ERIC Educational Resources Information Center
Ackerman, Terry A.
The purpose of this study was to investigate the effect of using multidimensional items in a computer adaptive test (CAT) setting which assumes a unidimensional item response theory (IRT) framework. Previous research has suggested that the composite of multidimensional abilities being estimated by a unidimensional IRT model is not constant…
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.
Effect of Rasch Calibration on Ability and DIF Estimation in Computer-Adaptive Tests.
ERIC Educational Resources Information Center
Zwick, Rebecca; And Others
1995-01-01
In a simulation study of ability and estimation of differential item functioning (DIF) in computerized adaptive tests, Rasch-based DIF statistics were highly correlated with generating DIF, but DIF statistics tended to be slightly smaller than in the three-parameter logistic model analyses. (SLD)
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,…
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.
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.
Phase estimation of coherent states with a noiseless linear amplifier
NASA Astrophysics Data System (ADS)
Assad, Syed M.; Bradshaw, Mark; Lam, Ping Koy
Amplification of quantum states is inevitably accompanied with the introduction of noise at the output. For protocols that are probabilistic with heralded success, noiseless linear amplification in theory may still be possible. When the protocol is successful, it can lead to an output that is a noiselessly amplified copy of the input. When the protocol is unsuccessful, the output state is degraded and is usually discarded. Probabilistic protocols may improve the performance of some quantum information protocols, but not for metrology if the whole statistics is taken into consideration. We calculate the precision limits on estimating the phase of coherent states using a noiseless linear amplifier by computing its quantum Fisher information and we show that on average, the noiseless linear amplifier does not improve the phase estimate. We also discuss the case where abstention from measurement can reduce the cost for estimation.
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.
Efficient sensor placement for state estimation in structural dynamics
NASA Astrophysics Data System (ADS)
Hernandez, Eric M.
2017-02-01
This paper derives a computationally efficient algorithm to determine optimal sequential sensor placement for state estimation in linear structural systems subjected to unmeasured excitations and noise contaminated measurements. The proposed algorithm is developed within the context of the Kalman filter and it minimizes the variance of the state estimate among all possible sequential sensor locations. The paper investigates the effects of measurement type, covariance matrix partition selection, spatial correlation of excitation and model selection on optimal sensor placement. The paper shows that the sequential approach reaches the optimal sensor placement as the number of sensor increases.
Automated mechanical ventilation: adapting decision making to different disease states.
Lozano-Zahonero, S; Gottlieb, D; Haberthür, C; Guttmann, J; Möller, K
2011-03-01
The purpose of the present study is to introduce a novel methodology for adapting and upgrading decision-making strategies concerning mechanical ventilation with respect to different disease states into our fuzzy-based expert system, AUTOPILOT-BT. The special features are: (1) Extraction of clinical knowledge in analogy to the daily routine. (2) An automated process to obtain the required information and to create fuzzy sets. (3) The controller employs the derived fuzzy rules to achieve the desired ventilation status. For demonstration this study focuses exclusively on the control of arterial CO(2) partial pressure (p(a)CO(2)). Clinical knowledge from 61 anesthesiologists was acquired using a questionnaire from which different disease-specific fuzzy sets were generated to control p(a)CO(2). For both, patients with healthy lung and with acute respiratory distress syndrome (ARDS) the fuzzy sets show different shapes. The fuzzy set "normal", i.e., "target p(a)CO(2) area", ranges from 35 to 39 mmHg for healthy lungs and from 39 to 43 mmHg for ARDS lungs. With the new fuzzy sets our AUTOPILOT-BT reaches the target p(a)CO(2) within maximal three consecutive changes of ventilator settings. Thus, clinical knowledge can be extended, updated, and the resulting mechanical ventilation therapies can be individually adapted, analyzed, and evaluated.
Aboy, Mateo; Márquez, Oscar W; McNames, James; Hornero, Roberto; Trong, Tran; Goldstein, Brahm
2005-08-01
We describe an algorithm to estimate the instantaneous power spectral density (PSD) of nonstationary signals. The algorithm is based on a dual Kalman filter that adaptively generates an estimate of the autoregressive model parameters at each time instant. The algorithm exhibits superior PSD tracking performance in nonstationary signals than classical nonparametric methodologies, and does not assume local stationarity of the data. Furthermore, it provides better time-frequency resolution, and is robust to model mismatches. We demonstrate its usefulness by a sample application involving PSD estimation of intracranial pressure signals (ICP) from patients with traumatic brain injury (TBI).
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.
Adaptive control of unknown unstable steady states of dynamical systems.
Pyragas, K; Pyragas, V; Kiss, I Z; Hudson, J L
2004-08-01
A simple adaptive controller based on a low-pass filter to stabilize unstable steady states of dynamical systems is considered. The controller is reference-free; it does not require knowledge of the location of the fixed point in the phase space. A topological limitation similar to that of the delayed feedback controller is discussed. We show that the saddle-type steady states cannot be stabilized by using the conventional low-pass filter. The limitation can be overcome by using an unstable low-pass filter. The use of the controller is demonstrated for several physical models, including the pendulum driven by a constant torque, the Lorenz system, and an electrochemical oscillator. Linear and nonlinear analyses of the models are performed and the problem of the basins of attraction of the stabilized steady states is discussed. The robustness of the controller is demonstrated in experiments and numerical simulations with an electrochemical oscillator, the dissolution of nickel in sulfuric acid; a comparison of the effect of using direct and indirect variables in the control is made. With the use of the controller, all unstable phase-space objects are successfully reconstructed experimentally.
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.
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
Campbell, D A; Chkrebtii, O
2013-12-01
Statistical inference for biochemical models often faces a variety of characteristic challenges. In this paper we examine state and parameter estimation for the JAK-STAT intracellular signalling mechanism, which exemplifies the implementation intricacies common in many biochemical inference problems. We introduce an extension to the Generalized Smoothing approach for estimating delay differential equation models, addressing selection of complexity parameters, choice of the basis system, and appropriate optimization strategies. Motivated by the JAK-STAT system, we further extend the generalized smoothing approach to consider a nonlinear observation process with additional unknown parameters, and highlight how the approach handles unobserved states and unevenly spaced observations. The methodology developed is generally applicable to problems of estimation for differential equation models with delays, unobserved states, nonlinear observation processes, and partially observed histories.
Adaptive urn designs for estimating several percentiles of a dose--response curve.
Mugno, Raymond; Zhus, Wei; Rosenberger, William F
2004-07-15
Dose--response experiments are crucial in biomedical studies. There are usually multiple objectives in such experiments and among the goals is the estimation of several percentiles on the dose--response curve. Here we present the first non-parametric adaptive design approach to estimate several percentiles simultaneously via generalized Pólya urns. Theoretical properties of these designs are investigated and their performance is gaged by the locally compound optimal designs. As an example, we re-investigated a psychophysical experiment where one of the goals was to estimate the three quartiles. We show that these multiple-objective adaptive designs are more efficient than the original single-objective adaptive design targeting the median only. We also show that urn designs which target the optimal designs are slightly more efficient than those which target the desired percentiles directly. Guidelines are given as to when to use which type of design. Overall we are pleased with the efficiency results and hope compound adaptive designs proposed in this work or their variants may prove to be a viable non-parametric alternative in multiple-objective dose--response studies.
Estimated status of osprey populations in the United States
Henny, C.J.; Ogden, J.C.
1970-01-01
The status of ospreys (Pandion haliaetus carolinensis) in seven states, as determined by comparison of recently observed rates of production to rates required to maintain a stable population, is discussed. It is estimated that 1.22-1.30 young must be produced per active nest (including unsuccesful nests) to maintain a stable population. The decline in numbers of ospreys was not uniform throughout the United States. The population at Florida Bay, Florida, seemed to be stable as opposed to the estimated 12-14 percent (minimal) annual decline in numbers during the 1960s of localized populations in Connecticut, Maine, Michigan, and Wisconsin. For the same time period, populations in Maryland and Minnesota were estimated to be declining at a 2-3 percent rate annually.
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.
Path Flow Estimation Using Time Varying Coefficient State Space Model
NASA Astrophysics Data System (ADS)
Jou, Yow-Jen; Lan, Chien-Lun
2009-08-01
The dynamic path flow information is very crucial in the field of transportation operation and management, i.e., dynamic traffic assignment, scheduling plan, and signal timing. Time-dependent path information, which is important in many aspects, is nearly impossible to be obtained. Consequently, researchers have been seeking estimation methods for deriving valuable path flow information from less expensive traffic data, primarily link traffic counts of surveillance systems. This investigation considers a path flow estimation problem involving the time varying coefficient state space model, Gibbs sampler, and Kalman filter. Numerical examples with part of a real network of the Taipei Mass Rapid Transit with real O-D matrices is demonstrated to address the accuracy of proposed model. Results of this study show that this time-varying coefficient state space model is very effective in the estimation of path flow compared to time-invariant model.
Aberration estimation from single point image in a simulated adaptive optics system.
Grisan, Enrico; Frassetto, Fabio; Da Deppo, Vania; Naletto, Giampiero; Ruggeri, Alfredo
2005-01-01
Adaptive optics has been recently applied for the development of ophthalmic devices, with the main objective of obtaining higher resolution images for diagnostic purposes or ideally correcting high-order eye aberrations. The core of every adaptive optics systems is an optical device that is able to modify the wavefront shape of the light entering a system: once the shape of the incoming wavefront has been estimated, by means of this device it is possible to correct the aberrations introduced along the optical path. The aim of this paper is to demonstrate the feasibility, although in a simulated system, of estimating and correcting the wavefront shape simply by means of an iterative software analysis of a single point source image, thus avoiding expensive wavefront sensors or the burdensome computation of the PSF of the optical system. To test the proposed algorithm, a simple optical system has been simulated with a ray-tracing software and a program to estimate the Zernike coefficients of the simulated aberration from the analysis of the source image has been developed. Numerical indexes were used to evaluate the capability of the software of correctly estimating the Zernike coefficients. Even if only defocus, astigmatism and coma were considered, the very satisfactory results obtained confirm the soundness of this new approach and encourage further work in this direction, in order to develop a system able to estimate also spherical aberration, tilt and field curvature. An implementation of this aberration estimation in a real AO system is also currently in progress.
Reducing the effect of respiration in baroreflex sensitivity estimation with adaptive filtering.
Tiinanen, Suvi; Tulppo, Mikko; Seppänen, Tapio
2008-01-01
Cardiac baroreflex is described by baroreflex sensitivity (BRS) from blood pressure and heart rate interval (RRi) fluctuations. However, respiration affects both blood pressure and RRi via mechanisms that are not necessarily of baroreflex origin. To separate the effects of baroreflex and respiration, metronome-guided breathing in a high frequency band (HF, 0.25-0.4 Hz) and a low frequency spectral band (LF, 0.04-0.15 Hz) have therefore been commonly used for BRS estimation. The controlled breathing may, however, change the natural functioning of the autonomic system and interfere BRS estimates. To enable usage of spontaneous breathing, we propose an adaptive LMS-based filter for removing the respiration effect from the BRS estimates. ECG, continuous blood pressure and respiration were measured during 5 min spontaneous and 5 min controlled breathing at 0.25 Hz in healthy males (n = 24, 33+/-7 years). BRS was calculated with spectral methods from the LF band with and without filtering. In those subjects whose spontaneous breathing rate was <0.15 Hz, the BRS(LF) values were overestimated, whereas the adaptive filtering reduced the bias significantly. As a conclusion, the adaptive filter reduces the distorting effect of respiration on BRS values, which enables more accurate estimation of BRS and the usage of spontaneous breathing as a measurement protocol.
Weak measurement-based state estimation of Gaussian states of one-variable quantum systems
NASA Astrophysics Data System (ADS)
Das, Debmalya; Arvind
2017-04-01
We present a scheme to estimate Gaussian states of one-dimensional continuous variable systems, based on weak (unsharp) quantum measurements. The estimation of a Gaussian state requires us to find position (q), momentum (p) and their second order moments. We measure q weakly and follow it up with a projective measurement of p on half of the ensemble, and on the other half we measure p weakly followed by a projective measurement of q. In each case we use the state twice before discarding it. We compare our results with projective measurements and demonstrate that under certain conditions such weak measurement-based estimation schemes, where recycling of the states is possible, can outperform projective measurement-based state estimation schemes. We establish beyond statistical fluctuations that our method works better for small ensemble sizes.
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.
State and parameter estimation for canonic models of neural oscillators.
Tyukin, Ivan; Steur, Erik; Nijmeijer, Henk; Fairhurst, David; Song, Inseon; Semyanov, Alexey; Van Leeuwen, Cees
2010-06-01
We consider the problem of how to recover the state and parameter values of typical model neurons, such as Hindmarsh-Rose, FitzHugh-Nagumo, Morris-Lecar, from in-vitro measurements of membrane potentials. In control theory, in terms of observer design, model neurons qualify as locally observable. However, unlike most models traditionally addressed in control theory, no parameter-independent diffeomorphism exists, such that the original model equations can be transformed into adaptive canonic observer form. For a large class of model neurons, however, state and parameter reconstruction is possible nevertheless. We propose a method which, subject to mild conditions on the richness of the measured signal, allows model parameters and state variables to be reconstructed up to an equivalence class.
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
2015-06-19
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.
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…
A Nonlinear Stochastic Filter for Continuous-Time State Estimation
Ghoreyshi, Atiyeh; Sanger, Terence D.
2015-01-01
Nonlinear filters produce a nonparametric estimate of the probability density of state at each point in time. Currently-known nonlinear filters include Particle Filters and the Kushner equation (and its un-normalized version: the Zakai equation). However, these filters have limited measurement models: Particle Filters require measurement at discrete times, and the Kushner and Zakai equations only apply when the measurement can be represented as a function of the state. We present a new nonlinear filter for continuous-time measurements with a much more general stochastic measurement model. It integrates to Bayes’ rule over short time intervals and provides Bayes-optimal estimates from quantized, intermittent, or ambiguous sensor measurements. The filter has a close link to Information Theory, and we show that the rate of change of entropy of the density estimate is equal to the mutual information between the measurement and the state and thus the maximum achievable. This is a fundamentally new class of filter that is widely applicable to nonlinear estimation for continuous-time control. PMID:26412871
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.
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.
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
Adaptive Parametric Spectral Estimation with Kalman Smoothing for Online Early Seizure Detection
Park, Yun S.; Hochberg, Leigh R.; Eskandar, Emad N.; Cash, Sydney S.; Truccolo, Wilson
2014-01-01
Tracking spectral changes in neural signals, such as local field potentials (LFPs) and scalp or intracranial electroencephalograms (EEG, iEEG), is an important problem in early detection and prediction of seizures. Most approaches have focused on either parametric or nonparametric spectral estimation methods based on moving time windows. Here, we explore an adaptive (time-varying) parametric ARMA approach for tracking spectral changes in neural signals based on the fixed-interval Kalman smoother. We apply the method to seizure detection based on spectral features of intracortical LFPs recorded from a person with pharmacologically intractable focal epilepsy. We also devise and test an approach for real-time tracking of spectra based on the adaptive parametric method with the fixed-interval Kalman smoother. The order of ARMA models is determined via the AIC computed in moving time windows. We quantitatively demonstrate the advantages of using the adaptive parametric estimation method in seizure detection over nonparametric alternatives based exclusively on moving time windows. Overall, the adaptive parametric approach significantly improves the statistical separability of interictal and ictal epochs. PMID:24663686
Gait Phase Estimation Based on Noncontact Capacitive Sensing and Adaptive Oscillators.
Zheng, Enhao; Manca, Silvia; Yan, Tingfang; Parri, Andrea; Vitiello, Nicola; Wang, Qining
2017-02-23
This paper presents a novel strategy aiming to acquire an accurate and walking-speed-adaptive estimation of the gait phase through noncontact capacitive sensing and adaptive oscillators (AOs). The capacitive sensing system is designed with two sensing cuffs that can measure the leg muscle shape changes during walking. The system can be dressed above the clothes and free human skin from contacting to electrodes. In order to track the capacitance signals, the gait phase estimator is designed based on the AOs dynamic system due to its ability of synchronizing with quasi-periodic signals. After the implementation of the whole system, we firstly evaluated the off-line estimation performance by experiments with twelve healthy subjects walking on treadmill with changing speeds. The strategy achieved an accurate and consistent gait phase estimation with only one channel of capacitance signal. The average root mean square errors in one stride were 0.19 rad (3.0% of one gait cycle) for constant walking speeds and 0.31 rad (4.9% of one gait cycle) for speed transitions even after the subjects re-wore the sensing cuffs. We then validated our strategy in a real-time gait phase estimation task with three subjects walking with changing speeds. Our study indicates that the strategy based on capacitive sensing and AOs is a promising alternative for the control of exoskeleton/orthosis.
Heat-Related Mortality and Adaptation to Heat in the United States
Peng, Roger D.; Bell, Michelle L.; Dominici, Francesca
2014-01-01
Background: In a changing climate, increasing temperatures are anticipated to have profound health impacts. These impacts could be mitigated if individuals and communities adapt to changing exposures; however, little is known about the extent to which the population may be adapting. Objective: We investigated the hypothesis that if adaptation is occurring, then heat-related mortality would be decreasing over time. Methods: We used a national database of daily weather, air pollution, and age-stratified mortality rates for 105 U.S. cities (covering 106 million people) during the summers of 1987–2005. Time-varying coefficient regression models and Bayesian hierarchical models were used to estimate city-specific, regional, and national temporal trends in heat-related mortality and to identify factors that might explain variation across cities. Results: On average across cities, the number of deaths (per 1,000 deaths) attributable to each 10°F increase in same-day temperature decreased from 51 [95% posterior interval (PI): 42, 61] in 1987 to 19 (95% PI: 12, 27) in 2005. This decline was largest among those ≥ 75 years of age, in northern regions, and in cities with cooler climates. Although central air conditioning (AC) prevalence has increased, we did not find statistically significant evidence of larger temporal declines among cities with larger increases in AC prevalence. Conclusions: The population has become more resilient to heat over time. Yet even with this increased resilience, substantial risks of heat-related mortality remain. Based on 2005 estimates, an increase in average temperatures by 5°F (central climate projection) would lead to an additional 1,907 deaths per summer across all cities. Citation: Bobb JF, Peng RD, Bell ML, Dominici F. 2014. Heat-related mortality and adaptation to heat in the United States. Environ Health Perspect 122:811–816; http://dx.doi.org/10.1289/ehp.1307392 PMID:24780880
The remarkable success of adaptive cosine estimator in hyperspectral target detection
NASA Astrophysics Data System (ADS)
Manolakis, D.; Pieper, M.; Truslow, E.; Cooley, T.; Brueggeman, M.; Lipson, S.
