Applications of adaptive state estimation theory
NASA Technical Reports Server (NTRS)
Moose, R. L.; Vanlandingham, H. F.; Mccabe, D. H.
1980-01-01
Two main areas of application of adaptive state estimation theory are presented. Following a review of the basic estimation approach, its application to both the control of nonlinear plants and to the problem of tracking maneuvering targets is presented. Results are brought together from these two areas of investigation to provide insight into the wide range of possible applications of the general estimation method.
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
NASA Astrophysics Data System (ADS)
D'Amato, Anthony M.
Input reconstruction is the process of using the output of a system to estimate its input. In some cases, input reconstruction can be accomplished by determining the output of the inverse of a model of the system whose input is the output of the original system. Inversion, however, requires an exact and fully known analytical model, and is limited by instabilities arising from nonminimum-phase zeros. The main contribution of this work is a novel technique for input reconstruction that does not require model inversion. This technique is based on a retrospective cost, which requires a limited number of Markov parameters. Retrospective cost input reconstruction (RCIR) does not require knowledge of nonminimum-phase zero locations or an analytical model of the system. RCIR provides a technique that can be used for model refinement, state estimation, and adaptive control. In the model refinement application, data are used to refine or improve a model of a system. It is assumed that the difference between the model output and the data is due to an unmodeled subsystem whose interconnection with the modeled system is inaccessible, that is, the interconnection signals cannot be measured and thus standard system identification techniques cannot be used. Using input reconstruction, these inaccessible signals can be estimated, and the inaccessible subsystem can be fitted. We demonstrate input reconstruction in a model refinement framework by identifying unknown physics in a space weather model and by estimating an unknown film growth in a lithium ion battery. The same technique can be used to obtain estimates of states that cannot be directly measured. Adaptive control can be formulated as a model-refinement problem, where the unknown subsystem is the idealized controller that minimizes a measured performance variable. Minimal modeling input reconstruction for adaptive control is useful for applications where modeling information may be difficult to obtain. We demonstrate
NASA Technical Reports Server (NTRS)
Balas, Mark J.; Thapa Magar, Kaman S.; Frost, Susan A.
2013-01-01
A theory called Adaptive Disturbance Tracking Control (ADTC) is introduced and used to track the Tip Speed Ratio (TSR) of 5 MW Horizontal Axis Wind Turbine (HAWT). Since ADTC theory requires wind speed information, a wind disturbance generator model is combined with lower order plant model to estimate the wind speed as well as partial states of the wind turbine. In this paper, we present a proof of stability and convergence of ADTC theory with lower order estimator and show that the state feedback can be adaptive.
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively. PMID:27438835
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix ‘R’ and the system noise V-C matrix ‘Q’. Then, the global filter uses R to calculate the information allocation factor ‘β’ for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively. PMID:27438835
Adaptive Kalman filter based state of charge estimation algorithm for lithium-ion battery
NASA Astrophysics Data System (ADS)
Zheng, Hong; Liu, Xu; Wei, Min
2015-09-01
In order to improve the accuracy of the battery state of charge (SOC) estimation, in this paper we take a lithium-ion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate. Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded. Project supported by the National Natural Science Foundation of China (Grant Nos. 61004048 and 61201010).
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.
NASA Astrophysics Data System (ADS)
Wang, Shuoqin; Verbrugge, Mark; Wang, John S.; Liu, Ping
2011-10-01
We report the development of an adaptive, multi-parameter battery state estimator based on the direct solution of the differential equations that govern an equivalent circuit representation of the battery. The core of the estimator includes two sets of inter-related equations corresponding to discharge and charge events respectively. Simulation results indicate that the estimator gives accurate prediction and numerically stable performance in the regression of model parameters. The estimator is implemented in a vehicle-simulated environment to predict the state of charge (SOC) and the charge and discharge power capabilities (state of power, SOP) of a lithium ion battery. Predictions for the SOC and SOP agree well with experimental measurements, demonstrating the estimator's application in battery management systems. In particular, this new approach appears to be very stable for high-frequency data streams.
NASA Astrophysics Data System (ADS)
Xiong, Rui; Gong, Xianzhi; Mi, Chunting Chris; Sun, Fengchun
2013-12-01
This paper presents a novel data-driven based approach for the estimation of the state of charge (SoC) of multiple types of lithium ion battery (LiB) cells with adaptive extended Kalman filter (AEKF). A modified second-order RC network based battery model is employed for the state estimation. Based on the battery model and experimental data, the SoC variation per mV voltage for different types of battery chemistry is analyzed and the parameters are identified. The AEKF algorithm is then employed to achieve accurate data-driven based SoC estimation, and the multi-parameter, closed loop feedback system is used to achieve robustness. The accuracy and convergence of the proposed approach is analyzed for different types of LiB cells, including convergence behavior of the model with a large initial SoC error. The results show that the proposed approach has good accuracy for different types of LiB cells, especially for C/LFP LiB cell that has a flat open circuit voltage (OCV) curve. The experimental results show good agreement with the estimation results with maximum error being less than 3%.
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.
NASA Astrophysics Data System (ADS)
Shankar, Praveen
The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. This algorithm is implemented on a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Self-Organizing Radial Basis Function Network (SORBFN) to minimize the effect of the inversion error which may occur due to imperfect modeling, approximate inversion or sudden changes in aircraft dynamics. The dynamic inversion controller is simulated for different situations including control surface failures, modeling errors and external disturbances with and without the adaptive network. A performance measure of maximum tracking error is specified for both the controllers a priori. Excellent tracking error minimization to a pre-specified level using the adaptive approximation based controller was achieved while the baseline dynamic inversion controller failed to meet this performance specification. The performance of the SORBFN based controller is also compared to a fixed RBF network
NASA Astrophysics Data System (ADS)
Bewley, Thomas
2015-11-01
Accurate long-term forecasts of the path and intensity of hurricanes are imperative to protect property and save lives. Accurate estimations and forecasts of the spread of large-scale contaminant plumes, such as those from Deepwater Horizon, Fukushima, and recent volcanic eruptions in Iceland, are essential for assessing environment impact, coordinating remediation efforts, and in certain cases moving folks out of harm's way. The challenges in estimating and forecasting such systems include: (a) environmental flow modeling, (b) high-performance real-time computing, (c) assimilating measured data into numerical simulations, and (d) acquiring in-situ data, beyond what can be measured from satellites, that is maximally relevant for reducing forecast uncertainty. This talk will focus on new techniques for addressing (c) and (d), namely, data assimilation and adaptive observation, in both hurricanes and large-scale environmental plumes. In particular, we will present a new technique for the energy-efficient coordination of swarms of sensor-laden balloons for persistent, in-situ, distributed, real-time measurement of developing hurricanes, leveraging buoyancy control only (coupled with the predictable and strongly stratified flowfield within the hurricane). Animations of these results are available at http://flowcontrol.ucsd.edu/3dhurricane.mp4 and http://flowcontrol.ucsd.edu/katrina.mp4. We also will survey our unique hybridization of the venerable Ensemble Kalman and Variational approaches to large-scale data assimilation in environmental flow systems, and how essentially the dual of this hybrid approach may be used to solve the adaptive observation problem in a uniquely effective and rigorous fashion.
Adaptive link selection algorithms for distributed estimation
NASA Astrophysics Data System (ADS)
Xu, Songcen; de Lamare, Rodrigo C.; Poor, H. Vincent
2015-12-01
This paper presents adaptive link selection algorithms for distributed estimation and considers their application to wireless sensor networks and smart grids. In particular, exhaustive search-based least mean squares (LMS) / recursive least squares (RLS) link selection algorithms and sparsity-inspired LMS / RLS link selection algorithms that can exploit the topology of networks with poor-quality links are considered. The proposed link selection algorithms are then analyzed in terms of their stability, steady-state, and tracking performance and computational complexity. In comparison with the existing centralized or distributed estimation strategies, the key features of the proposed algorithms are as follows: (1) more accurate estimates and faster convergence speed can be obtained and (2) the network is equipped with the ability of link selection that can circumvent link failures and improve the estimation performance. The performance of the proposed algorithms for distributed estimation is illustrated via simulations in applications of wireless sensor networks and smart grids.
NASA Astrophysics Data System (ADS)
Duong, Van-Huan; Bastawrous, Hany Ayad; Lim, KaiChin; See, Khay Wai; Zhang, Peng; Dou, Shi Xue
2015-11-01
This paper deals with the contradiction between simplicity and accuracy of the LiFePO4 battery states estimation in the electric vehicles (EVs) battery management system (BMS). State of charge (SOC) and state of health (SOH) are normally obtained from estimating the open circuit voltage (OCV) and the internal resistance of the equivalent electrical circuit model of the battery, respectively. The difficulties of the parameters estimation arise from their complicated variations and different dynamics which require sophisticated algorithms to simultaneously estimate multiple parameters. This, however, demands heavy computation resources. In this paper, we propose a novel technique which employs a simplified model and multiple adaptive forgetting factors recursive least-squares (MAFF-RLS) estimation to provide capability to accurately capture the real-time variations and the different dynamics of the parameters whilst the simplicity in computation is still retained. The validity of the proposed method is verified through two standard driving cycles, namely Urban Dynamometer Driving Schedule and the New European Driving Cycle. The proposed method yields experimental results that not only estimated the SOC with an absolute error of less than 2.8% but also characterized the battery model parameters accurately.
Adaptable state based control system
NASA Technical Reports Server (NTRS)
Rasmussen, Robert D. (Inventor); Dvorak, Daniel L. (Inventor); Gostelow, Kim P. (Inventor); Starbird, Thomas W. (Inventor); Gat, Erann (Inventor); Chien, Steve Ankuo (Inventor); Keller, Robert M. (Inventor)
2004-01-01
An autonomous controller, comprised of a state knowledge manager, a control executor, hardware proxies and a statistical estimator collaborates with a goal elaborator, with which it shares common models of the behavior of the system and the controller. The elaborator uses the common models to generate from temporally indeterminate sets of goals, executable goals to be executed by the controller. The controller may be updated to operate in a different system or environment than that for which it was originally designed by the replacement of shared statistical models and by the instantiation of a new set of state variable objects derived from a state variable class. The adaptation of the controller does not require substantial modification of the goal elaborator for its application to the new system or environment.
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.
Adaptive vehicle motion estimation and prediction
NASA Astrophysics Data System (ADS)
Zhao, Liang; Thorpe, Chuck E.
1999-01-01
Accurate motion estimation and reliable maneuver prediction enable an automated car to react quickly and correctly to the rapid maneuvers of the other vehicles, and so allow safe and efficient navigation. In this paper, we present a car tracking system which provides motion estimation, maneuver prediction and detection of the tracked car. The three strategies employed - adaptive motion modeling, adaptive data sampling, and adaptive model switching probabilities - result in an adaptive interacting multiple model algorithm (AIMM). The experimental results on simulated and real data demonstrate that our tracking system is reliable, flexible, and robust. The adaptive tracking makes the system intelligent and useful in various autonomous driving tasks.
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.
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.
Error magnitude estimation in model-reference adaptive systems
NASA Technical Reports Server (NTRS)
Colburn, B. K.; Boland, J. S., III
1975-01-01
A second order approximation is derived from a linearized error characteristic equation for Lyapunov designed model-reference adaptive systems and is used to estimate the maximum error between the model and plant states, and the time to reach this peak following a plant perturbation. The results are applicable in the analysis of plants containing magnitude-dependent nonlinearities.
Quantum state estimation with informationally overcomplete measurements
NASA Astrophysics Data System (ADS)
Zhu, Huangjun
2014-07-01
We study informationally overcomplete measurements for quantum state estimation so as to clarify their tomographic significance as compared with minimal informationally complete measurements. We show that informationally overcomplete measurements can improve the tomographic efficiency significantly over minimal measurements when the states of interest have high purities. Nevertheless, the efficiency is still too limited to be satisfactory with respect to figures of merit based on monotone Riemannian metrics, such as the Bures metric and quantum Chernoff metric. In this way, we also pinpoint the limitation of nonadaptive measurements and motivate the study of more sophisticated measurement schemes. In the course of our study, we introduce the best linear unbiased estimator and show that it is equally efficient as the maximum likelihood estimator in the large sample limit. This estimator may significantly outperform the canonical linear estimator for states with high purities. It is expected to play an important role in experimental designs and adaptive quantum state tomography besides its significance to the current study.
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.
Fast adaptive estimation of multidimensional psychometric functions.
DiMattina, Christopher
2015-01-01
Recently in vision science there has been great interest in understanding the perceptual representations of complex multidimensional stimuli. Therefore, it is becoming very important to develop methods for performing psychophysical experiments with multidimensional stimuli and efficiently estimating psychometric models that have multiple free parameters. In this methodological study, I analyze three efficient implementations of the popular Ψ method for adaptive data collection, two of which are novel approaches to psychophysical experiments. Although the standard implementation of the Ψ procedure is intractable in higher dimensions, I demonstrate that my implementations generalize well to complex psychometric models defined in multidimensional stimulus spaces and can be implemented very efficiently on standard laboratory computers. I show that my implementations may be of particular use for experiments studying how subjects combine multiple cues to estimate sensory quantities. I discuss strategies for speeding up experiments and suggest directions for future research in this rapidly growing area at the intersection of cognitive science, neuroscience, and machine learning. PMID:26200886
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.
Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions
Liu, Weidong; Luo, Xi
2014-01-01
This paper proposes a new method for estimating sparse precision matrices in the high dimensional setting. It has been popular to study fast computation and adaptive procedures for this problem. We propose a novel approach, called Sparse Column-wise Inverse Operator, to address these two issues. We analyze an adaptive procedure based on cross validation, and establish its convergence rate under the Frobenius norm. The convergence rates under other matrix norms are also established. This method also enjoys the advantage of fast computation for large-scale problems, via a coordinate descent algorithm. Numerical merits are illustrated using both simulated and real datasets. In particular, it performs favorably on an HIV brain tissue dataset and an ADHD resting-state fMRI dataset. PMID:25750463
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…
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
Runtime Verification with State Estimation
NASA Technical Reports Server (NTRS)
Stoller, Scott D.; Bartocci, Ezio; Seyster, Justin; Grosu, Radu; Havelund, Klaus; Smolka, Scott A.; Zadok, Erez
2011-01-01
We introduce the concept of Runtime Verification with State Estimation and show how this concept can be applied to estimate theprobability that a temporal property is satisfied by a run of a program when monitoring overhead is reduced by sampling. In such situations, there may be gaps in the observed program executions, thus making accurate estimation challenging. To deal with the effects of sampling on runtime verification, we view event sequences as observation sequences of a Hidden Markov Model (HMM), use an HMM model of the monitored program to "fill in" sampling-induced gaps in observation sequences, and extend the classic forward algorithm for HMM state estimation (which determines the probability of a state sequence, given an observation sequence) to compute the probability that the property is satisfied by an execution of the program. To validate our approach, we present a case study based on the mission software for a Mars rover. The results of our case study demonstrate high prediction accuracy for the probabilities computed by our algorithm. They also show that our technique is much more accurate than simply evaluating the temporal property on the given observation sequences, ignoring the gaps.
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…
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. PMID:25051769
Adaptive distributed video coding with correlation estimation using expectation propagation
NASA Astrophysics Data System (ADS)
Cui, Lijuan; Wang, Shuang; Jiang, Xiaoqian; Cheng, Samuel
2012-10-01
Distributed video coding (DVC) is rapidly increasing in popularity by the way of shifting the complexity from encoder to decoder, whereas no compression performance degrades, at least in theory. In contrast with conventional video codecs, the inter-frame correlation in DVC is explored at decoder based on the received syndromes of Wyner-Ziv (WZ) frame and side information (SI) frame generated from other frames available only at decoder. However, the ultimate decoding performances of DVC are based on the assumption that the perfect knowledge of correlation statistic between WZ and SI frames should be available at decoder. Therefore, the ability of obtaining a good statistical correlation estimate is becoming increasingly important in practical DVC implementations. Generally, the existing correlation estimation methods in DVC can be classified into two main types: pre-estimation where estimation starts before decoding and on-the-fly (OTF) estimation where estimation can be refined iteratively during decoding. As potential changes between frames might be unpredictable or dynamical, OTF estimation methods usually outperforms pre-estimation techniques with the cost of increased decoding complexity (e.g., sampling methods). In this paper, we propose a low complexity adaptive DVC scheme using expectation propagation (EP), where correlation estimation is performed OTF as it is carried out jointly with decoding of the factor graph-based DVC code. Among different approximate inference methods, EP generally offers better tradeoff between accuracy and complexity. Experimental results show that our proposed scheme outperforms the benchmark state-of-the-art DISCOVER codec and other cases without correlation tracking, and achieves comparable decoding performance but with significantly low complexity comparing with sampling method.
Model reference adaptive control, estimation and identification using only input and output signals
NASA Technical Reports Server (NTRS)
Carroll, R. L.; Monopoli, R. V.
1975-01-01
Significant recent advances in the application of stability theory to the adaptive control and identification of systems, and adaptive state estimation, are considered. Emphasis is on those methods which utilize only input and output measurements of the system, and do not require derivatives of the output signal.
J-Adaptive estimation with estimated noise statistics. [for orbit determination
NASA Technical Reports Server (NTRS)
Jazwinski, A. H.; Hipkins, C.
1975-01-01
The J-Adaptive estimator described by Jazwinski and Hipkins (1972) is extended to include the simultaneous estimation of the statistics of the unmodeled system accelerations. With the aid of simulations it is demonstrated that the J-Adaptive estimator with estimated noise statistics can automatically estimate satellite orbits to an accuracy comparable with the data noise levels, when excellent, continuous tracking coverage is available. Such tracking coverage will be available from satellite-to-satellite tracking.
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
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.
Finite element error estimation and adaptivity based on projected stresses
Jung, J.
