A selective-update affine projection algorithm with selective input vectors

NASA Astrophysics Data System (ADS)

Kong, NamWoong; Shin, JaeWook; Park, PooGyeon

2011-10-01

This paper proposes an affine projection algorithm (APA) with selective input vectors, which based on the concept of selective-update in order to reduce estimation errors and computations. The algorithm consists of two procedures: input- vector-selection and state-decision. The input-vector-selection procedure determines the number of input vectors by checking with mean square error (MSE) whether the input vectors have enough information for update. The state-decision procedure determines the current state of the adaptive filter by using the state-decision criterion. As the adaptive filter is in transient state, the algorithm updates the filter coefficients with the selected input vectors. On the other hand, as soon as the adaptive filter reaches the steady state, the update procedure is not performed. Through these two procedures, the proposed algorithm achieves small steady-state estimation errors, low computational complexity and low update complexity for colored input signals.

Restricted Complexity Framework for Nonlinear Adaptive Control in Complex Systems

NASA Astrophysics Data System (ADS)

Williams, Rube B.

2004-02-01

Control law adaptation that includes implicit or explicit adaptive state estimation, can be a fundamental underpinning for the success of intelligent control in complex systems, particularly during subsystem failures, where vital system states and parameters can be impractical or impossible to measure directly. A practical algorithm is proposed for adaptive state filtering and control in nonlinear dynamic systems when the state equations are unknown or are too complex to model analytically. The state equations and inverse plant model are approximated by using neural networks. A framework for a neural network based nonlinear dynamic inversion control law is proposed, as an extrapolation of prior developed restricted complexity methodology used to formulate the adaptive state filter. Examples of adaptive filter performance are presented for an SSME simulation with high pressure turbine failure to support extrapolations to adaptive control problems.

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.

Experimental demonstration of real-time adaptive one-qubit quantum-state tomography

NASA Astrophysics Data System (ADS)

Yin, Qi; Li, Li; Xiang, Xiao; Xiang, Guo-Yong; Li, Chuang-Feng; Guo, Guang-Can

2017-01-01

Quantum-state tomography plays a pivotal role in quantum computation and information processing. To improve the accuracy in estimating an unknown state, carefully designed measurement schemes, such as adopting an adaptive strategy, are necessarily needed, which have gained great interest recently. In this work, based on the proposal of Sugiyama et al. [Phys. Rev. A 85, 052107 (2012)], 10.1103/PhysRevA.85.052107, we experimentally realize an adaptive quantum-state tomography for one qubit in an optical system. Since this scheme gives an analytical solution to the optimal measurement basis problem, our experiment is updated in real time and the infidelity between the real state and the estimated state is tracked with the detected photons. We observe an almost 1 /N scaling rule of averaged infidelity against the overall number of photons, N , in our experiment, which outperforms 1 /√{N } of nonadaptive schemes.

Adaptive Control in the Presence of Simultaneous Sensor Bias and Actuator Failures

NASA Technical Reports Server (NTRS)

Joshi, Suresh M.

2012-01-01

The problem of simultaneously accommodating unknown sensor biases and unknown actuator failures in uncertain systems is considered in a direct model reference adaptive control (MRAC) setting for state tracking using state feedback. Sensor biases and actuator faults may be present at the outset or may occur at unknown instants of time during operation. A modified MRAC law is proposed, which combines sensor bias estimation with control gain adaptation for accommodation of sensor biases and actuator failures. This control law is shown to provide signal boundedness in the resulting system. For the case when an external asymptotically stable sensor bias estimator is available, an MRAC law is developed to accomplish asymptotic state tracking and signal boundedness. For a special case wherein biases are only present in the rate measurements and bias-free position measurements are available, an MRAC law is developed using a model-independent bias estimator, and is shown to provide asymptotic state tracking with signal boundedness.

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.

An adaptive state of charge estimation approach for lithium-ion series-connected battery system

NASA Astrophysics Data System (ADS)

Peng, Simin; Zhu, Xuelai; Xing, Yinjiao; Shi, Hongbing; Cai, Xu; Pecht, Michael

2018-07-01

Due to the incorrect or unknown noise statistics of a battery system and its cell-to-cell variations, state of charge (SOC) estimation of a lithium-ion series-connected battery system is usually inaccurate or even divergent using model-based methods, such as extended Kalman filter (EKF) and unscented Kalman filter (UKF). To resolve this problem, an adaptive unscented Kalman filter (AUKF) based on a noise statistics estimator and a model parameter regulator is developed to accurately estimate the SOC of a series-connected battery system. An equivalent circuit model is first built based on the model parameter regulator that illustrates the influence of cell-to-cell variation on the battery system. A noise statistics estimator is then used to attain adaptively the estimated noise statistics for the AUKF when its prior noise statistics are not accurate or exactly Gaussian. The accuracy and effectiveness of the SOC estimation method is validated by comparing the developed AUKF and UKF when model and measurement statistics noises are inaccurate, respectively. Compared with the UKF and EKF, the developed method shows the highest SOC estimation accuracy.