2013-05-01
A challenging problem of major importance in hyperspectral imaging applications is the detection of subpixel targets of military and civilian interest. The background clutter surrounding the target, acts as an interference source that simultaneously distorts the target spectrum and reduces its strength. Two additional limiting factors are the spectral variability of the background clutter and the spectral variability of the target. Since a result in applied statistics is only as reliable as the assumptions from which it is derived, it is important to investigate whether the basic assumptions used for the derivation of matched filter and adaptive cosine estimator algorithms are a reasonable description of the physical situation. Careful examination of the linear signal model used to derive these algorithms and the replacement signal model, which is a more realistic model for subpixel targets, reveals a serious discrepancy between modeling assumptions and the physical world. Despite this discrepancy and additional mismatches between assumed and actual signal and clutter models, the adaptive cosine estimator shows an amazing effectiveness in practical target detection applications. The objective of this paper is an attempt to explain this unbelievable effectiveness using a combination of classical statistical detection theory, geometrical interpretations, and a novel realistic performance prediction model for the adaptive cosine estimator.
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.
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
2016-07-06
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.
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
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.
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.
Basin structure of optimization based state and parameter estimation
NASA Astrophysics Data System (ADS)
Schumann-Bischoff, Jan; Parlitz, Ulrich; Abarbanel, Henry D. I.; Kostuk, Mark; Rey, Daniel; Eldridge, Michael; Luther, Stefan
2015-05-01
Most data based state and parameter estimation methods require suitable initial values or guesses to achieve convergence to the desired solution, which typically is a global minimum of some cost function. Unfortunately, however, other stable solutions (e.g., local minima) may exist and provide suboptimal or even wrong estimates. Here, we demonstrate for a 9-dimensional Lorenz-96 model how to characterize the basin size of the global minimum when applying some particular optimization based estimation algorithm. We compare three different strategies for generating suitable initial guesses, and we investigate the dependence of the solution on the given trajectory segment (underlying the measured time series). To address the question of how many state variables have to be measured for optimal performance, different types of multivariate time series are considered consisting of 1, 2, or 3 variables. Based on these time series, the local observability of state variables and parameters of the Lorenz-96 model is investigated and confirmed using delay coordinates. This result is in good agreement with the observation that correct state and parameter estimation results are obtained if the optimization algorithm is initialized with initial guesses close to the true solution. In contrast, initialization with other exact solutions of the model equations (different from the true solution used to generate the time series) typically fails, i.e., the optimization procedure ends up in local minima different from the true solution. Initialization using random values in a box around the attractor exhibits success rates depending on the number of observables and the available time series (trajectory segment).
Basin structure of optimization based state and parameter estimation.
Schumann-Bischoff, Jan; Parlitz, Ulrich; Abarbanel, Henry D I; Kostuk, Mark; Rey, Daniel; Eldridge, Michael; Luther, Stefan
2015-05-01
Most data based state and parameter estimation methods require suitable initial values or guesses to achieve convergence to the desired solution, which typically is a global minimum of some cost function. Unfortunately, however, other stable solutions (e.g., local minima) may exist and provide suboptimal or even wrong estimates. Here, we demonstrate for a 9-dimensional Lorenz-96 model how to characterize the basin size of the global minimum when applying some particular optimization based estimation algorithm. We compare three different strategies for generating suitable initial guesses, and we investigate the dependence of the solution on the given trajectory segment (underlying the measured time series). To address the question of how many state variables have to be measured for optimal performance, different types of multivariate time series are considered consisting of 1, 2, or 3 variables. Based on these time series, the local observability of state variables and parameters of the Lorenz-96 model is investigated and confirmed using delay coordinates. This result is in good agreement with the observation that correct state and parameter estimation results are obtained if the optimization algorithm is initialized with initial guesses close to the true solution. In contrast, initialization with other exact solutions of the model equations (different from the true solution used to generate the time series) typically fails, i.e., the optimization procedure ends up in local minima different from the true solution. Initialization using random values in a box around the attractor exhibits success rates depending on the number of observables and the available time series (trajectory segment).
Feature Detection for Model Assessment in State Estimation
1991-10-15
Gong Combat Control Systems Department S. C. Nardone University of Massachusetts Dartmouth DTICS ELECTE JUL 141992 DlNA Naval Underwater Systems...Assessment in State Estimation .AUTHOR(S) D. J. Ferkinhoff S. C. Nardone * J. G. Baylog K. F. Gong 7. PERFORLMING ORGANIZATION NAME(S) AND ADORESS(ES...VA 22203 11. SUPPLEMENTARY NOTES *S. C. Nardone is affiliated with the University of Massachusetts Dartmouth, North Dartmouth, MA 02747. 12
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.
State estimation using parallel extended Kalman filters on nonlinear measurements
Sheives, T.C.
1980-01-01
The extended Kalman filter applied to state estimation using nonlinear measurements has demonstrated divergence in many applications involving large initial uncertainties because of invalid linearizations performed by the filter on the nonlinear measurement functions. No quantitative results can be found as to how invalid these linearizations must be before divergence occurs. An attempt is made to fill this void by deriving the extended Kalman filter as an approximate nonlinear least squares estimator, through the minimization of a measurement squared error function. The convergence of the extended Kalman filter is then determined by examining the squared error function and verifying the usefulness of this function through Monte Carlo simulation. The results of this analysis are then used to specify parameters for the Gaussian sum approximation. Simulation results for nonlinear measurements generated by harmonic and Gauss-Markov processes demonstrate that successful state estimation occurs using the Gaussian sum approximation, through proper parameter specification obtained by the convergence analysis. This technique is then applied to estimating the errors in an inertial navigation system through the use of nonlinear radar altimeter measurements and terrain elevation data. The results of applying this technique to terrain-aided navigation demonstrate that the Gaussian sum approximation provides good terrain-aided navigation performance for large initial uncertainties.
Adaptive sampling of CT data for myocardial blood flow estimation from dose-reduced dynamic CT
NASA Astrophysics Data System (ADS)
Modgil, Dimple; Bindschadler, Michael D.; Alessio, Adam M.; La Rivière, Patrick J.
2015-03-01
Quantification of myocardial blood flow (MBF) can aid in the diagnosis and treatment of coronary artery disease (CAD). However, there are no widely accepted clinical methods for estimating MBF. Dynamic CT holds the promise of providing a quick and easy method to measure MBF quantitatively, however the need for repeated scans has raised concerns about the potential for high radiation dose. In our previous work, we explored techniques to reduce the patient dose by either uniformly reducing the tube current or by uniformly reducing the number of temporal frames in the dynamic CT sequence. These dose reduction techniques result in very noisy data, which can give rise to large errors in MBF estimation. In this work, we seek to investigate whether nonuniformly varying the tube current or sampling intervals can yield more accurate MBF estimates. Specifically, we try to minimize the dose and obtain the most accurate MBF estimate through addressing the following questions: when in the time attenuation curve (TAC) should the CT data be collected and at what tube current(s). We hypothesize that increasing the sampling rate and/or tube current during the time frames when the myocardial CT number is most sensitive to the flow rate, while reducing them elsewhere, can achieve better estimation accuracy for the same dose. We perform simulations of contrast agent kinetics and CT acquisitions to evaluate the relative MBF estimation performance of several clinically viable adaptive acquisition methods. We found that adaptive temporal and tube current sequences can be performed that impart an effective dose of about 5 mSv and allow for reductions in MBF estimation RMSE on the order of 11% compared to uniform acquisition sequences with comparable or higher radiation doses.
Millimeter Wave MIMO Channel Estimation Using Overlapped Beam Patterns and Rate Adaptation
NASA Astrophysics Data System (ADS)
Kokshoorn, Matthew; Chen, He; Wang, Peng; Li, Yonghui; Vucetic, Branka
2017-02-01
This paper is concerned with the channel estimation problem in Millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent sparse nature of the mmWave channel, we first propose a fast channel estimation (FCE) algorithm based on a novel overlapped beam pattern design, which can increase the amount of information carried by each channel measurement and thus reduce the required channel estimation time compared to the existing non-overlapped designs. We develop a maximum likelihood (ML) estimator to optimally extract the path information from the channel measurements. Then, we propose a novel rate-adaptive channel estimation (RACE) algorithm, which can dynamically adjust the number of channel measurements based on the expected probability of estimation error (PEE). The performance of both proposed algorithms is analyzed. For the FCE algorithm, an approximate closed-form expression for the PEE is derived. For the RACE algorithm, a lower bound for the minimum signal energy-to-noise ratio required for a given number of channel measurements is developed based on the Shannon-Hartley theorem. Simulation results show that the FCE algorithm significantly reduces the number of channel estimation measurements compared to the existing algorithms using non-overlapped beam patterns. By adopting the RACE algorithm, we can achieve up to a 6dB gain in signal energy-to-noise ratio for the same PEE compared to the existing algorithms.
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.
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
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.
NASA Astrophysics Data System (ADS)
Rajaona, Harizo; Septier, François; Armand, Patrick; Delignon, Yves; Olry, Christophe; Albergel, Armand; Moussafir, Jacques
2015-12-01
In the eventuality of an accidental or intentional atmospheric release, the reconstruction of the source term using measurements from a set of sensors is an important and challenging inverse problem. A rapid and accurate estimation of the source allows faster and more efficient action for first-response teams, in addition to providing better damage assessment. This paper presents a Bayesian probabilistic approach to estimate the location and the temporal emission profile of a pointwise source. The release rate is evaluated analytically by using a Gaussian assumption on its prior distribution, and is enhanced with a positivity constraint to improve the estimation. The source location is obtained by the means of an advanced iterative Monte-Carlo technique called Adaptive Multiple Importance Sampling (AMIS), which uses a recycling process at each iteration to accelerate its convergence. The proposed methodology is tested using synthetic and real concentration data in the framework of the Fusion Field Trials 2007 (FFT-07) experiment. The quality of the obtained results is comparable to those coming from the Markov Chain Monte Carlo (MCMC) algorithm, a popular Bayesian method used for source estimation. Moreover, the adaptive processing of the AMIS provides a better sampling efficiency by reusing all the generated samples.
Lee, Jeon; Song, Mi-hye; Shin, Dong-gu; Lee, Kyoung-joung
2012-08-01
In this paper, an event synchronous adaptive filter (ESAF) is proposed to estimate atrial activity (AA) from a single-lead AF ECG in real time. The proposed ESAF is a kind of adaptive filter designed to have the reference fed with the impulse train synchronized with the R peak in a raw atrial fibrillation (AF) ECG and to input the timely delayed AF ECG into the primary input. To assess the performance, for ten simulated AF ECGs, the cross-correlation coefficient (ρ) and the normalized mean square error (NMSE) between estimated AAs and ten original simulated AAs were calculated and, for ten real AF ECGs, the ventricular residue (VR) in QRS interval and similarity (S) in non-QRS interval were computed. As a result, these four parameters were revealed as ρ = 0.938 ± 0.016 and NMSE = 0.243 ± 0.051 for simulated AF ECGs and VR = 1.190 ± 0.476 and S = 0.967 ± 0.041 for real AF ECGs. These results were found to be better than those of the averaged beat subtraction (ABS) method, which had been previously considered the only way to estimate AA automatically in real time. In conclusion, even with single-lead AF ECGs, the proposed method estimated AAs accurately and calculated the atrial fibrillatory frequencies, the most valuable index in AF maintenance and therapy evaluation, with a remarkably low computational cost.
Modeling and Automatic Feedback Control of Tremor: Adaptive Estimation of Deep Brain Stimulation
Rehan, Muhammad; Hong, Keum-Shik
2013-01-01
This paper discusses modeling and automatic feedback control of (postural and rest) tremor for adaptive-control-methodology-based estimation of deep brain stimulation (DBS) parameters. The simplest linear oscillator-based tremor model, between stimulation amplitude and tremor, is investigated by utilizing input-output knowledge. Further, a nonlinear generalization of the oscillator-based tremor model, useful for derivation of a control strategy involving incorporation of parametric-bound knowledge, is provided. Using the Lyapunov method, a robust adaptive output feedback control law, based on measurement of the tremor signal from the fingers of a patient, is formulated to estimate the stimulation amplitude required to control the tremor. By means of the proposed control strategy, an algorithm is developed for estimation of DBS parameters such as amplitude, frequency and pulse width, which provides a framework for development of an automatic clinical device for control of motor symptoms. The DBS parameter estimation results for the proposed control scheme are verified through numerical simulations. PMID:23638163
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 .
Absence of rapid sensory adaptation in neocortex during information processing states.
Castro-Alamancos, Manuel A
2004-02-05
One prominent feature of sensory responses in neocortex is that they rapidly adapt to increases in frequency, a process called "sensory adaptation." Here we show that sensory adaptation mainly occurs during quiescent states such as anesthesia, slow-wave sleep, and awake immobility. In contrast, during behavior-ally activated states, sensory responses are already adapted. For instance, during learning of a behavioral task, when an animal is very alert and expectant, sensory adaptation is mostly absent. After learning occurs, and the task becomes routine, the level of alertness lessens and sensory adaptation becomes robust. The primary sensory thalamocortical pathway of alert and expectant animals is in the adapted state, which may be required for adequate sensory information processing.
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
Estimated HIV Incidence in the United States, 2006–2009
Prejean, Joseph; Song, Ruiguang; Hernandez, Angela; Ziebell, Rebecca; Green, Timothy; Walker, Frances; Lin, Lillian S.; An, Qian; Mermin, Jonathan; Lansky, Amy; Hall, H. Irene
2011-01-01
Background The estimated number of new HIV infections in the United States reflects the leading edge of the epidemic. Previously, CDC estimated HIV incidence in the United States in 2006 as 56,300 (95% CI: 48,200–64,500). We updated the 2006 estimate and calculated incidence for 2007–2009 using improved methodology. Methodology We estimated incidence using incidence surveillance data from 16 states and 2 cities and a modification of our previously described stratified extrapolation method based on a sample survey approach with multiple imputation, stratification, and extrapolation to account for missing data and heterogeneity of HIV testing behavior among population groups. Principal Findings Estimated HIV incidence among persons aged 13 years and older was 48,600 (95% CI: 42,400–54,700) in 2006, 56,000 (95% CI: 49,100–62,900) in 2007, 47,800 (95% CI: 41,800–53,800) in 2008 and 48,100 (95% CI: 42,200–54,000) in 2009. From 2006 to 2009 incidence did not change significantly overall or among specific race/ethnicity or risk groups. However, there was a 21% (95% CI:1.9%–39.8%; p = 0.017) increase in incidence for people aged 13–29 years, driven by a 34% (95% CI: 8.4%–60.4%) increase in young men who have sex with men (MSM). There was a 48% increase among young black/African American MSM (12.3%–83.0%; p<0.001). Among people aged 13–29, only MSM experienced significant increases in incidence, and among 13–29 year-old MSM, incidence increased significantly among young, black/African American MSM. In 2009, MSM accounted for 61% of new infections, heterosexual contact 27%, injection drug use (IDU) 9%, and MSM/IDU 3%. Conclusions/Significance Overall, HIV incidence in the United States was relatively stable 2006–2009; however, among young MSM, particularly black/African American MSM, incidence increased. HIV continues to be a major public health burden, disproportionately affecting several populations in the United States, especially MSM and
Vision science and adaptive optics, the state of the field.
Marcos, Susana; Werner, John S; Burns, Stephen A; Merigan, William H; Artal, Pablo; Atchison, David A; Hampson, Karen M; Legras, Richard; Lundstrom, Linda; Yoon, Geungyoung; Carroll, Joseph; Choi, Stacey S; Doble, Nathan; Dubis, Adam M; Dubra, Alfredo; Elsner, Ann; Jonnal, Ravi; Miller, Donald T; Paques, Michel; Smithson, Hannah E; Young, Laura K; Zhang, Yuhua; Campbell, Melanie; Hunter, Jennifer; Metha, Andrew; Palczewska, Grazyna; Schallek, Jesse; Sincich, Lawrence C
2017-03-01
Adaptive optics is a relatively new field, yet it is spreading rapidly and allows new questions to be asked about how the visual system is organized. The editors of this feature issue have posed a series of question to scientists involved in using adaptive optics in vision science. The questions are focused on three main areas. In the first we investigate the use of adaptive optics for psychophysical measurements of visual system function and for improving the optics of the eye. In the second, we look at the applications and impact of adaptive optics on retinal imaging and its promise for basic and applied research. In the third, we explore how adaptive optics is being used to improve our understanding of the neurophysiology of the visual system.
NASA Astrophysics Data System (ADS)
Liu, Yingzhe; Wang, Jinxiang; Fu, Fangfa
2013-04-01
The H.264/AVC video standard adopts a fixed search range (SR) and fixed reference frame (RF) for motion estimation. These fixed settings result in a heavy computational load in the video encoder. We propose a dynamic SR and multiframe selection algorithm to improve the computational efficiency of motion estimation. By exploiting the relationship between the predicted motion vector and the SR size, we develop an adaptive SR adjustment algorithm. We also design a RF selection scheme based on the correlation between the different block sizes of the macroblock. Experimental results show that our algorithm can significantly reduce the computational complexity of motion estimation compared with the JM15.1 reference software, with a negligible decrease in peak signal-to-noise ratio and a slight increase in bit rate. Our algorithm also outperforms existing methods in terms of its low complexity and high coding quality.
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
p-adaption for compressible flow problems using a goal-based error estimator
NASA Astrophysics Data System (ADS)
Ekelschot, Dirk; Moxey, David; Peiro, Joaquim; Sherwin, Spencer
2014-11-01
We present an approach of applying p-adaption to compressible flow problems using a dual-weighted error estimator. This technique has been implemented in the high-order h/p spectral element library Nektar + + . The compressible solver uses a high-order discontinuous Galerkin (DG) discretization. This approach is generally considered to be expensive and that is why the introduced p-adaption technique aims for lowering the computational cost while preserving the high-order accuracy and the exponential convergence properties. The numerical fluxes between the elements are discontinuous which allows one to use a different polynomial order in each element. After identifying and localizing the sources of error, the order of approximation of the solution within the element is improved. The solution to the adjoint equations for the compressible Euler equations is used to weigh the local residual of the primal solution. This provides both the error in the target quantity, which is typically the lift or drag coefficient, and an indication on how sensitive the local solution is to the target quantity. The dual-weighted error within each element serves then as a local refinement indicator that drives the p-adaptive algorithm. The performance of this p-adaptive method is demonstrated using a test case of subsonic flow past a 3D wing geometry.
State of the art in adaptive control of robotic systems
NASA Technical Reports Server (NTRS)
Tosunoglu, Sabri; Tesar, Delbert
1988-01-01
An up-to-date assessment of adaptive control technology as applied to robotics is presented. Although the field is relatively new and does not yet represent a mature discipline, considerable attention for the design of sophisticated robot controllers has occured. In this presentation, adaptive control methods are divided into model reference adaptive systems and self-tuning regulators, with further definition of various approaches given in each class. The similarity and distinct features of the designed controllers are delineated and tabulated to enhance comparative review.
Adaptive System Identification for Estimating Future Glucose Concentrations and Hypoglycemia Alarms.
