Sample records for target state estimation

  1. Accurate State Estimation and Tracking of a Non-Cooperative Target Vehicle

    NASA Technical Reports Server (NTRS)

    Thienel, Julie K.; Sanner, Robert M.

    2006-01-01

    Autonomous space rendezvous scenarios require knowledge of the target vehicle state in order to safely dock with the chaser vehicle. Ideally, the target vehicle state information is derived from telemetered data, or with the use of known tracking points on the target vehicle. However, if the target vehicle is non-cooperative and does not have the ability to maintain attitude control, or transmit attitude knowledge, the docking becomes more challenging. This work presents a nonlinear approach for estimating the body rates of a non-cooperative target vehicle, and coupling this estimation to a tracking control scheme. The approach is tested with the robotic servicing mission concept for the Hubble Space Telescope (HST). Such a mission would not only require estimates of the HST attitude and rates, but also precision control to achieve the desired rate and maintain the orientation to successfully dock with HST.

  2. Kalman filter data assimilation: targeting observations and parameter estimation.

    PubMed

    Bellsky, Thomas; Kostelich, Eric J; Mahalov, Alex

    2014-06-01

    This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.

  3. Kalman filter data assimilation: Targeting observations and parameter estimation

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

    Bellsky, Thomas, E-mail: bellskyt@asu.edu; Kostelich, Eric J.; Mahalov, Alex

    2014-06-15

    This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly locatedmore » observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.« less

  4. State estimation with incomplete nonlinear constraint

    NASA Astrophysics Data System (ADS)

    Huang, Yuan; Wang, Xueying; An, Wei

    2017-10-01

    A problem of state estimation with a new constraints named incomplete nonlinear constraint is considered. The targets are often move in the curve road, if the width of road is neglected, the road can be considered as the constraint, and the position of sensors, e.g., radar, is known in advance, this info can be used to enhance the performance of the tracking filter. The problem of how to incorporate the priori knowledge is considered. In this paper, a second-order sate constraint is considered. A fitting algorithm of ellipse is adopted to incorporate the priori knowledge by estimating the radius of the trajectory. The fitting problem is transformed to the nonlinear estimation problem. The estimated ellipse function is used to approximate the nonlinear constraint. Then, the typical nonlinear constraint methods proposed in recent works can be used to constrain the target state. Monte-Carlo simulation results are presented to illustrate the effectiveness proposed method in state estimation with incomplete constraint.

  5. Joint sparsity based heterogeneous data-level fusion for target detection and estimation

    NASA Astrophysics Data System (ADS)

    Niu, Ruixin; Zulch, Peter; Distasio, Marcello; Blasch, Erik; Shen, Dan; Chen, Genshe

    2017-05-01

    Typical surveillance systems employ decision- or feature-level fusion approaches to integrate heterogeneous sensor data, which are sub-optimal and incur information loss. In this paper, we investigate data-level heterogeneous sensor fusion. Since the sensors monitor the common targets of interest, whose states can be determined by only a few parameters, it is reasonable to assume that the measurement domain has a low intrinsic dimensionality. For heterogeneous sensor data, we develop a joint-sparse data-level fusion (JSDLF) approach based on the emerging joint sparse signal recovery techniques by discretizing the target state space. This approach is applied to fuse signals from multiple distributed radio frequency (RF) signal sensors and a video camera for joint target detection and state estimation. The JSDLF approach is data-driven and requires minimum prior information, since there is no need to know the time-varying RF signal amplitudes, or the image intensity of the targets. It can handle non-linearity in the sensor data due to state space discretization and the use of frequency/pixel selection matrices. Furthermore, for a multi-target case with J targets, the JSDLF approach only requires discretization in a single-target state space, instead of discretization in a J-target state space, as in the case of the generalized likelihood ratio test (GLRT) or the maximum likelihood estimator (MLE). Numerical examples are provided to demonstrate that the proposed JSDLF approach achieves excellent performance with near real-time accurate target position and velocity estimates.

  6. Disruption of State Estimation in the Human Lateral Cerebellum

    PubMed Central

    Miall, R. Chris; Christensen, Lars O. D; Cain, Owen; Stanley, James

    2007-01-01

    The cerebellum has been proposed to be a crucial component in the state estimation process that combines information from motor efferent and sensory afferent signals to produce a representation of the current state of the motor system. Such a state estimate of the moving human arm would be expected to be used when the arm is rapidly and skillfully reaching to a target. We now report the effects of transcranial magnetic stimulation (TMS) over the ipsilateral cerebellum as healthy humans were made to interrupt a slow voluntary movement to rapidly reach towards a visually defined target. Errors in the initial direction and in the final finger position of this reach-to-target movement were significantly higher for cerebellar stimulation than they were in control conditions. The average directional errors in the cerebellar TMS condition were consistent with the reaching movements being planned and initiated from an estimated hand position that was 138 ms out of date. We suggest that these results demonstrate that the cerebellum is responsible for estimating the hand position over this time interval and that TMS disrupts this state estimate. PMID:18044990

  7. SAR target recognition and posture estimation using spatial pyramid pooling within CNN

    NASA Astrophysics Data System (ADS)

    Peng, Lijiang; Liu, Xiaohua; Liu, Ming; Dong, Liquan; Hui, Mei; Zhao, Yuejin

    2018-01-01

    Many convolution neural networks(CNN) architectures have been proposed to strengthen the performance on synthetic aperture radar automatic target recognition (SAR-ATR) and obtained state-of-art results on targets classification on MSTAR database, but few methods concern about the estimation of depression angle and azimuth angle of targets. To get better effect on learning representation of hierarchies of features on both 10-class target classification task and target posture estimation tasks, we propose a new CNN architecture with spatial pyramid pooling(SPP) which can build high hierarchy of features map by dividing the convolved feature maps from finer to coarser levels to aggregate local features of SAR images. Experimental results on MSTAR database show that the proposed architecture can get high recognition accuracy as 99.57% on 10-class target classification task as the most current state-of-art methods, and also get excellent performance on target posture estimation tasks which pays attention to depression angle variety and azimuth angle variety. What's more, the results inspire us the application of deep learning on SAR target posture description.

  8. Multi-Target State Extraction for the SMC-PHD Filter

    PubMed Central

    Si, Weijian; Wang, Liwei; Qu, Zhiyu

    2016-01-01

    The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter has been demonstrated to be a favorable method for multi-target tracking. However, the time-varying target states need to be extracted from the particle approximation of the posterior PHD, which is difficult to implement due to the unknown relations between the large amount of particles and the PHD peaks representing potential target locations. To address this problem, a novel multi-target state extraction algorithm is proposed in this paper. By exploiting the information of measurements and particle likelihoods in the filtering stage, we propose a validation mechanism which aims at selecting effective measurements and particles corresponding to detected targets. Subsequently, the state estimates of the detected and undetected targets are performed separately: the former are obtained from the particle clusters directed by effective measurements, while the latter are obtained from the particles corresponding to undetected targets via clustering method. Simulation results demonstrate that the proposed method yields better estimation accuracy and reliability compared to existing methods. PMID:27322274

  9. Data and methodological problems in establishing state gasoline-conservation targets

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

    Greene, D.L.; Walton, G.H.

    The Emergency Energy Conservation Act of 1979 gives the President the authority to set gasoline-conservation targets for states in the event of a supply shortage. This paper examines data and methodological problems associated with setting state gasoline-conservation targets. The target-setting method currently used is examined and found to have some flaws. Ways of correcting these deficiencies through the use of Box-Jenkins time-series analysis are investigated. A successful estimation of Box-Jenkins models for all states included the estimation of the magnitude of the supply shortages of 1979 in each state and a preliminary estimation of state short-run price elasticities, which weremore » found to vary about a median value of -0.16. The time-series models identified were very simple in structure and lent support to the simple consumption growth model assumed by the current target method. The authors conclude that the flaws in the current method can be remedied either by replacing the current procedures with time-series models or by using the models in conjunction with minor modifications of the current method.« less

  10. Graph theoretic framework based cooperative control and estimation of multiple UAVs for target tracking

    NASA Astrophysics Data System (ADS)

    Ahmed, Mousumi

    topology switches to a different topology over particular time instants. Lyapunov analysis is performed to show stability in all cases. Another important aspect of this dissertation research is to implement the controller for the case, where perfect or full state information is not available. This necessitates the design of an estimator to estimate the system state. A nonlinear estimator, Extended Kalman Filter (EKF) is first developed for target tracking with a single UAV. The uncertainties involved with the measurement model and dynamics model are considered as zero mean Gaussian noises with some known covariances. The measurements of the full state of the target are not available and only the range, elevation, and azimuth angle are available from an onboard seeker sensor. A separate EKF is designed to estimate the UAV's own state where the state measurement is available through on-board sensors. The controller computes the three control commands based on the estimated states of target and its own states. Estimation based control laws is also implemented for colored noise measurement uncertainties, and the controller performance is shown with the simulation results. The estimation based control approach is then extended for the cooperative target tracking case. The target information is available to the network and a separate estimator is used to estimate target states. All of the UAVs in the network apply the same control law and the only difference is that each UAV updates the commands according to their connection. The simulation is performed for both cases of fixed and time varying communication topology. Monte Carlo simulation is also performed with different sample noises to investigate the performance of the estimator. The proposed technique is shown to be simple and robust to noisy environments.

  11. Ground target recognition using rectangle estimation.

    PubMed

    Grönwall, Christina; Gustafsson, Fredrik; Millnert, Mille

    2006-11-01

    We propose a ground target recognition method based on 3-D laser radar data. The method handles general 3-D scattered data. It is based on the fact that man-made objects of complex shape can be decomposed to a set of rectangles. The ground target recognition method consists of four steps; 3-D size and orientation estimation, target segmentation into parts of approximately rectangular shape, identification of segments that represent the target's functional/main parts, and target matching with CAD models. The core in this approach is rectangle estimation. The performance of the rectangle estimation method is evaluated statistically using Monte Carlo simulations. A case study on tank recognition is shown, where 3-D data from four fundamentally different types of laser radar systems are used. Although the approach is tested on rather few examples, we believe that the approach is promising.

  12. Pharmacokinetic Steady-States Highlight Interesting Target-Mediated Disposition Properties.

    PubMed

    Gabrielsson, Johan; Peletier, Lambertus A

    2017-05-01

    In this paper, we derive explicit expressions for the concentrations of ligand L, target R and ligand-target complex RL at steady state for the classical model describing target-mediated drug disposition, in the presence of a constant-rate infusion of ligand. We demonstrate that graphing the steady-state values of ligand, target and ligand-target complex, we obtain striking and often singular patterns, which yield a great deal of insight and understanding about the underlying processes. Deriving explicit expressions for the dependence of L, R and RL on the infusion rate, and displaying graphs of the relations between L, R and RL, we give qualitative and quantitive information for the experimentalist about the processes involved. Understanding target turnover is pivotal for optimising these processes when target-mediated drug disposition (TMDD) prevails. By a combination of mathematical analysis and simulations, we also show that the evolution of the three concentration profiles towards their respective steady-states can be quite complex, especially for lower infusion rates. We also show how parameter estimates obtained from iv bolus studies can be used to derive steady-state concentrations of ligand, target and complex. The latter may serve as a template for future experimental designs.

  13. Integrated detection, estimation, and guidance in pursuit of a maneuvering target

    NASA Astrophysics Data System (ADS)

    Dionne, Dany

    The thesis focuses on efficient solutions of non-cooperative pursuit-evasion games with imperfect information on the state of the system. This problem is important in the context of interception of future maneuverable ballistic missiles. However, the theoretical developments are expected to find application to a broad class of hybrid control and estimation problems in industry. The validity of the results is nevertheless confirmed using a benchmark problem in the area of terminal guidance. A specific interception scenario between an incoming target with no information and a single interceptor missile with noisy measurements is analyzed in the form of a linear hybrid system subject to additive abrupt changes. The general research is aimed to achieve improved homing accuracy by integrating ideas from detection theory, state estimation theory and guidance. The results achieved can be summarized as follows. (i) Two novel maneuver detectors are developed to diagnose abrupt changes in a class of hybrid systems (detection and isolation of evasive maneuvers): a new implementation of the GLR detector and the novel adaptive- H0 GLR detector. (ii) Two novel state estimators for target tracking are derived using the novel maneuver detectors. The state estimators employ parameterized family of functions to described possible evasive maneuvers. (iii) A novel adaptive Bayesian multiple model predictor of the ballistic miss is developed which employs semi-Markov models and ideas from detection theory. (iv) A novel integrated estimation and guidance scheme that significantly improves the homing accuracy is also presented. The integrated scheme employs banks of estimators and guidance laws, a maneuver detector, and an on-line governor; the scheme is adaptive with respect to the uncertainty affecting the probability density function of the filtered state. (v) A novel discretization technique for the family of continuous-time, game theoretic, bang-bang guidance laws is introduced. The

  14. An Inertial Dual-State State Estimator for Precision Planetary Landing with Hazard Detection and Avoidance

    NASA Technical Reports Server (NTRS)

    Bishop, Robert H.; DeMars, Kyle; Trawny, Nikolas; Crain, Tim; Hanak, Chad; Carson, John M.; Christian, John

    2016-01-01

    The navigation filter architecture successfully deployed on the Morpheus flight vehicle is presented. The filter was developed as a key element of the NASA Autonomous Landing and Hazard Avoidance Technology (ALHAT) project and over the course of 15 free fights was integrated into the Morpheus vehicle, operations, and flight control loop. Flight testing completed by demonstrating autonomous hazard detection and avoidance, integration of an altimeter, surface relative velocity (velocimeter) and hazard relative navigation (HRN) measurements into the onboard dual-state inertial estimator Kalman flter software, and landing within 2 meters of the vertical testbed GPS-based navigation solution at the safe landing site target. Morpheus followed a trajectory that included an ascent phase followed by a partial descent-to-landing, although the proposed filter architecture is applicable to more general planetary precision entry, descent, and landings. The main new contribution is the incorporation of a sophisticated hazard relative navigation sensor-originally intended to locate safe landing sites-into the navigation system and employed as a navigation sensor. The formulation of a dual-state inertial extended Kalman filter was designed to address the precision planetary landing problem when viewed as a rendezvous problem with an intended landing site. For the required precision navigation system that is capable of navigating along a descent-to-landing trajectory to a precise landing, the impact of attitude errors on the translational state estimation are included in a fully integrated navigation structure in which translation state estimation is combined with attitude state estimation. The map tie errors are estimated as part of the process, thereby creating a dual-state filter implementation. Also, the filter is implemented using inertial states rather than states relative to the target. External measurements include altimeter, velocimeter, star camera, terrain relative

  15. Quantum state tomography and fidelity estimation via Phaselift

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

    Lu, Yiping; Liu, Huan; Zhao, Qing, E-mail: qzhaoyuping@bit.edu.cn

    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 sixmore » 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.« less

  16. Reinforcement learning state estimator.

    PubMed

    Morimoto, Jun; Doya, Kenji

    2007-03-01

    In this study, we propose a novel use of reinforcement learning for estimating hidden variables and parameters of nonlinear dynamical systems. A critical issue in hidden-state estimation is that we cannot directly observe estimation errors. However, by defining errors of observable variables as a delayed penalty, we can apply a reinforcement learning frame-work to state estimation problems. Specifically, we derive a method to construct a nonlinear state estimator by finding an appropriate feedback input gain using the policy gradient method. We tested the proposed method on single pendulum dynamics and show that the joint angle variable could be successfully estimated by observing only the angular velocity, and vice versa. In addition, we show that we could acquire a state estimator for the pendulum swing-up task in which a swing-up controller is also acquired by reinforcement learning simultaneously. Furthermore, we demonstrate that it is possible to estimate the dynamics of the pendulum itself while the hidden variables are estimated in the pendulum swing-up task. Application of the proposed method to a two-linked biped model is also presented.

  17. Bias estimation for moving optical sensor measurements with targets of opportunity

    NASA Astrophysics Data System (ADS)

    Belfadel, Djedjiga; Osborne, Richard W.; Bar-Shalom, Yaakov

    2014-06-01

    Integration of space based sensors into a Ballistic Missile Defense System (BMDS) allows for detection and tracking of threats over a larger area than ground based sensors [1]. This paper examines the effect of sensor bias error on the tracking quality of a Space Tracking and Surveillance System (STSS) for the highly non-linear problem of tracking a ballistic missile. The STSS constellation consists of two or more satellites (on known trajectories) for tracking ballistic targets. Each satellite is equipped with an IR sensor that provides azimuth and elevation to the target. The tracking problem is made more difficult due to a constant or slowly varying bias error present in each sensor's line of sight measurements. It is important to correct for these bias errors so that the multiple sensor measurements and/or tracks can be referenced as accurately as possible to a common tracking coordinate system. The measurements provided by these sensors are assumed time-coincident (synchronous) and perfectly associated. The line of sight (LOS) measurements from the sensors can be fused into measurements which are the Cartesian target position, i.e., linear in the target state. We evaluate the Cramér-Rao Lower Bound (CRLB) on the covariance of the bias estimates, which serves as a quantification of the available information about the biases. Statistical tests on the results of simulations show that this method is statistically efficient, even for small sample sizes (as few as two sensors and six points on the (unknown) trajectory of a single target of opportunity). We also show that the RMS position error is significantly improved with bias estimation compared with the target position estimation using the original biased measurements.

  18. Range estimation of passive infrared targets through the atmosphere

    NASA Astrophysics Data System (ADS)

    Cho, Hoonkyung; Chun, Joohwan; Seo, Doochun; Choi, Seokweon

    2013-04-01

    Target range estimation is traditionally based on radar and active sonar systems in modern combat systems. However, jamming signals tremendously degrade the performance of such active sensor devices. We introduce a simple target range estimation method and the fundamental limits of the proposed method based on the atmosphere propagation model. Since passive infrared (IR) sensors measure IR signals radiating from objects in different wavelengths, this method has robustness against electromagnetic jamming. The measured target radiance of each wavelength at the IR sensor depends on the emissive properties of target material and various attenuation factors (i.e., the distance between sensor and target and atmosphere environment parameters). MODTRAN is a tool that models atmospheric propagation of electromagnetic radiation. Based on the results from MODTRAN and atmosphere propagation-based modeling, the target range can be estimated. To analyze the proposed method's performance statistically, we use maximum likelihood estimation (MLE) and evaluate the Cramer-Rao lower bound (CRLB) via the probability density function of measured radiance. We also compare CRLB and the variance of MLE using Monte-Carlo simulation.

  19. Moving target parameter estimation of SAR after two looks cancellation

    NASA Astrophysics Data System (ADS)

    Gan, Rongbing; Wang, Jianguo; Gao, Xiang

    2005-11-01

    Moving target detection of synthetic aperture radar (SAR) by two looks cancellation is studied. First, two looks are got by the first and second half of the synthetic aperture. After two looks cancellation, the moving targets are reserved and stationary targets are removed. After that, a Constant False Alarm Rate (CFAR) detector detects moving targets. The ground range velocity and cross-range velocity of moving target can be got by the position shift between the two looks. We developed a method to estimate the cross-range shift due to slant range moving. we estimate cross-range shift by Doppler frequency center. Wigner-Ville Distribution (WVD) is used to estimate the Doppler frequency center (DFC). Because the range position and cross range before correction is known, estimation of DFC is much easier and efficient. Finally experiments results show that our algorithms have good performance. With the algorithms we can estimate the moving target parameter accurately.

  20. An open source framework for tracking and state estimation ('Stone Soup')

    NASA Astrophysics Data System (ADS)

    Thomas, Paul A.; Barr, Jordi; Balaji, Bhashyam; White, Kruger

    2017-05-01

    The ability to detect and unambiguously follow all moving entities in a state-space is important in multiple domains both in defence (e.g. air surveillance, maritime situational awareness, ground moving target indication) and the civil sphere (e.g. astronomy, biology, epidemiology, dispersion modelling). However, tracking and state estimation researchers and practitioners have difficulties recreating state-of-the-art algorithms in order to benchmark their own work. Furthermore, system developers need to assess which algorithms meet operational requirements objectively and exhaustively rather than intuitively or driven by personal favourites. We have therefore commenced the development of a collaborative initiative to create an open source framework for production, demonstration and evaluation of Tracking and State Estimation algorithms. The initiative will develop a (MIT-licensed) software platform for researchers and practitioners to test, verify and benchmark a variety of multi-sensor and multi-object state estimation algorithms. The initiative is supported by four defence laboratories, who will contribute to the development effort for the framework. The tracking and state estimation community will derive significant benefits from this work, including: access to repositories of verified and validated tracking and state estimation algorithms, a framework for the evaluation of multiple algorithms, standardisation of interfaces and access to challenging data sets. Keywords: Tracking,

  1. Target Information Processing: A Joint Decision and Estimation Approach

    DTIC Science & Technology

    2012-03-29

    ground targets ( track - before - detect ) using computer cluster and graphics processing unit. Estimation and filtering theory is one of the most important...targets ( track - before - detect ) using computer cluster and graphics processing unit. Estimation and filtering theory is one of the most important

  2. Confidence level estimation in multi-target classification problems

    NASA Astrophysics Data System (ADS)

    Chang, Shi; Isaacs, Jason; Fu, Bo; Shin, Jaejeong; Zhu, Pingping; Ferrari, Silvia

    2018-04-01

    This paper presents an approach for estimating the confidence level in automatic multi-target classification performed by an imaging sensor on an unmanned vehicle. An automatic target recognition algorithm comprised of a deep convolutional neural network in series with a support vector machine classifier detects and classifies targets based on the image matrix. The joint posterior probability mass function of target class, features, and classification estimates is learned from labeled data, and recursively updated as additional images become available. Based on the learned joint probability mass function, the approach presented in this paper predicts the expected confidence level of future target classifications, prior to obtaining new images. The proposed approach is tested with a set of simulated sonar image data. The numerical results show that the estimated confidence level provides a close approximation to the actual confidence level value determined a posteriori, i.e. after the new image is obtained by the on-board sensor. Therefore, the expected confidence level function presented in this paper can be used to adaptively plan the path of the unmanned vehicle so as to optimize the expected confidence levels and ensure that all targets are classified with satisfactory confidence after the path is executed.

  3. 76 FR 34953 - Funding Opportunity Title: Risk Management Education in Targeted States (Targeted States Program...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-06-15

    ... Corporation Funding Opportunity Title: Risk Management Education in Targeted States (Targeted States Program... Corporation (FCIC), operating through the Risk Management Agency (RMA), announces its intent to award... same time as funding availability for similar but separate program, the Risk Management Education and...

  4. Influences on Adoption of Greenhouse Gas Reduction Targets among US States, 1998-2008

    PubMed Central

    Cale, Tabitha M.; Reams, Margaret A.

    2016-01-01

    While the United States has not established federal regulations for greenhouse gas (GHG) reduction targets, many US states have adopted their own standards and guidelines. In this study we examine state adoption of targets for GHG reductions during the ten-year period of 1998–2008, and identify factors that explain variation in target adoption. Potential influences are drawn from research from the public policy formulation and diffusion literature, and from studies specific to climate policy adoption. Potential influences on GHG reduction efforts among US states include socioeconomic attributes of residents, political and ideological orientations of citizens and state government, interest group activities, environmental pressures, and proximity to other states that have adopted GHG reduction targets. The findings of the multinomial logistic regression analysis indicate that states are more likely to adopt GHG reduction targets if they share a border with another state with a similar climate program and if their citizens are more ideologically liberal. Other factors including socioeconomic resources and interest group activities were not found to be associated with policy adoption. The findings yield insights into the conditions under which states are more likely to take action to reduce GHG’s, and are relevant both to state policy makers and residents with an interest in climate planning, and for researchers attempting to estimate future greenhouse gas reduction scenarios. PMID:27471657

  5. [Estimated mammogram coverage in Goiás State, Brazil].

    PubMed

    Corrêa, Rosangela da Silveira; Freitas-Júnior, Ruffo; Peixoto, João Emílio; Rodrigues, Danielle Cristina Netto; Lemos, Maria Eugênia da Fonseca; Marins, Lucy Aparecida Parreira; Silveira, Erika Aparecida da

    2011-09-01

    This cross-sectional study aimed to estimate mammogram coverage in the State of Goiás, Brazil, describing the supply, demand, and variations in different age groups, evaluating 98 mammography services as observational units. We estimated the mammogram rates by age group and type of health service, as well as the number of tests required to cover 70% and 100% of the target population. We assessed the association between mammograms, geographical distribution of mammography machines, type of service, and age group. Full coverage estimates, considering 100% of women in the 40-69 and 50-69-year age brackets, were 61% and 66%, of which the Brazilian Unified National Health System provided 13% and 14%, respectively. To achieve 70% coverage, 43,424 additional mammograms would be needed. All the associations showed statistically significant differences (p < 0.001). We conclude that mammogram coverage is unevenly distributed in the State of Goiás and that fewer tests are performed than required.

  6. Radiance and atmosphere propagation-based method for the target range estimation

    NASA Astrophysics Data System (ADS)

    Cho, Hoonkyung; Chun, Joohwan

    2012-06-01

    Target range estimation is traditionally based on radar and active sonar systems in modern combat system. However, the performance of such active sensor devices is degraded tremendously by jamming signal from the enemy. This paper proposes a simple range estimation method between the target and the sensor. Passive IR sensors measures infrared (IR) light radiance radiating from objects in dierent wavelength and this method shows robustness against electromagnetic jamming. The measured target radiance of each wavelength at the IR sensor depends on the emissive properties of target material and is attenuated by various factors, in particular the distance between the sensor and the target and atmosphere environment. MODTRAN is a tool that models atmospheric propagation of electromagnetic radiation. Based on the result from MODTRAN and measured radiance, the target range is estimated. To statistically analyze the performance of proposed method, we use maximum likelihood estimation (MLE) and evaluate the Cramer-Rao Lower Bound (CRLB) via the probability density function of measured radiance. And we also compare CRLB and the variance of and ML estimation using Monte-Carlo.

  7. 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.

  8. Foot placement relies on state estimation during visually guided walking.

    PubMed

    Maeda, Rodrigo S; O'Connor, Shawn M; Donelan, J Maxwell; Marigold, Daniel S

    2017-02-01

    As we walk, we must accurately place our feet to stabilize our motion and to navigate our environment. We must also achieve this accuracy despite imperfect sensory feedback and unexpected disturbances. In this study we tested whether the nervous system uses state estimation to beneficially combine sensory feedback with forward model predictions to compensate for these challenges. Specifically, subjects wore prism lenses during a visually guided walking task, and we used trial-by-trial variation in prism lenses to add uncertainty to visual feedback and induce a reweighting of this input. To expose altered weighting, we added a consistent prism shift that required subjects to adapt their estimate of the visuomotor mapping relationship between a perceived target location and the motor command necessary to step to that position. With added prism noise, subjects responded to the consistent prism shift with smaller initial foot placement error but took longer to adapt, compatible with our mathematical model of the walking task that leverages state estimation to compensate for noise. Much like when we perform voluntary and discrete movements with our arms, it appears our nervous systems uses state estimation during walking to accurately reach our foot to the ground. Accurate foot placement is essential for safe walking. We used computational models and human walking experiments to test how our nervous system achieves this accuracy. We find that our control of foot placement beneficially combines sensory feedback with internal forward model predictions to accurately estimate the body's state. Our results match recent computational neuroscience findings for reaching movements, suggesting that state estimation is a general mechanism of human motor control. Copyright © 2017 the American Physiological Society.

  9. Polarized Solid State Target

    NASA Astrophysics Data System (ADS)

    Dutz, Hartmut; Goertz, Stefan; Meyer, Werner

    2017-01-01

    The polarized solid state target is an indispensable experimental tool to study single and double polarization observables at low intensity particle beams like tagged photons. It was one of the major components of the Crystal-Barrel experiment at ELSA. Besides the operation of the 'CB frozen spin target' within the experimental program of the Crystal-Barrel collaboration both collaborative groups of the D1 project, the polarized target group of the Ruhr Universität Bochum and the Bonn polarized target group, have made significant developments in the field of polarized targets within the CRC16. The Bonn polarized target group has focused its work on the development of technically challenging polarized solid target systems towards the so called '4π continuous mode polarized target' to operate them in combination with 4π-particle detection systems. In parallel, the Bochum group has developed various highly polarized deuterated target materials and high precision NMR-systems, in the meantime used for polarization experiments at CERN, JLAB and MAMI, too.

  10. Non-Cooperative Target Imaging and Parameter Estimation with Narrowband Radar Echoes.

    PubMed

    Yeh, Chun-mao; Zhou, Wei; Lu, Yao-bing; Yang, Jian

    2016-01-20

    This study focuses on the rotating target imaging and parameter estimation with narrowband radar echoes, which is essential for radar target recognition. First, a two-dimensional (2D) imaging model with narrowband echoes is established in this paper, and two images of the target are formed on the velocity-acceleration plane at two neighboring coherent processing intervals (CPIs). Then, the rotating velocity (RV) is proposed to be estimated by utilizing the relationship between the positions of the scattering centers among two images. Finally, the target image is rescaled to the range-cross-range plane with the estimated rotational parameter. The validity of the proposed approach is confirmed using numerical simulations.

  11. Targeted estimation of nuisance parameters to obtain valid statistical inference.

    PubMed

    van der Laan, Mark J

    2014-01-01

    In order to obtain concrete results, we focus on estimation of the treatment specific mean, controlling for all measured baseline covariates, based on observing independent and identically distributed copies of a random variable consisting of baseline covariates, a subsequently assigned binary treatment, and a final outcome. The statistical model only assumes possible restrictions on the conditional distribution of treatment, given the covariates, the so-called propensity score. Estimators of the treatment specific mean involve estimation of the propensity score and/or estimation of the conditional mean of the outcome, given the treatment and covariates. In order to make these estimators asymptotically unbiased at any data distribution in the statistical model, it is essential to use data-adaptive estimators of these nuisance parameters such as ensemble learning, and specifically super-learning. Because such estimators involve optimal trade-off of bias and variance w.r.t. the infinite dimensional nuisance parameter itself, they result in a sub-optimal bias/variance trade-off for the resulting real-valued estimator of the estimand. We demonstrate that additional targeting of the estimators of these nuisance parameters guarantees that this bias for the estimand is second order and thereby allows us to prove theorems that establish asymptotic linearity of the estimator of the treatment specific mean under regularity conditions. These insights result in novel targeted minimum loss-based estimators (TMLEs) that use ensemble learning with additional targeted bias reduction to construct estimators of the nuisance parameters. In particular, we construct collaborative TMLEs (C-TMLEs) with known influence curve allowing for statistical inference, even though these C-TMLEs involve variable selection for the propensity score based on a criterion that measures how effective the resulting fit of the propensity score is in removing bias for the estimand. As a particular special

  12. 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.

  13. Reexamination of optimal quantum state estimation of pure states

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

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

    2005-09-15

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

  14. Sub-Second Parallel State Estimation

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

    Chen, Yousu; Rice, Mark J.; Glaesemann, Kurt R.

    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 statemore » 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

  15. Quantum State Tomography via Linear Regression Estimation

    PubMed Central

    Qi, Bo; Hou, Zhibo; Li, Li; Dong, Daoyi; Xiang, Guoyong; Guo, Guangcan

    2013-01-01

    A simple yet efficient state reconstruction algorithm of linear regression estimation (LRE) is presented for quantum state tomography. In this method, quantum state reconstruction is converted into a parameter estimation problem of a linear regression model and the least-squares method is employed to estimate the unknown parameters. An asymptotic mean squared error (MSE) upper bound for all possible states to be estimated is given analytically, which depends explicitly upon the involved measurement bases. This analytical MSE upper bound can guide one to choose optimal measurement sets. The computational complexity of LRE is O(d4) where d is the dimension of the quantum state. Numerical examples show that LRE is much faster than maximum-likelihood estimation for quantum state tomography. PMID:24336519

  16. Waveform Optimization for Target Estimation by Cognitive Radar with Multiple Antennas.

    PubMed

    Yao, Yu; Zhao, Junhui; Wu, Lenan

    2018-05-29

    A new scheme based on Kalman filtering to optimize the waveforms of an adaptive multi-antenna radar system for target impulse response (TIR) estimation is presented. This work aims to improve the performance of TIR estimation by making use of the temporal correlation between successive received signals, and minimize the mean square error (MSE) of TIR estimation. The waveform design approach is based upon constant learning from the target feature at the receiver. Under the multiple antennas scenario, a dynamic feedback loop control system is established to real-time monitor the change in the target features extracted form received signals. The transmitter adapts its transmitted waveform to suit the time-invariant environment. Finally, the simulation results show that, as compared with the waveform design method based on the MAP criterion, the proposed waveform design algorithm is able to improve the performance of TIR estimation for extended targets with multiple iterations, and has a relatively lower level of complexity.

  17. Estimating the Probability of a Diffusing Target Encountering a Stationary Sensor.

    DTIC Science & Technology

    1985-07-01

    7 RD-R1577 6- 44 ESTIMATING THE PROBABILITY OF A DIFFUSING TARGET i/i ENCOUNTERING R STATIONARY SENSOR(U) NAVAL POSTGRADUATE U SCHOOL MONTEREY CA...8217,: *.:.; - -*.. ,’.-,:;;’.’.. ’,. ,. .*.’.- 4 6 6- ..- .-,,.. : .-.;.- -. NPS55-85-013 NAVAL POSTGRADUATE SCHOOL Monterey, California ESTIMATING THE PROBABILITY OF A DIFFUSING TARGET...PROBABILITY OF A DIFFUSING Technical TARGET ENCOUNTERING A STATIONARY SENSOR S. PERFORMING ORG. REPORT NUMBER 7. AUTHOR(@) S. CONTRACT OR GRANT NUMBER(a

  18. Balancing Score Adjusted Targeted Minimum Loss-based Estimation

    PubMed Central

    Lendle, Samuel David; Fireman, Bruce; van der Laan, Mark J.

    2015-01-01

    Adjusting for a balancing score is sufficient for bias reduction when estimating causal effects including the average treatment effect and effect among the treated. Estimators that adjust for the propensity score in a nonparametric way, such as matching on an estimate of the propensity score, can be consistent when the estimated propensity score is not consistent for the true propensity score but converges to some other balancing score. We call this property the balancing score property, and discuss a class of estimators that have this property. We introduce a targeted minimum loss-based estimator (TMLE) for a treatment-specific mean with the balancing score property that is additionally locally efficient and doubly robust. We investigate the new estimator’s performance relative to other estimators, including another TMLE, a propensity score matching estimator, an inverse probability of treatment weighted estimator, and a regression-based estimator in simulation studies. PMID:26561539

  19. Estimation of Target Angular Position Under Mainbeam Jamming Conditions,

    DTIC Science & Technology

    1995-12-01

    technique, Multiple Signal Classification ( MUSIC ), is used to estimate the target Direction Of Arrival (DOA) from the processed data vectors. The model...used in the MUSIC technique takes into account the fact that the jammer has been cancelled in the target data vector. The performance of this algorithm

  20. MPN estimation of qPCR target sequence recoveries from whole cell calibrator samples.

    PubMed

    Sivaganesan, Mano; Siefring, Shawn; Varma, Manju; Haugland, Richard A

    2011-12-01

    DNA extracts from enumerated target organism cells (calibrator samples) have been used for estimating Enterococcus cell equivalent densities in surface waters by a comparative cycle threshold (Ct) qPCR analysis method. To compare surface water Enterococcus density estimates from different studies by this approach, either a consistent source of calibrator cells must be used or the estimates must account for any differences in target sequence recoveries from different sources of calibrator cells. In this report we describe two methods for estimating target sequence recoveries from whole cell calibrator samples based on qPCR analyses of their serially diluted DNA extracts and most probable number (MPN) calculation. The first method employed a traditional MPN calculation approach. The second method employed a Bayesian hierarchical statistical modeling approach and a Monte Carlo Markov Chain (MCMC) simulation method to account for the uncertainty in these estimates associated with different individual samples of the cell preparations, different dilutions of the DNA extracts and different qPCR analytical runs. The two methods were applied to estimate mean target sequence recoveries per cell from two different lots of a commercially available source of enumerated Enterococcus cell preparations. The mean target sequence recovery estimates (and standard errors) per cell from Lot A and B cell preparations by the Bayesian method were 22.73 (3.4) and 11.76 (2.4), respectively, when the data were adjusted for potential false positive results. Means were similar for the traditional MPN approach which cannot comparably assess uncertainty in the estimates. Cell numbers and estimates of recoverable target sequences in calibrator samples prepared from the two cell sources were also used to estimate cell equivalent and target sequence quantities recovered from surface water samples in a comparative Ct method. Our results illustrate the utility of the Bayesian method in accounting for

  1. UAV State Estimation Modeling Techniques in AHRS

    NASA Astrophysics Data System (ADS)

    Razali, Shikin; Zhahir, Amzari

    2017-11-01

    Autonomous unmanned aerial vehicle (UAV) system is depending on state estimation feedback to control flight operation. Estimation on the correct state improves navigation accuracy and achieves flight mission safely. One of the sensors configuration used in UAV state is Attitude Heading and Reference System (AHRS) with application of Extended Kalman Filter (EKF) or feedback controller. The results of these two different techniques in estimating UAV states in AHRS configuration are displayed through position and attitude graphs.

  2. Adaptive early detection ML/PDA estimator for LO targets with EO sensors

    NASA Astrophysics Data System (ADS)

    Chummun, Muhammad R.; Kirubarajan, Thiagalingam; Bar-Shalom, Yaakov

    2000-07-01

    The batch Maximum Likelihood Estimator, combined with Probabilistic Data (ML-PDA), has been shown to be effective in acquiring low observable (LO) - low SNR - non-maneuvering targets in the presence of heavy clutter. The use of signal strength or amplitude information (AI) in the ML-PDA estimator with AI in a sliding-window fashion, to detect high- speed targets in heavy clutter using electro-optical (EO) sensors. The initial time and the length of the sliding-window are adjusted adaptively according to the information content of the received measurements. A track validation scheme via hypothesis testing is developed to confirm the estimated track, that is, the presence of a target, in each window. The sliding-window ML-PDA approach, together with track validation, enables early detection by rejecting noninformative scans, target reacquisition in case of temporary target disappearance and the handling of targets with speeds evolving over time. The proposed algorithm is shown to detect the target, which is hidden in as many as 600 false alarms per scan, 10 frames earlier than the Multiple Hypothesis Tracking (MHT) algorithm.

  3. Accuracy of parameter estimates for closely spaced optical targets using multiple detectors

    NASA Astrophysics Data System (ADS)

    Dunn, K. P.

    1981-10-01

    In order to obtain the cross-scan position of an optical target, more than one scanning detector is used. As expected, the cross-scan position estimation performance degrades when two nearby optical targets interfere with each other. Theoretical bounds on the two-dimensional parameter estimation performance for two closely spaced optical targets are found. Two particular classes of scanning detector arrays, namely, the crow's foot and the brickwall (or mosaic) patterns, are considered.

  4. Unbiased Estimation of Refractive State of Aberrated Eyes

    PubMed Central

    Martin, Jesson; Vasudevan, Balamurali; Himebaugh, Nikole; Bradley, Arthur; Thibos, Larry

    2011-01-01

    To identify unbiased methods for estimating the target vergence required to maximize visual acuity based on wavefront aberration measurements. Experiments were designed to minimize the impact of confounding factors that have hampered previous research. Objective wavefront refractions and subjective acuity refractions were obtained for the same monochromatic wavelength. Accommodation and pupil fluctuations were eliminated by cycloplegia. Unbiased subjective refractions that maximize visual acuity for high contrast letters were performed with a computer controlled forced choice staircase procedure, using 0.125 diopter steps of defocus. All experiments were performed for two pupil diameters (3mm and 6mm). As reported in the literature, subjective refractive error does not change appreciably when the pupil dilates. For 3 mm pupils most metrics yielded objective refractions that were about 0.1D more hyperopic than subjective acuity refractions. When pupil diameter increased to 6 mm, this bias changed in the myopic direction and the variability between metrics also increased. These inaccuracies were small compared to the precision of the measurements, which implies that most metrics provided unbiased estimates of refractive state for medium and large pupils. A variety of image quality metrics may be used to determine ocular refractive state for monochromatic (635nm) light, thereby achieving accurate results without the need for empirical correction factors. PMID:21777601

  5. Wave-Based Algorithms and Bounds for Target Support Estimation

    DTIC Science & Technology

    2015-05-15

    vector electromagnetic formalism in [5]. This theory leads to three main variants of the optical theorem detector, in particular, three alternative...further expands the applicability for transient pulse change detection of ar- bitrary nonlinear-media and time-varying targets [9]. This report... electromagnetic methods a new methodology to estimate the minimum convex source region and the (possibly nonconvex) support of a scattering target from knowledge of

  6. 34 CFR 300.602 - State use of targets and reporting.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 34 Education 2 2011-07-01 2010-07-01 true State use of targets and reporting. 300.602 Section 300..., Technical Assistance, and Enforcement § 300.602 State use of targets and reporting. (a) General. Each State must use the targets established in the State's performance plan under § 300.601 and the priority areas...

  7. 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.

  8. Examining the target levels of state renewable portfolio standards

    NASA Astrophysics Data System (ADS)

    Helwig, Laurence Douglas

    At present 37 U.S. states have passed Renewable Portfolio Standards (RPS) or have a legislative driven goal that supports investment in renewable energy (RE) technologies. Previous research has identified economic, governmental, ideological and infrastructural characteristics as key predictors of policy adoption and renewable energy deployment efforts (Carley, 2009; Davis & Davis, 2009; Bohn & Lant, 2009; Lyon & Yin, 2010). To date, only a few studies have investigated the target levels of renewable portfolio standards. Carley & Miller (2012) found that policies of differing stringencies were motivated by systematically different factors that included governmental ideology. The purpose of this dissertation is to replicate and expand upon earlier models that predicted RPS adoption and RE deployment efforts by adding regulatory, infrastructural and spatial characteristics to predict RPS target levels. Hypotheses were tested using three alternative measurements of RPS target level strength to determine to what extent a combination of explanatory variables explain variation in policy target levels. Multivariate linear regression and global spatial autocorrelation results indicated that multiple state internal determinants influenced RPS target level including average electricity price, state government ideology and to a lesser extent actual RE potential capacity. In addition, some diffusion effects were found to exist that indicated that states are setting their RPS target levels lower than their neighboring states and a local geo-spatial clustering effect was observed in the target levels for a grouping of northeastern states.

  9. Kalman-variant estimators for state of charge in lithium-sulfur batteries

    NASA Astrophysics Data System (ADS)

    Propp, Karsten; Auger, Daniel J.; Fotouhi, Abbas; Longo, Stefano; Knap, Vaclav

    2017-03-01

    Lithium-sulfur batteries are now commercially available, offering high specific energy density, low production costs and high safety. However, there is no commercially-available battery management system for them, and there are no published methods for determining state of charge in situ. This paper describes a study to address this gap. The properties and behaviours of lithium-sulfur are briefly introduced, and the applicability of 'standard' lithium-ion state-of-charge estimation methods is explored. Open-circuit voltage methods and 'Coulomb counting' are found to have a poor fit for lithium-sulfur, and model-based methods, particularly recursive Bayesian filters, are identified as showing strong promise. Three recursive Bayesian filters are implemented: an extended Kalman filter (EKF), an unscented Kalman filter (UKF) and a particle filter (PF). These estimators are tested through practical experimentation, considering both a pulse-discharge test and a test based on the New European Driving Cycle (NEDC). Experimentation is carried out at a constant temperature, mirroring the environment expected in the authors' target automotive application. It is shown that the estimators, which are based on a relatively simple equivalent-circuit-network model, can deliver useful results. If the three estimators implemented, the unscented Kalman filter gives the most robust and accurate performance, with an acceptable computational effort.

  10. Constrained model predictive control, state estimation and coordination

    NASA Astrophysics Data System (ADS)

    Yan, Jun

    guarantee local stability or convergence to a target state. If these conditions are met for all subsystems, then this stability is inherited by the overall system. For the case when each subsystem suffers from disturbances in the dynamics, own self-measurement noises, and quantization errors on neighbors' information due to the finite-bit-rate channels, the constrained MPC strategy developed in Part (i) is appropriate to apply. In Part (iii), we discuss the local predictor design and bandwidth assignment problem in a coordinated vehicle formation context. The MPC controller used in Part (ii) relates the formation control performance and the information quality in the way that large standoff implies conservative performance. We first develop an LMI (Linear Matrix Inequality) formulation for cross-estimator design in a simple two-vehicle scenario with non-standard information: one vehicle does not have access to the other's exact control value applied at each sampling time, but to its known, pre-computed, coupling linear feedback control law. Then a similar LMI problem is formulated for the bandwidth assignment problem that minimizes the total number of bits by adjusting the prediction gain matrices and the number of bits assigned to each variable. (Abstract shortened by UMI.)

  11. 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

  12. Projection-based circular constrained state estimation and fusion over long-haul links

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

    Liu, Qiang; Rao, Nageswara S.

    In this paper, we consider a scenario where sensors are deployed over a large geographical area for tracking a target with circular nonlinear constraints on its motion dynamics. The sensor state estimates are sent over long-haul networks to a remote fusion center for fusion. We are interested in different ways to incorporate the constraints into the estimation and fusion process in the presence of communication loss. In particular, we consider closed-form projection-based solutions, including rules for fusing the estimates and for incorporating the constraints, which jointly can guarantee timely fusion often required in realtime systems. We test the performance ofmore » these methods in the long-haul tracking environment using a simple example.« less

  13. Estimating Power System Dynamic States Using Extended Kalman Filter

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

    Huang, Zhenyu; Schneider, Kevin P.; Nieplocha, Jaroslaw

    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 newmore » 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.« less

  14. Targeting excited states in all-trans polyenes with electron-pair states.

    PubMed

    Boguslawski, Katharina

    2016-12-21

    Wavefunctions restricted to electron pair states are promising models for strongly correlated systems. Specifically, the pair Coupled Cluster Doubles (pCCD) ansatz allows us to accurately describe bond dissociation processes and heavy-element containing compounds with multiple quasi-degenerate single-particle states. Here, we extend the pCCD method to model excited states using the equation of motion (EOM) formalism. As the cluster operator of pCCD is restricted to electron-pair excitations, EOM-pCCD allows us to target excited electron-pair states only. To model singly excited states within EOM-pCCD, we modify the configuration interaction ansatz of EOM-pCCD to contain also single excitations. Our proposed model represents a simple and cost-effective alternative to conventional EOM-CC methods to study singly excited electronic states. The performance of the excited state models is assessed against the lowest-lying excited states of the uranyl cation and the two lowest-lying excited states of all-trans polyenes. Our numerical results suggest that EOM-pCCD including single excitations is a good starting point to target singly excited states.

  15. Bayesian Small Area Estimates of Diabetes Incidence by United States County, 2009

    PubMed Central

    Barker, Lawrence E.; Thompson, Theodore J.; Kirtland, Karen A; Boyle, James P; Geiss, Linda S; McCauley, Mary M.; Albright, Ann L.

    2015-01-01

    In the United States, diabetes is common and costly. Programs to prevent new cases of diabetes are often carried out at the level of the county, a unit of local government. Thus, efficient targeting of such programs requires county-level estimates of diabetes incidence–the fraction of the non-diabetic population who received their diagnosis of diabetes during the past 12 months. Previously, only estimates of prevalence–the overall fraction of population who have the disease–have been available at the county level. Counties with high prevalence might or might not be the same as counties with high incidence, due to spatial variation in mortality and relocation of persons with incident diabetes to another county. Existing methods cannot be used to estimate county-level diabetes incidence, because the fraction of the population who receive a diabetes diagnosis in any year is too small. Here, we extend previously developed methods of Bayesian small-area estimation of prevalence, using diffuse priors, to estimate diabetes incidence for all U.S. counties based on data from a survey designed to yield state-level estimates. We found high incidence in the southeastern United States, the Appalachian region, and in scattered counties throughout the western U.S. Our methods might be applicable in other circumstances in which all cases of a rare condition also must be cases of a more common condition (in this analysis, “newly diagnosed cases of diabetes” and “cases of diabetes”). If appropriate data are available, our methods can be used to estimate proportion of the population with the rare condition at greater geographic specificity than the data source was designed to provide. PMID:26279666

  16. Traffic safety facts 1996 : state alcohol estimates

    DOT National Transportation Integrated Search

    1998-01-01

    The following data provide estimates of alcohol involvement in fatal crashes for the United States and individually for the 50 state, the District of Columbia, and Puerto Rico (not included in the national totals). These estimates are based on data f...

  17. Studies on Training Ground Observers to Estimate Range to Aerial Targets.

    ERIC Educational Resources Information Center

    McCluskey, Michael R.; And Others

    Six pilot studies were conducted to determine the effects of training on range estimation performance for aerial targets, and to identify some of the relevant variables. Observers were trained to estimate ranges of 350, 400, 800, 1,500, or 2,500 meters. Several variations of range estimation training methods were used, including immediate…

  18. State-Estimation Algorithm Based on Computer Vision

    NASA Technical Reports Server (NTRS)

    Bayard, David; Brugarolas, Paul

    2007-01-01

    An algorithm and software to implement the algorithm are being developed as means to estimate the state (that is, the position and velocity) of an autonomous vehicle, relative to a visible nearby target object, to provide guidance for maneuvering the vehicle. In the original intended application, the autonomous vehicle would be a spacecraft and the nearby object would be a small astronomical body (typically, a comet or asteroid) to be explored by the spacecraft. The algorithm could also be used on Earth in analogous applications -- for example, for guiding underwater robots near such objects of interest as sunken ships, mineral deposits, or submerged mines. It is assumed that the robot would be equipped with a vision system that would include one or more electronic cameras, image-digitizing circuitry, and an imagedata- processing computer that would generate feature-recognition data products.

  19. Link-state-estimation-based transmission power control in wireless body area networks.

    PubMed

    Kim, Seungku; Eom, Doo-Seop

    2014-07-01

    This paper presents a novel transmission power control protocol to extend the lifetime of sensor nodes and to increase the link reliability in wireless body area networks (WBANs). We first experimentally investigate the properties of the link states using the received signal strength indicator (RSSI). We then propose a practical transmission power control protocol based on both short- and long-term link-state estimations. Both the short- and long-term link-state estimations enable the transceiver to adapt the transmission power level and target the RSSI threshold range, respectively, to simultaneously satisfy the requirements of energy efficiency and link reliability. Finally, the performance of the proposed protocol is experimentally evaluated in two experimental scenarios-body posture change and dynamic body motion-and compared with the typical WBAN transmission power control protocols, a real-time reactive scheme, and a dynamic postural position inference mechanism. From the experimental results, it is found that the proposed protocol increases the lifetime of the sensor nodes by a maximum of 9.86% and enhances the link reliability by reducing the packet loss by a maximum of 3.02%.

  20. 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.

  1. 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.

  2. Monitoring the World Health Organization Global Target 2025 for Exclusive Breastfeeding: Experience From the United States.

    PubMed

    Gupta, Priya M; Perrine, Cria G; Chen, Jian; Elam-Evans, Laurie D; Flores-Ayala, Rafael

    2017-08-01

    Exclusive breastfeeding under 6 months, calculated from a single 24-hour recall among mothers of children 0 to 5 months of age, is a World Health Organization (WHO) indicator used to monitor progress on the 2025 global breastfeeding target. Many upper-middle-income and high-income countries, including the United States, do not have estimates for this indicator. Research aim: To describe the prevalence of exclusive breastfeeding under 6 months in the United States. We used a single 24-hour dietary recall from the National Health and Nutrition Examination Survey 2009-2012 to calculate the prevalence of exclusive breastfeeding under 6 months. We discuss our results in the context of routine breastfeeding surveillance, which is reported from a national survey with different methodology. Among children younger than 6 months, 24.4%, 95% confidence interval [17.6, 31.1], were exclusively breastfed the previous day. To our knowledge, this is the first estimate of the WHO indicator of exclusive breastfeeding under 6 months for the United States. This study supports the global surveillance and data strategy for reporting to the WHO on the 2025 target for exclusive breastfeeding.

  3. State energy data report 1996: Consumption estimates

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

    NONE

    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 themore » 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.« less

  4. State Energy Price and Expenditure Estimates

    EIA Publications

    2017-01-01

    The State Energy Price and Expenditure Estimates provide data on energy prices in current dollars per million Btu and expenditures in current dollars, by state and for the United States, by energy source and by sector in annual time-series back to 1970

  5. State policies targeting junk food in schools: racial/ethnic differences in the effect of policy change on soda consumption.

    PubMed

    Taber, Daniel R; Stevens, June; Evenson, Kelly R; Ward, Dianne S; Poole, Charles; Maciejewski, Matthew L; Murray, David M; Brownson, Ross C

    2011-09-01

    We estimated the association between state policy changes and adolescent soda consumption and body mass index (BMI) percentile, overall and by race/ethnicity. We obtained data on whether states required or recommended that schools prohibit junk food in vending machines, snack bars, concession stands, and parties from the 2000 and 2006 School Health Policies and Programs Study. We used linear mixed models to estimate the association between 2000-2006 policy changes and 2007 soda consumption and BMI percentile, as reported by 90 730 students in 33 states and the District of Columbia in the Youth Risk Behavior Survey, and to test for racial/ethnic differences in the associations. Policy changes targeting concession stands were associated with 0.09 fewer servings of soda per day among students (95% confidence interval [CI] = -0.17, -0.01); the association was more pronounced among non-Hispanic Blacks (0.19 fewer servings per day). Policy changes targeting parties were associated with 0.07 fewer servings per day (95% CI = -0.13, 0.00). Policy changes were not associated with BMI percentile in any group. State policies targeting junk food in schools may reduce racial/ethnic disparities in adolescent soda consumption, but their impact appears to be too weak to reduce adolescent BMI percentile.

  6. Perceiving the target's state or state provoked by the target? An analysis of the descriptive and evaluative knowledge in person perception.

    PubMed

    Mignon, Astrid; Mollaret, Patrick

    2012-12-01

    In line with the theory of traits as generalized affordances, the present article argues that target's states (TSs) and states provoked by a target (other's states (OSs) towards target) are two components of the meaning of traits referring, respectively, to a descriptive and to an evaluative knowledge of people. A preliminary study confirmed that TS and OS were equally representative of a trait. Two studies were designed to study the effects of practising the use of traits as either TS or OS categories (an induction procedure) on a subsequent person perception task, requiring participants to rate photographed targets on a series of traits. Results show that both the differentiation between targets and evaluative consistency of ratings were enhanced under the OS condition compared to TS and control (with no practice of traits) conditions. Importantly, Study 2 tends to show that the effects of the induction procedure are not limited to the practised traits but also generalize to unpractised traits. Implications of these findings for social perception research are discussed. ©2011 The British Psychological Society.

  7. Localised estimates and spatial mapping of poverty incidence in the state of Bihar in India—An application of small area estimation techniques

    PubMed Central

    Aditya, Kaustav; Sud, U. C.

    2018-01-01

    Poverty affects many people, but the ramifications and impacts affect all aspects of society. Information about the incidence of poverty is therefore an important parameter of the population for policy analysis and decision making. In order to provide specific, targeted solutions when addressing poverty disadvantage small area statistics are needed. Surveys are typically designed and planned to produce reliable estimates of population characteristics of interest mainly at higher geographic area such as national and state level. Sample sizes are usually not large enough to provide reliable estimates for disaggregated analysis. In many instances estimates are required for areas of the population for which the survey providing the data was unplanned. Then, for areas with small sample sizes, direct survey estimation of population characteristics based only on the data available from the particular area tends to be unreliable. This paper describes an application of small area estimation (SAE) approach to improve the precision of estimates of poverty incidence at district level in the State of Bihar in India by linking data from the Household Consumer Expenditure Survey 2011–12 of NSSO and the Population Census 2011. The results show that the district level estimates generated by SAE method are more precise and representative. In contrast, the direct survey estimates based on survey data alone are less stable. PMID:29879202

  8. Localised estimates and spatial mapping of poverty incidence in the state of Bihar in India-An application of small area estimation techniques.

    PubMed

    Chandra, Hukum; Aditya, Kaustav; Sud, U C

    2018-01-01

    Poverty affects many people, but the ramifications and impacts affect all aspects of society. Information about the incidence of poverty is therefore an important parameter of the population for policy analysis and decision making. In order to provide specific, targeted solutions when addressing poverty disadvantage small area statistics are needed. Surveys are typically designed and planned to produce reliable estimates of population characteristics of interest mainly at higher geographic area such as national and state level. Sample sizes are usually not large enough to provide reliable estimates for disaggregated analysis. In many instances estimates are required for areas of the population for which the survey providing the data was unplanned. Then, for areas with small sample sizes, direct survey estimation of population characteristics based only on the data available from the particular area tends to be unreliable. This paper describes an application of small area estimation (SAE) approach to improve the precision of estimates of poverty incidence at district level in the State of Bihar in India by linking data from the Household Consumer Expenditure Survey 2011-12 of NSSO and the Population Census 2011. The results show that the district level estimates generated by SAE method are more precise and representative. In contrast, the direct survey estimates based on survey data alone are less stable.

  9. Towards Real-Time Maneuver Detection: Automatic State and Dynamics Estimation with the Adaptive Optimal Control Based Estimator

    NASA Astrophysics Data System (ADS)

    Lubey, D.; Scheeres, D.

    control policy is zero for all times). In this paper, we relax this assumption on the nominal trajectory in order to allow for controlled nominal trajectories. This allows the estimator to be iterated to obtain a more accurate nonlinear solution for both the state and control estimates. Beyond these developments to the estimator, this paper also introduces a modified distance metric for maneuver detection. The original metric used in the OCBE only accounted for the estimated control and its uncertainty. This new metric accounts for measurement deviation and a priori state deviations, such that it accounts for all three major forms of uncertainty in orbit determination. This allows the user to understand the contributions of each source of uncertainty toward the total system mismodeling so that the user can properly account for them. Together these developments create an accurate orbit determination algorithm that is automated, robust to mismodeling, and capable of detecting and reconstructing the presence of mismodeling. These qualities make this algorithm a good foundation from which to approach the problem of real-time maneuver detection and reconstruction for Space Situational Awareness applications. This is further strengthened by the algorithm's general formulation that allows it to be applied to problems with an arbitrary target and observer.

  10. State-Level Estimates of Cancer-Related Absenteeism Costs

    PubMed Central

    Tangka, Florence K.; Trogdon, Justin G.; Nwaise, Isaac; Ekwueme, Donatus U.; Guy, Gery P.; Orenstein, Diane

    2016-01-01

    Background Cancer is one of the top five most costly diseases in the United States and leads to substantial work loss. Nevertheless, limited state-level estimates of cancer absenteeism costs have been published. Methods In analyses of data from the 2004–2008 Medical Expenditure Panel Survey, the 2004 National Nursing Home Survey, the U.S. Census Bureau for 2008, and the 2009 Current Population Survey, we used regression modeling to estimate annual state-level absenteeism costs attributable to cancer from 2004 to 2008. Results We estimated that the state-level median number of days of absenteeism per year among employed cancer patients was 6.1 days and that annual state-level cancer absenteeism costs ranged from $14.9 million to $915.9 million (median = $115.9 million) across states in 2010 dollars. Absenteeism costs are approximately 6.5% of the costs of premature cancer mortality. Conclusions The results from this study suggest that lost productivity attributable to cancer is a substantial cost to employees and employers and contributes to estimates of the overall impact of cancer in a state population. PMID:23969498

  11. State-level estimates of cancer-related absenteeism costs.

    PubMed

    Tangka, Florence K; Trogdon, Justin G; Nwaise, Isaac; Ekwueme, Donatus U; Guy, Gery P; Orenstein, Diane

    2013-09-01

    Cancer is one of the top five most costly diseases in the United States and leads to substantial work loss. Nevertheless, limited state-level estimates of cancer absenteeism costs have been published. In analyses of data from the 2004-2008 Medical Expenditure Panel Survey, the 2004 National Nursing Home Survey, the U.S. Census Bureau for 2008, and the 2009 Current Population Survey, we used regression modeling to estimate annual state-level absenteeism costs attributable to cancer from 2004 to 2008. We estimated that the state-level median number of days of absenteeism per year among employed cancer patients was 6.1 days and that annual state-level cancer absenteeism costs ranged from $14.9 million to $915.9 million (median = $115.9 million) across states in 2010 dollars. Absenteeism costs are approximately 6.5% of the costs of premature cancer mortality. The results from this study suggest that lost productivity attributable to cancer is a substantial cost to employees and employers and contributes to estimates of the overall impact of cancer in a state population.

  12. 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.

  13. DOA Estimation for Underwater Wideband Weak Targets Based on Coherent Signal Subspace and Compressed Sensing

    PubMed Central

    2018-01-01

    Direction of arrival (DOA) estimation is the basis for underwater target localization and tracking using towed line array sonar devices. A method of DOA estimation for underwater wideband weak targets based on coherent signal subspace (CSS) processing and compressed sensing (CS) theory is proposed. Under the CSS processing framework, wideband frequency focusing is accompanied by a two-sided correlation transformation, allowing the DOA of underwater wideband targets to be estimated based on the spatial sparsity of the targets and the compressed sensing reconstruction algorithm. Through analysis and processing of simulation data and marine trial data, it is shown that this method can accomplish the DOA estimation of underwater wideband weak targets. Results also show that this method can considerably improve the spatial spectrum of weak target signals, enhancing the ability to detect them. It can solve the problems of low directional resolution and unreliable weak-target detection in traditional beamforming technology. Compared with the conventional minimum variance distortionless response beamformers (MVDR), this method has many advantages, such as higher directional resolution, wider detection range, fewer required snapshots and more accurate detection for weak targets. PMID:29562642

  14. DOA Estimation for Underwater Wideband Weak Targets Based on Coherent Signal Subspace and Compressed Sensing.

    PubMed

    Li, Jun; Lin, Qiu-Hua; Kang, Chun-Yu; Wang, Kai; Yang, Xiu-Ting

    2018-03-18

    Direction of arrival (DOA) estimation is the basis for underwater target localization and tracking using towed line array sonar devices. A method of DOA estimation for underwater wideband weak targets based on coherent signal subspace (CSS) processing and compressed sensing (CS) theory is proposed. Under the CSS processing framework, wideband frequency focusing is accompanied by a two-sided correlation transformation, allowing the DOA of underwater wideband targets to be estimated based on the spatial sparsity of the targets and the compressed sensing reconstruction algorithm. Through analysis and processing of simulation data and marine trial data, it is shown that this method can accomplish the DOA estimation of underwater wideband weak targets. Results also show that this method can considerably improve the spatial spectrum of weak target signals, enhancing the ability to detect them. It can solve the problems of low directional resolution and unreliable weak-target detection in traditional beamforming technology. Compared with the conventional minimum variance distortionless response beamformers (MVDR), this method has many advantages, such as higher directional resolution, wider detection range, fewer required snapshots and more accurate detection for weak targets.