Optimal structure of metaplasticity for adaptive learning

PubMed Central

2017-01-01

Learning from reward feedback in a changing environment requires a high degree of adaptability, yet the precise estimation of reward information demands slow updates. In the framework of estimating reward probability, here we investigated how this tradeoff between adaptability and precision can be mitigated via metaplasticity, i.e. synaptic changes that do not always alter synaptic efficacy. Using the mean-field and Monte Carlo simulations we identified ‘superior’ metaplastic models that can substantially overcome the adaptability-precision tradeoff. These models can achieve both adaptability and precision by forming two separate sets of meta-states: reservoirs and buffers. Synapses in reservoir meta-states do not change their efficacy upon reward feedback, whereas those in buffer meta-states can change their efficacy. Rapid changes in efficacy are limited to synapses occupying buffers, creating a bottleneck that reduces noise without significantly decreasing adaptability. In contrast, more-populated reservoirs can generate a strong signal without manifesting any observable plasticity. By comparing the behavior of our model and a few competing models during a dynamic probability estimation task, we found that superior metaplastic models perform close to optimally for a wider range of model parameters. Finally, we found that metaplastic models are robust to changes in model parameters and that metaplastic transitions are crucial for adaptive learning since replacing them with graded plastic transitions (transitions that change synaptic efficacy) reduces the ability to overcome the adaptability-precision tradeoff. Overall, our results suggest that ubiquitous unreliability of synaptic changes evinces metaplasticity that can provide a robust mechanism for mitigating the tradeoff between adaptability and precision and thus adaptive learning. PMID:28658247

Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering.

PubMed

Wu, Dongjin; Xia, Linyuan; Geng, Jijun

2018-06-19

Pedestrian dead reckoning (PDR) using smart phone-embedded micro-electro-mechanical system (MEMS) sensors plays a key role in ubiquitous localization indoors and outdoors. However, as a relative localization method, it suffers from the problem of error accumulation which prevents it from long term independent running. Heading estimation error is one of the main location error sources, and therefore, in order to improve the location tracking performance of the PDR method in complex environments, an approach based on robust adaptive Kalman filtering (RAKF) for estimating accurate headings is proposed. In our approach, outputs from gyroscope, accelerometer, and magnetometer sensors are fused using the solution of Kalman filtering (KF) that the heading measurements derived from accelerations and magnetic field data are used to correct the states integrated from angular rates. In order to identify and control measurement outliers, a maximum likelihood-type estimator (M-estimator)-based model is used. Moreover, an adaptive factor is applied to resist the negative effects of state model disturbances. Extensive experiments under static and dynamic conditions were conducted in indoor environments. The experimental results demonstrate the proposed approach provides more accurate heading estimates and supports more robust and dynamic adaptive location tracking, compared with methods based on conventional KF.

The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.

PubMed

Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck

2016-07-16

This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively.

A new adaptive estimation method of spacecraft thermal mathematical model with an ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Akita, T.; Takaki, R.; Shima, E.

2012-04-01

An adaptive estimation method of spacecraft thermal mathematical model is presented. The method is based on the ensemble Kalman filter, which can effectively handle the nonlinearities contained in the thermal model. The state space equations of the thermal mathematical model is derived, where both temperature and uncertain thermal characteristic parameters are considered as the state variables. In the method, the thermal characteristic parameters are automatically estimated as the outputs of the filtered state variables, whereas, in the usual thermal model correlation, they are manually identified by experienced engineers using trial-and-error approach. A numerical experiment of a simple small satellite is provided to verify the effectiveness of the presented method.

Online vegetation parameter estimation using passive microwave remote sensing observations

USDA-ARS?s Scientific Manuscript database

In adaptive system identification the Kalman filter can be used to identify the coefficient of the observation operator of a linear system. Here the ensemble Kalman filter is tested for adaptive online estimation of the vegetation opacity parameter of a radiative transfer model. A state augmentatio...

Modeling, Control, and Estimation of Flexible, Aerodynamic Structures

NASA Astrophysics Data System (ADS)

Ray, Cody W.

Engineers have long been inspired by nature’s flyers. Such animals navigate complex environments gracefully and efficiently by using a variety of evolutionary adaptations for high-performance flight. Biologists have discovered a variety of sensory adaptations that provide flow state feedback and allow flying animals to feel their way through flight. A specialized skeletal wing structure and plethora of robust, adaptable sensory systems together allow nature’s flyers to adapt to myriad flight conditions and regimes. In this work, motivated by biology and the successes of bio-inspired, engineered aerial vehicles, linear quadratic control of a flexible, morphing wing design is investigated, helping to pave the way for truly autonomous, mission-adaptive craft. The proposed control algorithm is demonstrated to morph a wing into desired positions. Furthermore, motivated specifically by the sensory adaptations organisms possess, this work transitions to an investigation of aircraft wing load identification using structural response as measured by distributed sensors. A novel, recursive estimation algorithm is utilized to recursively solve the inverse problem of load identification, providing both wing structural and aerodynamic states for use in a feedback control, mission-adaptive framework. The recursive load identification algorithm is demonstrated to provide accurate load estimate in both simulation and experiment.

Optimized quantum sensing with a single electron spin using real-time adaptive measurements.

PubMed

Bonato, C; Blok, M S; Dinani, H T; Berry, D W; Markham, M L; Twitchen, D J; Hanson, R

2016-03-01

Quantum sensors based on single solid-state spins promise a unique combination of sensitivity and spatial resolution. The key challenge in sensing is to achieve minimum estimation uncertainty within a given time and with high dynamic range. Adaptive strategies have been proposed to achieve optimal performance, but their implementation in solid-state systems has been hindered by the demanding experimental requirements. Here, we realize adaptive d.c. sensing by combining single-shot readout of an electron spin in diamond with fast feedback. By adapting the spin readout basis in real time based on previous outcomes, we demonstrate a sensitivity in Ramsey interferometry surpassing the standard measurement limit. Furthermore, we find by simulations and experiments that adaptive protocols offer a distinctive advantage over the best known non-adaptive protocols when overhead and limited estimation time are taken into account. Using an optimized adaptive protocol we achieve a magnetic field sensitivity of 6.1 ± 1.7 nT Hz(-1/2) over a wide range of 1.78 mT. These results open up a new class of experiments for solid-state sensors in which real-time knowledge of the measurement history is exploited to obtain optimal performance.