Eren-Oruklu, Meriyan; Cinar, Ali; Rollins, Derrick K; Quinn, Lauretta
2012-08-01
Many patients with diabetes experience high variability in glucose concentrations that includes prolonged hyperglycemia or hypoglycemia. Models predicting a subject's future glucose concentrations can be used for preventing such conditions by providing early alarms. This paper presents a time-series model that captures dynamical changes in the glucose metabolism. Adaptive system identification is proposed to estimate model parameters which enable the adaptation of the model to inter-/intra-subject variation and glycemic disturbances. It consists of online parameter identification using the weighted recursive least squares method and a change detection strategy that monitors variation in model parameters. Univariate models developed from a subject's continuous glucose measurements are compared to multivariate models that are enhanced with continuous metabolic, physical activity and lifestyle information from a multi-sensor body monitor. A real life application for the proposed algorithm is demonstrated on early (30 min in advance) hypoglycemia detection.
Lepine, Nicholas N; Tajima, Takuro; Ogasawara, Takayuki; Kasahara, Ryoichi; Koizumi, Hiroshi; Lepine, Nicholas N; Tajima, Takuro; Ogasawara, Takayuki; Kasahara, Ryoichi; Koizumi, Hiroshi; Koizumi, Hiroshi; Ogasawara, Takayuki; Tajima, Takuro; Kasahara, Ryoichi; Lepine, Nicholas N
2016-08-01
An adaptive Kalman filter-based fusion algorithm capable of estimating respiration rate for unobtrusive respiratory monitoring is proposed. Using both signal characteristics and a priori information, the Kalman filter is adaptively optimized to improve accuracy. Furthermore, the system is able to combine the respiration-related signals extracted from a textile ECG sensor and an accelerometer to create a single robust measurement. We measured derived respiratory rates and, when compared to a reference, found root-mean-square error of 2.11 breaths-per-minute (BrPM) while lying down, 2.30 BrPM while sitting, 5.97 BrPM while walking, and 5.98 BrPM while running. These results demonstrate that the proposed system is applicable to unobtrusive monitoring for various applications.
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.
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
Real-time Adaptive Kinematic Model Estimation of Concentric Tube Robots.
Kim, Chunwoo; Ryu, Seok Chang; Dupont, Pierre E
2015-01-01
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.
Low-Rank Decomposition Based Restoration of Compressed Images via Adaptive Noise Estimation.
Zhang, Xinfeng; Lin, Weisi; Xiong, Ruiqin; Liu, Xianming; Ma, Siwei; Gao, Wen
2016-07-07
Images coded at low bit rates in real-world applications usually suffer from significant compression noise, which significantly degrades the visual quality. Traditional denoising methods are not suitable for the content-dependent compression noise, which usually assume that noise is independent and with identical distribution. In this paper, we propose a unified framework of content-adaptive estimation and reduction for compression noise via low-rank decomposition of similar image patches. We first formulate the framework of compression noise reduction based upon low-rank decomposition. Compression noises are removed by soft-thresholding the singular values in singular value decomposition (SVD) of every group of similar image patches. For each group of similar patches, the thresholds are adaptively determined according to compression noise levels and singular values. We analyze the relationship of image statistical characteristics in spatial and transform domains, and estimate compression noise level for every group of similar patches from statistics in both domains jointly with quantization steps. Finally, quantization constraint is applied to estimated images to avoid over-smoothing. Extensive experimental results show that the proposed method not only improves the quality of compressed images obviously for post-processing, but are also helpful for computer vision tasks as a pre-processing 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.
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.
The rejection of vibrations in adaptive optics systems using a DFT-based estimation method
NASA Astrophysics Data System (ADS)
Kania, Dariusz; Borkowski, Józef
2016-04-01
Adaptive optics systems are commonly used in many optical structures to reduce perturbations and to increase the system performance. The problem in such systems is undesirable vibrations due to some effects as shaking of the whole structure or the tracking process. This paper presents a frequency, amplitude and phase estimation method of a multifrequency signal that can be used to reject these vibrations in an adaptive method. The estimation method is based on using the FFT procedure. The undesirable signals are usually exponentially damped harmonic oscillations. The estimation error depends on several parameters and consists of a systematic component and a random component. The systematic error depends on the signal phase, the number of samples N in a measurement window, the value of CiR (number of signal periods in a measurement window), the THD value and the time window order H. The random error depends mainly on the variance of noise and the SNR value. This paper shows research on the sinusoidal signal phase and the estimation of exponentially damped sinusoids parameters. The shape of errors signals is periodical and it is associated with the signal period and with the sliding measurement window. For CiR=1.6 and the damping ratio 0.1% the error was in the order of 10-5 Hz/Hz, 10-4 V/V and 10-4 rad for the frequency, the amplitude and the phase estimation respectively. The information provided in this paper can be used to determine the approximate level of the efficiency of the vibrations elimination process before starting it.
Estimated use of water in the United States, 1965
Murray, Charles Richard
1968-01-01
Estimates of water use in the United States for 1965 indicate that an average of about 310 bgd (billion gallons per day) were withdrawn for public-supply, rural domestic and livestock, irrigation, and industrial (including thermoelectric power)uses--that is, about 1,600 gallons per capita per day. This represents an increase of 15 percent over the withdrawal of 270 bgd reported for 1960. Fresh water withdrawals for thermoelectric power generation increased nearly 25 percent during the 5 years, and saline water withdrawals increased 33 percent. An additional 2,300 bgd was used for hydroelectric power generation (waterpower), which also represented a 15-percent increase in 5 years. The quantity of water consumed-that is, water made unavailable for further possible withdrawal because of evaporation, incorporation in manufactured products, and other causes - was estimated to average 78 bgd for 1965, an increase of about 28 percent since 1960.Estimates made of the quantities of water withdrawn from surface and ground-water sources indicate withdrawals of 61 bgd of ground water, of which nearly 0.5 bgd was saline, and 250 bgd of surface water, of which 44 bgd was saline. The estimated amount of saline water used by industry increased 36 percent from 1960 to 1965. In addition to surface and ground water sources, reclaimed sewage supplied two-thirds of a billion gallons per day, mainly to irrigation and industry.The average annual streamflow in the United States is approximately 1,200 bgd, about four times the amount withdrawn for all purposes (except hydroelectric power) in 1965, and more than 15 times the estimated quantity of water consumed. However, comparisons of supply and demand in many river basins show that repeated use of the water is made, and at times in some basins all the available supply is consumed.In addition to tabulations of water-use data by States and by the water-use regions previously used, water-use tables are also given for the regions recently
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
An adaptive ARX model to estimate the RUL of aluminum plates based on its crack growth
NASA Astrophysics Data System (ADS)
Barraza-Barraza, Diana; Tercero-Gómez, Víctor G.; Beruvides, Mario G.; Limón-Robles, Jorge
2017-01-01
A wide variety of Condition-Based Maintenance (CBM) techniques deal with the problem of predicting the time for an asset fault. Most statistical approaches rely on historical failure data that might not be available in several practical situations. To address this issue, practitioners might require the use of self-starting approaches that consider only the available knowledge about the current degradation process and the asset operating context to update the prognostic model. Some authors use Autoregressive (AR) models for this purpose that are adequate when the asset operating context is constant, however, if it is variable, the accuracy of the models can be affected. In this paper, three autoregressive models with exogenous variables (ARX) were constructed, and their capability to estimate the remaining useful life (RUL) of a process was evaluated following the case of the aluminum crack growth problem. An existing stochastic model of aluminum crack growth was implemented and used to assess RUL estimation performance of the proposed ARX models through extensive Monte Carlo simulations. Point and interval estimations were made based only on individual history, behavior, operating conditions and failure thresholds. Both analytic and bootstrapping techniques were used in the estimation process. Finally, by including recursive parameter estimation and a forgetting factor, the ARX methodology adapts to changing operating conditions and maintain the focus on the current degradation level of an asset.
Shen, Yi
2013-05-01
A subject's sensitivity to a stimulus variation can be studied by estimating the psychometric function. Generally speaking, three parameters of the psychometric function are of interest: the performance threshold, the slope of the function, and the rate at which attention lapses occur. In the present study, three psychophysical procedures were used to estimate the three-parameter psychometric function for an auditory gap detection task. These were an up-down staircase (up-down) procedure, an entropy-based Bayesian (entropy) procedure, and an updated maximum-likelihood (UML) procedure. Data collected from four young, normal-hearing listeners showed that while all three procedures provided similar estimates of the threshold parameter, the up-down procedure performed slightly better in estimating the slope and lapse rate for 200 trials of data collection. When the lapse rate was increased by mixing in random responses for the three adaptive procedures, the larger lapse rate was especially detrimental to the efficiency of the up-down procedure, and the UML procedure provided better estimates of the threshold and slope than did the other two procedures.
State estimation for autonomous flight in cluttered environments
NASA Astrophysics Data System (ADS)
Langelaan, Jacob Willem
Safe, autonomous operation in complex, cluttered environments is a critical challenge facing autonomous mobile systems. The research described in this dissertation was motivated by a particularly difficult example of autonomous mobility: flight of a small Unmanned Aerial Vehicle (UAV) through a forest. In cluttered environments (such as forests or natural and urban canyons) signals from navigation beacons such as GPS may frequently be occluded. Direct measurements of vehicle position are therefore unavailable, and information required for flight control, obstacle avoidance, and navigation must be obtained using only on-board sensors. However, payload limitations of small UAVs restrict both the mass and physical dimensions of sensors that can be carried. This dissertation describes the development and proof-of-concept demonstration of a navigation system that uses only a low-cost inertial measurement unit and a monocular camera. Micro electromechanical inertial measurements units are well suited to small UAV applications and provide measurements of acceleration and angular rate. However, they do not provide information about nearby obstacles (needed for collision avoidance) and their noise and bias characteristics lead to unbounded growth in computed position. A monocular camera can provide bearings to nearby obstacles and landmarks. These bearings can be used both to enable obstacle avoidance and to aid navigation. Presented here is a solution to the problem of estimating vehicle state (position, orientation and velocity) as well as positions of obstacles in the environment using only inertial measurements and bearings to obstacles. This is a highly nonlinear estimation problem, and standard estimation techniques such as the Extended Kalman Filter are prone to divergence in this application. In this dissertation a Sigma Point Kalman Filter is implemented, resulting in an estimator which is able to cope with the significant nonlinearities in the system equations and
Treatment of inequality constraints in power system state estimation
Clements, K.A.; Davis, P.W.; Frey, K.D.
1995-05-01
A new formulation of the power system state estimation problem and a new solution technique are presented. The formulation allows for inequality constraints such as Var limits on generators and transformer tap ratio limits. In addition, unmeasured loads can be modeled as unknown but bounded quantities. The solution technique is an interior point method that uses logarithmic barrier functions to treat the inequality constraints. The authors describe computational issues arising in the implementation of the algorithm. Numerical results are given for systems ranging in size from six to 118 buses.
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.
Low-power metabolic equivalents estimation algorithm using adaptive acceleration sampling.
Tsukahara, Mio; Nakanishi, Motofumi; Izumi, Shintaro; Nakai, Yozaburo; Kawaguchi, Hiroshi; Yoshimoto, Masahiko; Tsukahara, Mio; Nakanishi, Motofumi; Izumi, Shintaro; Nakai, Yozaburo; Kawaguchi, Hiroshi; Yoshimoto, Masahiko; Izumi, Shintaro; Nakai, Yozaburo; Kawaguchi, Hiroshi; Yoshimoto, Masahiko; Tsukahara, Mio; Nakanishi, Motofumi
2016-08-01
This paper describes a proposed low-power metabolic equivalent estimation algorithm that can calculate the value of metabolic equivalents (METs) from triaxial acceleration at an adaptively changeable sampling rate. This algorithm uses four rates of 32, 16, 8 and 4 Hz. The mode of switching them is decided from synthetic acceleration. Applying this proposed algorithm to acceleration measured for 1 day, we achieved the low root mean squared error (RMSE) of calculated METs, with current consumption that was 41.5 % of the value at 32 Hz, and 75.4 % of the value at 16 Hz.
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.
Global coupled sea ice-ocean state estimation
NASA Astrophysics Data System (ADS)
Fenty, Ian; Menemenlis, Dimitris; Zhang, Hong
2015-09-01
We study the impact of synthesizing ocean and sea ice concentration data with a global, eddying coupled sea ice-ocean configuration of the Massachusetts Institute of Technology general circulation model with the goal of reproducing the 2004 three-dimensional time-evolving ice-ocean state. This work builds on the state estimation framework developed in the Estimating the Circulation and Climate of the Ocean consortium by seeking a reconstruction of the global sea ice-ocean system that is simultaneously consistent with (1) a suite of in situ and remotely-sensed ocean and ice data and (2) the physics encoded in the numerical model. This dual consistency is successfully achieved here by adjusting only the model's initial hydrographic state and its atmospheric boundary conditions such that misfits between the model and data are minimized in a least-squares sense. We show that synthesizing both ocean and sea ice concentration data is required for the model to adequately reproduce the observed details of the sea ice annual cycle in both hemispheres. Surprisingly, only modest adjustments to our first-guess atmospheric state and ocean initial conditions are necessary to achieve model-data consistency, suggesting that atmospheric reanalysis products remain a leading source of errors for sea ice-ocean model hindcasts and reanalyses. The synthesis of sea ice data is found to ameliorate misfits in the high latitude ocean, especially with respect to upper ocean stratification, temperature, and salinity. Constraining the model to sea ice concentration modestly reduces ICESat-derived Arctic ice thickness errors by improving the temporal and spatial evolution of seasonal ice. Further increases in the accuracy of global sea ice thickness in the model likely require the direct synthesis of sea ice thickness data.
Performance bounds on micro-Doppler estimation and adaptive waveform design using OFDM signals
NASA Astrophysics Data System (ADS)
Sen, Satyabrata; Barhen, Jacob; Glover, Charles W.
2014-05-01
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 Craḿer-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.
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.
Adaptive λ estimation in Lagrangian rate-distortion optimization for video coding
NASA Astrophysics Data System (ADS)
Chen, Lulin; Garbacea, Ilie
2006-01-01
In this paper, adaptive Lagrangian multiplier λ estimation in Larangian R-D optimization for video coding is presented that is based on the ρ-domain linear rate model and distortion model. It yields that λ is a function of rate, distortion and coding input statistics and can be written as λ(R, D, σ2) = β(ln(σ2/D) + δ)D/R + k 0, with β, δ and k 0 as coding constants, σ2 is variance of prediction error input. λ(R, D, σ2) describes its ubiquitous relationship with coding statistics and coding input in hybrid video coding such as H.263, MPEG-2/4 and H.264/AVC. The lambda evaluation is de-coupled with quantization parameters. The proposed lambda estimation enables a fine encoder design and encoder control.
NASA Astrophysics Data System (ADS)
Kang, Je-Won; Ryu, Soo-Kyung
2017-02-01
In this paper a sample-adaptive prediction technique is proposed to yield efficient coding performance in an intracoding for screen content video coding. The sample-based prediction is to reduce spatial redundancies in neighboring samples. To this aim, the proposed technique uses a weighted linear combination of neighboring samples and applies the robust optimization technique, namely, ridge estimation to derive the weights in a decoder side. The ridge estimation uses L2 norm based regularization term, and, thus the solution is more robust to high variance samples such as in sharp edges and high color contrasts exhibited in screen content videos. It is demonstrated with the experimental results that the proposed technique provides an improved coding gain as compared to the HEVC screen content video coding reference software.
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
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.
Adaptive external torque estimation by means of tracking a Lyapunov function
Schaub, H.; Junkins, J.L.; Robinett, R.D.
1996-03-01
A real-time method is presented to adoptively estimate three-dimensional unmodeled external torques acting on a spacecraft. This is accomplished by forcing the tracking error dynamics to follow the Lyapunov function underlying the feedback control law. For the case where the external torque is constant, the tracking error dynamics are shown to converge asypmtotically. The methodology applies not only to the control law used in this paper, but can also be applied to most Lyapunov derived feedback control laws. The adaptive external torque estimation is very robust in the presence of measurement noise, since a numerical integration is used instead of a numerical differentiation. Spacecraft modeling errors, such as in the inertia matrix, are also compensated for by this method. Several examples illustrate the practical significance of these ideas.
1983-03-01
AN ANALYSIS OF A FINITE ELEMENT METHOD FOR CONVECTION- DIFFUSION PROBLEMS PART II: A POSTERIORI ERROR ESTIMATES AND ADAPTIVITY by W. G. Szymczak Y 6a...PERIOD COVERED AN ANALYSIS OF A FINITE ELEMENT METHOD FOR final life of the contract CONVECTION- DIFFUSION PROBLEM S. Part II: A POSTERIORI ERROR ...Element Method for Convection- Diffusion Problems. Part II: A Posteriori Error Estimates and Adaptivity W. G. Szvmczak and I. Babu~ka# Laboratory for
Unbiased Estimation of Refractive State of Aberrated Eyes
Martin, Jesson; Vasudevan, Balamurali; Himebaugh, Nikole; Bradley, Arthur; Thibos, Larry
2011-01-01
To identify unbiased methods for estimating the target vergence required to maximize visual acuity based on wavefront aberration measurements. Experiments were designed to minimize the impact of confounding factors that have hampered previous research. Objective wavefront refractions and subjective acuity refractions were obtained for the same monochromatic wavelength. Accommodation and pupil fluctuations were eliminated by cycloplegia. Unbiased subjective refractions that maximize visual acuity for high contrast letters were performed with a computer controlled forced choice staircase procedure, using 0.125 diopter steps of defocus. All experiments were performed for two pupil diameters (3mm and 6mm). As reported in the literature, subjective refractive error does not change appreciably when the pupil dilates. For 3 mm pupils most metrics yielded objective refractions that were about 0.1D more hyperopic than subjective acuity refractions. When pupil diameter increased to 6 mm, this bias changed in the myopic direction and the variability between metrics also increased. These inaccuracies were small compared to the precision of the measurements, which implies that most metrics provided unbiased estimates of refractive state for medium and large pupils. A variety of image quality metrics may be used to determine ocular refractive state for monochromatic (635nm) light, thereby achieving accurate results without the need for empirical correction factors. PMID:21777601
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.
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, 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.