1990-08-01
This report investigates the behavior of a family of finite element error estimators based on projected stresses, i.e., continuous stresses that are a least squared error fit to the conventional Gauss point stresses. An error estimate based on element force equilibrium appears to be quite effective. Examples of adaptive mesh refinement for a one-dimensional problem are presented. Plans for two-dimensional adaptivity are discussed. 12 refs., 82 figs.
Rath, J J; Veluvolu, K C; Defoort, M
2014-01-01
The estimation of road excitation profile is important for evaluation of vehicle stability and vehicle suspension performance for autonomous vehicle control systems. In this work, the nonlinear dynamics of the active automotive system that is excited by the unknown road excitation profile are considered for modeling. To address the issue of estimation of road profile, we develop an adaptive supertwisting observer for state and unknown road profile estimation. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is proven. Simulation results with Ford Fiesta MK2 demonstrate the effectiveness of the proposed observer for state and unknown input estimation for nonlinear active suspension system. PMID:24683321
Rath, J. J.; Veluvolu, K. C.; Defoort, M.
2014-01-01
The estimation of road excitation profile is important for evaluation of vehicle stability and vehicle suspension performance for autonomous vehicle control systems. In this work, the nonlinear dynamics of the active automotive system that is excited by the unknown road excitation profile are considered for modeling. To address the issue of estimation of road profile, we develop an adaptive supertwisting observer for state and unknown road profile estimation. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is proven. Simulation results with Ford Fiesta MK2 demonstrate the effectiveness of the proposed observer for state and unknown input estimation for nonlinear active suspension system. PMID:24683321
Encoding attentional states during visuomotor adaptation.
Im, Hee Yeon; Bédard, Patrick; Song, Joo-Hyun
2015-01-01
We recently showed that visuomotor adaptation acquired under attentional distraction is better recalled under a similar level of distraction compared to no distraction. This paradoxical effect suggests that attentional state (e.g., divided or undivided) is encoded as an internal context during visuomotor learning and should be reinstated for successful recall (Song & Bédard, 2015). To investigate if there is a critical temporal window for encoding attentional state in visuomotor memory, we manipulated whether participants performed the secondary attention-demanding task concurrently in the early or late phase of visuomotor learning. Recall performance was enhanced when the attentional states between recall and the early phase of visuomotor learning were consistent. However, it reverted to untrained levels when tested under the attentional state of the late-phase learning. This suggests that attentional state is primarily encoded during the early phase of learning before motor errors decrease and reach an asymptote. Furthermore, we demonstrate that when divided and undivided attentional states were mixed during visuomotor adaptation, only divided attention was encoded as an internal cue for memory retrieval. Therefore, a single attentional state appears to be primarily integrated with visuomotor memory while motor error reduction is in progress during learning. PMID:26114683
Snowpack Estimates Improve Water Resources Climate-Change Adaptation Strategies
NASA Astrophysics Data System (ADS)
Lestak, L.; Molotch, N. P.; Guan, B.; Granger, S. L.; Nemeth, S.; Rizzardo, D.; Gehrke, F.; Franz, K. J.; Karsten, L. R.; Margulis, S. A.; Case, K.; Anderson, M.; Painter, T. H.; Dozier, J.
2010-12-01
Observed climate trends over the past 50 years indicate a reduction in snowpack water storage across the Western U.S. As the primary water source for the region, the loss in snowpack water storage presents significant challenges for managing water deliveries to meet agricultural, municipal, and hydropower demands. Improved snowpack information via remote sensing shows promise for improving seasonal water supply forecasts and for informing decadal scale infrastructure planning. An ongoing project in the California Sierra Nevada and examples from the Rocky Mountains indicate the tractability of estimating snowpack water storage on daily time steps using a distributed snowpack reconstruction model. Fractional snow covered area (FSCA) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data were used with modeled snowmelt from the snowpack model to estimate snow water equivalent (SWE) in the Sierra Nevada (64,515 km2). Spatially distributed daily SWE estimates were calculated for 10 years, 2000-2009, with detailed analysis for two anamolous years, 2006, a wet year and 2009, an over-forecasted year. Sierra-wide mean SWE was 0.8 cm for 01 April 2006 versus 0.4 cm for 01 April 2009, comparing favorably with known outflow. Modeled SWE was compared to in-situ (observed) SWE for 01 April 2006 for the Feather (northern Sierra, lower-elevation) and Merced (central Sierra, higher-elevation) basins, with mean modeled SWE 80% of observed SWE. Integration of spatial SWE estimates into forecasting operations will allow for better visualization and analysis of high-altitude late-season snow missed by in-situ snow sensors and inter-annual anomalies associated with extreme precipitation events/atmospheric rivers. Collaborations with state and local entities establish protocols on how to meet current and future information needs and improve climate-change adaptation strategies.
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.
Probability estimation in arithmetic and adaptive-Huffman entropy coders.
Duttweiler, D L; Chamzas, C
1995-01-01
Entropy coders, such as Huffman and arithmetic coders, achieve compression by exploiting nonuniformity in the probabilities under which a random variable to be coded takes on its possible values. Practical realizations generally require running adaptive estimates of these probabilities. An analysis of the relationship between estimation quality and the resulting coding efficiency suggests a particular scheme, dubbed scaled-count, for obtaining such estimates. It can optimally balance estimation accuracy against a need for rapid response to changing underlying statistics. When the symbols being coded are from a binary alphabet, simple hardware and software implementations requiring almost no computation are possible. A scaled-count adaptive probability estimator of the type described in this paper is used in the arithmetic coder of the JBIG and JPEG image coding standards. PMID:18289975
Adaptive Error Estimation in Linearized Ocean General Circulation Models
NASA Technical Reports Server (NTRS)
Chechelnitsky, Michael Y.
1999-01-01
Data assimilation methods are routinely used in oceanography. The statistics of the model and measurement errors need to be specified a priori. This study addresses the problem of estimating model and measurement error statistics from observations. We start by testing innovation based methods of adaptive error estimation with low-dimensional models in the North Pacific (5-60 deg N, 132-252 deg E) to TOPEX/POSEIDON (TIP) sea level anomaly data, acoustic tomography data from the ATOC project, and the MIT General Circulation Model (GCM). A reduced state linear model that describes large scale internal (baroclinic) error dynamics is used. The methods are shown to be sensitive to the initial guess for the error statistics and the type of observations. A new off-line approach is developed, the covariance matching approach (CMA), where covariance matrices of model-data residuals are "matched" to their theoretical expectations using familiar least squares methods. This method uses observations directly instead of the innovations sequence and is shown to be related to the MT method and the method of Fu et al. (1993). Twin experiments using the same linearized MIT GCM suggest that altimetric data are ill-suited to the estimation of internal GCM errors, but that such estimates can in theory be obtained using acoustic data. The CMA is then applied to T/P sea level anomaly data and a linearization of a global GFDL GCM which uses two vertical modes. We show that the CMA method can be used with a global model and a global data set, and that the estimates of the error statistics are robust. We show that the fraction of the GCM-T/P residual variance explained by the model error is larger than that derived in Fukumori et al.(1999) with the method of Fu et al.(1993). Most of the model error is explained by the barotropic mode. However, we find that impact of the change in the error statistics on the data assimilation estimates is very small. This is explained by the large
State energy data report 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.
Stability and error estimation for Component Adaptive Grid methods
NASA Technical Reports Server (NTRS)
Oliger, Joseph; Zhu, Xiaolei
1994-01-01
Component adaptive grid (CAG) methods for solving hyperbolic partial differential equations (PDE's) are discussed in this paper. Applying recent stability results for a class of numerical methods on uniform grids. The convergence of these methods for linear problems on component adaptive grids is established here. Furthermore, the computational error can be estimated on CAG's using the stability results. Using these estimates, the error can be controlled on CAG's. Thus, the solution can be computed efficiently on CAG's within a given error tolerance. Computational results for time dependent linear problems in one and two space dimensions are presented.
Adaptive frequency estimation by MUSIC (Multiple Signal Classification) method
NASA Astrophysics Data System (ADS)
Karhunen, Juha; Nieminen, Esko; Joutsensalo, Jyrki
During the last years, the eigenvector-based method called MUSIC has become very popular in estimating the frequencies of sinusoids in additive white noise. Adaptive realizations of the MUSIC method are studied using simulated data. Several of the adaptive realizations seem to give in practice equally good results as the nonadaptive standard realization. The only exceptions are instantaneous gradient type algorithms that need considerably more samples to achieve a comparable performance. A new method is proposed for constructing initial estimates to the signal subspace. The method improves often dramatically the performance of instantaneous gradient type algorithms. The new signal subspace estimate can also be used to define a frequency estimator directly or to simplify eigenvector computation.
Distributed estimation for adaptive sensor selection in wireless sensor networks
NASA Astrophysics Data System (ADS)
Mahmoud, Magdi S.; Hassan Hamid, Matasm M.
2014-05-01
Wireless sensor networks (WSNs) are usually deployed for monitoring systems with the distributed detection and estimation of sensors. Sensor selection in WSNs is considered for target tracking. A distributed estimation scenario is considered based on the extended information filter. A cost function using the geometrical dilution of precision measure is derived for active sensor selection. A consensus-based estimation method is proposed in this paper for heterogeneous WSNs with two types of sensors. The convergence properties of the proposed estimators are analyzed under time-varying inputs. Accordingly, a new adaptive sensor selection (ASS) algorithm is presented in which the number of active sensors is adaptively determined based on the absolute local innovations vector. Simulation results show that the tracking accuracy of the ASS is comparable to that of the other algorithms.
State energy data report 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.
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.
Estimation of cosmological parameters using adaptive importance sampling
Wraith, Darren; Kilbinger, Martin; Benabed, Karim; Prunet, Simon; Cappe, Olivier; Fort, Gersende; Cardoso, Jean-Francois; Robert, Christian P.
2009-07-15
We present a Bayesian sampling algorithm called adaptive importance sampling or population Monte Carlo (PMC), whose computational workload is easily parallelizable and thus has the potential to considerably reduce the wall-clock time required for sampling, along with providing other benefits. To assess the performance of the approach for cosmological problems, we use simulated and actual data consisting of CMB anisotropies, supernovae of type Ia, and weak cosmological lensing, and provide a comparison of results to those obtained using state-of-the-art Markov chain Monte Carlo (MCMC). For both types of data sets, we find comparable parameter estimates for PMC and MCMC, with the advantage of a significantly lower wall-clock time for PMC. In the case of WMAP5 data, for example, the wall-clock time scale reduces from days for MCMC to hours using PMC on a cluster of processors. Other benefits of the PMC approach, along with potential difficulties in using the approach, are analyzed and discussed.
Brain Network Adaptability across Task States
Davison, Elizabeth N.; Schlesinger, Kimberly J.; Bassett, Danielle S.; Lynall, Mary-Ellen; Miller, Michael B.; Grafton, Scott T.; Carlson, Jean M.
2015-01-01
Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific) and across (task-general) brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region) relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change. PMID:25569227
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.
State Energy Data Report, 1991: Consumption estimates
Not Available
1993-05-01
The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to the Government, policy makers, and the public; and (2) to provide the historical series necessary for EIA`s energy models.
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.
Estimating Skin Cancer Risk: Evaluating Mobile Computer-Adaptive Testing
Djaja, Ngadiman; Janda, Monika; Olsen, Catherine M; Whiteman, David C
2016-01-01
Background Response burden is a major detriment to questionnaire completion rates. Computer adaptive testing may offer advantages over non-adaptive testing, including reduction of numbers of items required for precise measurement. Objective Our aim was to compare the efficiency of non-adaptive (NAT) and computer adaptive testing (CAT) facilitated by Partial Credit Model (PCM)-derived calibration to estimate skin cancer risk. Methods We used a random sample from a population-based Australian cohort study of skin cancer risk (N=43,794). All 30 items of the skin cancer risk scale were calibrated with the Rasch PCM. A total of 1000 cases generated following a normal distribution (mean [SD] 0 [1]) were simulated using three Rasch models with three fixed-item (dichotomous, rating scale, and partial credit) scenarios, respectively. We calculated the comparative efficiency and precision of CAT and NAT (shortening of questionnaire length and the count difference number ratio less than 5% using independent t tests). Results We found that use of CAT led to smaller person standard error of the estimated measure than NAT, with substantially higher efficiency but no loss of precision, reducing response burden by 48%, 66%, and 66% for dichotomous, Rating Scale Model, and PCM models, respectively. Conclusions CAT-based administrations of the skin cancer risk scale could substantially reduce participant burden without compromising measurement precision. A mobile computer adaptive test was developed to help people efficiently assess their skin cancer risk. PMID:26800642
Precision of maximum likelihood estimation in adaptive designs.
Graf, Alexandra Christine; Gutjahr, Georg; Brannath, Werner
2016-03-15
There has been increasing interest in trials that allow for design adaptations like sample size reassessment or treatment selection at an interim analysis. Ignoring the adaptive and multiplicity issues in such designs leads to an inflation of the type 1 error rate, and treatment effect estimates based on the maximum likelihood principle become biased. Whereas the methodological issues concerning hypothesis testing are well understood, it is not clear how to deal with parameter estimation in designs were adaptation rules are not fixed in advanced so that, in practice, the maximum likelihood estimate (MLE) is used. It is therefore important to understand the behavior of the MLE in such designs. The investigation of Bias and mean squared error (MSE) is complicated by the fact that the adaptation rules need not be fully specified in advance and, hence, are usually unknown. To investigate Bias and MSE under such circumstances, we search for the sample size reassessment and selection rules that lead to the maximum Bias or maximum MSE. Generally, this leads to an overestimation of Bias and MSE, which can be reduced by imposing realistic constraints on the rules like, for example, a maximum sample size. We consider designs that start with k treatment groups and a common control and where selection of a single treatment and control is performed at the interim analysis with the possibility to reassess each of the sample sizes. We consider the case of unlimited sample size reassessments as well as several realistically restricted sample size reassessment rules. PMID:26459506
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.
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.
Optimal State Estimation for Cavity Optomechanical Systems.
Wieczorek, Witlef; Hofer, Sebastian G; Hoelscher-Obermaier, Jason; Riedinger, Ralf; Hammerer, Klemens; Aspelmeyer, Markus
2015-06-01
We demonstrate optimal state estimation for a cavity optomechanical system through Kalman filtering. By taking into account nontrivial experimental noise sources, such as colored laser noise and spurious mechanical modes, we implement a realistic state-space model. This allows us to obtain the conditional system state, i.e., conditioned on previous measurements, with a minimal least-squares estimation error. We apply this method to estimate the mechanical state, as well as optomechanical correlations both in the weak and strong coupling regime. The application of the Kalman filter is an important next step for achieving real-time optimal (classical and quantum) control of cavity optomechanical systems. PMID:26196621
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
Adaptive whitening of the electromyogram to improve amplitude estimation.
Clancy, E A; Farry, K A
2000-06-01
Previous research showed that whitening the surface electromyogram (EMG) can improve EMG amplitude estimation (where EMG amplitude is defined as the time-varying standard deviation of the EMG). However, conventional whitening via a linear filter seems to fail at low EMG amplitude levels, perhaps due to additive background noise in the measured EMG. This paper describes an adaptive whitening technique that overcomes this problem by cascading a nonadaptive whitening filter, an adaptive Wiener filter, and an adaptive gain correction. These stages can be calibrated from two, five second duration, constant-angle, constant-force contractions, one at a reference level [e.g., 50% maximum voluntary contraction (MVC)] and one at 0% MVC. In experimental studies, subjects used real-time EMG amplitude estimates to track a uniform-density, band-limited random target. With a 0.25-Hz bandwidth target, either adaptive whitening or multiple-channel processing reduced the tracking error roughly half-way to the error achieved using the dynamometer signal as the feedback. At the 1.00-Hz bandwidth, all of the EMG processors had errors equivalent to that of the dynamometer signal, reflecting that errors in this task were dominated by subjects' inability to track targets at this bandwidth. Increases in the additive noise level, smoothing window length, and tracking bandwidth diminish the advantages of whitening. PMID:10833845
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.
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.
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.
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
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. PMID:26831389
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.
State estimation for spacecraft power systems
NASA Technical Reports Server (NTRS)
Williamson, Susan H.; Sheble, Gerald B.
1990-01-01
A state estimator appropriate for spacecraft power systems is presented. Phasor voltage and current measurements are used to determine the system state. A weighted least squares algorithm with a multireference transmission cable model is used. Bad data are identified and resolved. Once the bad data have been identified, they are removed from the measurement set and the system state can be estimated from the remaining data. An observability analysis is performed on the remaining measurements to determine if the system state can be found from the reduced measurement set. An example of the algorithm for a sample spacecraft power system is presented.
State-Space Algorithms for Estimating Spike Rate Functions
Smith, Anne C.; Scalon, Joao D.; Wirth, Sylvia; Yanike, Marianna; Suzuki, Wendy A.; Brown, Emery N.