  15. An algorithm for automatic target recognition using passive radar and an EKF for estimating aircraft orientation

    NASA Astrophysics Data System (ADS)

    Ehrman, Lisa M.

    2005-07-01

    Rather than emitting pulses, passive radar systems rely on "illuminators of opportunity," such as TV and FM radio, to illuminate potential targets. These systems are attractive since they allow receivers to operate without emitting energy, rendering them covert. Until recently, most of the research regarding passive radar has focused on detecting and tracking targets. This dissertation focuses on extending the capabilities of passive radar systems to include automatic target recognition. The target recognition algorithm described in this dissertation uses the radar cross section (RCS) of potential targets, collected over a short period of time, as the key information for target recognition. To make the simulated RCS as accurate as possible, the received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. An extended Kalman filter (EKF) estimates the target's orientation (and uncertainty in the estimate) from velocity measurements obtained from the passive radar tracker. Coupling the aircraft orientation and state with the known antenna locations permits computation of the incident and observed azimuth and elevation angles. The Fast Illinois Solver Code (FISC) simulates the RCS of potential target classes as a function of these angles. Thus, the approximated incident and observed angles allow the appropriate RCS to be extracted from a database of FISC results. Using this process, the RCS of each aircraft in the target class is simulated as though each is executing the same maneuver as the target detected by the system. Two additional scaling processes are required to transform the RCS into a power profile (magnitude only) simulating the signal in the receiver. First, the RCS is scaled by the Advanced Refractive Effects Prediction System (AREPS) code to account for propagation losses that occur as functions of altitude and range. Then, the Numerical Electromagnetic Code (NEC2) computes the antenna gain pattern

  16. Precision Pointing Control to and Accurate Target Estimation of a Non-Cooperative Vehicle

    NASA Technical Reports Server (NTRS)

    VanEepoel, John; Thienel, Julie; Sanner, Robert M.

    2006-01-01

    In 2004, NASA began investigating a robotic servicing mission for the Hubble Space Telescope (HST). Such a mission would not only require estimates of the HST attitude and rates in order to achieve capture by the proposed Hubble Robotic Vehicle (HRV), but also precision control to achieve the desired rate and maintain the orientation to successfully dock with HST. To generalize the situation, HST is the target vehicle and HRV is the chaser. This work presents a nonlinear approach for estimating the body rates of a non-cooperative target vehicle, and coupling this estimation to a control scheme. Non-cooperative in this context relates to the target vehicle no longer having the ability to maintain attitude control or transmit attitude knowledge.

  17. State estimation improves prospects for ocean research

    NASA Astrophysics Data System (ADS)

    Stammer, Detlef; Wunsch, C.; Fukumori, I.; Marshall, J.

    Rigorous global ocean state estimation methods can now be used to produce dynamically consistent time-varying model/data syntheses, the results of which are being used to study a variety of important scientific problems. Figure 1 shows a schematic of a complete ocean observing and synthesis system that includes global observations and state-of-the-art ocean general circulation models (OGCM) run on modern computer platforms. A global observing system is described in detail in Smith and Koblinsky [2001],and the present status of ocean modeling and anticipated improvements are addressed by Griffies et al. [2001]. Here, the focus is on the third component of state estimation: the synthesis of the observations and a model into a unified, dynamically consistent estimate.

  18. A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

    PubMed

    Li, Qian; Li, Xudong; Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

    2011-03-22

    Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by

  19. A Network-Based Multi-Target Computational Estimation Scheme for Anticoagulant Activities of Compounds

    PubMed Central

    Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

    2011-01-01

    Background Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. Methodology We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. Conclusions This article proposes a network-based multi-target computational estimation method for

  20. State Policies Targeting Junk Food in Schools: Racial/Ethnic Differences in the Effect of Policy Change on Soda Consumption

    PubMed Central

    Stevens, June; Evenson, Kelly R.; Ward, Dianne S.; Poole, Charles; Maciejewski, Matthew L.; Murray, David M.; Brownson, Ross C.

    2011-01-01

    Objectives. We estimated the association between state policy changes and adolescent soda consumption and body mass index (BMI) percentile, overall and by race/ethnicity. Methods. We obtained data on whether states required or recommended that schools prohibit junk food in vending machines, snack bars, concession stands, and parties from the 2000 and 2006 School Health Policies and Programs Study. We used linear mixed models to estimate the association between 2000–2006 policy changes and 2007 soda consumption and BMI percentile, as reported by 90 730 students in 33 states and the District of Columbia in the Youth Risk Behavior Survey, and to test for racial/ethnic differences in the associations. Results. Policy changes targeting concession stands were associated with 0.09 fewer servings of soda per day among students (95% confidence interval [CI] = −0.17, −0.01); the association was more pronounced among non-Hispanic Blacks (0.19 fewer servings per day). Policy changes targeting parties were associated with 0.07 fewer servings per day (95% CI = −0.13, 0.00). Policy changes were not associated with BMI percentile in any group. Conclusions. State policies targeting junk food in schools may reduce racial/ethnic disparities in adolescent soda consumption, but their impact appears to be too weak to reduce adolescent BMI percentile. PMID:21778484

  1. The Mirror Illusion’s Effects on Body State Estimation

    PubMed Central

    Soliman, Tamer M.; Buxbaum, Laurel J.; Jax, Steven A.

    2016-01-01

    The mirror illusion uses a standard mirror to create a compelling illusion in which movements of one limb seem to be made by the other hidden limb. In this paper we adapt a motor control framework to examine which estimates of the body’s configuration are affected by the illusion. We propose that the illusion primarily alters estimates related to upcoming states of the body (the desired state and the predicted state), with smaller effects on the estimate of the body state prior to movement initiation. Support for this proposal is provided both by behavioral effects of the illusion as well as neuroimaging evidence from one neural region, V6A, that is critically involved in the mirror illusion and limb state estimation more generally. PMID:27390062

  2. 7 CFR 1944.525 - Targeting of TSA funds to States.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 13 2010-01-01 2009-01-01 true Targeting of TSA funds to States. 1944.525 Section... § 1944.525 Targeting of TSA funds to States. (a) The Administrator will determine, based on the most... portion of the available funds for TSA to these States, leaving the balance available for national...

  3. Estimating state-transition probabilities for unobservable states using capture-recapture/resighting data

    USGS Publications Warehouse

    Kendall, W.L.; Nichols, J.D.

    2002-01-01

    Temporary emigration was identified some time ago as causing potential problems in capture-recapture studies, and in the last five years approaches have been developed for dealing with special cases of this general problem. Temporary emigration can be viewed more generally as involving transitions to and from an unobservable state, and frequently the state itself is one of biological interest (e.g., 'nonbreeder'). Development of models that permit estimation of relevant parameters in the presence of an unobservable state requires either extra information (e.g., as supplied by Pollock's robust design) or the following classes of model constraints: reducing the order of Markovian transition probabilities, imposing a degree of determinism on transition probabilities, removing state specificity of survival probabilities, and imposing temporal constancy of parameters. The objective of the work described in this paper is to investigate estimability of model parameters under a variety of models that include an unobservable state. Beginning with a very general model and no extra information, we used numerical methods to systematically investigate the use of ancillary information and constraints to yield models that are useful for estimation. The result is a catalog of models for which estimation is possible. An example analysis of sea turtle capture-recapture data under two different models showed similar point estimates but increased precision for the model that incorporated ancillary data (the robust design) when compared to the model with deterministic transitions only. This comparison and the results of our numerical investigation of model structures lead to design suggestions for capture-recapture studies in the presence of an unobservable state.

  4. Design of a two-level power system linear state estimator

    NASA Astrophysics Data System (ADS)

    Yang, Tao

    The availability of synchro-phasor data has raised the possibility of a linear state estimator if the inputs are only complex currents and voltages and if there are enough such measurements to meet observability and redundancy requirements. Moreover, the new digital substations can perform some of the computation at the substation itself resulting in a more accurate two-level state estimator. The objective of this research is to develop a two-level linear state estimator processing synchro-phasor data and estimating the states at both the substation level and the control center level. Both the mathematical algorithms that are different from those in the present state estimation procedure and the layered architecture of databases, communications and application programs that are required to support this two-level linear state estimator are described in this dissertation. Besides, as the availability of phasor measurements at substations will increase gradually, this research also describes how the state estimator can be enhanced to handle both the traditional state estimator and the proposed linear state estimator simultaneously. This provides a way to immediately utilize the benefits in those parts of the system where such phasor measurements become available and provides a pathway to transition to the smart grid of the future. The design procedure of the two-level state estimator is applied to two study systems. The first study system is the IEEE-14 bus system. The second one is the 179 bus Western Electricity Coordinating Council (WECC) system. The static database for the substations is constructed from the power flow data of these systems and the real-time measurement database is produced by a power system dynamic simulating tool (TSAT). Time-skew problems that may be caused by communication delays are also considered and simulated. We used the Network Simulator (NS) tool to simulate a simple communication system and analyse its time delay performance. These

  5. 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.

  6. Dynamic state estimation assisted power system monitoring and protection

    NASA Astrophysics Data System (ADS)

    Cui, Yinan

    The advent of phasor measurement units (PMUs) has unlocked several novel methods to monitor, control, and protect bulk electric power systems. This thesis introduces the concept of "Dynamic State Estimation" (DSE), aided by PMUs, for wide-area monitoring and protection of power systems. Unlike traditional State Estimation where algebraic variables are estimated from system measurements, DSE refers to a process to estimate the dynamic states associated with synchronous generators. This thesis first establishes the viability of using particle filtering as a technique to perform DSE in power systems. The utility of DSE for protection and wide-area monitoring are then shown as potential novel applications. The work is presented as a collection of several journal and conference papers. In the first paper, we present a particle filtering approach to dynamically estimate the states of a synchronous generator in a multi-machine setting considering the excitation and prime mover control systems. The second paper proposes an improved out-of-step detection method for generators by means of angular difference. The generator's rotor angle is estimated with a particle filter-based dynamic state estimator and the angular separation is then calculated by combining the raw local phasor measurements with this estimate. The third paper introduces a particle filter-based dual estimation method for tracking the dynamic states of a synchronous generator. It considers the situation where the field voltage measurements are not readily available. The particle filter is modified to treat the field voltage as an unknown input which is sequentially estimated along with the other dynamic states. The fourth paper proposes a novel framework for event detection based on energy functions. The key idea is that any event in the system will leave a signature in WAMS data-sets. It is shown that signatures for four broad classes of disturbance events are buried in the components that constitute the

  7. Minimax estimation of qubit states with Bures risk

    NASA Astrophysics Data System (ADS)

    Acharya, Anirudh; Guţă, Mădălin

    2018-04-01

    The central problem of quantum statistics is to devise measurement schemes for the estimation of an unknown state, given an ensemble of n independent identically prepared systems. For locally quadratic loss functions, the risk of standard procedures has the usual scaling of 1/n. However, it has been noticed that for fidelity based metrics such as the Bures distance, the risk of conventional (non-adaptive) qubit tomography schemes scales as 1/\\sqrt{n} for states close to the boundary of the Bloch sphere. Several proposed estimators appear to improve this scaling, and our goal is to analyse the problem from the perspective of the maximum risk over all states. We propose qubit estimation strategies based on separate adaptive measurements, and collective measurements, that achieve 1/n scalings for the maximum Bures risk. The estimator involving local measurements uses a fixed fraction of the available resource n to estimate the Bloch vector direction; the length of the Bloch vector is then estimated from the remaining copies by measuring in the estimator eigenbasis. The estimator based on collective measurements uses local asymptotic normality techniques which allows us to derive upper and lower bounds to its maximum Bures risk. We also discuss how to construct a minimax optimal estimator in this setup. Finally, we consider quantum relative entropy and show that the risk of the estimator based on collective measurements achieves a rate O(n-1log n) under this loss function. Furthermore, we show that no estimator can achieve faster rates, in particular the ‘standard’ rate n ‑1.

  8. An estimation of distribution method for infrared target detection based on Copulas

    NASA Astrophysics Data System (ADS)

    Wang, Shuo; Zhang, Yiqun

    2015-10-01

    Track-before-detect (TBD) based target detection involves a hypothesis test of merit functions which measure each track as a possible target track. Its accuracy depends on the precision of the distribution of merit functions, which determines the threshold for a test. Generally, merit functions are regarded Gaussian, and on this basis the distribution is estimated, which is true for most methods such as the multiple hypothesis tracking (MHT). However, merit functions for some other methods such as the dynamic programming algorithm (DPA) are non-Guassian and cross-correlated. Since existing methods cannot reasonably measure the correlation, the exact distribution can hardly be estimated. If merit functions are assumed Guassian and independent, the error between an actual distribution and its approximation may occasionally over 30 percent, and is divergent by propagation. Hence, in this paper, we propose a novel estimation of distribution method based on Copulas, by which the distribution can be estimated precisely, where the error is less than 1 percent without propagation. Moreover, the estimation merely depends on the form of merit functions and the structure of a tracking algorithm, and is invariant to measurements. Thus, the distribution can be estimated in advance, greatly reducing the demand for real-time calculation of distribution functions.

  9. Assessing Methods for Generalizing Experimental Impact Estimates to Target Populations

    ERIC Educational Resources Information Center

    Kern, Holger L.; Stuart, Elizabeth A.; Hill, Jennifer; Green, Donald P.

    2016-01-01

    Randomized experiments are considered the gold standard for causal inference because they can provide unbiased estimates of treatment effects for the experimental participants. However, researchers and policymakers are often interested in using a specific experiment to inform decisions about other target populations. In education research,…

  10. MPN estimation of qPCR target sequence recoveries from whole cell calibrator samples

    EPA Science Inventory

    DNA extracts from enumerated target organism cells (calibrator samples) have been used for estimating Enterococcus cell equivalent densities in surface waters by a comparative cycle threshold (Ct) qPCR analysis method. To compare surface water Enterococcus density estimates from ...

  11. Sequential state estimation of nonlinear/non-Gaussian systems with stochastic input for turbine degradation estimation

    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.

  12. Application of Droplet Digital PCR for Estimating Vector Copy Number States in Stem Cell Gene Therapy.

    PubMed

    Lin, Huan-Ting; Okumura, Takashi; Yatsuda, Yukinori; Ito, Satoru; Nakauchi, Hiromitsu; Otsu, Makoto

    2016-10-01

    Stable gene transfer into target cell populations via integrating viral vectors is widely used in stem cell gene therapy (SCGT). Accurate vector copy number (VCN) estimation has become increasingly important. However, existing methods of estimation such as real-time quantitative PCR are more restricted in practicality, especially during clinical trials, given the limited availability of sample materials from patients. This study demonstrates the application of an emerging technology called droplet digital PCR (ddPCR) in estimating VCN states in the context of SCGT. Induced pluripotent stem cells (iPSCs) derived from a patient with X-linked chronic granulomatous disease were used as clonable target cells for transduction with alpharetroviral vectors harboring codon-optimized CYBB cDNA. Precise primer-probe design followed by multiplex analysis conferred assay specificity. Accurate estimation of per-cell VCN values was possible without reliance on a reference standard curve. Sensitivity was high and the dynamic range of detection was wide. Assay reliability was validated by observation of consistent, reproducible, and distinct VCN clustering patterns for clones of transduced iPSCs with varying numbers of transgene copies. Taken together, use of ddPCR appears to offer a practical and robust approach to VCN estimation with a wide range of clinical and research applications.

  13. Application of Droplet Digital PCR for Estimating Vector Copy Number States in Stem Cell Gene Therapy

    PubMed Central

    Lin, Huan-Ting; Okumura, Takashi; Yatsuda, Yukinori; Ito, Satoru; Nakauchi, Hiromitsu; Otsu, Makoto

    2016-01-01

    Stable gene transfer into target cell populations via integrating viral vectors is widely used in stem cell gene therapy (SCGT). Accurate vector copy number (VCN) estimation has become increasingly important. However, existing methods of estimation such as real-time quantitative PCR are more restricted in practicality, especially during clinical trials, given the limited availability of sample materials from patients. This study demonstrates the application of an emerging technology called droplet digital PCR (ddPCR) in estimating VCN states in the context of SCGT. Induced pluripotent stem cells (iPSCs) derived from a patient with X-linked chronic granulomatous disease were used as clonable target cells for transduction with alpharetroviral vectors harboring codon-optimized CYBB cDNA. Precise primer–probe design followed by multiplex analysis conferred assay specificity. Accurate estimation of per-cell VCN values was possible without reliance on a reference standard curve. Sensitivity was high and the dynamic range of detection was wide. Assay reliability was validated by observation of consistent, reproducible, and distinct VCN clustering patterns for clones of transduced iPSCs with varying numbers of transgene copies. Taken together, use of ddPCR appears to offer a practical and robust approach to VCN estimation with a wide range of clinical and research applications. PMID:27763786

  14. Chapter 16 - Predictive Analytics for Comprehensive Energy Systems State Estimation

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

    Zhang, Yingchen; Yang, Rui; Hodge, Brian S

    Energy sustainability is a subject of concern to many nations in the modern world. It is critical for electric power systems to diversify energy supply to include systems with different physical characteristics, such as wind energy, solar energy, electrochemical energy storage, thermal storage, bio-energy systems, geothermal, and ocean energy. Each system has its own range of control variables and targets. To be able to operate such a complex energy system, big-data analytics become critical to achieve the goal of predicting energy supplies and consumption patterns, assessing system operation conditions, and estimating system states - all providing situational awareness to powermore » system operators. This chapter presents data analytics and machine learning-based approaches to enable predictive situational awareness of the power systems.« less

  15. Exponentially convergent state estimation for delayed switched recurrent neural networks.

    PubMed

    Ahn, Choon Ki

    2011-11-01

    This paper deals with the delay-dependent exponentially convergent state estimation problem for delayed switched neural networks. A set of delay-dependent criteria is derived under which the resulting estimation error system is exponentially stable. It is shown that the gain matrix of the proposed state estimator is characterised in terms of the solution to a set of linear matrix inequalities (LMIs), which can be checked readily by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed state estimator.

  16. Road-Aided Ground Slowly Moving Target 2D Motion Estimation for Single-Channel Synthetic Aperture Radar.

    PubMed

    Wang, Zhirui; Xu, Jia; Huang, Zuzhen; Zhang, Xudong; Xia, Xiang-Gen; Long, Teng; Bao, Qian

    2016-03-16

    To detect and estimate ground slowly moving targets in airborne single-channel synthetic aperture radar (SAR), a road-aided ground moving target indication (GMTI) algorithm is proposed in this paper. First, the road area is extracted from a focused SAR image based on radar vision. Second, after stationary clutter suppression in the range-Doppler domain, a moving target is detected and located in the image domain via the watershed method. The target's position on the road as well as its radial velocity can be determined according to the target's offset distance and traffic rules. Furthermore, the target's azimuth velocity is estimated based on the road slope obtained via polynomial fitting. Compared with the traditional algorithms, the proposed method can effectively cope with slowly moving targets partly submerged in a stationary clutter spectrum. In addition, the proposed method can be easily extended to a multi-channel system to further improve the performance of clutter suppression and motion estimation. Finally, the results of numerical experiments are provided to demonstrate the effectiveness of the proposed algorithm.

  17. Optimal estimation of two-qubit pure-state entanglement

    NASA Astrophysics Data System (ADS)

    Acín, Antonio; Tarrach, Rolf; Vidal, Guifré

    2000-06-01

    We present optimal measuring strategies for an estimation of the entanglement of unknown two-qubit pure states and of the degree of mixing of unknown single-qubit mixed states, of which N identical copies are available. The most general measuring strategies are considered in both situations, to conclude in the first case that a local, although collective, measurement suffices to estimate entanglement, a nonlocal property, optimally.

  18. Traffic safety facts 1999 : state alcohol estimates

    DOT National Transportation Integrated Search

    2000-01-01

    The data in this traffic safety fact sheet provide estimates of alcohol involvement in fatal crashes for the United States and individually for the 50 states, the District of Columbia, and Puerto Rico (not included in the national totals). These esti...

  19. Vehicle Lateral State Estimation Based on Measured Tyre Forces

    PubMed Central

    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

  20. Estimating annualized earthquake losses for the conterminous United States

    USGS Publications Warehouse

    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.

  1. Occupancy estimation and modeling with multiple states and state uncertainty

    USGS Publications Warehouse

    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.

  2. The role of experience in location estimation: Target distributions shift location memory biases.

    PubMed

    Lipinski, John; Simmering, Vanessa R; Johnson, Jeffrey S; Spencer, John P

    2010-04-01

    Research based on the Category Adjustment model concluded that the spatial distribution of target locations does not influence location estimation responses [Huttenlocher, J., Hedges, L., Corrigan, B., & Crawford, L. E. (2004). Spatial categories and the estimation of location. Cognition, 93, 75-97]. This conflicts with earlier results showing that location estimation is biased relative to the spatial distribution of targets [Spencer, J. P., & Hund, A. M. (2002). Prototypes and particulars: Geometric and experience-dependent spatial categories. Journal of Experimental Psychology: General, 131, 16-37]. Here, we resolve this controversy by using a task based on Huttenlocher et al. (Experiment 4) with minor modifications to enhance our ability to detect experience-dependent effects. Results after the first block of trials replicate the pattern reported in Huttenlocher et al. After additional experience, however, participants showed biases that significantly shifted according to the target distributions. These results are consistent with the Dynamic Field Theory, an alternative theory of spatial cognition that integrates long-term memory traces across trials relative to the perceived structure of the task space. Copyright 2009 Elsevier B.V. All rights reserved.

  3. Steady-state evoked potentials possibilities for mental-state estimation

    NASA Technical Reports Server (NTRS)

    Junker, Andrew M.; Schnurer, John H.; Ingle, David F.; Downey, Craig W.

    1988-01-01

    The use of the human steady-state evoked potential (SSEP) as a possible measure of mental-state estimation is explored. A method for evoking a visual response to a sum-of-ten sine waves is presented. This approach provides simultaneous multiple frequency measurements of the human EEG to the evoking stimulus in terms of describing functions (gain and phase) and remnant spectra. Ways in which these quantities vary with the addition of performance tasks (manual tracking, grammatical reasoning, and decision making) are presented. Models of the describing function measures can be formulated using systems engineering technology. Relationships between model parameters and performance scores during manual tracking are discussed. Problems of unresponsiveness and lack of repeatability of subject responses are addressed in terms of a need for loop closure of the SSEP. A technique to achieve loop closure using a lock-in amplifier approach is presented. Results of a study designed to test the effectiveness of using feedback to consciously connect humans to their evoked response are presented. Findings indicate that conscious control of EEG is possible. Implications of these results in terms of secondary tasks for mental-state estimation and brain actuated control are addressed.

  4. Stated Preference Survey Estimating the Willingness to Pay ...

    EPA Pesticide Factsheets

    A national stated preference survey designed to elicit household willingness to pay for reductions in impinged and entrained fish at cooling water intake structures. To improve estimation of environmental benefits estimation

  5. Dynamic State Estimation of Terrestrial and Solar Plasmas

    NASA Astrophysics Data System (ADS)

    Kamalabadi, Farzad

    A pervasive problem in virtually all branches of space science is the estimation of multi-dimensional state parameters of a dynamical system from a collection of indirect, often incomplete, and imprecise measurements. Subsequent scientific inference is predicated on rigorous analysis, interpretation, and understanding of physical observations and on the reliability of the associated quantitative statistical bounds and performance characteristics of the algorithms used. In this work, we focus on these dynamic state estimation problems and illustrate their importance in the context of two timely activities in space remote sensing. First, we discuss the estimation of multi-dimensional ionospheric state parameters from UV spectral imaging measurements anticipated to be acquired the recently selected NASA Heliophysics mission, Ionospheric Connection Explorer (ICON). Next, we illustrate that similar state-space formulations provide the means for the estimation of 3D, time-dependent densities and temperatures in the solar corona from a series of white-light and EUV measurements. We demonstrate that, while a general framework for the stochastic formulation of the state estimation problem is suited for systematic inference of the parameters of a hidden Markov process, several challenges must be addressed in the assimilation of an increasing volume and diversity of space observations. These challenges are: (1) the computational tractability when faced with voluminous and multimodal data, (2) the inherent limitations of the underlying models which assume, often incorrectly, linear dynamics and Gaussian noise, and (3) the unavailability or inaccuracy of transition probabilities and noise statistics. We argue that pursuing answers to these questions necessitates cross-disciplinary research that enables progress toward systematically reconciling observational and theoretical understanding of the space environment.

  6. A minimalist approach to bias estimation for passive sensor measurements with targets of opportunity

    NASA Astrophysics Data System (ADS)

    Belfadel, Djedjiga; Osborne, Richard W.; Bar-Shalom, Yaakov

    2013-09-01

    In order to carry out data fusion, registration error correction is crucial in multisensor systems. This requires estimation of the sensor measurement biases. It is important to correct for these bias errors so that the multiple sensor measurements and/or tracks can be referenced as accurately as possible to a common tracking coordinate system. This paper provides a solution for bias estimation for the minimum number of passive sensors (two), when only targets of opportunity are available. The sensor measurements are assumed time-coincident (synchronous) and perfectly associated. Since these sensors provide only line of sight (LOS) measurements, the formation of a single composite Cartesian measurement obtained from fusing the LOS measurements from different sensors is needed to avoid the need for nonlinear filtering. We evaluate the Cramer-Rao Lower Bound (CRLB) on the covariance of the bias estimate, i.e., the quantification of the available information about the biases. Statistical tests on the results of simulations show that this method is statistically efficient, even for small sample sizes (as few as two sensors and six points on the trajectory of a single target of opportunity). We also show that the RMS position error is significantly improved with bias estimation compared with the target position estimation using the original biased measurements.

  7. State estimation for wave energy converters

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

    Bacelli, Giorgio; Coe, Ryan Geoffrey

    2017-04-01

    This report gives a brief discussion and examples on the topic of state estimation for wave energy converters (WECs). These methods are intended for use to enable real-time closed loop control of WECs.

  8. Target-depth estimation in active sonar: Cramer-Rao bounds for a bilinear sound-speed profile.

    PubMed

    Mours, Alexis; Ioana, Cornel; Mars, Jérôme I; Josso, Nicolas F; Doisy, Yves

    2016-09-01

    This paper develops a localization method to estimate the depth of a target in the context of active sonar, at long ranges. The target depth is tactical information for both strategy and classification purposes. The Cramer-Rao lower bounds for the target position as range and depth are derived for a bilinear profile. The influence of sonar parameters on the standard deviations of the target range and depth are studied. A localization method based on ray back-propagation with a probabilistic approach is then investigated. Monte-Carlo simulations applied to a summer Mediterranean sound-speed profile are performed to evaluate the efficiency of the estimator. This method is finally validated on data in an experimental tank.

  9. The Role of Experience in Location Estimation: Target Distributions Shift Location Memory Biases

    ERIC Educational Resources Information Center

    Lipinski, John; Simmering, Vanessa R.; Johnson, Jeffrey S.; Spencer, John P.

    2010-01-01

    Research based on the Category Adjustment model concluded that the spatial distribution of target locations does not influence location estimation responses [Huttenlocher, J., Hedges, L., Corrigan, B., & Crawford, L. E. (2004). Spatial categories and the estimation of location. "Cognition, 93", 75-97]. This conflicts with earlier results showing…

  10. Joint estimation over multiple individuals improves behavioural state inference from animal movement data.

    PubMed

    Jonsen, Ian

    2016-02-08

    State-space models provide a powerful way to scale up inference of movement behaviours from individuals to populations when the inference is made across multiple individuals. Here, I show how a joint estimation approach that assumes individuals share identical movement parameters can lead to improved inference of behavioural states associated with different movement processes. I use simulated movement paths with known behavioural states to compare estimation error between nonhierarchical and joint estimation formulations of an otherwise identical state-space model. Behavioural state estimation error was strongly affected by the degree of similarity between movement patterns characterising the behavioural states, with less error when movements were strongly dissimilar between states. The joint estimation model improved behavioural state estimation relative to the nonhierarchical model for simulated data with heavy-tailed Argos location errors. When applied to Argos telemetry datasets from 10 Weddell seals, the nonhierarchical model estimated highly uncertain behavioural state switching probabilities for most individuals whereas the joint estimation model yielded substantially less uncertainty. The joint estimation model better resolved the behavioural state sequences across all seals. Hierarchical or joint estimation models should be the preferred choice for estimating behavioural states from animal movement data, especially when location data are error-prone.

  11. Estimating the impact of newly arrived foreign-born persons on tuberculosis in the United States.

    PubMed

    Liu, Yecai; Painter, John A; Posey, Drew L; Cain, Kevin P; Weinberg, Michelle S; Maloney, Susan A; Ortega, Luis S; Cetron, Martin S

    2012-01-01

    Among approximately 163.5 million foreign-born persons admitted to the United States annually, only 500,000 immigrants and refugees are required to undergo overseas tuberculosis (TB) screening. It is unclear what extent of the unscreened nonimmigrant visitors contributes to the burden of foreign-born TB in the United States. We defined foreign-born persons within 1 year after arrival in the United States as "newly arrived", and utilized data from U.S. Department of Homeland Security, U.S. Centers for Disease Control and Prevention, and World Health Organization to estimate the incidence of TB among newly arrived foreign-born persons in the United States. During 2001 through 2008, 11,500 TB incident cases, including 291 multidrug-resistant TB incident cases, were estimated to occur among 20,989,738 person-years for the 1,479,542,654 newly arrived foreign-born persons in the United States. Of the 11,500 estimated TB incident cases, 41.6% (4,783) occurred among immigrants and refugees, 36.6% (4,211) among students/exchange visitors and temporary workers, 13.8% (1,589) among tourists and business travelers, and 7.3% (834) among Canadian and Mexican nonimmigrant visitors without an I-94 form (e.g., arrival-departure record). The top 3 newly arrived foreign-born populations with the largest estimated TB incident cases per 100,000 admissions were immigrants and refugees from high-incidence countries (e.g., 2008 WHO-estimated TB incidence rate of ≥100 cases/100,000 population/year; 235.8 cases/100,000 admissions, 95% confidence interval [CI], 228.3 to 243.3), students/exchange visitors and temporary workers from high-incidence countries (60.9 cases/100,000 admissions, 95% CI, 58.5 to 63.3), and immigrants and refugees from medium-incidence countries (e.g., 2008 WHO-estimated TB incidence rate of 15-99 cases/100,000 population/year; 55.2 cases/100,000 admissions, 95% CI, 51.6 to 58.8). Newly arrived nonimmigrant visitors contribute substantially to the burden of foreign

  12. Maximum profile likelihood estimation of differential equation parameters through model based smoothing state estimates.

    PubMed

    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. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

  13. Remediating Non-Positive Definite State Covariances for Collision Probability Estimation

    NASA Technical Reports Server (NTRS)

    Hall, Doyle T.; Hejduk, Matthew D.; Johnson, Lauren C.

    2017-01-01

    The NASA Conjunction Assessment Risk Analysis team estimates the probability of collision (Pc) for a set of Earth-orbiting satellites. The Pc estimation software processes satellite position+velocity states and their associated covariance matri-ces. On occasion, the software encounters non-positive definite (NPD) state co-variances, which can adversely affect or prevent the Pc estimation process. Inter-polation inaccuracies appear to account for the majority of such covariances, alt-hough other mechanisms contribute also. This paper investigates the origin of NPD state covariance matrices, three different methods for remediating these co-variances when and if necessary, and the associated effects on the Pc estimation process.

  14. Event-Based $H_\\infty $ State Estimation for Time-Varying Stochastic Dynamical Networks With State- and Disturbance-Dependent Noises.

    PubMed

    Sheng, Li; Wang, Zidong; Zou, Lei; Alsaadi, Fuad E

    2017-10-01

    In this paper, the event-based finite-horizon H ∞ state estimation problem is investigated for a class of discrete time-varying stochastic dynamical networks with state- and disturbance-dependent noises [also called (x,v) -dependent noises]. An event-triggered scheme is proposed to decrease the frequency of the data transmission between the sensors and the estimator, where the signal is transmitted only when certain conditions are satisfied. The purpose of the problem addressed is to design a time-varying state estimator in order to estimate the network states through available output measurements. By employing the completing-the-square technique and the stochastic analysis approach, sufficient conditions are established to ensure that the error dynamics of the state estimation satisfies a prescribed H ∞ performance constraint over a finite horizon. The desired estimator parameters can be designed via solving coupled backward recursive Riccati difference equations. Finally, a numerical example is exploited to demonstrate the effectiveness of the developed state estimation scheme.

  15. Optimization of training periods for the estimation model of three-dimensional target positions using an external respiratory surrogate.

    PubMed

    Iramina, Hiraku; Nakamura, Mitsuhiro; Iizuka, Yusuke; Mitsuyoshi, Takamasa; Matsuo, Yukinori; Mizowaki, Takashi; Kanno, Ikuo

    2018-04-19

    During therapeutic beam irradiation, an unvisualized three-dimensional (3D) target position should be estimated using an external surrogate with an estimation model. Training periods for the developed model with no additional imaging during beam irradiation were optimized using clinical data. Dual-source 4D-CBCT projection data for 20 lung cancer patients were used for validation. Each patient underwent one to three scans. The actual target positions of each scan were equally divided into two equal parts: one for the modeling and the other for the validating session. A quadratic target position estimation equation was constructed during the modeling session. Various training periods for the session-i.e., modeling periods (T M )-were employed: T M  ∈ {5,10,15,25,35} [s]. First, the equation was used to estimate target positions in the validating session of the same scan (intra-scan estimations). Second, the equation was then used to estimate target positions in the validating session of another temporally different scan (inter-scan estimations). The baseline drift of the surrogate and target between scans was corrected. Various training periods for the baseline drift correction-i.e., correction periods (T C s)-were employed: T C  ∈ {5,10,15; T C  ≤ T M } [s]. Evaluations were conducted with and without the correction. The difference between the actual and estimated target positions was evaluated by the root-mean-square error (RMSE). The range of mean respiratory period and 3D motion amplitude of the target was 2.4-13.0 s and 2.8-34.2 mm, respectively. On intra-scan estimation, the median 3D RMSE was within 1.5-2.1 mm, supported by previous studies. On inter-scan estimation, median elapsed time between scans was 10.1 min. All T M s exhibited 75th percentile 3D RMSEs of 5.0-6.4 mm due to baseline drift of the surrogate and the target. After the correction, those for each T M s fell by 1.4-2.3 mm. The median 3D RMSE for both the 10-s T M and

  16. Ocean state estimation for climate studies

    NASA Technical Reports Server (NTRS)

    Lee, T.

    2002-01-01

    Climate variabilities, which are of interest to CLIVAR, involve a broad range of spatial and temporal scales. Ocean state estimation (often referred to as ocean data assimilation), by optimally combining observations and models, becomes an important element of CLIVAR.

  17. 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

  18. Evaluation of the Washington State Target Zero teams project.

    DOT National Transportation Integrated Search

    2015-01-01

    As part of its Target Zero strategic highway safety plan that has the goal to reduce traffic fatalities in Washington to zero by the year 2030, the State of Washington established three detachments of Washington State Patrol (WSP) troopers to f...

  19. 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.

  20. Quantitative PET Imaging in Drug Development: Estimation of Target Occupancy.

    PubMed

    Naganawa, Mika; Gallezot, Jean-Dominique; Rossano, Samantha; Carson, Richard E

    2017-12-11

    Positron emission tomography, an imaging tool using radiolabeled tracers in humans and preclinical species, has been widely used in recent years in drug development, particularly in the central nervous system. One important goal of PET in drug development is assessing the occupancy of various molecular targets (e.g., receptors, transporters, enzymes) by exogenous drugs. The current linear mathematical approaches used to determine occupancy using PET imaging experiments are presented. These algorithms use results from multiple regions with different target content in two scans, a baseline (pre-drug) scan and a post-drug scan. New mathematical estimation approaches to determine target occupancy, using maximum likelihood, are presented. A major challenge in these methods is the proper definition of the covariance matrix of the regional binding measures, accounting for different variance of the individual regional measures and their nonzero covariance, factors that have been ignored by conventional methods. The novel methods are compared to standard methods using simulation and real human occupancy data. The simulation data showed the expected reduction in variance and bias using the proper maximum likelihood methods, when the assumptions of the estimation method matched those in simulation. Between-method differences for data from human occupancy studies were less obvious, in part due to small dataset sizes. These maximum likelihood methods form the basis for development of improved PET covariance models, in order to minimize bias and variance in PET occupancy studies.

  1. Guidelines for preparation of state water-use estimates for 2000

    USGS Publications Warehouse

    Kenny, Joan F.

    2004-01-01

    This report describes the water-use categories and data elements required for the 2000 national water-use compilation conducted by the U.S. Geological Survey (USGS) as part of its National Water Use Information Program. It identifies sources of water-use information, guidelines for estimating water use, and required documentation for preparation of the national compilation by State for the United States, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands. The data are published in USGS Circular 1268, Estimated Use of Water in the United States in 2000. USGS has published circulars on estimated use of water in the United States at 5-year intervals since 1950. As part of this USGS program to document water use on a national scale for the year 2000, all States prepare estimates of water withdrawals for public supply, industrial, irrigation, and thermoelectric power generation water uses at the county level. All States prepare estimates of domestifc use and population served by public supply at least at the State level. All States provide estimates of irrigated acres by irrigation system type (sprinkler, surface, or microirrigation) at the county level. County-level estimates of withdrawals for mining, livestock, and aquaculture uses are compiled by selected States that comprised the largest percentage of national use in 1995 for these categories, and are optional for other States. Ground-water withdrawals for public-supply, industrial, and irrigation use are aggregated by principal aquifer or aquifer system, as identified by the USGS Office of Ground Water. Some categories and data elements that were mandatory in previous compilations are optional for the 2000 compilation, in response to budget considerations at the State level. Optional categories are commercial, hydroelectric, and wastewater treatment. Estimation of deliveries from public supply to domestic, commercial, industrial, and thermoelectric uses, consumptive use for any category, and

  2. 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.

  3. Interacting multiple model forward filtering and backward smoothing for maneuvering target tracking

    NASA Astrophysics Data System (ADS)

    Nandakumaran, N.; Sutharsan, S.; Tharmarasa, R.; Lang, Tom; McDonald, Mike; Kirubarajan, T.

    2009-08-01

    The Interacting Multiple Model (IMM) estimator has been proven to be effective in tracking agile targets. Smoothing or retrodiction, which uses measurements beyond the current estimation time, provides better estimates of target states. Various methods have been proposed for multiple model smoothing in the literature. In this paper, a new smoothing method, which involves forward filtering followed by backward smoothing while maintaining the fundamental spirit of the IMM, is proposed. The forward filtering is performed using the standard IMM recursion, while the backward smoothing is performed using a novel interacting smoothing recursion. This backward recursion mimics the IMM estimator in the backward direction, where each mode conditioned smoother uses standard Kalman smoothing recursion. Resulting algorithm provides improved but delayed estimates of target states. Simulation studies are performed to demonstrate the improved performance with a maneuvering target scenario. The comparison with existing methods confirms the improved smoothing accuracy. This improvement results from avoiding the augmented state vector used by other algorithms. In addition, the new technique to account for model switching in smoothing is a key in improving the performance.

  4. Estimated Incidence of Antimicrobial Drug-Resistant Nontyphoidal Salmonella Infections, United States, 2004-2012.

    PubMed

    Medalla, Felicita; Gu, Weidong; Mahon, Barbara E; Judd, Michael; Folster, Jason; Griffin, Patricia M; Hoekstra, Robert M

    2016-01-01

    Salmonella infections are a major cause of illness in the United States. The antimicrobial agents used to treat severe infections include ceftriaxone, ciprofloxacin, and ampicillin. Antimicrobial drug resistance has been associated with adverse clinical outcomes. To estimate the incidence of resistant culture-confirmed nontyphoidal Salmonella infections, we used Bayesian hierarchical models of 2004-2012 data from the Centers for Disease Control and Prevention National Antimicrobial Resistance Monitoring System and Laboratory-based Enteric Disease Surveillance. We based 3 mutually exclusive resistance categories on susceptibility testing: ceftriaxone and ampicillin resistant, ciprofloxacin nonsusceptible but ceftriaxone susceptible, and ampicillin resistant but ceftriaxone and ciprofloxacin susceptible. We estimated the overall incidence of resistant infections as 1.07/100,000 person-years for ampicillin-only resistance, 0.51/100,000 person-years for ceftriaxone and ampicillin resistance, and 0.35/100,000 person-years for ciprofloxacin nonsusceptibility, or ≈6,200 resistant culture-confirmed infections annually. These national estimates help define the magnitude of the resistance problem so that control measures can be appropriately targeted.

  5. Decentralized state estimation for a large-scale spatially interconnected system.

    PubMed

    Liu, Huabo; Yu, Haisheng

    2018-03-01

    A decentralized state estimator is derived for the spatially interconnected systems composed of many subsystems with arbitrary connection relations. An optimization problem on the basis of linear matrix inequality (LMI) is constructed for the computations of improved subsystem parameter matrices. Several computationally effective approaches are derived which efficiently utilize the block-diagonal characteristic of system parameter matrices and the sparseness of subsystem connection matrix. Moreover, this decentralized state estimator is proved to converge to a stable system and obtain a bounded covariance matrix of estimation errors under certain conditions. Numerical simulations show that the obtained decentralized state estimator is attractive in the synthesis of a large-scale networked system. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  6. Demographic estimation methods for plants with unobservable life-states

    USGS Publications Warehouse

    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

  7. Accurate Initial State Estimation in a Monocular Visual–Inertial SLAM System

    PubMed Central

    Chen, Jing; Zhou, Zixiang; Leng, Zhen; Fan, Lei

    2018-01-01

    The fusion of monocular visual and inertial cues has become popular in robotics, unmanned vehicles and augmented reality fields. Recent results have shown that optimization-based fusion strategies outperform filtering strategies. Robust state estimation is the core capability for optimization-based visual–inertial Simultaneous Localization and Mapping (SLAM) systems. As a result of the nonlinearity of visual–inertial systems, the performance heavily relies on the accuracy of initial values (visual scale, gravity, velocity and Inertial Measurement Unit (IMU) biases). Therefore, this paper aims to propose a more accurate initial state estimation method. On the basis of the known gravity magnitude, we propose an approach to refine the estimated gravity vector by optimizing the two-dimensional (2D) error state on its tangent space, then estimate the accelerometer bias separately, which is difficult to be distinguished under small rotation. Additionally, we propose an automatic termination criterion to determine when the initialization is successful. Once the initial state estimation converges, the initial estimated values are used to launch the nonlinear tightly coupled visual–inertial SLAM system. We have tested our approaches with the public EuRoC dataset. Experimental results show that the proposed methods can achieve good initial state estimation, the gravity refinement approach is able to efficiently speed up the convergence process of the estimated gravity vector, and the termination criterion performs well. PMID:29419751

  8. Estimated use of water in the United States in 1995

    USGS Publications Warehouse

    Solley, Wayne B.; Pierce, Robert R.; Perlman, Howard A.

    1998-01-01

    The purpose of this report is to present consistent and current water-use estimates by state and water-resources region for the United States, Puerto Rico, the U.S. Virgin Islands, and the District of Columbia. Estimates of water withdrawn from surface- and ground-water sources, estimates of consumptive use, and estimates of instream use and wastewater releases during 1995 are presented in this report. This report discusses eight categories of offstream water use--public supply, domestic, commercial, irrigation, livestock, industrial, mining, and thermoelectric power--and one category of instream use: hydroelectric power.

  9. Adaptive bearing estimation and tracking of multiple targets in a realistic passive sonar scenario

    NASA Astrophysics Data System (ADS)

    Rajagopal, R.; Challa, Subhash; Faruqi, Farhan A.; Rao, P. R.

    1997-06-01

    In a realistic passive sonar environment, the received signal consists of multipath arrivals from closely separated moving targets. The signals are contaminated by spatially correlated noise. The differential MUSIC has been proposed to estimate the DOAs in such a scenario. This method estimates the 'noise subspace' in order to estimate the DOAs. However, the 'noise subspace' estimate has to be updated as and when new data become available. In order to save the computational costs, a new adaptive noise subspace estimation algorithm is proposed in this paper. The salient features of the proposed algorithm are: (1) Noise subspace estimation is done by QR decomposition of the difference matrix which is formed from the data covariance matrix. Thus, as compared to standard eigen-decomposition based methods which require O(N3) computations, the proposed method requires only O(N2) computations. (2) Noise subspace is updated by updating the QR decomposition. (3) The proposed algorithm works in a realistic sonar environment. In the second part of the paper, the estimated bearing values are used to track multiple targets. In order to achieve this, the nonlinear system/linear measurement extended Kalman filtering proposed is applied. Computer simulation results are also presented to support the theory.

  10. 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…

  11. Rankings & Estimates: Rankings of the States 2015 and Estimates of School Statistics 2016

    ERIC Educational Resources Information Center

    National Education Association, 2016

    2016-01-01

    The data presented in this combined report--"Rankings & Estimates"--provide facts about the extent to which local, state, and national governments commit resources to public education. As one might expect in a nation as diverse as the United States--with respect to economics, geography, and politics--the level of commitment to…

  12. Guidelines for preparation of State water-use estimates for 2015

    USGS Publications Warehouse

    Bradley, Michael W.

    2017-05-01

    The U.S. Geological Survey (USGS) has estimated the use of water in the United States at 5-year intervals since 1950. This report describes the water-use categories and data elements used for the national water-use compilation conducted as part of the USGS National Water-Use Science Project. The report identifies sources of water-use information, provides standard methods and techniques for estimating water use at the county level, and outlines steps for preparing documentation for the United States, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands.As part of this USGS program to document water use on a national scale, estimates of water withdrawals for the categories of public supply, self-supplied domestic, industrial, irrigation, and thermoelectric power are prepared for each county in each State, District, or territory by using the guidelines in this report. County estimates of water withdrawals for aquaculture, livestock, and mining are prepared for each State by using a county-based national model, although water-use programs in each State or Water Science Center have the option of producing independent county estimates of water withdrawals for these categories. Estimates of water withdrawals and consumptive use for thermoelectric power will be aggregated to the county level for each State by the national project; additionally, irrigation consumptive use at the county level will also be provided, although study chiefs in each State have the option of producing independent county estimates of water withdrawals and consumptive use for these categories.Estimates of deliveries of water from public supplies for domestic use by county also will be prepared for each State. As a result, total domestic water use can be determined for each State by combining self-supplied domestic withdrawals and public-supplied domestic deliveries. Fresh groundwater and surface-water estimates will be prepared for all categories of use, and saline groundwater and

  13. Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems.

    PubMed

    Chen, Tengpeng; Foo, Yi Shyh Eddy; Ling, K V; Chen, Xuebing

    2017-10-11

    In this paper, a distributed state estimation method based on moving horizon estimation (MHE) is proposed for the large-scale power system state estimation. The proposed method partitions the power systems into several local areas with non-overlapping states. Unlike the centralized approach where all measurements are sent to a processing center, the proposed method distributes the state estimation task to the local processing centers where local measurements are collected. Inspired by the partitioned moving horizon estimation (PMHE) algorithm, each local area solves a smaller optimization problem to estimate its own local states by using local measurements and estimated results from its neighboring areas. In contrast with PMHE, the error from the process model is ignored in our method. The proposed modified PMHE (mPMHE) approach can also take constraints on states into account during the optimization process such that the influence of the outliers can be further mitigated. Simulation results on the IEEE 14-bus and 118-bus systems verify that our method achieves comparable state estimation accuracy but with a significant reduction in the overall computation load.

  14. Using State Estimation Residuals to Detect Abnormal SCADA Data

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

    Ma, Jian; Chen, Yousu; Huang, Zhenyu

    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 asmore » 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.« less

  15. Molybdenum target specifications for cyclotron production of 99mTc based on patient dose estimates.

    PubMed

    Hou, X; Tanguay, J; Buckley, K; Schaffer, P; Bénard, F; Ruth, T J; Celler, A

    2016-01-21

    In response to the recognized fragility of reactor-produced (99)Mo supply, direct production of (99m)Tc via (100)Mo(p,2n)(99m)Tc reaction using medical cyclotrons has been investigated. However, due to the existence of other Molybdenum (Mo) isotopes in the target, in parallel with (99m)Tc, other technetium (Tc) radioactive isotopes (impurities) will be produced. They will be incorporated into the labeled radiopharmaceuticals and result in increased patient dose. The isotopic composition of the target and beam energy are main factors that determine production of impurities, thus also dose increases. Therefore, they both must be considered when selecting targets for clinical (99m)Tc production. Although for any given Mo target, the patient dose can be predicted based on complicated calculations of production yields for each Tc radioisotope, it would be very difficult to reverse these calculations to specify target composition based on dosimetry considerations. In this article, a relationship between patient dosimetry and Mo target composition is studied. A simple and easy algorithm for dose estimation, based solely on the knowledge of target composition and beam energy, is described. Using this algorithm, the patient dose increase due to every Mo isotope that could be present in the target is estimated. Most importantly, a technique to determine Mo target composition thresholds that would meet any given dosimetry requirement is proposed.

  16. Molybdenum target specifications for cyclotron production of 99mTc based on patient dose estimates

    NASA Astrophysics Data System (ADS)

    Hou, X.; Tanguay, J.; Buckley, K.; Schaffer, P.; Bénard, F.; Ruth, T. J.; Celler, A.

    2016-01-01

    In response to the recognized fragility of reactor-produced 99Mo supply, direct production of 99mTc via 100Mo(p,2n)99mTc reaction using medical cyclotrons has been investigated. However, due to the existence of other Molybdenum (Mo) isotopes in the target, in parallel with 99mTc, other technetium (Tc) radioactive isotopes (impurities) will be produced. They will be incorporated into the labeled radiopharmaceuticals and result in increased patient dose. The isotopic composition of the target and beam energy are main factors that determine production of impurities, thus also dose increases. Therefore, they both must be considered when selecting targets for clinical 99mTc production. Although for any given Mo target, the patient dose can be predicted based on complicated calculations of production yields for each Tc radioisotope, it would be very difficult to reverse these calculations to specify target composition based on dosimetry considerations. In this article, a relationship between patient dosimetry and Mo target composition is studied. A simple and easy algorithm for dose estimation, based solely on the knowledge of target composition and beam energy, is described. Using this algorithm, the patient dose increase due to every Mo isotope that could be present in the target is estimated. Most importantly, a technique to determine Mo target composition thresholds that would meet any given dosimetry requirement is proposed.

  17. Real-time state estimation in a flight simulator using fNIRS.

    PubMed

    Gateau, Thibault; Durantin, Gautier; Lancelot, Francois; Scannella, Sebastien; Dehais, Frederic

    2015-01-01

    Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot's instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot's mental state matched significantly better than chance with the pilot's real state (62% global accuracy, 58% specificity, and 72% sensitivity). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development.

  18. Real-Time State Estimation in a Flight Simulator Using fNIRS

    PubMed Central

    Gateau, Thibault; Durantin, Gautier; Lancelot, Francois; Scannella, Sebastien; Dehais, Frederic

    2015-01-01

    Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot’s instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot’s mental state matched significantly better than chance with the pilot’s real state (62% global accuracy, 58% specificity, and 72% sensitivity). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development. PMID:25816347

  19. Introduction to State Estimation of High-Rate System Dynamics.

    PubMed

    Hong, Jonathan; Laflamme, Simon; Dodson, Jacob; Joyce, Bryan

    2018-01-13

    Engineering systems experiencing high-rate dynamic events, including airbags, debris detection, and active blast protection systems, could benefit from real-time observability for enhanced performance. However, the task of high-rate state estimation is challenging, in particular for real-time applications where the rate of the observer's convergence needs to be in the microsecond range. This paper identifies the challenges of state estimation of high-rate systems and discusses the fundamental characteristics of high-rate systems. A survey of applications and methods for estimators that have the potential to produce accurate estimations for a complex system experiencing highly dynamic events is presented. It is argued that adaptive observers are important to this research. In particular, adaptive data-driven observers are advantageous due to their adaptability and lack of dependence on the system model.

  20. State-level estimates of obesity-attributable costs of absenteeism.

    PubMed

    Andreyeva, Tatiana; Luedicke, Joerg; Wang, Y Claire

    2014-11-01

    To provide state-level estimates of obesity-attributable costs of absenteeism among working adults in the United States. Nationally representative data from the National Health and Nutrition Examination Survey for 1998 to 2008 and from the Behavioral Risk Factor Surveillance System for 2012 are examined. The outcome is obesity-attributable workdays missed in the previous year because of health and their costs to states. Obesity, but not overweight, is associated with a significant increase in workdays absent, from 1.1 to 1.7 extra days missed annually compared with normal-weight employees. Obesity-attributable absenteeism among American workers costs the nation an estimated $8.65 billion per year. Obesity imposes a considerable financial burden on states, accounting for 6.5% to 12.6% of total absenteeism costs in the workplace. State legislatures and employers should seek effective ways to reduce these costs.

  1. Vehicle States Observer Using Adaptive Tire-Road Friction Estimator

    NASA Astrophysics Data System (ADS)

    Kwak, Byunghak; Park, Youngjin

    Vehicle stability control system is a new idea which can enhance the vehicle stability and handling in the emergency situation. This system requires the information of the yaw rate, sideslip angle and road friction in order to control the traction and braking forces at the individual wheels. This paper proposes an observer for the vehicle stability control system. This observer consisted of the state observer for vehicle motion estimation and the road condition estimator for the identification of the coefficient of the road friction. The state observer uses 2 degrees-of-freedom bicycle model and estimates the system variables based on the Kalman filter. The road condition estimator uses the same vehicle model and identifies the coefficient of the tire-road friction based on the recursive least square method. Both estimators make use of each other information. We show the effectiveness and feasibility of the proposed scheme under various road conditions through computer simulations of a fifteen degree-of-freedom non-linear vehicle model.

  2. Estimated Deaths Attributable to Social Factors in the United States

    PubMed Central

    Tracy, Melissa; Hoggatt, Katherine J.; DiMaggio, Charles; Karpati, Adam

    2011-01-01

    Objectives. We estimated the number of deaths attributable to social factors in the United States. Methods. We conducted a MEDLINE search for all English-language articles published between 1980 and 2007 with estimates of the relation between social factors and adult all-cause mortality. We calculated summary relative risk estimates of mortality, and we obtained and used prevalence estimates for each social factor to calculate the population-attributable fraction for each factor. We then calculated the number of deaths attributable to each social factor in the United States in 2000. Results. Approximately 245 000 deaths in the United States in 2000 were attributable to low education, 176 000 to racial segregation, 162 000 to low social support, 133 000 to individual-level poverty, 119 000 to income inequality, and 39 000 to area-level poverty. Conclusions. The estimated number of deaths attributable to social factors in the United States is comparable to the number attributed to pathophysiological and behavioral causes. These findings argue for a broader public health conceptualization of the causes of mortality and an expansive policy approach that considers how social factors can be addressed to improve the health of populations. PMID:21680937

  3. Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm

    PubMed Central

    Sun, Baoliang; Jiang, Chunlan; Li, Ming

    2016-01-01

    An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs. PMID:27809271

  4. Joint parameter and state estimation algorithms for real-time traffic monitoring.

    DOT National Transportation Integrated Search

    2013-12-01

    A common approach to traffic monitoring is to combine a macroscopic traffic flow model with traffic sensor data in a process called state estimation, data fusion, or data assimilation. The main challenge of traffic state estimation is the integration...

  5. Introduction to State Estimation of High-Rate System Dynamics

    PubMed Central

    Dodson, Jacob; Joyce, Bryan

    2018-01-01

    Engineering systems experiencing high-rate dynamic events, including airbags, debris detection, and active blast protection systems, could benefit from real-time observability for enhanced performance. However, the task of high-rate state estimation is challenging, in particular for real-time applications where the rate of the observer’s convergence needs to be in the microsecond range. This paper identifies the challenges of state estimation of high-rate systems and discusses the fundamental characteristics of high-rate systems. A survey of applications and methods for estimators that have the potential to produce accurate estimations for a complex system experiencing highly dynamic events is presented. It is argued that adaptive observers are important to this research. In particular, adaptive data-driven observers are advantageous due to their adaptability and lack of dependence on the system model. PMID:29342855

  6. Guidelines for preparation of State water-use estimates for 2005

    USGS Publications Warehouse

    Hutson, Susan S.

    2007-01-01

    The U.S. Geological Survey (USGS) has estimated the use of water in the United States at 5-year intervals since 1950. This report describes the water-use categories and data elements required for the 2005 national water-use compilation conducted as part of the USGS National Water Use Information Program. The report identifies sources of water-use information, provides standard methods and techniques for estimating water use at the county level, and outlines steps for preparing documentation for the United States, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands. As part of this USGS program to document water use on a national scale for the year 2005, estimates of water withdrawals for the categories of public supply, self-supplied domestic, industrial, irrigation, and thermoelectric power at the county level are prepared for each State using the guidelines in this report. Estimates of water withdrawals for aquaculture, livestock, and mining are prepared for each State using a county-based national model, although study chiefs in each State have the option of producing independent county estimates of water withdrawals for these categories. Estimates of deliveries of water from public supplies for domestic use by county also will be prepared for each State for 2005. As a result, domestic water use can be determined for each State by combining self-supplied domestic withdrawals and publicly supplied domestic deliveries. Fresh ground-water and surfacewater estimates will be prepared for all categories of use; and saline ground-water and surface-water estimates by county will be prepared for the categories of public supply, industrial, and thermoelectric power. Power production for thermoelectric power will be compiled for 2005. If data are available, reclaimed wastewater use will be compiled for the industrial and irrigation categories. Optional water-use categories are commercial, hydroelectric power, and wastewater treatment. Optional data elements

  7. Improved Battery State Estimation Using Novel Sensing Techniques

    NASA Astrophysics Data System (ADS)

    Abdul Samad, Nassim

    Lithium-ion batteries have been considered a great complement or substitute for gasoline engines due to their high energy and power density capabilities among other advantages. However, these types of energy storage devices are still yet not widespread, mainly because of their relatively high cost and safety issues, especially at elevated temperatures. This thesis extends existing methods of estimating critical battery states using model-based techniques augmented by real-time measurements from novel temperature and force sensors. Typically, temperature sensors are located near the edge of the battery, and away from the hottest core cell regions, which leads to slower response times and increased errors in the prediction of core temperatures. New sensor technology allows for flexible sensor placement at the cell surface between cells in a pack. This raises questions about the optimal locations of these sensors for best observability and temperature estimation. Using a validated model, which is developed and verified using experiments in laboratory fixtures that replicate vehicle pack conditions, it is shown that optimal sensor placement can lead to better and faster temperature estimation. Another equally important state is the state of health or the capacity fading of the cell. This thesis introduces a novel method of using force measurements for capacity fade estimation. Monitoring capacity is important for defining the range of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs). Current capacity estimation techniques require a full discharge to monitor capacity. The proposed method can complement or replace current methods because it only requires a shallow discharge, which is especially useful in EVs and PHEVs. Using the accurate state estimation accomplished earlier, a method for downsizing a battery pack is shown to effectively reduce the number of cells in a pack without compromising safety. The influence on the battery performance (e

  8. Estimated Incidence of Antimicrobial Drug–Resistant Nontyphoidal Salmonella Infections, United States, 2004–2012

    PubMed Central

    Gu, Weidong; Mahon, Barbara E.; Judd, Michael; Folster, Jason; Griffin, Patricia M.; Hoekstra, Robert M.

    2017-01-01

    Salmonella infections are a major cause of illness in the United States. The antimicrobial agents used to treat severe infections include ceftriaxone, ciprofloxacin, and ampicillin. Antimicrobial drug resistance has been associated with adverse clinical outcomes. To estimate the incidence of resistant culture-confirmed nontyphoidal Salmonella infections, we used Bayesian hierarchical models of 2004–2012 data from the Centers for Disease Control and Prevention National Antimicrobial Resistance Monitoring System and Laboratory-based Enteric Disease Surveillance. We based 3 mutually exclusive resistance categories on susceptibility testing: ceftriaxone and ampicillin resistant, ciprofloxacin nonsusceptible but ceftriaxone susceptible, and ampicillin resistant but ceftriaxone and ciprofloxacin susceptible. We estimated the overall incidence of resistant infections as 1.07/100,000 person-years for ampicillin-only resistance, 0.51/100,000 person-years for ceftriaxone and ampicillin resistance, and 0.35/100,000 person-years for ciprofloxacin nonsusceptibility, or ≈6,200 resistant culture-confirmed infections annually. These national estimates help define the magnitude of the resistance problem so that control measures can be appropriately targeted. PMID:27983506

  9. National intelligence estimates and the Failed State Index.

    PubMed

    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.