Optimized quantum sensing with a single electron spin using real-time adaptive measurements

NASA Astrophysics Data System (ADS)

Bonato, C.; Blok, M. S.; Dinani, H. T.; Berry, D. W.; Markham, M. L.; Twitchen, D. J.; Hanson, R.

2016-03-01

Quantum sensors based on single solid-state spins promise a unique combination of sensitivity and spatial resolution. The key challenge in sensing is to achieve minimum estimation uncertainty within a given time and with high dynamic range. Adaptive strategies have been proposed to achieve optimal performance, but their implementation in solid-state systems has been hindered by the demanding experimental requirements. Here, we realize adaptive d.c. sensing by combining single-shot readout of an electron spin in diamond with fast feedback. By adapting the spin readout basis in real time based on previous outcomes, we demonstrate a sensitivity in Ramsey interferometry surpassing the standard measurement limit. Furthermore, we find by simulations and experiments that adaptive protocols offer a distinctive advantage over the best known non-adaptive protocols when overhead and limited estimation time are taken into account. Using an optimized adaptive protocol we achieve a magnetic field sensitivity of 6.1 ± 1.7 nT Hz-1/2 over a wide range of 1.78 mT. These results open up a new class of experiments for solid-state sensors in which real-time knowledge of the measurement history is exploited to obtain optimal performance.

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter

PubMed Central

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Gu, Chengfan

2018-01-01

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation. PMID:29415509

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter.

PubMed

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Zhong, Yongmin; Gu, Chengfan

2018-02-06

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.

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

PubMed

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

2015-02-01

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

State-space self-tuner for on-line adaptive control

NASA Technical Reports Server (NTRS)

Shieh, L. S.

1994-01-01

Dynamic systems, such as flight vehicles, satellites and space stations, operating in real environments, constantly face parameter and/or structural variations owing to nonlinear behavior of actuators, failure of sensors, changes in operating conditions, disturbances acting on the system, etc. In the past three decades, adaptive control has been shown to be effective in dealing with dynamic systems in the presence of parameter uncertainties, structural perturbations, random disturbances and environmental variations. Among the existing adaptive control methodologies, the state-space self-tuning control methods, initially proposed by us, are shown to be effective in designing advanced adaptive controllers for multivariable systems. In our approaches, we have embedded the standard Kalman state-estimation algorithm into an online parameter estimation algorithm. Thus, the advanced state-feedback controllers can be easily established for digital adaptive control of continuous-time stochastic multivariable systems. A state-space self-tuner for a general multivariable stochastic system has been developed and successfully applied to the space station for on-line adaptive control. Also, a technique for multistage design of an optimal momentum management controller for the space station has been developed and reported in. Moreover, we have successfully developed various digital redesign techniques which can convert a continuous-time controller to an equivalent digital controller. As a result, the expensive and unreliable continuous-time controller can be implemented using low-cost and high performance microprocessors. Recently, we have developed a new hybrid state-space self tuner using a new dual-rate sampling scheme for on-line adaptive control of continuous-time uncertain systems.

The Effects of Test Difficulty Manipulation in Computerized Adaptive Testing and Self-Adapted Testing.

ERIC Educational Resources Information Center

Ponsoda, Vicente; Olea, Julio; Rodriguez, Maria Soledad; Revuelta, Javier

1999-01-01

Compared easy and difficult versions of self-adapted tests (SAT) and computerized adapted tests. No significant differences were found among the tests for estimated ability or posttest state anxiety in studies with 187 Spanish high school students, although other significant differences were found. Discusses implications for interpreting test…

Adaptive Parameter Estimation of Person Recognition Model in a Stochastic Human Tracking Process

NASA Astrophysics Data System (ADS)

Nakanishi, W.; Fuse, T.; Ishikawa, T.

2015-05-01

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general state space model. Firstly we explain the way to formulate human tracking in general state space model with their components. Then referring to previous researches, we use Bhattacharyya coefficient to formulate observation model of general state space model, which is corresponding to person recognition model. The observation model in this paper is a function of Bhattacharyya coefficient with one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed that sequential parameter estimation was succeeded and were consistent with observation situations such as occlusions.

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.

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.

Reliable video transmission over fading channels via channel state estimation

NASA Astrophysics Data System (ADS)

Kumwilaisak, Wuttipong; Kim, JongWon; Kuo, C.-C. Jay

2000-04-01

Transmission of continuous media such as video over time- varying wireless communication channels can benefit from the use of adaptation techniques in both source and channel coding. An adaptive feedback-based wireless video transmission scheme is investigated in this research with special emphasis on feedback-based adaptation. To be more specific, an interactive adaptive transmission scheme is developed by letting the receiver estimate the channel state information and send it back to the transmitter. By utilizing the feedback information, the transmitter is capable of adapting the level of protection by changing the flexible RCPC (rate-compatible punctured convolutional) code ratio depending on the instantaneous channel condition. The wireless channel is modeled as a fading channel, where the long-term and short- term fading effects are modeled as the log-normal fading and the Rayleigh flat fading, respectively. Then, its state (mainly the long term fading portion) is tracked and predicted by using an adaptive LMS (least mean squares) algorithm. By utilizing the delayed feedback on the channel condition, the adaptation performance of the proposed scheme is first evaluated in terms of the error probability and the throughput. It is then extended to incorporate variable size packets of ITU-T H.263+ video with the error resilience option. Finally, the end-to-end performance of wireless video transmission is compared against several non-adaptive protection schemes.