Improving Distribution Resiliency with Microgrids and State and Parameter Estimation
Tuffner, Francis K.; Williams, Tess L.; Schneider, Kevin P.; Elizondo, Marcelo A.; Sun, Yannan; Liu, Chen-Ching; Xu, Yin; Gourisetti, Sri Nikhil Gup
2015-09-30
Modern society relies on low-cost reliable electrical power, both to maintain industry, as well as provide basic social services to the populace. When major disturbances occur, such as Hurricane Katrina or Hurricane Sandy, the nation’s electrical infrastructure can experience significant outages. To help prevent the spread of these outages, as well as facilitating faster restoration after an outage, various aspects of improving the resiliency of the power system are needed. Two such approaches are breaking the system into smaller microgrid sections, and to have improved insight into the operations to detect failures or mis-operations before they become critical. Breaking the system into smaller sections of microgrid islands, power can be maintained in smaller areas where distribution generation and energy storage resources are still available, but bulk power generation is no longer connected. Additionally, microgrid systems can maintain service to local pockets of customers when there has been extensive damage to the local distribution system. However, microgrids are grid connected a majority of the time and implementing and operating a microgrid is much different than when islanded. This report discusses work conducted by the Pacific Northwest National Laboratory that developed improvements for simulation tools to capture the characteristics of microgrids and how they can be used to develop new operational strategies. These operational strategies reduce the cost of microgrid operation and increase the reliability and resilience of the nation’s electricity infrastructure. In addition to the ability to break the system into microgrids, improved observability into the state of the distribution grid can make the power system more resilient. State estimation on the transmission system already provides great insight into grid operations and detecting abnormal conditions by leveraging existing measurements. These transmission-level approaches are expanded to using
Improving our legacy: incorporation of adaptive management into state wildlife action plans.
Fontaine, Joseph J
2011-05-01
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.
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 .
NASA Astrophysics Data System (ADS)
Owens, A. R.; Kópházi, J.; Welch, J. A.; Eaton, M. D.
2017-04-01
In this paper a hanging-node, discontinuous Galerkin, isogeometric discretisation of the multigroup, discrete ordinates (SN) equations is presented in which each energy group has its own mesh. The equations are discretised using Non-Uniform Rational B-Splines (NURBS), which allows the coarsest mesh to exactly represent the geometry for a wide range of engineering problems of interest; this would not be the case using straight-sided finite elements. Information is transferred between meshes via the construction of a supermesh. This is a non-trivial task for two arbitrary meshes, but is significantly simplified here by deriving every mesh from a common coarsest initial mesh. In order to take full advantage of this flexible discretisation, goal-based error estimators are derived for the multigroup, discrete ordinates equations with both fixed (extraneous) and fission sources, and these estimators are used to drive an adaptive mesh refinement (AMR) procedure. The method is applied to a variety of test cases for both fixed and fission source problems. The error estimators are found to be extremely accurate for linear NURBS discretisations, with degraded performance for quadratic discretisations owing to a reduction in relative accuracy of the ;exact; adjoint solution required to calculate the estimators. Nevertheless, the method seems to produce optimal meshes in the AMR process for both linear and quadratic discretisations, and is ≈×100 more accurate than uniform refinement for the same amount of computational effort for a 67 group deep penetration shielding problem.
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.
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
HIV Trends in the United States: Diagnoses and Estimated Incidence
Song, Ruiguang; Tang, Tian; An, Qian; Prejean, Joseph; Dietz, Patricia; Hernandez, Angela L; Green, Timothy; Harris, Norma; McCray, Eugene; Mermin, Jonathan
2017-01-01
Background The best indicator of the impact of human immunodeficiency virus (HIV) prevention programs is the incidence of infection; however, HIV is a chronic infection and HIV diagnoses may include infections that occurred years before diagnosis. Alternative methods to estimate incidence use diagnoses, stage of disease, and laboratory assays of infection recency. Using a consistent, accurate method would allow for timely interpretation of HIV trends. Objective The objective of our study was to assess the recent progress toward reducing HIV infections in the United States overall and among selected population segments with available incidence estimation methods. Methods Data on cases of HIV infection reported to national surveillance for 2008-2013 were used to compare trends in HIV diagnoses, unadjusted and adjusted for reporting delay, and model-based incidence for the US population aged ≥13 years. Incidence was estimated using a biomarker for recency of infection (stratified extrapolation approach) and 2 back-calculation models (CD4 and Bayesian hierarchical models). HIV testing trends were determined from behavioral surveys for persons aged ≥18 years. Analyses were stratified by sex, race or ethnicity (black, Hispanic or Latino, and white), and transmission category (men who have sex with men, MSM). Results On average, HIV diagnoses decreased 4.0% per year from 48,309 in 2008 to 39,270 in 2013 (P<.001). Adjusting for reporting delays, diagnoses decreased 3.1% per year (P<.001). The CD4 model estimated an annual decrease in incidence of 4.6% (P<.001) and the Bayesian hierarchical model 2.6% (P<.001); the stratified extrapolation approach estimated a stable incidence. During these years, overall, the percentage of persons who ever had received an HIV test or had had a test within the past year remained stable; among MSM testing increased. For women, all 3 incidence models corroborated the decreasing trend in HIV diagnoses, and HIV diagnoses and 2 incidence
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.
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.
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
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
Dutra Filho, J A; Junior, T C; Simões Neto, D E
2014-08-29
We assessed the agroindustrial performance of 25 sugarcane genotypes adapted to the edaphoclimatic conditions of the State of Pernambuco, Brazil, within the microregions Mata Norte, Mata Sul, Região Central, Litoral Norte, and Litoral Sul. The variables analyzed were POL tonnage per hectare, sugarcane tonnage per hectare, fiber and total recoverable sugar tonnage per hectare, using a randomized block design with four repetitions. Combined variance of experiments, genetic parameter estimates, decomposition of the genotype-environment interaction, and environment stratification were analyzed. Phenotype adaptability and stability were also analyzed. The various genotypes presented great potential for improvement and a similar response pattern to the microregions Centro and Mata Sul of the state of Pernambuco. Genotypes RB863129, RB867515, RB92579, RB953180, SP81-3250, RB75126, and RB942520 were better in productivity and phenotype adaptability and stability compared to genotypes RB892700, RB943365, SP79-1011, Q138, RB943538, SP78-4764, RB953281, RB943066, RB928064, RB93509, RB72454, RB952675, RB952991, RB943161, RB942898, RB872552, RB952900, and RB942849. These genotypes are recommended as cultivation options in the sugarcane belt in the state of Pernambuco, since they stand out in terms of phenotype adaptability and stability as evaluated using the method by Annicchiarico, Lin and Bins, and the method by Eberhart and Russel.
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…
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…
The Role of Operator State Assessment in Adaptive Automation
2005-12-01
rate, and blink rate respiration rate. Prinzel, Freeman, Scerbo, Milkulka, & Pope (1998) looked at Event Related Potentials ( ERP ) as another...psychophysiological measure for adaptive aiding. This is a change in the electro encephalogram (EEG) after a specific event. Several components in the ERP ...P300 component and perceptual/cognitive processing demands were on ERPs elicited on the secondary task; a method that is therefore intrusive. The
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.
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.
State estimation Kalman filter using optical processings Noise statistics known
NASA Technical Reports Server (NTRS)
Jackson, J.; Casasent, D.
1984-01-01
Reference is made to a study by Casasent et al. (1983), which gave a description of a frequency-multiplexed acoustooptic processor and showed how it was capable of performing all the individual operations required in Kalman filtering. The data flow and organization of all required operations however, were not detailed in that study. Consideration is given here to a simpler Kalman filter state estimation problem. Equally spaced time-sampled intervals (k times T sub s, with k the iterative time index) are assumed. It is further assumed that the system noise vector w and the measurement noise vector v are uncorrelated and Gaussian distributed and that the noise statistics (Q and R) and the system model (Phi, Gamma, H) are known. The error covariance matrix P and the extrapolated error covariance matrix M can thus be precomputed and the Kalman gain matrix K sub k can be precomputed and stored for each input time sample.
Estimated use of water in the United States - 1950
MacKichan, Kenneth Allen
1951-01-01
An estimated 170,000 million gallons of water was withdrawn from the ground, lakes, or streams each day on the average during 1950 and used on the farms and in the homes, factories, and business establishments of the United States. An additional 1,100,000 million gallons per day was used to generate hydro-power. Water power is the largest user of water; however, irrigation and industry also are large users of both ground and surface water. More surface water was used for industrial purposes than for irrigation, whereas more ground water was used for irrigation than for industrial purposes (fig. 1). The total withdrawal of surface water was considerably in excess of ground-water withdrawal, as shown by figure 1. Large quantities of water were used also for purposes requiring no diversion, such as navigation, waste disposal, recreation, and support of wildlife.
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
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.
Enzymatic Synthesis of Ampicillin: Nonlinear Modeling, Kinetics Estimation, and Adaptive Control
Roman, Monica; Selişteanu, Dan
2012-01-01
Nowadays, the use of advanced control strategies in biotechnology is quite low. A main reason is the lack of quality of the data, and the fact that more sophisticated control strategies must be based on a model of the dynamics of bioprocesses. The nonlinearity of the bioprocesses and the absence of cheap and reliable instrumentation require an enhanced modeling effort and identification strategies for the kinetics. The present work approaches modeling and control strategies for the enzymatic synthesis of ampicillin that is carried out inside a fed-batch bioreactor. First, a nonlinear dynamical model of this bioprocess is obtained by using a novel modeling procedure for biotechnology: the bond graph methodology. Second, a high gain observer is designed for the estimation of the imprecisely known kinetics of the synthesis process. Third, by combining an exact linearizing control law with the on-line estimation kinetics algorithm, a nonlinear adaptive control law is designed. The case study discussed shows that a nonlinear feedback control strategy applied to the ampicillin synthesis bioprocess can cope with disturbances, noisy measurements, and parametric uncertainties. Numerical simulations performed with MATLAB environment are included in order to test the behavior and the performances of the proposed estimation and control strategies. PMID:22523470
Pilot-Assisted Adaptive Channel Estimation for Coded MC-CDMA with ICI Cancellation
NASA Astrophysics Data System (ADS)
Yui, Tatsunori; Tomeba, Hiromichi; Adachi, Fumiyuki
One of the promising wireless access techniques for the next generation mobile communications systems is multi-carrier code division multiple access (MC-CDMA). MC-CDMA can provide good transmission performance owing to the frequency diversity effect in a severe frequency-selective fading channel. However, the bit error rate (BER) performance of coded MC-CDMA is inferior to that of orthogonal frequency division multiplexing (OFDM) due to the residual inter-code interference (ICI) after frequency-domain equalization (FDE). Recently, we proposed a frequency-domain soft interference cancellation (FDSIC) to reduce the residual ICI and confirmed by computer simulation that the MC-CDMA with FDSIC provides better BER performance than OFDM. However, ideal channel estimation was assumed. In this paper, we propose adaptive decision-feedback channel estimation (ADFCE) and evaluate by computer simulation the average BER and throughput performances of turbo-coded MC-CDMA with FDSIC. We show that even if a practical channel estimation is used, MC-CDMA with FDSIC can still provide better performance than OFDM.
Enzymatic synthesis of ampicillin: nonlinear modeling, kinetics estimation, and adaptive control.
Roman, Monica; Selişteanu, Dan
2012-01-01
Nowadays, the use of advanced control strategies in biotechnology is quite low. A main reason is the lack of quality of the data, and the fact that more sophisticated control strategies must be based on a model of the dynamics of bioprocesses. The nonlinearity of the bioprocesses and the absence of cheap and reliable instrumentation require an enhanced modeling effort and identification strategies for the kinetics. The present work approaches modeling and control strategies for the enzymatic synthesis of ampicillin that is carried out inside a fed-batch bioreactor. First, a nonlinear dynamical model of this bioprocess is obtained by using a novel modeling procedure for biotechnology: the bond graph methodology. Second, a high gain observer is designed for the estimation of the imprecisely known kinetics of the synthesis process. Third, by combining an exact linearizing control law with the on-line estimation kinetics algorithm, a nonlinear adaptive control law is designed. The case study discussed shows that a nonlinear feedback control strategy applied to the ampicillin synthesis bioprocess can cope with disturbances, noisy measurements, and parametric uncertainties. Numerical simulations performed with MATLAB environment are included in order to test the behavior and the performances of the proposed estimation and control strategies.
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.
Link-state-estimation-based transmission power control in wireless body area networks.
Kim, Seungku; Eom, Doo-Seop
2014-07-01
This paper presents a novel transmission power control protocol to extend the lifetime of sensor nodes and to increase the link reliability in wireless body area networks (WBANs). We first experimentally investigate the properties of the link states using the received signal strength indicator (RSSI). We then propose a practical transmission power control protocol based on both short- and long-term link-state estimations. Both the short- and long-term link-state estimations enable the transceiver to adapt the transmission power level and target the RSSI threshold range, respectively, to simultaneously satisfy the requirements of energy efficiency and link reliability. Finally, the performance of the proposed protocol is experimentally evaluated in two experimental scenarios-body posture change and dynamic body motion-and compared with the typical WBAN transmission power control protocols, a real-time reactive scheme, and a dynamic postural position inference mechanism. From the experimental results, it is found that the proposed protocol increases the lifetime of the sensor nodes by a maximum of 9.86% and enhances the link reliability by reducing the packet loss by a maximum of 3.02%.
1996-12-01
discussion of the turbulence levels considered or their overall effect on the pose estimation algorithm (6:4). Additionally, the report states that their...is locally maximized at edges in the image. Therefore, by establishing an appropriate tolerance value, it is possible to use the Sobel technique to...They show their algorithm to be optimal in terms of both signal to noise ratio (SNR) and edge localization accuracy. The advantage of their
Local mapping of detector response for reliable quantum state estimation
Cooper, Merlin; Karpiński, Michał; Smith, Brian J.
2014-01-01
Improved measurement techniques are central to technological development and foundational scientific exploration. Quantum physics relies on detectors sensitive to non-classical features of systems, enabling precise tests of physical laws and quantum-enhanced technologies including precision measurement and secure communications. Accurate detector response calibration for quantum-scale inputs is key to future research and development in these cognate areas. To address this requirement, quantum detector tomography has been recently introduced. However, this technique becomes increasingly challenging as the complexity of the detector response and input space grow in a number of measurement outcomes and required probe states, leading to further demands on experiments and data analysis. Here we present an experimental implementation of a versatile, alternative characterization technique to address many-outcome quantum detectors that limits the input calibration region and does not involve numerical post processing. To demonstrate the applicability of this approach, the calibrated detector is subsequently used to estimate non-classical photon number states. PMID:25019300
Koenig, Alexander; Omlin, Ximena; Zimmerli, Lukas; Sapa, Mark; Krewer, Carmen; Bolliger, Marc; Müller, Friedemann; Riener, Robert
2011-01-01
Robot-assisted treadmill training is an established intervention used to improve walking ability in patients with neurological disorders. Although it has been shown that attention to the task is a key factor for successful rehabilitation, the psychological state of patients during robot-assisted gait therapy is often neglected. We presented 17 nondisabled subjects and 10 patients with neurological disorders a virtual-reality task with varying difficulty levels to induce feelings of being bored, excited, and overstressed. We developed an approach to automatically estimate and classify a patient's psychological state, i.e., his or her mental engagement, in real time during gait training. We used psychophysiological measurements to obtain an objective measure of the current psychological state. Automatic classification was performed by a neural network. We found that heart rate, skin conductance responses, and skin temperature can be used as markers for psychological states in the presence of physical effort induced by walking. The classifier achieved a classification error of 1.4% for nondisabled subjects and 2.1% for patients with neurological disorders. Using our new method, we processed the psychological state data in real time. Our method is a first step toward real-time auto-adaptive gait training with potential to improve rehabilitation results by optimally challenging patients at all times during exercise.
Signal Quality Estimation With Multichannel Adaptive Filtering in Intensive Care Settings
Lee, Joon; Mark, Roger G.
2013-01-01
A signal quality estimate of a physiological waveform can be an important initial step for automated processing of real-world data. This paper presents a new generic point-by-point signal quality index (SQI) based on adaptive multichannel prediction that does not rely on ad hoc morphological feature extraction from the target waveform. An application of this new SQI to photoplethysmograms (PPG), arterial blood pressure (ABP) measurements, and ECG showed that the SQI is monotonically related to signal-to-noise ratio (simulated by adding white Gaussian noise) and to subjective human quality assessment of 1361 multichannel waveform epochs. A receiver-operating-characteristic (ROC) curve analysis, with the human “bad” quality label as positive and the “good” quality label as negative, yielded areas under the ROC curve of 0.86 (PPG), 0.82 (ABP), and 0.68 (ECG). PMID:22717504
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.
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
Developing a new software package for PSF estimation and fitting of adaptive optics images
NASA Astrophysics Data System (ADS)
Schreiber, Laura; Diolaiti, Emiliano; Sollima, Antonio; Arcidiacono, Carmelo; Bellazzini, Michele; Ciliegi, Paolo; Falomo, Renato; Foppiani, Italo; Greggio, Laura; Lanzoni, Barbara; Lombini, Matteo; Montegriffo, Paolo; Dalessandro, Emanuele; Massari, Davide
2012-07-01
Adaptive Optics (AO) images are characterized by structured Point Spread Function (PSF), with sharp core and extended halo, and by significant variations across the field of view. In order to enable the extraction of high-precision quantitative information and improve the scientific exploitation of AO data, efforts in the PSF modeling and in the integration of suitable models in a code for image analysis are needed. We present the current status of a study on the modeling of AO PSFs based on observational data taken with present telescopes (VLT and LBT). The methods under development include parametric models and hybrid (i.e. analytical / numerical) models adapted to various types of PSFs that can show up in AO images. The specific features of AO data, such as the mainly radial variation of the PSF with respect to the guide star position in single-reference AO, are taken into account as much as possible. The final objective of this project is the development of a flexible software package, based on the Starfinder code (Diolaiati et Al 2000), specifically dedicated to the PSF estimation and to the astrometric and photometric analysis of AO images with complex and spatially variable PSF.
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.
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.
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.
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.
Online estimation of the wavefront outer scale profile from adaptive optics telemetry
NASA Astrophysics Data System (ADS)
Guesalaga, A.; Neichel, B.; Correia, C. M.; Butterley, T.; Osborn, J.; Masciadri, E.; Fusco, T.; Sauvage, J.-F.
2017-02-01
We describe an online method to estimate the wavefront outer scale profile, L0(h), for very large and future extremely large telescopes. The stratified information on this parameter impacts the estimation of the main turbulence parameters [turbulence strength, Cn2(h); Fried's parameter, r0; isoplanatic angle, θ0; and coherence time, τ0) and determines the performance of wide-field adaptive optics (AO) systems. This technique estimates L0(h) using data from the AO loop available at the facility instruments by constructing the cross-correlation functions of the slopes between two or more wavefront sensors, which are later fitted to a linear combination of the simulated theoretical layers having different altitudes and outer scale values. We analyse some limitations found in the estimation process: (i) its insensitivity to large values of L0(h) as the telescope becomes blind to outer scales larger than its diameter; (ii) the maximum number of observable layers given the limited number of independent inputs that the cross-correlation functions provide and (iii) the minimum length of data required for a satisfactory convergence of the turbulence parameters without breaking the assumption of statistical stationarity of the turbulence. The method is applied to the Gemini South multiconjugate AO system that comprises five wavefront sensors and two deformable mirrors. Statistics of L0(h) at Cerro Pachón from data acquired during 3 yr of campaigns show interesting resemblance to other independent results in the literature. A final analysis suggests that the impact of error sources will be substantially reduced in instruments of the next generation of giant telescopes.