  10. Sensitivity of postplanning target and OAR coverage estimates to dosimetric margin distribution sampling parameters.

    PubMed

    Xu, Huijun; Gordon, J James; Siebers, Jeffrey V

    2011-02-01

    A dosimetric margin (DM) is the margin in a specified direction between a structure and a specified isodose surface, corresponding to a prescription or tolerance dose. The dosimetric margin distribution (DMD) is the distribution of DMs over all directions. Given a geometric uncertainty model, representing inter- or intrafraction setup uncertainties or internal organ motion, the DMD can be used to calculate coverage Q, which is the probability that a realized target or organ-at-risk (OAR) dose metric D, exceeds the corresponding prescription or tolerance dose. Postplanning coverage evaluation quantifies the percentage of uncertainties for which target and OAR structures meet their intended dose constraints. The goal of the present work is to evaluate coverage probabilities for 28 prostate treatment plans to determine DMD sampling parameters that ensure adequate accuracy for postplanning coverage estimates. Normally distributed interfraction setup uncertainties were applied to 28 plans for localized prostate cancer, with prescribed dose of 79.2 Gy and 10 mm clinical target volume to planning target volume (CTV-to-PTV) margins. Using angular or isotropic sampling techniques, dosimetric margins were determined for the CTV, bladder and rectum, assuming shift invariance of the dose distribution. For angular sampling, DMDs were sampled at fixed angular intervals w (e.g., w = 1 degree, 2 degrees, 5 degrees, 10 degrees, 20 degrees). Isotropic samples were uniformly distributed on the unit sphere resulting in variable angular increments, but were calculated for the same number of sampling directions as angular DMDs, and accordingly characterized by the effective angular increment omega eff. In each direction, the DM was calculated by moving the structure in radial steps of size delta (=0.1, 0.2, 0.5, 1 mm) until the specified isodose was crossed. Coverage estimation accuracy deltaQ was quantified as a function of the sampling parameters omega or omega eff and delta. The

  11. Using State Estimation Residuals to Detect Abnormal SCADA Data

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

    Ma, Jian; Chen, Yousu; Huang, Zhenyu

    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 ismore » 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.« less

  12. The estimation error covariance matrix for the ideal state reconstructor with measurement noise

    NASA Technical Reports Server (NTRS)

    Polites, Michael E.

    1988-01-01

    A general expression is derived for the state estimation error covariance matrix for the Ideal State Reconstructor when the input measurements are corrupted by measurement noise. An example is presented which shows that the more measurements used in estimating the state at a given time, the better the estimator.

  13. Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

    NASA Astrophysics Data System (ADS)

    Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

    2016-03-01

    From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

  14. Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

    PubMed Central

    Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

    2016-01-01

    From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

  15. A weight modification sequential method for VSC-MTDC power system state estimation

    NASA Astrophysics Data System (ADS)

    Yang, Xiaonan; Zhang, Hao; Li, Qiang; Guo, Ziming; Zhao, Kun; Li, Xinpeng; Han, Feng

    2017-06-01

    This paper presents an effective sequential approach based on weight modification for VSC-MTDC power system state estimation, called weight modification sequential method. The proposed approach simplifies the AC/DC system state estimation algorithm through modifying the weight of state quantity to keep the matrix dimension constant. The weight modification sequential method can also make the VSC-MTDC system state estimation calculation results more ccurate and increase the speed of calculation. The effectiveness of the proposed weight modification sequential method is demonstrated and validated in modified IEEE 14 bus system.

  16. State-level estimates of childhood obesity prevalence in the United States corrected for report bias.

    PubMed

    Long, M W; Ward, Z J; Resch, S C; Cradock, A L; Wang, Y C; Giles, C M; Gortmaker, S L

    2016-10-01

    State-specific obesity prevalence data are critical to public health efforts to address the childhood obesity epidemic. However, few states administer objectively measured body mass index (BMI) surveillance programs. This study reports state-specific childhood obesity prevalence by age and sex correcting for parent-reported child height and weight bias. As part of the Childhood Obesity Intervention Cost Effectiveness Study (CHOICES), we developed childhood obesity prevalence estimates for states for the period 2005-2010 using data from the 2010 US Census and American Community Survey (ACS), 2003-2004 and 2007-2008 National Survey of Children's Health (NSCH) (n=133 213), and 2005-2010 National Health and Nutrition Examination Surveys (NHANES) (n=9377; ages 2-17). Measured height and weight data from NHANES were used to correct parent-report bias in NSCH using a non-parametric statistical matching algorithm. Model estimates were validated against surveillance data from five states (AR, FL, MA, PA and TN) that conduct censuses of children across a range of grades. Parent-reported height and weight resulted in the largest overestimation of childhood obesity in males ages 2-5 years (NSCH: 42.36% vs NHANES: 11.44%). The CHOICES model estimates for this group (12.81%) and for all age and sex categories were not statistically different from NHANES. Our modeled obesity prevalence aligned closely with measured data from five validation states, with a 0.64 percentage point mean difference (range: 0.23-1.39) and a high correlation coefficient (r=0.96, P=0.009). Estimated state-specific childhood obesity prevalence ranged from 11.0 to 20.4%. Uncorrected estimates of childhood obesity prevalence from NSCH vary widely from measured national data, from a 278% overestimate among males aged 2-5 years to a 44% underestimate among females aged 14-17 years. This study demonstrates the validity of the CHOICES matching methods to correct the bias of parent-reported BMI data and

  17. Estimating repetitive spatiotemporal patterns from resting-state brain activity data.

    PubMed

    Takeda, Yusuke; Hiroe, Nobuo; Yamashita, Okito; Sato, Masa-Aki

    2016-06-01

    Repetitive spatiotemporal patterns in spontaneous brain activities have been widely examined in non-human studies. These studies have reported that such patterns reflect past experiences embedded in neural circuits. In human magnetoencephalography (MEG) and electroencephalography (EEG) studies, however, spatiotemporal patterns in resting-state brain activities have not been extensively examined. This is because estimating spatiotemporal patterns from resting-state MEG/EEG data is difficult due to their unknown onsets. Here, we propose a method to estimate repetitive spatiotemporal patterns from resting-state brain activity data, including MEG/EEG. Without the information of onsets, the proposed method can estimate several spatiotemporal patterns, even if they are overlapping. We verified the performance of the method by detailed simulation tests. Furthermore, we examined whether the proposed method could estimate the visual evoked magnetic fields (VEFs) without using stimulus onset information. The proposed method successfully detected the stimulus onsets and estimated the VEFs, implying the applicability of this method to real MEG data. The proposed method was applied to resting-state functional magnetic resonance imaging (fMRI) data and MEG data. The results revealed informative spatiotemporal patterns representing consecutive brain activities that dynamically change with time. Using this method, it is possible to reveal discrete events spontaneously occurring in our brains, such as memory retrieval. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  18. A Unified Estimation Framework for State-Related Changes in Effective Brain Connectivity.

    PubMed

    Samdin, S Balqis; Ting, Chee-Ming; Ombao, Hernando; Salleh, Sh-Hussain

    2017-04-01

    This paper addresses the critical problem of estimating time-evolving effective brain connectivity. Current approaches based on sliding window analysis or time-varying coefficient models do not simultaneously capture both slow and abrupt changes in causal interactions between different brain regions. To overcome these limitations, we develop a unified framework based on a switching vector autoregressive (SVAR) model. Here, the dynamic connectivity regimes are uniquely characterized by distinct vector autoregressive (VAR) processes and allowed to switch between quasi-stationary brain states. The state evolution and the associated directed dependencies are defined by a Markov process and the SVAR parameters. We develop a three-stage estimation algorithm for the SVAR model: 1) feature extraction using time-varying VAR (TV-VAR) coefficients, 2) preliminary regime identification via clustering of the TV-VAR coefficients, 3) refined regime segmentation by Kalman smoothing and parameter estimation via expectation-maximization algorithm under a state-space formulation, using initial estimates from the previous two stages. The proposed framework is adaptive to state-related changes and gives reliable estimates of effective connectivity. Simulation results show that our method provides accurate regime change-point detection and connectivity estimates. In real applications to brain signals, the approach was able to capture directed connectivity state changes in functional magnetic resonance imaging data linked with changes in stimulus conditions, and in epileptic electroencephalograms, differentiating ictal from nonictal periods. The proposed framework accurately identifies state-dependent changes in brain network and provides estimates of connectivity strength and directionality. The proposed approach is useful in neuroscience studies that investigate the dynamics of underlying brain states.

  19. 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.

  20. Application of Smoothing Techniques for Tracking Maneuvering Targets. Multiple Target Tracking in Clutter: New Approaches

    DTIC Science & Technology

    1992-07-01

    target state estimation is affected not only by the measurement noise but also by the uncertainty in the origins of the measurements. To improve the...to identify targets in the presence of anticipated background noise (including earth, lunar, star backgrounds, complicated spacecraft structures...each other. Futhermore, those frames are often degraded versions of the original scene due to blur and noise . Through the task of image registration

  1. Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation.

    PubMed

    Liu, Xi; Qu, Hua; Zhao, Jihong; Yue, Pengcheng; Wang, Meng

    2016-09-20

    A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF) provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC), the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT) is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm.

  2. Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation

    PubMed Central

    Liu, Xi; Qu, Hua; Zhao, Jihong; Yue, Pengcheng; Wang, Meng

    2016-01-01

    A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF) provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC), the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT) is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm. PMID:27657069

  3. Soybean Crop Area Estimation and Mapping in Mato Grosso State, Brazil

    NASA Astrophysics Data System (ADS)

    Gusso, A.; Ducati, J. R.

    2012-07-01

    Evaluation of the MODIS Crop Detection Algorithm (MCDA) procedure for estimating historical planted soybean crop areas was done on fields in Mato Grosso State, Brazil. MCDA is based on temporal profiles of EVI (Enhanced Vegetation Index) derived from satellite data of the MODIS (Moderate Resolution Imaging Spectroradiometer) imager, and was previously developed for soybean area estimation in Rio Grande do Sul State, Brazil. According to the MCDA approach, in Mato Grosso soybean area estimates can be provided in December (1st forecast), using images from the sowing period, and in February (2nd forecast), using images from sowing and maximum crop development period. The results obtained by the MCDA were compared with Brazilian Institute of Geography and Statistics (IBGE) official estimates of soybean area at municipal level. Coefficients of determination were between 0.93 and 0.98, indicating a good agreement, and also the suitability of MCDA to estimations performed in Mato Grosso State. On average, the MCDA results explained 96% of the variation of the data estimated by the IBGE. In this way, MCDA calibration was able to provide annual thematic soybean maps, forecasting the planted area in the State, with results which are comparable to the official agricultural statistics.

  4. Methods for Estimating Water Withdrawals for Mining in the United States, 2005

    USGS Publications Warehouse

    Lovelace, John K.

    2009-01-01

    The mining water-use category includes groundwater and surface water that is withdrawn and used for nonfuels and fuels mining. Nonfuels mining includes the extraction of ores, stone, sand, and gravel. Fuels mining includes the extraction of coal, petroleum, and natural gas. Water is used for mineral extraction, quarrying, milling, and other operations directly associated with mining activities. For petroleum and natural gas extraction, water often is injected for secondary oil or gas recovery. Estimates of water withdrawals for mining are needed for water planning and management. This report documents methods used to estimate withdrawals of fresh and saline groundwater and surface water for mining during 2005 for each county and county equivalent in the United States, Puerto Rico, and the U.S. Virgin Islands. Fresh and saline groundwater and surface-water withdrawals during 2005 for nonfuels- and coal-mining operations in each county or county equivalent in the United States, Puerto Rico, and the U.S. Virgin Islands were estimated. Fresh and saline groundwater withdrawals for oil and gas operations in counties of six states also were estimated. Water withdrawals for nonfuels and coal mining were estimated by using mine-production data and water-use coefficients. Production data for nonfuels mining included the mine location and weight (in metric tons) of crude ore, rock, or mineral produced at each mine in the United States, Puerto Rico, and the U.S. Virgin Islands during 2004. Production data for coal mining included the weight, in metric tons, of coal produced in each county or county equivalent during 2004. Water-use coefficients for mined commodities were compiled from various sources including published reports and written communications from U.S. Geological Survey National Water-use Information Program (NWUIP) personnel in several states. Water withdrawals for oil and gas extraction were estimated for six States including California, Colorado, Louisiana, New

  5. Target-decoy Based False Discovery Rate Estimation for Large-scale Metabolite Identification.

    PubMed

    Wang, Xusheng; Jones, Drew R; Shaw, Timothy I; Cho, Ji-Hoon; Wang, Yuanyuan; Tan, Haiyan; Xie, Boer; Zhou, Suiping; Li, Yuxin; Peng, Junmin

    2018-05-23

    Metabolite identification is a crucial step in mass spectrometry (MS)-based metabolomics. However, it is still challenging to assess the confidence of assigned metabolites. In this study, we report a novel method for estimating false discovery rate (FDR) of metabolite assignment with a target-decoy strategy, in which the decoys are generated through violating the octet rule of chemistry by adding small odd numbers of hydrogen atoms. The target-decoy strategy was integrated into JUMPm, an automated metabolite identification pipeline for large-scale MS analysis, and was also evaluated with two other metabolomics tools, mzMatch and mzMine 2. The reliability of FDR calculation was examined by false datasets, which were simulated by altering MS1 or MS2 spectra. Finally, we used the JUMPm pipeline coupled with the target-decoy strategy to process unlabeled and stable-isotope labeled metabolomic datasets. The results demonstrate that the target-decoy strategy is a simple and effective method for evaluating the confidence of high-throughput metabolite identification.

  6. 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

  7. Time delay estimation using new spectral and adaptive filtering methods with applications to underwater target detection

    NASA Astrophysics Data System (ADS)

    Hasan, Mohammed A.

    1997-11-01

    In this dissertation, we present several novel approaches for detection and identification of targets of arbitrary shapes from the acoustic backscattered data and using the incident waveform. This problem is formulated as time- delay estimation and sinusoidal frequency estimation problems which both have applications in many other important areas in signal processing. Solving time-delay estimation problem allows the identification of the specular components in the backscattered signal from elastic and non-elastic targets. Thus, accurate estimation of these time delays would help in determining the existence of certain clues for detecting targets. Several new methods for solving these two problems in the time, frequency and wavelet domains are developed. In the time domain, a new block fast transversal filter (BFTF) is proposed for a fast implementation of the least squares (LS) method. This BFTF algorithm is derived by using data-related constrained block-LS cost function to guarantee global optimality. The new soft-constrained algorithm provides an efficient way of transferring weight information between blocks of data and thus it is computationally very efficient compared with other LS- based schemes. Additionally, the tracking ability of the algorithm can be controlled by varying the block length and/or a soft constrained parameter. The effectiveness of this algorithm is tested on several underwater acoustic backscattered data for elastic targets and non-elastic (cement chunk) objects. In the frequency domain, the time-delay estimation problem is converted to a sinusoidal frequency estimation problem by using the discrete Fourier transform. Then, the lagged sample covariance matrices of the resulting signal are computed and studied in terms of their eigen- structure. These matrices are shown to be robust and effective in extracting bases for the signal and noise subspaces. New MUSIC and matrix pencil-based methods are derived these subspaces. The effectiveness

  8. 34 CFR 300.602 - State use of targets and reporting.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 34 Education 2 2010-07-01 2010-07-01 false State use of targets and reporting. 300.602 Section 300.602 Education Regulations of the Offices of the Department of Education (Continued) OFFICE OF SPECIAL EDUCATION AND REHABILITATIVE SERVICES, DEPARTMENT OF EDUCATION ASSISTANCE TO STATES FOR THE EDUCATION OF...

  9. Evolution of the cerebellum as a neuronal machine for Bayesian state estimation

    NASA Astrophysics Data System (ADS)

    Paulin, M. G.

    2005-09-01

    The cerebellum evolved in association with the electric sense and vestibular sense of the earliest vertebrates. Accurate information provided by these sensory systems would have been essential for precise control of orienting behavior in predation. A simple model shows that individual spikes in electrosensory primary afferent neurons can be interpreted as measurements of prey location. Using this result, I construct a computational neural model in which the spatial distribution of spikes in a secondary electrosensory map forms a Monte Carlo approximation to the Bayesian posterior distribution of prey locations given the sense data. The neural circuit that emerges naturally to perform this task resembles the cerebellar-like hindbrain electrosensory filtering circuitry of sharks and other electrosensory vertebrates. The optimal filtering mechanism can be extended to handle dynamical targets observed from a dynamical platform; that is, to construct an optimal dynamical state estimator using spiking neurons. This may provide a generic model of cerebellar computation. Vertebrate motion-sensing neurons have specific fractional-order dynamical characteristics that allow Bayesian state estimators to be implemented elegantly and efficiently, using simple operations with asynchronous pulses, i.e. spikes. The computational neural models described in this paper represent a novel kind of particle filter, using spikes as particles. The models are specific and make testable predictions about computational mechanisms in cerebellar circuitry, while providing a plausible explanation of cerebellar contributions to aspects of motor control, perception and cognition.

  10. Sensitivity of postplanning target and OAR coverage estimates to dosimetric margin distribution sampling parameters

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

    Xu Huijun; Gordon, J. James; Siebers, Jeffrey V.

    2011-02-15

    Purpose: A dosimetric margin (DM) is the margin in a specified direction between a structure and a specified isodose surface, corresponding to a prescription or tolerance dose. The dosimetric margin distribution (DMD) is the distribution of DMs over all directions. Given a geometric uncertainty model, representing inter- or intrafraction setup uncertainties or internal organ motion, the DMD can be used to calculate coverage Q, which is the probability that a realized target or organ-at-risk (OAR) dose metric D{sub v} exceeds the corresponding prescription or tolerance dose. Postplanning coverage evaluation quantifies the percentage of uncertainties for which target and OAR structuresmore » meet their intended dose constraints. The goal of the present work is to evaluate coverage probabilities for 28 prostate treatment plans to determine DMD sampling parameters that ensure adequate accuracy for postplanning coverage estimates. Methods: Normally distributed interfraction setup uncertainties were applied to 28 plans for localized prostate cancer, with prescribed dose of 79.2 Gy and 10 mm clinical target volume to planning target volume (CTV-to-PTV) margins. Using angular or isotropic sampling techniques, dosimetric margins were determined for the CTV, bladder and rectum, assuming shift invariance of the dose distribution. For angular sampling, DMDs were sampled at fixed angular intervals {omega} (e.g., {omega}=1 deg., 2 deg., 5 deg., 10 deg., 20 deg.). Isotropic samples were uniformly distributed on the unit sphere resulting in variable angular increments, but were calculated for the same number of sampling directions as angular DMDs, and accordingly characterized by the effective angular increment {omega}{sub eff}. In each direction, the DM was calculated by moving the structure in radial steps of size {delta}(=0.1,0.2,0.5,1 mm) until the specified isodose was crossed. Coverage estimation accuracy {Delta}Q was quantified as a function of the sampling parameters

  11. Parameter Estimation and Image Reconstruction of Rotating Targets with Vibrating Interference in the Terahertz Band

    NASA Astrophysics Data System (ADS)

    Yang, Qi; Deng, Bin; Wang, Hongqiang; Qin, Yuliang

    2017-07-01

    Rotation is one of the typical micro-motions of radar targets. In many cases, rotation of the targets is always accompanied with vibrating interference, and it will significantly affect the parameter estimation and imaging, especially in the terahertz band. In this paper, we propose a parameter estimation method and an image reconstruction method based on the inverse Radon transform, the time-frequency analysis, and its inverse. The method can separate and estimate the rotating Doppler and the vibrating Doppler simultaneously and can obtain high-quality reconstructed images after vibration compensation. In addition, a 322-GHz radar system and a 25-GHz commercial radar are introduced and experiments on rotating corner reflectors are carried out in this paper. The results of the simulation and experiments verify the validity of the methods, which lay a foundation for the practical processing of the terahertz radar.

  12. An Empirical State Error Covariance Matrix for the Weighted Least Squares Estimation Method

    NASA Technical Reports Server (NTRS)

    Frisbee, Joseph H., Jr.

    2011-01-01

    State estimation techniques effectively provide mean state estimates. However, the theoretical state error covariance matrices provided as part of these techniques often suffer from a lack of confidence in their ability to describe the un-certainty in the estimated states. By a reinterpretation of the equations involved in the weighted least squares algorithm, it is possible to directly arrive at an empirical state error covariance matrix. This proposed empirical state error covariance matrix will contain the effect of all error sources, known or not. Results based on the proposed technique will be presented for a simple, two observer, measurement error only problem.

  13. Least-squares sequential parameter and state estimation for large space structures

    NASA Technical Reports Server (NTRS)

    Thau, F. E.; Eliazov, T.; Montgomery, R. C.

    1982-01-01

    This paper presents the formulation of simultaneous state and parameter estimation problems for flexible structures in terms of least-squares minimization problems. The approach combines an on-line order determination algorithm, with least-squares algorithms for finding estimates of modal approximation functions, modal amplitudes, and modal parameters. The approach combines previous results on separable nonlinear least squares estimation with a regression analysis formulation of the state estimation problem. The technique makes use of sequential Householder transformations. This allows for sequential accumulation of matrices required during the identification process. The technique is used to identify the modal prameters of a flexible beam.

  14. State-Level Estimates of Obesity-Attributable Costs of Absenteeism

    PubMed Central

    Andreyeva, Tatiana; Luedicke, Joerg; Wang, Y. Claire

    2014-01-01

    Objective To provide state-level estimates of obesity-attributable costs of absenteeism among working adults in the U.S. Methods Nationally-representative data from the National Health and Nutrition Examination Survey (NHANES) for 1998–2008 and from the Behavioral Risk Factor Surveillance System (BRFSS) for 2012 are examined. The outcome is obesity-attributable workdays missed in the previous year due to health, and their costs to states. Results Obesity, but not overweight, is associated with a significant increase in workdays absent, from 1.1 to 1.7 extra days missed annually compared to normal weight employees. Obesity-attributable absenteeism among American workers costs the nation an estimated $8.65 billion per year. Conclusion Obesity imposes a considerable financial burden on states, accounting for 6.5%–12.6% of total absenteeism costs in the workplace. State legislature and employers should seek effective ways to reduce these costs. PMID:25376405

  15. An Efficient Estimator for Moving Target Localization Using Multi-Station Dual-Frequency Radars.

    PubMed

    Huang, Jiyan; Zhang, Ying; Luo, Shan

    2017-12-15

    Localization of a moving target in a dual-frequency radars system has now gained considerable attention. The noncoherent localization approach based on a least squares (LS) estimator has been addressed in the literature. Compared with the LS method, a novel localization method based on a two-step weighted least squares estimator is proposed to increase positioning accuracy for a multi-station dual-frequency radars system in this paper. The effects of signal noise ratio and the number of samples on the performance of range estimation are also analyzed in the paper. Furthermore, both the theoretical variance and Cramer-Rao lower bound (CRLB) are derived. The simulation results verified the proposed method.

  16. Phase estimation of coherent states with a noiseless linear amplifier

    NASA Astrophysics Data System (ADS)

    Assad, Syed M.; Bradshaw, Mark; Lam, Ping Koy

    Amplification of quantum states is inevitably accompanied with the introduction of noise at the output. For protocols that are probabilistic with heralded success, noiseless linear amplification in theory may still be possible. When the protocol is successful, it can lead to an output that is a noiselessly amplified copy of the input. When the protocol is unsuccessful, the output state is degraded and is usually discarded. Probabilistic protocols may improve the performance of some quantum information protocols, but not for metrology if the whole statistics is taken into consideration. We calculate the precision limits on estimating the phase of coherent states using a noiseless linear amplifier by computing its quantum Fisher information and we show that on average, the noiseless linear amplifier does not improve the phase estimate. We also discuss the case where abstention from measurement can reduce the cost for estimation.

  17. A State Space Model for Spatial Updating of Remembered Visual Targets during Eye Movements

    PubMed Central

    Mohsenzadeh, Yalda; Dash, Suryadeep; Crawford, J. Douglas

    2016-01-01

    In the oculomotor system, spatial updating is the ability to aim a saccade toward a remembered visual target position despite intervening eye movements. Although this has been the subject of extensive experimental investigation, there is still no unifying theoretical framework to explain the neural mechanism for this phenomenon, and how it influences visual signals in the brain. Here, we propose a unified state-space model (SSM) to account for the dynamics of spatial updating during two types of eye movement; saccades and smooth pursuit. Our proposed model is a non-linear SSM and implemented through a recurrent radial-basis-function neural network in a dual Extended Kalman filter (EKF) structure. The model parameters and internal states (remembered target position) are estimated sequentially using the EKF method. The proposed model replicates two fundamental experimental observations: continuous gaze-centered updating of visual memory-related activity during smooth pursuit, and predictive remapping of visual memory activity before and during saccades. Moreover, our model makes the new prediction that, when uncertainty of input signals is incorporated in the model, neural population activity and receptive fields expand just before and during saccades. These results suggest that visual remapping and motor updating are part of a common visuomotor mechanism, and that subjective perceptual constancy arises in part from training the visual system on motor tasks. PMID:27242452

  18. State estimation of spatio-temporal phenomena

    NASA Astrophysics Data System (ADS)

    Yu, Dan

    This dissertation addresses the state estimation problem of spatio-temporal phenomena which can be modeled by partial differential equations (PDEs), such as pollutant dispersion in the atmosphere. After discretizing the PDE, the dynamical system has a large number of degrees of freedom (DOF). State estimation using Kalman Filter (KF) is computationally intractable, and hence, a reduced order model (ROM) needs to be constructed first. Moreover, the nonlinear terms, external disturbances or unknown boundary conditions can be modeled as unknown inputs, which leads to an unknown input filtering problem. Furthermore, the performance of KF could be improved by placing sensors at feasible locations. Therefore, the sensor scheduling problem to place multiple mobile sensors is of interest. The first part of the dissertation focuses on model reduction for large scale systems with a large number of inputs/outputs. A commonly used model reduction algorithm, the balanced proper orthogonal decomposition (BPOD) algorithm, is not computationally tractable for large systems with a large number of inputs/outputs. Inspired by the BPOD and randomized algorithms, we propose a randomized proper orthogonal decomposition (RPOD) algorithm and a computationally optimal RPOD (RPOD*) algorithm, which construct an ROM to capture the input-output behaviour of the full order model, while reducing the computational cost of BPOD by orders of magnitude. It is demonstrated that the proposed RPOD* algorithm could construct the ROM in real-time, and the performance of the proposed algorithms on different advection-diffusion equations. Next, we consider the state estimation problem of linear discrete-time systems with unknown inputs which can be treated as a wide-sense stationary process with rational power spectral density, while no other prior information needs to be known. We propose an autoregressive (AR) model based unknown input realization technique which allows us to recover the input

  19. Estimated Water Flows in 2005: United States

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

    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 inmore » 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.« less

  20. Discrete-time state estimation for stochastic polynomial systems over polynomial observations

    NASA Astrophysics Data System (ADS)

    Hernandez-Gonzalez, M.; Basin, M.; Stepanov, O.

    2018-07-01

    This paper presents a solution to the mean-square state estimation problem for stochastic nonlinear polynomial systems over polynomial observations confused with additive white Gaussian noises. The solution is given in two steps: (a) computing the time-update equations and (b) computing the measurement-update equations for the state estimate and error covariance matrix. A closed form of this filter is obtained by expressing conditional expectations of polynomial terms as functions of the state estimate and error covariance. As a particular case, the mean-square filtering equations are derived for a third-degree polynomial system with second-degree polynomial measurements. Numerical simulations show effectiveness of the proposed filter compared to the extended Kalman filter.

  1. Estimation of Hidden State Variables of the Intracranial System Using Constrained Nonlinear Kalman Filters

    PubMed Central

    Nenov, Valeriy; Bergsneider, Marvin; Glenn, Thomas C.; Vespa, Paul; Martin, Neil

    2007-01-01

    Impeded by the rigid skull, assessment of physiological variables of the intracranial system is difficult. A hidden state estimation approach is used in the present work to facilitate the estimation of unobserved variables from available clinical measurements including intracranial pressure (ICP) and cerebral blood flow velocity (CBFV). The estimation algorithm is based on a modified nonlinear intracranial mathematical model, whose parameters are first identified in an offline stage using a nonlinear optimization paradigm. Following the offline stage, an online filtering process is performed using a nonlinear Kalman filter (KF)-like state estimator that is equipped with a new way of deriving the Kalman gain satisfying the physiological constraints on the state variables. The proposed method is then validated by comparing different state estimation methods and input/output (I/O) configurations using simulated data. It is also applied to a set of CBFV, ICP and arterial blood pressure (ABP) signal segments from brain injury patients. The results indicated that the proposed constrained nonlinear KF achieved the best performance among the evaluated state estimators and that the state estimator combined with the I/O configuration that has ICP as the measured output can potentially be used to estimate CBFV continuously. Finally, the state estimator combined with the I/O configuration that has both ICP and CBFV as outputs can potentially estimate the lumped cerebral arterial radii, which are not measurable in a typical clinical environment. PMID:17281533

  2. State-Dependent Pseudo-Linear Filter for Spacecraft Attitude and Rate Estimation

    NASA Technical Reports Server (NTRS)

    Bar-Itzhack, Itzhack Y.; Harman, Richard R.

    2001-01-01

    This paper presents the development and performance of a special algorithm for estimating the attitude and angular rate of a spacecraft. The algorithm is a pseudo-linear Kalman filter, which is an ordinary linear Kalman filter that operates on a linear model whose matrices are current state estimate dependent. The nonlinear rotational dynamics equation of the spacecraft is presented in the state space as a state-dependent linear system. Two types of measurements are considered. One type is a measurement of the quaternion of rotation, which is obtained from a newly introduced star tracker based apparatus. The other type of measurement is that of vectors, which permits the use of a variety of vector measuring sensors like sun sensors and magnetometers. While quaternion measurements are related linearly to the state vector, vector measurements constitute a nonlinear function of the state vector. Therefore, in this paper, a state-dependent linear measurement equation is developed for the vector measurement case. The state-dependent pseudo linear filter is applied to simulated spacecraft rotations and adequate estimates of the spacecraft attitude and rate are obtained for the case of quaternion measurements as well as of vector measurements.

  3. Method of Enhancing On-Board State Estimation Using Communication Signals

    NASA Technical Reports Server (NTRS)

    Anzalone, Evan J. (Inventor); Chuang, Jason C. H. (Inventor)

    2015-01-01

    A method of enhancing on-board state estimation for a spacecraft utilizes a network of assets to include planetary-based assets and space-based assets. Communication signals transmitted from each of the assets into space are defined by a common protocol. Data is embedded in each communication signal transmitted by the assets. The data includes a time-of-transmission for a corresponding one of the communication signals and a position of a corresponding one of the assets at the time-of-transmission. A spacecraft is equipped to receive the communication signals, has a clock synchronized to the space-wide time reference frame, and has a processor programmed to generate state estimates of the spacecraft. Using its processor, the spacecraft determines a one-dimensional range from itself to at least one of the assets and then updates its state estimates using each one-dimensional range.

  4. Automatic Regionalization Algorithm for Distributed State Estimation in Power Systems

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

    Wang, Dexin; Yang, Liuqing; Florita, Anthony

    The deregulation of the power system and the incorporation of generation from renewable energy sources recessitates faster state estimation in the smart grid. Distributed state estimation (DSE) has become a promising and scalable solution to this urgent demand. In this paper, we investigate the regionalization algorithms for the power system, a necessary step before distributed state estimation can be performed. To the best of the authors' knowledge, this is the first investigation on automatic regionalization (AR). We propose three spectral clustering based AR algorithms. Simulations show that our proposed algorithms outperform the two investigated manual regionalization cases. With the helpmore » of AR algorithms, we also show how the number of regions impacts the accuracy and convergence speed of the DSE and conclude that the number of regions needs to be chosen carefully to improve the convergence speed of DSEs.« less

  5. Method for Estimating Water Withdrawals for Livestock in the United States, 2005

    USGS Publications Warehouse

    Lovelace, John K.

    2009-01-01

    Livestock water use includes ground water and surface water associated with livestock watering, feedlots, dairy operations, and other on-farm needs. The water may be used for drinking, cooling, sanitation, waste disposal, and other needs related to the animals. Estimates of water withdrawals for livestock are needed for water planning and management. This report documents a method used to estimate withdrawals of fresh ground water and surface water for livestock in 2005 for each county and county equivalent in the United States, Puerto Rico, and the U.S. Virgin Islands. Categories of livestock included dairy cattle, beef and other cattle, hogs and pigs, laying hens, broilers and other chickens, turkeys, sheep and lambs, all goats, and horses (including ponies, mules, burros, and donkeys). Use of the method described in this report could result in more consistent water-withdrawal estimates for livestock that can be used by water managers and planners to determine water needs and trends across the United States. Water withdrawals for livestock in 2005 were estimated by using water-use coefficients, in gallons per head per day for each animal type, and livestock-population data. Coefficients for various livestock for most States were obtained from U.S. Geological Survey water-use program personnel or U.S. Geological Survey water-use publications. When no coefficient was available for an animal type in a State, the median value of reported coefficients for that animal was used. Livestock-population data were provided by the National Agricultural Statistics Service. County estimates were further divided into ground-water and surface-water withdrawals for each county and county equivalent. County totals from 2005 were compared to county totals from 1995 and 2000. Large deviations from 1995 or 2000 livestock withdrawal estimates were investigated and generally were due to comparison with reported withdrawals, differences in estimation techniques, differences in livestock

  6. Real-time hydraulic interval state estimation for water transport networks: a case study

    NASA Astrophysics Data System (ADS)

    Vrachimis, Stelios G.; Eliades, Demetrios G.; Polycarpou, Marios M.

    2018-03-01

    Hydraulic state estimation in water distribution networks is the task of estimating water flows and pressures in the pipes and nodes of the network based on some sensor measurements. This requires a model of the network as well as knowledge of demand outflow and tank water levels. Due to modeling and measurement uncertainty, standard state estimation may result in inaccurate hydraulic estimates without any measure of the estimation error. This paper describes a methodology for generating hydraulic state bounding estimates based on interval bounds on the parametric and measurement uncertainties. The estimation error bounds provided by this method can be applied to determine the existence of unaccounted-for water in water distribution networks. As a case study, the method is applied to a modified transport network in Cyprus, using actual data in real time.

  7. Multisensor fusion for 3D target tracking using track-before-detect particle filter

    NASA Astrophysics Data System (ADS)

    Moshtagh, Nima; Romberg, Paul M.; Chan, Moses W.

    2015-05-01

    This work presents a novel fusion mechanism for estimating the three-dimensional trajectory of a moving target using images collected by multiple imaging sensors. The proposed projective particle filter avoids the explicit target detection prior to fusion. In projective particle filter, particles that represent the posterior density (of target state in a high-dimensional space) are projected onto the lower-dimensional observation space. Measurements are generated directly in the observation space (image plane) and a marginal (sensor) likelihood is computed. The particles states and their weights are updated using the joint likelihood computed from all the sensors. The 3D state estimate of target (system track) is then generated from the states of the particles. This approach is similar to track-before-detect particle filters that are known to perform well in tracking dim and stealthy targets in image collections. Our approach extends the track-before-detect approach to 3D tracking using the projective particle filter. The performance of this measurement-level fusion method is compared with that of a track-level fusion algorithm using the projective particle filter. In the track-level fusion algorithm, the 2D sensor tracks are generated separately and transmitted to a fusion center, where they are treated as measurements to the state estimator. The 2D sensor tracks are then fused to reconstruct the system track. A realistic synthetic scenario with a boosting target was generated, and used to study the performance of the fusion mechanisms.

  8. A Bayesian model for estimating multi-state disease progression.

    PubMed

    Shen, Shiwen; Han, Simon X; Petousis, Panayiotis; Weiss, Robert E; Meng, Frank; Bui, Alex A T; Hsu, William

    2017-02-01

    A growing number of individuals who are considered at high risk of cancer are now routinely undergoing population screening. However, noted harms such as radiation exposure, overdiagnosis, and overtreatment underscore the need for better temporal models that predict who should be screened and at what frequency. The mean sojourn time (MST), an average duration period when a tumor can be detected by imaging but with no observable clinical symptoms, is a critical variable for formulating screening policy. Estimation of MST has been long studied using continuous Markov model (CMM) with Maximum likelihood estimation (MLE). However, a lot of traditional methods assume no observation error of the imaging data, which is unlikely and can bias the estimation of the MST. In addition, the MLE may not be stably estimated when data is sparse. Addressing these shortcomings, we present a probabilistic modeling approach for periodic cancer screening data. We first model the cancer state transition using a three state CMM model, while simultaneously considering observation error. We then jointly estimate the MST and observation error within a Bayesian framework. We also consider the inclusion of covariates to estimate individualized rates of disease progression. Our approach is demonstrated on participants who underwent chest x-ray screening in the National Lung Screening Trial (NLST) and validated using posterior predictive p-values and Pearson's chi-square test. Our model demonstrates more accurate and sensible estimates of MST in comparison to MLE. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Meeting the Need for State-Level Estimates of Health Insurance Coverage: Use of State and Federal Survey Data

    PubMed Central

    Blewett, Lynn A; Davern, Michael

    2006-01-01

    Objective Critically review estimates of health insurance coverage available from different sources, including the federal government, state survey initiatives, and foundation-sponsored surveys for use in state policy research. Study Setting and Design We review the surveys in an attempt to flesh out the current weaknesses of survey data for state policy uses. The main data sources assessed in this analysis are federal government surveys (such as the Current Population Survey's Annual Social and Economic Supplement, and the National Health Interview Survey), foundation-supported surveys (National Survey of America's Families, and the Community Tracking Survey), and state-sponsored surveys. Principal Findings Despite information on estimates of health insurance coverage from six federal surveys, states find the data lacking for state policy purposes. We document the need for state representative data on the uninsured and the recent history of state data collection efforts spurred in part by the Health Resources Services Administration State Planning Grant program. We assess the state estimates of uninsurance from the Current Population Survey and make recommendations for a new consolidated federal survey with better state representative data. Conclusions We think there are several options to consider for coordinating a federal and state data collection strategy to inform state and national policy on coverage and access. PMID:16704521

  10. Load flow and state estimation algorithms for three-phase unbalanced power distribution systems

    NASA Astrophysics Data System (ADS)

    Madvesh, Chiranjeevi

    Distribution load flow and state estimation are two important functions in distribution energy management systems (DEMS) and advanced distribution automation (ADA) systems. Distribution load flow analysis is a tool which helps to analyze the status of a power distribution system under steady-state operating conditions. In this research, an effective and comprehensive load flow algorithm is developed to extensively incorporate the distribution system components. Distribution system state estimation is a mathematical procedure which aims to estimate the operating states of a power distribution system by utilizing the information collected from available measurement devices in real-time. An efficient and computationally effective state estimation algorithm adapting the weighted-least-squares (WLS) method has been developed in this research. Both the developed algorithms are tested on different IEEE test-feeders and the results obtained are justified.

  11. Cortical connective field estimates from resting state fMRI activity.

    PubMed

    Gravel, Nicolás; Harvey, Ben; Nordhjem, Barbara; Haak, Koen V; Dumoulin, Serge O; Renken, Remco; Curčić-Blake, Branislava; Cornelissen, Frans W

    2014-01-01

    One way to study connectivity in visual cortical areas is by examining spontaneous neural activity. In the absence of visual input, such activity remains shaped by the underlying neural architecture and, presumably, may still reflect visuotopic organization. Here, we applied population connective field (CF) modeling to estimate the spatial profile of functional connectivity in the early visual cortex during resting state functional magnetic resonance imaging (RS-fMRI). This model-based analysis estimates the spatial integration between blood-oxygen level dependent (BOLD) signals in distinct cortical visual field maps using fMRI. Just as population receptive field (pRF) mapping predicts the collective neural activity in a voxel as a function of response selectivity to stimulus position in visual space, CF modeling predicts the activity of voxels in one visual area as a function of the aggregate activity in voxels in another visual area. In combination with pRF mapping, CF locations on the cortical surface can be interpreted in visual space, thus enabling reconstruction of visuotopic maps from resting state data. We demonstrate that V1 ➤ V2 and V1 ➤ V3 CF maps estimated from resting state fMRI data show visuotopic organization. Therefore, we conclude that-despite some variability in CF estimates between RS scans-neural properties such as CF maps and CF size can be derived from resting state data.

  12. Comparison of several maneuvering target tracking models

    NASA Astrophysics Data System (ADS)

    McIntyre, Gregory A.; Hintz, Kenneth J.

    1998-07-01

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

  13. An Efficient Estimator for Moving Target Localization Using Multi-Station Dual-Frequency Radars

    PubMed Central

    Zhang, Ying; Luo, Shan

    2017-01-01

    Localization of a moving target in a dual-frequency radars system has now gained considerable attention. The noncoherent localization approach based on a least squares (LS) estimator has been addressed in the literature. Compared with the LS method, a novel localization method based on a two-step weighted least squares estimator is proposed to increase positioning accuracy for a multi-station dual-frequency radars system in this paper. The effects of signal noise ratio and the number of samples on the performance of range estimation are also analyzed in the paper. Furthermore, both the theoretical variance and Cramer–Rao lower bound (CRLB) are derived. The simulation results verified the proposed method. PMID:29244727

  14. Post Detection Target State Estimation Using Heuristic Information Processing - A Preliminary Investigation

    DTIC Science & Technology

    1977-09-01

    Interpolation algorithm allows this to be done when the transition boundaries are defined close together and parallel to one another. In this case the...in the variable kernel esti- -mates.) In [2] a goodness-of-fit criterion for a set of sam- One question of great interest to us in this study pies...an estimate /(x) is For the unimodal case the ab.olute minimum okV .based on the variables ocurs at k .= 100, ce 5. At this point we have j Mean

  15. Resting State Network Estimation in Individual Subjects

    PubMed Central

    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

  16. State estimation of stochastic non-linear hybrid dynamic system using an interacting multiple model algorithm.

    PubMed

    Elenchezhiyan, M; Prakash, J

    2015-09-01

    In this work, state estimation schemes for non-linear hybrid dynamic systems subjected to stochastic state disturbances and random errors in measurements using interacting multiple-model (IMM) algorithms are formulated. In order to compute both discrete modes and continuous state estimates of a hybrid dynamic system either an IMM extended Kalman filter (IMM-EKF) or an IMM based derivative-free Kalman filters is proposed in this study. The efficacy of the proposed IMM based state estimation schemes is demonstrated by conducting Monte-Carlo simulation studies on the two-tank hybrid system and switched non-isothermal continuous stirred tank reactor system. Extensive simulation studies reveal that the proposed IMM based state estimation schemes are able to generate fairly accurate continuous state estimates and discrete modes. In the presence and absence of sensor bias, the simulation studies reveal that the proposed IMM unscented Kalman filter (IMM-UKF) based simultaneous state and parameter estimation scheme outperforms multiple-model UKF (MM-UKF) based simultaneous state and parameter estimation scheme. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  17. A NEW RELEVANCE ESTIMATOR FOR THE COMPILATION AND VISUALIZATION OF DISEASE PATTERNS AND POTENTIAL DRUG TARGETS.

    PubMed

    VON Korff, Modest; Fink, Tobias; Sander, Thomas

    2017-01-01

    A new computational method is presented to extract disease patterns from heterogeneous and text-based data. For this study, 22 million PubMed records were mined for co-occurrences of gene name synonyms and disease MeSH terms. The resulting publication counts were transferred into a matrix Mdata. In this matrix, a disease was represented by a row and a gene by a column. Each field in the matrix represented the publication count for a co-occurring disease-gene pair. A second matrix with identical dimensions Mrelevance was derived from Mdata. To create Mrelevance the values from Mdata were normalized. The normalized values were multiplied by the column-wise calculated Gini coefficient. This multiplication resulted in a relevance estimator for every gene in relation to a disease. From Mrelevance the similarities between all row vectors were calculated. The resulting similarity matrix Srelevance related 5,000 diseases by the relevance estimators calculated for 15,000 genes. Three diseases were analyzed in detail for the validation of the disease patterns and the relevant genes. Cytoscape was used to visualize and to analyze Mrelevance and Srelevance together with the genes and diseases. Summarizing the results, it can be stated that the relevance estimator introduced here was able to detect valid disease patterns and to identify genes that encoded key proteins and potential targets for drug discovery projects.

  18. Support vector machines for nuclear reactor state estimation

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

    Zavaljevski, N.; Gross, K. C.

    2000-02-14

    Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformedmore » into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear power reactors. In particular, they implemented and tested kernels developed at Argonne National Laboratory for the Multivariate State Estimation Technique (MSET), a nonlinear, nonparametric estimation technique with a wide range of applications in nuclear reactors. The methodology has been applied to three data sets from experimental and commercial nuclear power reactor applications. The results are promising. The combination of MSET kernels with the SVM method has better noise reduction and generalization properties than the standard MSET algorithm.« less

  19. Optimal space-time attacks on system state estimation under a sparsity constraint

    NASA Astrophysics Data System (ADS)

    Lu, Jingyang; Niu, Ruixin; Han, Puxiao

    2016-05-01

    System state estimation in the presence of an adversary that injects false information into sensor readings has attracted much attention in wide application areas, such as target tracking with compromised sensors, secure monitoring of dynamic electric power systems, secure driverless cars, and radar tracking and detection in the presence of jammers. From a malicious adversary's perspective, the optimal strategy for attacking a multi-sensor dynamic system over sensors and over time is investigated. It is assumed that the system defender can perfectly detect the attacks and identify and remove sensor data once they are corrupted by false information injected by the adversary. With this in mind, the adversary's goal is to maximize the covariance matrix of the system state estimate by the end of attack period under a sparse attack constraint such that the adversary can only attack the system a few times over time and over sensors. The sparsity assumption is due to the adversary's limited resources and his/her intention to reduce the chance of being detected by the system defender. This becomes an integer programming problem and its optimal solution, the exhaustive search, is intractable with a prohibitive complexity, especially for a system with a large number of sensors and over a large number of time steps. Several suboptimal solutions, such as those based on greedy search and dynamic programming are proposed to find the attack strategies. Examples and numerical results are provided in order to illustrate the effectiveness and the reduced computational complexities of the proposed attack strategies.

  20. Cost estimation for the active debris removal of multiple priority targets

    NASA Astrophysics Data System (ADS)

    Braun, Vitali; Wiedemann, Carsten; Schulz, Eugen

    The increasing number of space debris objects, especially in distinct low Earth orbit (LEO) altitudes between 600 and 1000 km, leads to an increase in the potential collision risk between the objects and threatens active satellites in that region. Several recent studies show that active debris removal (ADR) has to be performed in order to prevent a collisional cascading effect, also known as the Kessler syndrome. In order to stabilize the population growth in the critical LEO region, a removal of five prioritized objects per year has been recognized as a significant figure. Various proposals are addressing the technical issues for ADR missions, including the de-orbiting of objects by means of a service satellite using a chemical or an electric propulsion system. The servicer would rendezvous with a preselected target, perform a docking maneuver and then provide a de-orbit burn to transfer the target on a trajectory where it re-enters the Earth’s atmosphere within a given time frame. In this paper the technical aspects are complemented by a cost estimation model, focusing on multi target missions, which are based on a service satellite capable of de-orbiting more than one target within a single mission. The cost model for ADR includes initial development cost, production cost, launch cost and operation cost as well as the modelling of the propulsion system of the servicer. Therefore, different scenarios are defined for chemical and electric propulsion systems as applied to multi target missions, based on a literature review of concepts currently being under discussion. The costs of multi target missions are compared to a scenario where only one target is removed. Also, the results allow to determine an optimum number of objects to be removed per mission and provide numbers which can be used in future studies, e.g. those related to ADR cost and benefit analyses.

  1. State Estimates of Disability in America. Disability Statistics Report 3.

    ERIC Educational Resources Information Center

    LaPlante, Mitchell P.

    This study presents and discusses existing data on disability by state, from the 1980 and 1990 censuses, the Current Population Survey (CPS), and the National Health Interview Survey (NHIS). The study used direct methods for states with large sample sizes and synthetic estimates for states with low sample sizes. The study's highlighted findings…

  2. Estimates of lifetime infertility from three states: the behavioral risk factor surveillance system.

    PubMed

    Crawford, Sara; Fussman, Chris; Bailey, Marie; Bernson, Dana; Jamieson, Denise J; Murray-Jordan, Melissa; Kissin, Dmitry M

    2015-07-01

    Knowledge of state-specific infertility is limited. The objectives of this study were to explore state-specific estimates of lifetime prevalence of having ever experienced infertility, sought treatment for infertility, types of treatments sought, and treatment outcomes. Male and female adult residents aged 18-50 years from three states involved in the States Monitoring Assisted Reproductive Technology Collaborative (Florida, Massachusetts, and Michigan) were asked state-added infertility questions as part of the 2012 Behavioral Risk Factor Surveillance System, a state-based, health-related telephone survey. Analysis involved estimation of lifetime prevalence of infertility. The estimated lifetime prevalence of infertility among 1,285 adults in Florida, 1,302 in Massachusetts, and 3,360 in Michigan was 9.7%, 6.0%, and 4.2%, respectively. Among 736 adults in Florida, 1,246 in Massachusetts, and 2,742 in Michigan that have ever tried to get pregnant, the lifetime infertility prevalence was 25.3% in Florida, 9.9% in Massachusetts, and 5.8% in Michigan. Among those with a history of infertility, over half sought treatment (60.7% in Florida, 70.6% in Massachusetts, and 51.6% in Michigan), the most common being non-assisted reproductive technology fertility treatments (61.3% in Florida, 66.0% in Massachusetts, and 75.9% in Michigan). State-specific estimates of lifetime infertility prevalence in Florida, Massachusetts, and Michigan varied. Variations across states are difficult to interpret, as they likely reflect both true differences in prevalence and differences in data collection questionnaires. State-specific estimates are needed for the prevention, detection, and management of infertility, but estimates should be based on a common set of questions appropriate for these goals.

  3. Estimates of Lifetime Infertility from Three States: The Behavioral Risk Factor Surveillance System

    PubMed Central

    Crawford, Sara; Fussman, Chris; Bailey, Marie; Bernson, Dana; Jamieson, Denise J.; Murray-Jordan, Melissa; Kissin, Dmitry M.

    2016-01-01

    Background Knowledge of state-specific infertility is limited. The objectives of this study were to explore state-specific estimates of lifetime prevalence of having ever experienced infertility, sought treatment for infertility, types of treatments sought, and treatment outcomes. Methods Male and female adult residents aged 18–50 years from three states involved in the States Monitoring Assisted Reproductive Technology Collaborative (Florida, Massachusetts, and Michigan) were asked state-added infertility questions as part of the 2012 Behavioral Risk Factor Surveillance System, a state-based, health-related telephone survey. Analysis involved estimation of lifetime prevalence of infertility. Results The estimated lifetime prevalence of infertility among 1,285 adults in Florida, 1,302 in Massachusetts, and 3,360 in Michigan was 9.7%, 6.0%, and 4.2%, respectively. Among 736 adults in Florida, 1,246 in Massachusetts, and 2,742 in Michigan that have ever tried to get pregnant, the lifetime infertility prevalence was 25.3% in Florida, 9.9% in Massachusetts, and 5.8% in Michigan. Among those with a history of infertility, over half sought treatment (60.7% in Florida, 70.6% in Massachusetts, and 51.6% in Michigan), the most common being non–assisted reproductive technology fertility treatments (61.3% in Florida, 66.0% in Massachusetts, and 75.9% in Michigan). Conclusion State-specific estimates of lifetime infertility prevalence in Florida, Massachusetts, and Michigan varied. Variations across states are difficult to interpret, as they likely reflect both true differences in prevalence and differences in data collection questionnaires. State-specific estimates are needed for the prevention, detection, and management of infertility, but estimates should be based on a common set of questions appropriate for these goals. PMID:26172998

  4. Estimating walking and bicycling at the state level.

    DOT National Transportation Integrated Search

    2017-03-01

    Estimates of vehicle miles traveled (VMT) drive policy and planning decisions for surface transportation. No similar : metric is computed for cycling and walking. What approaches could be used to compute such a metric on the state : level? This repor...

  5. 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.

  6. Fixed reproducible tangible wealth in the United States : revised estimates for 1995-97 and summary estimates for 1925-97

    DOT National Transportation Integrated Search

    1998-09-01

    The tables that follow present new estimates of fixed reproducible tangible wealth in the United States for 1997 and revised estimates for 199596. These estimates, which cover net stocks of privately owned and government owned durable equipment an...

  7. State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

    PubMed

    Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

    2016-04-01

    Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. Copyright © 2016 Elsevier Inc. All rights reserved.

  8. State-space model with deep learning for functional dynamics estimation in resting-state fMRI

    PubMed Central

    Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

    2017-01-01

    Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. PMID:26774612

  9. Hypersonic entry vehicle state estimation using nonlinearity-based adaptive cubature Kalman filters

    NASA Astrophysics Data System (ADS)

    Sun, Tao; Xin, Ming

    2017-05-01

    Guidance, navigation, and control of a hypersonic vehicle landing on the Mars rely on precise state feedback information, which is obtained from state estimation. The high uncertainty and nonlinearity of the entry dynamics make the estimation a very challenging problem. In this paper, a new adaptive cubature Kalman filter is proposed for state trajectory estimation of a hypersonic entry vehicle. This new adaptive estimation strategy is based on the measure of nonlinearity of the stochastic system. According to the severity of nonlinearity along the trajectory, the high degree cubature rule or the conventional third degree cubature rule is adaptively used in the cubature Kalman filter. This strategy has the benefit of attaining higher estimation accuracy only when necessary without causing excessive computation load. The simulation results demonstrate that the proposed adaptive filter exhibits better performance than the conventional third-degree cubature Kalman filter while maintaining the same performance as the uniform high degree cubature Kalman filter but with lower computation complexity.

  10. Accuracy of Noninvasive Estimation Techniques for the State of the Cochlear Amplifier

    NASA Astrophysics Data System (ADS)

    Dalhoff, Ernst; Gummer, Anthony W.

    2011-11-01

    Estimation of the function of the cochlea in human is possible only by deduction from indirect measurements, which may be subjective or objective. Therefore, for basic research as well as diagnostic purposes, it is important to develop methods to deduce and analyse error sources of cochlear-state estimation techniques. Here, we present a model of technical and physiologic error sources contributing to the estimation accuracy of hearing threshold and the state of the cochlear amplifier and deduce from measurements of human that the estimated standard deviation can be considerably below 6 dB. Experimental evidence is drawn from two partly independent objective estimation techniques for the auditory signal chain based on measurements of otoacoustic emissions.

  11. Stochastic spectral projection of electrochemical thermal model for lithium-ion cell state estimation

    NASA Astrophysics Data System (ADS)

    Tagade, Piyush; Hariharan, Krishnan S.; Kolake, Subramanya Mayya; Song, Taewon; Oh, Dukjin

    2017-03-01

    A novel approach for integrating a pseudo-two dimensional electrochemical thermal (P2D-ECT) model and data assimilation algorithm is presented for lithium-ion cell state estimation. This approach refrains from making any simplifications in the P2D-ECT model while making it amenable for online state estimation. Though deterministic, uncertainty in the initial states induces stochasticity in the P2D-ECT model. This stochasticity is resolved by spectrally projecting the stochastic P2D-ECT model on a set of orthogonal multivariate Hermite polynomials. Volume averaging in the stochastic dimensions is proposed for efficient numerical solution of the resultant model. A state estimation framework is developed using a transformation of the orthogonal basis to assimilate the measurables with this system of equations. Effectiveness of the proposed method is first demonstrated by assimilating the cell voltage and temperature data generated using a synthetic test bed. This validated method is used with the experimentally observed cell voltage and temperature data for state estimation at different operating conditions and drive cycle protocols. The results show increased prediction accuracy when the data is assimilated every 30s. High accuracy of the estimated states is exploited to infer temperature dependent behavior of the lithium-ion cell.

  12. Efficient Nonlocal M-Control and N-Target Controlled Unitary Gate Using Non-symmetric GHZ States

    NASA Astrophysics Data System (ADS)

    Chen, Li-Bing; Lu, Hong

    2018-03-01

    Efficient local implementation of a nonlocal M-control and N-target controlled unitary gate is considered. We first show that with the assistance of two non-symmetric qubit(1)-qutrit(N) Greenberger-Horne-Zeilinger (GHZ) states, a nonlocal 2-control and N-target controlled unitary gate can be constructed from 2 local two-qubit CNOT gates, 2 N local two-qutrit conditional SWAP gates, N local qutrit-qubit controlled unitary gates, and 2 N single-qutrit gates. At each target node, the two third levels of the two GHZ target qutrits are used to expose one and only one initial computational state to the local qutrit-qubit controlled unitary gate, instead of being used to hide certain states from the conditional dynamics. This scheme can be generalized straightforwardly to implement a higher-order nonlocal M-control and N-target controlled unitary gate by using M non-symmetric qubit(1)-qutrit(N) GHZ states as quantum channels. Neither the number of the additional levels of each GHZ target particle nor that of single-qutrit gates needs to increase with M. For certain realistic physical systems, the total gate time may be reduced compared with that required in previous schemes.

  13. Estimation of child vaccination coverage at state and national levels in India

    PubMed Central

    Gupta, Satish; Kumar, Rakesh; Haldar, Pradeep; Sethi, Raman; Bahl, Sunil

    2016-01-01

    Abstract Objective To review the data, for 1999–2013, on state-level child vaccination coverage in India and provide estimates of coverage at state and national levels. Methods We collated data from administrative reports, population-based surveys and other sources and used them to produce annual estimates of vaccination coverage. We investigated bacille Calmette–Guérin vaccine, the first and third doses of vaccine against diphtheria, tetanus and pertussis, the third dose of oral polio vaccine and the first dose of vaccine against measles. We obtained relevant data covering the period 1999–2013 for each of 16 states and territories and the period 2001–2013 for the state of Jharkhand – which was only created in 2000. We aggregated the resultant state-level estimates, using a population-weighted approach, to give national values. Findings For each of the vaccinations we investigated, about half of the 253 estimates of annual coverage at state level that we produced were based on survey results. The rest were based on interpolation between – or extrapolation from – so-called anchor points or, more rarely, on administrative data. Our national estimates indicated that, for each of the vaccines we investigated, coverage gradually increased between 1999 and 2010 but then levelled off. Conclusion The delivery of routine vaccination services to Indian children appears to have improved between 1999 and 2013. There remains considerable scope to improve the recording and reporting of childhood vaccination coverage in India and regular systematic reviews of the coverage data are recommended. PMID:27843162

  14. σ -SCF: A Direct Energy-targeting Method To Mean-field Excited States

    NASA Astrophysics Data System (ADS)

    Ye, Hongzhou; Welborn, Matthew; Ricke, Nathan; van Voorhis, Troy

    The mean-field solutions of electronic excited states are much less accessible than ground state (e.g. Hartree-Fock) solutions. Energy-based optimization methods for excited states, like Δ-SCF, tend to fall into the lowest solution consistent with a given symmetry - a problem known as ``variational collapse''. In this work, we combine the ideas of direct energy-targeting and variance-based optimization in order to describe excited states at the mean-field level. The resulting method, σ-SCF, has several advantages. First, it allows one to target any desired excited state by specifying a single parameter: a guess of the energy of that state. It can therefore, in principle, find all excited states. Second, it avoids variational collapse by using a variance-based, unconstrained local minimization. As a consequence, all states - ground or excited - are treated on an equal footing. Third, it provides an alternate approach to locate Δ-SCF solutions that are otherwise hardly accessible by the usual non-aufbau configuration initial guess. We present results for this new method for small atoms (He, Be) and molecules (H2, HF). This work was funded by a Grant from NSF (CHE-1464804).

  15. State estimation applications in aircraft flight-data analysis: A user's manual for SMACK

    NASA Technical Reports Server (NTRS)

    Bach, Ralph E., Jr.

    1991-01-01

    The evolution in the use of state estimation is traced for the analysis of aircraft flight data. A unifying mathematical framework for state estimation is reviewed, and several examples are presented that illustrate a general approach for checking instrument accuracy and data consistency, and for estimating variables that are difficult to measure. Recent applications associated with research aircraft flight tests and airline turbulence upsets are described. A computer program for aircraft state estimation is discussed in some detail. This document is intended to serve as a user's manual for the program called SMACK (SMoothing for AirCraft Kinematics). The diversity of the applications described emphasizes the potential advantages in using SMACK for flight-data analysis.

  16. 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.

  17. Learning to Estimate Dynamical State with Probabilistic Population Codes.

    PubMed

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

    2015-11-01

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

  18. State-Space Estimation of Soil Organic Carbon Stock

    NASA Astrophysics Data System (ADS)

    Ogunwole, Joshua O.; Timm, Luis C.; Obidike-Ugwu, Evelyn O.; Gabriels, Donald M.

    2014-04-01

    Understanding soil spatial variability and identifying soil parameters most determinant to soil organic carbon stock is pivotal to precision in ecological modelling, prediction, estimation and management of soil within a landscape. This study investigates and describes field soil variability and its structural pattern for agricultural management decisions. The main aim was to relate variation in soil organic carbon stock to soil properties and to estimate soil organic carbon stock from the soil properties. A transect sampling of 100 points at 3 m intervals was carried out. Soils were sampled and analyzed for soil organic carbon and other selected soil properties along with determination of dry aggregate and water-stable aggregate fractions. Principal component analysis, geostatistics, and state-space analysis were conducted on the analyzed soil properties. The first three principal components explained 53.2% of the total variation; Principal Component 1 was dominated by soil exchange complex and dry sieved macroaggregates clusters. Exponential semivariogram model described the structure of soil organic carbon stock with a strong dependence indicating that soil organic carbon values were correlated up to 10.8m.Neighbouring values of soil organic carbon stock, all waterstable aggregate fractions, and dithionite and pyrophosphate iron gave reliable estimate of soil organic carbon stock by state-space.