Adaptive disturbance compensation finite control set optimal control for PMSM systems based on sliding mode extended state observer

NASA Astrophysics Data System (ADS)

Wu, Yun-jie; Li, Guo-fei

2018-01-01

Based on sliding mode extended state observer (SMESO) technique, an adaptive disturbance compensation finite control set optimal control (FCS-OC) strategy is proposed for permanent magnet synchronous motor (PMSM) system driven by voltage source inverter (VSI). So as to improve robustness of finite control set optimal control strategy, a SMESO is proposed to estimate the output-effect disturbance. The estimated value is fed back to finite control set optimal controller for implementing disturbance compensation. It is indicated through theoretical analysis that the designed SMESO could converge in finite time. The simulation results illustrate that the proposed adaptive disturbance compensation FCS-OC possesses better dynamical response behavior in the presence of disturbance.

Developing Bayesian adaptive methods for estimating sensitivity thresholds (d′) in Yes-No and forced-choice tasks

PubMed Central

Lesmes, Luis A.; Lu, Zhong-Lin; Baek, Jongsoo; Tran, Nina; Dosher, Barbara A.; Albright, Thomas D.

2015-01-01

Motivated by Signal Detection Theory (SDT), we developed a family of novel adaptive methods that estimate the sensitivity threshold—the signal intensity corresponding to a pre-defined sensitivity level (d′ = 1)—in Yes-No (YN) and Forced-Choice (FC) detection tasks. Rather than focus stimulus sampling to estimate a single level of %Yes or %Correct, the current methods sample psychometric functions more broadly, to concurrently estimate sensitivity and decision factors, and thereby estimate thresholds that are independent of decision confounds. Developed for four tasks—(1) simple YN detection, (2) cued YN detection, which cues the observer's response state before each trial, (3) rated YN detection, which incorporates a Not Sure response, and (4) FC detection—the qYN and qFC methods yield sensitivity thresholds that are independent of the task's decision structure (YN or FC) and/or the observer's subjective response state. Results from simulation and psychophysics suggest that 25 trials (and sometimes less) are sufficient to estimate YN thresholds with reasonable precision (s.d. = 0.10–0.15 decimal log units), but more trials are needed for FC thresholds. When the same subjects were tested across tasks of simple, cued, rated, and FC detection, adaptive threshold estimates exhibited excellent agreement with the method of constant stimuli (MCS), and with each other. These YN adaptive methods deliver criterion-free thresholds that have previously been exclusive to FC methods. PMID:26300798

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

PubMed

Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani

2016-01-01

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

Multimodel Kalman filtering for adaptive nonuniformity correction in infrared sensors.

PubMed

Pezoa, Jorge E; Hayat, Majeed M; Torres, Sergio N; Rahman, Md Saifur

2006-06-01

We present an adaptive technique for the estimation of nonuniformity parameters of infrared focal-plane arrays that is robust with respect to changes and uncertainties in scene and sensor characteristics. The proposed algorithm is based on using a bank of Kalman filters in parallel. Each filter independently estimates state variables comprising the gain and the bias matrices of the sensor, according to its own dynamic-model parameters. The supervising component of the algorithm then generates the final estimates of the state variables by forming a weighted superposition of all the estimates rendered by each Kalman filter. The weights are computed and updated iteratively, according to the a posteriori-likelihood principle. The performance of the estimator and its ability to compensate for fixed-pattern noise is tested using both simulated and real data obtained from two cameras operating in the mid- and long-wave infrared regime.

Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO

NASA Astrophysics Data System (ADS)

Gao, Zhen; Dai, Linglong; Wang, Zhaocheng; Chen, Sheng

2015-12-01

This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a non-orthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. Additionally, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the non-orthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramer-Rao lower bound of the proposed scheme, which enlightens us to design the non-orthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.

Estimating the limits of adaptation from historical behaviour: Insights from the American Climate Prospectus

NASA Astrophysics Data System (ADS)

Jina, A.; Hsiang, S. M.; Kopp, R. E., III; Rasmussen, D.; Rising, J.

2014-12-01

The American Climate Prospectus (ACP), the technical analysis underlying the Risky Business project, quantitatively assessed the climate risks posed to the United States' economy in a number of economic sectors [1]. The main analysis presents projections of climate impacts with an assumption of "no adaptation". Yet, historically, when the climate imposed an economic cost upon society, adaptive responses were taken to minimise these costs. These adaptive behaviours, both autonomous and planned, can be expected to occur as climate impacts increase in the future. To understand the extent to which adaptation might decrease some of the worst impacts of climate change, we empirically estimate adaptive responses. We do this in three sectors considered in the analysis - crop yield, crime, and mortality - and estimate adaptive capacity in two steps. First, looking at changes in climate impacts through time, we identify a historical rate of adaptation. Second, spatial differences in climate impacts are then used to stratify regions into more adapted or less adapted based on climate averages. As these averages change across counties in the US, we allow each to become more adapted at the rate identified in step one. We are then able to estimate the residual damages, assuming that only the historical adaptive behaviours have taken place (fig 1). Importantly, we are unable to estimate any costs associated with these adaptations, nor are we able to estimate more novel (for example, new technological discoveries) or more disruptive (for example, migration) adaptive behaviours. However, an important insight is that historical adaptive behaviours may not be capable of reducing the worst impacts of climate change. The persistence of impacts in even the most exposed areas indicates that there are non-trivial costs associated with adaptation that will need to be met from other sources or through novel behavioural changes. References: [1] T. Houser et al. (2014), American Climate Prospectus, www.climateprospectus.org.

Entropy-based adaptive attitude estimation

NASA Astrophysics Data System (ADS)

Kiani, Maryam; Barzegar, Aylin; Pourtakdoust, Seid H.