NASA Astrophysics Data System (ADS)
Yu, Xiaolin; Zhang, Shaoqing; Lin, Xiaopei; Li, Mingkui
2017-03-01
The uncertainties in values of coupled model parameters are an important source of model bias that causes model climate drift. The values can be calibrated by a parameter estimation procedure that projects observational information onto model parameters. The signal-to-noise ratio of error covariance between the model state and the parameter being estimated directly determines whether the parameter estimation succeeds or not. With a conceptual climate model that couples the stochastic atmosphere and slow-varying ocean, this study examines the sensitivity of state-parameter covariance on the accuracy of estimated model states in different model components of a coupled system. Due to the interaction of multiple timescales, the fast-varying atmosphere
with a chaotic nature is the major source of the inaccuracy of estimated state-parameter covariance. Thus, enhancing the estimation accuracy of atmospheric states is very important for the success of coupled model parameter estimation, especially for the parameters in the air-sea interaction processes. The impact of chaotic-to-periodic ratio in state variability on parameter estimation is also discussed. This simple model study provides a guideline when real observations are used to optimize model parameters in a coupled general circulation model for improving climate analysis and predictions.
Neural adaptive chaotic control with constrained input using state and output feedback
NASA Astrophysics Data System (ADS)
Gao, Shi-Gen; Dong, Hai-Rong; Sun, Xu-Bin; Ning, Bin
2015-01-01
This paper presents neural adaptive control methods for a class of chaotic nonlinear systems in the presence of constrained input and unknown dynamics. To attenuate the influence of constrained input caused by actuator saturation, an effective auxiliary system is constructed to prevent the stability of closed loop system from being destroyed. Radial basis function neural networks (RBF-NNs) are used in the online learning of the unknown dynamics, which do not require an off-line training phase. Both state and output feedback control laws are developed. In the output feedback case, high-order sliding mode (HOSM) observer is utilized to estimate the unmeasurable system states. Simulation results are presented to verify the effectiveness of proposed schemes. Project supported by the National High Technology Research and Development Program of China (Grant No. 2012AA041701), the Fundamental Research Funds for Central Universities of China (Grant No. 2013JBZ007), the National Natural Science Foundation of China (Grant Nos. 61233001, 61322307, 61304196, and 61304157), and the Research Program of Beijing Jiaotong University, China (Grant No. RCS2012ZZ003).
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
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
Point estimation and p-values in phase II adaptive two-stage designs with a binary endpoint.
Kunzmann, K; Kieser, M
2017-03-15
Clinical trials in phase II of drug development are frequently conducted as single-arm two-stage studies with a binary endpoint. Recently, adaptive designs have been proposed for this setting that enable a midcourse modification of the sample size. While these designs are elaborated with respect to hypothesis testing by assuring control of the type I error rate, the topic of point estimation has up to now not been addressed. For adaptive designs with a prespecified sample size recalculation rule, we propose a new point estimator that both assures compatibility of estimation and test decision and minimizes average mean squared error. This estimator can be interpreted as a constrained posterior mean estimate based on the non-informative Jeffreys prior. A comparative investigation of the operating characteristics demonstrates the favorable properties of the proposed approach. Copyright © 2016 John Wiley & Sons, Ltd.
Baker, Nancy T.
2017-01-01
This dataset provides estimates of annual agricultural use of pesticide compounds by crop group at the state level for states in the conterminous United States, for the time period 1992-2014, compiled from data used to make county-level estimates by means of methods described in Thelin and Stone (2013) and Baker and Stone (2015). The source of this data is the same as the published county-level pesticide use estimates for 1992-2009 (Stone, 2013), estimates for 2008-2012 (Baker and Stone, 2015), and preliminary estimates for 2013 and 2014 respectively, Baker (2015), and Baker (2016). County level by-crop estimates are not published because of the increased uncertainty in estimating the geographic distribution of compounds applied to specific crops. County level estimates were aggregated to state level for high acreage crops such as corn and soybeans, and crop groups for lower acreage crops.
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
State estimation and prediction using clustered particle filters
Lee, Yoonsang; Majda, Andrew J.
2016-01-01
Particle filtering is an essential tool to improve uncertain model predictions by incorporating noisy observational data from complex systems including non-Gaussian features. A class of particle filters, clustered particle filters, is introduced for high-dimensional nonlinear systems, which uses relatively few particles compared with the standard particle filter. The clustered particle filter captures non-Gaussian features of the true signal, which are typical in complex nonlinear dynamical systems such as geophysical systems. The method is also robust in the difficult regime of high-quality sparse and infrequent observations. The key features of the clustered particle filtering are coarse-grained localization through the clustering of the state variables and particle adjustment to stabilize the method; each observation affects only neighbor state variables through clustering and particles are adjusted to prevent particle collapse due to high-quality observations. The clustered particle filter is tested for the 40-dimensional Lorenz 96 model with several dynamical regimes including strongly non-Gaussian statistics. The clustered particle filter shows robust skill in both achieving accurate filter results and capturing non-Gaussian statistics of the true signal. It is further extended to multiscale data assimilation, which provides the large-scale estimation by combining a cheap reduced-order forecast model and mixed observations of the large- and small-scale variables. This approach enables the use of a larger number of particles due to the computational savings in the forecast model. The multiscale clustered particle filter is tested for one-dimensional dispersive wave turbulence using a forecast model with model errors. PMID:27930332
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.
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
Goodness-of-Fit Tests and Nonparametric Adaptive Estimation for Spike Train Analysis.
Reynaud-Bouret, Patricia; Rivoirard, Vincent; Grammont, Franck; Tuleau-Malot, Christine
2014-04-17
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 MaterialThe online version of this article (doi:10.1186/2190-8567-4-3) contains supplementary material.
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.
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…
Adaptation of Adolescent Mexican Americans to United States Society
ERIC Educational Resources Information Center
Derbyshire, Robert L.
1969-01-01
In a paper prepared for the conference on "Migration and Behavioral Deviance, November 4-8, 1968, Dorado Beach, Puerto Rico, the author compares "the attitudes of Mexican American adolescents who were born and reared or whose parents were born and reared in the United States with those adolescents who migrated or whose parents migrated…
APL Revised: Its Uses and Adaptation in States.
ERIC Educational Resources Information Center
Fischer, Joan Keller; And Others
This report on the state level use and application of the 1971-1977 Adult Performance Level (APL) study contains two reports: "Competencies for Adult Basic Education and Diploma Programs: A Summary of Studies and Cross-Reference of Results." by Joan Keller Fischer and "The APL Study: Science, Dissemination, and the Nature of Adult…
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
Mariño, Inés P; Míguez, Joaquín
2005-11-01
We introduce a numerical approximation method for estimating an unknown parameter of a (primary) chaotic system which is partially observed through a scalar time series. Specifically, we show that the recursive minimization of a suitably designed cost function that involves the dynamic state of a fully observed (secondary) system and the observed time series can lead to the identical synchronization of the two systems and the accurate estimation of the unknown parameter. The salient feature of the proposed technique is that the only external input to the secondary system is the unknown parameter which needs to be adjusted. We present numerical examples for the Lorenz system which show how our algorithm can be considerably faster than some previously proposed methods.
NASA Astrophysics Data System (ADS)
Henke, D.; Schubert, A.; Small, D.; Meier, E.; Lüthi, M. P.; Vieli, A.
2014-12-01
A new method for glacier surface velocity (GSV) estimates is proposed here which combines ground- and space-based measurements with hidden state space modeling (HSSM). Examples of such a fusion of physical models with remote sensing (RS) observations were described in (Henke & Meier, Hidden State Space Models for Improved Remote Sensing Applications, ITISE 2014, p. 1242-1255) and are currently adapted for GSV estimation. GSV can be estimated using in situ measurements, RS methods or numerical simulations based on ice-flow models. In situ measurements ensure high accuracy but limited coverage and time consuming field work, while RS methods offer regular observations with high spatial coverage generally not possible with in situ methods. In particular, spaceborne Synthetic Aperture Radar (SAR) can obtain useful images independent of daytime and cloud cover. A ground portable radar interferometer (GPRI) is useful for investigating a particular area in more detail than is possible from space, but provides local coverage only. Several processing methods for deriving GSV from radar sensors have been established, including interferometry and offset tracking (Schubert et al, Glacier surface velocity estimation using repeat TerraSAR-X images. ISPRS Journal of P&RS, p. 49-62, 2013). On the other hand, it is also possible to derive glacier parameters from numerical ice-flow modeling alone. Given a well-parameterized model, GSV can in theory be derived and propagated continuously in time. However, uncertainties in the glacier flow dynamics and model errors increase with excessive propagation. All of these methods have been studied independently, but attempts to combine them have only rarely been made. The HSSM we propose recursively estimates the GSV based on 1) a process model making use of temporal and spatial interdependencies between adjacent states, and 2) observations (RS and optional in situ). The in situ and GPRI images currently being processed were acquired in the
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.
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.
Adaptive single replica multiple state transition interface sampling
NASA Astrophysics Data System (ADS)
Du, Wei-Na; Bolhuis, Peter G.
2013-07-01
The multiple state transition path sampling method allows sampling of rare transitions between many metastable states, but has the drawback that switching between qualitatively different pathways is difficult. Combination with replica exchange transition interface sampling can in principle alleviate this problem, but requires a large number of simultaneous replicas. Here we remove these drawbacks by introducing a single replica sampling algorithm that samples only one interface at a time, while efficiently walking through the entire path space using a Wang-Landau approach or, alternatively, a fixed bias. We illustrate the method on several model systems: a particle diffusing in a simple 2D potential, isomerization in a small Lennard Jones cluster, and isomerization of the alanine dipeptide in explicit water.
Global error estimation based on the tolerance proportionality for some adaptive Runge-Kutta codes
NASA Astrophysics Data System (ADS)
Calvo, M.; González-Pinto, S.; Montijano, J. I.
2008-09-01
Modern codes for the numerical solution of Initial Value Problems (IVPs) in ODEs are based in adaptive methods that, for a user supplied tolerance [delta], attempt to advance the integration selecting the size of each step so that some measure of the local error is [similar, equals][delta]. Although this policy does not ensure that the global errors are under the prescribed tolerance, after the early studies of Stetter [Considerations concerning a theory for ODE-solvers, in: R. Burlisch, R.D. Grigorieff, J. Schröder (Eds.), Numerical Treatment of Differential Equations, Proceedings of Oberwolfach, 1976, Lecture Notes in Mathematics, vol. 631, Springer, Berlin, 1978, pp. 188-200; Tolerance proportionality in ODE codes, in: R. März (Ed.), Proceedings of the Second Conference on Numerical Treatment of Ordinary Differential Equations, Humbold University, Berlin, 1980, pp. 109-123] and the extensions of Higham [Global error versus tolerance for explicit Runge-Kutta methods, IMA J. Numer. Anal. 11 (1991) 457-480; The tolerance proportionality of adaptive ODE solvers, J. Comput. Appl. Math. 45 (1993) 227-236; The reliability of standard local error control algorithms for initial value ordinary differential equations, in: Proceedings: The Quality of Numerical Software: Assessment and Enhancement, IFIP Series, Springer, Berlin, 1997], it has been proved that in many existing explicit Runge-Kutta codes the global errors behave asymptotically as some rational power of [delta]. This step-size policy, for a given IVP, determines at each grid point tn a new step-size hn+1=h(tn;[delta]) so that h(t;[delta]) is a continuous function of t. In this paper a study of the tolerance proportionality property under a discontinuous step-size policy that does not allow to change the size of the step if the step-size ratio between two consecutive steps is close to unity is carried out. This theory is applied to obtain global error estimations in a few problems that have been solved with
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.
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
Liu, Zong-Xiang; Wu, De-Hui; Xie, Wei-Xin; Li, Liang-Qun
2017-02-15
Tracking the target that maneuvers at a variable turn rate is a challenging problem. The traditional solution for this problem is the use of the switching multiple models technique, which includes several dynamic models with different turn rates for matching the motion mode of the target at each point in time. However, the actual motion mode of a target at any time may be different from all of the dynamic models, because these models are usually limited. To address this problem, we establish a formula for estimating the turn rate of a maneuvering target. By applying the estimation method of the turn rate to the multi-target Bayes (MB) filter, we develop a MB filter with an adaptive estimation of the turn rate, in order to track multiple maneuvering targets. Simulation results indicate that the MB filter with an adaptive estimation of the turn rate, is better than the existing filter at tracking the target that maneuvers at a variable turn rate.
NASA Astrophysics Data System (ADS)
Fenili, André
2012-11-01
In this paper the author investigates the angular position and vibration control of a nonlinear rigid-flexible two link robotic manipulator considering fast angular maneuvers. The nonlinear control technique named State-Dependent Riccati Equation (SDRE) is used here to achieve these aims. In a more realistic approach, it is considered that some states can be measured and some states cannot be measured. The states not measured are estimated in order to be used for the SDRE control. These states are all the angular velocities and the velocity of deformation of the flexible link. A state-dependent Riccati equation-based estimator is used here. Not only different initial conditions between the system to be controlled (here named "real" system) and the estimator but also a different mathematical model is considered as the estimation model in order to verify the limitations of the proposed estimation and control techniques. The mathematical model that emulates the real system to be controlled considers two modes expansion and the estimation model considers only one mode expansion. The results for the different approaches are compared and discussed.
Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints.
Chen, Ziting; Li, Zhijun; Chen, C L Philip
2016-03-17
An adaptive neural control strategy for multiple input multiple output nonlinear systems with various constraints is presented in this paper. To deal with the nonsymmetric input nonlinearity and the constrained states, the proposed adaptive neural control is combined with the backstepping method, radial basis function neural network, barrier Lyapunov function (BLF), and disturbance observer. By ensuring the boundedness of the BLF of the closed-loop system, it is demonstrated that the output tracking is achieved with all states remaining in the constraint sets and the general assumption on nonsingularity of unknown control coefficient matrices has been eliminated. The constructed adaptive neural control has been rigorously proved that it can guarantee the semiglobally uniformly ultimate boundedness of all signals in the closed-loop system. Finally, the simulation studies on a 2-DOF robotic manipulator system indicate that the designed adaptive control is effective.
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.
Climate adaptation wedges: a case study of premium wine in the western United States
NASA Astrophysics Data System (ADS)
Diffenbaugh, Noah S.; White, Michael A.; Jones, Gregory V.; Ashfaq, Moetasim
2011-04-01
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
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)
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.
Adaptive search range adjustment scheme for fast motion estimation in AVC/H.264
NASA Astrophysics Data System (ADS)
Lee, Sunyoung; Choi, Kiho; Jang, Euee S.
2011-06-01
AVC/H.264 supports the use of multiple reference frames (e.g., 5 frames in AVC/H.264) for motion estimation (ME), which demands a huge computational complexity in ME. We propose an adaptive search range adjustment scheme to reduce the computational complexity of ME by reducing the search range of each reference frame--from the (t-1)'th frame to the (t-5)'th frame--for each macroblock. Based on the statistical analysis that the 16×16 mode type is dominantly selected rather than the other block partition mode types, the proposed method reduces the search range of the remaining ME process in the given reference frame according to the motion vector (MV) position of the 16×16 block ME. In the case of the (t-1)'th frame, the MV position of the 8×8 block ME--in addition to that of 16×16 block ME--is also used for the search range reduction to sub-block partition mode types of the 8×8 block. The experimental results show that the proposed method reduces about 50% and 65% of the total encoding time over CIF/SIF and full HD test sequences, respectively, without any noticeable visual degradation, compared to the full search method of the AVC/H.264 encoder.
Technology Transfer Automated Retrieval System (TEKTRAN)
Our objective was to simulate the effect of child-friendly (CF) adaptations of the National Cancer Institute’s Automated Self-Administered 24-Hour Dietary Recall (ASA24) on estimates of nutrient intake. One hundred twenty children, 8–13 years old, entered their previous day’s intake using the ASA24 ...
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…
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
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.
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.
NASA Astrophysics Data System (ADS)
Li, Meng; Huang, Zhonghua
2016-10-01
Signal processing for an ultra-wideband radio fuze receiver involves some challenges: it requires high real-time performance; the output signal is mixed with broadband noise; and the signal-to-noise ratio (SNR) decreases with increased detection range. The adaptive line enhancement method is used to filter the output signal of the ultra-wideband radio fuze receiver, and thus suppress the wideband noise from the output signal of the receiver and extract the target characteristic signal. The filter input correlation matrix estimation algorithm is based on the delay factor of an adaptive line enhancer. The proposed adaptive algorithm was used to filter and reduce noise in the output signal from the fuze receiver. Simulation results showed that the SNR of the output signal after adaptive noise reduction was improved by 20 dB, which was higher than the SNR of the output signal after finite impulse response (FIR) filtering of around 10 dB.
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.
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.
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.
A new approach to state estimation in deterministic digital control systems
NASA Technical Reports Server (NTRS)
Polites, Michael E.
1987-01-01
The paper presents a new approach to state estimation in deterministic digital control systems. The scheme is based on sampling the output of the plant at a high rate and prefiltering the discrete measurements in a multi-input/multi-output moving average (MA) process. The coefficient matrices in the MA prefilter are selected so the estimated state equals the true state. An example is presented which illustrates the procedure to follow to completely design the estimator.
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.
Handschin, E.; Langer, M.; Kliokys, E.
1995-12-31
The possibility of power system state estimation with non-traditional measurement configuration is investigated. It is assumed that some substations are equipped with current magnitude measurements. Unique state estimation is possible, in such a situation, if currents are combined with voltage or power measurements and inequality constraints on node power injections are taken into account. The state estimation algorithm facilitating the efficient incorporation of inequality constraints is developed using an interior point optimization method. Simulation results showing the performance of the algorithm are presented. The method can be used for state estimation in medium voltage subtransmission and distribution networks.
NASA Astrophysics Data System (ADS)
Rallo, G.; Provenzano, G.; Manzano-Juárez, J.
2012-04-01
In the Mediterranean environment, where the period of crops growth does not coincide with the rainy season, the crop is subject to water stress periods that may be amplified with improper irrigation management. Agro-hydrological models can be considered an economic and simple tool to optimize irrigation water use, mainly when water represents a limiting factor for crop production. In the last two decades, agro-hydrological physically based models have been developed to simulate mass and energy exchange processes in the soil-plant-atmosphere system (Feddes et al., 1978; Bastiaanssen et al., 2007). Unfortunately these models, although very reliable, as a consequence of the high number of required variables and the complex computational analysis, cannot often be used. Therefore, simplified agro-hydrological models may represent an useful and simple tool for practical irrigation scheduling. The main objective of the work is to assess, for an olive orchard, the suitability of FAO-56 spreadsheet agro-hydrological model to estimate a long time series of field transpiration, soil water content and crop water stress dynamic. A modification of the spreadsheet is suggested in order to adapt the simulations to a crop tolerant to water stress. In particular, by implementing a new crop water stress function, actual transpiration fluxes and an ecophysiological stress indicator, i. e. the relative transpiration, are computed in order to evaluate a plant-based irrigation scheduling parameter. Validation of the proposed amendment is carried out by means of measured sap fluxes, measured on different plants and up-scaled to plot level. Spatial and temporal variability of soil water contents in the plot was measured, at several depths, using the Diviner 2000 capacitance probe (Sentek Environmental Technologies, 2000) and TDR-100 (Campbell scientific, Inc.) system. The detailed measurements of soil water content, allowed to explore the high spatial variability of soil water content due
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
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
2016-09-01
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.