  19. Redrawing the US Obesity Landscape: Bias-Corrected Estimates of State-Specific Adult Obesity Prevalence

    PubMed Central

    Ward, Zachary J.; Long, Michael W.; Resch, Stephen C.; Gortmaker, Steven L.; Cradock, Angie L.; Giles, Catherine; Hsiao, Amber; Wang, Y. Claire

    2016-01-01

    Background State-level estimates from the Centers for Disease Control and Prevention (CDC) underestimate the obesity epidemic because they use self-reported height and weight. We describe a novel bias-correction method and produce corrected state-level estimates of obesity and severe obesity. Methods Using non-parametric statistical matching, we adjusted self-reported data from the Behavioral Risk Factor Surveillance System (BRFSS) 2013 (n = 386,795) using measured data from the National Health and Nutrition Examination Survey (NHANES) (n = 16,924). We validated our national estimates against NHANES and estimated bias-corrected state-specific prevalence of obesity (BMI≥30) and severe obesity (BMI≥35). We compared these results with previous adjustment methods. Results Compared to NHANES, self-reported BRFSS data underestimated national prevalence of obesity by 16% (28.67% vs 34.01%), and severe obesity by 23% (11.03% vs 14.26%). Our method was not significantly different from NHANES for obesity or severe obesity, while previous methods underestimated both. Only four states had a corrected obesity prevalence below 30%, with four exceeding 40%–in contrast, most states were below 30% in CDC maps. Conclusions Twelve million adults with obesity (including 6.7 million with severe obesity) were misclassified by CDC state-level estimates. Previous bias-correction methods also resulted in underestimates. Accurate state-level estimates are necessary to plan for resources to address the obesity epidemic. PMID:26954566

  20. Automatic Regionalization Algorithm for Distributed State Estimation in Power Systems: Preprint

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

    Wang, Dexin; Yang, Liuqing; Florita, Anthony

    The deregulation of the power system and the incorporation of generation from renewable energy sources recessitates faster state estimation in the smart grid. Distributed state estimation (DSE) has become a promising and scalable solution to this urgent demand. In this paper, we investigate the regionalization algorithms for the power system, a necessary step before distributed state estimation can be performed. To the best of the authors' knowledge, this is the first investigation on automatic regionalization (AR). We propose three spectral clustering based AR algorithms. Simulations show that our proposed algorithms outperform the two investigated manual regionalization cases. With the helpmore » of AR algorithms, we also show how the number of regions impacts the accuracy and convergence speed of the DSE and conclude that the number of regions needs to be chosen carefully to improve the convergence speed of DSEs.« less

  1. Joint state and parameter estimation of the hemodynamic model by particle smoother expectation maximization method

    NASA Astrophysics Data System (ADS)

    Aslan, Serdar; Taylan Cemgil, Ali; Akın, Ata

    2016-08-01

    Objective. In this paper, we aimed for the robust estimation of the parameters and states of the hemodynamic model by using blood oxygen level dependent signal. Approach. In the fMRI literature, there are only a few successful methods that are able to make a joint estimation of the states and parameters of the hemodynamic model. In this paper, we implemented a maximum likelihood based method called the particle smoother expectation maximization (PSEM) algorithm for the joint state and parameter estimation. Main results. Former sequential Monte Carlo methods were only reliable in the hemodynamic state estimates. They were claimed to outperform the local linearization (LL) filter and the extended Kalman filter (EKF). The PSEM algorithm is compared with the most successful method called square-root cubature Kalman smoother (SCKS) for both state and parameter estimation. SCKS was found to be better than the dynamic expectation maximization (DEM) algorithm, which was shown to be a better estimator than EKF, LL and particle filters. Significance. PSEM was more accurate than SCKS for both the state and the parameter estimation. Hence, PSEM seems to be the most accurate method for the system identification and state estimation for the hemodynamic model inversion literature. This paper do not compare its results with Tikhonov-regularized Newton—CKF (TNF-CKF), a recent robust method which works in filtering sense.

  2. Implicit Particle Filter for Power System State Estimation with Large Scale Renewable Power Integration.

    NASA Astrophysics Data System (ADS)

    Uzunoglu, B.; Hussaini, Y.

    2017-12-01

    Implicit Particle Filter is a sequential Monte Carlo method for data assimilation that guides the particles to the high-probability by an implicit step . It optimizes a nonlinear cost function which can be inherited from legacy assimilation routines . Dynamic state estimation for almost real-time applications in power systems are becomingly increasingly more important with integration of variable wind and solar power generation. New advanced state estimation tools that will replace the old generation state estimation in addition to having a general framework of complexities should be able to address the legacy software and able to integrate the old software in a mathematical framework while allowing the power industry need for a cautious and evolutionary change in comparison to a complete revolutionary approach while addressing nonlinearity and non-normal behaviour. This work implements implicit particle filter as a state estimation tool for the estimation of the states of a power system and presents the first implicit particle filter application study on a power system state estimation. The implicit particle filter is introduced into power systems and the simulations are presented for a three-node benchmark power system . The performance of the filter on the presented problem is analyzed and the results are presented.

  3. Path Flow Estimation Using Time Varying Coefficient State Space Model

    NASA Astrophysics Data System (ADS)

    Jou, Yow-Jen; Lan, Chien-Lun

    2009-08-01

    The dynamic path flow information is very crucial in the field of transportation operation and management, i.e., dynamic traffic assignment, scheduling plan, and signal timing. Time-dependent path information, which is important in many aspects, is nearly impossible to be obtained. Consequently, researchers have been seeking estimation methods for deriving valuable path flow information from less expensive traffic data, primarily link traffic counts of surveillance systems. This investigation considers a path flow estimation problem involving the time varying coefficient state space model, Gibbs sampler, and Kalman filter. Numerical examples with part of a real network of the Taipei Mass Rapid Transit with real O-D matrices is demonstrated to address the accuracy of proposed model. Results of this study show that this time-varying coefficient state space model is very effective in the estimation of path flow compared to time-invariant model.

  4. Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR

    PubMed Central

    Li, Qingquan; Zhang, Liang; Mao, Qingzhou; Zou, Qin; Zhang, Pin; Feng, Shaojun; Ochieng, Washington

    2014-01-01

    This paper proposes a novel motion field estimation method based on a 3D light detection and ranging (LiDAR) sensor for motion sensing for intelligent driverless vehicles and active collision avoidance systems. Unlike multiple target tracking methods, which estimate the motion state of detected targets, such as cars and pedestrians, motion field estimation regards the whole scene as a motion field in which each little element has its own motion state. Compared to multiple target tracking, segmentation errors and data association errors have much less significance in motion field estimation, making it more accurate and robust. This paper presents an intact 3D LiDAR-based motion field estimation method, including pre-processing, a theoretical framework for the motion field estimation problem and practical solutions. The 3D LiDAR measurements are first projected to small-scale polar grids, and then, after data association and Kalman filtering, the motion state of every moving grid is estimated. To reduce computing time, a fast data association algorithm is proposed. Furthermore, considering the spatial correlation of motion among neighboring grids, a novel spatial-smoothing algorithm is also presented to optimize the motion field. The experimental results using several data sets captured in different cities indicate that the proposed motion field estimation is able to run in real-time and performs robustly and effectively. PMID:25207868

  5. Motion field estimation for a dynamic scene using a 3D LiDAR.

    PubMed

    Li, Qingquan; Zhang, Liang; Mao, Qingzhou; Zou, Qin; Zhang, Pin; Feng, Shaojun; Ochieng, Washington

    2014-09-09

    This paper proposes a novel motion field estimation method based on a 3D light detection and ranging (LiDAR) sensor for motion sensing for intelligent driverless vehicles and active collision avoidance systems. Unlike multiple target tracking methods, which estimate the motion state of detected targets, such as cars and pedestrians, motion field estimation regards the whole scene as a motion field in which each little element has its own motion state. Compared to multiple target tracking, segmentation errors and data association errors have much less significance in motion field estimation, making it more accurate and robust. This paper presents an intact 3D LiDAR-based motion field estimation method, including pre-processing, a theoretical framework for the motion field estimation problem and practical solutions. The 3D LiDAR measurements are first projected to small-scale polar grids, and then, after data association and Kalman filtering, the motion state of every moving grid is estimated. To reduce computing time, a fast data association algorithm is proposed. Furthermore, considering the spatial correlation of motion among neighboring grids, a novel spatial-smoothing algorithm is also presented to optimize the motion field. The experimental results using several data sets captured in different cities indicate that the proposed motion field estimation is able to run in real-time and performs robustly and effectively.

  6. Alternative method of quantum state tomography toward a typical target via a weak-value measurement

    NASA Astrophysics Data System (ADS)

    Chen, Xi; Dai, Hong-Yi; Yang, Le; Zhang, Ming

    2018-03-01

    There is usually a limitation of weak interaction on the application of weak-value measurement. This limitation dominates the performance of the quantum state tomography toward a typical target in the finite and high-dimensional complex-valued superposition of its basis states, especially when the compressive sensing technique is also employed. Here we propose an alternative method of quantum state tomography, presented as a general model, toward such typical target via weak-value measurement to overcome such limitation. In this model the pointer for the weak-value measurement is a qubit, and the target-pointer coupling interaction is no longer needed within the weak interaction limitation, meanwhile this interaction under the compressive sensing can be described with the Taylor series of the unitary evolution operator. The postselection state at the target is the equal superposition of all basis states, and the pointer readouts are gathered under multiple Pauli operator measurements. The reconstructed quantum state is generated from an optimization algorithm of total variation augmented Lagrangian alternating direction algorithm. Furthermore, we demonstrate an example of this general model for the quantum state tomography toward the planar laser-energy distribution and discuss the relations among some parameters at both our general model and the original first-order approximate model for this tomography.

  7. Effect of retransmission and retrodiction on estimation and fusion in long-haul sensor networks

    DOE PAGES

    Liu, Qiang; Wang, Xin; Rao, Nageswara S. V.; ...

    2016-01-01

    In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as target tracking. In this work, we study the scenario where sensors take measurements of one or more dynamic targets and send state estimates of the targets to a fusion center via satellite links. The severe loss and delay inherent over the satellite channels reduce the number of estimates successfully arriving at the fusion center, thereby limiting the potential fusion gain and resulting in suboptimal accuracy performance of the fused estimates. In addition, the errors in target-sensor data association can alsomore » degrade the estimation performance. To mitigate the effect of imperfect communications on state estimation and fusion, we consider retransmission and retrodiction. The system adopts certain retransmission-based transport protocols so that lost messages can be recovered over time. Besides, retrodiction/smoothing techniques are applied so that the chances of incurring excess delay due to retransmission are greatly reduced. We analyze the extent to which retransmission and retrodiction can improve the performance of delay-sensitive target tracking tasks under variable communication loss and delay conditions. Lastly, simulation results of a ballistic target tracking application are shown in the end to demonstrate the validity of our analysis.« less

  8. Utility Estimates of Disease-Specific Health States in Prostate Cancer from Three Different Perspectives.

    PubMed

    Gries, Katharine S; Regier, Dean A; Ramsey, Scott D; Patrick, Donald L

    2017-06-01

    To develop a statistical model generating utility estimates for prostate cancer specific health states, using preference weights derived from the perspectives of prostate cancer patients, men at risk for prostate cancer, and society. Utility estimate values were calculated using standard gamble (SG) methodology. Study participants valued 18 prostate-specific health states with the five attributes: sexual function, urinary function, bowel function, pain, and emotional well-being. Appropriateness of model (linear regression, mixed effects, or generalized estimating equation) to generate prostate cancer utility estimates was determined by paired t-tests to compare observed and predicted values. Mixed-corrected standard SG utility estimates to account for loss aversion were calculated based on prospect theory. 132 study participants assigned values to the health states (n = 40 men at risk for prostate cancer; n = 43 men with prostate cancer; n = 49 general population). In total, 792 valuations were elicited (six health states for each 132 participants). The most appropriate model for the classification system was a mixed effects model; correlations between the mean observed and predicted utility estimates were greater than 0.80 for each perspective. Developing a health-state classification system with preference weights for three different perspectives demonstrates the relative importance of main effects between populations. The predicted values for men with prostate cancer support the hypothesis that patients experiencing the disease state assign higher utility estimates to health states and there is a difference in valuations made by patients and the general population.

  9. Diagnostic Inspection of Pipelines for Estimating the State of Stress in Them

    NASA Astrophysics Data System (ADS)

    Subbotin, V. A.; Kolotilov, Yu. V.; Smirnova, V. Yu.; Ivashko, S. K.

    2017-12-01

    The diagnostic inspection used to estimate the technical state of a pipeline is described. The problems of inspection works are listed, and a functional-structural scheme is developed to estimate the state of stress in a pipeline. Final conclusions regarding the actual loading of a pipeline section are drawn upon a cross analysis of the entire information obtained during pipeline inspection.

  10. Estimating the Impact of Raising Prices and Eliminating Discounts on Cigarette Smoking Prevalence in the United States.

    PubMed

    Marynak, Kristy L; Xu, Xin; Wang, Xu; Holmes, Carissa Baker; Tynan, Michael A; Pechacek, Terry

    2016-01-01

    The average retail price per pack of cigarettes is less than $6, which is substantially lower than the $10 per-pack target established in 2014 by the Surgeon General to reduce the smoking rate. We estimated the impact of three cigarette pricing scenarios on smoking prevalence among teens aged 12-17 years, young adults aged 18-25 years, and adults aged ≥26 years, by state: (1) $0.94 federal tax increase on cigarettes, as proposed in the fiscal year 2017 President's budget; (2) $10 per-pack retail price, allowing discounts; and (3) $10 per-pack retail price, eliminating discounts. We conducted Monte Carlo simulations to generate point estimates of reductions in cigarette smoking prevalence by state. We found that each price scenario would substantially reduce cigarette smoking prevalence. A $10 per-pack retail price eliminating discounts could result in 637,270 fewer smokers aged 12-17 years; 4,186,954 fewer smokers aged 18-25 years; and 7,722,460 fewer smokers aged ≥26 years. Raising cigarette prices and eliminating discounts could substantially reduce cigarette smoking prevalence as well as smoking-related death and disease.

  11. A physics-based fractional order model and state of energy estimation for lithium ion batteries. Part II: Parameter identification and state of energy estimation for LiFePO4 battery

    NASA Astrophysics Data System (ADS)

    Li, Xiaoyu; Pan, Ke; Fan, Guodong; Lu, Rengui; Zhu, Chunbo; Rizzoni, Giorgio; Canova, Marcello

    2017-11-01

    State of energy (SOE) is an important index for the electrochemical energy storage system in electric vehicles. In this paper, a robust state of energy estimation method in combination with a physical model parameter identification method is proposed to achieve accurate battery state estimation at different operating conditions and different aging stages. A physics-based fractional order model with variable solid-state diffusivity (FOM-VSSD) is used to characterize the dynamic performance of a LiFePO4/graphite battery. In order to update the model parameter automatically at different aging stages, a multi-step model parameter identification method based on the lexicographic optimization is especially designed for the electric vehicle operating conditions. As the battery available energy changes with different applied load current profiles, the relationship between the remaining energy loss and the state of charge, the average current as well as the average squared current is modeled. The SOE with different operating conditions and different aging stages are estimated based on an adaptive fractional order extended Kalman filter (AFEKF). Validation results show that the overall SOE estimation error is within ±5%. The proposed method is suitable for the electric vehicle online applications.

  12. Estimating drug treatment needs among state prison inmates.

    PubMed

    Belenko, Steven; Peugh, Jordon

    2005-03-07

    Growing prison populations in the U.S. are largely due to drug-related crime and drug abuse. Yet, relatively few inmates receive treatment, existing interventions tend to be short-term or non-clinical, and better methods are needed to match drug-involved inmates to level of care. Using data from the 1997 Survey of Inmates in State Correctional Facilities, a nationally representative sample of 14,285 inmates from 275 state prisons, we present a framework for estimating their levels of treatment need. The framework is drawn partly from the American Society of Addiction Medicine Patient Placement Criteria and other client matching protocols, incorporating drug use severity, drug-related behavioral consequences, and other social and health problems. The results indicate high levels of drug involvement, but considerable variation in severity/recency of use and health and social consequences. We estimate that one-third of male and half of female inmates need residential treatment, but that half of male and one-third of female inmates may need no treatment or short-term interventions. Treatment capacity in state prisons is quite inadequate relative to need, and improvements in assessment, treatment matching, and inmate incentives are needed to conserve scarce treatment resources and facilitate inmate access to different levels of care.

  13. Estimated United States Transportation Energy Use 2005

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

    Smith, C A; Simon, A J; Belles, R D

    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 themore » transportation sector.« less

  14. Estimating Risk from Ambient Concentrations of Acrolein across the United States

    PubMed Central

    Woodruff, Tracey J.; Wells, Ellen M.; Holt, Elizabeth W.; Burgin, Deborah E.; Axelrad, Daniel A.

    2007-01-01

    Background Estimated ambient concentrations of acrolein, a hazardous air pollutant, are greater than the U.S. Environmental Protection Agency (EPA) reference concentration throughout the United States, making it a concern for human health. However, there is no method for assessing the extent of risk under the U.S. EPA noncancer risk assessment framework. Objectives We estimated excess risks from ambient concentrations of acrolein based on dose–response modeling of a study in rats with a relationship between acrolein and residual volume/total lung capacity ratio (RV/TLC) and specific compliance (sCL), markers for altered lung function. Methods Based on existing literature, we defined values above the 90th percentile for controls as “adverse.” We estimated the increase over baseline response that would occur in the human population from estimated ambient concentrations of acrolein, taken from the U.S. EPA’s National-Scale Air Toxics Assessment for 1999, after standard animal-to-human conversions and extrapolating to doses below the experimental data. Results The estimated median additional number of adverse sCL outcomes across the United States was approximately 2.5 cases per 1,000 people. The estimated range of additional outcomes from the 5th to the 95th percentile of acrolein concentration levels across census tracts was 0.28–14 cases per 1,000. For RV/TLC, the median additional outcome was 0.002 per 1,000, and the additional outcome at the 95th percentile was 0.13 per 1,000. Conclusions Although there are uncertainties in estimating human risks from animal data, this analysis demonstrates a method for estimating health risks for noncancer effects and suggests that acrolein could be associated with decreased respiratory function in the United States. PMID:17431491

  15. State Estimation for Landing Maneuver on High Performance Aircraft

    NASA Astrophysics Data System (ADS)

    Suresh, P. S.; Sura, Niranjan K.; Shankar, K.

    2018-01-01

    State estimation methods are popular means for validating aerodynamic database on aircraft flight maneuver performance characteristics. In this work, the state estimation method during landing maneuver is explored for the first of its kind, using upper diagonal adaptive extended Kalman filter (UD-AEKF) with fuzzy based adaptive tunning of process noise matrix. The mathematical model for symmetrical landing maneuver consists of non-linear flight mechanics equation representing Aircraft longitudinal dynamics. The UD-AEKF algorithm is implemented in MATLAB environment and the states with bias is considered to be the initial conditions just prior to the flare. The measurement data is obtained from a non-linear 6 DOF pilot in loop simulation using FORTRAN. These simulated measurement data is additively mixed with process and measurement noises, which are used as an input for UD-AEKF. Then, the governing states that dictate the landing loads at the instant of touch down are compared. The method is verified using flight data wherein, the vertical acceleration at the aircraft center of gravity (CG) is compared. Two possible outcome of purely relying on the aircraft measured data is highlighted. It is observed that, with the implementation of adaptive fuzzy logic based extended Kalman filter tuned to adapt for aircraft landing dynamics, the methodology improves the data quality of the states that are sourced from noisy measurements.

  16. Methods for Estimating Water Withdrawals for Aquaculture in the United States, 2005

    USGS Publications Warehouse

    Lovelace, John K.

    2009-01-01

    Aquaculture water use is associated with raising organisms that live in water - such as finfish and shellfish - for food, restoration, conservation, or sport. Aquaculture production occurs under controlled feeding, sanitation, and harvesting procedures primarily in ponds, flow-through raceways, and, to a lesser extent, cages, net pens, and tanks. Aquaculture ponds, raceways, and tanks usually require the withdrawal or diversion of water from a ground or surface source. Most water withdrawn or diverted for aquaculture production is used to maintain pond levels and/or water quality. Water typically is added for maintenance of levels, oxygenation, temperature control, and flushing of wastes. This report documents methods used to estimate withdrawals of fresh ground water and surface water for aqua-culture in 2005 for each county and county-equivalent in the United States, Puerto Rico, and the U.S. Virgin Islands by using aquaculture statistics and estimated water-use coefficients and water-replacement rates. County-level data for commercial and noncommercial operations compiled for the 2005 Census of Aquaculture were obtained from the National Agricultural Statistics Service. Withdrawals of water used at commercial and noncommercial operations for aquaculture ponds, raceways, tanks, egg incubators, and pens and cages for alligators were estimated and totaled by ground-water or surface-water source for each county and county equivalent. Use of the methods described in this report, when measured or reported data are unavailable, could result in more consistent water-withdrawal estimates for aquaculture that can be used by water managers and planners to determine water needs and trends across the United States. The results of this study were distributed to U.S. Geological Survey water-use personnel in each State during 2007. Water-use personnel are required to submit estimated withdrawals for all categories of use in their State to the U.S. Geological Survey National

  17. In vivo estimation of target registration errors during augmented reality laparoscopic surgery.

    PubMed

    Thompson, Stephen; Schneider, Crispin; Bosi, Michele; Gurusamy, Kurinchi; Ourselin, Sébastien; Davidson, Brian; Hawkes, David; Clarkson, Matthew J

    2018-06-01

    Successful use of augmented reality for laparoscopic surgery requires that the surgeon has a thorough understanding of the likely accuracy of any overlay. Whilst the accuracy of such systems can be estimated in the laboratory, it is difficult to extend such methods to the in vivo clinical setting. Herein we describe a novel method that enables the surgeon to estimate in vivo errors during use. We show that the method enables quantitative evaluation of in vivo data gathered with the SmartLiver image guidance system. The SmartLiver system utilises an intuitive display to enable the surgeon to compare the positions of landmarks visible in both a projected model and in the live video stream. From this the surgeon can estimate the system accuracy when using the system to locate subsurface targets not visible in the live video. Visible landmarks may be either point or line features. We test the validity of the algorithm using an anatomically representative liver phantom, applying simulated perturbations to achieve clinically realistic overlay errors. We then apply the algorithm to in vivo data. The phantom results show that using projected errors of surface features provides a reliable predictor of subsurface target registration error for a representative human liver shape. Applying the algorithm to in vivo data gathered with the SmartLiver image-guided surgery system shows that the system is capable of accuracies around 12 mm; however, achieving this reliably remains a significant challenge. We present an in vivo quantitative evaluation of the SmartLiver image-guided surgery system, together with a validation of the evaluation algorithm. This is the first quantitative in vivo analysis of an augmented reality system for laparoscopic surgery.

  18. Health-related quality of life among adults 65 years and older in the United States, 2011-2012: a multilevel small area estimation approach.

    PubMed

    Lin, Yu-Hsiu; McLain, Alexander C; Probst, Janice C; Bennett, Kevin J; Qureshi, Zaina P; Eberth, Jan M

    2017-01-01

    The purpose of this study was to develop county-level estimates of poor health-related quality of life (HRQOL) among aged 65 years and older U.S. adults and to identify spatial clusters of poor HRQOL using a multilevel, poststratification approach. Multilevel, random-intercept models were fit to HRQOL data (two domains: physical health and mental health) from the 2011-2012 Behavioral Risk Factor Surveillance System. Using a poststratification, small area estimation approach, we generated county-level probabilities of having poor HRQOL for each domain in U.S. adults aged 65 and older, and validated our model-based estimates against state and county direct estimates. County-level estimates of poor HRQOL in the United States ranged from 18.07% to 44.81% for physical health and 14.77% to 37.86% for mental health. Correlations between model-based and direct estimates were higher for physical than mental HRQOL. Counties located in the Arkansas, Kentucky, and Mississippi exhibited the worst physical HRQOL scores, but this pattern did not hold for mental HRQOL, which had the highest probability of mentally unhealthy days in Illinois, Indiana, and Vermont. Substantial geographic variation in physical and mental HRQOL scores exists among older U.S. adults. State and local policy makers should consider these local conditions in targeting interventions and policies to counties with high levels of poor HRQOL scores. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Learning to Estimate Dynamical State with Probabilistic Population Codes

    PubMed Central

    Sabes, Philip N.

    2015-01-01

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

  20. Estimated use of water in the United States, 1965

    USGS Publications Warehouse

    Murray, Charles Richard

    1968-01-01

    Estimates of water use in the United States for 1965 indicate that an average of about 310 bgd (billion gallons per day) were withdrawn for public-supply, rural domestic and livestock, irrigation, and industrial (including thermoelectric power)uses--that is, about 1,600 gallons per capita per day. This represents an increase of 15 percent over the withdrawal of 270 bgd reported for 1960. Fresh water withdrawals for thermoelectric power generation increased nearly 25 percent during the 5 years, and saline water withdrawals increased 33 percent. An additional 2,300 bgd was used for hydroelectric power generation (waterpower), which also represented a 15-percent increase in 5 years. The quantity of water consumed-that is, water made unavailable for further possible withdrawal because of evaporation, incorporation in manufactured products, and other causes - was estimated to average 78 bgd for 1965, an increase of about 28 percent since 1960.Estimates made of the quantities of water withdrawn from surface and ground-water sources indicate withdrawals of 61 bgd of ground water, of which nearly 0.5 bgd was saline, and 250 bgd of surface water, of which 44 bgd was saline. The estimated amount of saline water used by industry increased 36 percent from 1960 to 1965. In addition to surface and ground water sources, reclaimed sewage supplied two-thirds of a billion gallons per day, mainly to irrigation and industry.The average annual streamflow in the United States is approximately 1,200 bgd, about four times the amount withdrawn for all purposes (except hydroelectric power) in 1965, and more than 15 times the estimated quantity of water consumed. However, comparisons of supply and demand in many river basins show that repeated use of the water is made, and at times in some basins all the available supply is consumed.In addition to tabulations of water-use data by States and by the water-use regions previously used, water-use tables are also given for the regions recently

  1. Estimates of state-level health-care expenditures associated with disability.

    PubMed

    Anderson, Wayne L; Armour, Brian S; Finkelstein, Eric A; Wiener, Joshua M

    2010-01-01

    We estimated state-level disability-associated health-care expenditures (DAHE) for the U.S. adult population. We used a two-part model to estimate DAHE for the noninstitutionalized U.S. civilian adult population using data from the 2002-2003 Medical Expenditure Panel Survey and state-level data from the Behavioral Risk Factor Surveillance System. Administrative data for people in institutions were added to generate estimates for the total adult noninstitutionalized population. Individual-level data on total health-care expenditures along with demographic, socioeconomic, geographic, and payer characteristics were used in the models. The DAHE for all U.S. adults totaled $397.8 billion in 2006, with state expenditures ranging from $598 million in Wyoming to $40.1 billion in New York. Of the national total, the DAHE were $118.9 billion for the Medicare population, $161.1 billion for Medicaid recipients, and $117.8 billion for the privately insured and uninsured populations. For the total U.S. adult population, 26.7% of health-care expenditures were associated with disability, with proportions by state ranging from 16.9% in Hawaii to 32.8% in New York. This proportion varied greatly by payer, with 38.1% for Medicare expenditures, 68.7% for Medicaid expenditures, and 12.5% for nonpublic health-care expenditures associated with disability. DAHE vary greatly by state and are borne largely by the public sector, and particularly by Medicaid. Policy makers need to consider initiatives that will help reduce the prevalence of disabilities and disability-related health disparities, as well as improve the lives of people with disabilities.

  2. Comparative Analysis of State Fish Consumption Advisories Targeting Sensitive Populations

    PubMed Central

    Scherer, Alison C.; Tsuchiya, Ami; Younglove, Lisa R.; Burbacher, Thomas M.; Faustman, Elaine M.

    2008-01-01

    Objective Fish consumption advisories are issued to warn the public of possible toxicological threats from consuming certain fish species. Although developing fetuses and children are particularly susceptible to toxicants in fish, fish also contain valuable nutrients. Hence, formulating advice for sensitive populations poses challenges. We conducted a comparative analysis of advisory Web sites issued by states to assess health messages that sensitive populations might access. Data sources We evaluated state advisories accessed via the National Listing of Fish Advisories issued by the U.S. Environmental Protection Agency. Data extraction We created criteria to evaluate advisory attributes such as risk and benefit message clarity. Data synthesis All 48 state advisories issued at the time of this analysis targeted children, 90% (43) targeted pregnant women, and 58% (28) targeted women of childbearing age. Only six advisories addressed single contaminants, while the remainder based advice on 2–12 contaminants. Results revealed that advisories associated a dozen contaminants with specific adverse health effects. Beneficial health effects of any kind were specifically associated only with omega-3 fatty acids found in fish. Conclusions These findings highlight the complexity of assessing and communicating information about multiple contaminant exposure from fish consumption. Communication regarding potential health benefits conferred by specific fish nutrients was minimal and focused primarily on omega-3 fatty acids. This overview suggests some lessons learned and highlights a lack of both clarity and consistency in providing the breadth of information that sensitive populations such as pregnant women need to make public health decisions about fish consumption during pregnancy. PMID:19079708

  3. State of charge estimation in Ni-MH rechargeable batteries

    NASA Astrophysics Data System (ADS)

    Milocco, R. H.; Castro, B. E.

    In this work we estimate the state of charge (SOC) of Ni-MH rechargeable batteries using the Kalman filter based on a simplified electrochemical model. First, we derive the complete electrochemical model of the battery which includes diffusional processes and kinetic reactions in both Ni and MH electrodes. The full model is further reduced in a cascade of two parts, a linear time invariant dynamical sub-model followed by a static nonlinearity. Both parts are identified using the current and potential measured at the terminals of the battery with a simple 1-D minimization procedure. The inverse of the static nonlinearity together with a Kalman filter provide the SOC estimation as a linear estimation problem. Experimental results with commercial batteries are provided to illustrate the estimation procedure and to show the performance.

  4. Motion state analysis of space target based on optical cross section

    NASA Astrophysics Data System (ADS)

    Tian, Qichen; Li, Zhi; Xu, Can; Liu, Chenghao

    2017-10-01

    In order to solve the problem that the movement state analysis method of the space target based on OCS is not related to the real motion state. This paper proposes a method based on OCS for analyzing the state of space target motion. This paper first establish a three-dimensional model of real STSS satellite, then change the satellite's surface into element, and assign material to each panel according to the actual conditions of the satellite. This paper set up a motion scene according to the orbit parameters of STSS satellite in STK, and the motion states are set to three axis steady state and slowly rotating unstable state respectively. In these two states, the occlusion condition of the surface element is firstly determined, and the effective face element is selected. Then, the coordinates of the observation station and the solar coordinates in the satellite body coordinate system are input into the OCS calculation program, and the OCS variation curves of the three axis steady state and the slow rotating unstable state STSS satellite are obtained. Combining the satellite surface structure and the load situation, the OCS change curve of the three axis stabilized satellite is analyzed, and the conclude that the OCS curve fluctuates up and down when the sunlight is irradiated to the load area; By using Spectral analysis method, autocorrelation analysis and the cross residual method, the rotation speed of OCS satellite in slow rotating unstable state is analyzed, and the rotation speed of satellite is successfully reversed. By comparing the three methods, it is found that the cross residual method is more accurate.

  5. State Estimation Using Dependent Evidence Fusion: Application to Acoustic Resonance-Based Liquid Level Measurement.

    PubMed

    Xu, Xiaobin; Li, Zhenghui; Li, Guo; Zhou, Zhe

    2017-04-21

    Estimating the state of a dynamic system via noisy sensor measurement is a common problem in sensor methods and applications. Most state estimation methods assume that measurement noise and state perturbations can be modeled as random variables with known statistical properties. However in some practical applications, engineers can only get the range of noises, instead of the precise statistical distributions. Hence, in the framework of Dempster-Shafer (DS) evidence theory, a novel state estimatation method by fusing dependent evidence generated from state equation, observation equation and the actual observations of the system states considering bounded noises is presented. It can be iteratively implemented to provide state estimation values calculated from fusion results at every time step. Finally, the proposed method is applied to a low-frequency acoustic resonance level gauge to obtain high-accuracy measurement results.

  6. Complex sample survey estimation in static state-space

    Treesearch

    Raymond L. Czaplewski

    2010-01-01

    Increased use of remotely sensed data is a key strategy adopted by the Forest Inventory and Analysis Program. However, multiple sensor technologies require complex sampling units and sampling designs. The Recursive Restriction Estimator (RRE) accommodates this complexity. It is a design-consistent Empirical Best Linear Unbiased Prediction for the state-vector, which...

  7. Practical global oceanic state estimation

    NASA Astrophysics Data System (ADS)

    Wunsch, Carl; Heimbach, Patrick

    2007-06-01

    The problem of oceanographic state estimation, by means of an ocean general circulation model (GCM) and a multitude of observations, is described and contrasted with the meteorological process of data assimilation. In practice, all such methods reduce, on the computer, to forms of least-squares. The global oceanographic problem is at the present time focussed primarily on smoothing, rather than forecasting, and the data types are unlike meteorological ones. As formulated in the consortium Estimating the Circulation and Climate of the Ocean (ECCO), an automatic differentiation tool is used to calculate the so-called adjoint code of the GCM, and the method of Lagrange multipliers used to render the problem one of unconstrained least-squares minimization. Major problems today lie less with the numerical algorithms (least-squares problems can be solved by many means) than with the issues of data and model error. Results of ongoing calculations covering the period of the World Ocean Circulation Experiment, and including among other data, satellite altimetry from TOPEX/POSEIDON, Jason-1, ERS- 1/2, ENVISAT, and GFO, a global array of profiling floats from the Argo program, and satellite gravity data from the GRACE mission, suggest that the solutions are now useful for scientific purposes. Both methodology and applications are developing in a number of different directions.

  8. An Automated Technique for Estimating Daily Precipitation over the State of Virginia

    NASA Technical Reports Server (NTRS)

    Follansbee, W. A.; Chamberlain, L. W., III

    1981-01-01

    Digital IR and visible imagery obtained from a geostationary satellite located over the equator at 75 deg west latitude were provided by NASA and used to obtain a linear relationship between cloud top temperature and hourly precipitation. Two computer programs written in FORTRAN were used. The first program computes the satellite estimate field from the hourly digital IR imagery. The second program computes the final estimate for the entire state area by comparing five preliminary estimates of 24 hour precipitation with control raingage readings and determining which of the five methods gives the best estimate for the day. The final estimate is then produced by incorporating control gage readings into the winning method. In presenting reliable precipitation estimates for every cell in Virginia in near real time on a daily on going basis, the techniques require on the order of 125 to 150 daily gage readings by dependable, highly motivated observers distributed as uniformly as feasible across the state.

  9. Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks

    PubMed Central

    Sun, Xiaodian; Jin, Li; Xiong, Momiao

    2008-01-01

    It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks. PMID:19018286

  10. σ-SCF: A direct energy-targeting method to mean-field excited states.

    PubMed

    Ye, Hong-Zhou; Welborn, Matthew; Ricke, Nathan D; Van Voorhis, Troy

    2017-12-07

    The mean-field solutions of electronic excited states are much less accessible than ground state (e.g., Hartree-Fock) solutions. Energy-based optimization methods for excited states, like Δ-SCF (self-consistent field), tend to fall into the lowest solution consistent with a given symmetry-a problem known as "variational collapse." In this work, we combine the ideas of direct energy-targeting and variance-based optimization in order to describe excited states at the mean-field level. The resulting method, σ-SCF, has several advantages. First, it allows one to target any desired excited state by specifying a single parameter: a guess of the energy of that state. It can therefore, in principle, find all excited states. Second, it avoids variational collapse by using a variance-based, unconstrained local minimization. As a consequence, all states-ground or excited-are treated on an equal footing. Third, it provides an alternate approach to locate Δ-SCF solutions that are otherwise hardly accessible by the usual non-aufbau configuration initial guess. We present results for this new method for small atoms (He, Be) and molecules (H 2 , HF). We find that σ-SCF is very effective at locating excited states, including individual, high energy excitations within a dense manifold of excited states. Like all single determinant methods, σ-SCF shows prominent spin-symmetry breaking for open shell states and our results suggest that this method could be further improved with spin projection.

  11. Real-Time Radar-Based Tracking and State Estimation of Multiple Non-Conformant Aircraft

    NASA Technical Reports Server (NTRS)

    Cook, Brandon; Arnett, Timothy; Macmann, Owen; Kumar, Manish

    2017-01-01

    In this study, a novel solution for automated tracking of multiple unknown aircraft is proposed. Many current methods use transponders to self-report state information and augment track identification. While conformant aircraft typically report transponder information to alert surrounding aircraft of its state, vehicles may exist in the airspace that are non-compliant and need to be accurately tracked using alternative methods. In this study, a multi-agent tracking solution is presented that solely utilizes primary surveillance radar data to estimate aircraft state information. Main research challenges include state estimation, track management, data association, and establishing persistent track validity. In an effort to realize these challenges, techniques such as Maximum a Posteriori estimation, Kalman filtering, degree of membership data association, and Nearest Neighbor Spanning Tree clustering are implemented for this application.

  12. A Value-Added Estimate of Higher Education Quality of US States

    ERIC Educational Resources Information Center

    Zhang, Lei

    2009-01-01

    States differ substantially in higher education policies. Little is known about the effects of state policies on the performance of public colleges and universities, largely because no clear measures of college quality exist. In this paper, I estimate the average quality of public colleges of US states based on the value-added to individuals'…

  13. State estimation for networked control systems using fixed data rates

    NASA Astrophysics Data System (ADS)

    Liu, Qing-Quan; Jin, Fang

    2017-07-01

    This paper investigates state estimation for linear time-invariant systems where sensors and controllers are geographically separated and connected via a bandwidth-limited and errorless communication channel with the fixed data rate. All plant states are quantised, coded and converted together into a codeword in our quantisation and coding scheme. We present necessary and sufficient conditions on the fixed data rate for observability of such systems, and further develop the data-rate theorem. It is shown in our results that there exists a quantisation and coding scheme to ensure observability of the system if the fixed data rate is larger than the lower bound given, which is less conservative than the one in the literature. Furthermore, we also examine the role that the disturbances have on the state estimation problem in the case with data-rate limitations. Illustrative examples are given to demonstrate the effectiveness of the proposed method.

  14. Developing a Fundamental Model for an Integrated GPS/INS State Estimation System with Kalman Filtering

    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.

  15. State estimation for advanced control of wave energy converters

    DOE Data Explorer

    Coe, Ryan; Bacelli, Giorgio

    2017-04-25

    A report on state estimation for advanced control of wave energy converters (WECs), with supporting data models and slides from the overview presentation. The methods discussed are intended for use to enable real-time closed loop control of WECs.

  16. Estimating inpatient hospital prices from state administrative data and hospital financial reports.

    PubMed

    Levit, Katharine R; Friedman, Bernard; Wong, Herbert S

    2013-10-01

    To develop a tool for estimating hospital-specific inpatient prices for major payers. AHRQ Healthcare Cost and Utilization Project State Inpatient Databases and complete hospital financial reporting of revenues mandated in 10 states for 2006. Hospital discharge records and hospital financial information were merged to estimate revenue per stay by payer. Estimated prices were validated against other data sources. Hospital prices can be reasonably estimated for 10 geographically diverse states. All-payer price-to-charge ratios, an intermediate step in estimating prices, compare favorably to cost-to-charge ratios. Estimated prices also compare well with Medicare, MarketScan private insurance, and the Medical Expenditure Panel Survey prices for major payers, given limitations of each dataset. Public reporting of prices is a consumer resource in making decisions about health care treatment; for self-pay patients, they can provide leverage in negotiating discounts off of charges. Researchers can also use prices to increase understanding of the level and causes of price differentials among geographic areas. Prices by payer expand investigational tools available to study the interaction of inpatient hospital price setting among public and private payers--an important asset as the payer mix changes with the implementation of the Affordable Care Act. © Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

  17. σ-SCF: A direct energy-targeting method to mean-field excited states

    NASA Astrophysics Data System (ADS)

    Ye, Hong-Zhou; Welborn, Matthew; Ricke, Nathan D.; Van Voorhis, Troy

    2017-12-01

    The mean-field solutions of electronic excited states are much less accessible than ground state (e.g., Hartree-Fock) solutions. Energy-based optimization methods for excited states, like Δ-SCF (self-consistent field), tend to fall into the lowest solution consistent with a given symmetry—a problem known as "variational collapse." In this work, we combine the ideas of direct energy-targeting and variance-based optimization in order to describe excited states at the mean-field level. The resulting method, σ-SCF, has several advantages. First, it allows one to target any desired excited state by specifying a single parameter: a guess of the energy of that state. It can therefore, in principle, find all excited states. Second, it avoids variational collapse by using a variance-based, unconstrained local minimization. As a consequence, all states—ground or excited—are treated on an equal footing. Third, it provides an alternate approach to locate Δ-SCF solutions that are otherwise hardly accessible by the usual non-aufbau configuration initial guess. We present results for this new method for small atoms (He, Be) and molecules (H2, HF). We find that σ-SCF is very effective at locating excited states, including individual, high energy excitations within a dense manifold of excited states. Like all single determinant methods, σ-SCF shows prominent spin-symmetry breaking for open shell states and our results suggest that this method could be further improved with spin projection.

  18. Parameter estimation of qubit states with unknown phase parameter

    NASA Astrophysics Data System (ADS)

    Suzuki, Jun

    2015-02-01

    We discuss a problem of parameter estimation for quantum two-level system, qubit system, in presence of unknown phase parameter. We analyze trade-off relations for mean square errors (MSEs) when estimating relevant parameters with separable measurements based on known precision bounds; the symmetric logarithmic derivative (SLD) Cramér-Rao (CR) bound and Hayashi-Gill-Massar (HGM) bound. We investigate the optimal measurement which attains the HGM bound and discuss its properties. We show that the HGM bound for relevant parameters can be attained asymptotically by using some fraction of given n quantum states to estimate the phase parameter. We also discuss the Holevo bound which can be attained asymptotically by a collective measurement.

  19. State estimation for autonomous flight in cluttered environments

    NASA Astrophysics Data System (ADS)

    Langelaan, Jacob Willem

    Safe, autonomous operation in complex, cluttered environments is a critical challenge facing autonomous mobile systems. The research described in this dissertation was motivated by a particularly difficult example of autonomous mobility: flight of a small Unmanned Aerial Vehicle (UAV) through a forest. In cluttered environments (such as forests or natural and urban canyons) signals from navigation beacons such as GPS may frequently be occluded. Direct measurements of vehicle position are therefore unavailable, and information required for flight control, obstacle avoidance, and navigation must be obtained using only on-board sensors. However, payload limitations of small UAVs restrict both the mass and physical dimensions of sensors that can be carried. This dissertation describes the development and proof-of-concept demonstration of a navigation system that uses only a low-cost inertial measurement unit and a monocular camera. Micro electromechanical inertial measurements units are well suited to small UAV applications and provide measurements of acceleration and angular rate. However, they do not provide information about nearby obstacles (needed for collision avoidance) and their noise and bias characteristics lead to unbounded growth in computed position. A monocular camera can provide bearings to nearby obstacles and landmarks. These bearings can be used both to enable obstacle avoidance and to aid navigation. Presented here is a solution to the problem of estimating vehicle state (position, orientation and velocity) as well as positions of obstacles in the environment using only inertial measurements and bearings to obstacles. This is a highly nonlinear estimation problem, and standard estimation techniques such as the Extended Kalman Filter are prone to divergence in this application. In this dissertation a Sigma Point Kalman Filter is implemented, resulting in an estimator which is able to cope with the significant nonlinearities in the system equations and

  20. Auditory risk estimates for youth target shooting

    PubMed Central

    Meinke, Deanna K.; Murphy, William J.; Finan, Donald S.; Lankford, James E.; Flamme, Gregory A.; Stewart, Michael; Soendergaard, Jacob; Jerome, Trevor W.

    2015-01-01

    Objective To characterize the impulse noise exposure and auditory risk for youth recreational firearm users engaged in outdoor target shooting events. The youth shooting positions are typically standing or sitting at a table, which places the firearm closer to the ground or reflective surface when compared to adult shooters. Design Acoustic characteristics were examined and the auditory risk estimates were evaluated using contemporary damage-risk criteria for unprotected adult listeners and the 120-dB peak limit suggested by the World Health Organization (1999) for children. Study sample Impulses were generated by 26 firearm/ammunition configurations representing rifles, shotguns, and pistols used by youth. Measurements were obtained relative to a youth shooter’s left ear. Results All firearms generated peak levels that exceeded the 120 dB peak limit suggested by the WHO for children. In general, shooting from the seated position over a tabletop increases the peak levels, LAeq8 and reduces the unprotected maximum permissible exposures (MPEs) for both rifles and pistols. Pistols pose the greatest auditory risk when fired over a tabletop. Conclusion Youth should utilize smaller caliber weapons, preferably from the standing position, and always wear hearing protection whenever engaging in shooting activities to reduce the risk for auditory damage. PMID:24564688

  1. State-Targeted Funding and Technical Assistance to Increase Access to Medication Treatment for Opioid Use Disorder.

    PubMed

    Abraham, Amanda J; Andrews, Christina M; Grogan, Colleen M; Pollack, Harold A; D'Aunno, Thomas; Humphreys, Keith; Friedmann, Peter D

    2018-04-01

    As the United States grapples with an opioid epidemic, expanding access to effective treatment for opioid use disorder is a major public health priority. Identifying effective policy tools that can be used to expand access to care is critically important. This article examines the relationship between state-targeted funding and technical assistance and adoption of three medications for treating opioid use disorder: oral naltrexone, injectable naltrexone, and buprenorphine. This study draws from the 2013-2014 wave of the National Drug Abuse Treatment System Survey, a nationally representative, longitudinal study of substance use disorder treatment programs. The sample includes data from 695 treatment programs (85.5% response rate) and representatives from single-state agencies in 49 states and Washington, D.C. (98% response rate). Logistic regression was used to examine the relationships of single-state agency targeted funding and technical assistance to availability of opioid use disorder medications among treatment programs. State-targeted funding was associated with increased program-level adoption of oral naltrexone (adjusted odds ratio [AOR]=3.14, 95% confidence interval [CI]=1.49-6.60, p=.004) and buprenorphine (AOR=2.47, 95% CI=1.31-4.67, p=.006). Buprenorphine adoption was also correlated with state technical assistance to support medication provision (AOR=1.18, 95% CI=1.00-1.39, p=.049). State-targeted funding for medications may be a viable policy lever for increasing access to opioid use disorder medications. Given the historically low rates of opioid use disorder medication adoption in treatment programs, single-state agency targeted funding is a potentially important tool to reduce mortality and morbidity associated with opioid disorders and misuse.

  2. Remaining lifetime modeling using State-of-Health estimation

    NASA Astrophysics Data System (ADS)

    Beganovic, Nejra; Söffker, Dirk

    2017-08-01

    Technical systems and system's components undergo gradual degradation over time. Continuous degradation occurred in system is reflected in decreased system's reliability and unavoidably lead to a system failure. Therefore, continuous evaluation of State-of-Health (SoH) is inevitable to provide at least predefined lifetime of the system defined by manufacturer, or even better, to extend the lifetime given by manufacturer. However, precondition for lifetime extension is accurate estimation of SoH as well as the estimation and prediction of Remaining Useful Lifetime (RUL). For this purpose, lifetime models describing the relation between system/component degradation and consumed lifetime have to be established. In this contribution modeling and selection of suitable lifetime models from database based on current SoH conditions are discussed. Main contribution of this paper is the development of new modeling strategies capable to describe complex relations between measurable system variables, related system degradation, and RUL. Two approaches with accompanying advantages and disadvantages are introduced and compared. Both approaches are capable to model stochastic aging processes of a system by simultaneous adaption of RUL models to current SoH. The first approach requires a priori knowledge about aging processes in the system and accurate estimation of SoH. An estimation of SoH here is conditioned by tracking actual accumulated damage into the system, so that particular model parameters are defined according to a priori known assumptions about system's aging. Prediction accuracy in this case is highly dependent on accurate estimation of SoH but includes high number of degrees of freedom. The second approach in this contribution does not require a priori knowledge about system's aging as particular model parameters are defined in accordance to multi-objective optimization procedure. Prediction accuracy of this model does not highly depend on estimated SoH. This model

  3. Dynamic State Estimation and Parameter Calibration of DFIG based on Ensemble Kalman Filter

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

    Fan, Rui; Huang, Zhenyu; Wang, Shaobu

    2015-07-30

    With the growing interest in the application of wind energy, doubly fed induction generator (DFIG) plays an essential role in the industry nowadays. To deal with the increasing stochastic variations introduced by intermittent wind resource and responsive loads, dynamic state estimation (DSE) are introduced in any power system associated with DFIGs. However, sometimes this dynamic analysis canould not work because the parameters of DFIGs are not accurate enough. To solve the problem, an ensemble Kalman filter (EnKF) method is proposed for the state estimation and parameter calibration tasks. In this paper, a DFIG is modeled and implemented with the EnKFmore » method. Sensitivity analysis is demonstrated regarding the measurement noise, initial state errors and parameter errors. The results indicate this EnKF method has a robust performance on the state estimation and parameter calibration of DFIGs.« less

  4. Robustness enhancement of neurocontroller and state estimator

    NASA Technical Reports Server (NTRS)

    Troudet, Terry

    1993-01-01

    The feasibility of enhancing neurocontrol robustness, through training of the neurocontroller and state estimator in the presence of system uncertainties, is investigated on the example of a multivariable aircraft control problem. The performance and robustness of the newly trained neurocontroller are compared to those for an existing neurocontrol design scheme. The newly designed dynamic neurocontroller exhibits a better trade-off between phase and gain stability margins, and it is significantly more robust to degradations of the plant dynamics.

  5. A robust close-range photogrammetric target extraction algorithm for size and type variant targets

    NASA Astrophysics Data System (ADS)

    Nyarko, Kofi; Thomas, Clayton; Torres, Gilbert

    2016-05-01

    The Photo-G program conducted by Naval Air Systems Command at the Atlantic Test Range in Patuxent River, Maryland, uses photogrammetric analysis of large amounts of real-world imagery to characterize the motion of objects in a 3-D scene. Current approaches involve several independent processes including target acquisition, target identification, 2-D tracking of image features, and 3-D kinematic state estimation. Each process has its own inherent complications and corresponding degrees of both human intervention and computational complexity. One approach being explored for automated target acquisition relies on exploiting the pixel intensity distributions of photogrammetric targets, which tend to be patterns with bimodal intensity distributions. The bimodal distribution partitioning algorithm utilizes this distribution to automatically deconstruct a video frame into regions of interest (ROI) that are merged and expanded to target boundaries, from which ROI centroids are extracted to mark target acquisition points. This process has proved to be scale, position and orientation invariant, as well as fairly insensitive to global uniform intensity disparities.

  6. Weighted Optimization-Based Distributed Kalman Filter for Nonlinear Target Tracking in Collaborative Sensor Networks.

    PubMed

    Chen, Jie; Li, Jiahong; Yang, Shuanghua; Deng, Fang

    2017-11-01

    The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem in collaborative sensor networks. According to the adaptive Kalman filtering (KF) method, the nonlinearity and coupling can be regarded as the model noise covariance, and estimated by minimizing the innovation or residual errors of the states. However, the method requires large time window of data to achieve reliable covariance measurement, making it impractical for nonlinear systems which are rapidly changing. To deal with the problem, a weighted optimization-based distributed KF algorithm (WODKF) is proposed in this paper. The algorithm enlarges the data size of each sensor by the received measurements and state estimates from its connected sensors instead of the time window. A new cost function is set as the weighted sum of the bias and oscillation of the state to estimate the "best" estimate of the model noise covariance. The bias and oscillation of the state of each sensor are estimated by polynomial fitting a time window of state estimates and measurements of the sensor and its neighbors weighted by the measurement noise covariance. The best estimate of the model noise covariance is computed by minimizing the weighted cost function using the exhaustive method. The sensor selection method is in addition to the algorithm to decrease the computation load of the filter and increase the scalability of the sensor network. The existence, suboptimality and stability analysis of the algorithm are given. The local probability data association method is used in the proposed algorithm for the multitarget tracking case. The algorithm is demonstrated in simulations on tracking examples for a random signal, one nonlinear target, and four nonlinear targets. Results show the feasibility and superiority of WODKF against other filtering algorithms for a large class of systems.

  7. Development of advanced techniques for rotorcraft state estimation and parameter identification

    NASA Technical Reports Server (NTRS)

    Hall, W. E., Jr.; Bohn, J. G.; Vincent, J. H.

    1980-01-01

    An integrated methodology for rotorcraft system identification consists of rotorcraft mathematical modeling, three distinct data processing steps, and a technique for designing inputs to improve the identifiability of the data. These elements are as follows: (1) a Kalman filter smoother algorithm which estimates states and sensor errors from error corrupted data. Gust time histories and statistics may also be estimated; (2) a model structure estimation algorithm for isolating a model which adequately explains the data; (3) a maximum likelihood algorithm for estimating the parameters and estimates for the variance of these estimates; and (4) an input design algorithm, based on a maximum likelihood approach, which provides inputs to improve the accuracy of parameter estimates. Each step is discussed with examples to both flight and simulated data cases.

  8. Unauthorized Immigration to the United States: Annual Estimates and Components of Change, by State, 1990 to 2010

    PubMed Central

    Warren, Robert; Warren, John Robert

    2013-01-01

    We describe a method for producing annual estimates of the unauthorized immigrant population in the United Sates and components of population change, for each state and D.C., for 1990 to 2010. We quantify a sharp drop in the number of unauthorized immigrants arriving since 2000, and we demonstrate the role of departures from the population (emigration, adjustment to legal status, removal by the Department of Homeland Security (DHS), and deaths) in reducing population growth from one million in 2000 to population losses in 2008 and 2009. The number arriving in the U.S. peaked at more than one million in 1999 to 2001, and then declined rapidly through 2009. We provide evidence that population growth stopped after 2007 primarily because entries declined and not because emigration increased during the economic crisis. Our estimates of the total unauthorized immigrant population in the U.S. and in the top ten states are comparable to those produced by DHS and the Pew Hispanic Center. For the remaining states and D.C., our data and methods produce estimates with smaller ranges of sampling error. PMID:23956482

  9. Unauthorized Immigration to the United States: Annual Estimates and Components of Change, by State, 1990 to 2010.

    PubMed

    Warren, Robert; Warren, John Robert

    2013-06-01

    We describe a method for producing annual estimates of the unauthorized immigrant population in the United Sates and components of population change, for each state and D.C., for 1990 to 2010. We quantify a sharp drop in the number of unauthorized immigrants arriving since 2000, and we demonstrate the role of departures from the population (emigration, adjustment to legal status, removal by the Department of Homeland Security (DHS), and deaths) in reducing population growth from one million in 2000 to population losses in 2008 and 2009. The number arriving in the U.S. peaked at more than one million in 1999 to 2001, and then declined rapidly through 2009. We provide evidence that population growth stopped after 2007 primarily because entries declined and not because emigration increased during the economic crisis. Our estimates of the total unauthorized immigrant population in the U.S. and in the top ten states are comparable to those produced by DHS and the Pew Hispanic Center. For the remaining states and D.C., our data and methods produce estimates with smaller ranges of sampling error.

  10. H∞ state estimation of stochastic memristor-based neural networks with time-varying delays.

    PubMed

    Bao, Haibo; Cao, Jinde; Kurths, Jürgen; Alsaedi, Ahmed; Ahmad, Bashir

    2018-03-01

    This paper addresses the problem of H ∞ state estimation for a class of stochastic memristor-based neural networks with time-varying delays. Under the framework of Filippov solution, the stochastic memristor-based neural networks are transformed into systems with interval parameters. The present paper is the first to investigate the H ∞ state estimation problem for continuous-time Itô-type stochastic memristor-based neural networks. By means of Lyapunov functionals and some stochastic technique, sufficient conditions are derived to ensure that the estimation error system is asymptotically stable in the mean square with a prescribed H ∞ performance. An explicit expression of the state estimator gain is given in terms of linear matrix inequalities (LMIs). Compared with other results, our results reduce control gain and control cost effectively. Finally, numerical simulations are provided to demonstrate the efficiency of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

  11. Simultaneous estimation of multiple phases in digital holographic interferometry using state space analysis

    NASA Astrophysics Data System (ADS)

    Kulkarni, Rishikesh; Rastogi, Pramod

    2018-05-01

    A new approach is proposed for the multiple phase estimation from a multicomponent exponential phase signal recorded in multi-beam digital holographic interferometry. It is capable of providing multidimensional measurements in a simultaneous manner from a single recording of the exponential phase signal encoding multiple phases. Each phase within a small window around each pixel is appproximated with a first order polynomial function of spatial coordinates. The problem of accurate estimation of polynomial coefficients, and in turn the unwrapped phases, is formulated as a state space analysis wherein the coefficients and signal amplitudes are set as the elements of a state vector. The state estimation is performed using the extended Kalman filter. An amplitude discrimination criterion is utilized in order to unambiguously estimate the coefficients associated with the individual signal components. The performance of proposed method is stable over a wide range of the ratio of signal amplitudes. The pixelwise phase estimation approach of the proposed method allows it to handle the fringe patterns that may contain invalid regions.

  12. Estimating irrigation water use in the humid eastern United States

    USGS Publications Warehouse

    Levin, Sara B.; Zarriello, Phillip J.

    2013-01-01

    Accurate accounting of irrigation water use is an important part of the U.S. Geological Survey National Water-Use Information Program and the WaterSMART initiative to help maintain sustainable water resources in the Nation. Irrigation water use in the humid eastern United States is not well characterized because of inadequate reporting and wide variability associated with climate, soils, crops, and farming practices. To better understand irrigation water use in the eastern United States, two types of predictive models were developed and compared by using metered irrigation water-use data for corn, cotton, peanut, and soybean crops in Georgia and turf farms in Rhode Island. Reliable metered irrigation data were limited to these areas. The first predictive model that was developed uses logistic regression to predict the occurrence of irrigation on the basis of antecedent climate conditions. Logistic regression equations were developed for corn, cotton, peanut, and soybean crops by using weekly irrigation water-use data from 36 metered sites in Georgia in 2009 and 2010 and turf farms in Rhode Island from 2000 to 2004. For the weeks when irrigation was predicted to take place, the irrigation water-use volume was estimated by multiplying the average metered irrigation application rate by the irrigated acreage for a given crop. The second predictive model that was developed is a crop-water-demand model that uses a daily soil water balance to estimate the water needs of a crop on a given day based on climate, soil, and plant properties. Crop-water-demand models were developed independently of reported irrigation water-use practices and relied on knowledge of plant properties that are available in the literature. Both modeling approaches require accurate accounting of irrigated area and crop type to estimate total irrigation water use. Water-use estimates from both modeling methods were compared to the metered irrigation data from Rhode Island and Georgia that were used to

  13. [Criteria of estimation of state forensic-medical expert activity administration].

    PubMed

    Klevno, V A; Loban, I E

    2008-01-01

    Criteria of estimation of state forensic medical activity administration were systematized and their content was considered. New integral index of administration efficiency - index of technological efficiency - was developed and proved.

  14. Estimating litter carbon stocks on forest land in the United States

    Treesearch

    Grant M. Domke; Charles H. (Hobie) Perry; Brian F. Walters; Christopher W. Woodall; Matthew B. Russell; James E. Smith

    2016-01-01

    Forest ecosystems are the largest terrestrial carbon sink on earth, withmore than half of their net primary productionmoving to the soil via the decomposition of litter biomass. Therefore, changes in the litter carbon (C) pool have important implications for global carbon budgets and carbon emissions reduction targets and negotiations. Litter accounts for an estimated...

  15. The design of nonlinear observers for wind turbine dynamic state and parameter estimation

    NASA Astrophysics Data System (ADS)

    Ritter, B.; Schild, A.; Feldt, M.; Konigorski, U.

    2016-09-01

    This contribution addresses the dynamic state and parameter estimation problem which arises with more advanced wind turbine controllers. These control devices need precise information about the system's current state to outperform conventional industrial controllers effectively. First, the necessity of a profound scientific treatment on nonlinear observers for wind turbine application is highlighted. Secondly, the full estimation problem is introduced and the variety of nonlinear filters is discussed. Finally, a tailored observer architecture is proposed and estimation results of an illustrative application example from a complex simulation set-up are presented.

  16. Methods for Estimating Annual Wastewater Nutrient Loads in the Southeastern United States

    USGS Publications Warehouse

    McMahon, Gerard; Tervelt, Larinda; Donehoo, William

    2007-01-01

    This report describes an approach for estimating annual total nitrogen and total phosphorus loads from point-source dischargers in the southeastern United States. Nutrient load estimates for 2002 were used in the calibration and application of a regional nutrient model, referred to as the SPARROW (SPAtially Referenced Regression On Watershed attributes) watershed model. Loads from dischargers permitted under the National Pollutant Discharge Elimination System were calculated using data from the U.S. Environmental Protection Agency Permit Compliance System database and individual state databases. Site information from both state and U.S. Environmental Protection Agency databases, including latitude and longitude and monitored effluent data, was compiled into a project database. For sites with a complete effluent-monitoring record, effluent-flow and nutrient-concentration data were used to develop estimates of annual point-source nitrogen and phosphorus loads. When flow data were available but nutrient-concentration data were missing or incomplete, typical pollutant-concentration values of total nitrogen and total phosphorus were used to estimate load. In developing typical pollutant-concentration values, the major factors assumed to influence wastewater nutrient-concentration variability were the size of the discharger (the amount of flow), the season during which discharge occurred, and the Standard Industrial Classification code of the discharger. One insight gained from this study is that in order to gain access to flow, concentration, and location data, close communication and collaboration are required with the agencies that collect and manage the data. In addition, the accuracy and usefulness of the load estimates depend on the willingness of the states and the U.S. Environmental Protection Agency to provide guidance and review for at least a subset of the load estimates that may be problematic.