2010-01-01
The accurate characterization of spike firing rates including the determination of when changes in activity occur is a fundamental issue in the analysis of neurophysiological data. Here we describe a state-space model for estimating the spike rate function that provides a maximum likelihood estimate of the spike rate, model goodness-of-fit assessments, as well as confidence intervals for the spike rate function and any other associated quantities of interest. Using simulated spike data, we first compare the performance of the state-space approach with that of Bayesian adaptive regression splines (BARS) and a simple cubic spline smoothing algorithm. We show that the state-space model is computationally efficient and comparable with other spline approaches. Our results suggest both a theoretically sound and practical approach for estimating spike rate functions that is applicable to a wide range of neurophysiological data. PMID:19911062
ERIC Educational Resources Information Center
Raiche, Gilles; Blais, Jean-Guy
In a computerized adaptive test (CAT), it would be desirable to obtain an acceptable precision of the proficiency level estimate using an optimal number of items. Decreasing the number of items is accompanied, however, by a certain degree of bias when the true proficiency level differs significantly from the a priori estimate. G. Raiche (2000) has…
NASA Astrophysics Data System (ADS)
Fleischer, Christian; Waag, Wladislaw; Heyn, Hans-Martin; Sauer, Dirk Uwe
2014-08-01
Lithium-ion battery systems employed in high power demanding systems such as electric vehicles require a sophisticated monitoring system to ensure safe and reliable operation. Three major states of the battery are of special interest and need to be constantly monitored, these include: battery state of charge (SoC), battery state of health (capcity fade determination, SoH), and state of function (power fade determination, SoF). In a series of two papers, we propose a system of algorithms based on a weighted recursive least quadratic squares parameter estimator, that is able to determine the battery impedance and diffusion parameters for accurate state estimation. The functionality was proven on different battery chemistries with different aging conditions. The first paper investigates the general requirements on BMS for HEV/EV applications. In parallel, the commonly used methods for battery monitoring are reviewed to elaborate their strength and weaknesses in terms of the identified requirements for on-line applications. Special emphasis will be placed on real-time capability and memory optimized code for cost-sensitive industrial or automotive applications in which low-cost microcontrollers must be used. Therefore, a battery model is presented which includes the influence of the Butler-Volmer kinetics on the charge-transfer process. Lastly, the mass transport process inside the battery is modeled in a novel state-space representation.
Impact of PMU Technology in State Estimation
Avila-Rosales, Rene; Rice, Mark; Lopez, Rafael; Beard, Lisa; Mathur, Tanya; Galvan, Floyd; Gupta, Vinit; James, Lambert; Graffy, James; Papic, Milorad
2008-01-01
Recent blackouts and the need to manage larger and larger power systems closer to their stability limits are driving electricity utilities to deploy synchronized phasor measurements (PMU) for wide area monitoring and control. At the same time, modern TSOs need accurate, fast and reliable estimation of their networks' real-time conditions. State Estimator (SE), a fundamental function in dispatching control centers, is expected to perform reliably around the clock since it provides the foundation for subsequent critical security analyses, market revenue calculations, etc. PMU technology provides accurate, satellite-synchronized measurements of both magnitude and angle, which naturally fit in the SE algorithm and enhance its robustness and the quality of the results. This paper presents current results of ongoing experiences with electric utilities on the usage of PMU data in state estimation. It focuses on practical implementation aspects, such as data communication and interfacing to the control center EMS, metrics for evaluation of State Estimator results and improvements in state estimation behavior and results thanks to PMU data.
Energy Detection Based Estimation of Channel Occupancy Rate with Adaptive Noise Estimation
NASA Astrophysics Data System (ADS)
Lehtomäki, Janne J.; Vuohtoniemi, Risto; Umebayashi, Kenta; Mäkelä, Juha-Pekka
Recently, there has been growing interest in opportunistically utilizing the 2.4GHz ISM-band. Numerous spectrum occupancy measurements covering the ISM-band have been performed to analyze the spectrum usage. However, in these campaigns the verification of the correctness of the obtained occupancy values for the highly dynamic ISM-band has not been presented. In this paper, we propose and verify channel occupancy rate (COR) estimation utilizing energy detection mechanism with a novel adaptive energy detection threshold setting method. The results are compared with the true reference COR values. Several different types of verification measurements showed that our setup can estimate the COR values of 802.11 traffic well, with negligible overestimation. The results from real-time real-life measurements also confirm that the proposed adaptive threshold setting method enables accurate thresholds even in the situations where multiple interferers are present in the received signal.
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.
Estimation after subpopulation selection in adaptive seamless trials.
Kimani, Peter K; Todd, Susan; Stallard, Nigel
2015-08-15
During the development of new therapies, it is not uncommon to test whether a new treatment works better than the existing treatment for all patients who suffer from a condition (full population) or for a subset of the full population (subpopulation). One approach that may be used for this objective is to have two separate trials, where in the first trial, data are collected to determine if the new treatment benefits the full population or the subpopulation. The second trial is a confirmatory trial to test the new treatment in the population selected in the first trial. In this paper, we consider the more efficient two-stage adaptive seamless designs (ASDs), where in stage 1, data are collected to select the population to test in stage 2. In stage 2, additional data are collected to perform confirmatory analysis for the selected population. Unlike the approach that uses two separate trials, for ASDs, stage 1 data are also used in the confirmatory analysis. Although ASDs are efficient, using stage 1 data both for selection and confirmatory analysis introduces selection bias and consequently statistical challenges in making inference. We will focus on point estimation for such trials. In this paper, we describe the extent of bias for estimators that ignore multiple hypotheses and selecting the population that is most likely to give positive trial results based on observed stage 1 data. We then derive conditionally unbiased estimators and examine their mean squared errors for different scenarios. PMID:25903293
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.
Cryptography and system state estimation using polarization states
NASA Astrophysics Data System (ADS)
Kak, Subhash; Verma, Pramode; MacDonald, Greg
2011-09-01
We present new results on cryptography and system state estimation using polarization states of photons. Current quantum cryptography applications are based on the BB84 protocol which is not secure against photon siphoning attacks. Recent research has established that the information that can be obtained from a pure state in repeated experiments is potentially infinite. This can be harnessed by sending a burst of photons confined to a very narrow time window, each such burst containing several bits of information. The proposed method represents a new way of transmitting secret information. While polarization shift-keying methods have been proposed earlier, our method is somewhat different in that it proposes to discover the polarization state of identical photons in a burst from a laser which codes binary information. We also present results on estimating the state of a system based on the polarization of the received photons which can have applications in intrusion detection.
A state estimation of Liu equations
NASA Astrophysics Data System (ADS)
Ananyev, B. I.
2015-11-01
This paper is concerned with state estimation problems for so-called Liu equations. These equations are counterparts of well-known Ito ones and they were introduced by B. Liu under elaboration of his uncertain theory. The Liu equations may be solved backward and they represent a more convenient object for the state estimation problem solution especially for the case when distributions of disturbances are unknown. Using the dynamic programming principle, we derive an equation for the informational set consisting of all states that are compatible with measuring data. Special cases of Liu equations and constraints for disturbances are examined. Among them the linear equations with quadratic constraints are considered in most details. Some examples are also given.
Zorgani, Youssef Agrebi; Koubaa, Yassine; Boussak, Mohamed
2016-03-01
This paper presents a novel method for estimating the load torque of a sensorless indirect stator flux oriented controlled (ISFOC) induction motor drive based on the model reference adaptive system (MRAS) scheme. As a matter of fact, this method is meant to inter-connect a speed estimator with the load torque observer. For this purpose, a MRAS has been applied to estimate the rotor speed with tuned load torque in order to obtain a high performance ISFOC induction motor drive. The reference and adjustable models, developed in the stationary stator reference frame, are used in the MRAS scheme in an attempt to estimate the speed of the measured terminal voltages and currents. The load torque is estimated by means of a Luenberger observer defined throughout the mechanical equation. Every observer state matrix depends on the mechanical characteristics of the machine taking into account the vicious friction coefficient and inertia moment. Accordingly, some simulation results are presented to validate the proposed method and to highlight the influence of the variation of the inertia moment and the friction coefficient on the speed and the estimated load torque. The experimental results, concerning to the sensorless speed with a load torque estimation, are elaborated in order to validate the effectiveness of the proposed method. The complete sensorless ISFOC with load torque estimation is successfully implemented in real time using a digital signal processor board DSpace DS1104 for a laboratory 3 kW induction motor. PMID:26775088
Optimal error regions for quantum state estimation
NASA Astrophysics Data System (ADS)
Shang, Jiangwei; Khoon Ng, Hui; Sehrawat, Arun; Li, Xikun; Englert, Berthold-Georg
2013-12-01
An estimator is a state that represents one's best guess of the actual state of the quantum system for the given data. Such estimators are points in the state space. To be statistically meaningful, they have to be endowed with error regions, the generalization of error bars beyond one dimension. As opposed to standard ad hoc constructions of error regions, we introduce the maximum-likelihood region—the region of largest likelihood among all regions of the same size—as the natural counterpart of the popular maximum-likelihood estimator. Here, the size of a region is its prior probability. A related concept is the smallest credible region—the smallest region with pre-chosen posterior probability. In both cases, the optimal error region has constant likelihood on its boundary. This surprisingly simple characterization permits concise reporting of the error regions, even in high-dimensional problems. For illustration, we identify optimal error regions for single-qubit and two-qubit states from computer-generated data that simulate incomplete tomography with few measured copies.
Estimated Water Flows in 2005: United States
Smith, C A; Belles, R D; Simon, A J
2011-03-16
Flow charts depicting water use in the United States have been constructed from publicly available data and estimates of water use patterns. Approximately 410,500 million gallons per day of water are managed throughout the United States for use in farming, power production, residential, commercial, and industrial applications. Water is obtained from four major resource classes: fresh surface-water, saline (ocean) surface-water, fresh groundwater and saline (brackish) groundwater. Water that is not consumed or evaporated during its use is returned to surface bodies of water. The flow patterns are represented in a compact 'visual atlas' of 52 state-level (all 50 states in addition to Puerto Rico and the Virgin Islands) and one national water flow chart representing a comprehensive systems view of national water resources, use, and disposition.
An Empirical State Error Covariance Matrix for Batch State Estimation
NASA Technical Reports Server (NTRS)
Frisbee, Joseph H., Jr.
2011-01-01
State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. Consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. It then follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully account for the error in the state estimate. By way of a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm, it is possible to arrive at an appropriate, and formally correct, empirical state error covariance matrix. The first specific step of the method is to use the average form of the weighted measurement residual variance performance index rather than its usual total weighted residual form. Next it is helpful to interpret the solution to the normal equations as the average of a collection of sample vectors drawn from a hypothetical parent population. From here, using a standard statistical analysis approach, it directly follows as to how to determine the standard empirical state error covariance matrix. This matrix will contain the total uncertainty in the
Parallel State Estimation Assessment with Practical Data
Chen, Yousu; Jin, Shuangshuang; Rice, Mark J.; Huang, Zhenyu
2013-07-31
This paper presents a parallel state estimation (PSE) implementation using a preconditioned gradient algorithm and an orthogonal decomposition-based algorithm. The preliminary tests against a commercial Energy Management System (EMS) State Estimation (SE) tool using real-world data are performed. The results show that while the precondition gradient algorithm can solve the SE problem quicker with the help of parallel computing techniques, it might not be good for real-world data due to the large condition number of gain matrix introduced by the wide range of measurement weights. With the help of PETSc package and considering one iteration of the SE process, the orthogonal decomposition-based PSE algorithm can achieve 5-20 times speedup comparing against the commercial EMS tool. It is very promising that the developed PSE can solve the SE problem for large power systems at the SCADA rate, to improve grid reliability.
Estimated United States Transportation Energy Use 2005
Smith, C A; Simon, A J; Belles, R D
2011-11-09
A flow chart depicting energy flow in the transportation sector of the United States economy in 2005 has been constructed from publicly available data and estimates of national energy use patterns. Approximately 31,000 trillion British Thermal Units (trBTUs) of energy were used throughout the United States in transportation activities. Vehicles used in these activities include automobiles, motorcycles, trucks, buses, airplanes, rail, and ships. The transportation sector is powered primarily by petroleum-derived fuels (gasoline, diesel and jet fuel). Biomass-derived fuels, electricity and natural gas-derived fuels are also used. The flow patterns represent a comprehensive systems view of energy used within the transportation sector.
Codon Usage Selection Can Bias Estimation of the Fraction of Adaptive Amino Acid Fixations.
Matsumoto, Tomotaka; John, Anoop; Baeza-Centurion, Pablo; Li, Boyang; Akashi, Hiroshi
2016-06-01
A growing number of molecular evolutionary studies are estimating the proportion of adaptive amino acid substitutions (α) from comparisons of ratios of polymorphic and fixed DNA mutations. Here, we examine how violations of two of the model assumptions, neutral evolution of synonymous mutations and stationary base composition, affect α estimation. We simulated the evolution of coding sequences assuming weak selection on synonymous codon usage bias and neutral protein evolution, α = 0. We show that weak selection on synonymous mutations can give polymorphism/divergence ratios that yield α-hat (estimated α) considerably larger than its true value. Nonstationary evolution (changes in population size, selection, or mutation) can exacerbate such biases or, in some scenarios, give biases in the opposite direction, α-hat < α. These results demonstrate that two factors that appear to be prevalent among taxa, weak selection on synonymous mutations and non-steady-state nucleotide composition, should be considered when estimating α. Estimates of the proportion of adaptive amino acid fixations from large-scale analyses of Drosophila melanogaster polymorphism and divergence data are positively correlated with codon usage bias. Such patterns are consistent with α-hat inflation from weak selection on synonymous mutations and/or mutational changes within the examined gene trees. PMID:26873577
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 neuro-fuzzy estimation of optimal lens system parameters
NASA Astrophysics Data System (ADS)
Petković, Dalibor; Pavlović, Nenad T.; Shamshirband, Shahaboddin; Mat Kiah, Miss Laiha; Badrul Anuar, Nor; Idna Idris, Mohd Yamani
2014-04-01
Due to the popularization of digital technology, the demand for high-quality digital products has become critical. The quantitative assessment of image quality is an important consideration in any type of imaging system. Therefore, developing a design that combines the requirements of good image quality is desirable. Lens system design represents a crucial factor for good image quality. Optimization procedure is the main part of the lens system design methodology. Lens system optimization is a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. Therefore lens system design provides ideal problems for intelligent optimization algorithms. There are many tools which can be used to measure optical performance. One very useful tool is the spot diagram. The spot diagram gives an indication of the image of a point object. In this paper, one optimization criterion for lens system, the spot size radius, is considered. This paper presents new lens optimization methods based on adaptive neuro-fuzzy inference strategy (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated.
Discrete Inverse and State Estimation Problems
NASA Astrophysics Data System (ADS)
Wunsch, Carl
2006-06-01
The problems of making inferences about the natural world from noisy observations and imperfect theories occur in almost all scientific disciplines. This book addresses these problems using examples taken from geophysical fluid dynamics. It focuses on discrete formulations, both static and time-varying, known variously as inverse, state estimation or data assimilation problems. Starting with fundamental algebraic and statistical ideas, the book guides the reader through a range of inference tools including the singular value decomposition, Gauss-Markov and minimum variance estimates, Kalman filters and related smoothers, and adjoint (Lagrange multiplier) methods. The final chapters discuss a variety of practical applications to geophysical flow problems. Discrete Inverse and State Estimation Problems is an ideal introduction to the topic for graduate students and researchers in oceanography, meteorology, climate dynamics, and geophysical fluid dynamics. It is also accessible to a wider scientific audience; the only prerequisite is an understanding of linear algebra. Provides a comprehensive introduction to discrete methods of inference from incomplete information Based upon 25 years of practical experience using real data and models Develops sequential and whole-domain analysis methods from simple least-squares Contains many examples and problems, and web-based support through MIT opencourseware
Resting State Network Estimation in Individual Subjects
Hacker, Carl D.; Laumann, Timothy O.; Szrama, Nicholas P.; Baldassarre, Antonello; Snyder, Abraham Z.
2014-01-01
Resting-state functional magnetic resonance imaging (fMRI) has been used to study brain networks associated with both normal and pathological cognitive function. The objective of this work is to reliably compute resting state network (RSN) topography in single participants. We trained a supervised classifier (multi-layer perceptron; MLP) to associate blood oxygen level dependent (BOLD) correlation maps corresponding to pre-defined seeds with specific RSN identities. Hard classification of maps obtained from a priori seeds was highly reliable across new participants. Interestingly, continuous estimates of RSN membership retained substantial residual error. This result is consistent with the view that RSNs are hierarchically organized, and therefore not fully separable into spatially independent components. After training on a priori seed-based maps, we propagated voxel-wise correlation maps through the MLP to produce estimates of RSN membership throughout the brain. The MLP generated RSN topography estimates in individuals consistent with previous studies, even in brain regions not represented in the training data. This method could be used in future studies to relate RSN topography to other measures of functional brain organization (e.g., task-evoked responses, stimulation mapping, and deficits associated with lesions) in individuals. The multi-layer perceptron was directly compared to two alternative voxel classification procedures, specifically, dual regression and linear discriminant analysis; the perceptron generated more spatially specific RSN maps than either alternative. PMID:23735260
Adaptive optimal stochastic state feedback control of resistive wall modes in tokamaks
Sun, Z.; Sen, A.K.; Longman, R.W.
2006-01-15
An adaptive optimal stochastic state feedback control is developed to stabilize the resistive wall mode (RWM) instability in tokamaks. The extended least-square method with exponential forgetting factor and covariance resetting is used to identify (experimentally determine) the time-varying stochastic system model. A Kalman filter is used to estimate the system states. The estimated system states are passed on to an optimal state feedback controller to construct control inputs. The Kalman filter and the optimal state feedback controller are periodically redesigned online based on the identified system model. This adaptive controller can stabilize the time-dependent RWM in a slowly evolving tokamak discharge. This is accomplished within a time delay of roughly four times the inverse of the growth rate for the time-invariant model used.
Spin State Estimation of Tumbling Small Bodies
NASA Astrophysics Data System (ADS)
Olson, Corwin; Russell, Ryan P.; Bhaskaran, Shyam
2016-06-01
It is expected that a non-trivial percentage of small bodies that future missions may visit are in non-principal axis rotation (i.e. "tumbling"). The primary contribution of this paper is the application of the Extended Kalman Filter (EKF) Simultaneous Localization and Mapping (SLAM) method to estimate the small body spin state, mass, and moments of inertia; the spacecraft position and velocity; and the surface landmark locations. The method uses optical landmark measurements, and an example scenario based on the Rosetta mission is used. The SLAM method proves effective, with order of magnitude decreases in the spacecraft and small body spin state errors after less than a quarter of the comet characterization phase. The SLAM method converges nicely for initial small body angular velocity errors several times larger than the true rates (effectively having no a priori knowledge of the angular velocity). Surface landmark generation and identification are not treated in this work, but significant errors in the initial body-fixed landmark positions are effectively estimated. The algorithm remains effective for a range of different truth spin states, masses, and center of mass offsets that correspond to expected tumbling small bodies throughout the solar system.