Liu, Yan-Jun; Li, Jing; Tong, Shaocheng; Chen, C L Philip
2016-07-01
In order to stabilize a class of uncertain nonlinear strict-feedback systems with full-state constraints, an adaptive neural network control method is investigated in this paper. The state constraints are frequently emerged in the real-life plants and how to avoid the violation of state constraints is an important task. By introducing a barrier Lyapunov function (BLF) to every step in a backstepping procedure, a novel adaptive backstepping design is well developed to ensure that the full-state constraints are not violated. At the same time, one remarkable feature is that the minimal learning parameters are employed in BLF backstepping design. By making use of Lyapunov analysis, we can prove that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded and the output is well driven to follow the desired output. Finally, a simulation is given to verify the effectiveness of the method.
Nenov, Valeriy; Bergsneider, Marvin; Glenn, Thomas C.; Vespa, Paul; Martin, Neil
2007-01-01
Impeded by the rigid skull, assessment of physiological variables of the intracranial system is difficult. A hidden state estimation approach is used in the present work to facilitate the estimation of unobserved variables from available clinical measurements including intracranial pressure (ICP) and cerebral blood flow velocity (CBFV). The estimation algorithm is based on a modified nonlinear intracranial mathematical model, whose parameters are first identified in an offline stage using a nonlinear optimization paradigm. Following the offline stage, an online filtering process is performed using a nonlinear Kalman filter (KF)-like state estimator that is equipped with a new way of deriving the Kalman gain satisfying the physiological constraints on the state variables. The proposed method is then validated by comparing different state estimation methods and input/output (I/O) configurations using simulated data. It is also applied to a set of CBFV, ICP and arterial blood pressure (ABP) signal segments from brain injury patients. The results indicated that the proposed constrained nonlinear KF achieved the best performance among the evaluated state estimators and that the state estimator combined with the I/O configuration that has ICP as the measured output can potentially be used to estimate CBFV continuously. Finally, the state estimator combined with the I/O configuration that has both ICP and CBFV as outputs can potentially estimate the lumped cerebral arterial radii, which are not measurable in a typical clinical environment. PMID:17281533
State estimates of total medical expenditures attributable to cigarette smoking, 1993.
Miller, L S; Zhang, X; Rice, D P; Max, W
1998-01-01
OBJECTIVE: To estimate state-by-state totals of medical expenditures attributable to cigarette smoking for calendar year 1993. METHODS: The smoking-attributable fractions (SAFs) of total state medical expenditures, by type of expenditure, were estimated using a national model that describes the relationship between smoking and medical expenditures, controlling for a variety of sociodemographic, economic, and behavioral factors. Employing data from the Behavioral Risk Factor Surveillance System, the authors used the national model to estimate SAFs for the 50 states and the District of Columbia, then applied these SAFs to published state medical expenditures, by type of expenditures, to estimate total 1993 state medical expenditures attributable to smoking. National estimates are the sums of state estimates. RESULTS: In 1993, the estimated proportion of total medical expenditures attributable to smoking for the U.S. as a whole was 11.8%, with a range across states from 6.6% to 14.1%. By type of expenditure, SAFs ranged from a low of 8.0% for home health expenditures to a high of 15.9% for nursing home expenditures for the nation as a whole. Total U.S. medical expenditures attributable to smoking amounted to an estimated $72.7 billion in 1993 (95% interval estimate $48.0-$97.4 billion). Estimates of total smoking-attributable state medical expenditures (SAEs) ranged from $79.6 million to $8.72 billion. CONCLUSIONS: Cigarette smoking accounted for a substantial portion of state and national medical expenditures in 1993, with considerable variation among states. The range across states was due to differences in smoking prevalence, health status, other socioeconomic variables used in the model, and the magnitude and patterns of state medical expenditures. PMID:9769770
The Constitution of the United States (Adapted). For Use in EMR Classes.
ERIC Educational Resources Information Center
Miller, Louise A.
This special instructional handbook presents a unit of study on the United States Constitution for educable mentally retarded students at the secondary level. The adapted material explains the structure of the national government, and is designed to promote citizenship and patriotic values. Preliminary information includes a glossary of new words,…
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...
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.
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
Some Considerations for Eliminating Biases in Ability Estimation in Computerized Adaptive Testing.
ERIC Educational Resources Information Center
Samejima, Fumiko
Item response theory (IRT) has been adapted as the theoretical foundation of computerized adaptive testing (CAT) for several decades. In applying IRT to CAT, there are certain considerations that are essential, and yet tend to be neglected. These essential issues are addressed in this paper, and then several ways of eliminating noise and bias in…
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.
On the state estimation of structures with second order observers
NASA Technical Reports Server (NTRS)
Belvin, W. Keith; Park, K. C.
1989-01-01
The use of Linear Quadratic Regulator (LQR) control synthesis techniques implies the availability of full state feedback. For vibration control of structures, usually only a limited number of states are measured from which an observer model reconstructs the full state. It is shown that using second order observers is a viable technique for reconstructing the unmeasured states of structures under mildly restrictive conditions. Moreover, the computational advantages of the second order observer as compared to a first order observer indicate that significantly larger observer models may be utilized. Numerical examples are used to demonstrate the performance of second order observers. The implications of second order observers in the development of Controls-Structures Interaction (CSI) technology is discussed.
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.
Estimation of health state utilities in breast cancer
Kim, Seon-Ha; Jo, Min-Woo; Ock, Minsu; Lee, Hyeon-Jeong; Lee, Jong-Won
2017-01-01
Purpose The aim of this study is to determine the utility of breast cancer health states using the standard gamble (SG) and visual analog scale (VAS) methods in the Korean general population. Materials and methods Eight hypothetical breast cancer health states were developed based on patient education material and previous publications. Data from 509 individuals from the Korean general population were used to evaluate breast cancer health states using the VAS and the SG methods, which were obtained via computer-assisted personal interviews. Mean utility values were calculated for each human papillomavirus (HPV)-related health state. Results The rank of health states was identical between two valuation methods. SG values were higher than VAS values in all health states. The utility values derived from SG were 0.801 (noninvasive breast cancer with mastectomy and followed by reconstruction), 0.790 (noninvasive breast cancer with mastectomy only), 0.779 (noninvasive breast cancer with breast-conserving surgery and radiation therapy), 0.731 (invasive breast cancer with surgery, radiation therapy, and/or chemotherapy), 0.610 (locally advanced breast cancer with radical mastectomy with radiation therapy), 0.587 (inoperable locally advanced breast cancer), 0.496 (loco-regional recurrent breast cancer), and 0.352 (metastatic breast cancer). Conclusion Our findings might be useful for economic evaluation of breast cancer screening and interventions in general populations. PMID:28352159
Sheng, Li; Wang, Zidong; Zou, Lei; Alsaadi, Fuad E
2016-07-20
In this paper, the event-based finite-horizon H∞ state estimation problem is investigated for a class of discrete time-varying stochastic dynamical networks with state- and disturbance-dependent noises [also called (x,v)-dependent noises]. An event-triggered scheme is proposed to decrease the frequency of the data transmission between the sensors and the estimator, where the signal is transmitted only when certain conditions are satisfied. The purpose of the problem addressed is to design a time-varying state estimator in order to estimate the network states through available output measurements. By employing the completing-the-square technique and the stochastic analysis approach, sufficient conditions are established to ensure that the error dynamics of the state estimation satisfies a prescribed H∞ performance constraint over a finite horizon. The desired estimator parameters can be designed via solving coupled backward recursive Riccati difference equations. Finally, a numerical example is exploited to demonstrate the effectiveness of the developed state estimation scheme.
Thompson, Renee J.; Mata, Jutta; Jaeggi, Susanne M.; Buschkuehl, Martin; Jonides, John; Gotlib, Ian H.
2010-01-01
Rumination has consistently been found to be associated with the onset and duration of major depressive episodes. Little research, however, has examined factors that may weaken the association between maladaptive coping, such as rumination, and depressive symptoms. In three samples of participants, including 149 never-depressed adolescent girls, 41 never-depressed women, and 39 depressed women, we examined whether generally adaptive forms of coping interacted with generally maladaptive coping to predict depressive symptoms. Age-appropriate measures of coping and depression were administered to participants in each sample. In never-depressed females, maladaptive coping / rumination were more strongly related to depressive symptoms in the presence of lower levels of adaptive coping. The relation between depression and maladaptive coping / rumination was weaker in the context of higher levels of adaptive coping. In contrast, for the depressed females, we found main effects for rumination and adaptive coping, with higher levels of rumination and lower levels of adaptive coping being associated with higher levels of depressive symptoms. The present findings highlight how adaptive coping and maladaptive coping, including rumination, differentially relate to each other and depressive symptoms depending on individuals’ current depressive state. PMID:20211463
Integration of remote measurement calibration with state estimation; A feasibility study
Adibi, M.M. ); Clements, K.A. ); Kafka, R.J. ); Stovall, J.P. )
1992-08-01
This paper describes the integration of measurement calibration and state estimation methodologies for increasing the confidence level in the real-time data base. The objectives are to improve performance of state estimators and to reduce the system engineering effort which goes into its installations and the related measurement calibrations.
Blewett, Lynn A; Davern, Michael
2006-01-01
Objective Critically review estimates of health insurance coverage available from different sources, including the federal government, state survey initiatives, and foundation-sponsored surveys for use in state policy research. Study Setting and Design We review the surveys in an attempt to flesh out the current weaknesses of survey data for state policy uses. The main data sources assessed in this analysis are federal government surveys (such as the Current Population Survey's Annual Social and Economic Supplement, and the National Health Interview Survey), foundation-supported surveys (National Survey of America's Families, and the Community Tracking Survey), and state-sponsored surveys. Principal Findings Despite information on estimates of health insurance coverage from six federal surveys, states find the data lacking for state policy purposes. We document the need for state representative data on the uninsured and the recent history of state data collection efforts spurred in part by the Health Resources Services Administration State Planning Grant program. We assess the state estimates of uninsurance from the Current Population Survey and make recommendations for a new consolidated federal survey with better state representative data. Conclusions We think there are several options to consider for coordinating a federal and state data collection strategy to inform state and national policy on coverage and access. PMID:16704521
NASA Astrophysics Data System (ADS)
Feischl, Michael; Gantner, Gregor; Praetorius, Dirk
2015-06-01
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.
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
Distributed bounded-error state estimation based on practical robust positive invariance
NASA Astrophysics Data System (ADS)
Riverso, Stefano; Rubini, Daria; Ferrari-Trecate, Giancarlo
2015-11-01
We propose a state estimator for linear discrete-time systems composed by coupled subsystems affected by bounded disturbances. The architecture is distributed in the sense that each subsystem is equipped with a local state estimator that exploits suitable pieces of information from parent subsystems. Furthermore, each local estimator reconstructs the state of the corresponding subsystem only. Different from methods based on moving horizon estimation, our approach does not require the online solution to optimisation problems. Our state estimation scheme, which is based on the notion of practical robust positive invariance, also guarantees satisfaction of constraints on local estimation errors and it can be updated with a limited computational effort when subsystems are added or removed.
The ECCO Family of State Estimates: An Overview
NASA Astrophysics Data System (ADS)
Wunsch, C.
2008-12-01
The idea of ECCO (Estimating the Circulation and Climate of the Ocean)originated in the middle 1980s, when it became apparent that a global oceanographic observing system for the general circulation would become a reality as it did through the World Ocean Circulation Experiment. Observational design involved extremely diverse technologies and oceanic flow regimes. To be physically interpretable, these diverse data and physical processes would need to be combined into a useful, coherent, whole. Such a synthesis can only be done with a skillful GCM having useful resolution. ECCO originated as an experiment to demonstrate the technical feasibility of such a synthesis and to determine if any of several possible methods was preferable. In contrast to a number of other superficially similar efforts, mainly derived from weather forecasting methods, the ECCO goal was to estimate the long-term circulation mean and its variability on climate (decadal and longer) time scales in a form exactly satisfying known equations of motion. ECCO was made feasible with the simultaneous construction of a new GCM (MIT) along with the development of an automatic differentiation (AD) software tool(now called TAF) which rendered practical the method of Lagrange multipliers (called the adjoint method in oceanography). Parallel developments of simplified sequential methods (smoothers) provided an alternative, also practical, methodology. One can now use the existing (publicly available) machinery to discuss the ocean circulation and its variability. The huge variety of issues connected with the global circulation has meant that an entire family of estimates has grown up, each having different emphases (primarily global; but some primarily regional---the tropics, the Southern Ocean); some focussed on physics---the role of eddies or sea ice). The methodology leads, usefully, to intense scrutiny of data and model errors and spatio-temporal coverage. As with any estimation problem, no uniquely
State Space Composition Technique for Intelligent Wheel Chair Adapting to Environment.
NASA Astrophysics Data System (ADS)
Hamagami, Tomoki; Hirata, Hironori
This paper describes a state space composition technique for the adaptation to environment in the autonomous behavior of intelligent wheel chair (IWC).In the product like IWC with actual sensors, composing state space is difficult problem since environmental information can not be observed sufficiently from restricted sensor inputs.A lot of states observed from same environment position raise the fail of the learning and adaptation with active learning approach.In order to compensate for the effects of the sensor configuration, that is sensor position, angle and precision, a normalization processing of position detector is introduced.In sensor normalization process, IWC scans present environment via range sensors with executing spot-turn, and prepare scan-patterns of each sensor.Then the normalization process adjusts the phase and dynamic range of each pattern to the reference sensor scan-pattern, analyzing phase differences and scale factors of each pattern against reference pattern.Using phase difference and scale factors, automated state space composition is possible.From the simulation experiment with both artificial and real-worlddraft, the automated state space construction is confirmed as a practical approach for pre-processing for environment learning and adaptation.
Implementation of phasor measurements in state estimator at Sevillana de Electricidad
Slutsker, I.W.; Mokhtari, S.; Jaques, L.A.; Provost, J.M.G.; Perez, M.B.; Sierra, J.B.; Gonzalez, F.G.; Figueroa, J.M.M.
1995-12-31
The paper describes the first implementation of phase angle measurements in an industrial state estimator.Transducers for measuring phase angles using satellite clock synchronization were installed at Sevillana de Electricidad and incorporated into the data acquisition subsystem of the EMS. The State Estimator was modified to support the processing of phasor telemetry. The enhancements to the state estimator algorithm are described in the paper. The modified state estimator was subjected to intensive testing prior to installation in the field. The results of all experiments as well as classification of phasor measurement effectiveness are reported in the paper. Phasor measurements were found to provide valuable information about the power network and improve the accuracy of the state estimation.
NASA Astrophysics Data System (ADS)
Hugelius, G.
2014-12-01
Current global scale estimates of soil organic carbon (SOC) stocks do not account for pedogenic processes unique to permafrost environments. The Northern Circumpolar Soil Carbon Database (NCSCD) was compiled to address this lack of knowledge of permafrost affected soils. The NCSCD links pedon data in the 0-3 m depth range from the northern permafrost regions to several digitized regional soil classification maps to produce a combined circumpolar coverage. While these different soil classification maps have been harmonized to a common soil classification system, the maps are of different origin and age and they were produced at a range of different scales (fig. 1). The spatial distribution of soil pedon data is also highly uneven with notable data-gaps in central Siberia, the High Arctic and Alpine regions. The NCSCD is thus a dataset with its roots in traditional soil survey and soil map data and with substantial uncertainties and data-gaps. The original maps are subdivided into polygons where soil specialists have mapped different coverages of soil orders. The NCSCD has been gridded and is currently in use for many different model-based studies. This conversion of polygon-based data to the gridded world of spatial or process-based models can affect accuracy, precision and usability of a dataset. There are also inherent difficulties in assessing the uncertainty of stock or process quantifications based on polygon soil maps such as the NCSCD. Soil science is moving into a fully digital world where new maps of soil order distribution or soil properties are based on gridded remote sensing products. Such data can more easily be scaled and adapted for use in grid-based soil models. However, it is argued that the traditional soil maps are still extremely valuable data-sets that contain information that may be lost in fully digital soil mapping approaches. This presentation discusses these issues and gives some examples of how existing pedon databases and soil maps can be
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.
A simplified approach to estimating the maximal lactate steady state.
Snyder, A C; Woulfe, T; Welsh, R; Foster, C
1994-01-01
The exercise intensity associated with an elevated but stable blood lactate (HLa) concentration during constant load work (the maximal steady state, MSS) has received attention as a candidate for the "optimal" exercise intensity for endurance training. Identification of MSS ordinarily demands direct measurement of HLa or respiratory metabolism. The purpose of this study was to test the ability of heart rate (HR) to identify MSS during steady state exercise, similar to that used in conventional exercise prescription. Trained runners (n = 9) and cyclists (n = 12) performed incremental and steady state exercise. MSS was defined as the highest intensity in which blood lactate concentration increased < 1.0 mM from minutes 10 to 30. The next higher intensity workbout completed was defined as > MSS. HR models related to the presence or absence of steady state conditions were developed from the upper 95% confidence interval of MSS and the lower 95% confidence interval of > MSS. Cross validation of the model to predict MSS was performed using 21 running and 45 cycling exercise bouts in a separate group. Using the MSS upper 95% confidence interval model 84% and 76% of workbouts were correctly predicted in cyclists and runners, respectively. Using the > MSS lower 95% confidence interval model, 76% and 81% of workbouts were correctly predicted in cyclists and runners, respectively. Prediction errors tended to incorrectly predict non-steady state conditions when steady state had occurred (16/26) (62%). We conclude that use of these simple HR models may predict MSS with sufficient accuracy to be useful when direct HLa measurement is not available.
An adaptive detector and channel estimator for deep space optical communications
NASA Technical Reports Server (NTRS)
Mukai, R.; Arabshahi, P.; Yan, T. Y.
2001-01-01
This paper will discuss the design and testing of both the channel parameter identification system, and the adaptive threshold system, and illustrate their advantages and performance under simulated channel degradation conditions.