  17. A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric Sensors.

    PubMed

    Song, Yu; Nuske, Stephen; Scherer, Sebastian

    2016-12-22

    State estimation is the most critical capability for MAV (Micro-Aerial Vehicle) localization, autonomous obstacle avoidance, robust flight control and 3D environmental mapping. There are three main challenges for MAV state estimation: (1) it can deal with aggressive 6 DOF (Degree Of Freedom) motion; (2) it should be robust to intermittent GPS (Global Positioning System) (even GPS-denied) situations; (3) it should work well both for low- and high-altitude flight. In this paper, we present a state estimation technique by fusing long-range stereo visual odometry, GPS, barometric and IMU (Inertial Measurement Unit) measurements. The new estimation system has two main parts, a stochastic cloning EKF (Extended Kalman Filter) estimator that loosely fuses both absolute state measurements (GPS, barometer) and the relative state measurements (IMU, visual odometry), and is derived and discussed in detail. A long-range stereo visual odometry is proposed for high-altitude MAV odometry calculation by using both multi-view stereo triangulation and a multi-view stereo inverse depth filter. The odometry takes the EKF information (IMU integral) for robust camera pose tracking and image feature matching, and the stereo odometry output serves as the relative measurements for the update of the state estimation. Experimental results on a benchmark dataset and our real flight dataset show the effectiveness of the proposed state estimation system, especially for the aggressive, intermittent GPS and high-altitude MAV flight.

  18. A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology

    NASA Astrophysics Data System (ADS)

    Ait-El-Fquih, Boujemaa; El Gharamti, Mohamad; Hoteit, Ibrahim

    2016-08-01

    Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface groundwater models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model's state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKFOSA. Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25 % more accurate state and parameter estimations than the joint and dual approaches.

  19. Distributed and decentralized state estimation in gas networks as distributed parameter systems.

    PubMed

    Ahmadian Behrooz, Hesam; Boozarjomehry, R Bozorgmehry

    2015-09-01

    In this paper, a framework for distributed and decentralized state estimation in high-pressure and long-distance gas transmission networks (GTNs) is proposed. The non-isothermal model of the plant including mass, momentum and energy balance equations are used to simulate the dynamic behavior. Due to several disadvantages of implementing a centralized Kalman filter for large-scale systems, the continuous/discrete form of extended Kalman filter for distributed and decentralized estimation (DDE) has been extended for these systems. Accordingly, the global model is decomposed into several subsystems, called local models. Some heuristic rules are suggested for system decomposition in gas pipeline networks. In the construction of local models, due to the existence of common states and interconnections among the subsystems, the assimilation and prediction steps of the Kalman filter are modified to take the overlapping and external states into account. However, dynamic Riccati equation for each subsystem is constructed based on the local model, which introduces a maximum error of 5% in the estimated standard deviation of the states in the benchmarks studied in this paper. The performance of the proposed methodology has been shown based on the comparison of its accuracy and computational demands against their counterparts in centralized Kalman filter for two viable benchmarks. In a real life network, it is shown that while the accuracy is not significantly decreased, the real-time factor of the state estimation is increased by a factor of 10. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  20. Marketing and alcohol-related traffic fatalities: impact of alcohol advertising targeting minors.

    PubMed

    Smith, Ryan C; Geller, E Scott

    2009-10-01

    Alcohol-related youth traffic fatalities continue as a major public-health concern. While state and federal laws can be useful in tackling this problem, the efficacy of many laws has not been empirically demonstrated. We examined the impact of state laws prohibiting alcohol advertising to target minors. Using statistics obtained from the Fatality Analysis Reporting System (FARS), youth alcohol-related, single-vehicle, driver traffic fatalities were compared by state as a function of whether the state has a law prohibiting alcohol advertising that targets minors. Overall, states possessing this law experienced 32.9% fewer of the above specified traffic fatalities. DISCUSSION AND IMPACT ON INDUSTRY: The results suggest that not only are youth drinking rates affected by alcohol advertisements targeting youth, but also drink-driving behaviors. Indeed, we estimate that if this type of legislation were adopted in the 26 states that do not prohibit targeting of minors with alcohol advertising, then 400 youth lives could be saved annually.

  1. Estimated use of water in the New England States, 1990

    USGS Publications Warehouse

    Korzendorfer, B.A.; Horn, M.A.

    1995-01-01

    Data on freshwater withdrawals in 1990 were compiled for the New England States. An estimated 4,160 Mgal/d (million gallons per day) of freshwater was withdrawn in 1990 in the six States. Of this total, 1,430 Mgal/d was withdrawn by public suppliers and delivered to users, and 2,720 Mgal/d was withdrawn by domestic, commercial, industrial, agricultural, mining, and thermoelectric power-generation users. More than 83 percent of the freshwater was from surface-water sources. Massachusetts, with the largest population, had the largest withdrawals of water. Data on saline water withdraw, and instream flow at hydroelectric plants were also compiled. An estimated 9, 170 Mgal/d of saline water was used for thermoelectric-power generation and industrial use in Connecticut, Maine, Massachusetts, New Hampshire, and Rhode Island. Return flow fro public wastewater-treatment plants totaled 1,750 Mgal/d; more than half (55 percent) of this return flow was in Massachusetts. In addition, about 178,000 Mgal/d was used for instream hydroelectric power generation; the largest users were Maine (about 83,000 Mgal/d) and New Hampshire (46,000 Mgal/d). These data, some of which were based on site-specific water-use information and some based on estimation techniques, were compiled through joint efforts by the U.S. Geological Survey and State cooperators for the 1990 national water-use compilation.

  2. Adaptive Disturbance Tracking Theory with State Estimation and State Feedback for Region II Control of Large Wind Turbines

    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.

  3. Airborne target tracking algorithm against oppressive decoys in infrared imagery

    NASA Astrophysics Data System (ADS)

    Sun, Xiechang; Zhang, Tianxu

    2009-10-01

    This paper presents an approach for tracking airborne target against oppressive infrared decoys. Oppressive decoy lures infrared guided missile by its high infrared radiation. Traditional tracking algorithms have degraded stability even come to tracking failure when airborne target continuously throw out many decoys. The proposed approach first determines an adaptive tracking window. The center of the tracking window is set at a predicted target position which is computed based on uniform motion model. Different strategies are applied for determination of tracking window size according to target state. The image within tracking window is segmented and multi features of candidate targets are extracted. The most similar candidate target is associated to the tracking target by using a decision function, which calculates a weighted sum of normalized feature differences between two comparable targets. Integrated intensity ratio of association target and tracking target, and target centroid are examined to estimate target state in the presence of decoys. The tracking ability and robustness of proposed approach has been validated by processing available real-world and simulated infrared image sequences containing airborne targets and oppressive decoys.

  4. Variable input observer for state estimation of high-rate dynamics

    NASA Astrophysics Data System (ADS)

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

    2017-04-01

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

  5. High maternal mortality in Jigawa State, Northern Nigeria estimated using the sisterhood method.

    PubMed

    Sharma, Vandana; Brown, Willa; Kainuwa, Muhammad Abdullahi; Leight, Jessica; Nyqvist, Martina Bjorkman

    2017-06-02

    Maternal mortality is extremely high in Nigeria. Accurate estimation of maternal mortality is challenging in low-income settings such as Nigeria where vital registration is incomplete. The objective of this study was to estimate the lifetime risk (LTR) of maternal death and the maternal mortality ratio (MMR) in Jigawa State, Northern Nigeria using the Sisterhood Method. Interviews with 7,069 women aged 15-49 in 96 randomly selected clusters of communities in 24 Local Government Areas (LGAs) across Jigawa state were conducted. A retrospective cohort of their sisters of reproductive age was constructed to calculate the lifetime risk of maternal mortality. Using most recent estimates of total fertility for the state, the MMR was estimated. The 7,069 respondents reported 10,957 sisters who reached reproductive age. Of the 1,026 deaths in these sisters, 300 (29.2%) occurred during pregnancy, childbirth or within 42 days after delivery. This corresponds to a LTR of 6.6% and an estimated MMR for the study areas of 1,012 maternal deaths per 100,000 live births (95% CI: 898-1,126) with a time reference of 2001. Jigawa State has an extremely high maternal mortality ratio underscoring the urgent need for health systems improvement and interventions to accelerate reductions in MMR. The trial is registered at clinicaltrials.gov ( NCT01487707 ). Initially registered on December 6, 2011.

  6. Estimated use of water in the United States in 1990

    USGS Publications Warehouse

    Solley, Wayne B.; Pierce, Robert R.; Perlman, Howard A.

    1993-01-01

    Water withdrawals in the United States during 1990 were estimated to average 408,000 million gallons per day (Mgal/d) of freshwater and saline water for offstream uses--2 percent more than the 1985 estimate. Total freshwater withdrawals were an estimated 339,000 Mgal/d during 1990, about the same as during 1985. Average per-capita use for all offstream uses was 1,620 gallons per day (gal/d) of freshwater and saline water combined and 1,340 gal/d of freshwater.Offstream water-use categories are classified in this report as public supply, domestic, commercial, irrigation, livestock, industrial, mining, and thermoelectric power. During 1990, public-supply withdrawals were an estimated 38,500 Mgal/d, and self-supplied withdrawals were estimated as follows: domestic, 3,390 Mgal/d; commercial, 2,390 Mgal/d; irrigation, 137,000 Mgal/d; livestock, 4,500 Mgal/d; industrial, 22,600 Mgal/d, of which 3,270 Mgal/d was saline water; mining, 4,960 Mgal/d, of which 1,650 Mgal/d was saline; and thermoelectric power, 195,000 Mgal/d, of which 64,500 Mgal/d was saline.Water use for hydroelectric power generation, the only instream use compiled in this report, was estimated to be 3,290,000 Mgal/d during 1990, or 8 percent more than during 1985 and about the same as estimated for 1975 and 1980.Estimates of withdrawals by source indicate that during 1990, total surface-water withdrawals were 327,000 Mgal/d, or 1 percent more than during 1985, and total ground-water withdrawals were 80,600 Mgal/d, or 9 percent more than during 1985. Total saline-water withdrawals during 1990 were 69,400 Mgal/d, or 15 percent more than during 1985, most of which was saline surface water. Reclaimed wastewater averaged about 750 Mgal/d during 1990, or 30 percent more than during 1985.Total freshwater consumptive use was an estimated 94,000 Mgal/d during 1990, or 2 percent more than during 1985. Consumptive use by irrigation accounted for the largest part of total consumptive use, and was an estimated 76

  7. Optimal estimation of parameters and states in stochastic time-varying systems with time delay

    NASA Astrophysics Data System (ADS)

    Torkamani, Shahab; Butcher, Eric A.

    2013-08-01

    In this study estimation of parameters and states in stochastic linear and nonlinear delay differential systems with time-varying coefficients and constant delay is explored. The approach consists of first employing a continuous time approximation to approximate the stochastic delay differential equation with a set of stochastic ordinary differential equations. Then the problem of parameter estimation in the resulting stochastic differential system is represented as an optimal filtering problem using a state augmentation technique. By adapting the extended Kalman-Bucy filter to the resulting system, the unknown parameters of the time-delayed system are estimated from noise-corrupted, possibly incomplete measurements of the states.

  8. State-of-charge estimation in lithium-ion batteries: A particle filter approach

    NASA Astrophysics Data System (ADS)

    Tulsyan, Aditya; Tsai, Yiting; Gopaluni, R. Bhushan; Braatz, Richard D.

    2016-11-01

    The dynamics of lithium-ion batteries are complex and are often approximated by models consisting of partial differential equations (PDEs) relating the internal ionic concentrations and potentials. The Pseudo two-dimensional model (P2D) is one model that performs sufficiently accurately under various operating conditions and battery chemistries. Despite its widespread use for prediction, this model is too complex for standard estimation and control applications. This article presents an original algorithm for state-of-charge estimation using the P2D model. Partial differential equations are discretized using implicit stable algorithms and reformulated into a nonlinear state-space model. This discrete, high-dimensional model (consisting of tens to hundreds of states) contains implicit, nonlinear algebraic equations. The uncertainty in the model is characterized by additive Gaussian noise. By exploiting the special structure of the pseudo two-dimensional model, a novel particle filter algorithm that sweeps in time and spatial coordinates independently is developed. This algorithm circumvents the degeneracy problems associated with high-dimensional state estimation and avoids the repetitive solution of implicit equations by defining a 'tether' particle. The approach is illustrated through extensive simulations.

  9. Maneuver Algorithm for Bearings-Only Target Tracking with Acceleration and Field of View Constraints

    NASA Astrophysics Data System (ADS)

    Roh, Heekun; Shim, Sang-Wook; Tahk, Min-Jea

    2018-05-01

    This paper proposes a maneuver algorithm for the agent performing target tracking with bearing angle information only. The goal of the agent is to estimate the target position and velocity based only on the bearing angle data. The methods of bearings-only target state estimation are outlined. The nature of bearings-only target tracking problem is then addressed. Based on the insight from above-mentioned properties, the maneuver algorithm for the agent is suggested. The proposed algorithm is composed of a nonlinear, hysteresis guidance law and the estimation accuracy assessment criteria based on the theory of Cramer-Rao bound. The proposed guidance law generates lateral acceleration command based on current field of view angle. The accuracy criteria supply the expected estimation variance, which acts as a terminal criterion for the proposed algorithm. The aforementioned algorithm is verified with a two-dimensional simulation.

  10. A Two-Stage Kalman Filter Approach for Robust and Real-Time Power System State Estimation

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

    Zhang, Jinghe; Welch, Greg; Bishop, Gary

    2014-04-01

    As electricity demand continues to grow and renewable energy increases its penetration in the power grid, realtime state estimation becomes essential for system monitoring and control. Recent development in phasor technology makes it possible with high-speed time-synchronized data provided by Phasor Measurement Units (PMU). In this paper we present a two-stage Kalman filter approach to estimate the static state of voltage magnitudes and phase angles, as well as the dynamic state of generator rotor angles and speeds. Kalman filters achieve optimal performance only when the system noise characteristics have known statistical properties (zero-mean, Gaussian, and spectrally white). However in practicemore » the process and measurement noise models are usually difficult to obtain. Thus we have developed the Adaptive Kalman Filter with Inflatable Noise Variances (AKF with InNoVa), an algorithm that can efficiently identify and reduce the impact of incorrect system modeling and/or erroneous measurements. In stage one, we estimate the static state from raw PMU measurements using the AKF with InNoVa; then in stage two, the estimated static state is fed into an extended Kalman filter to estimate the dynamic state. Simulations demonstrate its robustness to sudden changes of system dynamics and erroneous measurements.« less

  11. Improved GGIW-PHD filter for maneuvering non-ellipsoidal extended targets or group targets tracking based on sub-random matrices.

    PubMed

    Liang, Zhibing; Liu, Fuxian; Gao, Jiale

    2018-01-01

    For non-ellipsoidal extended targets and group targets tracking (NETT and NGTT), using an ellipsoid to approximate the target extension may not be accurate enough because of the lack of shape and orientation information. In consideration of this, we model a non-ellipsoidal extended target or target group as a combination of multiple ellipsoidal sub-objects, each represented by a random matrix. Based on these models, an improved gamma Gaussian inverse Wishart probability hypothesis density (GGIW-PHD) filter is proposed to estimate the measurement rates, kinematic states, and extension states of the sub-objects for each extended target or target group. For maneuvering NETT and NGTT, a multi-model (MM) approach based GGIW-PHD (MM-GGIW-PHD) filter is proposed. The common and the individual dynamics of the sub-objects belonging to the same extended target or target group are described by means of the combination between the overall maneuver model and the sub-object models. For the merging of updating components, an improved merging criterion and a new merging method are derived. A specific implementation of prediction partition with pseudo-likelihood method is presented. Two scenarios for non-maneuvering and maneuvering NETT and NGTT are simulated. The results demonstrate the effectiveness of the proposed algorithms.

  12. Improved GGIW-PHD filter for maneuvering non-ellipsoidal extended targets or group targets tracking based on sub-random matrices

    PubMed Central

    Liu, Fuxian; Gao, Jiale

    2018-01-01

    For non-ellipsoidal extended targets and group targets tracking (NETT and NGTT), using an ellipsoid to approximate the target extension may not be accurate enough because of the lack of shape and orientation information. In consideration of this, we model a non-ellipsoidal extended target or target group as a combination of multiple ellipsoidal sub-objects, each represented by a random matrix. Based on these models, an improved gamma Gaussian inverse Wishart probability hypothesis density (GGIW-PHD) filter is proposed to estimate the measurement rates, kinematic states, and extension states of the sub-objects for each extended target or target group. For maneuvering NETT and NGTT, a multi-model (MM) approach based GGIW-PHD (MM-GGIW-PHD) filter is proposed. The common and the individual dynamics of the sub-objects belonging to the same extended target or target group are described by means of the combination between the overall maneuver model and the sub-object models. For the merging of updating components, an improved merging criterion and a new merging method are derived. A specific implementation of prediction partition with pseudo-likelihood method is presented. Two scenarios for non-maneuvering and maneuvering NETT and NGTT are simulated. The results demonstrate the effectiveness of the proposed algorithms. PMID:29444144

  13. Risk maps for targeting exotic plant pest detection programs in the United States

    Treesearch

    R.D. Magarey; D.M. Borchert; J.S. Engle; M Garcia-Colunga; Frank H. Koch; et al

    2011-01-01

    In the United States, pest risk maps are used by the Cooperative Agricultural Pest Survey for spatial and temporal targeting of exotic plant pest detection programs. Methods are described to create standardized host distribution, climate and pathway risk maps for the top nationally ranked exotic pest targets. Two examples are provided to illustrate the risk mapping...

  14. Quantum state estimation when qubits are lost: a no-data-left-behind approach

    DOE PAGES

    Williams, Brian P.; Lougovski, Pavel

    2017-04-06

    We present an approach to Bayesian mean estimation of quantum states using hyperspherical parametrization and an experiment-specific likelihood which allows utilization of all available data, even when qubits are lost. With this method, we report the first closed-form Bayesian mean and maximum likelihood estimates for the ideal single qubit. Due to computational constraints, we utilize numerical sampling to determine the Bayesian mean estimate for a photonic two-qubit experiment in which our novel analysis reduces burdens associated with experimental asymmetries and inefficiencies. This method can be applied to quantum states of any dimension and experimental complexity.

  15. Series resistance compensation for whole-cell patch-clamp studies using a membrane state estimator

    PubMed Central

    Sherman, AJ; Shrier, A; Cooper, E

    1999-01-01

    Whole-cell patch-clamp techniques are widely used to measure membrane currents from isolated cells. While suitable for a broad range of ionic currents, the series resistance (R(s)) of the recording pipette limits the bandwidth of the whole-cell configuration, making it difficult to measure rapid ionic currents. To increase bandwidth, it is necessary to compensate for R(s). Most methods of R(s) compensation become unstable at high bandwidth, making them hard to use. We describe a novel method of R(s) compensation that overcomes the stability limitations of standard designs. This method uses a state estimator, implemented with analog computation, to compute the membrane potential, V(m), which is then used in a feedback loop to implement a voltage clamp; we refer to this as state estimator R(s) compensation. To demonstrate the utility of this approach, we built an amplifier incorporating state estimator R(s) compensation. In benchtop tests, our amplifier showed significantly higher bandwidths and improved stability when compared with a commercially available amplifier. We demonstrated that state estimator R(s) compensation works well in practice by recording voltage-gated Na(+) currents under voltage-clamp conditions from dissociated neonatal rat sympathetic neurons. We conclude that state estimator R(s) compensation should make it easier to measure large rapid ionic currents with whole-cell patch-clamp techniques. PMID:10545359

  16. A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric Sensors

    PubMed Central

    Song, Yu; Nuske, Stephen; Scherer, Sebastian

    2016-01-01

    State estimation is the most critical capability for MAV (Micro-Aerial Vehicle) localization, autonomous obstacle avoidance, robust flight control and 3D environmental mapping. There are three main challenges for MAV state estimation: (1) it can deal with aggressive 6 DOF (Degree Of Freedom) motion; (2) it should be robust to intermittent GPS (Global Positioning System) (even GPS-denied) situations; (3) it should work well both for low- and high-altitude flight. In this paper, we present a state estimation technique by fusing long-range stereo visual odometry, GPS, barometric and IMU (Inertial Measurement Unit) measurements. The new estimation system has two main parts, a stochastic cloning EKF (Extended Kalman Filter) estimator that loosely fuses both absolute state measurements (GPS, barometer) and the relative state measurements (IMU, visual odometry), and is derived and discussed in detail. A long-range stereo visual odometry is proposed for high-altitude MAV odometry calculation by using both multi-view stereo triangulation and a multi-view stereo inverse depth filter. The odometry takes the EKF information (IMU integral) for robust camera pose tracking and image feature matching, and the stereo odometry output serves as the relative measurements for the update of the state estimation. Experimental results on a benchmark dataset and our real flight dataset show the effectiveness of the proposed state estimation system, especially for the aggressive, intermittent GPS and high-altitude MAV flight. PMID:28025524

  17. Enhancing interferometer phase estimation, sensing sensitivity, and resolution using robust entangled states

    NASA Astrophysics Data System (ADS)

    Smith, James F.

    2017-11-01

    With the goal of designing interferometers and interferometer sensors, e.g., LADARs with enhanced sensitivity, resolution, and phase estimation, states using quantum entanglement are discussed. These states include N00N states, plain M and M states (PMMSs), and linear combinations of M and M states (LCMMS). Closed form expressions for the optimal detection operators; visibility, a measure of the state's robustness to loss and noise; a resolution measure; and phase estimate error, are provided in closed form. The optimal resolution for the maximum visibility and minimum phase error are found. For the visibility, comparisons between PMMSs, LCMMS, and N00N states are provided. For the minimum phase error, comparisons between LCMMS, PMMSs, N00N states, separate photon states (SPSs), the shot noise limit (SNL), and the Heisenberg limit (HL) are provided. A representative collection of computational results illustrating the superiority of LCMMS when compared to PMMSs and N00N states is given. It is found that for a resolution 12 times the classical result LCMMS has visibility 11 times that of N00N states and 4 times that of PMMSs. For the same case, the minimum phase error for LCMMS is 10.7 times smaller than that of PMMS and 29.7 times smaller than that of N00N states.

  18. H∞ state estimation for discrete-time memristive recurrent neural networks with stochastic time-delays

    NASA Astrophysics Data System (ADS)

    Liu, Hongjian; Wang, Zidong; Shen, Bo; Alsaadi, Fuad E.

    2016-07-01

    This paper deals with the robust H∞ state estimation problem for a class of memristive recurrent neural networks with stochastic time-delays. The stochastic time-delays under consideration are governed by a Bernoulli-distributed stochastic sequence. The purpose of the addressed problem is to design the robust state estimator such that the dynamics of the estimation error is exponentially stable in the mean square, and the prescribed ? performance constraint is met. By utilizing the difference inclusion theory and choosing a proper Lyapunov-Krasovskii functional, the existence condition of the desired estimator is derived. Based on it, the explicit expression of the estimator gain is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is employed to demonstrate the effectiveness and applicability of the proposed estimation approach.

  19. A hybrid deconvolution approach for estimation of in vivo non-displaceable binding for brain PET targets without a reference region

    PubMed Central

    Mann, J. John; Ogden, R. Todd

    2017-01-01

    Background and aim Estimation of a PET tracer’s non-displaceable distribution volume (VND) is required for quantification of specific binding to its target of interest. VND is generally assumed to be comparable brain-wide and is determined either from a reference region devoid of the target, often not available for many tracers and targets, or by imaging each subject before and after blocking the target with another molecule that has high affinity for the target, which is cumbersome and involves additional radiation exposure. Here we propose, and validate for the tracers [11C]DASB and [11C]CUMI-101, a new data-driven hybrid deconvolution approach (HYDECA) that determines VND at the individual level without requiring either a reference region or a blocking study. Methods HYDECA requires the tracer metabolite-corrected concentration curve in blood plasma and uses a singular value decomposition to estimate the impulse response function across several brain regions from measured time activity curves. HYDECA decomposes each region’s impulse response function into the sum of a parametric non-displaceable component, which is a function of VND, assumed common across regions, and a nonparametric specific component. These two components differentially contribute to each impulse response function. Different regions show different contributions of the two components, and HYDECA examines data across regions to find a suitable common VND. HYDECA implementation requires determination of two tuning parameters, and we propose two strategies for objectively selecting these parameters for a given tracer: using data from blocking studies, and realistic simulations of the tracer. Using available test-retest data, we compare HYDECA estimates of VND and binding potentials to those obtained based on VND estimated using a purported reference region. Results For [11C]DASB and [11C]CUMI-101, we find that regardless of the strategy used to optimize the tuning parameters, HYDECA provides

  20. Airborne data measurement system errors reduction through state estimation and control optimization

    NASA Astrophysics Data System (ADS)

    Sebryakov, G. G.; Muzhichek, S. M.; Pavlov, V. I.; Ermolin, O. V.; Skrinnikov, A. A.

    2018-02-01

    The paper discusses the problem of airborne data measurement system errors reduction through state estimation and control optimization. The approaches are proposed based on the methods of experiment design and the theory of systems with random abrupt structure variation. The paper considers various control criteria as applied to an aircraft data measurement system. The physics of criteria is explained, the mathematical description and the sequence of steps for each criterion application is shown. The formula is given for airborne data measurement system state vector posterior estimation based for systems with structure variations.

  1. Assimilation of thermospheric measurements for ionosphere-thermosphere state estimation

    NASA Astrophysics Data System (ADS)

    Miladinovich, Daniel S.; Datta-Barua, Seebany; Bust, Gary S.; Makela, Jonathan J.

    2016-12-01

    We develop a method that uses data assimilation to estimate ionospheric-thermospheric (IT) states during midlatitude nighttime storm conditions. The algorithm Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE) uses time-varying electron densities in the F region, derived primarily from total electron content data, to estimate two drivers of the IT: neutral winds and electric potential. A Kalman filter is used to update background models based on ingested plasma densities and neutral wind measurements. This is the first time a Kalman filtering technique is used with the EMPIRE algorithm and the first time neutral wind measurements from 630.0 nm Fabry-Perot interferometers (FPIs) are ingested to improve estimates of storm time ion drifts and neutral winds. The effects of assimilating remotely sensed neutral winds from FPI observations are studied by comparing results of ingesting: electron densities (N) only, N plus half the measurements from a single FPI, and then N plus all of the FPI data. While estimates of ion drifts and neutral winds based on N give estimates similar to the background models, this study's results show that ingestion of the FPI data can significantly change neutral wind and ion drift estimation away from background models. In particular, once neutral winds are ingested, estimated neutral winds agree more with validation wind data, and estimated ion drifts in the magnetic field-parallel direction are more sensitive to ingestion than the field-perpendicular zonal and meridional directions. Also, data assimilation with FPI measurements helps provide insight into the effects of contamination on 630.0 nm emissions experienced during geomagnetic storms.

  2. Power system observability and dynamic state estimation for stability monitoring using synchrophasor measurements

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

    Sun, Kai; Qi, Junjian; Kang, Wei

    2016-08-01

    Growing penetration of intermittent resources such as renewable generations increases the risk of instability in a power grid. This paper introduces the concept of observability and its computational algorithms for a power grid monitored by the wide-area measurement system (WAMS) based on synchrophasors, e.g. phasor measurement units (PMUs). The goal is to estimate real-time states of generators, especially for potentially unstable trajectories, the information that is critical for the detection of rotor angle instability of the grid. The paper studies the number and siting of synchrophasors in a power grid so that the state of the system can be accuratelymore » estimated in the presence of instability. An unscented Kalman filter (UKF) is adopted as a tool to estimate the dynamic states that are not directly measured by synchrophasors. The theory and its computational algorithms are illustrated in detail by using a 9-bus 3-generator power system model and then tested on a 140-bus 48-generator Northeast Power Coordinating Council power grid model. Case studies on those two systems demonstrate the performance of the proposed approach using a limited number of synchrophasors for dynamic state estimation for stability assessment and its robustness against moderate inaccuracies in model parameters.« less

  3. A Track Initiation Method for the Underwater Target Tracking Environment

    NASA Astrophysics Data System (ADS)

    Li, Dong-dong; Lin, Yang; Zhang, Yao

    2018-04-01

    A novel efficient track initiation method is proposed for the harsh underwater target tracking environment (heavy clutter and large measurement errors): track splitting, evaluating, pruning and merging method (TSEPM). Track initiation demands that the method should determine the existence and initial state of a target quickly and correctly. Heavy clutter and large measurement errors certainly pose additional difficulties and challenges, which deteriorate and complicate the track initiation in the harsh underwater target tracking environment. There are three primary shortcomings for the current track initiation methods to initialize a target: (a) they cannot eliminate the turbulences of clutter effectively; (b) there may be a high false alarm probability and low detection probability of a track; (c) they cannot estimate the initial state for a new confirmed track correctly. Based on the multiple hypotheses tracking principle and modified logic-based track initiation method, in order to increase the detection probability of a track, track splitting creates a large number of tracks which include the true track originated from the target. And in order to decrease the false alarm probability, based on the evaluation mechanism, track pruning and track merging are proposed to reduce the false tracks. TSEPM method can deal with the track initiation problems derived from heavy clutter and large measurement errors, determine the target's existence and estimate its initial state with the least squares method. What's more, our method is fully automatic and does not require any kind manual input for initializing and tuning any parameter. Simulation results indicate that our new method improves significantly the performance of the track initiation in the harsh underwater target tracking environment.

  4. Estimating the State of Aerodynamic Flows in the Presence of Modeling Errors

    NASA Astrophysics Data System (ADS)

    da Silva, Andre F. C.; Colonius, Tim

    2017-11-01

    The ensemble Kalman filter (EnKF) has been proven to be successful in fields such as meteorology, in which high-dimensional nonlinear systems render classical estimation techniques impractical. When the model used to forecast state evolution misrepresents important aspects of the true dynamics, estimator performance may degrade. In this work, parametrization and state augmentation are used to track misspecified boundary conditions (e.g., free stream perturbations). The resolution error is modeled as a Gaussian-distributed random variable with the mean (bias) and variance to be determined. The dynamics of the flow past a NACA 0009 airfoil at high angles of attack and moderate Reynolds number is represented by a Navier-Stokes equations solver with immersed boundaries capabilities. The pressure distribution on the airfoil or the velocity field in the wake, both randomized by synthetic noise, are sampled as measurement data and incorporated into the estimated state and bias following Kalman's analysis scheme. Insights about how to specify the modeling error covariance matrix and its impact on the estimator performance are conveyed. This work has been supported in part by a Grant from AFOSR (FA9550-14-1-0328) with Dr. Douglas Smith as program manager, and by a Science without Borders scholarship from the Ministry of Education of Brazil (Capes Foundation - BEX 12966/13-4).

  5. A particle filter for multi-target tracking in track before detect context

    NASA Astrophysics Data System (ADS)

    Amrouche, Naima; Khenchaf, Ali; Berkani, Daoud

    2016-10-01

    The track-before-detect (TBD) approach can be used to track a single target in a highly noisy radar scene. This is because it makes use of unthresholded observations and incorporates a binary target existence variable into its target state estimation process when implemented as a particle filter (PF). This paper proposes the recursive PF-TBD approach to detect multiple targets in low-signal-to noise ratios (SNR). The algorithm's successful performance is demonstrated using a simulated two target example.

  6. Estimating short-run and long-run interaction mechanisms in interictal state.

    PubMed

    Ozkaya, Ata; Korürek, Mehmet

    2010-04-01

    We address the issue of analyzing electroencephalogram (EEG) from seizure patients in order to test, model and determine the statistical properties that distinguish between EEG states (interictal, pre-ictal, ictal) by introducing a new class of time series analysis methods. In the present study: firstly, we employ statistical methods to determine the non-stationary behavior of focal interictal epileptiform series within very short time intervals; secondly, for such intervals that are deemed non-stationary we suggest the concept of Autoregressive Integrated Moving Average (ARIMA) process modelling, well known in time series analysis. We finally address the queries of causal relationships between epileptic states and between brain areas during epileptiform activity. We estimate the interaction between different EEG series (channels) in short time intervals by performing Granger-causality analysis and also estimate such interaction in long time intervals by employing Cointegration analysis, both analysis methods are well-known in econometrics. Here we find: first, that the causal relationship between neuronal assemblies can be identified according to the duration and the direction of their possible mutual influences; second, that although the estimated bidirectional causality in short time intervals yields that the neuronal ensembles positively affect each other, in long time intervals neither of them is affected (increasing amplitudes) from this relationship. Moreover, Cointegration analysis of the EEG series enables us to identify whether there is a causal link from the interictal state to ictal state.

  7. Use of uninformative priors to initialize state estimation for dynamical systems

    NASA Astrophysics Data System (ADS)

    Worthy, Johnny L.; Holzinger, Marcus J.

    2017-10-01

    The admissible region must be expressed probabilistically in order to be used in Bayesian estimation schemes. When treated as a probability density function (PDF), a uniform admissible region can be shown to have non-uniform probability density after a transformation. An alternative approach can be used to express the admissible region probabilistically according to the Principle of Transformation Groups. This paper uses a fundamental multivariate probability transformation theorem to show that regardless of which state space an admissible region is expressed in, the probability density must remain the same under the Principle of Transformation Groups. The admissible region can be shown to be analogous to an uninformative prior with a probability density that remains constant under reparameterization. This paper introduces requirements on how these uninformative priors may be transformed and used for state estimation and the difference in results when initializing an estimation scheme via a traditional transformation versus the alternative approach.

  8. IMM estimator with out-of-sequence measurements

    NASA Astrophysics Data System (ADS)

    Bar-Shalom, Yaakov; Chen, Huimin

    2004-08-01

    In multisensor tracking systems that operate in a centralized information processing architecture, measurements from the same target obtained by different sensors can arrive at the processing center out of sequence. In order to avoid either a delay in the output or the need for reordering and reprocessing an entire sequence of measurements, such measurements have to be processed as out-of-sequence measurements (OOSM). Recent work developed procedures for incorporating OOSMs into a Kalman filter (KF). Since the state of the art tracker for real (maneuvering) targets is the Interacting Multiple Model (IMM) estimator, this paper presents the algorithm for incorporating OOSMs into an IMM estimator. Both data association and estimation are considered. Simulation results are presented for two realistic problems using measurements from two airborne GMTI sensors. It is shown that the proposed algorithm for incorporating OOSMs into an IMM estimator yields practically the same performance as the reordering and in-sequence reprocessing of the measurements.

  9. MRAS state estimator for speed sensorless ISFOC induction motor drives with Luenberger load torque estimation.

    PubMed

    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. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  10. Target Detection of Quantum Illumination Receiver Based on Photon-subtracted Entanglement State

    NASA Astrophysics Data System (ADS)

    Chi, Jiao; Liu, HongJun; Huang, Nan; Wang, ZhaoLu

    2017-12-01

    We theoretically propose a quantum illumination receiver based on the ideal photon-subtracted two-mode squeezed state (PSTMSS) to efficiently detect the noise-hidden target. This receiver is generated by applying an optical parametric amplifier (OPA) to the cross correlation detection. With analyzing the output performance, it is found that OPA as a preposition technology of the receiver can contribute to the PSTMSS by significantly reducing the error probability than that of the general two-mode squeezed state (TMSS). Comparing with TMSS, the signal-to-noise ratio of quantum illumination based on ideal PSTMSS and OPA is improved more than 4 dB under an optimal gain of OPA. This work may provide a potential improvement in the application of accurate target detection when two kinds of resource have the identical real squeezing parameter.

  11. Customized Steady-State Constraints for Parameter Estimation in Non-Linear Ordinary Differential Equation Models

    PubMed Central

    Rosenblatt, Marcus; Timmer, Jens; Kaschek, Daniel

    2016-01-01

    Ordinary differential equation models have become a wide-spread approach to analyze dynamical systems and understand underlying mechanisms. Model parameters are often unknown and have to be estimated from experimental data, e.g., by maximum-likelihood estimation. In particular, models of biological systems contain a large number of parameters. To reduce the dimensionality of the parameter space, steady-state information is incorporated in the parameter estimation process. For non-linear models, analytical steady-state calculation typically leads to higher-order polynomial equations for which no closed-form solutions can be obtained. This can be circumvented by solving the steady-state equations for kinetic parameters, which results in a linear equation system with comparatively simple solutions. At the same time multiplicity of steady-state solutions is avoided, which otherwise is problematic for optimization. When solved for kinetic parameters, however, steady-state constraints tend to become negative for particular model specifications, thus, generating new types of optimization problems. Here, we present an algorithm based on graph theory that derives non-negative, analytical steady-state expressions by stepwise removal of cyclic dependencies between dynamical variables. The algorithm avoids multiple steady-state solutions by construction. We show that our method is applicable to most common classes of biochemical reaction networks containing inhibition terms, mass-action and Hill-type kinetic equations. Comparing the performance of parameter estimation for different analytical and numerical methods of incorporating steady-state information, we show that our approach is especially well-tailored to guarantee a high success rate of optimization. PMID:27243005

  12. Customized Steady-State Constraints for Parameter Estimation in Non-Linear Ordinary Differential Equation Models.

    PubMed

    Rosenblatt, Marcus; Timmer, Jens; Kaschek, Daniel

    2016-01-01

    Ordinary differential equation models have become a wide-spread approach to analyze dynamical systems and understand underlying mechanisms. Model parameters are often unknown and have to be estimated from experimental data, e.g., by maximum-likelihood estimation. In particular, models of biological systems contain a large number of parameters. To reduce the dimensionality of the parameter space, steady-state information is incorporated in the parameter estimation process. For non-linear models, analytical steady-state calculation typically leads to higher-order polynomial equations for which no closed-form solutions can be obtained. This can be circumvented by solving the steady-state equations for kinetic parameters, which results in a linear equation system with comparatively simple solutions. At the same time multiplicity of steady-state solutions is avoided, which otherwise is problematic for optimization. When solved for kinetic parameters, however, steady-state constraints tend to become negative for particular model specifications, thus, generating new types of optimization problems. Here, we present an algorithm based on graph theory that derives non-negative, analytical steady-state expressions by stepwise removal of cyclic dependencies between dynamical variables. The algorithm avoids multiple steady-state solutions by construction. We show that our method is applicable to most common classes of biochemical reaction networks containing inhibition terms, mass-action and Hill-type kinetic equations. Comparing the performance of parameter estimation for different analytical and numerical methods of incorporating steady-state information, we show that our approach is especially well-tailored to guarantee a high success rate of optimization.

  13. Precision targeting in guided munition using IR sensor and MmW radar

    NASA Astrophysics Data System (ADS)

    Sreeja, S.; Hablani, H. B.; Arya, H.

    2015-10-01

    Conventional munitions are not guided with sensors and therefore miss the target, particularly if the target is mobile. The miss distance of these munitions can be decreased by incorporating sensors to detect the target and guide the munition during flight. This paper is concerned with a Precision Guided Munition(PGM) equipped with an infrared sensor and a millimeter wave radar [IR and MmW, for short]. Three-dimensional flight of the munition and its pitch and yaw motion models are developed and simulated. The forward and lateral motion of a target tank on the ground is modeled as two independent second-order Gauss-Markov process. To estimate the target location on the ground and the line-of-sight rate to intercept it an Extended Kalman Filter is composed whose state vector consists of cascaded state vectors of missile dynamics and target dynamics. The line-of-sight angle measurement from the infrared seeker is by centroiding the target image in 40 Hz. The centroid estimation of the images in the focal plane is at a frequency of 10 Hz. Every 10 Hz, centroids of four consecutive images are averaged, yielding a time-averaged centroid, implying some measurement delay. The miss distance achieved by including by image processing delays is 1:45m.

  14. Precision targeting in guided munition using infrared sensor and millimeter wave radar

    NASA Astrophysics Data System (ADS)

    Sulochana, Sreeja; Hablani, Hari B.; Arya, Hemendra

    2016-07-01

    Conventional munitions are not guided with sensors and therefore miss the target, particularly if the target is mobile. The miss distance of these munitions can be decreased by incorporating sensors to detect the target and guide the munition during flight. This paper is concerned with a precision guided munition equipped with an infrared (IR) sensor and a millimeter wave radar (MmW). Three-dimensional flight of the munition and its pitch and yaw motion models are developed and simulated. The forward and lateral motion of a target tank on the ground is modeled as two independent second-order Gauss-Markov processes. To estimate the target location on the ground and the line-of-sight (LOS) rate to intercept it, an extended Kalman filter is composed whose state vector consists of cascaded state vectors of missile dynamics and target dynamics. The LOS angle measurement from the IR seeker is by centroiding the target image in 40 Hz. The centroid estimation of the images in the focal plane is at a frequency of 10 Hz. Every 10 Hz, centroids of four consecutive images are averaged, yielding a time-averaged centroid, implying some measurement delay. The miss distance achieved by including image processing delays is 1.45 m.

  15. Estimation of Dynamic Sparse Connectivity Patterns From Resting State fMRI.

    PubMed

    Cai, Biao; Zille, Pascal; Stephen, Julia M; Wilson, Tony W; Calhoun, Vince D; Wang, Yu Ping

    2018-05-01

    Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI) time series, especially during resting state periods, provides a powerful tool to assess human brain functional architecture in health, disease, and developmental states. Recently, the focus of connectivity analysis has shifted toward the subnetworks of the brain, which reveals co-activating patterns over time. Most prior works produced a dense set of high-dimensional vectors, which are hard to interpret. In addition, their estimations to a large extent were based on an implicit assumption of spatial and temporal stationarity throughout the fMRI scanning session. In this paper, we propose an approach called dynamic sparse connectivity patterns (dSCPs), which takes advantage of both matrix factorization and time-varying fMRI time series to improve the estimation power of FC. The feasibility of analyzing dynamic FC with our model is first validated through simulated experiments. Then, we use our framework to measure the difference between young adults and children with real fMRI data set from the Philadelphia Neurodevelopmental Cohort (PNC). The results from the PNC data set showed significant FC differences between young adults and children in four different states. For instance, young adults had reduced connectivity between the default mode network and other subnetworks, as well as hyperconnectivity within the visual system in states 1 and 3, and hypoconnectivity in state 2. Meanwhile, they exhibited temporal correlation patterns that changed over time within functional subnetworks. In addition, the dSCPs model indicated that older people tend to spend more time within a relatively connected FC pattern. Overall, the proposed method provides a valid means to assess dynamic FC, which could facilitate the study of brain networks.

  16. Single-shot quantum state estimation via a continuous measurement in the strong backaction regime

    NASA Astrophysics Data System (ADS)

    Cook, Robert L.; Riofrío, Carlos A.; Deutsch, Ivan H.

    2014-09-01

    We study quantum tomography based on a stochastic continuous-time measurement record obtained from a probe field collectively interacting with an ensemble of identically prepared systems. In comparison to previous studies, we consider here the case in which the measurement-induced backaction has a non-negligible effect on the dynamical evolution of the ensemble. We formulate a maximum likelihood estimate for the initial quantum state given only a single instance of the continuous diffusive measurement record. We apply our estimator to the simplest problem: state tomography of a single pure qubit, which, during the course of the measurement, is also subjected to dynamical control. We identify a regime where the many-body system is well approximated at all times by a separable pure spin coherent state, whose Bloch vector undergoes a conditional stochastic evolution. We simulate the results of our estimator and show that we can achieve close to the upper bound of fidelity set by the optimal generalized measurement. This estimate is compared to, and significantly outperforms, an equivalent estimator that ignores measurement backaction.

  17. Speckle pattern sequential extraction metric for estimating the focus spot size on a remote diffuse target.

    PubMed

    Yu, Zhan; Li, Yuanyang; Liu, Lisheng; Guo, Jin; Wang, Tingfeng; Yang, Guoqing

    2017-11-10

    The speckle pattern (line by line) sequential extraction (SPSE) metric is proposed by the one-dimensional speckle intensity level crossing theory. Through the sequential extraction of received speckle information, the speckle metrics for estimating the variation of focusing spot size on a remote diffuse target are obtained. Based on the simulation, we will give some discussions about the SPSE metric range of application under the theoretical conditions, and the aperture size will affect the metric performance of the observation system. The results of the analyses are verified by the experiment. This method is applied to the detection of relative static target (speckled jitter frequency is less than the CCD sampling frequency). The SPSE metric can determine the variation of the focusing spot size over a long distance, moreover, the metric will estimate the spot size under some conditions. Therefore, the monitoring and the feedback of far-field spot will be implemented laser focusing system applications and help the system to optimize the focusing performance.

  18. Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package

    PubMed Central

    Decker, Anna L.; Hubbard, Alan; Crespi, Catherine M.; Seto, Edmund Y.W.; Wang, May C.

    2015-01-01

    While child and adolescent obesity is a serious public health concern, few studies have utilized parameters based on the causal inference literature to examine the potential impacts of early intervention. The purpose of this analysis was to estimate the causal effects of early interventions to improve physical activity and diet during adolescence on body mass index (BMI), a measure of adiposity, using improved techniques. The most widespread statistical method in studies of child and adolescent obesity is multi-variable regression, with the parameter of interest being the coefficient on the variable of interest. This approach does not appropriately adjust for time-dependent confounding, and the modeling assumptions may not always be met. An alternative parameter to estimate is one motivated by the causal inference literature, which can be interpreted as the mean change in the outcome under interventions to set the exposure of interest. The underlying data-generating distribution, upon which the estimator is based, can be estimated via a parametric or semi-parametric approach. Using data from the National Heart, Lung, and Blood Institute Growth and Health Study, a 10-year prospective cohort study of adolescent girls, we estimated the longitudinal impact of physical activity and diet interventions on 10-year BMI z-scores via a parameter motivated by the causal inference literature, using both parametric and semi-parametric estimation approaches. The parameters of interest were estimated with a recently released R package, ltmle, for estimating means based upon general longitudinal treatment regimes. We found that early, sustained intervention on total calories had a greater impact than a physical activity intervention or non-sustained interventions. Multivariable linear regression yielded inflated effect estimates compared to estimates based on targeted maximum-likelihood estimation and data-adaptive super learning. Our analysis demonstrates that sophisticated

  19. The MAP Spacecraft Angular State Estimation After Sensor Failure

    NASA Technical Reports Server (NTRS)

    Bar-Itzhack, Itzhack Y.; Harman, Richard R.

    2003-01-01

    This work describes two algorithms for computing the angular rate and attitude in case of a gyro and a Star Tracker failure in the Microwave Anisotropy Probe (MAP) satellite, which was placed in the L2 parking point from where it collects data to determine the origin of the universe. The nature of the problem is described, two algorithms are suggested, an observability study is carried out and real MAP data are used to determine the merit of the algorithms. It is shown that one of the algorithms yields a good estimate of the rates but not of the attitude whereas the other algorithm yields a good estimate of the rate as well as two of the three attitude angles. The estimation of the third angle depends on the initial state estimate. There is a contradiction between this result and the outcome of the observability analysis. An explanation of this contradiction is given in the paper. Although this work treats a particular spacecraft, the conclusions have a far reaching consequence.

  20. Using Tensor Completion Method to Achieving Better Coverage of Traffic State Estimation from Sparse Floating Car Data

    PubMed Central

    Ran, Bin; Song, Li; Cheng, Yang; Tan, Huachun

    2016-01-01

    Traffic state estimation from the floating car system is a challenging problem. The low penetration rate and random distribution make available floating car samples usually cover part space and time points of the road networks. To obtain a wide range of traffic state from the floating car system, many methods have been proposed to estimate the traffic state for the uncovered links. However, these methods cannot provide traffic state of the entire road networks. In this paper, the traffic state estimation is transformed to solve a missing data imputation problem, and the tensor completion framework is proposed to estimate missing traffic state. A tensor is constructed to model traffic state in which observed entries are directly derived from floating car system and unobserved traffic states are modeled as missing entries of constructed tensor. The constructed traffic state tensor can represent spatial and temporal correlations of traffic data and encode the multi-way properties of traffic state. The advantage of the proposed approach is that it can fully mine and utilize the multi-dimensional inherent correlations of traffic state. We tested the proposed approach on a well calibrated simulation network. Experimental results demonstrated that the proposed approach yield reliable traffic state estimation from very sparse floating car data, particularly when dealing with the floating car penetration rate is below 1%. PMID:27448326

  1. Using Tensor Completion Method to Achieving Better Coverage of Traffic State Estimation from Sparse Floating Car Data.

    PubMed

    Ran, Bin; Song, Li; Zhang, Jian; Cheng, Yang; Tan, Huachun

    2016-01-01

    Traffic state estimation from the floating car system is a challenging problem. The low penetration rate and random distribution make available floating car samples usually cover part space and time points of the road networks. To obtain a wide range of traffic state from the floating car system, many methods have been proposed to estimate the traffic state for the uncovered links. However, these methods cannot provide traffic state of the entire road networks. In this paper, the traffic state estimation is transformed to solve a missing data imputation problem, and the tensor completion framework is proposed to estimate missing traffic state. A tensor is constructed to model traffic state in which observed entries are directly derived from floating car system and unobserved traffic states are modeled as missing entries of constructed tensor. The constructed traffic state tensor can represent spatial and temporal correlations of traffic data and encode the multi-way properties of traffic state. The advantage of the proposed approach is that it can fully mine and utilize the multi-dimensional inherent correlations of traffic state. We tested the proposed approach on a well calibrated simulation network. Experimental results demonstrated that the proposed approach yield reliable traffic state estimation from very sparse floating car data, particularly when dealing with the floating car penetration rate is below 1%.

  2. A Method for Determining Pseudo-measurement State Values for Topology Observability of State Estimation in Power Systems

    NASA Astrophysics Data System (ADS)

    Urano, Shoichi; Mori, Hiroyuki

    This paper proposes a new technique for determining of state values in power systems. Recently, it is useful for carrying out state estimation with data of PMU (Phasor Measurement Unit). The authors have developed a method for determining state values with artificial neural network (ANN) considering topology observability in power systems. ANN has advantage to approximate nonlinear functions with high precision. The method evaluates pseudo-measurement state values of the data which are lost in power systems. The method is successfully applied to the IEEE 14-bus system.

  3. A method and implementation for incorporating heuristic knowledge into a state estimator through the use of a fuzzy model

    NASA Astrophysics Data System (ADS)

    Swanson, Steven Roy

    The objective of the dissertation is to improve state estimation performance, as compared to a Kalman filter, when non-constant, or changing, biases exist in the measurement data. The state estimation performance increase will come from the use of a fuzzy model to determine the position and velocity gains of a state estimator. A method is proposed for incorporating heuristic knowledge into a state estimator through the use of a fuzzy model. This method consists of using a fuzzy model to determine the gains of the state estimator, converting the heuristic knowledge into the fuzzy model, and then optimizing the fuzzy model with a genetic algorithm. This method is applied to the problem of state estimation of a cascaded global positioning system (GPS)/inertial reference unit (IRU) navigation system. The GPS position data contains two major sources for position bias. The first bias is due to satellite errors and the second is due to the time delay or lag from when the GPS position is calculated until it is used in the state estimator. When a change in the bias of the measurement data occurs, a state estimator will converge on the new measurement data solution. This will introduce errors into a Kalman filter's estimated state velocities, which in turn will cause a position overshoot as it converges. By using a fuzzy model to determine the gains of a state estimator, the velocity errors and their associated deficiencies can be reduced.

  4. National and State Estimates of the Numbers of Adults and Children with Active Epilepsy - United States, 2015.

    PubMed

    Zack, Matthew M; Kobau, Rosemarie

    2017-08-11

    Epilepsy, a brain disorder leading to recurring seizures, has garnered increased public health focus because persons with epilepsy experience pronounced and persistent health and socioeconomic disparities despite treatment advances, public awareness programs, and expanded rights for persons with disabilities (1,2). For almost all states, epilepsy prevalence estimates do not exist. CDC used national data sources including the 2015 National Health Interview Survey (NHIS) for adults (aged ≥18 years), the 2011-2012 National Survey of Children's Health (NSCH), and the 2015 Current Population Survey data, describing 2014 income levels, to estimate prevalent cases of active epilepsy, overall and by state, to provide information for state public health planning. In 2015, 1.2% of the U.S. population (3.4 million persons: 3 million adults and 470,000 children) reported active epilepsy (self-reported doctor-diagnosed epilepsy and under treatment or with recent seizures within 12 months of interview) or current epilepsy (parent-reported doctor-diagnosed epilepsy and current epilepsy). Estimated numbers of persons with active epilepsy, after accounting for income and age differences by state, ranged from 5,900 in Wyoming to 427,700 in California. NHIS data from 2010-2015 indicate increases in the number of persons with active epilepsy, probably because of population growth. This study provides updated national and modeled state-specific numbers of active epilepsy cases. Public health practitioners, health care providers, policy makers, epilepsy researchers, and other epilepsy stakeholders, including family members and people with epilepsy, can use these findings to ensure that evidence-based programs meet the complex needs of adults and children with epilepsy and reduce the disparities resulting from it.

  5. Estimated United States Residential Energy Use in 2005

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

    Smith, C A; Johnson, D M; Simon, A J

    2011-12-12

    A flow chart depicting energy flow in the residential sector of the United States economy in 2005 has been constructed from publicly available data and estimates of national energy use patterns. Approximately 11,000 trillion British Thermal Units (trBTUs) of electricity and fuels were used throughout the United States residential sector in lighting, electronics, air conditioning, space heating, water heating, washing appliances, cooking appliances, refrigerators, and other appliances. The residential sector is powered mainly by electricity and natural gas. Other fuels used include petroleum products (fuel oil, liquefied petroleum gas and kerosene), biomass (wood), and on-premises solar, wind, and geothermal energy.more » The flow patterns represent a comprehensive systems view of energy used within the residential sector.« less

  6. State of Charge estimation of lithium ion battery based on extended Kalman filtering algorithm

    NASA Astrophysics Data System (ADS)

    Yang, Fan; Feng, Yiming; Pan, Binbiao; Wan, Renzhuo; Wang, Jun

    2017-08-01

    Accurate estimation of state-of-charge (SOC) for lithium ion battery is crucial for real-time diagnosis and prognosis in green energy vehicles. In this paper, a state space model of the battery based on Thevenin model is adopted. The strategy of estimating state of charge (SOC) based on extended Kalman fil-ter is presented, as well as to combine with ampere-hour counting (AH) and open circuit voltage (OCV) methods. The comparison between simulation and experiments indicates that the model’s performance matches well with that of lithium ion battery. The algorithm of extended Kalman filter keeps a good accura-cy precision and less dependent on its initial value in full range of SOC, which is proved to be suitable for online SOC estimation.

  7. Simultaneous Mean and Covariance Correction Filter for Orbit Estimation.

    PubMed

    Wang, Xiaoxu; Pan, Quan; Ding, Zhengtao; Ma, Zhengya

    2018-05-05

    This paper proposes a novel filtering design, from a viewpoint of identification instead of the conventional nonlinear estimation schemes (NESs), to improve the performance of orbit state estimation for a space target. First, a nonlinear perturbation is viewed or modeled as an unknown input (UI) coupled with the orbit state, to avoid the intractable nonlinear perturbation integral (INPI) required by NESs. Then, a simultaneous mean and covariance correction filter (SMCCF), based on a two-stage expectation maximization (EM) framework, is proposed to simply and analytically fit or identify the first two moments (FTM) of the perturbation (viewed as UI), instead of directly computing such the INPI in NESs. Orbit estimation performance is greatly improved by utilizing the fit UI-FTM to simultaneously correct the state estimation and its covariance. Third, depending on whether enough information is mined, SMCCF should outperform existing NESs or the standard identification algorithms (which view the UI as a constant independent of the state and only utilize the identified UI-mean to correct the state estimation, regardless of its covariance), since it further incorporates the useful covariance information in addition to the mean of the UI. Finally, our simulations demonstrate the superior performance of SMCCF via an orbit estimation example.

  8. Evaluating the national land cover database tree canopy and impervious cover estimates across the conterminous United States: a comparison with photo-interpreted estimates

    Treesearch

    David J. Nowak; Eric J. Greenfield

    2010-01-01

    The 2001 National Land Cover Database (NLCD) provides 30-m resolution estimates of percentage tree canopy and percentage impervious cover for the conterminous United States. Previous estimates that compared NLCD tree canopy and impervious cover estimates with photo-interpreted cover estimates within selected counties and places revealed that NLCD underestimates tree...

  9. A stochastic approach to online vehicle state and parameter estimation, with application to inertia estimation for rollover prevention and battery charge/health estimation.

    DOT National Transportation Integrated Search

    2013-08-01

    This report summarizes research conducted at Penn State, Virginia Tech, and West Virginia University on the development of algorithms based on the generalized polynomial chaos (gpc) expansion for the online estimation of automotive and transportation...

  10. Enhancing transit polio vaccination in collaboration with targeted stakeholders in Kaduna State, Nigeria: Lessons learnt: 2014-2015.

    PubMed

    Musa, Audu; Abba, Bashir; Ningi, Adamu M I; Gali, Emanuel; Bawa, Samuel; Manneh, Fadninding; Mkanda, Pascal; Banda, Richard; Yehuluashet, Yared G; Tegegne, Sisay G; Umeh, Gregory; Nsubuga, Peter; Etsano, Andrew; Shuaib, Faisal; Mohammed, Ado; Vaz, Rui G

    2016-10-10

    In Kaduna State of Nigeria, the high influx of people from neighboring states with eligible children for polio vaccination represents a significant proportion of the target population. Many of these children are often missed by the vaccination team. The purpose of the study was to determine the contribution of targeted stakeholders in transit polio vaccination. We used the trends of vaccinated children at transit points, motor parks and markets, well as total children vaccinated by transit teams in Chikun, Igabi and Sabon Gari Local Government Areas (LGAs) of Kaduna State, Nigeria, four rounds before and after the introduction of transit polio vaccination with targeted stakeholders in Kaduna State. A total of 87,502 under-5 children were vaccinated by the various transit teams in the three LGAs, which accounted for 3.2% of the total 2,781,162 children vaccinated by the three LGAs. For transit point vaccination, the number of vaccinated children increased from 1026 to 19,289 (302%), while motor park vaccination increased from 1289 to 4106 (318%) and market vaccination increased from 10,488 to 14,511 (138%), four rounds after the introduction of transit polio vaccination with targeted stakeholders. Engagement of targeted stakeholders significantly enhanced transit polio vaccination in Kaduna State, Nigeria. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  11. Robust state estimation for uncertain fuzzy bidirectional associative memory networks with time-varying delays

    NASA Astrophysics Data System (ADS)

    Vadivel, P.; Sakthivel, R.; Mathiyalagan, K.; Arunkumar, A.

    2013-09-01

    This paper addresses the issue of robust state estimation for a class of fuzzy bidirectional associative memory (BAM) neural networks with time-varying delays and parameter uncertainties. By constructing the Lyapunov-Krasovskii functional, which contains the triple-integral term and using the free-weighting matrix technique, a set of sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to estimate the neuron states through available output measurements such that the dynamics of the estimation error system is robustly asymptotically stable. In particular, we consider a generalized activation function in which the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. More precisely, the design of the state estimator for such BAM neural networks can be obtained by solving some LMIs, which are dependent on the size of the time derivative of the time-varying delays. Finally, a numerical example with simulation result is given to illustrate the obtained theoretical results.