2018-03-01

Gaussian approximation filters have increasingly been developed to enhance the accuracy of attitude estimation in space missions. The effective employment of these algorithms demands accurate knowledge of system dynamics and measurement models, as well as their noise characteristics, which are usually unavailable or unreliable. An innovation-based adaptive filtering approach has been adopted as a solution to this problem; however, it exhibits two major challenges, namely appropriate window size selection and guaranteed assurance of positive definiteness for the estimated noise covariance matrices. The current work presents two novel techniques based on relative entropy and confidence level concepts in order to address the abovementioned drawbacks. The proposed adaptation techniques are applied to two nonlinear state estimation algorithms of the extended Kalman filter and cubature Kalman filter for attitude estimation of a low earth orbit satellite equipped with three-axis magnetometers and Sun sensors. The effectiveness of the proposed adaptation scheme is demonstrated by means of comprehensive sensitivity analysis on the system and environmental parameters by using extensive independent Monte Carlo simulations.

Brownian motion with adaptive drift for remaining useful life prediction: Revisited

NASA Astrophysics Data System (ADS)

Wang, Dong; Tsui, Kwok-Leung

2018-01-01

Linear Brownian motion with constant drift is widely used in remaining useful life predictions because its first hitting time follows the inverse Gaussian distribution. State space modelling of linear Brownian motion was proposed to make the drift coefficient adaptive and incorporate on-line measurements into the first hitting time distribution. Here, the drift coefficient followed the Gaussian distribution, and it was iteratively estimated by using Kalman filtering once a new measurement was available. Then, to model nonlinear degradation, linear Brownian motion with adaptive drift was extended to nonlinear Brownian motion with adaptive drift. However, in previous studies, an underlying assumption used in the state space modelling was that in the update phase of Kalman filtering, the predicted drift coefficient at the current time exactly equalled the posterior drift coefficient estimated at the previous time, which caused a contradiction with the predicted drift coefficient evolution driven by an additive Gaussian process noise. In this paper, to alleviate such an underlying assumption, a new state space model is constructed. As a result, in the update phase of Kalman filtering, the predicted drift coefficient at the current time evolves from the posterior drift coefficient at the previous time. Moreover, the optimal Kalman filtering gain for iteratively estimating the posterior drift coefficient at any time is mathematically derived. A discussion that theoretically explains the main reasons why the constructed state space model can result in high remaining useful life prediction accuracies is provided. Finally, the proposed state space model and its associated Kalman filtering gain are applied to battery prognostics.

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

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.

Accommodating Sensor Bias in MRAC for State Tracking

NASA Technical Reports Server (NTRS)

Patre, Parag; Joshi, Suresh M.

2011-01-01

The problem of accommodating unknown sensor bias is considered in a direct model reference adaptive control (MRAC) setting for state tracking using state feedback. Sensor faults can occur during operation, and if the biased state measurements are directly used with a standard MRAC control law, neither closed-loop signal boundedness, nor asymptotic tracking can be guaranteed and the resulting tracking errors may be unbounded or unacceptably large. A modified MRAC law is proposed, which combines a bias estimator with control gain adaptation, and it is shown that signal boundedness can be accomplished, although the tracking error may not go to zero. Further, for the case wherein an asymptotically stable sensor bias estimator is available, an MRAC control law is proposed to accomplish asymptotic tracking and signal boundedness. Such a sensor bias estimator can be designed if additional sensor measurements are available, as illustrated for the case wherein bias is present in the rate gyro and airspeed measurements. Numerical example results are presented to illustrate each of the schemes.

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.

Distributed Damage Estimation for Prognostics based on Structural Model Decomposition

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil

2011-01-01

Model-based prognostics approaches capture system knowledge in the form of physics-based models of components, and how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is projected forward in time to determine end of life. However, the damage estimation problem is often multi-dimensional and computationally intensive. We propose a model decomposition approach adapted from the diagnosis community, called possible conflicts, in order to both improve the computational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state estimate from which prediction is performed. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the approach.

Observer-Based Adaptive NN Control for a Class of Uncertain Nonlinear Systems With Nonsymmetric Input Saturation.

PubMed

Yong-Feng Gao; Xi-Ming Sun; Changyun Wen; Wei Wang

2017-07-01

This paper is concerned with the problem of adaptive tracking control for a class of uncertain nonlinear systems with nonsymmetric input saturation and immeasurable states. The radial basis function of neural network (NN) is employed to approximate unknown functions, and an NN state observer is designed to estimate the immeasurable states. To analyze the effect of input saturation, an auxiliary system is employed. By the aid of adaptive backstepping technique, an adaptive tracking control approach is developed. Under the proposed adaptive tracking controller, the boundedness of all the signals in the closed-loop system is achieved. Moreover, distinct from most of the existing references, the tracking error can be bounded by an explicit function of design parameters and saturation input error. Finally, an example is given to show the effectiveness of the proposed method.

Sequential Adaptive Multi-Modality Target Detection and Classification Using Physics Based Models

DTIC Science & Technology

2006-09-01

estimation," R. Raghuram, R. Raich and A.O. Hero, IEEE Intl. Conf. on Acoustics, Speech , and Signal Processing, Toulouse France, June 2006, <http...can then be solved using off-the-shelf classifiers such as radial basis functions, SVM, or kNN classifier structures. When applied to mine detection we...stage waveform selection for adaptive resource constrained state estimation," 2006 IEEE Intl. Conf. on Acoustics, Speech , and Signal Processing

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.

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

Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

NASA Technical Reports Server (NTRS)

Mitra, Sunanda; Pemmaraju, Surya

1992-01-01

Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

Fuzzy Adaptive Decentralized Optimal Control for Strict Feedback Nonlinear Large-Scale Systems.