Spin State Estimation of Tumbling Small Bodies
NASA Astrophysics Data System (ADS)
Olson, Corwin; Russell, Ryan P.; Bhaskaran, Shyam
2016-02-01
It is expected that a non-trivial percentage of small bodies that future missions may visit are in non-principal axis rotation (i.e. "tumbling"). The primary contribution of this paper is the application of the Extended Kalman Filter (EKF) Simultaneous Localization and Mapping (SLAM) method to estimate the small body spin state, mass, and moments of inertia; the spacecraft position and velocity; and the surface landmark locations. The method uses optical landmark measurements, and an example scenario based on the Rosetta mission is used. The SLAM method proves effective, with order of magnitude decreases in the spacecraft and small body spin state errors after less than a quarter of the comet characterization phase. The SLAM method converges nicely for initial small body angular velocity errors several times larger than the true rates (effectively having no a priori knowledge of the angular velocity). Surface landmark generation and identification are not treated in this work, but significant errors in the initial body-fixed landmark positions are effectively estimated. The algorithm remains effective for a range of different truth spin states, masses, and center of mass offsets that correspond to expected tumbling small bodies throughout the solar system.
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.
Adaptive importance sampling of random walks on continuous state spaces
Baggerly, K.; Cox, D.; Picard, R.
1998-11-01
The authors consider adaptive importance sampling for a random walk with scoring in a general state space. Conditions under which exponential convergence occurs to the zero-variance solution are reviewed. These results generalize previous work for finite, discrete state spaces in Kollman (1993) and in Kollman, Baggerly, Cox, and Picard (1996). This paper is intended for nonstatisticians and includes considerable explanatory material.
Hellander, Andreas; Lawson, Michael J; Drawert, Brian; Petzold, Linda
2015-01-01
The efficiency of exact simulation methods for the reaction-diffusion master equation (RDME) is severely limited by the large number of diffusion events if the mesh is fine or if diffusion constants are large. Furthermore, inherent properties of exact kinetic-Monte Carlo simulation methods limit the efficiency of parallel implementations. Several approximate and hybrid methods have appeared that enable more efficient simulation of the RDME. A common feature to most of them is that they rely on splitting the system into its reaction and diffusion parts and updating them sequentially over a discrete timestep. This use of operator splitting enables more efficient simulation but it comes at the price of a temporal discretization error that depends on the size of the timestep. So far, existing methods have not attempted to estimate or control this error in a systematic manner. This makes the solvers hard to use for practitioners since they must guess an appropriate timestep. It also makes the solvers potentially less efficient than if the timesteps are adapted to control the error. Here, we derive estimates of the local error and propose a strategy to adaptively select the timestep when the RDME is simulated via a first order operator splitting. While the strategy is general and applicable to a wide range of approximate and hybrid methods, we exemplify it here by extending a previously published approximate method, the Diffusive Finite-State Projection (DFSP) method, to incorporate temporal adaptivity. PMID:26865735
NASA Astrophysics Data System (ADS)
Hellander, Andreas; Lawson, Michael J.; Drawert, Brian; Petzold, Linda
2014-06-01
The efficiency of exact simulation methods for the reaction-diffusion master equation (RDME) is severely limited by the large number of diffusion events if the mesh is fine or if diffusion constants are large. Furthermore, inherent properties of exact kinetic-Monte Carlo simulation methods limit the efficiency of parallel implementations. Several approximate and hybrid methods have appeared that enable more efficient simulation of the RDME. A common feature to most of them is that they rely on splitting the system into its reaction and diffusion parts and updating them sequentially over a discrete timestep. This use of operator splitting enables more efficient simulation but it comes at the price of a temporal discretization error that depends on the size of the timestep. So far, existing methods have not attempted to estimate or control this error in a systematic manner. This makes the solvers hard to use for practitioners since they must guess an appropriate timestep. It also makes the solvers potentially less efficient than if the timesteps were adapted to control the error. Here, we derive estimates of the local error and propose a strategy to adaptively select the timestep when the RDME is simulated via a first order operator splitting. While the strategy is general and applicable to a wide range of approximate and hybrid methods, we exemplify it here by extending a previously published approximate method, the diffusive finite-state projection (DFSP) method, to incorporate temporal adaptivity.
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…
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.
Characteristics of Bayesian multiple model adaptive estimation for tracking airborne targets
NASA Astrophysics Data System (ADS)
Netzer, A. S.
1985-12-01
Previous studies at the Air Force Institute of Technology have led to the development of a multiple model adaptive filter (MMAF) tracking algorithm which provides significant improvements in tracker performance against highly-dynamic airborne targets over the currently used correlation trackers. A forward looking infra-red (FLIR) sensor is used to provide a target shape function to the tracking algorithm in the form of an 8 x 8 array of intensities projected onto a field of view (FOV). This target image measurement is correlated with an estimate of the target image template, to produce linear offset pseudo-measurements from the center of the FOV, which are provided as measurements to a bank of linear Kalman filters, in the multiple model adaptive filtering (MMAF) structure. The output of the MMAF provides the state estimates used in pointing the FLIR sensor, and generating the new target image estimate. This study investigates the characteristics of this algorithm in order to evaluate its performance against various target scenarios.
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. PMID:25100233
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 adaptive quantum tomography of two-qubit states
NASA Astrophysics Data System (ADS)
Struchalin, G. I.; Pogorelov, I. A.; Straupe, S. S.; Kravtsov, K. S.; Radchenko, I. V.; Kulik, S. P.
2016-01-01
We report an experimental realization of adaptive Bayesian quantum state tomography for two-qubit states. Our implementation is based on the adaptive experimental design strategy proposed in the work by Huszár and Houlsby [F. Huszár and N. M. T. Houlsby, Phys. Rev. A 85, 052120 (2012)., 10.1103/PhysRevA.85.052120] and provides an optimal measurement approach in terms of the information gain. We address the practical questions which one faces in any experimental application: the influence of technical noise and the behavior of the tomographic algorithm for an easy-to-implement class of factorized measurements. In an experiment with polarization states of entangled photon pairs, we observe a lower instrumental noise floor and superior reconstruction accuracy for nearly pure states of the adaptive protocol compared to a nonadaptive protocol. At the same time, we show that for the mixed states, the restriction to factorized measurements results in no advantage for adaptive measurements, so general measurements have to be used.
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
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.
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.
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.
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
Nonparametric estimation of quantum states, processes and measurements
NASA Astrophysics Data System (ADS)
Lougovski, Pavel; Bennink, Ryan
Quantum state, process, and measurement estimation methods traditionally use parametric models, in which the number and role of relevant parameters is assumed to be known. When such an assumption cannot be justified, a common approach in many disciplines is to fit the experimental data to multiple models with different sets of parameters and utilize an information criterion to select the best fitting model. However, it is not always possible to assume a model with a finite (countable) number of parameters. This typically happens when there are unobserved variables that stem from hidden correlations that can only be unveiled after collecting experimental data. How does one perform quantum characterization in this situation? We present a novel nonparametric method of experimental quantum system characterization based on the Dirichlet Process (DP) that addresses this problem. Using DP as a prior in conjunction with Bayesian estimation methods allows us to increase model complexity (number of parameters) adaptively as the number of experimental observations grows. We illustrate our approach for the one-qubit case and show how a probability density function for an unknown quantum process can be estimated.
A frequency-domain estimator for use in adaptive control systems
NASA Technical Reports Server (NTRS)
Lamaire, Richard O.; Valavani, Lena; Athans, Michael; Stein, Gunter
1991-01-01
This paper presents a frequency-domain estimator that can identify both a parametrized nominal model of a plant as well as a frequency-domain bounding function on the modeling error associated with this nominal model. This estimator, which we call a robust estimator, can be used in conjunction with a robust control-law redesign algorithm to form a robust adaptive controller.
A frequency-domain estimator for use in adaptive control systems
NASA Technical Reports Server (NTRS)
Lamaire, Richard O.; Valavani, Lena; Athans, Michael; Stein, Gunter
1987-01-01
The paper presents a frequency-domain estimator which can identify both a nominal model of a plant as well as a frequency-domain bounding function on the modeling error associated with this nominal model. This estimator, which is called a robust estimator, can be used in conjunction with a robust control-law redesign algorithm to form a robust adaptive controller.
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.
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…
An Adaptive Displacement Estimation Algorithm for Improved Reconstruction of Thermal Strain
Ding, Xuan; Dutta, Debaditya; Mahmoud, Ahmed M.; Tillman, Bryan; Leers, Steven A.; Kim, Kang
2014-01-01
Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas’ estimator and time-shift estimators like normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas’ estimator is limited by phase-wrapping and NXcorr performs poorly when the signal-to-noise ratio (SNR) is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas’ estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex-vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas’ estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas’ estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI using Field II showed that the adaptive displacement estimator was less biased than either Loupas’ estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7–350% and the spatial accuracy by 1.2–23.0% (p < 0.001). An ex-vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and results in improved strain reconstruction. PMID:25585398
Magnitude Estimation with Noisy Integrators Linked by an Adaptive Reference
Thurley, Kay
2016-01-01
Judgments of physical stimuli show characteristic biases; relatively small stimuli are overestimated whereas relatively large stimuli are underestimated (regression effect). Such biases likely result from a strategy that seeks to minimize errors given noisy estimates about stimuli that itself are drawn from a distribution, i.e., the statistics of the environment. While being conceptually well described, it is unclear how such a strategy could be implemented neurally. The present paper aims toward answering this question. A theoretical approach is introduced that describes magnitude estimation as two successive stages of noisy (neural) integration. Both stages are linked by a reference memory that is updated with every new stimulus. The model reproduces the behavioral characteristics of magnitude estimation and makes several experimentally testable predictions. Moreover, the model identifies the regression effect as a means of minimizing estimation errors and explains how this optimality strategy depends on the subject's discrimination abilities and on the stimulus statistics. The latter influence predicts another property of magnitude estimation, the so-called range effect. Beyond being successful in describing decision-making, the present work suggests that noisy integration may also be important in processing magnitudes. PMID:26909028
Magnitude Estimation with Noisy Integrators Linked by an Adaptive Reference.
Thurley, Kay
2016-01-01
Judgments of physical stimuli show characteristic biases; relatively small stimuli are overestimated whereas relatively large stimuli are underestimated (regression effect). Such biases likely result from a strategy that seeks to minimize errors given noisy estimates about stimuli that itself are drawn from a distribution, i.e., the statistics of the environment. While being conceptually well described, it is unclear how such a strategy could be implemented neurally. The present paper aims toward answering this question. A theoretical approach is introduced that describes magnitude estimation as two successive stages of noisy (neural) integration. Both stages are linked by a reference memory that is updated with every new stimulus. The model reproduces the behavioral characteristics of magnitude estimation and makes several experimentally testable predictions. Moreover, the model identifies the regression effect as a means of minimizing estimation errors and explains how this optimality strategy depends on the subject's discrimination abilities and on the stimulus statistics. The latter influence predicts another property of magnitude estimation, the so-called range effect. Beyond being successful in describing decision-making, the present work suggests that noisy integration may also be important in processing magnitudes. PMID:26909028
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
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.
Guo, Qing; Sun, Ping; Yin, Jing-Min; Yu, Tian; Jiang, Dan
2016-05-01
Some unknown parameter estimation of electro-hydraulic system (EHS) should be considered in hydraulic controller design due to many parameter uncertainties in practice. In this study, a parametric adaptive backstepping control method is proposed to improve the dynamic behavior of EHS under parametric uncertainties and unknown disturbance (i.e., hydraulic parameters and external load). The unknown parameters of EHS model are estimated by the parametric adaptive estimation law. Then the recursive backstepping controller is designed by Lyapunov technique to realize the displacement control of EHS. To avoid explosion of virtual control in traditional backstepping, a decayed memory filter is presented to re-estimate the virtual control and the dynamic external load. The effectiveness of the proposed controller has been demonstrated by comparison with the controller without adaptive and filter estimation. The comparative experimental results in critical working conditions indicate the proposed approach can achieve better dynamic performance on the motion control of Two-DOF robotic arm. PMID:26920086
Identification of linear system models and state estimators for controls
NASA Technical Reports Server (NTRS)
Chen, Chung-Wen
1992-01-01
The following paper is presented in viewgraph format and covers topics including: (1) linear state feedback control system; (2) Kalman filter state estimation; (3) relation between residual and stochastic part of output; (4) obtaining Kalman filter gain; (5) state estimation under unknown system model and unknown noises; and (6) relationship between filter Markov parameters and system Markov parameters.
Metabolic flux estimation--a self-adaptive evolutionary algorithm with singular value decomposition.
Yang, Jing; Wongsa, Sarawan; Kadirkamanathan, Visakan; Billings, Stephen A; Wright, Phillip C
2007-01-01
Metabolic flux analysis is important for metabolic system regulation and intracellular pathway identification. A popular approach for intracellular flux estimation involves using 13C tracer experiments to label states that can be measured by nuclear magnetic resonance spectrometry or gas chromatography mass spectrometry. However, the bilinear balance equations derived from 13C tracer experiments and the noisy measurements require a nonlinear optimization approach to obtain the optimal solution. In this paper, the flux quantification problem is formulated as an error-minimization problem with equality and inequality constraints through the 13C balance and stoichiometric equations. The stoichiometric constraints are transformed to a null space by singular value decomposition. Self-adaptive evolutionary algorithms are then introduced for flux quantification. The performance of the evolutionary algorithm is compared with ordinary least squares estimation by the simulation of the central pentose phosphate pathway. The proposed algorithm is also applied to the central metabolism of Corynebacterium glutamicum under lysine-producing conditions. A comparison between the results from the proposed algorithm and data from the literature is given. The complexity of a metabolic system with bidirectional reactions is also investigated by analyzing the fluctuations in the flux estimates when available measurements are varied. PMID:17277420
Estimating daily pan evaporation using adaptive neural-based fuzzy inference system
NASA Astrophysics Data System (ADS)
Keskin, M. Erol; Terzi, Özlem; Taylan, Dilek
2009-09-01
Estimation of evaporation is important for water planning, management, and hydrological practices. There are many available methods to estimate evaporation from a water surface, comprising both direct and indirect methods. All the evaporation models are based on crisp conceptions with no uncertainty element coupled into the model structure although in daily evaporation variations there are uncontrollable effects to a certain extent. The probabilistic, statistical, and stochastic approaches require large amounts of data for the modeling purposes and therefore are not practical in local evaporation studies. It is therefore necessary to adopt a better approach for evaporation modeling, which is the fuzzy sets and adaptive neural-based fuzzy inference system (ANFIS) as used in this paper. ANFIS and fuzzy sets have been evaluated for its applicability to estimate evaporation from meteorological data which is including air and water temperatures, solar radiation, and air pressure obtained from Automated GroWheather meteorological station located near Lake Eğirdir and daily pan evaporation values measured by XVIII. District Directorate of State Hydraulic Works. Results of ANFIS and fuzzy logic approaches were analyzed and compared with measured daily pan evaporation values. ANFIS approach could be employed more successfully in modeling the evaporation process than fuzzy sets.
Adaptive noise estimation and suppression for improving microseismic event detection
NASA Astrophysics Data System (ADS)
Mousavi, S. Mostafa; Langston, Charles A.
2016-09-01
Microseismic data recorded by surface arrays are often strongly contaminated by unwanted noise. This background noise makes the detection of small magnitude events difficult. A noise level estimation and noise reduction algorithm is presented for microseismic data analysis based upon minimally controlled recursive averaging and neighborhood shrinkage estimators. The method might not be compared with more sophisticated and computationally expensive denoising algorithm in terms of preserving detailed features of seismic signal. However, it is fast and data-driven and can be applied in real-time processing of continuous data for event detection purposes. Results from application of this algorithm to synthetic and real seismic data show that it holds a great promise for improving microseismic event detection.
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.
State Alcohol-Impaired-Driving Estimates
... estimates are based on data from NHTSA’s Fatality Analysis Reporting System (FARS). Unfortunately, blood alcohol concentration (BAC) ... involvement); and NHTSA’s National Center for Statistics and Analysis 1200 New Jersey Avenue SE., Washington, DC 20590 ...
Solid state, autonomous attitude estimation system
NASA Astrophysics Data System (ADS)
Rose, C. E.; Runyan, D. P.
This paper describes the means by which star tracker measurements can be used to estimate the rotational position of a space vehicle upon which the trackers are mounted. A nonlinear least squares approach is taken in which a novel method of normalizing the attitude quaternion is developed. Examples of convergence histories are included, as well as an estimate of the memory needed to hold the algorithm.
Bounded Linear Stability Analysis - A Time Delay Margin Estimation Approach for Adaptive Control
NASA Technical Reports Server (NTRS)
Nguyen, Nhan T.; Ishihara, Abraham K.; Krishnakumar, Kalmanje Srinlvas; Bakhtiari-Nejad, Maryam
2009-01-01
This paper presents a method for estimating time delay margin for model-reference adaptive control of systems with almost linear structured uncertainty. The bounded linear stability analysis method seeks to represent the conventional model-reference adaptive law by a locally bounded linear approximation within a small time window using the comparison lemma. The locally bounded linear approximation of the combined adaptive system is cast in a form of an input-time-delay differential equation over a small time window. The time delay margin of this system represents a local stability measure and is computed analytically by a matrix measure method, which provides a simple analytical technique for estimating an upper bound of time delay margin. Based on simulation results for a scalar model-reference adaptive control system, both the bounded linear stability method and the matrix measure method are seen to provide a reasonably accurate and yet not too conservative time delay margin estimation.
Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.
Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu
2015-08-01
This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm. PMID:25265622
Geometric-Algebra LMS Adaptive Filter and Its Application to Rotation Estimation
NASA Astrophysics Data System (ADS)
Lopes, Wilder B.; Al-Nuaimi, Anas; Lopes, Cassio G.
2016-06-01
This paper exploits Geometric (Clifford) Algebra (GA) theory in order to devise and introduce a new adaptive filtering strategy. From a least-squares cost function, the gradient is calculated following results from Geometric Calculus (GC), the extension of GA to handle differential and integral calculus. The novel GA least-mean-squares (GA-LMS) adaptive filter, which inherits properties from standard adaptive filters and from GA, is developed to recursively estimate a rotor (multivector), a hypercomplex quantity able to describe rotations in any dimension. The adaptive filter (AF) performance is assessed via a 3D point-clouds registration problem, which contains a rotation estimation step. Calculating the AF computational complexity suggests that it can contribute to reduce the cost of a full-blown 3D registration algorithm, especially when the number of points to be processed grows. Moreover, the employed GA/GC framework allows for easily applying the resulting filter to estimating rotors in higher dimensions.
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.
Adaptation of the Steady-state PERG in Early Glaucoma
Porciatti, Vittorio; Bosse, Brandon; Parekh, Prashant K.; Shif, Olga A.; Feuer, William J.; Ventura, Lori M.
2013-01-01
Purpose Previous studies have shown that the onset of high-contrast, fast reversing patterned stimuli induces rapid blood flow increase in retinal vessels in association with slow changes of the steady-state PERG signal. We tested the hypothesis that adaptive PERG changes of normal controls (NC) differed from those of glaucoma suspects (GS) and patients with early manifest glaucoma (EMG). Methods Subjects were 42 GS (SAP MD −0.89 ±1.8 dB), 22 EMG (MD −2.12 ±2.4 dB) with visual acuity of ≥20/20 and 16 age-matched NC from a previous study. The PERG signal was sampled every ~15 s over 4 minutes in response to gratings (1.6 cyc/deg, 100% contrast) reversing 16.28 times/s. Amplitude/phase values of successive PERG samples were fitted with a non-parametric LOWESS smoothing function to retrieve the initial and final values and calculate their difference (delta) and the residual standard deviation around the fitted function (SDr). The magnitude of PERG adaptive change compared to random variability was calculated as log10 of percentage coefficient of variation CoV=100*SDr ÷ |delta|. Grand-average PERGs were also obtained by averaging all samples of the same series. Results The grand-average PERG amplitude (ANOVA, p=0.02), but not phase (ANOVA, p=0.63), decreased with increasing severity of disease. Adaptive changes (log10 (CoV) of PERG amplitude were not significantly associated with disease severity (ANOVA, p=0.27), but adaptive changes (log10 (CoV) of PERG phase were (ANOVA, p=0.037; linear trend, p=0.011). Conclusions The steady-state PERG signal displayed slow adaptive changes over time that could be isolated from random variability. PERG adaptive changes differed from those of grand-average PERGs (corresponding the standard steady-state PERG), thus representing a new source of biological information about retinal ganglion cell function that may have potential in the study of glaucoma and optic nerve diseases. PMID:23429613
Tsai, M.F.; Tzou, Y.Y.
1997-03-01
In this paper, the authors design and implement an adaptive speed controller that can estimate load torque for ac induction motor drives employing a transputer-based parallel processing technique. The adaptive speed controller, which precedes the field-oriented control loop, consists of a two-degree-of-freedom controller and a feedforward load-torque compensator. The two-degree-of-freedom controller is designed by a pole-placement technique with polynomial manipulations. Its parameters are adjusted adaptively in terms of estimated model parameters. Estimating the model parameters entails a second-order least-squares estimator with constant trace to avoid estimator windup. The design of the feedforward compensator is based on an estimated load-torque model. Estimating the load torque entails a first-order least-squares estimator with variable forgetting factor and covariance resetting, the purposes of which are to detect any slow or sudden changes of torque disturbance, respectively. The resulting adaptive controller is implemented in parallel by IMS T800-20 transputers. Experimental results demonstrate the robustness of the proposed control method in contending with varying load and torque disturbance.
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.
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.
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
Image signal-to-noise ratio estimation using adaptive slope nearest-neighbourhood model.
Sim, K S; Teh, V
2015-12-01
A new technique based on nearest neighbourhood method is proposed. In this paper, considering the noise as Gaussian additive white noise, new technique single-image-based estimator is proposed. The performance of this new technique such as adaptive slope nearest neighbourhood is compared with three of the existing method which are original nearest neighbourhood (simple method), first-order interpolation method and shape-preserving piecewise cubic hermite autoregressive moving average. In a few cases involving images with different brightness and edges, this adaptive slope nearest neighbourhood is found to deliver an optimum solution for signal-to-noise ratio estimation problems. For different values of noise variance, the adaptive slope nearest neighbourhood has highest accuracy and less percentage estimation error. Being more robust with white noise, the new proposed technique estimator has efficiency that is significantly greater than those of the three methods. PMID:26292081
Estimating unbiased phenological trends by adapting site-occupancy models.
Roth, Tobias; Strebel, Nicolas; Amrhein, Valentin
2014-08-01
As a response to climate warming, many animals and plants have been found to shift phenologies, such as appearance in spring or timing of reproduction. However, traditional measures for shifts in phenology that are based on observational data likely are biased due to a large influence of population size, observational effort, starting date of a survey, or other causes that may affect the probability of detecting a species. Understanding phenological responses of species to climate change, however, requires a robust measure that could be compared among studies and study years. Here, we developed a new method for estimating arrival and departure dates based on site-occupancy models. Using simulated data, we show that our method provided virtually unbiased estimates of phenological events even if detection probability or the number of sites occupied by the species is changing over time. To illustrate the flexibility of our method, we analyzed spring arrival of two long-distance migrant songbirds and the length of the flight period of two butterfly species, using data from a long-term biodiversity monitoring program in Switzerland. In contrast to many birds that migrate short distances, the two long-distance migrant songbirds tended to postpone average spring arrival by -0.5 days per year between 1995 and 2012. Furthermore, the flight period of the short-distance-flying butterfly species apparently became even shorter over the study period, while the flight period of the longer-distance-flying butterfly species remained relatively stable. Our method could be applied to temporally and spatially extensive data from a wide range of monitoring programs and citizen science projects, to help unravel how species and communities respond to global warming. PMID:25230466
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.
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.
Sakaguchi, Yutaka; Takano, Mitsuo
2004-09-01
This article proposes an adaptive action-selection method for a model-free reinforcement learning system, based on the concept of the 'reliability of internal prediction/estimation'. This concept is realized using an internal variable, called the Reliability Index (RI), which estimates the accuracy of the internal estimator. We define this index for a value function of a temporal difference learning system and substitute it for the temperature parameter of the Boltzmann action-selection rule. Accordingly, the weight of exploratory actions adaptively changes depending on the uncertainty of the prediction. We use this idea for tabular and weighted-sum type value functions. Moreover, we use the RI to adjust the learning coefficient in addition to the temperature parameter, meaning that the reliability becomes a general basis for meta-learning. Numerical experiments were performed to examine the behavior of the proposed method. The RI-based Q-learning system demonstrated its features when the adaptive learning coefficient and large RI-discount rate (which indicate how the RI values of future states are reflected in the RI value of the current state) were introduced. Statistical tests confirmed that the algorithm spent more time exploring in the initial phase of learning, but accelerated learning from the midpoint of learning. It is also shown that the proposed method does not work well with the actor-critic models. The limitations of the proposed method and its relationship to relevant research are discussed. PMID:15312837
Design of optimal second-order state estimators
NASA Technical Reports Server (NTRS)
Joshi, Suresh M.
1991-01-01
The present consideration of the design of online computation-saving second-order state estimators for second-order vector-matrix differential systems proposes a class of such estimators which is proven to possess guaranteed convergence. A class of optimal second-order estimators is then obtained, and the conditions required for optimality are identified. The estimator proposed offers high performance in conjunction with online computation reductions sufficiently great to allow the estimation of the large number of state variables associated with control of large, flexible space structures represented by high-dimensional second-order systems.
Error estimation and adaptive order nodal method for solving multidimensional transport problems
Zamonsky, O.M.; Gho, C.J.; Azmy, Y.Y.
1998-01-01
The authors propose a modification of the Arbitrarily High Order Transport Nodal method whereby they solve each node and each direction using different expansion order. With this feature and a previously proposed a posteriori error estimator they develop an adaptive order scheme to automatically improve the accuracy of the solution of the transport equation. They implemented the modified nodal method, the error estimator and the adaptive order scheme into a discrete-ordinates code for solving monoenergetic, fixed source, isotropic scattering problems in two-dimensional Cartesian geometry. They solve two test problems with large homogeneous regions to test the adaptive order scheme. The results show that using the adaptive process the storage requirements are reduced while preserving the accuracy of the results.
Mode estimation and adaptive feedforward control for stabilization of a flexible gun tube
NASA Astrophysics Data System (ADS)
Vandegrift, Mark W.; DiRenzo, Michael T.
1998-07-01
In this paper we describe an approach for designing a pointing and stabilization system for an unbalanced, flexible gun. Our approach is based upon classical control techniques as well as system identification and adaptive feedforward techniques. Adaptive algorithms identify the flexible modes of the system and estimate the dynamics unbalance. This information is used to update the control law in order to improve the stabilization accuracy of the system.
Masud, Muhammad Mehedi; Junsheng, Ha; Akhtar, Rulia; Al-Amin, Abul Quasem; Kari, Fatimah Binti
2015-02-01
This paper estimates Malaysian farmers' willingness to pay (WTP) for a planned adaptation programme for addressing climate issues in the Malaysian agricultural sector. We used the contingent valuation method (CVM) for a monetary valuation of farmers' preferences for a planned adaptation programme by ascertaining the value attached to address climatic issues in the Malaysian agricultural sector. Structured questionnaires were distributed among the sampled farmers. The study found that 74 % of respondents were willing to pay for a planned adaptation programme and that several socioeconomic and motivation factors have greater influence on their WTP. This paper clearly specifies the steps needed for all institutional bodies to better address issues in climate change. The outcomes of this paper will support policy makers to better design an efficient adaptation framework for adapting to the adverse impacts of climate change. PMID:25632900
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.
Risk, resources and state-dependent adaptive behavioural syndromes
Luttbeg, Barney; Sih, Andrew
2010-01-01
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. PMID:21078650
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.
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-01-01
Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback-Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. PMID:27249002
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-01-01
Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback–Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. PMID:27249002
NASA Astrophysics Data System (ADS)
Dai, Haifeng; Zhu, Letao; Zhu, Jiangong; Wei, Xuezhe; Sun, Zechang
2015-10-01
The accurate monitoring of battery cell temperature is indispensible to the design of battery thermal management system. To obtain the internal temperature of a battery cell online, an adaptive temperature estimation method based on Kalman filtering and an equivalent time-variant electrical network thermal (EENT) model is proposed. The EENT model uses electrical components to simulate the battery thermodynamics, and the model parameters are obtained with a least square algorithm. With a discrete state-space description of the EENT model, a Kalman filtering (KF) based internal temperature estimator is developed. Moreover, considering the possible time-varying external heat exchange coefficient, a joint Kalman filtering (JKF) based estimator is designed to simultaneously estimate the internal temperature and the external thermal resistance. Several experiments using the hard-cased LiFePO4 cells with embedded temperature sensors have been conducted to validate the proposed method. Validation results show that, the EENT model expresses the battery thermodynamics well, the KF based temperature estimator tracks the real central temperature accurately even with a poor initialization, and the JKF based estimator can simultaneously estimate both central temperature and external thermal resistance precisely. The maximum estimation errors of the KF- and JKF-based estimators are less than 1.8 °C and 1 °C respectively.
NASA Astrophysics Data System (ADS)
Wu, Heng
2000-10-01
In this thesis, an a-posteriori error estimator is presented and employed for solving viscous incompressible flow problems. In an effort to detect local flow features, such as vortices and separation, and to resolve flow details precisely, a velocity angle error estimator e theta which is based on the spatial derivative of velocity direction fields is designed and constructed. The a-posteriori error estimator corresponds to the antisymmetric part of the deformation-rate-tensor, and it is sensitive to the second derivative of the velocity angle field. Rationality discussions reveal that the velocity angle error estimator is a curvature error estimator, and its value reflects the accuracy of streamline curves. It is also found that the velocity angle error estimator contains the nonlinear convective term of the Navier-Stokes equations, and it identifies and computes the direction difference when the convective acceleration direction and the flow velocity direction have a disparity. Through benchmarking computed variables with the analytic solution of Kovasznay flow or the finest grid of cavity flow, it is demonstrated that the velocity angle error estimator has a better performance than the strain error estimator. The benchmarking work also shows that the computed profile obtained by using etheta can achieve the best matching outcome with the true theta field, and that it is asymptotic to the true theta variation field, with a promise of fewer unknowns. Unstructured grids are adapted by employing local cell division as well as unrefinement of transition cells. Using element class and node class can efficiently construct a hierarchical data structure which provides cell and node inter-reference at each adaptive level. Employing element pointers and node pointers can dynamically maintain the connection of adjacent elements and adjacent nodes, and thus avoids time-consuming search processes. The adaptive scheme is applied to viscous incompressible flow at different
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.
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
NASA Astrophysics Data System (ADS)
Moore, F. C.; Lobell, D. B.
2013-12-01
Agriculture is one of the economic sectors most exposed to climate change and estimating the sensitivity of food production to these changes is critical for determining the severity of climate change impacts and for informing both adaptation and mitigation policy. While climate change might have adverse effects in many areas, it has long been recognized that farmers have a suite of adaptation options at their disposal including, inter alia, changing planting date, varieties, crops, or the mix and quantity of inputs applied. These adaptations may significantly reduce the adverse impacts of climate change but the potential effectiveness of these options and the speed with which farmers will adopt them remain uncertain. We estimate the sensitivity of crop yields and farm profits in western Europe to climate change with and without the adoption of on-farm adaptations. We use cross-sectional variation across farms to define the long-run response function that includes adaptation and inter-annual variation within farms to define the short-run response function without adaptation. The difference between these can be interpreted as the potential for adaptation. We find that future warming will have a large adverse impact on wheat and barley yields and that adaptation will only be able to mitigate a small fraction of this. Maize, oilseed and sugarbeet yields are more modestly affected and adaptation is more effective for these crops. Farm profits could increase slightly under moderate amounts of warming if adaptations are adopted but will decline in the absence of adaptation. A decomposition of variance gives the relative importance of different sources of uncertainty in projections of climate change impacts. We find that in most cases uncertainty over future adaptation pathways (whether farmers will or will not adopt beneficial adaptations) is the most important source of uncertainty in projecting the effect of temperature changes on crop yields and farm profits. This
Nonlinear State Estimation and Modeling of a Helicopter UAV
NASA Astrophysics Data System (ADS)
Barczyk, Martin
Experimentally-validated nonlinear flight control of a helicopter UAV has two necessary conditions: an estimate of the vehicle’s states from noisy multirate output measurements, and a nonlinear dynamics model with minimum complexity, physically controllable inputs and experimentally identified parameter values. This thesis addresses both these objectives for the Applied Nonlinear Controls Lab (ANCL)'s helicopter UAV project. A magnetometer-plus-GPS aided Inertial Navigation System (INS) for outdoor flight as well as an Attitude and Heading Reference System (AHRS) for indoor testing are designed, implemented and experimentally validated employing an Extended Kalman Filter (EKF), using a novel calibration technique for the magnetometer aiding sensor added to remove the limitations of an earlier GPS-only aiding design. Next the recently-developed nonlinear observer design methodology of invariant observers is adapted to the aided INS and AHRS examples, employing a rotation matrix representation for the state manifold to obtain designs amenable to global stability analysis, obtaining a direct nonlinear design for gains of the AHRS observer, modifying the previously-proposed Invariant EKF systematic method for computing gains, and culminating in simulation and experimental validation of the observers. Lastly a nonlinear control-oriented model of the helicopter UAV is derived from first principles, using a rigid-body dynamics formulation augmented with models of the on-board subsystems: main rotor forces and blade flapping dynamics, the Bell-Hiller system and flybar flapping dynamics, tail rotor forces, tail gyro unit, engine and rotor speed, servo operation, fuselage drag, and tail stabilizer forces. The parameter values in the resulting models are identified experimentally. Using these the model is further simplified to be tractable for model-based control design.
Power system state estimation for a spacecraft power system
NASA Technical Reports Server (NTRS)
Berry, F. C.; Benitez, N. L.; Cox, M. D.
1990-01-01
An application of the maximum likelihood state estimator to a space-based power system is presented. The state estimator uses current and voltage measurements to generate estimates of node voltages for an electrical power distribution system for the Space Shuttle. Preliminary results on the effect of noisy measurements on estimated parameters are reported. The software used in generating these results is part of an overall package being developed at Louisiana Tech University. Intended applications of this package include the analysis of power systems and real-time parallel processing on the Space Shuttle.
State estimation for networked systems with randomly occurring quantisations
NASA Astrophysics Data System (ADS)
He, Xiao; Wang, Zidong; Ji, Y. D.; Zhou, D. H.