  12. Burden of Severe Pneumonia, Pneumococcal Pneumonia and Pneumonia Deaths in Indian States: Modelling Based Estimates

    PubMed Central

    Farooqui, Habib; Jit, Mark; Heymann, David L.; Zodpey, Sanjay

    2015-01-01

    The burden of severe pneumonia in terms of morbidity and mortality is unknown in India especially at sub-national level. In this context, we aimed to estimate the number of severe pneumonia episodes, pneumococcal pneumonia episodes and pneumonia deaths in children younger than 5 years in 2010. We adapted and parameterized a mathematical model based on the epidemiological concept of potential impact fraction developed CHERG for this analysis. The key parameters that determine the distribution of severe pneumonia episode across Indian states were state-specific under-5 population, state-specific prevalence of selected definite pneumonia risk factors and meta-estimates of relative risks for each of these risk factors. We applied the incidence estimates and attributable fraction of risk factors to population estimates for 2010 of each Indian state. We then estimated the number of pneumococcal pneumonia cases by applying the vaccine probe methodology to an existing trial. We estimated mortality due to severe pneumonia and pneumococcal pneumonia by combining incidence estimates with case fatality ratios from multi-centric hospital-based studies. Our results suggest that in 2010, 3.6 million (3.3–3.9 million) episodes of severe pneumonia and 0.35 million (0.31–0.40 million) all cause pneumonia deaths occurred in children younger than 5 years in India. The states that merit special mention include Uttar Pradesh where 18.1% children reside but contribute 24% of pneumonia cases and 26% pneumonia deaths, Bihar (11.3% children, 16% cases, 22% deaths) Madhya Pradesh (6.6% children, 9% cases, 12% deaths), and Rajasthan (6.6% children, 8% cases, 11% deaths). Further, we estimated that 0.56 million (0.49–0.64 million) severe episodes of pneumococcal pneumonia and 105 thousand (92–119 thousand) pneumococcal deaths occurred in India. The top contributors to India’s pneumococcal pneumonia burden were Uttar Pradesh, Bihar, Madhya Pradesh and Rajasthan in that order. Our

  13. An Estimate of Avian Mortality at Communication Towers in the United States and Canada

    PubMed Central

    Longcore, Travis; Rich, Catherine; Mineau, Pierre; MacDonald, Beau; Bert, Daniel G.; Sullivan, Lauren M.; Mutrie, Erin; Gauthreaux, Sidney A.; Avery, Michael L.; Crawford, Robert L.; Manville, Albert M.; Travis, Emilie R.; Drake, David

    2012-01-01

    Avian mortality at communication towers in the continental United States and Canada is an issue of pressing conservation concern. Previous estimates of this mortality have been based on limited data and have not included Canada. We compiled a database of communication towers in the continental United States and Canada and estimated avian mortality by tower with a regression relating avian mortality to tower height. This equation was derived from 38 tower studies for which mortality data were available and corrected for sampling effort, search efficiency, and scavenging where appropriate. Although most studies document mortality at guyed towers with steady-burning lights, we accounted for lower mortality at towers without guy wires or steady-burning lights by adjusting estimates based on published studies. The resulting estimate of mortality at towers is 6.8 million birds per year in the United States and Canada. Bootstrapped subsampling indicated that the regression was robust to the choice of studies included and a comparison of multiple regression models showed that incorporating sampling, scavenging, and search efficiency adjustments improved model fit. Estimating total avian mortality is only a first step in developing an assessment of the biological significance of mortality at communication towers for individual species or groups of species. Nevertheless, our estimate can be used to evaluate this source of mortality, develop subsequent per-species mortality estimates, and motivate policy action. PMID:22558082

  14. An estimate of avian mortality at communication towers in the United States and Canada.

    PubMed

    Longcore, Travis; Rich, Catherine; Mineau, Pierre; MacDonald, Beau; Bert, Daniel G; Sullivan, Lauren M; Mutrie, Erin; Gauthreaux, Sidney A; Avery, Michael L; Crawford, Robert L; Manville, Albert M; Travis, Emilie R; Drake, David

    2012-01-01

    Avian mortality at communication towers in the continental United States and Canada is an issue of pressing conservation concern. Previous estimates of this mortality have been based on limited data and have not included Canada. We compiled a database of communication towers in the continental United States and Canada and estimated avian mortality by tower with a regression relating avian mortality to tower height. This equation was derived from 38 tower studies for which mortality data were available and corrected for sampling effort, search efficiency, and scavenging where appropriate. Although most studies document mortality at guyed towers with steady-burning lights, we accounted for lower mortality at towers without guy wires or steady-burning lights by adjusting estimates based on published studies. The resulting estimate of mortality at towers is 6.8 million birds per year in the United States and Canada. Bootstrapped subsampling indicated that the regression was robust to the choice of studies included and a comparison of multiple regression models showed that incorporating sampling, scavenging, and search efficiency adjustments improved model fit. Estimating total avian mortality is only a first step in developing an assessment of the biological significance of mortality at communication towers for individual species or groups of species. Nevertheless, our estimate can be used to evaluate this source of mortality, develop subsequent per-species mortality estimates, and motivate policy action.

  15. Methodology for estimating soil carbon for the forest carbon budget model of the United States, 2001

    Treesearch

    L. S. Heath; R. A. Birdsey; D. W. Williams

    2002-01-01

    The largest carbon (C) pool in United States forests is the soil C pool. We present methodology and soil C pool estimates used in the FORCARB model, which estimates and projects forest carbon budgets for the United States. The methodology balances knowledge, uncertainties, and ease of use. The estimates are calculated using the USDA Natural Resources Conservation...

  16. Fast super-resolution estimation of DOA and DOD in bistatic MIMO Radar with off-grid targets

    NASA Astrophysics Data System (ADS)

    Zhang, Dong; Zhang, Yongshun; Zheng, Guimei; Feng, Cunqian; Tang, Jun

    2018-05-01

    In this paper, we focus on the problem of joint DOA and DOD estimation in Bistatic MIMO Radar using sparse reconstruction method. In traditional ways, we usually convert the 2D parameter estimation problem into 1D parameter estimation problem by Kronecker product which will enlarge the scale of the parameter estimation problem and bring more computational burden. Furthermore, it requires that the targets must fall on the predefined grids. In this paper, a 2D-off-grid model is built which can solve the grid mismatch problem of 2D parameters estimation. Then in order to solve the joint 2D sparse reconstruction problem directly and efficiently, three kinds of fast joint sparse matrix reconstruction methods are proposed which are Joint-2D-OMP algorithm, Joint-2D-SL0 algorithm and Joint-2D-SOONE algorithm. Simulation results demonstrate that our methods not only can improve the 2D parameter estimation accuracy but also reduce the computational complexity compared with the traditional Kronecker Compressed Sensing method.

  17. Finite time state and disturbance estimation for robust performance of motion control systems using sliding modes

    NASA Astrophysics Data System (ADS)

    Tamhane, Bhagyashri; Kurode, Shailaja

    2018-05-01

    In this paper, simultaneous state and disturbance estimation of a drive system composed of motor connected to a load is proposed. Such a system is represented by a two mass model realising in a fourth-order plant. Backlash is introduced as the nonlinear disturbance in gears which is proposed to be estimated and in turn compensated. For this motion control system, a two-stage higher order sliding-mode observer is proposed for state and backlash estimation. The novelty lies in the fact that for this fourth-order system, output is considered from the motor end only, i.e. its angular displacement. The unmeasured states consisting of output derivative, load-side angular displacement and its derivative along with backlash are estimated in finite time. This disturbance due to backlash is unmatched in nature. The estimated states and disturbance are used to devise a robust sliding-mode control. This proposed scheme is validated in simulation and experimentation.

  18. Elastic registration of prostate MR images based on state estimation of dynamical systems

    NASA Astrophysics Data System (ADS)

    Marami, Bahram; Ghoul, Suha; Sirouspour, Shahin; Capson, David W.; Davidson, Sean R. H.; Trachtenberg, John; Fenster, Aaron

    2014-03-01

    Magnetic resonance imaging (MRI) is being increasingly used for image-guided biopsy and focal therapy of prostate cancer. A combined rigid and deformable registration technique is proposed to register pre-treatment diagnostic 3T magnetic resonance (MR) images, with the identified target tumor(s), to the intra-treatment 1.5T MR images. The pre-treatment 3T images are acquired with patients in strictly supine position using an endorectal coil, while 1.5T images are obtained intra-operatively just before insertion of the ablation needle with patients in the lithotomy position. An intensity-based registration routine rigidly aligns two images in which the transformation parameters is initialized using three pairs of manually selected approximate corresponding points. The rigid registration is followed by a deformable registration algorithm employing a generic dynamic linear elastic deformation model discretized by the finite element method (FEM). The model is used in a classical state estimation framework to estimate the deformation of the prostate based on a similarity metric between pre- and intra-treatment images. Registration results using 10 sets of prostate MR images showed that the proposed method can significantly improve registration accuracy in terms of target registration error (TRE) for all prostate substructures. The root mean square (RMS) TRE of 46 manually identified fiducial points was found to be 2.40+/-1.20 mm, 2.51+/-1.20 mm, and 2.28+/-1.22mm for the whole gland (WG), central gland (CG), and peripheral zone (PZ), respectively after deformable registration. These values are improved from 3.15+/-1.60 mm, 3.09+/-1.50 mm, and 3.20+/-1.73mm in the WG, CG and PZ, respectively resulted from rigid registration. Registration results are also evaluated based on the Dice similarity coefficient (DSC), mean absolute surface distances (MAD) and maximum absolute surface distances (MAXD) of the WG and CG in the prostate images.

  19. A regularized auxiliary particle filtering approach for system state estimation and battery life prediction

    NASA Astrophysics Data System (ADS)

    Liu, Jie; Wang, Wilson; Ma, Fai

    2011-07-01

    System current state estimation (or condition monitoring) and future state prediction (or failure prognostics) constitute the core elements of condition-based maintenance programs. For complex systems whose internal state variables are either inaccessible to sensors or hard to measure under normal operational conditions, inference has to be made from indirect measurements using approaches such as Bayesian learning. In recent years, the auxiliary particle filter (APF) has gained popularity in Bayesian state estimation; the APF technique, however, has some potential limitations in real-world applications. For example, the diversity of the particles may deteriorate when the process noise is small, and the variance of the importance weights could become extremely large when the likelihood varies dramatically over the prior. To tackle these problems, a regularized auxiliary particle filter (RAPF) is developed in this paper for system state estimation and forecasting. This RAPF aims to improve the performance of the APF through two innovative steps: (1) regularize the approximating empirical density and redraw samples from a continuous distribution so as to diversify the particles; and (2) smooth out the rather diffused proposals by a rejection/resampling approach so as to improve the robustness of particle filtering. The effectiveness of the proposed RAPF technique is evaluated through simulations of a nonlinear/non-Gaussian benchmark model for state estimation. It is also implemented for a real application in the remaining useful life (RUL) prediction of lithium-ion batteries.

  20. Event triggered state estimation techniques for power systems with integrated variable energy resources.

    PubMed

    Francy, Reshma C; Farid, Amro M; Youcef-Toumi, Kamal

    2015-05-01

    For many decades, state estimation (SE) has been a critical technology for energy management systems utilized by power system operators. Over time, it has become a mature technology that provides an accurate representation of system state under fairly stable and well understood system operation. The integration of variable energy resources (VERs) such as wind and solar generation, however, introduces new fast frequency dynamics and uncertainties into the system. Furthermore, such renewable energy is often integrated into the distribution system thus requiring real-time monitoring all the way to the periphery of the power grid topology and not just the (central) transmission system. The conventional solution is two fold: solve the SE problem (1) at a faster rate in accordance with the newly added VER dynamics and (2) for the entire power grid topology including the transmission and distribution systems. Such an approach results in exponentially growing problem sets which need to be solver at faster rates. This work seeks to address these two simultaneous requirements and builds upon two recent SE methods which incorporate event-triggering such that the state estimator is only called in the case of considerable novelty in the evolution of the system state. The first method incorporates only event-triggering while the second adds the concept of tracking. Both SE methods are demonstrated on the standard IEEE 14-bus system and the results are observed for a specific bus for two difference scenarios: (1) a spike in the wind power injection and (2) ramp events with higher variability. Relative to traditional state estimation, the numerical case studies showed that the proposed methods can result in computational time reductions of 90%. These results were supported by a theoretical discussion of the computational complexity of three SE techniques. The work concludes that the proposed SE techniques demonstrate practical improvements to the computational complexity of

  1. Log-polar mapping-based scale space tracking with adaptive target response

    NASA Astrophysics Data System (ADS)

    Li, Dongdong; Wen, Gongjian; Kuai, Yangliu; Zhang, Ximing

    2017-05-01

    Correlation filter-based tracking has exhibited impressive robustness and accuracy in recent years. Standard correlation filter-based trackers are restricted to translation estimation and equipped with fixed target response. These trackers produce an inferior performance when encountered with a significant scale variation or appearance change. We propose a log-polar mapping-based scale space tracker with an adaptive target response. This tracker transforms the scale variation of the target in the Cartesian space into a shift along the logarithmic axis in the log-polar space. A one-dimensional scale correlation filter is learned online to estimate the shift along the logarithmic axis. With the log-polar representation, scale estimation is achieved accurately without a multiresolution pyramid. To achieve an adaptive target response, a variance of the Gaussian function is computed from the response map and updated online with a learning rate parameter. Our log-polar mapping-based scale correlation filter and adaptive target response can be combined with any correlation filter-based trackers. In addition, the scale correlation filter can be extended to a two-dimensional correlation filter to achieve joint estimation of the scale variation and in-plane rotation. Experiments performed on an OTB50 benchmark demonstrate that our tracker achieves superior performance against state-of-the-art trackers.

  2. Cost-effectiveness of targeted screening for abdominal aortic aneurysm. Monte Carlo-based estimates.

    PubMed

    Pentikäinen, T J; Sipilä, T; Rissanen, P; Soisalon-Soininen, S; Salo, J

    2000-01-01

    This article reports a cost-effectiveness analysis of targeted screening for abdominal aortic aneurysm (AAA). A major emphasis was on the estimation of distributions of costs and effectiveness. We performed a Monte Carlo simulation using C programming language in a PC environment. Data on survival and costs, and a majority of screening probabilities, were from our own empirical studies. Natural history data were based on the literature. Each screened male gained 0.07 life-years at an incremental cost of FIM 3,300. The expected values differed from zero very significantly. For females, expected gains were 0.02 life-years at an incremental cost of FIM 1,100, which was not statistically significant. Cost-effectiveness ratios and their 95% confidence intervals were FIM 48,000 (27,000-121,000) and 54,000 (22,000-infinity) for males and females, respectively. Sensitivity analysis revealed that the results for males were stable. Individual variation in life-year gains was high. Males seemed to benefit from targeted AAA screening, and the results were stable. As far as the cost-effectiveness ratio is considered acceptable, screening for males seemed to be justified. However, our assumptions about growth and rupture behavior of AAAs might be improved with further clinical and epidemiological studies. As a point estimate, females benefited in a similar manner, but the results were not statistically significant. The evidence of this study did not justify screening of females.

  3. Efficient Ensemble State-Parameters Estimation Techniques in Ocean Ecosystem Models: Application to the North Atlantic

    NASA Astrophysics Data System (ADS)

    El Gharamti, M.; Bethke, I.; Tjiputra, J.; Bertino, L.

    2016-02-01

    Given the recent strong international focus on developing new data assimilation systems for biological models, we present in this comparative study the application of newly developed state-parameters estimation tools to an ocean ecosystem model. It is quite known that the available physical models are still too simple compared to the complexity of the ocean biology. Furthermore, various biological parameters remain poorly unknown and hence wrong specifications of such parameters can lead to large model errors. Standard joint state-parameters augmentation technique using the ensemble Kalman filter (Stochastic EnKF) has been extensively tested in many geophysical applications. Some of these assimilation studies reported that jointly updating the state and the parameters might introduce significant inconsistency especially for strongly nonlinear models. This is usually the case for ecosystem models particularly during the period of the spring bloom. A better handling of the estimation problem is often carried out by separating the update of the state and the parameters using the so-called Dual EnKF. The dual filter is computationally more expensive than the Joint EnKF but is expected to perform more accurately. Using a similar separation strategy, we propose a new EnKF estimation algorithm in which we apply a one-step-ahead smoothing to the state. The new state-parameters estimation scheme is derived in a consistent Bayesian filtering framework and results in separate update steps for the state and the parameters. Unlike the classical filtering path, the new scheme starts with an update step and later a model propagation step is performed. We test the performance of the new smoothing-based schemes against the standard EnKF in a one-dimensional configuration of the Norwegian Earth System Model (NorESM) in the North Atlantic. We use nutrients profile (up to 2000 m deep) data and surface partial CO2 measurements from Mike weather station (66o N, 2o E) to estimate

  4. Tracking Multiple Video Targets with an Improved GM-PHD Tracker

    PubMed Central

    Zhou, Xiaolong; Yu, Hui; Liu, Honghai; Li, Youfu

    2015-01-01

    Tracking multiple moving targets from a video plays an important role in many vision-based robotic applications. In this paper, we propose an improved Gaussian mixture probability hypothesis density (GM-PHD) tracker with weight penalization to effectively and accurately track multiple moving targets from a video. First, an entropy-based birth intensity estimation method is incorporated to eliminate the false positives caused by noisy video data. Then, a weight-penalized method with multi-feature fusion is proposed to accurately track the targets in close movement. For targets without occlusion, a weight matrix that contains all updated weights between the predicted target states and the measurements is constructed, and a simple, but effective method based on total weight and predicted target state is proposed to search the ambiguous weights in the weight matrix. The ambiguous weights are then penalized according to the fused target features that include spatial-colour appearance, histogram of oriented gradient and target area and further re-normalized to form a new weight matrix. With this new weight matrix, the tracker can correctly track the targets in close movement without occlusion. For targets with occlusion, a robust game-theoretical method is used. Finally, the experiments conducted on various video scenarios validate the effectiveness of the proposed penalization method and show the superior performance of our tracker over the state of the art. PMID:26633422

  5. Transfer Alignment Error Compensator Design Based on Robust State Estimation

    NASA Astrophysics Data System (ADS)

    Lyou, Joon; Lim, You-Chol

    This paper examines the transfer alignment problem of the StrapDown Inertial Navigation System (SDINS), which is subject to the ship’s roll and pitch. Major error sources for velocity and attitude matching are lever arm effect, measurement time delay and ship-body flexure. To reduce these alignment errors, an error compensation method based on state augmentation and robust state estimation is devised. A linearized error model for the velocity and attitude matching transfer alignment system is derived first by linearizing the nonlinear measurement equation with respect to its time delay and dominant Y-axis flexure, and by augmenting the delay state and flexure state into conventional linear state equations. Then an H∞ filter is introduced to account for modeling uncertainties of time delay and the ship-body flexure. The simulation results show that this method considerably decreases azimuth alignment errors considerably.

  6. Constrained target controllability of complex networks

    NASA Astrophysics Data System (ADS)

    Guo, Wei-Feng; Zhang, Shao-Wu; Wei, Ze-Gang; Zeng, Tao; Liu, Fei; Zhang, Jingsong; Wu, Fang-Xiang; Chen, Luonan

    2017-06-01

    It is of great theoretical interest and practical significance to study how to control a system by applying perturbations to only a few driver nodes. Recently, a hot topic of modern network researches is how to determine driver nodes that allow the control of an entire network. However, in practice, to control a complex network, especially a biological network, one may know not only the set of nodes which need to be controlled (i.e. target nodes), but also the set of nodes to which only control signals can be applied (i.e. constrained control nodes). Compared to the general concept of controllability, we introduce the concept of constrained target controllability (CTC) of complex networks, which concerns the ability to drive any state of target nodes to their desirable state by applying control signals to the driver nodes from the set of constrained control nodes. To efficiently investigate the CTC of complex networks, we further design a novel graph-theoretic algorithm called CTCA to estimate the ability of a given network to control targets by choosing driver nodes from the set of constrained control nodes. We extensively evaluate the CTC of numerous real complex networks. The results indicate that biological networks with a higher average degree are easier to control than biological networks with a lower average degree, while electronic networks with a lower average degree are easier to control than web networks with a higher average degree. We also show that our CTCA can more efficiently produce driver nodes for target-controlling the networks than existing state-of-the-art methods. Moreover, we use our CTCA to analyze two expert-curated bio-molecular networks and compare to other state-of-the-art methods. The results illustrate that our CTCA can efficiently identify proven drug targets and new potentials, according to the constrained controllability of those biological networks.

  7. Adaptive optimisation-offline cyber attack on remote state estimator

    NASA Astrophysics Data System (ADS)

    Huang, Xin; Dong, Jiuxiang

    2017-10-01

    Security issues of cyber-physical systems have received increasing attentions in recent years. In this paper, deception attacks on the remote state estimator equipped with the chi-squared failure detector are considered, and it is assumed that the attacker can monitor and modify all the sensor data. A novel adaptive optimisation-offline cyber attack strategy is proposed, where using the current and previous sensor data, the attack can yield the largest estimation error covariance while ensuring to be undetected by the chi-squared monitor. From the attacker's perspective, the attack is better than the existing linear deception attacks to degrade the system performance. Finally, some numerical examples are provided to demonstrate theoretical results.

  8. Estimation of annual agricultural pesticide use for counties of the conterminous United States, 1992-2009

    USGS Publications Warehouse

    Thelin, Gail P.; Stone, Wesley W.

    2013-01-01

    A method was developed to calculate annual county level pesticide use for selected herbicides, insecticides, and fungicides applied to agricultural crops grown in the conterminous United States from 1992 through 2009. Pesticide-use data compiled by proprietary surveys of farm operations located within Crop Reporting Districts were used in conjunction with annual harvested-crop acreage reported by the U.S. Department of Agriculture National Agricultural Statistics Service (NASS) to calculate use rates per harvested crop acre, or an 'estimated pesticide use' (EPest) rate, for each crop by year. Pesticide-use data were not available for all Crop Reporting Districts and years. When data were unavailable for a Crop Reporting District in a particular year, EPest extrapolated rates were calculated from adjoining or nearby Crop Reporting Districts to ensure that pesticide use was estimated for all counties that reported harvested-crop acreage. EPest rates were applied to county harvested-crop acreage differently to obtain EPest-low and EPest-high estimates of pesticide-use for counties and states, with the exception of use estimates for California, which were taken from annual Department of Pesticide Regulation Pesticide Use Reports. Annual EPest-low and EPest-high use totals were compared with other published pesticide-use reports for selected pesticides, crops, and years. EPest-low and EPest-high national totals for five of seven herbicides were in close agreement with U.S. Environmental Protection Agency and National Pesticide Use Data estimates, but greater than most NASS national totals. A second set of analyses compared EPest and NASS annual state totals and state-by-crop totals for selected crops. Overall, EPest and NASS use totals were not significantly different for the majority of crop-stateyear combinations evaluated. Furthermore, comparisons of EPest and NASS use estimates for most pesticides had rank correlation coefficients greater than 0.75 and median

  9. New cost estimates for carbon sequestration through afforestation in the United States

    Treesearch

    Anne Sofie Elburg Nielsen; Andrew J. Plantinga; Ralph J. Alig

    2014-01-01

    This report provides new cost estimates for carbon sequestration through afforestation in the United States. We extend existing studies of carbon sequestration costs in several important ways, while ensuring the transparency of our approach. We clearly identify all components of our cost estimates so that other researchers can reconstruct our results as well as use our...

  10. LuciPHOr: Algorithm for Phosphorylation Site Localization with False Localization Rate Estimation Using Modified Target-Decoy Approach*

    PubMed Central

    Fermin, Damian; Walmsley, Scott J.; Gingras, Anne-Claude; Choi, Hyungwon; Nesvizhskii, Alexey I.

    2013-01-01

    The localization of phosphorylation sites in peptide sequences is a challenging problem in large-scale phosphoproteomics analysis. The intense neutral loss peaks and the coexistence of multiple serine/threonine and/or tyrosine residues are limiting factors for objectively scoring site patterns across thousands of peptides. Various computational approaches for phosphorylation site localization have been proposed, including Ascore, Mascot Delta score, and ProteinProspector, yet few address direct estimation of the false localization rate (FLR) in each experiment. Here we propose LuciPHOr, a modified target-decoy-based approach that uses mass accuracy and peak intensities for site localization scoring and FLR estimation. Accurate estimation of the FLR is a difficult task at the individual-site level because the degree of uncertainty in localization varies significantly across different peptides. LuciPHOr carries out simultaneous localization on all candidate sites in each peptide and estimates the FLR based on the target-decoy framework, where decoy phosphopeptides generated by placing artificial phosphorylation(s) on non-candidate residues compete with the non-decoy phosphopeptides. LuciPHOr also reports approximate site-level confidence scores for all candidate sites as a means to localize additional sites from multiphosphorylated peptides in which localization can be partially achieved. Unlike the existing tools, LuciPHOr is compatible with any search engine output processed through the Trans-Proteomic Pipeline. We evaluated the performance of LuciPHOr in terms of the sensitivity and accuracy of FLR estimates using two synthetic phosphopeptide libraries and a phosphoproteomic dataset generated from complex mouse brain samples. PMID:23918812

  11. Variable selection for confounder control, flexible modeling and Collaborative Targeted Minimum Loss-based Estimation in causal inference

    PubMed Central

    Schnitzer, Mireille E.; Lok, Judith J.; Gruber, Susan

    2015-01-01

    This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low-and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios. PMID:26226129

  12. Variable Selection for Confounder Control, Flexible Modeling and Collaborative Targeted Minimum Loss-Based Estimation in Causal Inference.

    PubMed

    Schnitzer, Mireille E; Lok, Judith J; Gruber, Susan

    2016-05-01

    This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010 [27]) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low- and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios.

  13. Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models

    PubMed Central

    Schwab, Joshua; Gruber, Susan; Blaser, Nello; Schomaker, Michael; van der Laan, Mark

    2015-01-01

    This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time-dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention-specific counterfactual outcome as a function of the intervention, time point, and possibly a subset of baseline covariates. Because the true shape of this function is rarely known, the marginal structural model is used as a working model. The causal quantity of interest is defined as the projection of the true function onto this working model. Iterated conditional expectation double robust estimators for marginal structural model parameters were previously proposed by Robins (2000, 2002) and Bang and Robins (2005). Here we build on this work and present a pooled TMLE for the parameters of marginal structural working models. We compare this pooled estimator to a stratified TMLE (Schnitzer et al. 2014) that is based on estimating the intervention-specific mean separately for each intervention of interest. The performance of the pooled TMLE is compared to the performance of the stratified TMLE and the performance of inverse probability weighted (IPW) estimators using simulations. Concepts are illustrated using an example in which the aim is to estimate the causal effect of delayed switch following immunological failure of first line antiretroviral therapy among HIV-infected patients. Data from the International Epidemiological Databases to Evaluate AIDS, Southern Africa are analyzed to investigate this question using both TML and IPW estimators. Our results demonstrate practical advantages of the

  14. Target-type probability combining algorithms for multisensor tracking

    NASA Astrophysics Data System (ADS)

    Wigren, Torbjorn

    2001-08-01

    Algorithms for the handing of target type information in an operational multi-sensor tracking system are presented. The paper discusses recursive target type estimation, computation of crosses from passive data (strobe track triangulation), as well as the computation of the quality of the crosses for deghosting purposes. The focus is on Bayesian algorithms that operate in the discrete target type probability space, and on the approximations introduced for computational complexity reduction. The centralized algorithms are able to fuse discrete data from a variety of sensors and information sources, including IFF equipment, ESM's, IRST's as well as flight envelopes estimated from track data. All algorithms are asynchronous and can be tuned to handle clutter, erroneous associations as well as missed and erroneous detections. A key to obtain this ability is the inclusion of data forgetting by a procedure for propagation of target type probability states between measurement time instances. Other important properties of the algorithms are their abilities to handle ambiguous data and scenarios. The above aspects are illustrated in a simulations study. The simulation setup includes 46 air targets of 6 different types that are tracked by 5 airborne sensor platforms using ESM's and IRST's as data sources.

  15. The direct cost of epilepsy in the United States: A systematic review of estimates.

    PubMed

    Begley, Charles E; Durgin, Tracy L

    2015-09-01

    To develop estimates of the direct cost of epilepsy in the United States for the general epilepsy population and sub-populations by systematically comparing similarities and differences in types of estimates and estimation methods from recently published studies. Papers published since 1995 were identified by systematic literature search. Information on types of estimates, study designs, data sources, types of epilepsy, and estimation methods was extracted from each study. Annual per person cost estimates from methodologically similar studies were identified, converted to 2013 U.S. dollars, and compared. From 4,104 publications discovered in the literature search, 21 were selected for review. Three were added that were published after the search. Eighteen were identified that reported estimates of average annual direct costs for the general epilepsy population in the United States. For general epilepsy populations (comprising all clinically defined subgroups), total direct healthcare costs per person ranged from $10,192 to $47,862 and epilepsy-specific costs ranged from $1,022 to $19,749. Four recent studies using claims data from large general populations yielded relatively similar epilepsy-specific annual cost estimates ranging from $8,412 to $11,354. Although more difficult to compare, studies examining direct cost differences for epilepsy sub-populations indicated a consistent pattern of markedly higher costs for those with uncontrolled or refractory epilepsy, and for those with comorbidities. This systematic review found that various approaches have been used to estimate the direct costs of epilepsy in the United States. However, recent studies using large claims databases and similar methods allow estimation of the direct cost burden of epilepsy for the general disease population, and show that it is greater for some patient subgroups. Additional research is needed to further understand the broader economic burden of epilepsy and how it varies across

  16. Carbon in U.S. forests and wood products, 1987-1997: state-by-state estimates

    Treesearch

    R.A. Birdsey; G.M. Lewis

    2003-01-01

    Estimated changes in carbon stocks are reported for the forests and wood products of the 50 U.S. States. Carbon stocks on forest land and in harvested wood products increased between 1987 and 1997 at an annual rate of 190 million metric tons. Most of this increase was in biomass, followed closely by wood products and landfills. Changes in land use since 1987 caused a...

  17. Estimating ecosystem carbon stocks at Redwood National and State Parks

    USGS Publications Warehouse

    van Mantgem, Phillip J.; Madej, Mary Ann; Seney, Joseph; Deshais, Janelle

    2013-01-01

    Accounting for ecosystem carbon is increasingly important for park managers. In this case study we present our efforts to estimate carbon stocks and the effects of management on carbon stocks for Redwood National and State Parks in northern California. Using currently available information, we estimate that on average these parks’ soils contain approximately 89 tons of carbon per acre (200 Mg C per ha), while vegetation contains about 130 tons C per acre (300 Mg C per ha). estoration activities at the parks (logging-road removal, second-growth forest management) were shown to initially reduce ecosystem carbon, but may provide for enhanced ecosystem carbon storage over the long term. We highlight currently available tools that could be used to estimate ecosystem carbon at other units of the National Park System.

  18. Metropolitan-area estimates of binge drinking in the United States.

    PubMed

    Nelson, David E; Naimi, Timothy S; Brewer, Robert D; Bolen, Julie; Wells, Henry E

    2004-04-01

    We estimated adult binge drinking prevalence in US metropolitan areas. We analyzed 1997 and 1999 Behavioral Risk Factor Surveillance System data for 120 metropolitan areas in 48 states and the District of Columbia. The prevalence of binge drinking varied substantially across metropolitan areas, from 4.1% in Chattanooga, Tenn, to 23.9% in San Antonio, Tex, (median = 14.5%). Seventeen of the 20 metropolitan areas with the highest estimates were located in the upper Midwest, Texas, and Nevada. In 13 of these areas, at least one third of persons aged 18 to 34 years were binge drinkers. There were significant intrastate differences for binge drinking among metropolitan areas in New York, Tennessee, and Utah. Metropolitan-area estimates can be used to guide local efforts to reduce binge drinking.

  19. State Estimation of International Space Station Centrifuge Rotor With Incomplete Knowledge of Disturbance Inputs

    NASA Technical Reports Server (NTRS)

    Sullivan, Michael J.

    2005-01-01

    This thesis develops a state estimation algorithm for the Centrifuge Rotor (CR) system where only relative measurements are available with limited knowledge of both rotor imbalance disturbances and International Space Station (ISS) thruster disturbances. A Kalman filter is applied to a plant model augmented with sinusoidal disturbance states used to model both the effect of the rotor imbalance and the 155 thrusters on the CR relative motion measurement. The sinusoidal disturbance states compensate for the lack of the availability of plant inputs for use in the Kalman filter. Testing confirms that complete disturbance modeling is necessary to ensure reliable estimation. Further testing goes on to show that increased estimator operational bandwidth can be achieved through the expansion of the disturbance model within the filter dynamics. In addition, Monte Carlo analysis shows the varying levels of robustness against defined plant/filter uncertainty variations.

  20. Improving Distribution Resiliency with Microgrids and State and Parameter Estimation

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

    Tuffner, Francis K.; Williams, Tess L.; Schneider, Kevin P.

    Modern society relies on low-cost reliable electrical power, both to maintain industry, as well as provide basic social services to the populace. When major disturbances occur, such as Hurricane Katrina or Hurricane Sandy, the nation’s electrical infrastructure can experience significant outages. To help prevent the spread of these outages, as well as facilitating faster restoration after an outage, various aspects of improving the resiliency of the power system are needed. Two such approaches are breaking the system into smaller microgrid sections, and to have improved insight into the operations to detect failures or mis-operations before they become critical. Breaking themore » system into smaller sections of microgrid islands, power can be maintained in smaller areas where distribution generation and energy storage resources are still available, but bulk power generation is no longer connected. Additionally, microgrid systems can maintain service to local pockets of customers when there has been extensive damage to the local distribution system. However, microgrids are grid connected a majority of the time and implementing and operating a microgrid is much different than when islanded. This report discusses work conducted by the Pacific Northwest National Laboratory that developed improvements for simulation tools to capture the characteristics of microgrids and how they can be used to develop new operational strategies. These operational strategies reduce the cost of microgrid operation and increase the reliability and resilience of the nation’s electricity infrastructure. In addition to the ability to break the system into microgrids, improved observability into the state of the distribution grid can make the power system more resilient. State estimation on the transmission system already provides great insight into grid operations and detecting abnormal conditions by leveraging existing measurements. These transmission-level approaches are expanded to

  1. Estimating aboveground live understory vegetation carbon in the United States

    NASA Astrophysics Data System (ADS)

    Johnson, Kristofer D.; Domke, Grant M.; Russell, Matthew B.; Walters, Brian; Hom, John; Peduzzi, Alicia; Birdsey, Richard; Dolan, Katelyn; Huang, Wenli

    2017-12-01

    Despite the key role that understory vegetation plays in ecosystems and the terrestrial carbon cycle, it is often overlooked and has few quantitative measurements, especially at national scales. To understand the contribution of understory carbon to the United States (US) carbon budget, we developed an approach that relies on field measurements of understory vegetation cover and height on US Department of Agriculture Forest Service, Forest Inventory and Analysis (FIA) subplots. Allometric models were developed to estimate aboveground understory carbon. A spatial model based on stand characteristics and remotely sensed data was also applied to estimate understory carbon on all FIA plots. We found that most understory carbon was comprised of woody shrub species (64%), followed by nonwoody forbs and graminoid species (35%) and seedlings (1%). The largest estimates were found in temperate or warm humid locations such as the Pacific Northwest and southeastern US, thus following the same broad trend as aboveground tree biomass. The average understory aboveground carbon density was estimated to be 0.977 Mg ha-1, for a total estimate of 272 Tg carbon across all managed forest land in the US (approximately 2% of the total aboveground live tree carbon pool). This estimate is more than twice as low as previous FIA modeled estimates that did not rely on understory measurements, suggesting that this pool may currently be overestimated in US National Greenhouse Gas reporting.

  2. Negotiating targets with patients: choice of target in relation to occupational state.

    PubMed

    Robinson, Sandra M; Walker, David J

    2012-02-01

    Following the recent National Institute for Health and Clinical Excellence guidance on the management of RA, we were interested to see if we could negotiate targets for treatment with patients in routine clinics, how they would express this and whether staying at work would be a target. One hundred RA patients were recruited. They were consecutive within clinics, but not all clinics were used. They were asked their understanding of the DAS score and a target for treatment negotiated. Any impact of the RA on their paid employment was then explored. Four participants were unable to specify a target for their RA. Negotiated targets were expressed as restricted activities and either as maintaining an activity (70) if the disease was stable, or regaining an activity (26) if the treatment was being increased. Targets were walking a distance for 50% of patients; leisure activities for 18%; domestic activities for 17%; work for 14% and personal care for 2%. For the 21 participants currently working, maintaining work was the target for 12, with 1 wishing to regain lost hours. No patient currently not working expressed returning to work as a target. There were some differences in targets between men and women. Patients are able to negotiate a target for their treatment, expressed as maintaining or regaining a physical activity. Work ceases to be a target once it is lost. Therefore, preventing loss of occupation is likely to be more effective than trying to regain it.

  3. Validation of State Counts of Handicapped Children. Volume II--Estimation of the Number of Handicapped Children in Each State.

    ERIC Educational Resources Information Center

    Kaskowitz, David H.

    The booklet provides detailed estimates on handicapping conditions for school aged populations. The figures are intended to help the federal government validate state child count data as required by P.L. 94-142, the Education for All Handicapped Children. Section I uncovers the methodology used to arrive at the estimates, and it identifies the…

  4. Subduction in an Eddy-Resolving State Estimate of the Northeast Atlantic Ocean

    NASA Technical Reports Server (NTRS)

    Gebbie, Geoffrey

    2004-01-01

    Are eddies an important contributor to subduction in the eastern subtropical gyre? Here, an adjoint model is used to combine a regional, eddy-resolving numerical model with observations to produce a state estimate of the ocean circulation. The estimate is a synthesis of a variety of in- situ observations from the Subduction Experiment, TOPEX/POSEIDON altimetry, and the MTI General Circulation Model. The adjoint method is successful because the Northeast Atlantic Ocean is only weakly nonlinear. The state estimate provides a physically-interpretable, eddy-resolving information source to diagnose subduction. Estimates of eddy subduction for the eastern subtropical gyre of the North Atlantic are larger than previously calculated from parameterizations in coarse-resolution models. Furthermore, eddy subduction rates have typical magnitudes of 15% of the total subduction rate. Eddies contribute as much as 1 Sverdrup to water-mass transformation, and hence subduction, in the North Equatorial Current and the Azores Current. The findings of this thesis imply that the inability to resolve or accurately parameterize eddy subduction in climate models would lead to an accumulation of error in the structure of the main thermocline, even in the relatively-quiescent eastern subtropical gyre.

  5. The Use of Collateral Information in Proficiency Estimation for the Trial State Assessment.

    ERIC Educational Resources Information Center

    Mazzeo, John; And Others

    The adequacy of several approaches to estimation of proficiency distributions for the Trial State Assessment (TSA) in eighth grade mathematics of the National Assessment of Educational Progress was examined. These approaches are more restrictive than the estimation procedures originally used, with the same kind of plausible-values approach that…

  6. Multivariate analysis for the estimation of target localization errors in fiducial marker-based radiotherapy

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

    Takamiya, Masanori; Nakamura, Mitsuhiro, E-mail: m-nkmr@kuhp.kyoto-u.ac.jp; Akimoto, Mami

    Purpose: To assess the target localization error (TLE) in terms of the distance between the target and the localization point estimated from the surrogates (|TMD|), the average of respiratory motion for the surrogates and the target (|aRM|), and the number of fiducial markers used for estimating the target (n). Methods: This study enrolled 17 lung cancer patients who subsequently underwent four fractions of real-time tumor tracking irradiation. Four or five fiducial markers were implanted around the lung tumor. The three-dimensional (3D) distance between the tumor and markers was at maximum 58.7 mm. One of the markers was used as themore » target (P{sub t}), and those markers with a 3D |TMD{sub n}| ≤ 58.7 mm at end-exhalation were then selected. The estimated target position (P{sub e}) was calculated from a localization point consisting of one to three markers except P{sub t}. Respiratory motion for P{sub t} and P{sub e} was defined as the root mean square of each displacement, and |aRM| was calculated from the mean value. TLE was defined as the root mean square of each difference between P{sub t} and P{sub e} during the monitoring of each fraction. These procedures were performed repeatedly using the remaining markers. To provide the best guidance on the answer with n and |TMD|, fiducial markers with a 3D |aRM ≥ 10 mm were selected. Finally, a total of 205, 282, and 76 TLEs that fulfilled the 3D |TMD| and 3D |aRM| criteria were obtained for n = 1, 2, and 3, respectively. Multiple regression analysis (MRA) was used to evaluate TLE as a function of |TMD| and |aRM| in each n. Results: |TMD| for n = 1 was larger than that for n = 3. Moreover, |aRM| was almost constant for all n, indicating a similar scale for the marker’s motion near the lung tumor. MRA showed that |aRM| in the left–right direction was the major cause of TLE; however, the contribution made little difference to the 3D TLE because of the small amount of motion in the left–right direction

  7. Housing accessibility for senior citizens in Sweden: Estimation of the effects of targeted elimination of environmental barriers.

    PubMed

    Pettersson, Cecilia; Slaug, Björn; Granbom, Marianne; Kylberg, Marianne; Iwarsson, Susanne

    2017-01-24

    To estimate the effects of targeted elimination of environmental barriers (EB) in the ordinary housing stock in Sweden, and to explore the estimated effects on accessibility at a population level in relation to (a) residents with different functional profiles, (b) different housing types and (c) building periods. Data on dwellings from existing Swedish research databases were utilized. EB and accessibility were assessed by means of the Housing Enabler instrument. In simulations of EB removal, five items that correspond to the most common housing adaptations were selected. The simulations were applied to four functional profiles of different complexity. EB known to be commonly removed by housing adaptations exist in large proportions of the existing ordinary housing stock. Estimated targeted elimination of selected barriers would have the largest accessibility effects for the more complex functional profiles. The effects would be consistently larger for one-family houses, and for all types of dwellings built before 1960. The elimination of the EB most commonly addressed by housing adaptations could result in a reduction of the housing accessibility problems that community-living older people are facing. For society to solve the housing situation for the ageing population well-informed and efficient upgrading of ordinary housing is imperative.

  8. Synchrophasor Data Correction under GPS Spoofing Attack: A State Estimation Based Approach

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

    Fan, Xiaoyuan; Du, Liang; Duan, Dongliang

    GPS spoofing attack (GSA) has been shown to be one of the most imminent threats to almost all cyber-physical systems incorporated with the civilian GPS signal. Specifically, for our current agenda of the modernization of the power grid, this may greatly jeopardize the benefits provided by the pervasively installed phasor measurement units (PMU). In this study, we consider the case where synchrophasor data from PMUs are compromised due to the presence of a single GSA, and show that it can be corrected by signal processing techniques. In particular, we introduce a statistical model for synchrophasorbased power system state estimation (SE),more » and then derive the spoofing-matched algorithms for synchrophasor data correction against GPS spoofing attack. Different testing scenarios in IEEE 14-, 30-, 57-, 118-bus systems are simulated to show the proposed algorithms’ performance on GSA detection and state estimation. Numerical results demonstrate that our proposed algorithms can consistently locate and correct the spoofed synchrophasor data with good accuracy as long as the system observability is satisfied. Finally, the accuracy of state estimation is significantly improved compared with the traditional weighted least square method and approaches the performance under the Genie-aided method.« less

  9. Synchrophasor Data Correction under GPS Spoofing Attack: A State Estimation Based Approach

    DOE PAGES

    Fan, Xiaoyuan; Du, Liang; Duan, Dongliang

    2017-02-01

    GPS spoofing attack (GSA) has been shown to be one of the most imminent threats to almost all cyber-physical systems incorporated with the civilian GPS signal. Specifically, for our current agenda of the modernization of the power grid, this may greatly jeopardize the benefits provided by the pervasively installed phasor measurement units (PMU). In this study, we consider the case where synchrophasor data from PMUs are compromised due to the presence of a single GSA, and show that it can be corrected by signal processing techniques. In particular, we introduce a statistical model for synchrophasorbased power system state estimation (SE),more » and then derive the spoofing-matched algorithms for synchrophasor data correction against GPS spoofing attack. Different testing scenarios in IEEE 14-, 30-, 57-, 118-bus systems are simulated to show the proposed algorithms’ performance on GSA detection and state estimation. Numerical results demonstrate that our proposed algorithms can consistently locate and correct the spoofed synchrophasor data with good accuracy as long as the system observability is satisfied. Finally, the accuracy of state estimation is significantly improved compared with the traditional weighted least square method and approaches the performance under the Genie-aided method.« less

  10. Infrared dim-small target tracking via singular value decomposition and improved Kernelized correlation filter

    NASA Astrophysics Data System (ADS)

    Qian, Kun; Zhou, Huixin; Rong, Shenghui; Wang, Bingjian; Cheng, Kuanhong

    2017-05-01

    Infrared small target tracking plays an important role in applications including military reconnaissance, early warning and terminal guidance. In this paper, an effective algorithm based on the Singular Value Decomposition (SVD) and the improved Kernelized Correlation Filter (KCF) is presented for infrared small target tracking. Firstly, the super performance of the SVD-based algorithm is that it takes advantage of the target's global information and obtains a background estimation of an infrared image. A dim target is enhanced by subtracting the corresponding estimated background with update from the original image. Secondly, the KCF algorithm is combined with Gaussian Curvature Filter (GCF) to eliminate the excursion problem. The GCF technology is adopted to preserve the edge and eliminate the noise of the base sample in the KCF algorithm, helping to calculate the classifier parameter for a small target. At last, the target position is estimated with a response map, which is obtained via the kernelized classifier. Experimental results demonstrate that the presented algorithm performs favorably in terms of efficiency and accuracy, compared with several state-of-the-art algorithms.

  11. Robust infrared targets tracking with covariance matrix representation

    NASA Astrophysics Data System (ADS)

    Cheng, Jian

    2009-07-01

    Robust infrared target tracking is an important and challenging research topic in many military and security applications, such as infrared imaging guidance, infrared reconnaissance, scene surveillance, etc. To effectively tackle the nonlinear and non-Gaussian state estimation problems, particle filtering is introduced to construct the theory framework of infrared target tracking. Under this framework, the observation probabilistic model is one of main factors for infrared targets tracking performance. In order to improve the tracking performance, covariance matrices are introduced to represent infrared targets with the multi-features. The observation probabilistic model can be constructed by computing the distance between the reference target's and the target samples' covariance matrix. Because the covariance matrix provides a natural tool for integrating multiple features, and is scale and illumination independent, target representation with covariance matrices can hold strong discriminating ability and robustness. Two experimental results demonstrate the proposed method is effective and robust for different infrared target tracking, such as the sensor ego-motion scene, and the sea-clutter scene.

  12. Online Kinematic and Dynamic-State Estimation for Constrained Multibody Systems Based on IMUs

    PubMed Central

    Torres-Moreno, José Luis; Blanco-Claraco, José Luis; Giménez-Fernández, Antonio; Sanjurjo, Emilio; Naya, Miguel Ángel

    2016-01-01

    This article addresses the problems of online estimations of kinematic and dynamic states of a mechanism from a sequence of noisy measurements. In particular, we focus on a planar four-bar linkage equipped with inertial measurement units (IMUs). Firstly, we describe how the position, velocity, and acceleration of all parts of the mechanism can be derived from IMU signals by means of multibody kinematics. Next, we propose the novel idea of integrating the generic multibody dynamic equations into two variants of Kalman filtering, i.e., the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), in a way that enables us to handle closed-loop, constrained mechanisms, whose state space variables are not independent and would normally prevent the direct use of such estimators. The proposal in this work is to apply those estimators over the manifolds of allowed positions and velocities, by means of estimating a subset of independent coordinates only. The proposed techniques are experimentally validated on a testbed equipped with encoders as a means of establishing the ground-truth. Estimators are run online in real-time, a feature not matched by any previous procedure of those reported in the literature on multibody dynamics. PMID:26959027

  13. The public costs of births resulting from unintended pregnancies: national and state-level estimates.

    PubMed

    Sonfield, Adam; Kost, Kathryn; Gold, Rachel Benson; Finer, Lawrence B

    2011-06-01

    Births resulting from unintended pregnancies are associated with substantial maternity and infant care costs to the federal and state governments; these costs have never been estimated at the national and state levels. The proportions of births paid for by public insurance programs in 2006 were estimated, by pregnancy intention status, using data from the Pregnancy Risk Assessment Monitoring System and similar state surveys, or were predicted by multivariate linear regression. Public costs were calculated using state-level estimates of the number of births, by intention status, and of the cost of a publicly funded birth. In 2006, 64% of births resulting from unintended pregnancies were publicly funded, compared with 48% of all births and 35% of births resulting from intended pregnancies. The proportion of births resulting from unintended pregnancies that were publicly funded varied by state, from 42% to 81%. Of the 2.0 million publicly funded births, 51% resulted from unintended pregnancies, accounting for $11.1 billion in costs-half of the total public expenditures on births. In seven states, the costs for births from unintended pregnancies exceeded a half billion dollars. Public insurance programs are central in assisting American families in affording pregnancy and childbirth; however, they pay for a disproportionately high number of births resulting from unintended pregnancy. The resulting budgetary impact warrants increased public efforts to reduce unintended pregnancy. Copyright © 2011 by the Guttmacher Institute.

  14. State and Parameter Estimation for a Coupled Ocean--Atmosphere Model

    NASA Astrophysics Data System (ADS)

    Ghil, M.; Kondrashov, D.; Sun, C.

    2006-12-01

    The El-Nino/Southern-Oscillation (ENSO) dominates interannual climate variability and plays, therefore, a key role in seasonal-to-interannual prediction. Much is known by now about the main physical mechanisms that give rise to and modulate ENSO, but the values of several parameters that enter these mechanisms are an important unknown. We apply Extended Kalman Filtering (EKF) for both model state and parameter estimation in an intermediate, nonlinear, coupled ocean--atmosphere model of ENSO. The coupled model consists of an upper-ocean, reduced-gravity model of the Tropical Pacific and a steady-state atmospheric response to the sea surface temperature (SST). The model errors are assumed to be mainly in the atmospheric wind stress, and assimilated data are equatorial Pacific SSTs. Model behavior is very sensitive to two key parameters: (i) μ, the ocean-atmosphere coupling coefficient between SST and wind stress anomalies; and (ii) δs, the surface-layer coefficient. Previous work has shown that δs determines the period of the model's self-sustained oscillation, while μ measures the degree of nonlinearity. Depending on the values of these parameters, the spatio-temporal pattern of model solutions is either that of a delayed oscillator or of a westward propagating mode. Estimation of these parameters is tested first on synthetic data and allows us to recover the delayed-oscillator mode starting from model parameter values that correspond to the westward-propagating case. Assimilation of SST data from the NCEP-NCAR Reanalysis-2 shows that the parameters can vary on fairly short time scales and switch between values that approximate the two distinct modes of ENSO behavior. Rapid adjustments of these parameters occur, in particular, during strong ENSO events. Ways to apply EKF parameter estimation efficiently to state-of-the-art coupled ocean--atmosphere GCMs will be discussed.

  15. Generalizing Evidence From Randomized Clinical Trials to Target Populations

    PubMed Central

    Cole, Stephen R.; Stuart, Elizabeth A.

    2010-01-01

    Properly planned and conducted randomized clinical trials remain susceptible to a lack of external validity. The authors illustrate a model-based method to standardize observed trial results to a specified target population using a seminal human immunodeficiency virus (HIV) treatment trial, and they provide Monte Carlo simulation evidence supporting the method. The example trial enrolled 1,156 HIV-infected adult men and women in the United States in 1996, randomly assigned 577 to a highly active antiretroviral therapy and 579 to a largely ineffective combination therapy, and followed participants for 52 weeks. The target population was US people infected with HIV in 2006, as estimated by the Centers for Disease Control and Prevention. Results from the trial apply, albeit muted by 12%, to the target population, under the assumption that the authors have measured and correctly modeled the determinants of selection that reflect heterogeneity in the treatment effect. In simulations with a heterogeneous treatment effect, a conventional intent-to-treat estimate was biased with poor confidence limit coverage, but the proposed estimate was largely unbiased with appropriate confidence limit coverage. The proposed method standardizes observed trial results to a specified target population and thereby provides information regarding the generalizability of trial results. PMID:20547574

  16. Influence of the optimization methods on neural state estimation quality of the drive system with elasticity.

    PubMed

    Orlowska-Kowalska, Teresa; Kaminski, Marcin

    2014-01-01

    The paper deals with the implementation of optimized neural networks (NNs) for state variable estimation of the drive system with an elastic joint. The signals estimated by NNs are used in the control structure with a state-space controller and additional feedbacks from the shaft torque and the load speed. High estimation quality is very important for the correct operation of a closed-loop system. The precision of state variables estimation depends on the generalization properties of NNs. A short review of optimization methods of the NN is presented. Two techniques typical for regularization and pruning methods are described and tested in detail: the Bayesian regularization and the Optimal Brain Damage methods. Simulation results show good precision of both optimized neural estimators for a wide range of changes of the load speed and the load torque, not only for nominal but also changed parameters of the drive system. The simulation results are verified in a laboratory setup.

  17. Estimation of reach in peripersonal and extrapersonal space: a developmental view.

    PubMed

    Gabbard, Carl; Cordova, Alberto; Ammar, Diala

    2007-01-01

    This study explored the developmental nature of action processing via estimation of reach in peripersonal and extrapersonal space. Children 5 to 11 years of age and adults were tested for estimates of reach to targets presented randomly at seven midline locations. Target distances were scaled to the individual based on absolute maximum reach. While there was no difference between age groups for total error, a significant distinction emerged in reference to space. With children, significantly more error was exhibited in extrapersonal space; no difference was found with adults. The groups did not differ in peripersonal space; however, adults were substantially more accurate with extrapersonal targets. Furthermore, children displayed a greater tendency to overestimate. In essence, these data reveal a body-scaling problem in children in estimating reach in extrapersonal space. Future work should focus on possible developmental differences in use of visual information and state of confidence.

  18. Estimating debt capacity of New York State Health facilities.

    PubMed

    Hogan, A J

    1985-01-01

    A measure of the capacity to take on new debt is developed for health facilities. This measure is a function of the current financial position of the facility, future financial market conditions (interest rates and bond/loan maturities), and a policy variable (the debt service coverage ratio) to be set by state health policy makers. The quality of this measure was shown to depend on the quality of current health facility financial accounting data, on the quality of forecasts of interest rates and future cashflow, and on the appropriateness of the criterion debt service coverage ratio. Some of the limitations of the estimate are discussed. Consideration of the debt capacity estimate serves to highlight some crucial issues in imposing capital expenditure limits, namely the interrelationships between financial viability, interest rates and access to capital markets.

  19. Female Genital Mutilation/Cutting in the United States: Updated Estimates of Women and Girls at Risk, 2012

    PubMed Central

    Stupp, Paul; Okoroh, Ekwutosi; Besera, Ghenet; Goodman, David; Danel, Isabella

    2016-01-01

    Objectives In 1996, the U.S. Congress passed legislation making female genital mutilation/cutting (FGM/C) illegal in the United States. CDC published the first estimates of the number of women and girls at risk for FGM/C in 1997. Since 2012, various constituencies have again raised concerns about the practice in the United States. We updated an earlier estimate of the number of women and girls in the United States who were at risk for FGM/C or its consequences. Methods We estimated the number of women and girls who were at risk for undergoing FGM/C or its consequences in 2012 by applying country-specific prevalence of FGM/C to the estimated number of women and girls living in the United States who were born in that country or who lived with a parent born in that country. Results Approximately 513,000 women and girls in the United States were at risk for FGM/C or its consequences in 2012, which was more than three times higher than the earlier estimate, based on 1990 data. The increase in the number of women and girls younger than 18 years of age at risk for FGM/C was more than four times that of previous estimates. Conclusion The estimated increase was wholly a result of rapid growth in the number of immigrants from FGM/C-practicing countries living in the United States and not from increases in FGM/C prevalence in those countries. Scientifically valid information regarding whether women or their daughters have actually undergone FGM/C and related information that can contribute to efforts to prevent the practice in the United States and provide needed health services to women who have undergone FGM/C are needed. PMID:26957669

  20. Female Genital Mutilation/Cutting in the United States: Updated Estimates of Women and Girls at Risk, 2012.

    PubMed

    Goldberg, Howard; Stupp, Paul; Okoroh, Ekwutosi; Besera, Ghenet; Goodman, David; Danel, Isabella

    2016-01-01

    In 1996, the U.S. Congress passed legislation making female genital mutilation/cutting (FGM/C) illegal in the United States. CDC published the first estimates of the number of women and girls at risk for FGM/C in 1997. Since 2012, various constituencies have again raised concerns about the practice in the United States. We updated an earlier estimate of the number of women and girls in the United States who were at risk for FGM/C or its consequences. We estimated the number of women and girls who were at risk for undergoing FGM/C or its consequences in 2012 by applying country-specific prevalence of FGM/C to the estimated number of women and girls living in the United States who were born in that country or who lived with a parent born in that country. Approximately 513,000 women and girls in the United States were at risk for FGM/C or its consequences in 2012, which was more than three times higher than the earlier estimate, based on 1990 data. The increase in the number of women and girls younger than 18 years of age at risk for FGM/C was more than four times that of previous estimates. The estimated increase was wholly a result of rapid growth in the number of immigrants from FGM/C-practicing countries living in the United States and not from increases in FGM/C prevalence in those countries. Scientifically valid information regarding whether women or their daughters have actually undergone FGM/C and related information that can contribute to efforts to prevent the practice in the United States and provide needed health services to women who have undergone FGM/C are needed.

  1. Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

    DOE PAGES

    Liu, Qiang; Brigham, Katharine; Rao, Nageswara S. V.

    2017-02-01

    In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics – such as the position – is expected to possess much improved quality. In this paper, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). Finally, the joint effect of (i)more » imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.« less

  2. Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

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

    Liu, Qiang; Brigham, Katharine; Rao, Nageswara S. V.

    In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics – such as the position – is expected to possess much improved quality. In this paper, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). Finally, the joint effect of (i)more » imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.« less

  3. Markov state models from short non-equilibrium simulations—Analysis and correction of estimation bias

    NASA Astrophysics Data System (ADS)

    Nüske, Feliks; Wu, Hao; Prinz, Jan-Hendrik; Wehmeyer, Christoph; Clementi, Cecilia; Noé, Frank

    2017-03-01

    Many state-of-the-art methods for the thermodynamic and kinetic characterization of large and complex biomolecular systems by simulation rely on ensemble approaches, where data from large numbers of relatively short trajectories are integrated. In this context, Markov state models (MSMs) are extremely popular because they can be used to compute stationary quantities and long-time kinetics from ensembles of short simulations, provided that these short simulations are in "local equilibrium" within the MSM states. However, over the last 15 years since the inception of MSMs, it has been controversially discussed and not yet been answered how deviations from local equilibrium can be detected, whether these deviations induce a practical bias in MSM estimation, and how to correct for them. In this paper, we address these issues: We systematically analyze the estimation of MSMs from short non-equilibrium simulations, and we provide an expression for the error between unbiased transition probabilities and the expected estimate from many short simulations. We show that the unbiased MSM estimate can be obtained even from relatively short non-equilibrium simulations in the limit of long lag times and good discretization. Further, we exploit observable operator model (OOM) theory to derive an unbiased estimator for the MSM transition matrix that corrects for the effect of starting out of equilibrium, even when short lag times are used. Finally, we show how the OOM framework can be used to estimate the exact eigenvalues or relaxation time scales of the system without estimating an MSM transition matrix, which allows us to practically assess the discretization quality of the MSM. Applications to model systems and molecular dynamics simulation data of alanine dipeptide are included for illustration. The improved MSM estimator is implemented in PyEMMA of version 2.3.

  4. Estimation of electronegativity values of elements in different valence states.

    PubMed

    Li, Keyan; Xue, Dongfeng

    2006-10-05

    The electronegativities of 82 elements in different valence states and with the most common coordination numbers have been quantitatively calculated on the basis of an effective ionic potential defined by the ionization energy and ionic radius. It is found that for a given cation, the electronegativity increases with increasing oxidation state and decreases with increasing coordination number. For the transition-metal cations, the electronegativity of the low-spin state is higher than that of the high-spin state. The ligand field stabilization, the first filling of p orbitals, the transition-metal contraction, and especially the lanthanide contraction are well-reflected by the relative values of our proposed electronegativity. This new scale is useful for us to estimate some quantities (e.g., the Lewis acid strength for the main group elements and the hydration free energy for the first transition series) and predict the structure and property of materials.

  5. Estimating mercury emissions resulting from wildfire in forests of the Western United States.

    PubMed

    Webster, Jackson P; Kane, Tyler J; Obrist, Daniel; Ryan, Joseph N; Aiken, George R

    2016-10-15

    Understanding the emissions of mercury (Hg) from wildfires is important for quantifying the global atmospheric Hg sources. Emissions of Hg from soils resulting from wildfires in the Western United States was estimated for the 2000 to 2013 period, and the potential emission of Hg from forest soils was assessed as a function of forest type and soil-heating. Wildfire released an annual average of 3100±1900kg-Hgy(-1) for the years spanning 2000-2013 in the 11 states within the study area. This estimate is nearly 5-fold lower than previous estimates for the study region. Lower emission estimates are attributed to an inclusion of fire severity within burn perimeters. Within reported wildfire perimeters, the average distribution of low, moderate, and high severity burns was 52, 29, and 19% of the total area, respectively. Review of literature data suggests that that low severity burning does not result in soil heating, moderate severity fire results in shallow soil heating, and high severity fire results in relatively deep soil heating (<5cm). Using this approach, emission factors for high severity burns ranged from 58 to 640μg-Hgkg-fuel(-1). In contrast, low severity burns have emission factors that are estimated to be only 18-34μg-Hgkg-fuel(-1). In this estimate, wildfire is predicted to release 1-30gHgha(-1) from Western United States forest soils while above ground fuels are projected to contribute an additional 0.9 to 7.8gHgha(-1). Land cover types with low biomass (desert scrub) are projected to release less than 1gHgha(-1). Following soil sources, fuel source contributions to total Hg emissions generally followed the order of duff>wood>foliage>litter>branches. Copyright © 2016 Elsevier B.V. All rights reserved.

  6. Pharmacophore based design of some multi-targeted compounds targeted against pathways of diabetic complications.

    PubMed

    Chadha, Navriti; Silakari, Om

    2017-09-01

    Diabetic complications is a complex metabolic disorder developed primarily due to prolonged hyperglycemia in the body. The complexity of the disease state as well as the unifying pathophysiology discussed in the literature reports exhibited that the use of multi-targeted agents with multiple complementary biological activities may offer promising therapy for the intervention of the disease over the single-target drugs. In the present study, novel thiazolidine-2,4-dione analogues were designed as multi-targeted agents implicated against the molecular pathways involved in diabetic complications using knowledge based as well as in-silico approaches such as pharmacophore mapping, molecular docking etc. The hit molecules were duly synthesized and biochemical estimation of these molecules against aldose reductase (ALR2), protein kinase Cβ (PKCβ) and poly (ADP-ribose) polymerase 1 (PARP-1) led to identification of compound 2 that showed good potency against PARP-1 and ALR2 enzymes. These positive results support the progress of a low cost multi-targeted agent with putative roles in diabetic complications. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. An optimal state estimation model of sensory integration in human postural balance

    NASA Astrophysics Data System (ADS)

    Kuo, Arthur D.