PubMed

Sun, Kangkang; Sui, Shuai; Tong, Shaocheng

2018-04-01

This paper considers the optimal decentralized fuzzy adaptive control design problem for a class of interconnected large-scale nonlinear systems in strict feedback form and with unknown nonlinear functions. The fuzzy logic systems are introduced to learn the unknown dynamics and cost functions, respectively, and a state estimator is developed. By applying the state estimator and the backstepping recursive design algorithm, a decentralized feedforward controller is established. By using the backstepping decentralized feedforward control scheme, the considered interconnected large-scale nonlinear system in strict feedback form is changed into an equivalent affine large-scale nonlinear system. Subsequently, an optimal decentralized fuzzy adaptive control scheme is constructed. The whole optimal decentralized fuzzy adaptive controller is composed of a decentralized feedforward control and an optimal decentralized control. It is proved that the developed optimal decentralized controller can ensure that all the variables of the control system are uniformly ultimately bounded, and the cost functions are the smallest. Two simulation examples are provided to illustrate the validity of the developed optimal decentralized fuzzy adaptive control scheme.

Utilizing Adjoint-Based Error Estimates for Surrogate Models to Accurately Predict Probabilities of Events

DOE PAGES

Butler, Troy; Wildey, Timothy

2018-01-01

In thist study, we develop a procedure to utilize error estimates for samples of a surrogate model to compute robust upper and lower bounds on estimates of probabilities of events. We show that these error estimates can also be used in an adaptive algorithm to simultaneously reduce the computational cost and increase the accuracy in estimating probabilities of events using computationally expensive high-fidelity models. Specifically, we introduce the notion of reliability of a sample of a surrogate model, and we prove that utilizing the surrogate model for the reliable samples and the high-fidelity model for the unreliable samples gives preciselymore » the same estimate of the probability of the output event as would be obtained by evaluation of the original model for each sample. The adaptive algorithm uses the additional evaluations of the high-fidelity model for the unreliable samples to locally improve the surrogate model near the limit state, which significantly reduces the number of high-fidelity model evaluations as the limit state is resolved. Numerical results based on a recently developed adjoint-based approach for estimating the error in samples of a surrogate are provided to demonstrate (1) the robustness of the bounds on the probability of an event, and (2) that the adaptive enhancement algorithm provides a more accurate estimate of the probability of the QoI event than standard response surface approximation methods at a lower computational cost.« less

Utilizing Adjoint-Based Error Estimates for Surrogate Models to Accurately Predict Probabilities of Events

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

Butler, Troy; Wildey, Timothy

In thist study, we develop a procedure to utilize error estimates for samples of a surrogate model to compute robust upper and lower bounds on estimates of probabilities of events. We show that these error estimates can also be used in an adaptive algorithm to simultaneously reduce the computational cost and increase the accuracy in estimating probabilities of events using computationally expensive high-fidelity models. Specifically, we introduce the notion of reliability of a sample of a surrogate model, and we prove that utilizing the surrogate model for the reliable samples and the high-fidelity model for the unreliable samples gives preciselymore » the same estimate of the probability of the output event as would be obtained by evaluation of the original model for each sample. The adaptive algorithm uses the additional evaluations of the high-fidelity model for the unreliable samples to locally improve the surrogate model near the limit state, which significantly reduces the number of high-fidelity model evaluations as the limit state is resolved. Numerical results based on a recently developed adjoint-based approach for estimating the error in samples of a surrogate are provided to demonstrate (1) the robustness of the bounds on the probability of an event, and (2) that the adaptive enhancement algorithm provides a more accurate estimate of the probability of the QoI event than standard response surface approximation methods at a lower computational cost.« less

Quantum-enhanced multiparameter estimation in multiarm interferometers

PubMed Central

Ciampini, Mario A.; Spagnolo, Nicolò; Vitelli, Chiara; Pezzè, Luca; Smerzi, Augusto; Sciarrino, Fabio

2016-01-01

Quantum metrology is the state-of-the-art measurement technology. It uses quantum resources to enhance the sensitivity of phase estimation over that achievable by classical physics. While single parameter estimation theory has been widely investigated, much less is known about the simultaneous estimation of multiple phases, which finds key applications in imaging and sensing. In this manuscript we provide conditions of useful particle (qudit) entanglement for multiphase estimation and adapt them to multiarm Mach-Zehnder interferometry. We theoretically discuss benchmark multimode Fock states containing useful qudit entanglement and overcoming the sensitivity of separable qudit states in three and four arm Mach-Zehnder-like interferometers - currently within the reach of integrated photonics technology. PMID:27381743

An adaptive and generalizable closed-loop system for control of medically induced coma and other states of anesthesia

NASA Astrophysics Data System (ADS)

Yang, Yuxiao; Shanechi, Maryam M.

2016-12-01

Objective. Design of closed-loop anesthetic delivery (CLAD) systems is an important topic, particularly for medically induced coma, which needs to be maintained for long periods. Current CLADs for medically induced coma require a separate offline experiment for model parameter estimation, which causes interruption in treatment and is difficult to perform. Also, CLADs may exhibit bias due to inherent time-variation and non-stationarity, and may have large infusion rate variations at steady state. Finally, current CLADs lack theoretical performance guarantees. We develop the first adaptive CLAD for medically induced coma, which addresses these limitations. Further, we extend our adaptive system to be generalizable to other states of anesthesia. Approach. We designed general parametric pharmacodynamic, pharmacokinetic and neural observation models with associated guidelines, and derived a novel adaptive controller. We further penalized large steady-state drug infusion rate variations in the controller. We derived theoretical guarantees that the adaptive system has zero steady-state bias. Using simulations that resembled real time-varying and noisy environments, we tested the closed-loop system for control of two different anesthetic states, burst suppression in medically induced coma and unconsciousness in general anesthesia. Main results. In 1200 simulations, the adaptive system achieved precise control of both anesthetic states despite non-stationarity, time-variation, noise, and no initial parameter knowledge. In both cases, the adaptive system performed close to a baseline system that knew the parameters exactly. In contrast, a non-adaptive system resulted in large steady-state bias and error. The adaptive system also resulted in significantly smaller steady-state infusion rate variations compared to prior systems. Significance. These results have significant implications for clinically viable CLAD design for a wide range of anesthetic states, with potential cost-saving and therapeutic benefits.