NASA Astrophysics Data System (ADS)
Ijima, Yusuke; Nose, Takashi; Tachibana, Makoto; Kobayashi, Takao
In this paper, we propose a rapid model adaptation technique for emotional speech recognition which enables us to extract paralinguistic information as well as linguistic information contained in speech signals. This technique is based on style estimation and style adaptation using a multiple-regression HMM (MRHMM). In the MRHMM, the mean parameters of the output probability density function are controlled by a low-dimensional parameter vector, called a style vector, which corresponds to a set of the explanatory variables of the multiple regression. The recognition process consists of two stages. In the first stage, the style vector that represents the emotional expression category and the intensity of its expressiveness for the input speech is estimated on a sentence-by-sentence basis. Next, the acoustic models are adapted using the estimated style vector, and then standard HMM-based speech recognition is performed in the second stage. We assess the performance of the proposed technique in the recognition of simulated emotional speech uttered by both professional narrators and non-professional speakers.
NASA Astrophysics Data System (ADS)
Sun, Fengchun; Xiong, Rui; He, Hongwen
2014-08-01
Battery state-of-charge (SoC) and state-of-power capability (SoP) are two of the most significant decision factors for energy management system in electrified vehicles. This paper tries to make two contributions to the existing literature. (1) Based on the adaptive extended Kalman filter algorithm, a data-driven joint estimator for battery SoC and SoP against varying degradations has been developed. (2) To achieve accurate estimations of SoC and SoP in the whole calendar-life of battery, the need for model parameter updates with lowest computation burden has been discussed and studied. The robustness of the joint estimator against dynamic loading profiles and varying health conditions is evaluated. We subsequently used data from cells that have different aging levels to assess the robustness of the SoC and SoP estimation algorithm. The results show that battery SoP has close relationship with its aging levels. And the prediction precision would be significantly improved if recalibrating the parameter of battery capacity and resistance timely. What's more, the method reaches accuracies for new and aged battery cells in electrified vehicle applications of better than 97.5%.
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
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.
Nondestructive Estimation of the Eigenvectors of Mixed State
NASA Astrophysics Data System (ADS)
Gao, Jingliang
2017-02-01
Given n qubits prepared according to the same mixed state ρ, we propose a measuring scheme which approximately yields the eigenvectors of ρ. By performing a sequence of measurements, we can scan the Bloch sphere like radar to search for the eigenvectors. It is shown that this scheme is nondestructive when n is large. This result is based on the quantum typical subspace theory and it actually reveals an interesting mathematical structure of the n-fold Hilbert space.
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.
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
NASA Astrophysics Data System (ADS)
Kania, Dariusz
2015-05-01
In adaptive optics systems, there is a problem of a sinusoidal oscillations rejection. This paper presents the estimation method that can be used to reject these oscillations on the example of the photovoltaic system. In such a system, photovoltaic panels generate the DC signal converted by the inverter to the AC signal with specified parameters. This paper focuses on the fast and accurate estimation of these parameters taking into account the presence of harmonics in the sinusoidal signal. The estimation method is based on using maximum decay sidelobes windows and the Fast Fourier Transform procedure. In reality, the AC signal is not a pure sinusoid and it is often distorted in a deterministic manner by harmonics, and in a random manner by white, "colored" or quantization noise. The estimation error depends on the systematic error, i.e. the error caused by the quantization noise and the error caused by harmonic components. Several parameters determine which error component is dominant in the estimation results. The value of the error caused by harmonic components depends mainly on the distance between the harmonic component and the fundamental component in a frequency domain and the THD (Total Harmonic Distortion) ratio of the signal. The level of this maximum relative error is approximately 10-3 for the tested signal with THD=50%. It is important to use a filter that reduces unwanted harmonics before the data processing. The information provided in this paper can be used to determine the approximate level of estimation error before starting the estimation process.
State estimation for Markovian jumping genetic regulatory networks with random delays
NASA Astrophysics Data System (ADS)
Liu, Jinliang; Tian, Engang; Gu, Zhou; Zhang, Yuanyuan
2014-07-01
In this paper, the state estimation problem is investigated for stochastic genetic regulatory networks (GRNs) with random delays and Markovian jumping parameters. The delay considered is assumed to be satisfying a certain stochastic characteristic. Meantime, the delays of GRNs are described by a binary switching sequence satisfying a conditional probability distribution. The aim of this paper is to design a state estimator to estimate the true states of the considered GRNs through the available output measurements. By using Lyapunov functional and some stochastic analysis techniques, the stability criteria of the estimation error systems are obtained in the form of linear matrix inequalities under which the estimation error dynamics is globally asymptotically stable. Then, the explicit expression of the desired estimator is shown. Finally, a numerical example is presented to show the effectiveness of the proposed results.
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.
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.
Khuwaja, Salma A; Selwyn, Beatrice J; Mgbere, Osaro; Khuwaja, Alam; Kapadia, Asha; McCurdy, Sheryl; Hsu, Chiehwen E
2013-04-01
This study explored post-migration experiences of recently migrated Pakistani Muslim adolescent females residing in the United States. In-depth, semi-structured interviews were conducted with thirty Pakistani Muslim adolescent females between the ages of 15 and 18 years living with their families in Houston, Texas. Data obtained from the interviews were evaluated using discourse analysis to identify major reoccurring themes. Participants discussed factors associated with the process of adaptation to the American culture. The results revealed that the main factors associated with adaptation process included positive motivation for migration, family bonding, social support networks, inter-familial communication, aspiration of adolescents to learn other cultures, availability of English-as-second-language programs, participation in community rebuilding activities, and faith practices, English proficiency, peer pressure, and inter-generational conflicts. This study provided much needed information on factors associated with adaptation process of Pakistani Muslim adolescent females in the United States. The results have important implications for improving the adaptation process of this group and offer potential directions for intervention and counseling services.
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.
Liu, Zong-xiang; Wu, De-hui; Xie, Wei-xin; Li, Liang-qun
2017-01-01
Tracking the target that maneuvers at a variable turn rate is a challenging problem. The traditional solution for this problem is the use of the switching multiple models technique, which includes several dynamic models with different turn rates for matching the motion mode of the target at each point in time. However, the actual motion mode of a target at any time may be different from all of the dynamic models, because these models are usually limited. To address this problem, we establish a formula for estimating the turn rate of a maneuvering target. By applying the estimation method of the turn rate to the multi-target Bayes (MB) filter, we develop a MB filter with an adaptive estimation of the turn rate, in order to track multiple maneuvering targets. Simulation results indicate that the MB filter with an adaptive estimation of the turn rate, is better than the existing filter at tracking the target that maneuvers at a variable turn rate. PMID:28212291
NASA Astrophysics Data System (ADS)
Vo, Thanh Tu; Chen, Xiaopeng; Shen, Weixiang; Kapoor, Ajay
2015-01-01
In this paper, a new charging strategy of lithium-polymer batteries (LiPBs) has been proposed based on the integration of Taguchi method (TM) and state of charge estimation. The TM is applied to search an optimal charging current pattern. An adaptive switching gain sliding mode observer (ASGSMO) is adopted to estimate the SOC which controls and terminates the charging process. The experimental results demonstrate that the proposed charging strategy can successfully charge the same types of LiPBs with different capacities and cycle life. The proposed charging strategy also provides much shorter charging time, narrower temperature variation and slightly higher energy efficiency than the equivalent constant current constant voltage charging method.
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.
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
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.
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,…
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'…
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.
Consistent thermodynamic derivative estimates for tabular equations of state.
NASA Astrophysics Data System (ADS)
Dilts, Gary
2005-07-01
A valid fluid equation of state must satisfy the thermodynamic differential conditions of consistency (derivation from a free energy) and stability (positive sound speed squared). Typical software interfaces to tabular equations of state based on polynomial or rational interpolants compute derivatives of pressure and energy and may enforce the stability conditions, but do not enforce the consistency condition and its derivatives, which is important for the computation of dimensionless quantities associated with more sensitive artificial viscosities and Riemann solvers that accurately model shock structure in regions near phase transitions. We describe a new type of table interface derived from a constrained local least squares regression technique. Application to several SESAME tables shows the consistency condition can be satisfied to round-off with third-order accuracy. An improvement of 14 orders of magnitude over conventional derivatives is demonstrated, although the new method is two orders of magnitude slower, due to solving an 11-dimensional nonlinear system. The new approach can be used to construct consistent and stable tables of derivatives, however.
Lan, Ling; Datta, Somnath
2010-04-01
As a type of multivariate survival data, multistate models have a wide range of applications, notably in cancer and infectious disease progression studies. In this article, we revisit the problem of estimation of state occupation, entry and exit times in a multistate model where various estimators have been proposed in the past under a variety of parametric and non-parametric assumptions. We focus on two non-parametric approaches, one using a product limit formula as recently proposed in Datta and Sundaram(1) and a novel approach using a fractional risk set calculation followed by a subtraction formula to calculate the state occupation probability of a transient state. A numerical comparison between the two methods is presented using detailed simulation studies. We show that the new estimators have lower statistical errors of estimation of state occupation probabilities for the distant states. We illustrate the two methods using a pubertal development data set obtained from the NHANES III.(2).
Ferguson, Gail M; Bornstein, Marc H; Pottinger, Audrey M
2012-01-01
A bidimensional acculturation framework cannot account for multiple destination cultures within contemporary settlement societies. A tridimensional model is proposed and tested among Jamaican adolescent-mother dyads in the United States compared to 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 nonimmigrant peers in Jamaica and the United States. However, assimilated adolescents, particularly first generation immigrants, have worse sociocultural adaptation than integrated and separated adolescents.
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.
NASA Astrophysics Data System (ADS)
Diodati, Gianluca; Concilio, Antonio; Ricci, Sergio; De Gaspari, Alessandro; Huvelin, Fabien; Dumont, Antoine; Godard, Jean-Luc
2013-03-01
This paper deals with the estimation of the performance of a medium-size aircraft (3-hour flight range) equipped with an adaptive trailing edge device (ATED) that runs span-wise from the wing root in the flap zone and extends chord-wise for a limited percentage of the MAC. Computations are calculated referring to the full wing and do not refer to the complete aircraft configuration. Aerodynamic computations, taking into account ideal shapes, have been performed by using both Euler and Navier- Stokes method in order to extract the wing polars for the reference and the optimal wing, implementing an ATED, deflected upwards and downwards. A comparison of the achieved results is discussed. Considering the shape domain, a suitable interpolation procedure has been set up to obtain the wing polar envelop of the adaptive wing, intended as the set of "best" values, picked by each different polar. At the end, the performances of the complete reference and adaptive wing are computed and compared for a symmetric, centered, leveled and steady cruise flight for a medium size aircraft. A significant fuel burn reduction estimate or, alternatively, an increased range capability is demonstrated, with margins of further improvements. The research leading to these results has gratefully received funding from the European Union Seventh Framework Programme (FP7/2007- 2013) under Grant Agreement n° 284562.
Vazquez, Alejandro; Statton, Matthew A.; Busgang, Stefanie A.
2015-01-01
Motor learning during reaching not only recalibrates movement but can also lead to small but consistent changes in the sense of arm position. Studies have suggested that this sensory effect may be the result of recalibration of a forward model that associates motor commands with their sensory consequences. Here we investigated whether similar perceptual changes occur in the lower limbs after learning a new walking pattern on a split-belt treadmill—a task that critically involves proprioception. Specifically, we studied how this motor learning task affects perception of leg speed during walking, perception of leg position during standing or walking, and perception of contact force during stepping. Our results show that split-belt adaptation leads to robust motor aftereffects and alters the perception of leg speed during walking. This is specific to the direction of walking that was trained during adaptation (i.e., backward or forward). The change in leg speed perception accounts for roughly half of the observed motor aftereffect. In contrast, split-belt adaptation does not alter the perception of leg position during standing or walking and does not change the perception of stepping force. Our results demonstrate that there is a recalibration of a sensory percept specific to the domain of the perturbation that was applied during walking (i.e., speed but not position or force). Furthermore, the motor and sensory consequences of locomotor adaptation may be linked, suggesting overlapping mechanisms driving changes in the motor and sensory domains. PMID:26424576
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.
Consistent thermodynamic derivative estimates for tabular equations of state.
Dilts, Gary A
2006-06-01
A valid fluid equation of state (EOS) must satisfy the thermodynamic conditions of consistency (derivation from a free energy) and stability (positive sound speed squared). Numerical simulations of compressible fluid flow for realistic materials require a tabular EOS, but typical software interfaces to such tables based on polynomial or rational interpolants may enforce the stability conditions, but do not enforce the consistency condition and its derivatives. The consistency condition is important for the computation of various dimensionless parameters of an EOS that may involve derivatives of up to second order which are important for the development of more sensitive artificial viscosities and Riemann solvers that accurately model shock structure in regions near phase transitions. We describe a table interface based on the tuned regression method, which is derived from a constrained local least-squares regression technique. It is applied to several SESAME EOS showing how the consistency and stability conditions can be satisfied to round-off while computing first and second derivatives with demonstrated second-order convergence. An improvement of 14 orders of magnitude over conventional derivatives is demonstrated, although the method is apparently two orders of magnitude slower, due to the fact that every evaluation requires solving an 11-dimensional nonlinear system. Application is made to the computation of the fundamental derivative.
Consistent thermodynamic derivative estimates for tabular equations of state
NASA Astrophysics Data System (ADS)
Dilts, Gary A.
2006-06-01
A valid fluid equation of state (EOS) must satisfy the thermodynamic conditions of consistency (derivation from a free energy) and stability (positive sound speed squared). Numerical simulations of compressible fluid flow for realistic materials require a tabular EOS, but typical software interfaces to such tables based on polynomial or rational interpolants may enforce the stability conditions, but do not enforce the consistency condition and its derivatives. The consistency condition is important for the computation of various dimensionless parameters of an EOS that may involve derivatives of up to second order which are important for the development of more sensitive artificial viscosities and Riemann solvers that accurately model shock structure in regions near phase transitions. We describe a table interface based on the tuned regression method, which is derived from a constrained local least-squares regression technique. It is applied to several SESAME EOS showing how the consistency and stability conditions can be satisfied to round-off while computing first and second derivatives with demonstrated second-order convergence. An improvement of 14 orders of magnitude over conventional derivatives is demonstrated, although the method is apparently two orders of magnitude slower, due to the fact that every evaluation requires solving an 11-dimensional nonlinear system. Application is made to the computation of the fundamental derivative.
Systematic variational method for statistical nonlinear state and parameter estimation
NASA Astrophysics Data System (ADS)
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.
1990-12-01
the ensemble average given a perfect adaptation. The last algorithm is the calculation of /. 3-5 As stated earlier, the gain constant /s is determined...Vita .. .. .. .. ... ... .. ... ... ... ... ... ... ... .. VITA-i vii List of Figures Figure Page 2.1. Ensemble Average of SDAT, M...2-8 2.6. Ensemble Average of N1 . . . . . . . . . . . . . . . . . . . . . . . . . 2-10 2.7. Variance of N
Plitt, Mark; Barnes, Kelly Anne; Wallace, Gregory L.; Kenworthy, Lauren; Martin, Alex
2015-01-01
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. PMID:26627261
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.
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.
Orlowska-Kowalska, Teresa; Kaminski, Marcin
2014-01-01
The paper deals with the implementation of optimized neural networks (NNs) for state variable estimation of the drive system with an elastic joint. The signals estimated by NNs are used in the control structure with a state-space controller and additional feedbacks from the shaft torque and the load speed. High estimation quality is very important for the correct operation of a closed-loop system. The precision of state variables estimation depends on the generalization properties of NNs. A short review of optimization methods of the NN is presented. Two techniques typical for regularization and pruning methods are described and tested in detail: the Bayesian regularization and the Optimal Brain Damage methods. Simulation results show good precision of both optimized neural estimators for a wide range of changes of the load speed and the load torque, not only for nominal but also changed parameters of the drive system. The simulation results are verified in a laboratory setup.
1997-03-01
ADAPTATION OF THE AIR WEATHER SERVICE FO MODEL TO FORECAST RADIATION FOG EVENT IN THE SOUTHEAST UNITED STATES THESIS Andrew C. Goodnite, Captain...ENP/97M-06 ADAPTATION OF THE AIR WEATHER SERVICE FOG MODEL TO FORECAST RADIATION FOG EVENTS IN THE SOUTHEAST UNITED STATES THESIS Andrew C. Goodnite...AIR WEATHER SERVICE FOG MODEL TO FORECAST RADIATION FOG EVENTS IN THE SOUTHEAST UNITED STATES THESIS Presented to the Faculty of the Graduate School of
An Ensemble Adjustment Filter for Tropical Pacific State Estimation.
NASA Astrophysics Data System (ADS)
Karspeck, A. R.; Anderson, J.
2006-12-01
While a significant amount of attention has been paid to ensemble based methods applied to short time scales in the atmosphere, the use of ensemble methods applied to the ENSO system, which operates as a coupled ocean/atmosphere system on a much longer timescale, has heretofore been limited. In this study, an Ensemble Adjustment Kalman Filter (EaKF) has been used to assimilate sea surface temperature data into an intermediate coupled model of the ENSO system. The model is the most recent version of the Zebiak-Cane model, dubbed LDEO5. Variants of the ZC model have been used to make operational ENSO forecasts for over 15 years. We pay particular attention to issues related to the efficacy of covariance localization for improving the quality of the analysis. Contrary to the traditional thinking on localization, we find that in this system localization tends to degrade the quality of the analysis even when the ensemble size is relatively low. We also demonstrate the utility of allowing observations of SST to impact all the physical variables in the state. This represents a break from the more common practice in 3DVAR of restricting any cross-variable impacts. Insights gained in the context of "perfect model experiments" are then used to assimilate SST from the independent analysis of Kaplan et al. (1998). The analyzed wind and thermocline depth fields will be compared with recent, independent data to assess the possibility of creating reconstructions back to 1856 (the earliest date of the Kaplan SST). Future work will focus of the application of the method to probabilistic forecast initialization.
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.
Nonsteady state carbon sequestration in forest ecosystems of China estimated by data assimilation
NASA Astrophysics Data System (ADS)
Zhou, Tao; Shi, Peijun; Jia, Gensuo; Luo, Yiqi
2013-12-01
sequestration occurs only when terrestrial ecosystems are at nonsteady states. Despite of their ubiquity in the real world, the nonsteady states of ecosystems have not been well quantified, especially at regional and global scales. In this study, we developed a two-step data assimilation scheme to estimate carbon sink strength in China's forest ecosystems. Specifically, the two-step scheme consists of a steady state step and a nonsteady state step. In the steady state step, we constrained a process-based model (Terrestrial Ecosystem Regional (TECO-R) model) against biometric data (net primary production NPP, biomass, litter, and soil organic carbon) in mature forests. With a subset of the parameter values estimated from the steady state data assimilation being fixed, the nonsteady state data assimilation was performed to estimate carbon sequestration in China's forest ecosystems. Our results indicated that 17 out of the 22 total parameters in the TECO-R model were well constrained by the biometric data with the steady state data assimilation. When observations from both mature and developing forests were used, all the 10 parameters related to carbon sequestration in vegetation and soil carbon pools were well constrained at the nonsteady state step. The estimated mean vegetation carbon sink in China's forests is 89.7 ± 16.8 gC m-2 yr-1, comparable with the values estimated from the forest inventory and other process-based regional models. The estimated mean soil and litter carbon sinks in China's forests are 14.1 ± 20.7 and 4.7 ± 6.5 gC m-2 yr-1. This study demonstrated that a two-step data assimilation scheme can be a potent tool to estimate regional carbon sequestration in nonsteady state ecosystems.