    2005-09-01

    We propose a model for human postural balance, combining state feedback control with optimal state estimation. State estimation uses an internal model of body and sensor dynamics to process sensor information and determine body orientation. Three sensory modalities are modeled: joint proprioception, vestibular organs in the inner ear, and vision. These are mated with a two degree-of-freedom model of body dynamics in the sagittal plane. Linear quadratic optimal control is used to design state feedback and estimation gains. Nine free parameters define the control objective and the signal-to-noise ratios of the sensors. The model predicts statistical properties of human sway in terms of covariance of ankle and hip motion. These predictions are compared with normal human responses to alterations in sensory conditions. With a single parameter set, the model successfully reproduces the general nature of postural motion as a function of sensory environment. Parameter variations reveal that the model is highly robust under normal sensory conditions, but not when two or more sensors are inaccurate. This behavior is similar to that of normal human subjects. We propose that age-related sensory changes may be modeled with decreased signal-to-noise ratios, and compare the model's behavior with degraded sensors against experimental measurements from older adults. We also examine removal of the model's vestibular sense, which leads to instability similar to that observed in bilateral vestibular loss subjects. The model may be useful for predicting which sensors are most critical for balance, and how much they can deteriorate before posture becomes unstable.

  8. Computation of the target state and feedback controls for time optimal consensus in multi-agent systems

    NASA Astrophysics Data System (ADS)

    Mulla, Ameer K.; Patil, Deepak U.; Chakraborty, Debraj

    2018-02-01

    N identical agents with bounded inputs aim to reach a common target state (consensus) in the minimum possible time. Algorithms for computing this time-optimal consensus point, the control law to be used by each agent and the time taken for the consensus to occur, are proposed. Two types of multi-agent systems are considered, namely (1) coupled single-integrator agents on a plane and, (2) double-integrator agents on a line. At the initial time instant, each agent is assumed to have access to the state information of all the other agents. An algorithm, using convexity of attainable sets and Helly's theorem, is proposed, to compute the final consensus target state and the minimum time to achieve this consensus. Further, parts of the computation are parallelised amongst the agents such that each agent has to perform computations of O(N2) run time complexity. Finally, local feedback time-optimal control laws are synthesised to drive each agent to the target point in minimum time. During this part of the operation, the controller for each agent uses measurements of only its own states and does not need to communicate with any neighbouring agents.

  9. A chemodynamic approach for estimating losses of target organic chemicals from water during sample holding time

    USGS Publications Warehouse

    Capel, P.D.; Larson, S.J.

    1995-01-01

    Minimizing the loss of target organic chemicals from environmental water samples between the time of sample collection and isolation is important to the integrity of an investigation. During this sample holding time, there is a potential for analyte loss through volatilization from the water to the headspace, sorption to the walls and cap of the sample bottle; and transformation through biotic and/or abiotic reactions. This paper presents a chemodynamic-based, generalized approach to estimate the most probable loss processes for individual target organic chemicals. The basic premise is that the investigator must know which loss process(es) are important for a particular analyte, based on its chemodynamic properties, when choosing the appropriate method(s) to prevent loss.

  10. The modular modality frame model: continuous body state estimation and plausibility-weighted information fusion.

    PubMed

    Ehrenfeld, Stephan; Butz, Martin V

    2013-02-01

    Humans show admirable capabilities in movement planning and execution. They can perform complex tasks in various contexts, using the available sensory information very effectively. Body models and continuous body state estimations appear necessary to realize such capabilities. We introduce the Modular Modality Frame (MMF) model, which maintains a highly distributed, modularized body model continuously updating, modularized probabilistic body state estimations over time. Modularization is realized with respect to modality frames, that is, sensory modalities in particular frames of reference and with respect to particular body parts. We evaluate MMF performance on a simulated, nine degree of freedom arm in 3D space. The results show that MMF is able to maintain accurate body state estimations despite high sensor and motor noise. Moreover, by comparing the sensory information available in different modality frames, MMF can identify faulty sensory measurements on the fly. In the near future, applications to lightweight robot control should be pursued. Moreover, MMF may be enhanced with neural encodings by introducing neural population codes and learning techniques. Finally, more dexterous goal-directed behavior should be realized by exploiting the available redundant state representations.

  11. Estimation of the Parameters in a Two-State System Coupled to a Squeezed Bath

    NASA Astrophysics Data System (ADS)

    Hu, Yao-Hua; Yang, Hai-Feng; Tan, Yong-Gang; Tao, Ya-Ping

    2018-04-01

    Estimation of the phase and weight parameters of a two-state system in a squeezed bath by calculating quantum Fisher information is investigated. The results show that, both for the phase estimation and for the weight estimation, the quantum Fisher information always decays with time and changes periodically with the phases. The estimation precision can be enhanced by choosing the proper values of the phases and the squeezing parameter. These results can be provided as an analysis reference for the practical application of the parameter estimation in a squeezed bath.

  12. NOAA Atlas 14: Updated Precipitation Frequency Estimates for the United States

    NASA Astrophysics Data System (ADS)

    Pavlovic, S.; Perica, S.; Martin, D.; Roy, I.; StLaurent, M.; Trypaluk, C.; Unruh, D.; Yekta, M.; Bonnin, G. M.

    2013-12-01

    NOAA Atlas 14 precipitation frequency estimates, developed by the National Weather Service's Hydrometeorological Design Studies Center, serve as the de-facto standards for a wide variety of design and planning activities under federal, state, and local regulations. Precipitation frequency estimates are used in the design of drainage for highways, culverts, bridges, parking lots, as well as in sizing sewer and stormwater infrastructure. Water resources engineers use them to estimate the amount of runoff, to estimate the volume of detention basins and size detention-basin outlet structures, and to estimate the volume of sediment or the amount of erosion. They are also used by floodplain managers to delineate floodplains and regulate the development in floodplains, which is crucial for all communities in the National Flood Insurance Program. Hydrometeorological Design Studies Center now provides more than 35,000 downloads per month to its Precipitation Frequency Data Server. Precipitation frequency estimates are often used in engineering design without any understanding how these estimates have been developed or without any understanding of the uncertainties associated with these estimates. This presentation will describe novel tools and techniques that have being developed in the last years to determine precipitation frequency estimates in NOAA Atlas 14. Particular attention will be given to the regional frequency analysis approach based on L-moment statistics calculated from annual maximum series, selected statistics obtained in determining and parameterizing the probability distribution functions, and the potential implication for engineering design of recently published estimates.

  13. NOAA Atlas 14: Updated Precipitation Frequency Estimates for the United States

    NASA Astrophysics Data System (ADS)

    Pavlovic, S.; Perica, S.; Martin, D.; Roy, I.; StLaurent, M.; Trypaluk, C.; Unruh, D.; Yekta, M.; Bonnin, G. M.

    2011-12-01

    NOAA Atlas 14 precipitation frequency estimates, developed by the National Weather Service's Hydrometeorological Design Studies Center, serve as the de-facto standards for a wide variety of design and planning activities under federal, state, and local regulations. Precipitation frequency estimates are used in the design of drainage for highways, culverts, bridges, parking lots, as well as in sizing sewer and stormwater infrastructure. Water resources engineers use them to estimate the amount of runoff, to estimate the volume of detention basins and size detention-basin outlet structures, and to estimate the volume of sediment or the amount of erosion. They are also used by floodplain managers to delineate floodplains and regulate the development in floodplains, which is crucial for all communities in the National Flood Insurance Program. Hydrometeorological Design Studies Center now provides more than 35,000 downloads per month to its Precipitation Frequency Data Server. Precipitation frequency estimates are often used in engineering design without any understanding how these estimates have been developed or without any understanding of the uncertainties associated with these estimates. This presentation will describe novel tools and techniques that have being developed in the last years to determine precipitation frequency estimates in NOAA Atlas 14. Particular attention will be given to the regional frequency analysis approach based on L-moment statistics calculated from annual maximum series, selected statistics obtained in determining and parameterizing the probability distribution functions, and the potential implication for engineering design of recently published estimates.

  14. Direct estimations of linear and nonlinear functionals of a quantum state.

    PubMed

    Ekert, Artur K; Alves, Carolina Moura; Oi, Daniel K L; Horodecki, Michał; Horodecki, Paweł; Kwek, L C

    2002-05-27

    We present a simple quantum network, based on the controlled-SWAP gate, that can extract certain properties of quantum states without recourse to quantum tomography. It can be used as a basic building block for direct quantum estimations of both linear and nonlinear functionals of any density operator. The network has many potential applications ranging from purity tests and eigenvalue estimations to direct characterization of some properties of quantum channels. Experimental realizations of the proposed network are within the reach of quantum technology that is currently being developed.

  15. The Estimated Annual Cost of Uterine Leiomyomata in the United States

    PubMed Central

    CARDOZO, Eden R.; CLARK, Andrew D.; BANKS, Nicole K.; HENNE, Melinda B.; STEGMANN, Barbara J.; SEGARS, James H.

    2011-01-01

    Objective To estimate the total annual societal cost of uterine fibroids in the United States, based on direct and indirect costs, including associated obstetric complications. Study Design A systematic review of the literature was conducted to estimate the number of women seeking treatment for symptomatic fibroids annually, the costs of medical and surgical treatment, work lost and obstetric complications attributable to fibroids. Total annual costs were converted to 2010 U.S. dollars. A sensitivity analysis was performed. Results The estimated annual direct costs (surgery, hospital admissions, outpatient visits, medications) were $4.1 to $9.4 billion. Estimated lost work costs ranged from $1.55 to $17.2 billion annually. Obstetric outcomes attributed to fibroids resulted in a cost of $238 million to $7.76 billion annually. Uterine fibroids were estimated to cost the US $5.9 to $34.4 billion annually. Conclusions Obstetric complications associated with fibroids contributed significantly to their economic burden. Lost work costs may account for the largest proportion of societal costs due to fibroids. PMID:22244472

  16. Target selection and mass estimation for manned NEO exploration using a baseline mission design

    NASA Astrophysics Data System (ADS)

    Boden, Ralf C.; Hein, Andreas M.; Kawaguchi, Junichiro

    2015-06-01

    In recent years Near-Earth Objects (NEOs) have received an increased amount of interest as a target for human exploration. NEOs offer scientifically interesting targets, and at the same time function as a stepping stone for achieving future Mars missions. The aim of this research is to identify promising targets from the large number of known NEOs that qualify for a manned sample-return mission with a maximum duration of one year. By developing a baseline mission design and a mass estimation model, mission opportunities are evaluated based on on-orbit mass requirements, safety considerations, and the properties of the potential targets. A selection of promising NEOs is presented and the effects of mission requirements and restrictions are discussed. Regarding safety aspects, the use of free-return trajectories provides the lowest on-orbit mass, when compared to an alternative design that uses system redundancies to ensure return of the spacecraft to Earth. It is discovered that, although a number of targets are accessible within the analysed time frame, no NEO offers both easy access and high incentive for its exploration. Under the discussed aspects a first human exploration mission going beyond the vicinity of Earth will require a trade off between targets that provide easy access and those that are of scientific interest. This lack of optimal mission opportunities can be seen in the small number of only 4 NEOs that meet all requirements for a sample-return mission and remain below an on-orbit mass of 500 metric Tons (mT). All of them require a mass between 315 and 492 mT. Even less ideal, smaller asteroids that are better accessible require an on-orbit mass that exceeds the launch capability of future heavy lift vehicles (HLV) such as SLS by at least 30 mT. These mass requirements show that additional efforts are necessary to increase the number of available targets and reduce on-orbit mass requirements through advanced mission architectures. The need for on

  17. Constrained State Estimation for Individual Localization in Wireless Body Sensor Networks

    PubMed Central

    Feng, Xiaoxue; Snoussi, Hichem; Liang, Yan; Jiao, Lianmeng

    2014-01-01

    Wireless body sensor networks based on ultra-wideband radio have recently received much research attention due to its wide applications in health-care, security, sports and entertainment. Accurate localization is a fundamental problem to realize the development of effective location-aware applications above. In this paper the problem of constrained state estimation for individual localization in wireless body sensor networks is addressed. Priori knowledge about geometry among the on-body nodes as additional constraint is incorporated into the traditional filtering system. The analytical expression of state estimation with linear constraint to exploit the additional information is derived. Furthermore, for nonlinear constraint, first-order and second-order linearizations via Taylor series expansion are proposed to transform the nonlinear constraint to the linear case. Examples between the first-order and second-order nonlinear constrained filters based on interacting multiple model extended kalman filter (IMM-EKF) show that the second-order solution for higher order nonlinearity as present in this paper outperforms the first-order solution, and constrained IMM-EKF obtains superior estimation than IMM-EKF without constraint. Another brownian motion individual localization example also illustrates the effectiveness of constrained nonlinear iterative least square (NILS), which gets better filtering performance than NILS without constraint. PMID:25390408

  18. The capability of professional- and lay-rescuers to estimate the chest compression-depth target: a short, randomized experiment.

    PubMed

    van Tulder, Raphael; Laggner, Roberta; Kienbacher, Calvin; Schmid, Bernhard; Zajicek, Andreas; Haidvogel, Jochen; Sebald, Dieter; Laggner, Anton N; Herkner, Harald; Sterz, Fritz; Eisenburger, Philip

    2015-04-01

    In CPR, sufficient compression depth is essential. The American Heart Association ("at least 5cm", AHA-R) and the European Resuscitation Council ("at least 5cm, but not to exceed 6cm", ERC-R) recommendations differ, and both are hardly achieved. This study aims to investigate the effects of differing target depth instructions on compression depth performances of professional and lay-rescuers. 110 professional-rescuers and 110 lay-rescuers were randomized (1:1, 4 groups) to estimate the AHA-R or ERC-R on a paper sheet (given horizontal axis) using a pencil and to perform chest compressions according to AHA-R or ERC-R on a manikin. Distance estimation and compression depth were the outcome variables. Professional-rescuers estimated the distance according to AHA-R in 19/55 (34.5%) and to ERC-R in 20/55 (36.4%) cases (p=0.84). Professional-rescuers achieved correct compression depth according to AHA-R in 39/55 (70.9%) and to ERC-R in 36/55 (65.4%) cases (p=0.97). Lay-rescuers estimated the distance correctly according to AHA-R in 18/55 (32.7%) and to ERC-R in 20/55 (36.4%) cases (p=0.59). Lay-rescuers yielded correct compression depth according to AHA-R in 39/55 (70.9%) and to ERC-R in 26/55 (47.3%) cases (p=0.02). Professional and lay-rescuers have severe difficulties in correctly estimating distance on a sheet of paper. Professional-rescuers are able to yield AHA-R and ERC-R targets likewise. In lay-rescuers AHA-R was associated with significantly higher success rates. The inability to estimate distance could explain the failure to appropriately perform chest compressions. For teaching lay-rescuers, the AHA-R with no upper limit of compression depth might be preferable. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  19. Hierarchical State-Space Estimation of Leatherback Turtle Navigation Ability

    PubMed Central

    Mills Flemming, Joanna; Jonsen, Ian D.; Field, Christopher A.

    2010-01-01

    Remotely sensed tracking technology has revealed remarkable migration patterns that were previously unknown; however, models to optimally use such data have developed more slowly. Here, we present a hierarchical Bayes state-space framework that allows us to combine tracking data from a collection of animals and make inferences at both individual and broader levels. We formulate models that allow the navigation ability of animals to be estimated and demonstrate how information can be combined over many animals to allow improved estimation. We also show how formal hypothesis testing regarding navigation ability can easily be accomplished in this framework. Using Argos satellite tracking data from 14 leatherback turtles, 7 males and 7 females, during their southward migration from Nova Scotia, Canada, we find that the circle of confusion (the radius around an animal's location within which it is unable to determine its location precisely) is approximately 96 km. This estimate suggests that the turtles' navigation does not need to be highly accurate, especially if they are able to use more reliable cues as they near their destination. Moreover, for the 14 turtles examined, there is little evidence to suggest that male and female navigation abilities differ. Because of the minimal assumptions made about the movement process, our approach can be used to estimate and compare navigation ability for many migratory species that are able to carry electronic tracking devices. PMID:21203382

  20. Targeted alpha therapy of mCRPC: Dosimetry estimate of 213Bismuth-PSMA-617.

    PubMed

    Kratochwil, Clemens; Schmidt, Karl; Afshar-Oromieh, Ali; Bruchertseifer, Frank; Rathke, Hendrik; Morgenstern, Alfred; Haberkorn, Uwe; Giesel, Frederik L

    2018-01-01

    PSMA-617 is a small molecule targeting the prostate-specific membrane antigen (PSMA). In this work, we estimate the radiation dosimetry for this ligand labeled with the alpha-emitter 213 Bi. Three patients with metastatic prostate cancer underwent PET scans 0.1 h, 1 h, 2 h, 3 h, 4 h and 5 h after injection of 68 Ga-PSMA-617. Source organs were kidneys, liver, spleen, salivary glands, bladder, red marrow and representative tumor lesions. The imaging nuclide 68 Ga was extrapolated to the half-life of 213 Bi. The residence times of 213 Bi were forwarded to the instable daughter nuclides. OLINDA was used for dosimetry calculation. Results are discussed in comparison to literature data for 225 Ac-PSMA-617. Assuming a relative biological effectiveness of 5 for alpha radiation, the dosimetry estimate revealed equivalent doses of mean 8.1 Sv RBE5 /GBq for salivary glands, 8.1 Sv RBE5 /GBq for kidneys and 0.52 Sv RBE5 /GBq for red marrow. Liver (1.2 Sv RBE5 /GBq), spleen (1.4 Sv RBE5 /GBq), bladder (0.28 Sv RBE5 /GBq) and other organs (0.26 Sv RBE5 /GBq) were not dose-limiting. The effective dose is 0.56 Sv RBE5 /GBq. Tumor lesions were in the range 3.2-9.0 Sv RBE5 /GBq (median 7.6 Sv RBE5 /GBq). Kidneys would limit the cumulative treatment activity to 3.7 GBq; red marrow might limit the maximum single fraction to 2 GBq. Despite promising results, the therapeutic index was inferior compared to 225 Ac-PSMA-617. Dosimetry of 213 Bi-PSMA-617 is in a range traditionally considered reasonable for clinical application. Nevertheless, compared to 225 Ac-PSMA-617, it suffers from higher perfusion-dependent off-target radiation and a longer biological half-life of PSMA-617 in dose-limiting organs than the physical half-life of 213 Bi, rendering this nuclide as a second choice radiolabel for targeted alpha therapy of prostate cancer.

  1. Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery

    NASA Astrophysics Data System (ADS)

    Wei, Zhongbao; Tseng, King Jet; Wai, Nyunt; Lim, Tuti Mariana; Skyllas-Kazacos, Maria

    2016-11-01

    Reliable state estimate depends largely on an accurate battery model. However, the parameters of battery model are time varying with operating condition variation and battery aging. The existing co-estimation methods address the model uncertainty by integrating the online model identification with state estimate and have shown improved accuracy. However, the cross interference may arise from the integrated framework to compromise numerical stability and accuracy. Thus this paper proposes the decoupling of model identification and state estimate to eliminate the possibility of cross interference. The model parameters are online adapted with the recursive least squares (RLS) method, based on which a novel joint estimator based on extended Kalman Filter (EKF) is formulated to estimate the state of charge (SOC) and capacity concurrently. The proposed joint estimator effectively compresses the filter order which leads to substantial improvement in the computational efficiency and numerical stability. Lab scale experiment on vanadium redox flow battery shows that the proposed method is highly authentic with good robustness to varying operating conditions and battery aging. The proposed method is further compared with some existing methods and shown to be superior in terms of accuracy, convergence speed, and computational cost.

  2. Radar Imaging of Non-Uniformly Rotating Targets via a Novel Approach for Multi-Component AM-FM Signal Parameter Estimation

    PubMed Central

    Wang, Yong

    2015-01-01

    A novel radar imaging approach for non-uniformly rotating targets is proposed in this study. It is assumed that the maneuverability of the non-cooperative target is severe, and the received signal in a range cell can be modeled as multi-component amplitude-modulated and frequency-modulated (AM-FM) signals after motion compensation. Then, the modified version of Chirplet decomposition (MCD) based on the integrated high order ambiguity function (IHAF) is presented for the parameter estimation of AM-FM signals, and the corresponding high quality instantaneous ISAR images can be obtained from the estimated parameters. Compared with the MCD algorithm based on the generalized cubic phase function (GCPF) in the authors’ previous paper, the novel algorithm presented in this paper is more accurate and efficient, and the results with simulated and real data demonstrate the superiority of the proposed method. PMID:25806870

  3. Monitoring of Batch Industrial Crystallization with Growth, Nucleation, and Agglomeration. Part 2: Structure Design for State Estimation with Secondary Measurements

    PubMed Central

    2017-01-01

    This work investigates the design of alternative monitoring tools based on state estimators for industrial crystallization systems with nucleation, growth, and agglomeration kinetics. The estimation problem is regarded as a structure design problem where the estimation model and the set of innovated states have to be chosen; the estimator is driven by the available measurements of secondary variables. On the basis of Robust Exponential estimability arguments, it is found that the concentration is distinguishable with temperature and solid fraction measurements while the crystal size distribution (CSD) is not. Accordingly, a state estimator structure is selected such that (i) the concentration (and other distinguishable states) are innovated by means of the secondary measurements processed with the geometric estimator (GE), and (ii) the CSD is estimated by means of a rigorous model in open loop mode. The proposed estimator has been tested through simulations showing good performance in the case of mismatch in the initial conditions, parametric plant-model mismatch, and noisy measurements. PMID:28890604

  4. Monitoring of Batch Industrial Crystallization with Growth, Nucleation, and Agglomeration. Part 2: Structure Design for State Estimation with Secondary Measurements.

    PubMed

    Porru, Marcella; Özkan, Leyla

    2017-08-30

    This work investigates the design of alternative monitoring tools based on state estimators for industrial crystallization systems with nucleation, growth, and agglomeration kinetics. The estimation problem is regarded as a structure design problem where the estimation model and the set of innovated states have to be chosen; the estimator is driven by the available measurements of secondary variables. On the basis of Robust Exponential estimability arguments, it is found that the concentration is distinguishable with temperature and solid fraction measurements while the crystal size distribution (CSD) is not. Accordingly, a state estimator structure is selected such that (i) the concentration (and other distinguishable states) are innovated by means of the secondary measurements processed with the geometric estimator (GE), and (ii) the CSD is estimated by means of a rigorous model in open loop mode. The proposed estimator has been tested through simulations showing good performance in the case of mismatch in the initial conditions, parametric plant-model mismatch, and noisy measurements.

  5. Performance Analysis for Joint Target Parameter Estimation in UMTS-Based Passive Multistatic Radar with Antenna Arrays Using Modified Cramér-Rao Lower Bounds

    PubMed Central

    Wang, Fei; Salous, Sana; Zhou, Jianjiang

    2017-01-01

    In this study, the modified Cramér-Rao lower bounds (MCRLBs) on the joint estimation of target position and velocity is investigated for a universal mobile telecommunication system (UMTS)-based passive multistatic radar system with antenna arrays. First, we analyze the log-likelihood redfunction of the received signal for a complex Gaussian extended target. Then, due to the non-deterministic transmitted data symbols, the analytically closed-form expressions of the MCRLBs on the Cartesian coordinates of target position and velocity are derived for a multistatic radar system with Nt UMTS-based transmit station of Lt antenna elements and Nr receive stations of Lr antenna elements. With the aid of numerical simulations, it is shown that increasing the number of receiving elements in each receive station can reduce the estimation errors. In addition, it is demonstrated that the MCRLB is not only a function of signal-to-noise ratio (SNR), the number of receiving antenna elements and the properties of the transmitted UMTS signals, but also a function of the relative geometric configuration between the target and the multistatic radar system.The analytical expressions for MCRLB will open up a new dimension for passive multistatic radar system by aiding the optimal placement of receive stations to improve the target parameter estimation performance. PMID:29057805

  6. Estimation of death rates in US states with small subpopulations.

    PubMed

    Voulgaraki, Anastasia; Wei, Rong; Kedem, Benjamin

    2015-05-20

    In US states with small subpopulations, the observed mortality rates are often zero, particularly among young ages. Because in life tables, death rates are reported mostly on a log scale, zero mortality rates are problematic. To overcome the observed zero death rates problem, appropriate probability models are used. Using these models, observed zero mortality rates are replaced by the corresponding expected values. This enables logarithmic transformations and, in some cases, the fitting of the eight-parameter Heligman-Pollard model to produce mortality estimates for ages 0-130 years, a procedure illustrated in terms of mortality data from several states. Copyright © 2014 John Wiley & Sons, Ltd.

  7. Using National Data to Estimate Average Cost Effectiveness of EFNEP Outcomes by State/Territory

    ERIC Educational Resources Information Center

    Baral, Ranju; Davis, George C.; Blake, Stephanie; You, Wen; Serrano, Elena

    2013-01-01

    This report demonstrates how existing national data can be used to first calculate upper limits on the average cost per participant and per outcome per state/territory for the Expanded Food and Nutrition Education Program (EFNEP). These upper limits can then be used by state EFNEP administrators to obtain more precise estimates for their states,…

  8. Margin estimation and disturbances of irradiation field in layer-stacking carbon-ion beams for respiratory moving targets.

    PubMed

    Tajiri, Shinya; Tashiro, Mutsumi; Mizukami, Tomohiro; Tsukishima, Chihiro; Torikoshi, Masami; Kanai, Tatsuaki

    2017-11-01

    Carbon-ion therapy by layer-stacking irradiation for static targets has been practised in clinical treatments. In order to apply this technique to a moving target, disturbances of carbon-ion dose distributions due to respiratory motion have been studied based on the measurement using a respiratory motion phantom, and the margin estimation given by the square root of the summation Internal margin2+Setup margin2 has been assessed. We assessed the volume in which the variation in the ratio of the dose for a target moving due to respiration relative to the dose for a static target was within 5%. The margins were insufficient for use with layer-stacking irradiation of a moving target, and an additional margin was required. The lateral movement of a target converts to the range variation, as the thickness of the range compensator changes with the movement of the target. Although the additional margin changes according to the shape of the ridge filter, dose uniformity of 5% can be achieved for a spherical target 93 mm in diameter when the upward range variation is limited to 5 mm and the additional margin of 2.5 mm is applied in case of our ridge filter. Dose uniformity in a clinical target largely depends on the shape of the mini-peak as well as on the bolus shape. We have shown the relationship between range variation and dose uniformity. In actual therapy, the upper limit of target movement should be considered by assessing the bolus shape. © The Author 2017. Published by Oxford University Press on behalf of The Japan Radiation Research Society and Japanese Society for Radiation Oncology.

  9. Enhancing emotional-based target prediction

    NASA Astrophysics Data System (ADS)

    Gosnell, Michael; Woodley, Robert

    2008-04-01

    This work extends existing agent-based target movement prediction to include key ideas of behavioral inertia, steady states, and catastrophic change from existing psychological, sociological, and mathematical work. Existing target prediction work inherently assumes a single steady state for target behavior, and attempts to classify behavior based on a single emotional state set. The enhanced, emotional-based target prediction maintains up to three distinct steady states, or typical behaviors, based on a target's operating conditions and observed behaviors. Each steady state has an associated behavioral inertia, similar to the standard deviation of behaviors within that state. The enhanced prediction framework also allows steady state transitions through catastrophic change and individual steady states could be used in an offline analysis with additional modeling efforts to better predict anticipated target reactions.

  10. Composing problem solvers for simulation experimentation: a case study on steady state estimation.

    PubMed

    Leye, Stefan; Ewald, Roland; Uhrmacher, Adelinde M

    2014-01-01

    Simulation experiments involve various sub-tasks, e.g., parameter optimization, simulation execution, or output data analysis. Many algorithms can be applied to such tasks, but their performance depends on the given problem. Steady state estimation in systems biology is a typical example for this: several estimators have been proposed, each with its own (dis-)advantages. Experimenters, therefore, must choose from the available options, even though they may not be aware of the consequences. To support those users, we propose a general scheme to aggregate such algorithms to so-called synthetic problem solvers, which exploit algorithm differences to improve overall performance. Our approach subsumes various aggregation mechanisms, supports automatic configuration from training data (e.g., via ensemble learning or portfolio selection), and extends the plugin system of the open source modeling and simulation framework James II. We show the benefits of our approach by applying it to steady state estimation for cell-biological models.

  11. Mercury emission estimates from fires: an initial inventory for the United States.

    PubMed

    Wiedinmyer, Christine; Friedli, Hans

    2007-12-01

    Recent studies have shown that emissions of mercury (Hg), a hazardous air pollutant, from fires can be significant. However, to date, these emissions have not been well-quantified for the entire United States. Daily emissions of Hg from fires in the lower 48 states of the United States (LOWER48) and in Alaska were estimated for 2002-2006 using a simple fire emissions model. Emission factors of Hg from fires in different ecosystems were compiled from published plume studies and from soil-based assessments. Annual averaged emissions of Hg from fires in the LOWER48 and Alaska were 44 (20-65) metric tons yr(-1), equivalent to approximately 30% of the U.S. EPA 2002 National Emissions Inventory for Hg. Alaska had the highest averaged monthly emissions of all states; however, the emissions have a high temporal variability. Emissions from forests dominate the inventory, suggesting that Hg emissions from agricultural fires are not significant on an annual basis. The uncertainty in the Hg emission factors due to limited data leads to an uncertainty in the emission estimates on the order of +/-50%. Research is still needed to better constrain Hg emission factors from fires, particularly in the eastern U.S. and for ecosystems other than forests.

  12. Estimating the breeding population of long-billed curlew in the United States

    USGS Publications Warehouse

    Stanley, T.R.; Skagen, S.K.

    2007-01-01

    Determining population size and long-term trends in population size for species of high concern is a priority of international, national, and regional conservation plans. Long-billed curlews (Numenius americanus) are a species of special concern in North America due to apparent declines in their population. Because long-billed curlews are not adequately monitored by existing programs, we undertook a 2-year study with the goals of 1) determining present long-billed curlew distribution and breeding population size in the United States and 2) providing recommendations for a long-term long-billed curlew monitoring protocol. We selected a stratified random sample of survey routes in 16 western states for sampling in 2004 and 2005, and we analyzed count data from these routes to estimate detection probabilities and abundance. In addition, we evaluated habitat along roadsides to determine how well roadsides represented habitat throughout the sampling units. We estimated there were 164,515 (SE = 42,047) breeding long-billed curlews in 2004, and 109,533 (SE = 31,060) breeding individuals in 2005. These estimates far exceed currently accepted estimates based on expert opinion. We found that habitat along roadsides was representative of long-billed curlew habitat in general. We make recommendations for improving sampling methodology, and we present power curves to provide guidance on minimum sample sizes required to detect trends in abundance.

  13. Estimating watershed evapotranspiration across the United States using multiple methods

    Treesearch

    Ge Sun; Shanlei Sun; Jingfeng Xiao; Peter Caldwell; Devendra Amatya; Suat Irmak; Prasanna H. Gowda; Sudhanshu Panda; Steve McNulty; Yang Zhang

    2016-01-01

    Evapotranspiration (ET) is the largest watershed water balance component only next to precipitation in the United States. ET is closely coupled with ecosystem carbon and energy fluxes, affects flooding or drought magnitude, and is also a good predictor for biodiversity at a regional scale.Thus, accurately estimating ET is of paramount importance to quantify the effects...

  14. Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans

    PubMed Central

    Kim, Hyoungkyu; Hudetz, Anthony G.; Lee, Joseph; Mashour, George A.; Lee, UnCheol; Avidan, Michael S.

    2018-01-01

    The integrated information theory (IIT) proposes a quantitative measure, denoted as Φ, of the amount of integrated information in a physical system, which is postulated to have an identity relationship with consciousness. IIT predicts that the value of Φ estimated from brain activities represents the level of consciousness across phylogeny and functional states. Practical limitations, such as the explosive computational demands required to estimate Φ for real systems, have hindered its application to the brain and raised questions about the utility of IIT in general. To achieve practical relevance for studying the human brain, it will be beneficial to establish the reliable estimation of Φ from multichannel electroencephalogram (EEG) and define the relationship of Φ to EEG properties conventionally used to define states of consciousness. In this study, we introduce a practical method to estimate Φ from high-density (128-channel) EEG and determine the contribution of each channel to Φ. We examine the correlation of power, frequency, functional connectivity, and modularity of EEG with regional Φ in various states of consciousness as modulated by diverse anesthetics. We find that our approximation of Φ alone is insufficient to discriminate certain states of anesthesia. However, a multi-dimensional parameter space extended by four parameters related to Φ and EEG connectivity is able to differentiate all states of consciousness. The association of Φ with EEG connectivity during clinically defined anesthetic states represents a new practical approach to the application of IIT, which may be used to characterize various physiological (sleep), pharmacological (anesthesia), and pathological (coma) states of consciousness in the human brain. PMID:29503611

  15. Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans.

    PubMed

    Kim, Hyoungkyu; Hudetz, Anthony G; Lee, Joseph; Mashour, George A; Lee, UnCheol

    2018-01-01

    The integrated information theory (IIT) proposes a quantitative measure, denoted as Φ, of the amount of integrated information in a physical system, which is postulated to have an identity relationship with consciousness. IIT predicts that the value of Φ estimated from brain activities represents the level of consciousness across phylogeny and functional states. Practical limitations, such as the explosive computational demands required to estimate Φ for real systems, have hindered its application to the brain and raised questions about the utility of IIT in general. To achieve practical relevance for studying the human brain, it will be beneficial to establish the reliable estimation of Φ from multichannel electroencephalogram (EEG) and define the relationship of Φ to EEG properties conventionally used to define states of consciousness. In this study, we introduce a practical method to estimate Φ from high-density (128-channel) EEG and determine the contribution of each channel to Φ. We examine the correlation of power, frequency, functional connectivity, and modularity of EEG with regional Φ in various states of consciousness as modulated by diverse anesthetics. We find that our approximation of Φ alone is insufficient to discriminate certain states of anesthesia. However, a multi-dimensional parameter space extended by four parameters related to Φ and EEG connectivity is able to differentiate all states of consciousness. The association of Φ with EEG connectivity during clinically defined anesthetic states represents a new practical approach to the application of IIT, which may be used to characterize various physiological (sleep), pharmacological (anesthesia), and pathological (coma) states of consciousness in the human brain.

  16. Prevalence estimates for primary brain tumors in the United States by age, gender, behavior, and histology.

    PubMed

    Porter, Kimberly R; McCarthy, Bridget J; Freels, Sally; Kim, Yoonsang; Davis, Faith G

    2010-06-01

    Prevalence is the best indicator of cancer survivorship in the population, but few studies have focused on brain tumor prevalence because of previous data limitations. Hence, the full impact of primary brain tumors on the healthcare system in the United States is not completely described. The present study provides an estimate of the prevalence of disease in the United States, updating an earlier prevalence study. Incidence data for 2004 and survival data for 1985-2005 were obtained by the Central Brain Tumor Registry of the United States from selected regions, modeled under 2 different survival assumptions, to estimate prevalence rates for the year 2004 and projected estimates for 2010. The overall incidence rate for primary brain tumors was 18.1 per 100 000 person-years with 2-, 5-, 10-, and 20-year observed survival rates of 62%, 54%, 45%, and 30%, respectively. On the basis of the sum of nonmalignant and averaged malignant estimates, the overall prevalence rate of individuals with a brain tumor was estimated to be 209.0 per 100 000 in 2004 and 221.8 per 100 000 in 2010. The female prevalence rate (264.8 per 100 000) was higher than that in males (158.7 per 100 000). The averaged prevalence rate for malignant tumors (42.5 per 100 000) was lower than the prevalence for nonmalignant tumors (166.5 per 100 000). This study provides estimates of the 2004 (n = 612 770) and 2010 (n = 688 096) expected number of individuals living with primary brain tumor diagnoses in the United States, providing more current and robust estimates for aiding healthcare planning and patient advocacy for an aging US population.

  17. 76 FR 21750 - State Median Income Estimate for a Four-Person Family: Notice of the Federal Fiscal Year (FFY...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-04-18

    ... Income Estimate for a Four-Person Family: Notice of the Federal Fiscal Year (FFY) 2012 State Median Income Estimates for Use Under the Low Income Home Energy Assistance Program (LIHEAP) AGENCY.... ACTION: Notice of State median income estimates for FFY 2012. SUMMARY: This notice announces to LIHEAP...

  18. Prevalence of Prostate Cancer Clinical States and Mortality in the United States: Estimates Using a Dynamic Progression Model

    PubMed Central

    Scher, Howard I.; Solo, Kirk; Valant, Jason; Todd, Mary B.; Mehra, Maneesha

    2015-01-01

    Objective To identify patient populations most in need of treatment across the prostate cancer disease continuum, we developed a novel dynamic transition model based on risk of disease progression and mortality. Design and Outcome Measurements We modeled the flow of patient populations through eight prostate cancer clinical states (PCCS) that are characterized by the status of the primary tumor, presence of metastases, prior and current treatment, and testosterone levels. Simulations used published US incidence rates for each year from 1990. Progression and mortality rates were derived from published clinical trials, meta-analyses, and observational studies. Model outputs included the incidence, prevalence, and mortality for each PCCS. The impact of novel treatments was modeled in three distinct scenarios: metastatic castration-resistant prostate cancer (mCRPC), non-metastatic CRPC (nmCRPC), or both. Results and Limitations The model estimated the prevalence of prostate cancer as 2,219,280 in the US in 2009 and 3,072,480 in 2020, and incidence of mCRPC as 36,100 and 42,970, respectively. All-cause mortality in prostate cancer was estimated at 168,290 in 2009 and 219,360 in 2020, with 20.5% and 19.5% of these deaths, respectively, occurring in men with mCRPC. The majority (86%) of incidence flow into mCRPC states was from the nmCRPC clinical state. In the scenario with novel interventions for nmCRPC states, the progression to mCRPC is reduced, thus decreasing mCRPC incidence by 12% in 2020, with a sustained decline in mCRPC mortality. A limitation of the model is that it does not estimate prostate cancer—specific mortality. Conclusion The model informs clinical trial design for prostate cancer by quantifying outcomes in PCCS, and demonstrates the impact of an effective therapy applied in an earlier clinical state of nmCRPC on the incidence of mCRPC morbidity and subsequent mortality. PMID:26460686

  19. Single snapshot DOA estimation

    NASA Astrophysics Data System (ADS)

    Häcker, P.; Yang, B.

    2010-10-01

    In array signal processing, direction of arrival (DOA) estimation has been studied for decades. Many algorithms have been proposed and their performance has been studied thoroughly. Yet, most of these works are focused on the asymptotic case of a large number of snapshots. In automotive radar applications like driver assistance systems, however, only a small number of snapshots of the radar sensor array or, in the worst case, a single snapshot is available for DOA estimation. In this paper, we investigate and compare different DOA estimators with respect to their single snapshot performance. The main focus is on the estimation accuracy and the angular resolution in multi-target scenarios including difficult situations like correlated targets and large target power differences. We will show that some algorithms lose their ability to resolve targets or do not work properly at all. Other sophisticated algorithms do not show a superior performance as expected. It turns out that the deterministic maximum likelihood estimator is a good choice under these hard conditions.

  20. Pathologic Criteria to Estimate the State of the Liver in Potential Donors.

    PubMed

    Shaimardanova, Caliya; Fedotovskikh, Galina; Savchuk, Aleksandr; Doszhan, Ainura; Smagulova, Aigerim; Gaipov, Abduzhappar

    2015-11-01

    The pathologic evaluation of the liver is one of the most important issues in liver transplants. We evaluated the histopathological condition of livers in potential donors. After liver biopsy, 37 potential donor livers were histologically studied. Liver tissue was stained by hematoxylin and eosin as well as Masson Trichrome. The results of the study showed the morphologic criteria used to estimate the state of the liver in potential donors is not only steatosis and fibrosis, but other important histologic criteria, such as proteinosis, necrosis of hepatocytes, endotheliitis of central veins, inflammatory infiltration, endarteritis in portal tracts and phlebitis in portal tracts, proliferation of the bile ducts, and cholestasis. Results of the study showed that the morphologic criteria to estimate the state of the liver in potential donors includes not only steatosis and fibrosis, but other important histologic criteria as well.

  1. Estimating mercury emissions resulting from wildfire in forests of the Western United States

    USGS Publications Warehouse

    Webster, Jackson; Kane, Tyler J.; Obrist, Daniel; Ryan, Joseph N.; Aiken, George R.

    2016-01-01

    Understanding the emissions of mercury (Hg) from wildfires is important for quantifying the global atmospheric Hg sources. Emissions of Hg from soils resulting from wildfires in the Western United States was estimated for the 2000 to 2013 period, and the potential emission of Hg from forest soils was assessed as a function of forest type and soil-heating. Wildfire released an annual average of 3100 ± 1900 kg-Hg y− 1 for the years spanning 2000–2013 in the 11 states within the study area. This estimate is nearly 5-fold lower than previous estimates for the study region. Lower emission estimates are attributed to an inclusion of fire severity within burn perimeters. Within reported wildfire perimeters, the average distribution of low, moderate, and high severity burns was 52, 29, and 19% of the total area, respectively. Review of literature data suggests that that low severity burning does not result in soil heating, moderate severity fire results in shallow soil heating, and high severity fire results in relatively deep soil heating (< 5 cm). Using this approach, emission factors for high severity burns ranged from 58 to 640 μg-Hg kg-fuel− 1. In contrast, low severity burns have emission factors that are estimated to be only 18–34 μg-Hg kg-fuel− 1. In this estimate, wildfire is predicted to release 1–30 g Hg ha− 1 from Western United States forest soils while above ground fuels are projected to contribute an additional 0.9 to 7.8 g Hg ha− 1. Land cover types with low biomass (desert scrub) are projected to release less than 1 g Hg ha− 1. Following soil sources, fuel source contributions to total Hg emissions generally followed the order of duff > wood > foliage > litter > branches.

  2. A probabilistic estimate of maximum acceleration in rock in the contiguous United States

    USGS Publications Warehouse

    Algermissen, Sylvester Theodore; Perkins, David M.

    1976-01-01

    This paper presents a probabilistic estimate of the maximum ground acceleration to be expected from earthquakes occurring in the contiguous United States. It is based primarily upon the historic seismic record which ranges from very incomplete before 1930 to moderately complete after 1960. Geologic data, primarily distribution of faults, have been employed only to a minor extent, because most such data have not been interpreted yet with earthquake hazard evaluation in mind.The map provides a preliminary estimate of the relative hazard in various parts of the country. The report provides a method for evaluating the relative importance of the many parameters and assumptions in hazard analysis. The map and methods of evaluation described reflect the current state of understanding and are intended to be useful for engineering purposes in reducing the effects of earthquakes on buildings and other structures.Studies are underway on improved methods for evaluating the relativ( earthquake hazard of different regions. Comments on this paper are invited to help guide future research and revisions of the accompanying map.The earthquake hazard in the United States has been estimated in a variety of ways since the initial effort by Ulrich (see Roberts and Ulrich, 1950). In general, the earlier maps provided an estimate of the severity of ground shaking or damage but the frequency of occurrence of the shaking or damage was not given. Ulrich's map showed the distribution of expected damage in terms of no damage (zone 0), minor damage (zone 1), moderate damage (zone 2), and major damage (zone 3). The zones were not defined further and the frequency of occurrence of damage was not suggested. Richter (1959) and Algermissen (1969) estimated the ground motion in terms of maximum Modified Mercalli intensity. Richter used the terms "occasional" and "frequent" to characterize intensity IX shaking and Algermissen included recurrence curves for various parts of the country in the paper

  3. Effects of window size and shape on accuracy of subpixel centroid estimation of target images

    NASA Technical Reports Server (NTRS)

    Welch, Sharon S.

    1993-01-01

    A new algorithm is presented for increasing the accuracy of subpixel centroid estimation of (nearly) point target images in cases where the signal-to-noise ratio is low and the signal amplitude and shape vary from frame to frame. In the algorithm, the centroid is calculated over a data window that is matched in width to the image distribution. Fourier analysis is used to explain the dependency of the centroid estimate on the size of the data window, and simulation and experimental results are presented which demonstrate the effects of window size for two different noise models. The effects of window shape were also investigated for uniform and Gaussian-shaped windows. The new algorithm was developed to improve the dynamic range of a close-range photogrammetric tracking system that provides feedback for control of a large gap magnetic suspension system (LGMSS).

  4. Ground moving target geo-location from monocular camera mounted on a micro air vehicle

    NASA Astrophysics Data System (ADS)

    Guo, Li; Ang, Haisong; Zheng, Xiangming

    2011-08-01

    The usual approaches to unmanned air vehicle(UAV)-to-ground target geo-location impose some severe constraints to the system, such as stationary objects, accurate geo-reference terrain database, or ground plane assumption. Micro air vehicle(MAV) works with characteristics including low altitude flight, limited payload and onboard sensors' low accuracy. According to these characteristics, a method is developed to determine the location of ground moving target which imaged from the air using monocular camera equipped on MAV. This method eliminates the requirements for terrain database (elevation maps) and altimeters that can provide MAV's and target's altitude. Instead, the proposed method only requires MAV flight status provided by its inherent onboard navigation system which includes inertial measurement unit(IMU) and global position system(GPS). The key is to get accurate information on the altitude of the ground moving target. First, Optical flow method extracts background static feature points. Setting a local region around the target in the current image, The features which are on the same plane with the target in this region are extracted, and are retained as aided features. Then, inverse-velocity method calculates the location of these points by integrated with aircraft status. The altitude of object, which is calculated by using position information of these aided features, combining with aircraft status and image coordinates, geo-locate the target. Meanwhile, a framework with Bayesian estimator is employed to eliminate noise caused by camera, IMU and GPS. Firstly, an extended Kalman filter(EKF) provides a simultaneous localization and mapping solution for the estimation of aircraft states and aided features location which defines the moving target local environment. Secondly, an unscented transformation(UT) method determines the estimated mean and covariance of target location from aircraft states and aided features location, and then exports them for the

  5. Estimating quality weights for EQ-5D health states with the time trade-off method in South Korea.

    PubMed

    Jo, Min-Woo; Yun, Sung-Cheol; Lee, Sang-Il

    2008-12-01

    To estimate quality weights of EQ-5D health states with the time trade-off (TTO) method in the general population of South Korea. A total of 500 respondents valued 42 hypothetical EQ-5D health states using the TTO and visual analog scale. The quality weights for all EQ-5D health states were estimated by a random effects model and compared with those from studies in other countries. Overall estimated quality weights for all EQ-5D health states from this study were highly correlated with those from previous studies, but quality weights of individual states were substantially different from those of their corresponding states in other studies. The Korean value set differed from value sets from other countries. Special caution is needed when a value set from one country is applied to another with a different culture.

  6. Combined Parameter and State Estimation Problem in a Complex Domain: RF Hyperthermia Treatment Using Nanoparticles

    NASA Astrophysics Data System (ADS)

    Bermeo Varon, L. A.; Orlande, H. R. B.; Eliçabe, G. E.

    2016-09-01

    The particle filter methods have been widely used to solve inverse problems with sequential Bayesian inference in dynamic models, simultaneously estimating sequential state variables and fixed model parameters. This methods are an approximation of sequences of probability distributions of interest, that using a large set of random samples, with presence uncertainties in the model, measurements and parameters. In this paper the main focus is the solution combined parameters and state estimation in the radiofrequency hyperthermia with nanoparticles in a complex domain. This domain contains different tissues like muscle, pancreas, lungs, small intestine and a tumor which is loaded iron oxide nanoparticles. The results indicated that excellent agreements between estimated and exact value are obtained.

  7. Using cost and health impacts to prioritize the targeted testing of tuberculosis in the United States.

    PubMed

    Miller, Thaddeus L; Hilsenrath, Peter; Lykens, Kristine; McNabb, Scott J N; Moonan, Patrick K; Weis, Stephen E

    2006-04-01

    Evaluation improves efficiency and effectiveness. Current U.S. tuberculosis (TB) control policies emphasize the treatment of latent TB infection (LTBI). However, this policy, if not targeted, may be inefficient. We determined the efficiency of a state-law mandated TB screening program and a non state-law mandated one in terms of cost, morbidity, treatment, and disease averted. We evaluated two publicly funded metropolitan TB prevention and control programs through retrospective analyses and modeling. Main outcomes measured were TB incidence and prevalence, TB cases averted, and cost. A non state-law mandated TB program for homeless persons in Tarrant County screened 4.5 persons to identify one with LTBI and 82 persons to identify one with TB. A state-law mandated TB program for jail inmates screened 109 persons to identify one with LTBI and 3274 persons to identify one with TB. The number of patients with LTBI treated to prevent one TB case was 12.1 and 15.3 for the homeless and jail inmate TB programs, respectively. Treatment of LTBI by the homeless and jail inmate TB screening programs will avert 11.9 and 7.9 TB cases at a cost of 14,350 US dollars and 34,761 US dollars per TB case, respectively. Mandated TB screening programs should be risk-based, not population-based. Non mandated targeted testing for TB in congregate settings for the homeless was more efficient than state-law mandated targeted testing for TB among jailed inmates.

  8. NATIONAL- AND STATE-LEVEL EMISSIONS ESTIMATES OF RADIATIVELY IMPORTANT TRACE GASES (RITGS) FROM ANTHROPOGENIC SOURCES

    EPA Science Inventory

    The report documents the development of national- and state- level emissions estimates of radiatively important trace gases (RlTGs). Emissions estimates are presented for the principal anthropogenic sources of carbon dioxide (CO2), methane (CH4), chlorofluorocarbons (CFCs), and o...

  9. Asynchronous State Estimation for Discrete-Time Switched Complex Networks With Communication Constraints.

    PubMed

    Zhang, Dan; Wang, Qing-Guo; Srinivasan, Dipti; Li, Hongyi; Yu, Li

    2018-05-01

    This paper is concerned with the asynchronous state estimation for a class of discrete-time switched complex networks with communication constraints. An asynchronous estimator is designed to overcome the difficulty that each node cannot access to the topology/coupling information. Also, the event-based communication, signal quantization, and the random packet dropout problems are studied due to the limited communication resource. With the help of switched system theory and by resorting to some stochastic system analysis method, a sufficient condition is proposed to guarantee the exponential stability of estimation error system in the mean-square sense and a prescribed performance level is also ensured. The characterization of the desired estimator gains is derived in terms of the solution to a convex optimization problem. Finally, the effectiveness of the proposed design approach is demonstrated by a simulation example.

  10. Using a Regression Method for Estimating Performance in a Rapid Serial Visual Presentation Target-Detection Task

    DTIC Science & Technology

    2017-12-01

    values designating each stimulus as a target ( true ) or nontarget (false). Both stim_time and stim_label should have length equal to the number of...position unless so designated by other authorized documents. Citation of manufacturer’s or trade names does not constitute an official endorsement or...depend strongly on the true values of hit rate and false-alarm rate. Based on its better estimation of hit rate and false-alarm rate, the regression

  11. Strong Tracking Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking.

    PubMed

    Liu, Hua; Wu, Wen

    2017-03-31

    Conventional spherical simplex-radial cubature Kalman filter (SSRCKF) for maneuvering target tracking may decline in accuracy and even diverge when a target makes abrupt state changes. To overcome this problem, a novel algorithm named strong tracking spherical simplex-radial cubature Kalman filter (STSSRCKF) is proposed in this paper. The proposed algorithm uses the spherical simplex-radial (SSR) rule to obtain a higher accuracy than cubature Kalman filter (CKF) algorithm. Meanwhile, by introducing strong tracking filter (STF) into SSRCKF and modifying the predicted states' error covariance with a time-varying fading factor, the gain matrix is adjusted on line so that the robustness of the filter and the capability of dealing with uncertainty factors is improved. In this way, the proposed algorithm has the advantages of both STF's strong robustness and SSRCKF's high accuracy. Finally, a maneuvering target tracking problem with abrupt state changes is used to test the performance of the proposed filter. Simulation results show that the STSSRCKF algorithm can get better estimation accuracy and greater robustness for maneuvering target tracking.

  12. A General Simulator Using State Estimation for a Space Tug Navigation System. [computerized simulation, orbital position estimation and flight mechanics

    NASA Technical Reports Server (NTRS)

    Boland, J. S., III

    1975-01-01

    A general simulation program is presented (GSP) involving nonlinear state estimation for space vehicle flight navigation systems. A complete explanation of the iterative guidance mode guidance law, derivation of the dynamics, coordinate frames, and state estimation routines are given so as to fully clarify the assumptions and approximations involved so that simulation results can be placed in their proper perspective. A complete set of computer acronyms and their definitions as well as explanations of the subroutines used in the GSP simulator are included. To facilitate input/output, a complete set of compatable numbers, with units, are included to aid in data development. Format specifications, output data phrase meanings and purposes, and computer card data input are clearly spelled out. A large number of simulation and analytical studies were used to determine the validity of the simulator itself as well as various data runs.

  13. Performance Analysis for Joint Target Parameter Estimation in UMTS-Based Passive Multistatic Radar with Antenna Arrays Using Modified Cramér-Rao Lower Bounds.

    PubMed

    Shi, Chenguang; Wang, Fei; Salous, Sana; Zhou, Jianjiang

    2017-10-18

    In this study, the modified Cramér-Rao lower bounds (MCRLBs) on the joint estimation of target position and velocity is investigated for a universal mobile telecommunication system (UMTS)-based passive multistatic radar system with antenna arrays. First, we analyze the log-likelihood redfunction of the received signal for a complex Gaussian extended target. Then, due to the non-deterministic transmitted data symbols, the analytically closed-form expressions of the MCRLBs on the Cartesian coordinates of target position and velocity are derived for a multistatic radar system with N t UMTS-based transmit station of L t antenna elements and N r receive stations of L r antenna elements. With the aid of numerical simulations, it is shown that increasing the number of receiving elements in each receive station can reduce the estimation errors. In addition, it is demonstrated that the MCRLB is not only a function of signal-to-noise ratio (SNR), the number of receiving antenna elements and the properties of the transmitted UMTS signals, but also a function of the relative geometric configuration between the target and the multistatic radar system.The analytical expressions for MCRLB will open up a new dimension for passive multistatic radar system by aiding the optimal placement of receive stations to improve the target parameter estimation performance.

  14. Lithium-ion battery state of function estimation based on fuzzy logic algorithm with associated variables

    NASA Astrophysics Data System (ADS)

    Gan, L.; Yang, F.; Shi, Y. F.; He, H. L.

    2017-11-01

    Many occasions related to batteries demand to know how much continuous and instantaneous power can batteries provide such as the rapidly developing electric vehicles. As the large-scale applications of lithium-ion batteries, lithium-ion batteries are used to be our research object. Many experiments are designed to get the lithium-ion battery parameters to ensure the relevance and reliability of the estimation. To evaluate the continuous and instantaneous load capability of a battery called state-of-function (SOF), this paper proposes a fuzzy logic algorithm based on battery state-of-charge(SOC), state-of-health(SOH) and C-rate parameters. Simulation and experimental results indicate that the proposed approach is suitable for battery SOF estimation.

  15. State-space models’ dirty little secrets: even simple linear Gaussian models can have estimation problems

    NASA Astrophysics Data System (ADS)

    Auger-Méthé, Marie; Field, Chris; Albertsen, Christoffer M.; Derocher, Andrew E.; Lewis, Mark A.; Jonsen, Ian D.; Mills Flemming, Joanna

    2016-05-01

    State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results.

  16. County-level estimates of nitrogen and phosphorus fertilizer use in the United States, 1945 to 1985

    USGS Publications Warehouse

    Alexander, Richard B.; Smith, Richard A.

    1990-01-01

    Estimates of nitrogen and phosphorus fertilizer use were made for counties in the United States for the period 1945 to 1985. County fertilizer use estimates were obtained through the disaggregation of state-level fertilizer use in proportion to the amount of state fertilized acreage reported to exist in counties. Numerical values of nitrogen and phosphorus fertilizer use by county are not presented in the text of this report because of the size of the data file, but are available in machine-readable form upon request. Graphical summaries of national, state, and county nitrogen and phosphorus fertilizer use are presented to briefly describe the spatial and temporal variability that exist in the data.

  17. A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization

    DOE PAGES

    Zhao, Junbo; Wang, Shaobu; Mili, Lamine; ...

    2018-01-08

    Here, this paper develops a robust power system state estimation framework with the consideration of measurement correlations and imperfect synchronization. In the framework, correlations of SCADA and Phasor Measurements (PMUs) are calculated separately through unscented transformation and a Vector Auto-Regression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model with robustly estimated parameters using projection statistics approach. The latter takes into account the temporal and spatial correlations of PMU measurements and provides redundant measurements to suppress bad data and mitigate imperfect synchronization. In case where the SCADAmore » and PMU measurements are not time synchronized, either the forecasted PMU measurements or the prior SCADA measurements from the last estimation run are leveraged to restore system observability. Then, a robust generalized maximum-likelihood (GM)-estimator is extended to integrate measurement error correlations and to handle the outliers in the SCADA and PMU measurements. Simulation results that stem from a comprehensive comparison with other alternatives under various conditions demonstrate the benefits of the proposed framework.« less

  18. A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization

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

    Zhao, Junbo; Wang, Shaobu; Mili, Lamine

    Here, this paper develops a robust power system state estimation framework with the consideration of measurement correlations and imperfect synchronization. In the framework, correlations of SCADA and Phasor Measurements (PMUs) are calculated separately through unscented transformation and a Vector Auto-Regression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model with robustly estimated parameters using projection statistics approach. The latter takes into account the temporal and spatial correlations of PMU measurements and provides redundant measurements to suppress bad data and mitigate imperfect synchronization. In case where the SCADAmore » and PMU measurements are not time synchronized, either the forecasted PMU measurements or the prior SCADA measurements from the last estimation run are leveraged to restore system observability. Then, a robust generalized maximum-likelihood (GM)-estimator is extended to integrate measurement error correlations and to handle the outliers in the SCADA and PMU measurements. Simulation results that stem from a comprehensive comparison with other alternatives under various conditions demonstrate the benefits of the proposed framework.« less

  19. Multiple Target Laser Designator (MTLD)

    DTIC Science & Technology

    2007-03-01

    Optimized Liquid Crystal Scanning Element Optimize the Nonimaging Predictive Algorithm for Target Ranging, Tracking, and Position Estimation...commercial potential. 3.0 PROGRESS THIS QUARTER 3.1 Optimization of Nonimaging Holographic Antenna for Target Tracking and Position Estimation (Task 6) In

  20. Estimating Domestic Values for EQ-5D Health States Using Survey Data From External Sources.

    PubMed

    Chuang, Ling-Hsiang; Zarate, Victor; Kind, Paul

    2009-02-01

    Health status measures used to quantify outcomes for economic evaluation must be capable of representing health gain in a single index, usually calibrated in terms of the social preferences elicited from "the relevant population." The general problem faced in the majority of countries where social preferences are required for cost-effectiveness analysis is the absence of a value set based on domestic data sources. This article establishes a methodology for estimating domestic visual analog scale (VAS)-based values for EQ-5D health states by adjusting data sets from countries where valuation studies have been carried out. building upon the relationship between the values for respondents' real health states and hypothetical health states, 2 models are investigated. One assumes that the link between VAS scores for real and hypothetical health state is constant across 2 countries (R1), whereas the other adopts the assumption that the relationship of VAS scores for hypothetical heath states between 2 countries functionally corresponds to variation in scores for real health states (R2). Data from national UK and US population surveys were selected to test both methods. The R2 model performed better in generating estimated scores that were closer to observed values. The R2 model seems to offer a viable method for estimating domestic values of health. Such a method could help to bridge the gap between countries as well as region within a country.

  1. 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

  2. County-level estimates of nitrogen and phosphorus from commercial fertilizer for the Conterminous United States, 1987–2006

    USGS Publications Warehouse

    Gronberg, Jo Ann M.; Spahr, Norman E.

    2012-01-01

    The U.S. Geological Survey’s National Water-Quality Assessment program requires nutrient input for analysis of the national and regional assessment of water quality. Detailed information on nutrient inputs to the environment are needed to understand and address the many serious problems that arise from excess nutrients in the streams and groundwater of the Nation. This report updates estimated county-level farm and nonfarm nitrogen and phosphorus input from commercial fertilizer sales for the conterminous United States for 1987 through 2006. Estimates were calculated from the Association of American Plant Food Control Officials fertilizer sales data, Census of Agriculture fertilizer expenditures, and U.S. Census Bureau county population. A previous national approach for deriving farm and nonfarm fertilizer nutrient estimates was evaluated, and a revised method for selecting representative states to calculate national farm and nonfarm proportions was developed. A national approach was used to estimate farm and nonfarm fertilizer inputs because not all states distinguish between farm and nonfarm use, and the quality of fertilizer reporting varies from year to year. For states that distinguish between farm and nonfarm use, the spatial distribution of the ratios of nonfarm-to-total fertilizer estimates for nitrogen and phosphorus calculated using the national-based farm and nonfarm proportions were similar to the spatial distribution of the ratios generated using state-based farm and nonfarm proportions. In addition, the relative highs and lows in the temporal distribution of farm and nonfarm nitrogen and phosphorus input at the state level were maintained—the periods of high and low usage coincide between national- and state-based values. With a few exceptions, nonfarm nitrogen estimates were found to be reasonable when compared to the amounts that would result if the lawn application rates recommended by state and university agricultural agencies were used. Also

  3. Acute Pesticide Illnesses Associated with Off-Target Pesticide Drift from Agricultural Applications: 11 States, 1998–2006

    PubMed Central

    Lee, Soo-Jeong; Mehler, Louise; Beckman, John; Diebolt-Brown, Brienne; Prado, Joanne; Lackovic, Michelle; Waltz, Justin; Mulay, Prakash; Schwartz, Abby; Mitchell, Yvette; Moraga-McHaley, Stephanie; Gergely, Rita

    2011-01-01

    Background: Pesticides are widely used in agriculture, and off-target pesticide drift exposes workers and the public to harmful chemicals. Objective: We estimated the incidence of acute illnesses from pesticide drift from outdoor agricultural applications and characterized drift exposure and illnesses. Methods: Data were obtained from the National Institute for Occupational Safety and Health’s Sentinel Event Notification System for Occupational Risks–Pesticides program and the California Department of Pesticide Regulation. Drift included off-target movement of pesticide spray, volatiles, and contaminated dust. Acute illness cases were characterized by demographics, pesticide and application variables, health effects, and contributing factors. Results: From 1998 through 2006, we identified 2,945 cases associated with agricultural pesticide drift from 11 states. Our findings indicate that 47% were exposed at work, 92% experienced low-severity illness, and 14% were children (< 15 years). The annual incidence ranged from 1.39 to 5.32 per million persons over the 9-year period. The overall incidence (in million person-years) was 114.3 for agricultural workers, 0.79 for other workers, 1.56 for nonoccupational cases, and 42.2 for residents in five agriculture-intensive counties in California. Soil applications with fumigants were responsible for the largest percentage (45%) of cases. Aerial applications accounted for 24% of cases. Common factors contributing to drift cases included weather conditions, improper seal of the fumigation site, and applicator carelessness near nontarget areas. Conclusions: Agricultural workers and residents in agricultural regions had the highest rate of pesticide poisoning from drift exposure, and soil fumigations were a major hazard, causing large drift incidents. Our findings highlight areas where interventions to reduce off-target drift could be focused. PMID:21642048

  4. Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability

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

    Qi, Junjian; Sun, Kai; Wang, Jianhui

    In this paper, in order to enhance the numerical stability of the unscented Kalman filter (UKF) used for power system dynamic state estimation, a new UKF with guaranteed positive semidifinite estimation error covariance (UKFGPS) is proposed and compared with five existing approaches, including UKFschol, UKF-kappa, UKFmodified, UKF-Delta Q, and the squareroot UKF (SRUKF). These methods and the extended Kalman filter (EKF) are tested by performing dynamic state estimation on WSCC 3-machine 9-bus system and NPCC 48-machine 140-bus system. For WSCC system, all methods obtain good estimates. However, for NPCC system, both EKF and the classic UKF fail. It is foundmore » that UKFschol, UKF-kappa, and UKF-Delta Q do not work well in some estimations while UKFGPS works well in most cases. UKFmodified and SRUKF can always work well, indicating their better scalability mainly due to the enhanced numerical stability.« less

  5. A spline-based parameter and state estimation technique for static models of elastic surfaces

    NASA Technical Reports Server (NTRS)

    Banks, H. T.; Daniel, P. L.; Armstrong, E. S.