An adaptive and generalizable closed-loop system for control of medically induced coma and other states of anesthesia.

PubMed

Yang, Yuxiao; Shanechi, Maryam M

2016-12-01

Design of closed-loop anesthetic delivery (CLAD) systems is an important topic, particularly for medically induced coma, which needs to be maintained for long periods. Current CLADs for medically induced coma require a separate offline experiment for model parameter estimation, which causes interruption in treatment and is difficult to perform. Also, CLADs may exhibit bias due to inherent time-variation and non-stationarity, and may have large infusion rate variations at steady state. Finally, current CLADs lack theoretical performance guarantees. We develop the first adaptive CLAD for medically induced coma, which addresses these limitations. Further, we extend our adaptive system to be generalizable to other states of anesthesia. We designed general parametric pharmacodynamic, pharmacokinetic and neural observation models with associated guidelines, and derived a novel adaptive controller. We further penalized large steady-state drug infusion rate variations in the controller. We derived theoretical guarantees that the adaptive system has zero steady-state bias. Using simulations that resembled real time-varying and noisy environments, we tested the closed-loop system for control of two different anesthetic states, burst suppression in medically induced coma and unconsciousness in general anesthesia. In 1200 simulations, the adaptive system achieved precise control of both anesthetic states despite non-stationarity, time-variation, noise, and no initial parameter knowledge. In both cases, the adaptive system performed close to a baseline system that knew the parameters exactly. In contrast, a non-adaptive system resulted in large steady-state bias and error. The adaptive system also resulted in significantly smaller steady-state infusion rate variations compared to prior systems. These results have significant implications for clinically viable CLAD design for a wide range of anesthetic states, with potential cost-saving and therapeutic benefits.

Linear quadratic Gaussian control of a deformable mirror adaptive optics system with time-delayed measurements

NASA Astrophysics Data System (ADS)

Paschall, Randall N.; Anderson, David J.

1993-11-01

A linear quadratic Gaussian method is proposed for a deformable mirror adaptive optics system control. Estimates of system states describing the distortion are generated by a Kalman filter based on Hartmann wave front measurements of the wave front gradient.

Systems identification and the adaptive management of waterfowl in the United States

USGS Publications Warehouse

Williams, B.K.; Nichols, J.D.

2001-01-01

Waterfowl management in the United States is one of the more visible conservation success stories in the United States. It is authorized and supported by appropriate legislative authorities, based on large-scale monitoring programs, and widely accepted by the public. The process is one of only a limited number of large-scale examples of effective collaboration between research and management, integrating scientific information with management in a coherent framework for regulatory decision-making. However, harvest management continues to face some serious technical problems, many of which focus on sequential identification of the resource system in a context of optimal decision-making. The objective of this paper is to provide a theoretical foundation of adaptive harvest management, the approach currently in use in the United States for regulatory decision-making. We lay out the legal and institutional framework for adaptive harvest management and provide a formal description of regulatory decision-making in terms of adaptive optimization. We discuss some technical and institutional challenges in applying adaptive harvest management and focus specifically on methods of estimating resource states for linear resource systems.

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.

Nonlinear model predictive control for chemical looping process

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

Joshi, Abhinaya; Lei, Hao; Lou, Xinsheng

A control system for optimizing a chemical looping ("CL") plant includes a reduced order mathematical model ("ROM") that is designed by eliminating mathematical terms that have minimal effect on the outcome. A non-linear optimizer provides various inputs to the ROM and monitors the outputs to determine the optimum inputs that are then provided to the CL plant. An estimator estimates the values of various internal state variables of the CL plant. The system has one structure adapted to control a CL plant that only provides pressure measurements in the CL loops A and B, a second structure adapted to amore » CL plant that provides pressure measurements and solid levels in both loops A, and B, and a third structure adapted to control a CL plant that provides full information on internal state variables. A final structure provides a neural network NMPC controller to control operation of loops A and B.« less

Real-Time State Estimation and Long-Term Model Adaptation: A Two-Sided Approach toward Personalized Diagnosis of Glucose and Insulin Levels

PubMed Central

Eberle, Claudia; Ament, Christoph

2012-01-01

Background With continuous glucose sensors (CGSs), it is possible to obtain a dynamical signal of the patient’s subcutaneous glucose concentration in real time. How could that information be exploited? We suggest a model-based diagnosis system with a twofold objective: real-time state estimation and long-term model parameter identification. Methods To obtain a dynamical model, Bergman’s nonlinear minimal model (considering plasma glucose G, insulin I, and interstitial insulin X) is extended by two states describing first and second insulin response. Furthermore, compartments for oral glucose and subcutaneous insulin inputs as well as for subcutaneous glucose measurement are added. The observability of states and external inputs as well as the identifiability of model parameters are assessed using the empirical observability Gramian. Signals are estimated for different nondiabetic and diabetic scenarios by unscented Kalman filter. Results (1) Observability of different state subsets is evaluated, e.g., from CGSs, {G, I} or {G, X} can be observed and the set {G, I, X} cannot. (2) Model parameters are included, e.g., it is possible to estimate the second-phase insulin response gain kG2 additionally. This can be used for model adaptation and as a diagnostic parameter that is almost zero for diabetes patients. (3) External inputs are considered, e.g., oral glucose is theoretically observable for nondiabetic patients, but estimation scenarios show that the time delay of 1 h limits application. Conclusions A real-time estimation of states (such as plasma insulin I) and parameters (such as kG2) is possible, which allows an improved real-time state prediction and a personalized model. PMID:23063042

Adaptive Fuzzy Control for Nonstrict Feedback Systems With Unmodeled Dynamics and Fuzzy Dead Zone via Output Feedback.