2013-07-01
In this article, the state estimation problem is investigated for a class of discrete-time networked systems with randomly occurring quantisations. Logarithmic quantisers with different quantisation laws are considered and a Bernoulli distributed stochastic sequence is utilised to determine which quantiser is used at a certain time instant. After converting the quantisation effects into sector-bounded parameter uncertainties, a sufficient condition ensuring the existence of desirable estimators is proposed by using Lyapunov function approach, and parameters of the desired estimator are further obtained. Simulation is carried out on a networked three-tank system in order to illustrate the applicability of the proposed state estimation technique.
Distributed Dynamic State Estimation with Extended Kalman Filter
Du, Pengwei; Huang, Zhenyu; Sun, Yannan; Diao, Ruisheng; Kalsi, Karanjit; Anderson, Kevin K.; Li, Yulan; Lee, Barry
2011-08-04
Increasing complexity associated with large-scale renewable resources and novel smart-grid technologies necessitates real-time monitoring and control. Our previous work applied the extended Kalman filter (EKF) with the use of phasor measurement data (PMU) for dynamic state estimation. However, high computation complexity creates significant challenges for real-time applications. In this paper, the problem of distributed dynamic state estimation is investigated. One domain decomposition method is proposed to utilize decentralized computing resources. The performance of distributed dynamic state estimation is tested on a 16-machine, 68-bus test system.
Estimation of the covariance matrix of macroscopic quantum states
NASA Astrophysics Data System (ADS)
Ruppert, László; Usenko, Vladyslav C.; Filip, Radim
2016-05-01
For systems analogous to a linear harmonic oscillator, the simplest way to characterize the state is by a covariance matrix containing the symmetrically ordered moments of operators analogous to position and momentum. We show that using Stokes-like detectors without direct access to either position or momentum, the estimation of the covariance matrix of a macroscopic signal is still possible using interference with a classical noisy and low-intensity reference. Such a detection technique will allow one to estimate macroscopic quantum states of electromagnetic radiation without a coherent high-intensity local oscillator. It can be directly applied to estimate the covariance matrix of macroscopically bright squeezed states of light.
Quasi-static shape estimation and control of adaptive truss structures
NASA Technical Reports Server (NTRS)
Kuwao, Fumihiro; Chen, Gun-Shing; Wada, Ben K.
1991-01-01
Methods for estimating the deformation of adaptive truss structures are proposed which employ internal displacement sensors to measure changes in the length of selected truss members. Based on the measured data from the instrumented truss member, the total truss deformation pattern can be estimated through direct interpolation. To verify the validity of the methods presented here, numerical simulations are carried out for simple plane trusses, a beam truss, and a tetrahedral truss.
Adaptive algorithm for cloud cover estimation from all-sky images over the sea
NASA Astrophysics Data System (ADS)
Krinitskiy, M. A.; Sinitsyn, A. V.
2016-05-01
A new algorithm for cloud cover estimation has been formulated and developed based on the synthetic control index, called the grayness rate index, and an additional algorithm step of adaptive filtering of the Mie scattering contribution. A setup for automated cloud cover estimation has been designed, assembled, and tested under field conditions. The results shows a significant advantage of the new algorithm over currently commonly used procedures.
NASA Astrophysics Data System (ADS)
Bo, Yizhou; Shifa, Naima
2013-09-01
An estimator for finding the abundance of a rare, clustered and mobile population has been introduced. This model is based on adaptive cluster sampling (ACS) to identify the location of the population and negative binomial distribution to estimate the total in each site. To identify the location of the population we consider both sampling with replacement (WR) and sampling without replacement (WOR). Some mathematical properties of the model are also developed.
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.
A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops
NASA Astrophysics Data System (ADS)
Lu, Hai-liang; Wen, Xi-shan; Lan, Lei; An, Yun-zhu; Li, Xiao-ping
2015-01-01
A self-adaptive genetic algorithm for estimating Jiles-Atherton (JA) magnetic hysteresis model parameters is presented. The fitness function is established based on the distances between equidistant key points of normalized hysteresis loops. Linearity function and logarithm function are both adopted to code the five parameters of JA model. Roulette wheel selection is used and the selection pressure is adjusted adaptively by deducting a proportional which depends on current generation common value. The Crossover operator is established by combining arithmetic crossover and multipoint crossover. Nonuniform mutation is improved by adjusting the mutation ratio adaptively. The algorithm is used to estimate the parameters of one kind of silicon-steel sheet's hysteresis loops, and the results are in good agreement with published data.
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.
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…
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. PMID:21069471
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.
New Estimates of Private Sector Unionism in the United States.
ERIC Educational Resources Information Center
Freeman, Richard B.; Medoff, James L.
1979-01-01
The study presents new estimates of two measures of unionism in the United States, the percentage of private sector workers covered by union agreements, and the percentage who are union members. These figures are compared with each other and with previous estimates, showing a decline in private sector unionism. (MF)
Mehta, Cyrus; Liu, Lingyun
2016-02-10
Over the past 25 years, adaptive designs have gradually gained acceptance and are being used with increasing frequency in confirmatory clinical trials. Recent surveys of submissions to the regulatory agencies reveal that the most popular type of adaptation is unblinded sample size re-estimation. Concerns have nevertheless been raised that this type of adaptation is inefficient.We intend to show in our discussion that such concerns are greatly exaggerated in any practical setting and that the advantages of adaptive sample size re-estimation usually outweigh any minor loss of efficiency. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26757953
NASA Astrophysics Data System (ADS)
Alvey, Brendan; Zare, Alina; Cook, Matthew; Ho, Dominic K. C.
2016-05-01
The adaptive coherence estimator (ACE) estimates the squared cosine of the angle between a known target vector and a sample vector in a transformed coordinate space. The space is transformed according to an estimation of the background statistics, which directly effects the performance of the statistic as a target detector. In this paper, the ACE detection statistic is used to detect buried explosive hazards with data from a Wideband Electromagnetic Induction (WEMI) sensor. Target signatures are based on a dictionary defined using a Discrete Spectrum of Relaxation Frequencies (DSRF) model. Results are summarized as a receiver operator curve (ROC) and compared to other leading methods.
F-8C adaptive flight control extensions. [for maximum likelihood estimation
NASA Technical Reports Server (NTRS)
Stein, G.; Hartmann, G. L.
1977-01-01
An adaptive concept which combines gain-scheduled control laws with explicit maximum likelihood estimation (MLE) identification to provide the scheduling values is described. The MLE algorithm was improved by incorporating attitude data, estimating gust statistics for setting filter gains, and improving parameter tracking during changing flight conditions. A lateral MLE algorithm was designed to improve true air speed and angle of attack estimates during lateral maneuvers. Relationships between the pitch axis sensors inherent in the MLE design were examined and used for sensor failure detection. Design details and simulation performance are presented for each of the three areas investigated.
NASA Astrophysics Data System (ADS)
Cofaru, Corneliu; Philips, Wilfried; Van Paepegem, Wim
2011-09-01
Digital image processing methods represent a viable and well acknowledged alternative to strain gauges and interferometric techniques for determining full-field displacements and strains in materials under stress. This paper presents an image adaptive technique for dense motion and strain estimation using high-resolution speckle images that show the analyzed material in its original and deformed states. The algorithm starts by dividing the speckle image showing the original state into irregular cells taking into consideration both spatial and gradient image information present. Subsequently the Newton-Raphson digital image correlation technique is applied to calculate the corresponding motion for each cell. Adaptive spatial regularization in the form of the Geman- McClure robust spatial estimator is employed to increase the spatial consistency of the motion components of a cell with respect to the components of neighbouring cells. To obtain the final strain information, local least-squares fitting using a linear displacement model is performed on the horizontal and vertical displacement fields. To evaluate the presented image partitioning and strain estimation techniques two numerical and two real experiments are employed. The numerical experiments simulate the deformation of a specimen with constant strain across the surface as well as small rigid-body rotations present while real experiments consist specimens that undergo uniaxial stress. The results indicate very good accuracy of the recovered strains as well as better rotation insensitivity compared to classical techniques.
Kwon, Oh-Sang; Knill, David C
2013-03-12
Because of uncertainty and noise, the brain should use accurate internal models of the statistics of objects in scenes to interpret sensory signals. Moreover, the brain should adapt its internal models to the statistics within local stimulus contexts. Consider the problem of hitting a baseball. The impoverished nature of the visual information available makes it imperative that batters use knowledge of the temporal statistics and history of previous pitches to accurately estimate pitch speed. Using a laboratory analog of hitting a baseball, we tested the hypothesis that the brain uses adaptive internal models of the statistics of object speeds to plan hand movements to intercept moving objects. We fit Bayesian observer models to subjects' performance to estimate the statistical environments in which subjects' performance would be ideal and compared the estimated statistics with the true statistics of stimuli in an experiment. A first experiment showed that subjects accurately estimated and used the variance of object speeds in a stimulus set to time hitting behavior but also showed serial biases that are suboptimal for stimuli that were uncorrelated over time. A second experiment showed that the strength of the serial biases depended on the temporal correlations within a stimulus set, even when the biases were estimated from uncorrelated stimulus pairs subsampled from the larger set. Taken together, the results show that subjects adapted their internal models of the variance and covariance of object speeds within a stimulus set to plan interceptive movements but retained a bias to positive correlations. PMID:23440185
Estimating undersupply of nursing home beds in states.
Swan, J H; Harrington, C
1986-01-01
This examination of nursing home bed supply estimates undersupply in each of the states for the purpose of identifying the states with the greatest undersupply of beds. New data on state nursing home bed supply for the period 1979-1982 are used. The study employs selected independent variables in two different types of analyses to estimate bed supply for each state. Where a state is found to have a bed shortage, state public policymakers may wish to employ policies that differ from those suitable for states with an adequate supply of beds. Because of limitations in the data, issues of oversupply and of the extent of undersupply could not be examined. PMID:3519534
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.
Estimation of HIV Incidence in the United States
Hall, H. Irene; Song, Ruiguang; Rhodes, Philip; Prejean, Joseph; An, Qian; Lee, Lisa M.; Karon, John; Brookmeyer, Ron; Kaplan, Edward H.; McKenna, Matthew T.; Janssen, Robert S.
2010-01-01
Context HIV incidence in the United States has not been directly measured. New assays that differentiate recent versus long-standing HIV infections allow improved estimation of HIV incidence. Objective To estimate HIV incidence in the United States. Design Remnant diagnostic serum specimens from patients diagnosed with HIV during 2006 in 22 states were tested with the BED HIV-1 capture enzyme immunoassay to classify infections as recent or long-standing. Information was reported to the Centers for Disease Control and Prevention through June 2007. HIV incidence in the 22 states during 2006 was estimated using a statistical approach with adjustment for testing frequency and extrapolated to the U.S. Results were corroborated with back-calculation of HIV incidence for 1977–2006 based on HIV diagnoses from 40 states and on AIDS incidence from 50 states and the District of Columbia. Setting Data from 22 states were extrapolated to the U.S. Patients Persons newly diagnosed with HIV (age ≥ 13 years). Main outcome measure Estimated HIV incidence. Results An estimated 39,400 persons were diagnosed with HIV in 2006 in the 22 states. Of 6,864 diagnostic specimens tested using the BED assay, 2,133 (31%) were classified as recent infections. Based on extrapolations from these data, the estimated number of new infections for the U.S. in 2006 was 56,300 (95% confidence interval [CI] 48,200, 64,500); the estimated incidence rate was 22.8 per 100,000 population (95% CI 19.5, 26.1). Forty-five percent of infections were among blacks and 53% among men who have sex with men. The back-calculation (n=1.230 million HIV/AIDS cases reported by the end of 2006) yielded an estimate of 55,400 (95% CI 52,700, 58,100) new infections per year for 2003–2006, and indicated that HIV incidence increased in the mid-1990s, then slightly declined after 1999 and has been stable thereafter. Conclusions The estimates are the first direct estimate of HIV incidence in the United States using laboratory
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…
Development of the One-Sided Nonlinear Adaptive Doppler Shift Estimation
NASA Technical Reports Server (NTRS)
Beyon, Jeffrey Y.; Koch, Grady J.; Singh, Upendra N.; Kavaya, Michael J.; Serror, Judith A.
2009-01-01
The new development of a one-sided nonlinear adaptive shift estimation technique (NADSET) is introduced. The background of the algorithm and a brief overview of NADSET are presented. The new technique is applied to the wind parameter estimates from a 2-micron wavelength coherent Doppler lidar system called VALIDAR located in NASA Langley Research Center in Virginia. The new technique enhances wind parameters such as Doppler shift and power estimates in low Signal-To-Noise-Ratio (SNR) regimes using the estimates in high SNR regimes as the algorithm scans the range bins from low to high altitude. The original NADSET utilizes the statistics in both the lower and the higher range bins to refine the wind parameter estimates in between. The results of the two different approaches of NADSET are compared.
NASA Astrophysics Data System (ADS)
Zhong, Fuli; Li, Hui; Zhong, Shouming; Zhong, Qishui; Yin, Chun
2015-07-01
A state of charge (SOC) estimation approach based on an adaptive sliding mode observer (SMO) and a fractional order equivalent circuit model (FOECM) for lithium-ion batteries is proposed in this paper. In order to design the adaptive sliding mode observer (SMO) for the SOC estimation, the state equations based on a FOECM of battery are derived. A new self-adjusting strategy for the observer gains is presented to adjust the observer in the estimating process, which helps to reduce chattering and convergence time. Furthermore, a continuous and smooth function called hyperbolic tangent function is applied to balance the chattering affection and the disturbance. At last, a battery simulation model is established to test the SOC estimation performance of the designed SMOs, and the results show the proposed approach is feasible and effective.
An Adaptive Objective Function for Evaporation Duct Estimations from Radar Sea Echo
NASA Astrophysics Data System (ADS)
Zhang, Jin-Peng; Wu, Zhen-Sen; Wang, Bo
2011-03-01
In the process of atmospheric refractivity estimation from radar sea echo, the objective function that calculates the match between the predicted and observed field plays an important role. To reduce the effect of noises from long ranges on the objective function, we present a selection method of final ranges for inversion. An adaptive objective function is introduced with a linear distance weight added to the least squares error function (LSEF). Through an evaporation duct height (EDH) retrieving process, the performance of the adaptive objective function is evaluated. The result illustrates that the present method performs better than the LSEF in EDH inversions from clutters with different clutter-to-noise ratios.
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.
NASA Astrophysics Data System (ADS)
Brüggemann, Matthias; Kays, Rüdiger; Springer, Paul; Erdler, Oliver
2015-03-01
In this paper we present a combination of block-matching and differential motion field estimation. We initialize the motion field using a predictive hierarchical block-matching approach. This vector field is refined by a pixel-recursive differential motion estimation method. We integrate image warping and adaptive filter kernels into the Horn and Schunck differential optical flow estimation approach to break the block structure of the initial correspondence vector fields and compute motion field updates to fulfill the smoothness constraint inside motion boundaries. The influence of occlusion areas is reduced by integrating an in-the-loop occlusion detection and adjusting the adaptive filter weights in the iteration process. We integrate the combined estimation into a hierarchical multi-scale framework. The refined motion on the current scale is upscaled and used as prediction for block-matching motion estimation on the next scale. With the proposed system we are able to combine the advantages of block-matching and differential motion estimation and achieve a dense vector field with floating point precision even for large motion.
2013-01-01
Background Elective patient admission and assignment planning is an important task of the strategic and operational management of a hospital and early on became a central topic of clinical operations research. The management of hospital beds is an important subtask. Various approaches have been proposed, involving the computation of efficient assignments with regard to the patients’ condition, the necessity of the treatment, and the patients’ preferences. However, these approaches are mostly based on static, unadaptable estimates of the length of stay and, thus, do not take into account the uncertainty of the patient’s recovery. Furthermore, the effect of aggregated bed capacities have not been investigated in this context. Computer supported bed management, combining an adaptable length of stay estimation with the treatment of shared resources (aggregated bed capacities) has not yet been sufficiently investigated. The aim of our work is: 1) to define a cost function for patient admission taking into account adaptable length of stay estimations and aggregated resources, 2) to define a mathematical program formally modeling the assignment problem and an architecture for decision support, 3) to investigate four algorithmic methodologies addressing the assignment problem and one base-line approach, and 4) to evaluate these methodologies w.r.t. cost outcome, performance, and dismissal ratio. Methods The expected free ward capacity is calculated based on individual length of stay estimates, introducing Bernoulli distributed random variables for the ward occupation states and approximating the probability densities. The assignment problem is represented as a binary integer program. Four strategies for solving the problem are applied and compared: an exact approach, using the mixed integer programming solver SCIP; and three heuristic strategies, namely the longest expected processing time, the shortest expected processing time, and random choice. A baseline approach
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
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
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…
Estimating annualized earthquake losses for the conterminous United States
Jaiswal, Kishor S.; Bausch, Douglas; Chen, Rui; Bouabid, Jawhar; Seligson, Hope
2015-01-01
We make use of the most recent National Seismic Hazard Maps (the years 2008 and 2014 cycles), updated census data on population, and economic exposure estimates of general building stock to quantify annualized earthquake loss (AEL) for the conterminous United States. The AEL analyses were performed using the Federal Emergency Management Agency's (FEMA) Hazus software, which facilitated a systematic comparison of the influence of the 2014 National Seismic Hazard Maps in terms of annualized loss estimates in different parts of the country. The losses from an individual earthquake could easily exceed many tens of billions of dollars, and the long-term averaged value of losses from all earthquakes within the conterminous U.S. has been estimated to be a few billion dollars per year. This study estimated nationwide losses to be approximately $4.5 billion per year (in 2012$), roughly 80% of which can be attributed to the States of California, Oregon and Washington. We document the change in estimated AELs arising solely from the change in the assumed hazard map. The change from the 2008 map to the 2014 map results in a 10 to 20% reduction in AELs for the highly seismic States of the Western United States, whereas the reduction is even more significant for Central and Eastern United States.