    1983-01-01

    Parameter and state estimation techniques for an elliptic system arising in a developmental model for the antenna surface in the Maypole Hoop/Column antenna are discussed. A computational algorithm based on spline approximations for the state and elastic parameters is given and numerical results obtained using this algorithm are summarized.

  6. State Estimation for Linear Systems Driven Simultaneously by Wiener and Poisson Processes.

    DTIC Science & Technology

    1978-12-01

    The state estimation problem of linear stochastic systems driven simultaneously by Wiener and Poisson processes is considered, especially the case...where the incident intensities of the Poisson processes are low and the system is observed in an additive white Gaussian noise. The minimum mean squared

  7. Tradeoffs among watershed model calibration targets for parameter estimation

    EPA Science Inventory

    Hydrologic models are commonly calibrated by optimizing a single objective function target to compare simulated and observed flows, although individual targets are influenced by specific flow modes. Nash-Sutcliffe efficiency (NSE) emphasizes flood peaks in evaluating simulation f...

  8. Estimation of time- and state-dependent delays and other parameters in functional differential equations

    NASA Technical Reports Server (NTRS)

    Murphy, K. A.

    1988-01-01

    A parameter estimation algorithm is developed which can be used to estimate unknown time- or state-dependent delays and other parameters (e.g., initial condition) appearing within a nonlinear nonautonomous functional differential equation. The original infinite dimensional differential equation is approximated using linear splines, which are allowed to move with the variable delay. The variable delays are approximated using linear splines as well. The approximation scheme produces a system of ordinary differential equations with nice computational properties. The unknown parameters are estimated within the approximating systems by minimizing a least-squares fit-to-data criterion. Convergence theorems are proved for time-dependent delays and state-dependent delays within two classes, which say essentially that fitting the data by using approximations will, in the limit, provide a fit to the data using the original system. Numerical test examples are presented which illustrate the method for all types of delay.

  9. Estimation of time- and state-dependent delays and other parameters in functional differential equations

    NASA Technical Reports Server (NTRS)

    Murphy, K. A.

    1990-01-01

    A parameter estimation algorithm is developed which can be used to estimate unknown time- or state-dependent delays and other parameters (e.g., initial condition) appearing within a nonlinear nonautonomous functional differential equation. The original infinite dimensional differential equation is approximated using linear splines, which are allowed to move with the variable delay. The variable delays are approximated using linear splines as well. The approximation scheme produces a system of ordinary differential equations with nice computational properties. The unknown parameters are estimated within the approximating systems by minimizing a least-squares fit-to-data criterion. Convergence theorems are proved for time-dependent delays and state-dependent delays within two classes, which say essentially that fitting the data by using approximations will, in the limit, provide a fit to the data using the original system. Numerical test examples are presented which illustrate the method for all types of delay.

  10. Estimating earthquake magnitudes from reported intensities in the central and eastern United States

    USGS Publications Warehouse

    Boyd, Oliver; Cramer, Chris H.

    2014-01-01

    A new macroseismic intensity prediction equation is derived for the central and eastern United States and is used to estimate the magnitudes of the 1811–1812 New Madrid, Missouri, and 1886 Charleston, South Carolina, earthquakes. This work improves upon previous derivations of intensity prediction equations by including additional intensity data, correcting magnitudes in the intensity datasets to moment magnitude, and accounting for the spatial and temporal population distributions. The new relation leads to moment magnitude estimates for the New Madrid earthquakes that are toward the lower range of previous studies. Depending on the intensity dataset to which the new macroseismic intensity prediction equation is applied, mean estimates for the 16 December 1811, 23 January 1812, and 7 February 1812 mainshocks, and 16 December 1811 dawn aftershock range from 6.9 to 7.1, 6.8 to 7.1, 7.3 to 7.6, and 6.3 to 6.5, respectively. One‐sigma uncertainties on any given estimate could be as high as 0.3–0.4 magnitude units. We also estimate a magnitude of 6.9±0.3 for the 1886 Charleston, South Carolina, earthquake. We find a greater range of magnitude estimates when also accounting for multiple macroseismic intensity prediction equations. The inability to accurately and precisely ascertain magnitude from intensities increases the uncertainty of the central United States earthquake hazard by nearly a factor of two. Relative to the 2008 national seismic hazard maps, our range of possible 1811–1812 New Madrid earthquake magnitudes increases the coefficient of variation of seismic hazard estimates for Memphis, Tennessee, by 35%–42% for ground motions expected to be exceeded with a 2% probability in 50 years and by 27%–35% for ground motions expected to be exceeded with a 10% probability in 50 years.

  11. State, Parameter, and Unknown Input Estimation Problems in Active Automotive Safety Applications

    NASA Astrophysics Data System (ADS)

    Phanomchoeng, Gridsada

    A variety of driver assistance systems such as traction control, electronic stability control (ESC), rollover prevention and lane departure avoidance systems are being developed by automotive manufacturers to reduce driver burden, partially automate normal driving operations, and reduce accidents. The effectiveness of these driver assistance systems can be significant enhanced if the real-time values of several vehicle parameters and state variables, namely tire-road friction coefficient, slip angle, roll angle, and rollover index, can be known. Since there are no inexpensive sensors available to measure these variables, it is necessary to estimate them. However, due to the significant nonlinear dynamics in a vehicle, due to unknown and changing plant parameters, and due to the presence of unknown input disturbances, the design of estimation algorithms for this application is challenging. This dissertation develops a new approach to observer design for nonlinear systems in which the nonlinearity has a globally (or locally) bounded Jacobian. The developed approach utilizes a modified version of the mean value theorem to express the nonlinearity in the estimation error dynamics as a convex combination of known matrices with time varying coefficients. The observer gains are then obtained by solving linear matrix inequalities (LMIs). A number of illustrative examples are presented to show that the developed approach is less conservative and more useful than the standard Lipschitz assumption based nonlinear observer. The developed nonlinear observer is utilized for estimation of slip angle, longitudinal vehicle velocity, and vehicle roll angle. In order to predict and prevent vehicle rollovers in tripped situations, it is necessary to estimate the vertical tire forces in the presence of unknown road disturbance inputs. An approach to estimate unknown disturbance inputs in nonlinear systems using dynamic model inversion and a modified version of the mean value theorem is

  12. Coupling BCI and cortical stimulation for brain-state-dependent stimulation: methods for spectral estimation in the presence of stimulation after-effects

    PubMed Central

    Walter, Armin; Murguialday, Ander R.; Rosenstiel, Wolfgang; Birbaumer, Niels; Bogdan, Martin

    2012-01-01

    Brain-state-dependent stimulation (BSDS) combines brain-computer interfaces (BCIs) and cortical stimulation into one paradigm that allows the online decoding for example of movement intention from brain signals while simultaneously applying stimulation. If the BCI decoding is performed by spectral features, stimulation after-effects such as artefacts and evoked activity present a challenge for a successful implementation of BSDS because they can impair the detection of targeted brain states. Therefore, efficient and robust methods are needed to minimize the influence of the stimulation-induced effects on spectral estimation without violating the real-time constraints of the BCI. In this work, we compared four methods for spectral estimation with autoregressive (AR) models in the presence of pulsed cortical stimulation. Using combined EEG-TMS (electroencephalography-transcranial magnetic stimulation) as well as combined electrocorticography (ECoG) and epidural electrical stimulation, three patients performed a motor task using a sensorimotor-rhythm BCI. Three stimulation paradigms were varied between sessions: (1) no stimulation, (2) single stimulation pulses applied independently (open-loop), or (3) coupled to the BCI output (closed-loop) such that stimulation was given only while an intention to move was detected using neural data. We found that removing the stimulation after-effects by linear interpolation can introduce a bias in the estimation of the spectral power of the sensorimotor rhythm, leading to an overestimation of decoding performance in the closed-loop setting. We propose the use of the Burg algorithm for segmented data to deal with stimulation after-effects. This work shows that the combination of BCIs controlled with spectral features and cortical stimulation in a closed-loop fashion is possible when the influence of stimulation after-effects on spectral estimation is minimized. PMID:23162436

  13. Estimated use of water in the United States in 1970

    USGS Publications Warehouse

    Murray, Charles Richard; Reeves, E. Bodette

    1972-01-01

    The average annual streamflow--simplified measure of the total available water supply--is approximately 1,200 bgd in the conterminous United States. Total water withdrawn in 1970 for off-channel uses (withdrawals other than for hydroelectric power) amounted to about 30 percent of the average annual streamflow: 7 percent of the 1,200 bgd basic supply was consumed. However, comparisons of Water Resources Council regions indicate that the rate of withdrawal was higher than the locally dependable supply in the Middle Atlantic, Texas-Gulf, Rio Grande, Lower Colorado, and California-South Pacific regions. Consumption amounted to nearly 25 percent of withdrawals in the conterminous United States; however, fresh-water consumption amounted to only 14 percent of off-channel withdrawals in the 31 Eastern States and ranged from 30 percent to nearly 70 percent of off-channel withdrawals in the Water Resources Council regions in the West. In the Rio Grande and Lower Colorado regions, fresh-water consumption in 1970 exceeded the estimated dependable supply of fresh water.

  14. Estimating the Availability of Potential Homes for Unwanted Horses in the United States

    PubMed Central

    Weiss, Emily; Dolan, Emily D.; Mohan-Gibbons, Heather; Gramann, Shannon; Slater, Margaret R.

    2017-01-01

    Simple Summary There are approximately 200,000 unwanted horses annually in the United States. Many are shipped to slaughter, enter rescue facilities, or are held on federal lands. This study aimed to estimate a potential number of available homes for unwanted horses in order to examine broadly the viability of pursuing re-homing policies as an option for the thousands of unwanted horses in the U.S. The results of this survey suggest there could be an estimated 1.2 million homes who have both the perceived resources and desire to house an unwanted horse. This number exceeds the approximately 200,000 unwanted horses living each year in the United States. These data suggest that efforts to reduce unwanted horses could involve matching such horses with adoptive homes and enhancing opportunities to keep horses in the homes they already have. Abstract There are approximately 200,000 unwanted horses annually in the United States. This study aimed to better understand the potential homes for horses that need to be re-homed. Using an independent survey company through an Omnibus telephone (land and cell) survey, we interviewed a nationally projectable sample of 3036 adults (using both landline and cellular phone numbers) to learn of their interest and capacity to adopt a horse. Potential adopters with interest in horses with medical and/or behavioral problems and self-assessed perceived capacity to adopt, constituted 0.92% of the total sample. Extrapolating the results of this survey using U.S. Census data, suggests there could be an estimated 1.25 million households who have both the self-reported and perceived resources and desire to house an unwanted horse. This number exceeds the estimated number of unwanted horses living each year in the United States. This study points to opportunities and need to increase communication and support between individuals and organizations that have unwanted horses to facilitate re-homing with people in their community willing to adopt

  15. A novel approach of battery pack state of health estimation using artificial intelligence optimization algorithm

    NASA Astrophysics Data System (ADS)

    Zhang, Xu; Wang, Yujie; Liu, Chang; Chen, Zonghai

    2018-02-01

    An accurate battery pack state of health (SOH) estimation is important to characterize the dynamic responses of battery pack and ensure the battery work with safety and reliability. However, the different performances in battery discharge/charge characteristics and working conditions in battery pack make the battery pack SOH estimation difficult. In this paper, the battery pack SOH is defined as the change of battery pack maximum energy storage. It contains all the cells' information including battery capacity, the relationship between state of charge (SOC) and open circuit voltage (OCV), and battery inconsistency. To predict the battery pack SOH, the method of particle swarm optimization-genetic algorithm is applied in battery pack model parameters identification. Based on the results, a particle filter is employed in battery SOC and OCV estimation to avoid the noise influence occurring in battery terminal voltage measurement and current drift. Moreover, a recursive least square method is used to update cells' capacity. Finally, the proposed method is verified by the profiles of New European Driving Cycle and dynamic test profiles. The experimental results indicate that the proposed method can estimate the battery states with high accuracy for actual operation. In addition, the factors affecting the change of SOH is analyzed.

  16. Improved Spatial Registration and Target Tracking Method for Sensors on Multiple Missiles.

    PubMed

    Lu, Xiaodong; Xie, Yuting; Zhou, Jun

    2018-05-27

    Inspired by the problem that the current spatial registration methods are unsuitable for three-dimensional (3-D) sensor on high-dynamic platform, this paper focuses on the estimation for the registration errors of cooperative missiles and motion states of maneuvering target. There are two types of errors being discussed: sensor measurement biases and attitude biases. Firstly, an improved Kalman Filter on Earth-Centered Earth-Fixed (ECEF-KF) coordinate algorithm is proposed to estimate the deviations mentioned above, from which the outcomes are furtherly compensated to the error terms. Secondly, the Pseudo Linear Kalman Filter (PLKF) and the nonlinear scheme the Unscented Kalman Filter (UKF) with modified inputs are employed for target tracking. The convergence of filtering results are monitored by a position-judgement logic, and a low-pass first order filter is selectively introduced before compensation to inhibit the jitter of estimations. In the simulation, the ECEF-KF enhancement is proven to improve the accuracy and robustness of the space alignment, while the conditional-compensation-based PLKF method is demonstrated to be the optimal performance in target tracking.

  17. Alternating steady state free precession for estimation of current-induced magnetic flux density: A feasibility study.

    PubMed

    Lee, Hyunyeol; Jeong, Woo Chul; Kim, Hyung Joong; Woo, Eung Je; Park, Jaeseok

    2016-05-01

    To develop a novel, current-controlled alternating steady-state free precession (SSFP)-based conductivity imaging method and corresponding MR signal models to estimate current-induced magnetic flux density (Bz ) and conductivity distribution. In the proposed method, an SSFP pulse sequence, which is in sync with alternating current pulses, produces dual oscillating steady states while yielding nonlinear relation between signal phase and Bz . A ratiometric signal model between the states was analytically derived using the Bloch equation, wherein Bz was estimated by solving a nonlinear inverse problem for conductivity estimation. A theoretical analysis on the signal-to-noise ratio of Bz was given. Numerical and experimental studies were performed using SSFP-FID and SSFP-ECHO with current pulses positioned either before or after signal encoding to investigate the feasibility of the proposed method in conductivity estimation. Given all SSFP variants herein, SSFP-FID with alternating current pulses applied before signal encoding exhibits the highest Bz signal-to-noise ratio and conductivity contrast. Additionally, compared with conventional conductivity imaging, the proposed method benefits from rapid SSFP acquisition without apparent loss of conductivity contrast. We successfully demonstrated the feasibility of the proposed method in estimating current-induced Bz and conductivity distribution. It can be a promising, rapid imaging strategy for quantitative conductivity imaging. © 2015 Wiley Periodicals, Inc.

  18. Estimating Elevation Angles From SAR Crosstalk

    NASA Technical Reports Server (NTRS)

    Freeman, Anthony

    1994-01-01

    Scheme for processing polarimetric synthetic-aperture-radar (SAR) image data yields estimates of elevation angles along radar beam to target resolution cells. By use of estimated elevation angles, measured distances along radar beam to targets (slant ranges), and measured altitude of aircraft carrying SAR equipment, one can estimate height of target terrain in each resolution cell. Monopulselike scheme yields low-resolution topographical data.

  19. Mean-square state and parameter estimation for stochastic linear systems with Gaussian and Poisson noises

    NASA Astrophysics Data System (ADS)

    Basin, M.; Maldonado, J. J.; Zendejo, O.

    2016-07-01

    This paper proposes new mean-square filter and parameter estimator design for linear stochastic systems with unknown parameters over linear observations, where unknown parameters are considered as combinations of Gaussian and Poisson white noises. The problem is treated by reducing the original problem to a filtering problem for an extended state vector that includes parameters as additional states, modelled as combinations of independent Gaussian and Poisson processes. The solution to this filtering problem is based on the mean-square filtering equations for incompletely polynomial states confused with Gaussian and Poisson noises over linear observations. The resulting mean-square filter serves as an identifier for the unknown parameters. Finally, a simulation example shows effectiveness of the proposed mean-square filter and parameter estimator.

  20. Simultaneous Estimation of Model State Variables and Observation and Forecast Biases Using a Two-Stage Hybrid Kalman Filter

    NASA Technical Reports Server (NTRS)

    Pauwels, V. R. N.; DeLannoy, G. J. M.; Hendricks Franssen, H.-J.; Vereecken, H.

    2013-01-01

    In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

  1. Gasoline tax as a corrective tax: Estimates for the United States, 1970-1991

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

    Haughton, J.; Sarkar, S.

    1996-12-01

    The debate over the appropriate level of gasoline taxes in the United States (US) surfaces every few years. For every gallon of gasoline tax collected 14.1 cents was for the federal government and 17.6 cents on average for state governments, far less than $2.30 per gallon collected in Western Europe. The author offers estimates of benefits gained by taxing at various levels. 42 refs., 4 tabs.

  2. Practical state of health estimation of power batteries based on Delphi method and grey relational grade analysis

    NASA Astrophysics Data System (ADS)

    Sun, Bingxiang; Jiang, Jiuchun; Zheng, Fangdan; Zhao, Wei; Liaw, Bor Yann; Ruan, Haijun; Han, Zhiqiang; Zhang, Weige

    2015-05-01

    The state of health (SOH) estimation is very critical to battery management system to ensure the safety and reliability of EV battery operation. Here, we used a unique hybrid approach to enable complex SOH estimations. The approach hybridizes the Delphi method known for its simplicity and effectiveness in applying weighting factors for complicated decision-making and the grey relational grade analysis (GRGA) for multi-factor optimization. Six critical factors were used in the consideration for SOH estimation: peak power at 30% state-of-charge (SOC), capacity, the voltage drop at 30% SOC with a C/3 pulse, the temperature rises at the end of discharge and charge at 1C; respectively, and the open circuit voltage at the end of charge after 1-h rest. The weighting of these factors for SOH estimation was scored by the 'experts' in the Delphi method, indicating the influencing power of each factor on SOH. The parameters for these factors expressing the battery state variations are optimized by GRGA. Eight battery cells were used to illustrate the principle and methodology to estimate the SOH by this hybrid approach, and the results were compared with those based on capacity and power capability. The contrast among different SOH estimations is discussed.

  3. Kalman filter for onboard state of charge estimation and peak power capability analysis of lithium-ion batteries

    NASA Astrophysics Data System (ADS)

    Dong, Guangzhong; Wei, Jingwen; Chen, Zonghai

    2016-10-01

    To evaluate the continuous and instantaneous load capability of a battery, this paper describes a joint estimator for state-of-charge (SOC) and state-of-function (SOF) of lithium-ion batteries (LIB) based on Kalman filter (KF). The SOC is a widely used index for remain useful capacity left in a battery. The SOF represents the peak power capability of the battery. It can be determined by real-time SOC estimation and terminal voltage prediction, which can be derived from impedance parameters. However, the open-circuit-voltage (OCV) of LiFePO4 is highly nonlinear with SOC, which leads to the difficulties in SOC estimation. To solve these problems, this paper proposed an onboard SOC estimation method. Firstly, a simplified linearized equivalent-circuit-model is developed to simulate the dynamic characteristics of a battery, where the OCV is regarded as a linearized function of SOC. Then, the system states are estimated based on the KF. Besides, the factors that influence peak power capability are analyzed according to statistical data. Finally, the performance of the proposed methodology is demonstrated by experiments conducted on a LiFePO4 LIBs under different operating currents and temperatures. Experimental results indicate that the proposed approach is suitable for battery onboard SOC and SOF estimation.

  4. The Economic Impact of Organized Camping in the United States in 1982: An Estimate and Analysis.

    ERIC Educational Resources Information Center

    University of Southern Maine, Portland. Organized Camping Resources Center for Research and Advanced Study.

    This study explored the economic value of organized camping in the United States. Organized camping provides: (1) job opportunities for target unemployment groups; (2) expenditures for food, insurance, equipment, supplies, improvements and services; and (3) an investment in recreational facilities. Separate state reports list total population of…

  5. Handling target obscuration through Markov chain observations

    NASA Astrophysics Data System (ADS)

    Kouritzin, Michael A.; Wu, Biao

    2008-04-01

    Target Obscuration, including foliage or building obscuration of ground targets and landscape or horizon obscuration of airborne targets, plagues many real world filtering problems. In particular, ground moving target identification Doppler radar, mounted on a surveillance aircraft or unattended airborne vehicle, is used to detect motion consistent with targets of interest. However, these targets try to obscure themselves (at least partially) by, for example, traveling along the edge of a forest or around buildings. This has the effect of creating random blockages in the Doppler radar image that move dynamically and somewhat randomly through this image. Herein, we address tracking problems with target obscuration by building memory into the observations, eschewing the usual corrupted, distorted partial measurement assumptions of filtering in favor of dynamic Markov chain assumptions. In particular, we assume the observations are a Markov chain whose transition probabilities depend upon the signal. The state of the observation Markov chain attempts to depict the current obscuration and the Markov chain dynamics are used to handle the evolution of the partially obscured radar image. Modifications of the classical filtering equations that allow observation memory (in the form of a Markov chain) are given. We use particle filters to estimate the position of the moving targets. Moreover, positive proof-of-concept simulations are included.

  6. On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models part 2. Parameter and state estimation

    NASA Astrophysics Data System (ADS)

    Fleischer, Christian; Waag, Wladislaw; Heyn, Hans-Martin; Sauer, Dirk Uwe

    2014-09-01

    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 (capacity fade determination, SoH), and state of function (power fade determination, SoF). The second paper concludes the series by presenting a multi-stage online parameter identification technique based on a weighted recursive least quadratic squares parameter estimator to determine the parameters of the proposed battery model from the first paper during operation. A novel mutation based algorithm is developed to determine the nonlinear current dependency of the charge-transfer resistance. The influence of diffusion is determined by an on-line identification technique and verified on several batteries at different operation conditions. This method guarantees a short response time and, together with its fully recursive structure, assures a long-term stable monitoring of the battery parameters. The relative dynamic voltage prediction error of the algorithm is reduced to 2%. The changes of parameters are used to determine the states of the battery. The algorithm is real-time capable and can be implemented on embedded systems.

  7. Dominant root locus in state estimator design for material flow processes: A case study of hot strip rolling.

    PubMed

    Fišer, Jaromír; Zítek, Pavel; Skopec, Pavel; Knobloch, Jan; Vyhlídal, Tomáš

    2017-05-01

    The purpose of the paper is to achieve a constrained estimation of process state variables using the anisochronic state observer tuned by the dominant root locus technique. The anisochronic state observer is based on the state-space time delay model of the process. Moreover the process model is identified not only as delayed but also as non-linear. This model is developed to describe a material flow process. The root locus technique combined with the magnitude optimum method is utilized to investigate the estimation process. Resulting dominant roots location serves as a measure of estimation process performance. The higher the dominant (natural) frequency in the leftmost position of the complex plane the more enhanced performance with good robustness is achieved. Also the model based observer control methodology for material flow processes is provided by means of the separation principle. For demonstration purposes, the computer-based anisochronic state observer is applied to the strip temperatures estimation in the hot strip finishing mill composed of seven stands. This application was the original motivation to the presented research. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Estimation of the tritium retention in ITER tungsten divertor target using macroscopic rate equations simulations

    NASA Astrophysics Data System (ADS)

    Hodille, E. A.; Bernard, E.; Markelj, S.; Mougenot, J.; Becquart, C. S.; Bisson, R.; Grisolia, C.

    2017-12-01

    Based on macroscopic rate equation simulations of tritium migration in an actively cooled tungsten (W) plasma facing component (PFC) using the code MHIMS (migration of hydrogen isotopes in metals), an estimation has been made of the tritium retention in ITER W divertor target during a non-uniform exponential distribution of particle fluxes. Two grades of materials are considered to be exposed to tritium ions: an undamaged W and a damaged W exposed to fast fusion neutrons. Due to strong temperature gradient in the PFC, Soret effect’s impacts on tritium retention is also evaluated for both cases. Thanks to the simulation, the evolutions of the tritium retention and the tritium migration depth are obtained as a function of the implanted flux and the number of cycles. From these evolutions, extrapolation laws are built to estimate the number of cycles needed for tritium to permeate from the implantation zone to the cooled surface and to quantify the corresponding retention of tritium throughout the W PFC.

  9. An Accurate Non-Cooperative Method for Measuring Textureless Spherical Target Based on Calibrated Lasers.

    PubMed

    Wang, Fei; Dong, Hang; Chen, Yanan; Zheng, Nanning

    2016-12-09

    Strong demands for accurate non-cooperative target measurement have been arising recently for the tasks of assembling and capturing. Spherical objects are one of the most common targets in these applications. However, the performance of the traditional vision-based reconstruction method was limited for practical use when handling poorly-textured targets. In this paper, we propose a novel multi-sensor fusion system for measuring and reconstructing textureless non-cooperative spherical targets. Our system consists of four simple lasers and a visual camera. This paper presents a complete framework of estimating the geometric parameters of textureless spherical targets: (1) an approach to calibrate the extrinsic parameters between a camera and simple lasers; and (2) a method to reconstruct the 3D position of the laser spots on the target surface and achieve the refined results via an optimized scheme. The experiment results show that our proposed calibration method can obtain a fine calibration result, which is comparable to the state-of-the-art LRF-based methods, and our calibrated system can estimate the geometric parameters with high accuracy in real time.

  10. An Accurate Non-Cooperative Method for Measuring Textureless Spherical Target Based on Calibrated Lasers

    PubMed Central

    Wang, Fei; Dong, Hang; Chen, Yanan; Zheng, Nanning

    2016-01-01

    Strong demands for accurate non-cooperative target measurement have been arising recently for the tasks of assembling and capturing. Spherical objects are one of the most common targets in these applications. However, the performance of the traditional vision-based reconstruction method was limited for practical use when handling poorly-textured targets. In this paper, we propose a novel multi-sensor fusion system for measuring and reconstructing textureless non-cooperative spherical targets. Our system consists of four simple lasers and a visual camera. This paper presents a complete framework of estimating the geometric parameters of textureless spherical targets: (1) an approach to calibrate the extrinsic parameters between a camera and simple lasers; and (2) a method to reconstruct the 3D position of the laser spots on the target surface and achieve the refined results via an optimized scheme. The experiment results show that our proposed calibration method can obtain a fine calibration result, which is comparable to the state-of-the-art LRF-based methods, and our calibrated system can estimate the geometric parameters with high accuracy in real time. PMID:27941705

  11. Equations for estimating stand establishment, release, and thinning costs in the Lake States.

    Treesearch

    Jeffrey T. Olson; Allen L. Lundgren; Dietmar Rose

    1978-01-01

    Equations for estimating project costs for certain silvicultural treatments in the Lake States have been developed from project records of public forests. Treatments include machine site preparation, hand planting, aerial spraying, prescribed burning, manual release, and thinning.

  12. A Framework for Reviewing EPA's State Administrative Cost Estimates: A Case Study (2007)

    EPA Pesticide Factsheets

    This report contains the findings of the set of case studies that look at EPA’s and the states’ information and methods used to estimate the costs to states charged with administering a selection of EPA regulations.

  13. Correlation between clinical signs of depth of anaesthesia and cerebral state index responses in dogs with different target-controlled infusions of propofol.

    PubMed

    Ribeiro, Lénio M; Ferreira, David A; Brás, Susana; Gonzalo-Orden, Jose M; Antunes, Luis M

    2012-01-01

    To evaluate if the cerebral state index (CSI), measured by a Cerebral State Monitor (CSM), can predict depth of anaesthesia as assessed clinically or by estimated propofol plasma concentrations. Prospective clinical study. Fourteen mixed breed dogs, weighing 24.5 ± 4.7 kg, scheduled to undergo neutering procedures. Dogs were premedicated with 0.05 mg kg(-1) acepromazine intramuscularly. The CSM and cardiovascular monitoring equipment were attached. Anaesthesia was induced with propofol using a target controlled infusion (TCI) to varying plasma propofol targets (PropCp). Following endotracheal intubation the dogs were ventilated with oxygen. Anaesthetic maintenance was with propofol by TCI. A PropCp of 3 μg dL(-1) was set initially, then PropCps were increased in 1 μg dL(-1) steps to 7, 9 and then 11 μg dL(-1). Each PropCp was held constant for a 5 minute period, at the end of which depth of anaesthesia was classified using a previously evaluated scale of 'planes' based on palpebral and corneal reflexes and eye position. Cerebral state index (CSI), burst suppression (BSR) and electromyogram were measured at these time points. The prediction probability (PK) of these variables, or of the PropCp in predicting depth of anaesthesia was calculated. The PKs for predicting anaesthetic planes were 0.74, 0.91, 0.76 and 0.78 for CSI, BSR, EMG and PropCp, respectively. The PKs for PropCp to predict CSI, BSR and EMG were 0.65, 0.71 and 0.65 respectively. The Cerebral State Monitor was able to detect very deep planes of anaesthesia when BSR occurs, but was not able to distinguish between the intermediate anaesthetic planes likely to be used in clinical anaesthesia. © 2011 The Authors. Veterinary Anaesthesia and Analgesia. © 2011 Association of Veterinary Anaesthetists and the American College of Veterinary Anesthesiologists.

  14. Earthquake catalog for estimation of maximum earthquake magnitude, Central and Eastern United States: Part B, historical earthquakes

    USGS Publications Warehouse

    Wheeler, Russell L.

    2014-01-01

    Computation of probabilistic earthquake hazard requires an estimate of Mmax: the moment magnitude of the largest earthquake that is thought to be possible within a specified geographic region. The region specified in this report is the Central and Eastern United States and adjacent Canada. Parts A and B of this report describe the construction of a global catalog of moderate to large earthquakes that occurred worldwide in tectonic analogs of the Central and Eastern United States. Examination of histograms of the magnitudes of these earthquakes allows estimation of Central and Eastern United States Mmax. The catalog and Mmax estimates derived from it are used in the 2014 edition of the U.S. Geological Survey national seismic-hazard maps. Part A deals with prehistoric earthquakes, and this part deals with historical events.

  15. An Automatic Multi-Target Independent Analysis Framework for Non-Planar Infrared-Visible Registration.

    PubMed

    Sun, Xinglong; Xu, Tingfa; Zhang, Jizhou; Zhao, Zishu; Li, Yuankun

    2017-07-26

    In this paper, we propose a novel automatic multi-target registration framework for non-planar infrared-visible videos. Previous approaches usually analyzed multiple targets together and then estimated a global homography for the whole scene, however, these cannot achieve precise multi-target registration when the scenes are non-planar. Our framework is devoted to solving the problem using feature matching and multi-target tracking. The key idea is to analyze and register each target independently. We present a fast and robust feature matching strategy, where only the features on the corresponding foreground pairs are matched. Besides, new reservoirs based on the Gaussian criterion are created for all targets, and a multi-target tracking method is adopted to determine the relationships between the reservoirs and foreground blobs. With the matches in the corresponding reservoir, the homography of each target is computed according to its moving state. We tested our framework on both public near-planar and non-planar datasets. The results demonstrate that the proposed framework outperforms the state-of-the-art global registration method and the manual global registration matrix in all tested datasets.

  16. Optimizing focal plane electric field estimation for detecting exoplanets

    NASA Astrophysics Data System (ADS)

    Groff, T.; Kasdin, N. J.; Riggs, A. J. E.

    Detecting extrasolar planets with angular separations and contrast levels similar to Earth requires a large space-based observatory and advanced starlight suppression techniques. This paper focuses on techniques employing an internal coronagraph, which is highly sensitive to optical errors and must rely on focal plane wavefront control techniques to achieve the necessary contrast levels. To maximize the available science time for a coronagraphic mission we demonstrate an estimation scheme using a discrete time Kalman filter. The state estimate feedback inherent to the filter allows us to minimize the number of exposures required to estimate the electric field. We also show progress including a bias estimate into the Kalman filter to eliminate incoherent light from the estimate. Since the exoplanets themselves are incoherent to the star, this has the added benefit of using the control history to gain certainty in the location of exoplanet candidates as the signal-to-noise between the planets and speckles improves. Having established a purely focal plane based wavefront estimation technique, we discuss a sensor fusion concept where alternate wavefront sensors feedforward a time update to the focal plane estimate to improve robustness to time varying speckle. The overall goal of this work is to reduce the time required for wavefront control on a target, thereby improving the observatory's planet detection performance by increasing the number of targets reachable during the lifespan of the mission.

  17. Efficient dynamical correction of the transition state theory rate estimate for a flat energy barrier.

    PubMed

    Mökkönen, Harri; Ala-Nissila, Tapio; Jónsson, Hannes

    2016-09-07

    The recrossing correction to the transition state theory estimate of a thermal rate can be difficult to calculate when the energy barrier is flat. This problem arises, for example, in polymer escape if the polymer is long enough to stretch between the initial and final state energy wells while the polymer beads undergo diffusive motion back and forth over the barrier. We present an efficient method for evaluating the correction factor by constructing a sequence of hyperplanes starting at the transition state and calculating the probability that the system advances from one hyperplane to another towards the product. This is analogous to what is done in forward flux sampling except that there the hyperplane sequence starts at the initial state. The method is applied to the escape of polymers with up to 64 beads from a potential well. For high temperature, the results are compared with direct Langevin dynamics simulations as well as forward flux sampling and excellent agreement between the three rate estimates is found. The use of a sequence of hyperplanes in the evaluation of the recrossing correction speeds up the calculation by an order of magnitude as compared with the traditional approach. As the temperature is lowered, the direct Langevin dynamics simulations as well as the forward flux simulations become computationally too demanding, while the harmonic transition state theory estimate corrected for recrossings can be calculated without significant increase in the computational effort.

  18. Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout.

    PubMed

    Das, Anup; Pradhapan, Paruthi; Groenendaal, Willemijn; Adiraju, Prathyusha; Rajan, Raj Thilak; Catthoor, Francky; Schaafsma, Siebren; Krichmar, Jeffrey L; Dutt, Nikil; Van Hoof, Chris

    2018-03-01

    Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine learning technique to estimate heart-rate from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery-life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects is considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices. Copyright © 2018 Elsevier Ltd. All rights reserved.

  19. Statistically-Estimated Tree Composition for the Northeastern United States at Euro-American Settlement.

    PubMed

    Paciorek, Christopher J; Goring, Simon J; Thurman, Andrew L; Cogbill, Charles V; Williams, John W; Mladenoff, David J; Peters, Jody A; Zhu, Jun; McLachlan, Jason S

    2016-01-01

    We present a gridded 8 km-resolution data product of the estimated composition of tree taxa at the time of Euro-American settlement of the northeastern United States and the statistical methodology used to produce the product from trees recorded by land surveyors. Composition is defined as the proportion of stems larger than approximately 20 cm diameter at breast height for 22 tree taxa, generally at the genus level. The data come from settlement-era public survey records that are transcribed and then aggregated spatially, giving count data. The domain is divided into two regions, eastern (Maine to Ohio) and midwestern (Indiana to Minnesota). Public Land Survey point data in the midwestern region (ca. 0.8-km resolution) are aggregated to a regular 8 km grid, while data in the eastern region, from Town Proprietor Surveys, are aggregated at the township level in irregularly-shaped local administrative units. The product is based on a Bayesian statistical model fit to the count data that estimates composition on the 8 km grid across the entire domain. The statistical model is designed to handle data from both the regular grid and the irregularly-shaped townships and allows us to estimate composition at locations with no data and to smooth over noise caused by limited counts in locations with data. Critically, the model also allows us to quantify uncertainty in our composition estimates, making the product suitable for applications employing data assimilation. We expect this data product to be useful for understanding the state of vegetation in the northeastern United States prior to large-scale Euro-American settlement. In addition to specific regional questions, the data product can also serve as a baseline against which to investigate how forests and ecosystems change after intensive settlement. The data product is being made available at the NIS data portal as version 1.0.

  20. Estimated Use of Water in the United States in 2000

    USGS Publications Warehouse

    Hutson, Susan S.; Barber, Nancy L.; Kenny, Joan F.; Linsey, Kristin S.; Lumia, Deborah S.; Maupin, Molly A.

    2004-01-01

    Estimates of water use in the United States indicate that about 408 billion gallons per day (one thousand million gallons per day, abbreviated Bgal/d) were withdrawn for all uses during 2000. This total has varied less than 3 percent since 1985 as withdrawals have stabilized for the two largest uses?thermoelectric power and irrigation. Fresh ground-water withdrawals (83.3 Bgal/d) during 2000 were 14 percent more than during 1985. Fresh surface-water withdrawals for 2000 were 262 Bgal/d, varying less than 2 percent since 1985. About 195 Bgal/d, or 48 percent of all freshwater and saline-water withdrawals for 2000, were used for thermoelectric power. Most of this water was derived from surface water and used for once-through cooling at power plants. About 52 percent of fresh surface-water withdrawals and about 96 percent of saline-water withdrawals were for thermoelectric-power use. Withdrawals for thermoelectric power have been relatively stable since 1985. Irrigation remained the largest use of freshwater in the United States and totaled 137 Bgal/d for 2000. Since 1950, irrigation has accounted for about 65 percent of total water withdrawals, excluding those for thermoelectric power. Historically, more surface water than ground water has been used for irrigation. However, the percentage of total irrigation withdrawals from ground water has continued to increase, from 23 percent in 1950 to 42 percent in 2000. Total irrigation withdrawals were 2 percent more for 2000 than for 1995, because of a 16-percent increase in ground-water withdrawals and a small decrease in surface-water withdrawals. Irrigated acreage more than doubled between 1950 and 1980, then remained constant before increasing nearly 7 percent between 1995 and 2000. The number of acres irrigated with sprinkler and microirrigation systems has continued to increase and now comprises more than one-half the total irrigated acreage. Public-supply withdrawals were more than 43 Bgal/d for 2000. Public

  1. Aging persons' estimates of vehicular motion.

    PubMed

    Schiff, W; Oldak, R; Shah, V

    1992-12-01

    Estimated arrival times of moving autos were examined in relation to viewer age, gender, motion trajectory, and velocity. Direct push-button judgments were compared with verbal estimates derived from velocity and distance, which were based on assumptions that perceivers compute arrival time from perceived distance and velocity. Experiment 1 showed that direct estimates of younger Ss were most accurate. Older women made the shortest (highly cautious) estimates of when cars would arrive. Verbal estimates were much lower than direct estimates, with little correlation between them. Experiment 2 extended target distances and velocities of targets, with the results replicating the main findings of Experiment 1. Judgment accuracy increased with target velocity, and verbal estimates were again poorer estimates of arrival time than direct ones, with different patterns of findings. Using verbal estimates to approximate judgments in traffic situations appears questionable.

  2. Comparing floral and isotopic paleoelevation estimates: Examples from the western United States

    NASA Astrophysics Data System (ADS)

    Hyland, E. G.; Huntington, K. W.; Sheldon, N. D.; Smith, S. Y.; Strömberg, C. A. E.

    2016-12-01

    Describing paleoelevations is crucial to understanding tectonic processes and deconvolving the effects of uplift and climate on environmental change in the past. Decades of work has gone into estimating past elevation from various proxy archives, particularly using modern relationships between elevation and temperature, floral assemblage compositions, or oxygen isotope values. While these methods have been used widely and refined through time, they are rarely applied in tandem; here we provide two examples from the western United States using new multiproxy methods: 1) combining clumped isotopes and macrofloral assemblages to estimate paleoelevations along the Colorado Plateau, and 2) combining oxygen isotopes and phytolith methods to estimate paleoelevations within the greater Yellowstone region. Clumped isotope measurements and refined floral coexistence methods from sites on the northern Colorado Plateau like Florissant and Creede (CO) consistently estimate low (< 2km) elevations through the Eocene/Oligocene, suggesting slower uplift and a south-north propagation of the plateau. Oxygen isotope measurements and C4 phytolith estimates from sites surrounding the Yellowstone hotspot consistently estimate moderate uplift (0.2-0.7km) propagating along the hotspot track, suggesting migrating dynamic topography associated with the region. These examples provide support for the emerging practice of using multiproxy methods to estimate paleoelevations for important time periods, and can help integrate environmental and tectonic records of the past.

  3. Dynamic State Estimation of Power Systems With Quantization Effects: A Recursive Filter Approach.

    PubMed

    Hu, Liang; Wang, Zidong; Liu, Xiaohui

    2016-08-01

    In this paper, a recursive filter algorithm is developed to deal with the state estimation problem for power systems with quantized nonlinear measurements. The measurements from both the remote terminal units and the phasor measurement unit are subject to quantizations described by a logarithmic quantizer. Attention is focused on the design of a recursive filter such that, in the simultaneous presence of nonlinear measurements and quantization effects, an upper bound for the estimation error covariance is guaranteed and subsequently minimized. Instead of using the traditional approximation methods in nonlinear estimation that simply ignore the linearization errors, we treat both the linearization and quantization errors as norm-bounded uncertainties in the algorithm development so as to improve the performance of the estimator. For the power system with such kind of introduced uncertainties, a filter is designed in the framework of robust recursive estimation, and the developed filter algorithm is tested on the IEEE benchmark power system to demonstrate its effectiveness.

  4. A framework for estimating health state utility values within a discrete choice experiment: modeling risky choices.

    PubMed

    Robinson, Angela; Spencer, Anne; Moffatt, Peter

    2015-04-01

    There has been recent interest in using the discrete choice experiment (DCE) method to derive health state utilities for use in quality-adjusted life year (QALY) calculations, but challenges remain. We set out to develop a risk-based DCE approach to derive utility values for health states that allowed 1) utility values to be anchored directly to normal health and death and 2) worse than dead health states to be assessed in the same manner as better than dead states. Furthermore, we set out to estimate alternative models of risky choice within a DCE model. A survey was designed that incorporated a risk-based DCE and a "modified" standard gamble (SG). Health state utility values were elicited for 3 EQ-5D health states assuming "standard" expected utility (EU) preferences. The DCE model was then generalized to allow for rank-dependent expected utility (RDU) preferences, thereby allowing for probability weighting. A convenience sample of 60 students was recruited and data collected in small groups. Under the assumption of "standard" EU preferences, the utility values derived within the DCE corresponded fairly closely to the mean results from the modified SG. Under the assumption of RDU preferences, the utility values estimated are somewhat lower than under the assumption of standard EU, suggesting that the latter may be biased upward. Applying the correct model of risky choice is important whether a modified SG or a risk-based DCE is deployed. It is, however, possible to estimate a probability weighting function within a DCE and estimate "unbiased" utility values directly, which is not possible within a modified SG. We conclude by setting out the relative strengths and weaknesses of the 2 approaches in this context. © The Author(s) 2014.

  5. A Real-Time Brain-Machine Interface Combining Motor Target and Trajectory Intent Using an Optimal Feedback Control Design

    PubMed Central

    Shanechi, Maryam M.; Williams, Ziv M.; Wornell, Gregory W.; Hu, Rollin C.; Powers, Marissa; Brown, Emery N.

    2013-01-01

    Real-time brain-machine interfaces (BMI) have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system. PMID:23593130

  6. Estimating State-Specific Contributions to PM2.5- and O3-Related Health Burden from Residential Combustion and Electricity Generating Unit Emissions in the United States.

    PubMed

    Penn, Stefani L; Arunachalam, Saravanan; Woody, Matthew; Heiger-Bernays, Wendy; Tripodis, Yorghos; Levy, Jonathan I

    2017-03-01

    Residential combustion (RC) and electricity generating unit (EGU) emissions adversely impact air quality and human health by increasing ambient concentrations of fine particulate matter (PM 2.5 ) and ozone (O 3 ). Studies to date have not isolated contributing emissions by state of origin (source-state), which is necessary for policy makers to determine efficient strategies to decrease health impacts. In this study, we aimed to estimate health impacts (premature mortalities) attributable to PM 2.5 and O 3 from RC and EGU emissions by precursor species, source sector, and source-state in the continental United States for 2005. We used the Community Multiscale Air Quality model employing the decoupled direct method to quantify changes in air quality and epidemiological evidence to determine concentration-response functions to calculate associated health impacts. We estimated 21,000 premature mortalities per year from EGU emissions, driven by sulfur dioxide emissions forming PM 2.5 . More than half of EGU health impacts are attributable to emissions from eight states with significant coal combustion and large downwind populations. We estimate 10,000 premature mortalities per year from RC emissions, driven by primary PM 2.5 emissions. States with large populations and significant residential wood combustion dominate RC health impacts. Annual mortality risk per thousand tons of precursor emissions (health damage functions) varied significantly across source-states for both source sectors and all precursor pollutants. Our findings reinforce the importance of pollutant-specific, location-specific, and source-specific models of health impacts in design of health-risk minimizing emissions control policies. Citation: Penn SL, Arunachalam S, Woody M, Heiger-Bernays W, Tripodis Y, Levy JI. 2017. Estimating state-specific contributions to PM 2.5 - and O 3 -related health burden from residential combustion and electricity generating unit emissions in the United States. Environ

  7. Estimate of the direct and indirect annual cost of bacterial conjunctivitis in the United States

    PubMed Central

    2009-01-01

    Background The aim of this study was to estimate both the direct and indirect annual costs of treating bacterial conjunctivitis (BC) in the United States. This was a cost of illness study performed from a U.S. healthcare payer perspective. Methods A comprehensive review of the medical literature was supplemented by data on the annual incidence of BC which was obtained from an analysis of the National Ambulatory Medical Care Survey (NAMCS) database for the year 2005. Cost estimates for medical visits and laboratory or diagnostic tests were derived from published Medicare CPT fee codes. The cost of prescription drugs was obtained from standard reference sources. Indirect costs were calculated as those due to lost productivity. Due to the acute nature of BC, no cost discounting was performed. All costs are expressed in 2007 U.S. dollars. Results The number of BC cases in the U.S. for 2005 was estimated at approximately 4 million yielding an estimated annual incidence rate of 135 per 10,000. Base-case analysis estimated the total direct and indirect cost of treating patients with BC in the United States at $ 589 million. One- way sensitivity analysis, assuming either a 20% variation in the annual incidence of BC or treatment costs, generated a cost range of $ 469 million to $ 705 million. Two-way sensitivity analysis, assuming a 20% variation in both the annual incidence of BC and treatment costs occurring simultaneously, resulted in an estimated cost range of $ 377 million to $ 857 million. Conclusion The economic burden posed by BC is significant. The findings may prove useful to decision makers regarding the allocation of healthcare resources necessary to address the economic burden of BC in the United States. PMID:19939250

  8. A comparison of above-ground dry-biomass estimators for trees in the Northeastern United States

    Treesearch

    James A. Westfall

    2012-01-01

    In the northeastern United States, both component and total aboveground tree dry-biomass estimates are available from several sources. In this study, comparisons were made among four methods to promote understanding of the similarities and differences in live-tree biomass estimators. The methods use various equations developed from biomass data collected in the United...

  9. Estimation of steady-state leakage current in polycrystalline PZT thin films

    NASA Astrophysics Data System (ADS)

    Podgorny, Yury; Vorotilov, Konstantin; Sigov, Alexander

    2016-09-01

    Estimation of the steady state (or "true") leakage current Js in polycrystalline ferroelectric PZT films with the use of the voltage-step technique is discussed. Curie-von Schweidler (CvS) and sum of exponents (Σ exp ) models are studied for current-time J (t) data fitting. Σ exp model (sum of three or two exponents) gives better fitting characteristics and provides good accuracy of Js estimation at reduced measurement time thus making possible to avoid film degradation, whereas CvS model is very sensitive to both start and finish time points and give in many cases incorrect results. The results give rise to suggest an existence of low-frequency relaxation processes in PZT films with characteristic duration of tens and hundreds of seconds.

  10. Preliminary estimates of annual agricultural pesticide use for counties of the conterminous United States, 2010-11

    USGS Publications Warehouse

    Baker, Nancy T.; Stone, Wesley W.

    2013-01-01

    This report provides preliminary estimates of annual agricultural use of 374 pesticide compounds in counties of the conterminous United States in 2010 and 2011, compiled by means of methods described in Thelin and Stone (2013). U.S. Department of Agriculture (USDA) county-level data for harvested-crop acreage were used in conjunction with proprietary Crop Reporting District (CRD)-level pesticide-use data to estimate county-level pesticide use. Estimated pesticide use (EPest) values were calculated with both the EPest-high and EPest-low methods. The distinction between the EPest-high method and the EPest-low method is that there are more counties with estimated pesticide use for EPest-high compared to EPest-low, owing to differing assumptions about missing survey data (Thelin and Stone, 2013). Preliminary estimates in this report will be revised upon availability of updated crop acreages in the 2012 Agricultural Census, to be published by the USDA in 2014. In addition, estimates for 2008 and 2009 previously published by Stone (2013) will be updated subsequent to the 2012 Agricultural Census release. Estimates of annual agricultural pesticide use are provided as downloadable, tab-delimited files, which are organized by compound, year, state Federal Information Processing Standard (FIPS) code, county FIPS code, and kg (amount in kilograms).

  11. Optical model calculations of heavy-ion target fragmentation

    NASA Technical Reports Server (NTRS)

    Townsend, L. W.; Wilson, J. W.; Cucinotta, F. A.; Norbury, J. W.

    1986-01-01

    The fragmentation of target nuclei by relativistic protons and heavy ions is described within the context of a simple abrasion-ablation-final-state interaction model. Abrasion is described by a quantum mechanical formalism utilizing an optical model potential approximation. Nuclear charge distributions of the excited prefragments are calculated by both a hypergeometric distribution and a method based upon the zero-point oscillations of the giant dipole resonance. Excitation energies are estimated from the excess surface energy resulting from the abrasion process and the additional energy deposited by frictional spectator interactions of the abraded nucleons. The ablation probabilities are obtained from the EVA-3 computer program. Isotope production cross sections for the spallation of copper targets by relativistic protons and for the fragmenting of carbon targets by relativistic carbon, neon, and iron projectiles are calculated and compared with available experimental data.

  12. Estimation and Uncertainty Analysis of Impacts of Future Heat Waves on Mortality in the Eastern United States

    PubMed Central

    Wu, Jianyong; Zhou, Ying; Gao, Yang; Fu, Joshua S.; Johnson, Brent A.; Huang, Cheng; Kim, Young-Min

    2013-01-01

    Background: Climate change is anticipated to influence heat-related mortality in the future. However, estimates of excess mortality attributable to future heat waves are subject to large uncertainties and have not been projected under the latest greenhouse gas emission scenarios. Objectives: We estimated future heat wave mortality in the eastern United States (approximately 1,700 counties) under two Representative Concentration Pathways (RCPs) and investigated sources of uncertainty. Methods: Using dynamically downscaled hourly temperature projections for 2057–2059, we projected heat wave days that were defined using four heat wave metrics and estimated the excess mortality attributable to them. We apportioned the sources of uncertainty in excess mortality estimates using a variance-decomposition method. Results: Estimates suggest that excess mortality attributable to heat waves in the eastern United States would result in 200–7,807 deaths/year (mean 2,379 deaths/year) in 2057–2059. Average excess mortality projections under RCP4.5 and RCP8.5 scenarios were 1,403 and 3,556 deaths/year, respectively. Excess mortality would be relatively high in the southern states and eastern coastal areas (excluding Maine). The major sources of uncertainty were the relative risk estimates for mortality on heat wave versus non–heat wave days, the RCP scenarios, and the heat wave definitions. Conclusions: Mortality risks from future heat waves may be an order of magnitude higher than the mortality risks reported in 2002–2004, with thousands of heat wave–related deaths per year in the study area projected under the RCP8.5 scenario. Substantial spatial variability in county-level heat mortality estimates suggests that effective mitigation and adaptation measures should be developed based on spatially resolved data. Citation: Wu J, Zhou Y, Gao Y, Fu JS, Johnson BA, Huang C, Kim YM, Liu Y. 2014. Estimation and uncertainty analysis of impacts of future heat waves on mortality

  13. Estimating the Impacts of Educational Interventions Using State Tests or Study-Administered Tests. NCEE 2012-4016

    ERIC Educational Resources Information Center

    Olsen, Robert B.; Unlu, Fatih; Price, Cristofer; Jaciw, Andrew P.

    2011-01-01

    This report examines the differences in impact estimates and standard errors that arise when these are derived using state achievement tests only (as pre-tests and post-tests), study-administered tests only, or some combination of state- and study-administered tests. State tests may yield different evaluation results relative to a test that is…

  14. Estimating the Probability of Elevated Nitrate Concentrations in Ground Water in Washington State

    USGS Publications Warehouse

    Frans, Lonna M.

    2008-01-01

    Logistic regression was used to relate anthropogenic (manmade) and natural variables to the occurrence of elevated nitrate concentrations in ground water in Washington State. Variables that were analyzed included well depth, ground-water recharge rate, precipitation, population density, fertilizer application amounts, soil characteristics, hydrogeomorphic regions, and land-use types. Two models were developed: one with and one without the hydrogeomorphic regions variable. The variables in both models that best explained the occurrence of elevated nitrate concentrations (defined as concentrations of nitrite plus nitrate as nitrogen greater than 2 milligrams per liter) were the percentage of agricultural land use in a 4-kilometer radius of a well, population density, precipitation, soil drainage class, and well depth. Based on the relations between these variables and measured nitrate concentrations, logistic regression models were developed to estimate the probability of nitrate concentrations in ground water exceeding 2 milligrams per liter. Maps of Washington State were produced that illustrate these estimated probabilities for wells drilled to 145 feet below land surface (median well depth) and the estimated depth to which wells would need to be drilled to have a 90-percent probability of drawing water with a nitrate concentration less than 2 milligrams per liter. Maps showing the estimated probability of elevated nitrate concentrations indicated that the agricultural regions are most at risk followed by urban areas. The estimated depths to which wells would need to be drilled to have a 90-percent probability of obtaining water with nitrate concentrations less than 2 milligrams per liter exceeded 1,000 feet in the agricultural regions; whereas, wells in urban areas generally would need to be drilled to depths in excess of 400 feet.

  15. The estimated lifetime probability of acquiring human papillomavirus in the United States.

    PubMed

    Chesson, Harrell W; Dunne, Eileen F; Hariri, Susan; Markowitz, Lauri E

    2014-11-01

    Estimates of the lifetime probability of acquiring human papillomavirus (HPV) can help to quantify HPV incidence, illustrate how common HPV infection is, and highlight the importance of HPV vaccination. We developed a simple model, based primarily on the distribution of lifetime numbers of sex partners across the population and the per-partnership probability of acquiring HPV, to estimate the lifetime probability of acquiring HPV in the United States in the time frame before HPV vaccine availability. We estimated the average lifetime probability of acquiring HPV among those with at least 1 opposite sex partner to be 84.6% (range, 53.6%-95.0%) for women and 91.3% (range, 69.5%-97.7%) for men. Under base case assumptions, more than 80% of women and men acquire HPV by age 45 years. Our results are consistent with estimates in the existing literature suggesting a high lifetime probability of HPV acquisition and are supported by cohort studies showing high cumulative HPV incidence over a relatively short period, such as 3 to 5 years.

  16. Bayesian approach to estimate AUC, partition coefficient and drug targeting index for studies with serial sacrifice design.

    PubMed

    Wang, Tianli; Baron, Kyle; Zhong, Wei; Brundage, Richard; Elmquist, William

    2014-03-01

    The current study presents a Bayesian approach to non-compartmental analysis (NCA), which provides the accurate and precise estimate of AUC 0 (∞) and any AUC 0 (∞) -based NCA parameter or derivation. In order to assess the performance of the proposed method, 1,000 simulated datasets were generated in different scenarios. A Bayesian method was used to estimate the tissue and plasma AUC 0 (∞) s and the tissue-to-plasma AUC 0 (∞) ratio. The posterior medians and the coverage of 95% credible intervals for the true parameter values were examined. The method was applied to laboratory data from a mice brain distribution study with serial sacrifice design for illustration. Bayesian NCA approach is accurate and precise in point estimation of the AUC 0 (∞) and the partition coefficient under a serial sacrifice design. It also provides a consistently good variance estimate, even considering the variability of the data and the physiological structure of the pharmacokinetic model. The application in the case study obtained a physiologically reasonable posterior distribution of AUC, with a posterior median close to the value estimated by classic Bailer-type methods. This Bayesian NCA approach for sparse data analysis provides statistical inference on the variability of AUC 0 (∞) -based parameters such as partition coefficient and drug targeting index, so that the comparison of these parameters following destructive sampling becomes statistically feasible.

  17. A novel method for state of charge estimation of lithium-ion batteries using a nonlinear observer

    NASA Astrophysics Data System (ADS)

    Xia, Bizhong; Chen, Chaoren; Tian, Yong; Sun, Wei; Xu, Zhihui; Zheng, Weiwei

    2014-12-01

    The state of charge (SOC) is important for the safety and reliability of battery operation since it indicates the remaining capacity of a battery. However, as the internal state of each cell cannot be directly measured, the value of the SOC has to be estimated. In this paper, a novel method for SOC estimation in electric vehicles (EVs) using a nonlinear observer (NLO) is presented. One advantage of this method is that it does not need complicated matrix operations, so the computation cost can be reduced. As a key step in design of the nonlinear observer, the state-space equations based on the equivalent circuit model are derived. The Lyapunov stability theory is employed to prove the convergence of the nonlinear observer. Four experiments are carried out to evaluate the performance of the presented method. The results show that the SOC estimation error converges to 3% within 130 s while the initial SOC error reaches 20%, and does not exceed 4.5% while the measurement suffers both 2.5% voltage noise and 5% current noise. Besides, the presented method has advantages over the extended Kalman filter (EKF) and sliding mode observer (SMO) algorithms in terms of computation cost, estimation accuracy and convergence rate.

  18. State and actuator fault estimation observer design integrated in a riderless bicycle stabilization system.

    PubMed

    Brizuela Mendoza, Jorge Aurelio; Astorga Zaragoza, Carlos Manuel; Zavala Río, Arturo; Pattalochi, Leo; Canales Abarca, Francisco

    2016-03-01

    This paper deals with an observer design for Linear Parameter Varying (LPV) systems with high-order time-varying parameter dependency. The proposed design, considered as the main contribution of this paper, corresponds to an observer for the estimation of the actuator fault and the system state, considering measurement noise at the system outputs. The observer gains are computed by considering the extension of linear systems theory to polynomial LPV systems, in such a way that the observer reaches the characteristics of LPV systems. As a result, the actuator fault estimation is ready to be used in a Fault Tolerant Control scheme, where the estimated state with reduced noise should be used to generate the control law. The effectiveness of the proposed methodology has been tested using a riderless bicycle model with dependency on the translational velocity v, where the control objective corresponds to the system stabilization towards the upright position despite the variation of v along the closed-loop system trajectories. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  19. Economic productivity by age and sex: 2007 estimates for the United States.

    PubMed

    Grosse, Scott D; Krueger, Kurt V; Mvundura, Mercy

    2009-07-01

    Human capital estimates of labor productivity are often used to estimate the economic impact of diseases and injuries that cause incapacitation or death. Estimates of average hourly, annual, and lifetime economic productivity, both market and household, were calculated in 2007 US dollars for 5-year age groups for men, women, and both sexes in the United States. Data from the American Time Use Survey were used to estimate hours of paid work and household services and hourly and annual earnings and household productivity. Present values of discounted lifetime earnings were calculated for each age group using the 2004 US life tables and a discount rate of 3% per year and assuming future productivity growth of 1% per year. The estimates of hours and productivity were calculated using the time diaries of 72,922 persons included in the American Time Use Survey for the years 2003 to 2007. The present value of lifetime productivity is approximately $1.2 million in 2007 dollars for children under 5 years of age. For adults in their 20s and 30s, it is approximately $1.6 million and then it declines with increasing age. Productivity estimates are higher for males than for females, more for market productivity than for total productivity. Changes in hours of paid employment and household services can affect economic productivity by age and sex. This is the first publication to include estimates of household services based on contemporary time use data for the US population.

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

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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