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.
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.
Transition probability estimates for non-Markov multi-state models.
Titman, Andrew C
2015-12-01
Non-parametric estimation of the transition probabilities in multi-state models is considered for non-Markov processes. Firstly, a generalization of the estimator of Pepe et al., (1991) (Statistics in Medicine) is given for a class of progressive multi-state models based on the difference between Kaplan-Meier estimators. Secondly, a general estimator for progressive or non-progressive models is proposed based upon constructed univariate survival or competing risks processes which retain the Markov property. The properties of the estimators and their associated standard errors are investigated through simulation. The estimators are demonstrated on datasets relating to survival and recurrence in patients with colon cancer and prothrombin levels in liver cirrhosis patients.
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.
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.
Adaptations to the "Whole of Government" Approach by the United States Department of State
2010-06-11
individuals do not have the means to purchase land, grow food, or purchase food outright. Natural disasters and climate changes also result in loss...The topography of power has changed . Neither national nor international governments have the power or influence they once enjoyed. Sweeping changes ...have exposed the vulnerability of nation states and their challenge to reformulate their roles and responsibilities. Patterns of conflict have changed
Dual extended Kalman filter for combined estimation of vehicle state and road friction
NASA Astrophysics Data System (ADS)
Zong, Changfu; Hu, Dan; Zheng, Hongyu
2013-03-01
Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute(HSRI) tire model; the dual extended Kalman filter (DEKF) technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions(high-friction, low-friction, and joint-friction). On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.
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.
Error Estimation and h-Adaptivity for Optimal Finite Element Analysis
NASA Technical Reports Server (NTRS)
Cwik, Tom; Lou, John
1997-01-01
The objective of adaptive meshing and automatic error control in finite element analysis is to eliminate the need for the application engineer from re-meshing and re-running design simulations to verify numerical accuracy. The user should only need to enter the component geometry and a coarse finite element mesh. The software will then autonomously and adaptively refine this mesh where needed, reducing the error in the fields to a user prescribed value. The ideal end result of the simulation is a measurable quantity (e.g. scattered field, input impedance), calculated to a prescribed error, in less time and less machine memory than if the user applied typical uniform mesh refinement by hand. It would also allow for the simulation of larger objects since an optimal mesh is created.
NASA Astrophysics Data System (ADS)
Hassane, Mamadou Maina F. Z.; Ackerer, P.
2017-02-01
In the context of parameter identification by inverse methods, an optimized adaptive downscaling parameterization is described in this work. The adaptive downscaling parameterization consists of (i) defining a parameter mesh for each parameter, independent of the flow model mesh, (ii) optimizing the parameters set related to the parameter mesh, and (iii) if the match between observed and computed heads is not accurate enough, creating a new parameter mesh via refinement (downscaling) and performing a new optimization of the parameters. Refinement and coarsening indicators are defined to optimize the parameter mesh refinement. The robustness of the refinement and coarsening indicators was tested by comparing the results of inversions using refinement without indicators, refinement with only refinement indicators and refinement with coarsening and refinement indicators. These examples showed that the indicators significantly reduce the number of degrees of freedom necessary to solve the inverse problem without a loss of accuracy. They, therefore, limit over-parameterization.
State-Bound Estimation for Nonlinear Systems Using Randomized Mu-Analysis
2014-04-30
Jae-Hun Park, Ho J. Lee, Hong S. Min, and Seon-Dong Kim. The effects of geothermal heating on the East/Japan Sea circulation. J. Geophys. Res. Oceans...AFRL-AFOSR-UK-TR-2014-0020 State-bound estimation for nonlinear systems using randomized mu-analysis Jongrae Kim THE UNIVERSITY OF GLASGOW...COVERED (From – To) 28 February 2013 – 27 February 2014 4. TITLE AND SUBTITLE State-bound estimation for nonlinear systems using randomized mu
The report documents the development of national- and state- level emissions estimates of radiatively important trace gases (RlTGs). Emissions estimates are presented for the principal anthropogenic sources of carbon dioxide (CO2), methane (CH4), chlorofluorocarbons (CFCs), and o...
NASA Astrophysics Data System (ADS)
Zou, Yuan; Hu, Xiaosong; Ma, Hongmin; Li, Shengbo Eben
2015-01-01
A combined SOC (State Of Charge) and SOH (State Of Health) estimation method over the lifespan of a lithium-ion battery is proposed. First, the SOC dependency of the nominal parameters of a first-order RC (resistor-capacitor) model is determined, and the performance degradation of the nominal model over the battery lifetime is quantified. Second, two Extended Kalman Filters with different time scales are used for combined SOC/SOH monitoring: the SOC is estimated in real-time, and the SOH (the capacity and internal ohmic resistance) is updated offline. The time scale of the SOH estimator is determined based on model accuracy deterioration. The SOC and SOH estimation results are demonstrated by using large amounts of testing data over the battery lifetime.
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.
Kunz, Cornelia U; Stallard, Nigel; Parsons, Nicholas; Todd, Susan; Friede, Tim
2017-03-01
Regulatory authorities require that the sample size of a confirmatory trial is calculated prior to the start of the trial. However, the sample size quite often depends on parameters that might not be known in advance of the study. Misspecification of these parameters can lead to under- or overestimation of the sample size. Both situations are unfavourable as the first one decreases the power and the latter one leads to a waste of resources. Hence, designs have been suggested that allow a re-assessment of the sample size in an ongoing trial. These methods usually focus on estimating the variance. However, for some methods the performance depends not only on the variance but also on the correlation between measurements. We develop and compare different methods for blinded estimation of the correlation coefficient that are less likely to introduce operational bias when the blinding is maintained. Their performance with respect to bias and standard error is compared to the unblinded estimator. We simulated two different settings: one assuming that all group means are the same and one assuming that different groups have different means. Simulation results show that the naïve (one-sample) estimator is only slightly biased and has a standard error comparable to that of the unblinded estimator. However, if the group means differ, other estimators have better performance depending on the sample size per group and the number of groups.
Gut Estimates: Pregnant women adapt to changing possibilities for squeezing through doorways
Franchak, John M.; Adolph, Karen E.
2014-01-01
Possibilities for action depend on the fit between the body and the environment. Perceiving what actions are possible is challenging because the body and the environment are always changing. How do people adapt to changes in body size and compression? In Experiment 1, we tested pregnant women monthly over the course of pregnancy to determine whether they adapted to changing possibilities for squeezing through doorways. As women gained belly girth and weight, previously passable doorways were no longer passable, but women’s decisions to attempt passage tracked their changing abilities. Moreover, their accuracy was equivalent to that of non-pregnant adults. In Experiment 2, non-pregnant adults wore a “pregnancy pack” that instantly increased the size of their bellies and judged whether doorways were passable. Accuracy in “pregnant” participants was only marginally worse than that of actual pregnant women, suggesting that participants adapted to the prosthesis during the test session. In Experiment 3, participants wore the pregnancy pack and gauged passability before and after attempting passage. Judgments were grossly inaccurate prior to receiving feedback. Findings indicate that experience facilitates perceptual-motor recalibration for certain types of actions. PMID:24338434
NASA Astrophysics Data System (ADS)
Yuan, Chunhua; Wang, Jiang; Yi, Guosheng
2017-03-01
Estimation of ion channel parameters is crucial to spike initiation of neurons. The biophysical neuron models have numerous ion channel parameters, but only a few of them play key roles in the firing patterns of the models. So we choose three parameters featuring the adaptation in the Ermentrout neuron model to be estimated. However, the traditional particle swarm optimization (PSO) algorithm is still easy to fall into local optimum and has the premature convergence phenomenon in the study of some problems. In this paper, we propose an improved method that uses a concave function and dynamic logistic chaotic mapping mixed to adjust the inertia weights of the fitness value, effectively improve the global convergence ability of the algorithm. The perfect predicting firing trajectories of the rebuilt model using the estimated parameters prove that only estimating a few important ion channel parameters can establish the model well and the proposed algorithm is effective. Estimations using two classic PSO algorithms are also compared to the improved PSO to verify that the algorithm proposed in this paper can avoid local optimum and quickly converge to the optimal value. The results provide important theoretical foundations for building biologically realistic neuron models.
Adaptation of the Z-cone model to the estimation of the energy of a bcc foam
NASA Astrophysics Data System (ADS)
Murtagh, R.; Whyte, D.; Weaire, D.; Hutzler, S.
2015-12-01
The Z-cone model, which provides an analytic approach to the estimation of the energy of a foam for simple crystal structures, has been adapted to the important case of the body-centred cubic foam. This involves defining two types of cones, corresponding to the two sets of neighbouring bubbles that come into contact. We obtain results which compare favourably with accurate computations using the Surface Evolver. The precise dependence of energy on liquid fraction close to the loss of the ? contacts remains to be resolved.
NASA Astrophysics Data System (ADS)
Urano, Shoichi; Mori, Hiroyuki
This paper proposes a new technique for determining of state values in power systems. Recently, it is useful for carrying out state estimation with data of PMU (Phasor Measurement Unit). The authors have developed a method for determining state values with artificial neural network (ANN) considering topology observability in power systems. ANN has advantage to approximate nonlinear functions with high precision. The method evaluates pseudo-measurement state values of the data which are lost in power systems. The method is successfully applied to the IEEE 14-bus system.
Wu, Jianyong; Zhou, Ying; Gao, Yang; Fu, Joshua S.; Johnson, Brent; Huang, Cheng; Kim, Young-Min; Liu, Yang
2014-01-01
Background: It is anticipated that climate change will influence heat-related mortality in the future. However, the estimation of excess mortality attributable to future heat waves is subject to large uncertainties, which have not been examined under the latest greenhouse gas emission scenarios. Objectives: We estimated the future heat wave impact on mortality in the eastern United States (~ 1,700 counties) under two Representative Concentration Pathways (RCPs) and analyzed the sources of uncertainties. Methods Using dynamically downscaled hourly temperature projections in 2057-2059, we calculated heat wave days and episodes based on four heat wave metrics, and estimated the excess mortality attributable to them. The sources of uncertainty in estimated excess mortality were apportioned using a variance-decomposition method. Results: In the eastern U.S., the excess mortality attributable to heat waves could range from 200-7,807 with the mean of 2,379 persons/year in 2057-2059. The projected average excess mortality in RCP 4.5 and 8.5 scenarios was 1,403 and 3,556 persons/year, respectively. Excess mortality would be relatively high in the southern and eastern coastal areas. The major sources of uncertainty in the estimates are relative risk of heat wave mortality, the RCP scenarios, and the heat wave definitions. Conclusions: The estimated mortality risks from future heat waves are likely an order of magnitude higher than its current level and lead to thousands of deaths each year under the RCP8.5 scenario. The substantial spatial variability in estimated county-level heat mortality suggests that effective mitigation and adaptation measures should be developed based on spatially resolved data.
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.
State Variability in Children's Medicaid/CHIP Crowd-Out Estimates
Muhlestein, David B.; Seiber, Eric E.
2013-01-01
Background Health insurance crowd-out occurs when individuals enrolled in a public health insurance plan would have enrolled in a private plan but for the public option. The crowding-out of private insurance is often used to criticize state Medicaid and Children's Health Insurance Program (CHIP) expansion, as already insured children move their coverage to the states at the public's expense. A difficulty in discussing crowd-out comes from inconsistent estimates. Previous work focusing on the expansion of public programs has led to estimates ranging from 0% to 50% of the children newly insured on public plans being crowded-out. Methods We apply a regression discontinuity approach to estimate how many children near the state Medicaid/CHIP threshold are crowded-out of private insurance. This approach allows estimates of crowd-out near the eligibility threshold independent of any expansion. Data from the American Community Survey's yearly survey of American households allows for state-level estimates of crowd-out. Results We find considerable heterogeneity in the crowd-out that occurs in each state, ranging from no crowd-out to over 18% in states with similar eligibility thresholds. Additionally, we found that as state eligibility thresholds increase, children are less likely to be crowded-out. Discussion This research indicates that national estimates of crowd-out are inappropriate, as state-specific Medicaid and CHIP programs have state-specific crowd-out. Additionally, it indicates that wealthier families that are eligible for public insurance are less likely to switch from private to public coverage than families earning less. Future work should identify reasons for the heterogeneity among states. PMID:24753969
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
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.
Adaptive relaxation for the steady-state analysis of Markov chains
NASA Technical Reports Server (NTRS)
Horton, Graham
1994-01-01
We consider a variant of the well-known Gauss-Seidel method for the solution of Markov chains in steady state. Whereas the standard algorithm visits each state exactly once per iteration in a predetermined order, the alternative approach uses a dynamic strategy. A set of states to be visited is maintained which can grow and shrink as the computation progresses. In this manner, we hope to concentrate the computational work in those areas of the chain in which maximum improvement in the solution can be achieved. We consider the adaptive approach both as a solver in its own right and as a relaxation method within the multi-level algorithm. Experimental results show significant computational savings in both cases.
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.
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.
NASA Astrophysics Data System (ADS)
Azam, Sazuan N. M.
2017-01-01
In this paper, we used the modified quadruple tank system that represents a multi-input-multi-output (MIMO) system as an example to present the realization of a linear discrete-time state space model and to obtain the state estimation using Kalman filter in a methodical mannered. First, an existing dynamics of the system of stochastic differential equations is linearized to produce the deterministic-stochastic linear transfer function. Then the linear transfer function is discretized to produce a linear discrete-time state space model that has a deterministic and a stochastic component. The filtered part of the Kalman filter is used to estimates the current state, based on the model and the measurements. The static and dynamic Kalman filter is compared and all results is demonstrated through simulations.
A spline-based parameter and state estimation technique for static models of elastic surfaces
NASA Technical Reports Server (NTRS)
Banks, H. T.; Daniel, P. L.; Armstrong, E. S.
1983-01-01
Parameter and state estimation techniques for an elliptic system arising in a developmental model for the antenna surface in the Maypole Hoop/Column antenna are discussed. A computational algorithm based on spline approximations for the state and elastic parameters is given and numerical results obtained using this algorithm are summarized.
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.
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.
Finite-Time State Estimation for Coupled Markovian Neural Networks With Sensor Nonlinearities.
Wang, Zhuo; Xu, Yong; Lu, Renquan; Peng, Hui
2017-03-01
This paper investigates the issue of finite-time state estimation for coupled Markovian neural networks subject to sensor nonlinearities, where the Markov chain with partially unknown transition probabilities is considered. A Luenberger-type state estimator is proposed based on incomplete measurements, and the estimation error system is derived by using the Kronecker product. By using the Lyapunov method, sufficient conditions are established, which guarantee that the estimation error system is stochastically finite-time bounded and stochastically finite-time stable, respectively. Then, the estimator gains are obtained via solving a set of coupled linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the proposed new design method.
Improvement of ocean state estimation by assimilating mapped Argo drift data.
Masuda, Shuhei; Sugiura, Nozomi; Osafune, Satoshi; Doi, Toshimasa
2014-01-01
We investigated the impact of assimilating a mapped dataset of subsurface ocean currents into an ocean state estimation. We carried out two global ocean state estimations from 2000 to 2007 using the K7 four-dimensional variational data synthesis system, one of which included an additional map of climatological geostrophic currents estimated from the global set of Argo floats. We assessed the representativeness of the volume transport in the two exercises. The assimilation of Argo ocean current data at only one level, 1000 dbar depth, had subtle impacts on the estimated volume transports, which were strongest in the subtropical North Pacific. The corrections at 10(°)N, where the impact was most notable, arose through the nearly complete offset of wind stress curl by the data synthesis system in conjunction with the first mode baroclinic Rossby wave adjustment. Our results imply that subsurface current data can be effective for improving the estimation of global oceanic circulation by a data synthesis.
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…
Comparison of Adaptive Spectral Estimation for Vehicle Speed Measurement with Radar Sensors.
Shariff, Khairul Khaizi Mohd; Hoare, Edward; Daniel, Liam; Antoniou, Michail; Cherniakov, Mikhail
2017-04-02
Vehicle speed-over-ground (SoG) radar offers significant advantages over conventional speed measurement systems. Radar sensors enable contactless speed measurement, which is free from wheel slip. One of the key issues in SoG radar is the development of the Doppler shift estimation algorithm. In this paper, we compared two algorithms to estimate a mean Doppler frequency accurately. The first is the center-of-mass algorithm, which based on spectrum center-of-mass estimation with a bandwidth-limiting technique. The second is the cross-correlation algorithm, which is based on a cross-correlation technique by cross-correlating Doppler spectrum with a theoretical Gaussian curve. Analysis shows that both algorithms are computationally efficient and suitable for real-time SoG systems. Our extensive simulated and experimental results show both methods achieved low estimation error between 0.5% and 1.5% for flat road conditions. In terms of reliability, the cross-correlation method shows good performance under low Signal-to-Noise Ratio (SNR) while the center-of-mass method failed in this condition.
NASA Astrophysics Data System (ADS)
Tagade, Piyush; Hariharan, Krishnan S.; Kolake, Subramanya Mayya; Song, Taewon; Oh, Dukjin
2017-03-01
A novel approach for integrating a pseudo-two dimensional electrochemical thermal (P2D-ECT) model and data assimilation algorithm is presented for lithium-ion cell state estimation. This approach refrains from making any simplifications in the P2D-ECT model while making it amenable for online state estimation. Though deterministic, uncertainty in the initial states induces stochasticity in the P2D-ECT model. This stochasticity is resolved by spectrally projecting the stochastic P2D-ECT model on a set of orthogonal multivariate Hermite polynomials. Volume averaging in the stochastic dimensions is proposed for efficient numerical solution of the resultant model. A state estimation framework is developed using a transformation of the orthogonal basis to assimilate the measurables with this system of equations. Effectiveness of the proposed method is first demonstrated by assimilating the cell voltage and temperature data generated using a synthetic test bed. This validated method is used with the experimentally observed cell voltage and temperature data for state estimation at different operating conditions and drive cycle protocols. The results show increased prediction accuracy when the data is assimilated every 30s. High accuracy of the estimated states is exploited to infer temperature dependent behavior of the lithium-ion cell.
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.
NASA Astrophysics Data System (ADS)
Siddlingeshwar, B.; Hanagodimath, S. M.
2009-04-01
The ground state ( μg) and the excited state ( μe) 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 ( μg and μe) 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 ( ETN). It was observed that dipole moment values of excited states ( μe) were higher than corresponding ground state values ( μg), indicating a substantial redistribution of the π-electron densities in a more polar excited state for all the molecules investigated.