Estimation of State Transition Probabilities: A Neural Network Model
NASA Astrophysics Data System (ADS)
Saito, Hiroshi; Takiyama, Ken; Okada, Masato
2015-12-01
Humans and animals can predict future states on the basis of acquired knowledge. This prediction of the state transition is important for choosing the best action, and the prediction is only possible if the state transition probability has already been learned. However, how our brains learn the state transition probability is unknown. Here, we propose a simple algorithm for estimating the state transition probability by utilizing the state prediction error. We analytically and numerically confirmed that our algorithm is able to learn the probability completely with an appropriate learning rate. Furthermore, our learning rule reproduced experimentally reported psychometric functions and neural activities in the lateral intraparietal area in a decision-making task. Thus, our algorithm might describe the manner in which our brains learn state transition probabilities and predict future states.
Rankings & Estimates: Rankings of the States 2010 and Estimates of School Statistics 2011
ERIC Educational Resources Information Center
National Education Association Research Department, 2010
2010-01-01
The data presented in this combined report--"Rankings & Estimates"--provide facts about the extent to which local, state, and national governments commit resources to public education. As one might expect in a nation as diverse as the United States--with respect to economics, geography, and politics--the level of commitment to education varies on…
Rankings & Estimates: Rankings of the States 2009 and Estimates of School Statistics 2010
ERIC Educational Resources Information Center
National Education Association Research Department, 2009
2009-01-01
The data presented in this combined report--"Rankings & Estimates"--provide facts about the extent to which local, state, and national governments commit resources to public education. As one might expect in a nation as diverse as the United States--with respect to economics, geography, and politics--the level of commitment to education varies on…
Rankings & Estimates: Rankings of the States 2008 and Estimates of School Statistics 2009
ERIC Educational Resources Information Center
National Education Association Research Department, 2008
2008-01-01
The data presented in this combined report--"Rankings & Estimates"--provide facts about the extent to which local, state, and national governments commit resources to public education. As one might expect in a nation as diverse as the United States--with respect to economics, geography, and politics--the level of commitment to education varies on…
Rankings & Estimates: Rankings of the States 2004 and Estimates of School Statistics 2005
ERIC Educational Resources Information Center
National Education Association Research Department, 2005
2005-01-01
The data presented in this combined report--"Rankings & Estimates"--provide facts about the extent to which local, state, and national governments commit resources to public education. As one might expect in a nation as diverse as the United States--with respect to economics, geography, and politics--the level of commitment to education varies on…
Photoconversion from the light-adapted to the dark-adapted state of bacteriorhodopsin
NASA Technical Reports Server (NTRS)
Kouyama, T.; Bogomolni, R. A.; Stoeckenius, W.
1985-01-01
The dark and light adaptation of the bR(trans)570 (bacteriorhodopsin) and bR(cis)550 isomers is analyzed. The equilibrium between the two bR isomers in light-adapted purple membrane films is studied in terms of the wavelength of actinic light and hydration levels. Absorption spectra observed after light adaptations with red and yellow light reveal that red light is less efficient in converting bR(cis)550 to bR(trans)570 than yellow light and the amount of bR(cis)550 in a light-adapted sample increases with decreasing hydration. The rate constants of dark and light adaptation are evaluated; the rate constant of dark adaptation is independent of the hydration level and the rate constant of light adaptation increases with hydration. The acceleration of a dark adaptation by red light is investigated; the dependence of the accelerated dark adaptation on the light intensity is discussed. The action spectrum of light adaptation in a purple membrane suspension is compared with the absorption spectrum of bR(cis)550; correlation between the spectra reveals that cis-to-transconversion is due to excitation of bR(cis)550 and the mechanism of cis-to-trans conversion in film is not affected by humidity levels. It is noted that the light-driven trans-to-cis conversion is a single photon process. The branching at M410 from the all-trans into the 13-cis photocycle is examined.
NASA Astrophysics Data System (ADS)
Xue, Ming; Wang, Jiang; Jia, Chenhui; Yu, Haitao; Deng, Bin; Wei, Xile; Che, Yanqiu
2013-03-01
In this paper, we proposed a new approach to estimate unknown parameters and topology of a neuronal network based on the adaptive synchronization control scheme. A virtual neuronal network is constructed as an observer to track the membrane potential of the corresponding neurons in the original network. When they achieve synchronization, the unknown parameters and topology of the original network are obtained. The method is applied to estimate the real-time status of the connection in the feedforward network and the neurotransmitter release probability of unreliable synapses is obtained by statistic computation. Numerical simulations are also performed to demonstrate the effectiveness of the proposed adaptive controller. The obtained results may have important implications in system identification in neural science.
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
Adaptation strategies for high order discontinuous Galerkin methods based on Tau-estimation
NASA Astrophysics Data System (ADS)
Kompenhans, Moritz; Rubio, Gonzalo; Ferrer, Esteban; Valero, Eusebio
2016-02-01
In this paper three p-adaptation strategies based on the minimization of the truncation error are presented for high order discontinuous Galerkin methods. The truncation error is approximated by means of a τ-estimation procedure and enables the identification of mesh regions that require adaptation. Three adaptation strategies are developed and termed a posteriori, quasi-a priori and quasi-a priori corrected. All strategies require fine solutions, which are obtained by enriching the polynomial order, but while the former needs time converged solutions, the last two rely on non-converged solutions, which lead to faster computations. In addition, the high order method permits the spatial decoupling for the estimated errors and enables anisotropic p-adaptation. These strategies are verified and compared in terms of accuracy and computational cost for the Euler and the compressible Navier-Stokes equations. It is shown that the two quasi-a priori methods achieve a significant reduction in computational cost when compared to a uniform polynomial enrichment. Namely, for a viscous boundary layer flow, we obtain a speedup of 6.6 and 7.6 for the quasi-a priori and quasi-a priori corrected approaches, respectively.
NASA Technical Reports Server (NTRS)
Linares, Irving; Mersereau, Russell M.; Smith, Mark J. T.
1994-01-01
Two representative sample images of Band 4 of the Landsat Thematic Mapper are compressed with the JPEG algorithm at 8:1, 16:1 and 24:1 Compression Ratios for experimental browsing purposes. We then apply the Optimal PSNR Estimated Spectra Adaptive Postfiltering (ESAP) algorithm to reduce the DCT blocking distortion. ESAP reduces the blocking distortion while preserving most of the image's edge information by adaptively postfiltering the decoded image using the block's spectral information already obtainable from each block's DCT coefficients. The algorithm iteratively applied a one dimensional log-sigmoid weighting function to the separable interpolated local block estimated spectra of the decoded image until it converges to the optimal PSNR with respect to the original using a 2-D steepest ascent search. Convergence is obtained in a few iterations for integer parameters. The optimal logsig parameters are transmitted to the decoder as a negligible byte of overhead data. A unique maxima is guaranteed due to the 2-D asymptotic exponential overshoot shape of the surface generated by the algorithm. ESAP is based on a DFT analysis of the DCT basis functions. It is implemented with pixel-by-pixel spatially adaptive separable FIR postfilters. PSNR objective improvements between 0.4 to 0.8 dB are shown together with their corresponding optimal PSNR adaptive postfiltered images.
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.
Adaptive feedforward of estimated ripple improves the closed loop system performance significantly
Kwon, S.; Regan, A.; Wang, Y.M.; Rohlev, T.
1998-12-31
The Low Energy Demonstration Accelerator (LEDA) being constructed at Los Alamos National Laboratory will serve as the prototype for the low energy section of Acceleration Production of Tritium (APT) accelerator. This paper addresses the problem of LLRF control system for LEDA. The authors propose an estimator of the ripple and its time derivative and a control law which is based on PID control and adaptive feedforward of estimated ripple. The control law reduces the effect of the deterministic cathode ripple that is due to high voltage power supply and achieves tracking of desired set points.
[Polynesian adaptation of the Mini-Mental State Examination].
Wong, H; Larre, P; Ghawché, F
2015-04-01
This study aimed to develop and validate a Polynesian version of the MMSE (Mini-Mental State Examination). For this study a sample (n=112) of healthy people were evaluated with the French version of the consensual version of the MMSE, to target and modify some inadequate items for French Polynesia. Subsequently, a second sample (n=112) with the same characteristics (age, educational level) as well as 46 healthy people aged 60 years and more were evaluated with the adaptive version of the MMSE (P-MMSE). This version was then applied to 17 participants with Alzheimer disease. The control subjects were selected according to their age and educational level. The variables gender and evaluation sites were checked. An analysis of the results showed a significant dissociation between the two versions as well as a meaningful effect on global performance of the variables age (r=-0.45) and educational level (r=-0.25). Cut-off scores taking into consideration these variables were defined. The sensitivity and specificity values of the new cut-off scores were much greater than 0.5. Various global cut-off scores were also analyzed. A general cut-off score (≤23) was defined and yielded 82 % sensitivity and 75 % specificity in detecting Alzheimer disease. PMID:25575608
Rescue of endemic states in interconnected networks with adaptive coupling.
Vazquez, F; Serrano, M Ángeles; Miguel, M San
2016-01-01
We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network -and therefore on the interconnected system- the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime. PMID:27380771
Rescue of endemic states in interconnected networks with adaptive coupling
NASA Astrophysics Data System (ADS)
Vazquez, F.; Serrano, M. Ángeles; Miguel, M. San
2016-07-01
We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network –and therefore on the interconnected system– the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime.
Rescue of endemic states in interconnected networks with adaptive coupling
Vazquez, F.; Serrano, M. Ángeles; Miguel, M. San
2016-01-01
We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network –and therefore on the interconnected system– the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime. PMID:27380771
Using State Estimation Residuals to Detect Abnormal SCADA Data
Ma, Jian; Chen, Yousu; Huang, Zhenyu; Wong, Pak C.
2010-04-30
Detection of abnormal supervisory control and data acquisition (SCADA) data is critically important for safe and secure operation of modern power systems. In this paper, a methodology of abnormal SCADA data detection based on state estimation residuals is presented. Preceded with a brief overview of outlier detection methods and bad SCADA data detection for state estimation, the framework of the proposed methodology is described. Instead of using original SCADA measurements as the bad data sources, the residuals calculated based on the results of the state estimator are used as the input for the outlier detection algorithm. The BACON algorithm is applied to the outlier detection task. The IEEE 118-bus system is used as a test base to evaluate the effectiveness of the proposed methodology. The accuracy of the BACON method is compared with that of the 3-σ method for the simulated SCADA measurements and residuals.
Using State Estimation Residuals to Detect Abnormal SCADA Data
Ma, Jian; Chen, Yousu; Huang, Zhenyu; Wong, Pak C.
2010-06-14
Detection of manipulated supervisory control and data acquisition (SCADA) data is critically important for the safe and secure operation of modern power systems. In this paper, a methodology of detecting manipulated SCADA data based on state estimation residuals is presented. A framework of the proposed methodology is described. Instead of using original SCADA measurements as the bad data sources, the residuals calculated based on the results of the state estimator are used as the input for the outlier detection process. The BACON algorithm is applied to detect outliers in the state estimation residuals. The IEEE 118-bus system is used as a test case to evaluate the effectiveness of the proposed methodology. The accuracy of the BACON method is compared with that of the 3-σ method for the simulated SCADA measurements and residuals.
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.
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.
Parameter estimation using NOON states over a relativistic quantum channel
NASA Astrophysics Data System (ADS)
Hosler, Dominic; Kok, Pieter
2013-11-01
We study the effect of the acceleration of the observer on a parameter estimation protocol using NOON states. An inertial observer, Alice, prepares a NOON state in Unruh modes of the quantum field, and sends it to an accelerated observer, Rob. We calculate the quantum Fisher information of the state received by Rob. We find the counterintuitive result that the single-rail encoding outperforms the dual rail. The NOON states have an optimal N for the maximum information extractable by Rob, given his acceleration. This optimal N decreases with increasing acceleration.
National intelligence estimates and the Failed State Index.
Voracek, Martin
2013-10-01
Across 177 countries around the world, the Failed State Index, a measure of state vulnerability, was reliably negatively associated with the estimates of national intelligence. Psychometric analysis of the Failed State Index, compounded of 12 social, economic, and political indicators, suggested factorial unidimensionality of this index. The observed correspondence of higher national intelligence figures to lower state vulnerability might arise through these two macro-level variables possibly being proxies of even more pervasive historical and societal background variables that affect both. PMID:24597444
State Medicaid Pharmacy Payments and Their Relation to Estimated Costs
Adams, E. Kathleen; Kreling, David H.; Gondek, Kathleen
1994-01-01
Although prescription drugs do not appear to be a primary source of recent surges in Medicaid spending, their share of Medicaid expenditures has risen despite efforts to control costs. As part of a general concern with prescription drug policy, Congress mandated a study of the adequacy of Medicaid payments to pharmacies. In this study, several data sources were used to develop 1991 estimates of average pharmacy ingredient and dispensing costs. A simulation was used to estimate the amounts States pay. Nationally, simulated payments averaged 96 percent of estimated costs overall but were lower for dispensing costs (79 percent) and higher for ingredient costs (102 percent). PMID:10137796
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.
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
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 .
NASA Astrophysics Data System (ADS)
Yi, J.; Choi, C.
2014-12-01
Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.
Zuo, K; Bellanger, J J; Yang, C; Shu, H; Le Bouquin Jeannés, R
2013-01-01
This paper aims at estimating causal relationships between signals to detect flow propagation in autoregressive and physiological models. The main challenge of the ongoing work is to discover whether neural activity in a given structure of the brain influences activity in another area during epileptic seizures. This question refers to the concept of effective connectivity in neuroscience, i.e. to the identification of information flows and oriented propagation graphs. Past efforts to determine effective connectivity rooted to Wiener causality definition adapted in a practical form by Granger with autoregressive models. A number of studies argue against such a linear approach when nonlinear dynamics are suspected in the relationship between signals. Consequently, nonlinear nonparametric approaches, such as transfer entropy (TE), have been introduced to overcome linear methods limitations and promoted in many studies dealing with electrophysiological signals. Until now, even though many TE estimators have been developed, further improvement can be expected. In this paper, we investigate a new strategy by introducing an adaptive kernel density estimator to improve TE estimation. PMID:24110694
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. PMID:23579098
Stochastic EM algorithm for nonlinear state estimation with model uncertainties
NASA Astrophysics Data System (ADS)
Zia, Amin; Kirubarajan, Thiagalingam; Reilly, James P.; Shirani, Shahram
2004-01-01
In most solutions to state estimation problems like, for example, target tracking, it is generally assumed that the state evolution and measurement models are known a priori. The model parameters include process and measurement matrices or functions as well as the corresponding noise statistics. However, there are situations where the model parameters are not known a priori or are known only partially (i.e., with some uncertainty). Moreover, there are situations where the measurement is biased. In these scenarios, standard estimation algorithms like the Kalman filter and the extended Kalman Filter (EKF), which assume perfect knowledge of the model parameters, are not accurate. In this paper, the problem with uncertain model parameters is considered as a special case of maximum likelihood estimation with incomplete-data, for which a standard solution called the expectation-maximization (EM) algorithm exists. In this paper a new extension to the EM algorithm is proposed to solve the more general problem of joint state estimation and model parameter identification for nonlinear systems with possibly non-Gaussian noise. In the expectation (E) step, it is shown that the best variational distribution over the state variables is the conditional posterior distribution of states given all the available measurements and inputs. Therefore, a particular type of particle filter is used to estimate and update the posterior distribution. In the maximization (M) step the nonlinear measurement process parameters are approximated using a nonlinear regression method for adjusting the parameters of a mixture of Gaussians (MofG). The proposed algorithm is used to solve a nonlinear bearing-only tracking problem similar to the one reported recently with uncertain measurement process. It is shown that the algorithm is capable of accurately tracking the state vector while identifying the unknown measurement dynamics. Simulation results show the advantages of the new technique over standard
Stochastic EM algorithm for nonlinear state estimation with model uncertainties
NASA Astrophysics Data System (ADS)
Zia, Amin; Kirubarajan, Thiagalingam; Reilly, James P.; Shirani, Shahram
2003-12-01
In most solutions to state estimation problems like, for example, target tracking, it is generally assumed that the state evolution and measurement models are known a priori. The model parameters include process and measurement matrices or functions as well as the corresponding noise statistics. However, there are situations where the model parameters are not known a priori or are known only partially (i.e., with some uncertainty). Moreover, there are situations where the measurement is biased. In these scenarios, standard estimation algorithms like the Kalman filter and the extended Kalman Filter (EKF), which assume perfect knowledge of the model parameters, are not accurate. In this paper, the problem with uncertain model parameters is considered as a special case of maximum likelihood estimation with incomplete-data, for which a standard solution called the expectation-maximization (EM) algorithm exists. In this paper a new extension to the EM algorithm is proposed to solve the more general problem of joint state estimation and model parameter identification for nonlinear systems with possibly non-Gaussian noise. In the expectation (E) step, it is shown that the best variational distribution over the state variables is the conditional posterior distribution of states given all the available measurements and inputs. Therefore, a particular type of particle filter is used to estimate and update the posterior distribution. In the maximization (M) step the nonlinear measurement process parameters are approximated using a nonlinear regression method for adjusting the parameters of a mixture of Gaussians (MofG). The proposed algorithm is used to solve a nonlinear bearing-only tracking problem similar to the one reported recently with uncertain measurement process. It is shown that the algorithm is capable of accurately tracking the state vector while identifying the unknown measurement dynamics. Simulation results show the advantages of the new technique over standard
NASA Astrophysics Data System (ADS)
Hanachi, Houman; Liu, Jie; Banerjee, Avisekh; Chen, Ying
2016-05-01
Health state estimation of inaccessible components in complex systems necessitates effective state estimation techniques using the observable variables of the system. The task becomes much complicated when the system is nonlinear/non-Gaussian and it receives stochastic input. In this work, a novel sequential state estimation framework is developed based on particle filtering (PF) scheme for state estimation of general class of nonlinear dynamical systems with stochastic input. Performance of the developed framework is then validated with simulation on a Bivariate Non-stationary Growth Model (BNGM) as a benchmark. In the next step, three-year operating data of an industrial gas turbine engine (GTE) are utilized to verify the effectiveness of the developed framework. A comprehensive thermodynamic model for the GTE is therefore developed to formulate the relation of the observable parameters and the dominant degradation symptoms of the turbine, namely, loss of isentropic efficiency and increase of the mass flow. The results confirm the effectiveness of the developed framework for simultaneous estimation of multiple degradation symptoms in complex systems with noisy measured inputs.
Optimal PMU Placement Evaluation for Power System Dynamic State Estimation
Zhang, Jinghe; Welch, Greg; Bishop, Gary; Huang, Zhenyu
2010-10-10
Abstract - The synchronized phaor measurements unit (PMU), developed in the 1980s, is concidered to be one of the most important devices in the future of power systems. The recent development of PMU technology provides high-speed, precisely synchronized sensor data, which has been found to be usefule for dynamic, state estimation of power the power grid.
Fault detection in electromagnetic suspension systems with state estimation methods
Sinha, P.K.; Zhou, F.B.; Kutiyal, R.S. . Dept. of Engineering)
1993-11-01
High-speed maglev vehicles need a high level of safety that depends on the whole vehicle system's reliability. There are many ways of attaining high reliability for the system. Conventional method uses redundant hardware with majority vote logic circuits. Hardware redundancy costs more, weigh more and occupy more space than that of analytically redundant methods. Analytically redundant systems use parameter identification and state estimation methods based on the system models to detect and isolate the fault of instruments (sensors), actuator and components. In this paper the authors use the Luenberger observer to estimate three state variables of the electromagnetic suspension system: position (airgap), vehicle velocity, and vertical acceleration. These estimates are compared with the corresponding sensor outputs for fault detection. In this paper, they consider FDI of the accelerometer, the sensor which provides the ride quality.
State estimation and absolute image registration for geosynchronous satellites
NASA Technical Reports Server (NTRS)
Nankervis, R.; Koch, D. W.; Sielski, H.
1980-01-01
Spacecraft state estimation and the absolute registration of Earth images acquired by cameras onboard geosynchronous satellites are described. The basic data type of the procedure consists of line and element numbers of image points called landmarks whose geodetic coordinates, relative to United States Geodetic Survey topographic maps, are known. A conventional least squares process is used to estimate navigational parameters and camera pointing biases from observed minus computed landmark line and element numbers. These estimated parameters along with orbit and attitude dynamic models are used to register images, using an automated grey level correlation technique, inside the span represented by the landmark data. In addition, the dynamic models can be employed to register images outside of the data span in a near real time mode. An important application of this mode is in support of meteorological studies where rapid data reduction is required for the rapid tracking and predicting of dynamic phenomena.
Spectral Doppler estimation utilizing 2-D spatial information and adaptive signal processing.
Ekroll, Ingvild K; Torp, Hans; Løvstakken, Lasse
2012-06-01
The trade-off between temporal and spectral resolution in conventional pulsed wave (PW) Doppler may limit duplex/triplex quality and the depiction of rapid flow events. It is therefore desirable to reduce the required observation window (OW) of the Doppler signal while preserving the frequency resolution. This work investigates how the required observation time can be reduced by adaptive spectral estimation utilizing 2-D spatial information obtained by parallel receive beamforming. Four adaptive estimation techniques were investigated, the power spectral Capon (PSC) method, the amplitude and phase estimation (APES) technique, multiple signal classification (MUSIC), and a projection-based version of the Capon technique. By averaging radially and laterally, the required covariance matrix could successfully be estimated without temporal averaging. Useful PW spectra of high resolution and contrast could be generated from ensembles corresponding to those used in color flow imaging (CFI; OW = 10). For a given OW, the frequency resolution could be increased compared with the Welch approach, in cases in which the transit time was higher or comparable to the observation time. In such cases, using short or long pulses with unfocused or focused transmit, an increase in temporal resolution of up to 4 to 6 times could be obtained in in vivo examples. It was further shown that by using adaptive signal processing, velocity spectra may be generated without high-pass filtering the Doppler signal. With the proposed approach, spectra retrospectively calculated from CFI may become useful for unfocused as well as focused imaging. This application may provide new clinical information by inspection of velocity spectra simultaneously from several spatial locations. PMID:22711413
On-Board Event-Based State Estimation for Trajectory Approaching and Tracking of a Vehicle.
Martínez-Rey, Miguel; Espinosa, Felipe; Gardel, Alfredo; Santos, Carlos
2015-01-01
For the problem of pose estimation of an autonomous vehicle using networked external sensors, the processing capacity and battery consumption of these sensors, as well as the communication channel load should be optimized. Here, we report an event-based state estimator (EBSE) consisting of an unscented Kalman filter that uses a triggering mechanism based on the estimation error covariance matrix to request measurements from the external sensors. This EBSE generates the events of the estimator module on-board the vehicle and, thus, allows the sensors to remain in stand-by mode until an event is generated. The proposed algorithm requests a measurement every time the estimation distance root mean squared error (DRMS) value, obtained from the estimator's covariance matrix, exceeds a threshold value. This triggering threshold can be adapted to the vehicle's working conditions rendering the estimator even more efficient. An example of the use of the proposed EBSE is given, where the autonomous vehicle must approach and follow a reference trajectory. By making the threshold a function of the distance to the reference location, the estimator can halve the use of the sensors with a negligible deterioration in the performance of the approaching maneuver. PMID:26102489
On-Board Event-Based State Estimation for Trajectory Approaching and Tracking of a Vehicle
Martínez-Rey, Miguel; Espinosa, Felipe; Gardel, Alfredo; Santos, Carlos
2015-01-01
For the problem of pose estimation of an autonomous vehicle using networked external sensors, the processing capacity and battery consumption of these sensors, as well as the communication channel load should be optimized. Here, we report an event-based state estimator (EBSE) consisting of an unscented Kalman filter that uses a triggering mechanism based on the estimation error covariance matrix to request measurements from the external sensors. This EBSE generates the events of the estimator module on-board the vehicle and, thus, allows the sensors to remain in stand-by mode until an event is generated. The proposed algorithm requests a measurement every time the estimation distance root mean squared error (DRMS) value, obtained from the estimator's covariance matrix, exceeds a threshold value. This triggering threshold can be adapted to the vehicle's working conditions rendering the estimator even more efficient. An example of the use of the proposed EBSE is given, where the autonomous vehicle must approach and follow a reference trajectory. By making the threshold a function of the distance to the reference location, the estimator can halve the use of the sensors with a negligible deterioration in the performance of the approaching maneuver. PMID:26102489
NASA Astrophysics Data System (ADS)
Shi, Lei; Wang, Z. J.
2015-08-01
Adjoint-based mesh adaptive methods are capable of distributing computational resources to areas which are important for predicting an engineering output. In this paper, we develop an adjoint-based h-adaptation approach based on the high-order correction procedure via reconstruction formulation (CPR) to minimize the output or functional error. A dual-consistent CPR formulation of hyperbolic conservation laws is developed and its dual consistency is analyzed. Super-convergent functional and error estimate for the output with the CPR method are obtained. Factors affecting the dual consistency, such as the solution point distribution, correction functions, boundary conditions and the discretization approach for the non-linear flux divergence term, are studied. The presented method is then used to perform simulations for the 2D Euler and Navier-Stokes equations with mesh adaptation driven by the adjoint-based error estimate. Several numerical examples demonstrate the ability of the presented method to dramatically reduce the computational cost comparing with uniform grid refinement.
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.
Trapping phenomenon of the parameter estimation in asymptotic quantum states
NASA Astrophysics Data System (ADS)
Berrada, K.
2016-09-01
In this paper, we study in detail the behavior of the precision of the parameter estimation in open quantum systems using the quantum Fisher information (QFI). In particular, we study the sensitivity of the estimation on a two-qubit system evolving under Kossakowski-type quantum dynamical semigroups of completely positive maps. In such an environment, the precision of the estimation can even persist asymptotically for different effects of the initial parameters. We find that the QFI can be resistant to the action of the environment with respect to the initial asymptotic states, and it can persist even in the asymptotic long-time regime. In addition, our results provide further evidence that the initial pure and separable mixed states of the input state may enhance quantum metrology. These features make quantum states in this kind of environment a good candidate for the implementation of different schemes of quantum optics and information with high precision. Finally, we show that this quantity may be proposed to detect the amount of the total quantum information that the whole state contains with respect to projective measurements.
Estimation of beryllium ground state energy by Monte Carlo simulation
NASA Astrophysics Data System (ADS)
Kabir, K. M. Ariful; Halder, Amal
2015-05-01
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.
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.
NASA Astrophysics Data System (ADS)
Farmann, Alexander; Waag, Wladislaw; Sauer, Dirk Uwe
2015-12-01
Robust algorithms using reduced order equivalent circuit model (ECM) for an accurate and reliable estimation of battery states in various applications become more popular. In this study, a novel adaptive, self-learning heuristic algorithm for on-board impedance parameters and voltage estimation of lithium-ion batteries (LIBs) in electric vehicles is introduced. The presented approach is verified using LIBs with different composition of chemistries (NMC/C, NMC/LTO, LFP/C) at different aging states. An impedance-based reduced order ECM incorporating ohmic resistance and a combination of a constant phase element and a resistance (so-called ZARC-element) is employed. Existing algorithms in vehicles are much more limited in the complexity of the ECMs. The algorithm is validated using seven day real vehicle data with high temperature variation including very low temperatures (from -20 °C to +30 °C) at different Depth-of-Discharges (DoDs). Two possibilities to approximate both ZARC-elements with finite number of RC-elements on-board are shown and the results of the voltage estimation are compared. Moreover, the current dependence of the charge-transfer resistance is considered by employing Butler-Volmer equation. Achieved results indicate that both models yield almost the same grade of accuracy.
Adaptive RBF network for parameter estimation and stable air-fuel ratio control.
Wang, Shiwei; Yu, D L
2008-01-01
In the application of variable structure control to engine air-fuel ratio, the ratio is subjected to chattering due to system uncertainty, such as unknown parameters or time varying dynamics. This paper proposes an adaptive neural network method to estimate two immeasurable physical parameters on-line and to compensate for the model uncertainty and engine time varying dynamics, so that the chattering is substantially reduced and the air-fuel ratio is regulated within the desired range of the stoichiometric value. The adaptive law of the neural network is derived using the Lyapunov method, so that the stability of the whole system and the convergence of the networks are guaranteed. Computer simulations based on a mean value engine model demonstrate the effectiveness of the technique. PMID:18166378
NASA Astrophysics Data System (ADS)
Zhang, Xiangwen; Xu, Yong; Pan, Ming; Ren, Fenghua
2014-04-01
A sliding-mode observer is designed to estimate the vehicle velocity with the measured vehicle acceleration, the wheel speeds and the braking torques. Based on the Burckhardt tyre model, the extended Kalman filter is designed to estimate the parameters of the Burckhardt model with the estimated vehicle velocity, the measured wheel speeds and the vehicle acceleration. According to the estimated parameters of the Burckhardt tyre model, the tyre/road friction coefficients and the optimal slip ratios are calculated. A vehicle adaptive sliding-mode control (SMC) algorithm is presented with the estimated vehicle velocity, the tyre/road friction coefficients and the optimal slip ratios. And the adjustment method of the sliding-mode gain factors is discussed. Based on the adaptive SMC algorithm, a vehicle's antilock braking system (ABS) control system model is built with the Simulink Toolbox. Under the single-road condition as well as the different road conditions, the performance of the vehicle ABS system is simulated with the vehicle velocity observer, the tyre/road friction coefficient estimator and the adaptive SMC algorithm. The results indicate that the estimated errors of the vehicle velocity and the tyre/road friction coefficients are acceptable and the vehicle ABS adaptive SMC algorithm is effective. So the proposed adaptive SMC algorithm can be used to control the vehicle ABS without the information of the vehicle velocity and the road conditions.
Optimization of an adaptive SPECT system with the scanning linear estimator
NASA Astrophysics Data System (ADS)
Ghanbari, Nasrin; Clarkson, Eric; Kupinski, Matthew A.; Li, Xin
2015-08-01
The adaptive single-photon emission computed tomography (SPECT) system studied here acquires an initial scout image to obtain preliminary information about the object. Then the configuration is adjusted by selecting the size of the pinhole and the magnification that optimize system performance on an ensemble of virtual objects generated to be consistent with the scout data. In this study the object is a lumpy background that contains a Gaussian signal with a variable width and amplitude. The virtual objects in the ensemble are imaged by all of the available configurations and the subsequent images are evaluated with the scanning linear estimator to obtain an estimate of the signal width and amplitude. The ensemble mean squared error (EMSE) on the virtual ensemble between the estimated and the true parameters serves as the performance figure of merit for selecting the optimum configuration. The results indicate that variability in the original object background, noise and signal parameters leads to a specific optimum configuration in each case. A statistical study carried out for a number of objects show that the adaptive system on average performs better than its nonadaptive counterpart.
Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology
NASA Astrophysics Data System (ADS)
Petković, Dalibor; Shamshirband, Shahaboddin; Pavlović, Nenad T.; Anuar, Nor Badrul; Kiah, Miss Laiha Mat
2014-07-01
The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.
Battery state-of-charge estimation using approximate least squares
NASA Astrophysics Data System (ADS)
Unterrieder, C.; Zhang, C.; Lunglmayr, M.; Priewasser, R.; Marsili, S.; Huemer, M.
2015-03-01
In recent years, much effort has been spent to extend the runtime of battery-powered electronic applications. In order to improve the utilization of the available cell capacity, high precision estimation approaches for battery-specific parameters are needed. In this work, an approximate least squares estimation scheme is proposed for the estimation of the battery state-of-charge (SoC). The SoC is determined based on the prediction of the battery's electromotive force. The proposed approach allows for an improved re-initialization of the Coulomb counting (CC) based SoC estimation method. Experimental results for an implementation of the estimation scheme on a fuel gauge system on chip are illustrated. Implementation details and design guidelines are presented. The performance of the presented concept is evaluated for realistic operating conditions (temperature effects, aging, standby current, etc.). For the considered test case of a GSM/UMTS load current pattern of a mobile phone, the proposed method is able to re-initialize the CC-method with a high accuracy, while state-of-the-art methods fail to perform a re-initialization.
Attention control learning in the decision space using state estimation
NASA Astrophysics Data System (ADS)
Gharaee, Zahra; Fatehi, Alireza; Mirian, Maryam S.; Nili Ahmadabadi, Majid
2016-05-01
The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information.
Second-order state estimation experiments using acceleration measurements
NASA Technical Reports Server (NTRS)
Belvin, W. K.
1992-01-01
The estimation of dynamic states for feedback control of structural systems using second-order differential equations and acceleration measurements is described. The formulation of the observer model, and the design of the observer gains is discussed in detail. It is shown the second-order observer is highly stable because the stability constraints on the observer gains are model independent. The limitation of the proposed observer is the need for 'nearly' collocated actuators and accelerometers. Experimental results using a control-structure interaction testbed are presented that show the second-order observer provided more stability than a Kalman filter estimator without decreasing closed-loop performance.
Epidemic processes over adaptive state-dependent networks
NASA Astrophysics Data System (ADS)
Ogura, Masaki; Preciado, Victor M.
2016-06-01
In this paper we study the dynamics of epidemic processes taking place in adaptive networks of arbitrary topology. We focus our study on the adaptive susceptible-infected-susceptible (ASIS) model, where healthy individuals are allowed to temporarily cut edges connecting them to infected nodes in order to prevent the spread of the infection. In this paper we derive a closed-form expression for a lower bound on the epidemic threshold of the ASIS model in arbitrary networks with heterogeneous node and edge dynamics. For networks with homogeneous node and edge dynamics, we show that the resulting lower bound is proportional to the epidemic threshold of the standard SIS model over static networks, with a proportionality constant that depends on the adaptation rates. Furthermore, based on our results, we propose an efficient algorithm to optimally tune the adaptation rates in order to eradicate epidemic outbreaks in arbitrary networks. We confirm the tightness of the proposed lower bounds with several numerical simulations and compare our optimal adaptation rates with popular centrality measures.
Epidemic processes over adaptive state-dependent networks.
Ogura, Masaki; Preciado, Victor M
2016-06-01
In this paper we study the dynamics of epidemic processes taking place in adaptive networks of arbitrary topology. We focus our study on the adaptive susceptible-infected-susceptible (ASIS) model, where healthy individuals are allowed to temporarily cut edges connecting them to infected nodes in order to prevent the spread of the infection. In this paper we derive a closed-form expression for a lower bound on the epidemic threshold of the ASIS model in arbitrary networks with heterogeneous node and edge dynamics. For networks with homogeneous node and edge dynamics, we show that the resulting lower bound is proportional to the epidemic threshold of the standard SIS model over static networks, with a proportionality constant that depends on the adaptation rates. Furthermore, based on our results, we propose an efficient algorithm to optimally tune the adaptation rates in order to eradicate epidemic outbreaks in arbitrary networks. We confirm the tightness of the proposed lower bounds with several numerical simulations and compare our optimal adaptation rates with popular centrality measures. PMID:27415289
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.
The State of Literacy in America: Estimates at the Local, State, and National Levels.
ERIC Educational Resources Information Center
National Inst. for Literacy, Washington, DC.
This document presents synthesized estimates of the rates of level 1 literacy by congressional district in the 50 states and District of Columbia. The estimates are extrapolations of the National Adult Literacy Survey (NALS) that were based on the findings of approximately 26,000 interviews. The document begins with an introduction containing the…