PubMed

Wang, Lijie; Li, Hongyi; Zhou, Qi; Lu, Renquan

2017-09-01

This paper investigates the problem of observer-based adaptive fuzzy control for a category of nonstrict feedback systems subject to both unmodeled dynamics and fuzzy dead zone. Through constructing a fuzzy state observer and introducing a center of gravity method, unmeasurable states are estimated and the fuzzy dead zone is defuzzified, respectively. By employing fuzzy logic systems to identify the unknown functions. And combining small-gain approach with adaptive backstepping control technique, a novel adaptive fuzzy output feedback control strategy is developed, which ensures that all signals involved are semi-globally uniformly bounded. Simulation results are given to demonstrate the effectiveness of the presented method.

Empirical Estimation of Climate Impacts Under Adaptation

NASA Astrophysics Data System (ADS)

Rising, J. A.; Jina, A.; Hsiang, S. M.

2016-12-01

Estimating the impacts of climate change requires a careful account of both the present levels of adaptation observed in different regions and the adaptive capacity those regions might show under climate change. To date, little empirical evidence on either of these components. We present a general approach for empirically estimating the impacts of climate change under both forms of adaptation, applied to the United States. We draw upon relationships between daily temperatures and impacts on mortality, agriculture, and crime, from the econometric climate impacts literature. These are estimated using year-to-year temperature variation within each location. The degree to which regions are vulnerable to high temperatures varies across the US, with warmer regions generally showing less vulnerability. As climate changes, cooler regions will adopt behaviors from warmer regions, such as greater use of air conditioning, and their impact relationships will change accordingly. The rate at which regions have adapted is estimated from changes in these relationships over recent decades. We use these results to model future changes in each US county. as they are exposed to warmer temperatures and adopt characteristics of currently warmer areas. We do this across a full range of climate and statistical uncertainty. The median degree to which adaptation alleviates impacts varies by sector, with 10% lower rates of temperature-induced crime, 15% lower yield losses to maize, to 80% lower rates of heat-related mortality. However, the uncertainty in adaptive capacity remains greater than these changes. Uncertainty in regional response relationships and the rate of adaptation dominate the uncertainty in our total result. We perform two thought experiments to explore the extreme potential for adaptation in light of this uncertainty. We replace the regional relationships with a uniform approach to complete temperature insensitivity, using the normal estimated rate of adaptation. We also apply instantaneous adaptation in each year to the regional relationship corresponding to each region's new climate. We show that the rate of adaptation is the critical obstacle to further mortality benefits, while the small observed range of regional adaptations is causing the low adaptive benefits in crime.

Demosaicking of noisy Bayer-sampled color images with least-squares luma-chroma demultiplexing and noise level estimation.

PubMed

Jeon, Gwanggil; Dubois, Eric

2013-01-01

This paper adapts the least-squares luma-chroma demultiplexing (LSLCD) demosaicking method to noisy Bayer color filter array (CFA) images. A model is presented for the noise in white-balanced gamma-corrected CFA images. A method to estimate the noise level in each of the red, green, and blue color channels is then developed. Based on the estimated noise parameters, one of a finite set of configurations adapted to a particular level of noise is selected to demosaic the noisy data. The noise-adaptive demosaicking scheme is called LSLCD with noise estimation (LSLCD-NE). Experimental results demonstrate state-of-the-art performance over a wide range of noise levels, with low computational complexity. Many results with several algorithms, noise levels, and images are presented on our companion web site along with software to allow reproduction of our results.

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 performance of the novel algorithms is assessed for the scenario of a pursuit-evasion engagement between a randomly maneuvering ballistic missile and an interceptor. Extensive Monte Carlo simulations are employed to evaluate the main statistical properties of the algorithms. (Abstract shortened by UMI.)

An Adaptive Low-Cost INS/GNSS Tightly-Coupled Integration Architecture Based on Redundant Measurement Noise Covariance Estimation.

PubMed

Li, Zheng; Zhang, Hai; Zhou, Qifan; Che, Huan

2017-09-05

The main objective of the introduced study is to design an adaptive Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) tightly-coupled integration system that can provide more reliable navigation solutions by making full use of an adaptive Kalman filter (AKF) and satellite selection algorithm. To achieve this goal, we develop a novel redundant measurement noise covariance estimation (RMNCE) theorem, which adaptively estimates measurement noise properties by analyzing the difference sequences of system measurements. The proposed RMNCE approach is then applied to design both a modified weighted satellite selection algorithm and a type of adaptive unscented Kalman filter (UKF) to improve the performance of the tightly-coupled integration system. In addition, an adaptive measurement noise covariance expanding algorithm is developed to mitigate outliers when facing heavy multipath and other harsh situations. Both semi-physical simulation and field experiments were conducted to evaluate the performance of the proposed architecture and were compared with state-of-the-art algorithms. The results validate that the RMNCE provides a significant improvement in the measurement noise covariance estimation and the proposed architecture can improve the accuracy and reliability of the INS/GNSS tightly-coupled systems. The proposed architecture can effectively limit positioning errors under conditions of poor GNSS measurement quality and outperforms all the compared schemes.

An Adaptive Low-Cost INS/GNSS Tightly-Coupled Integration Architecture Based on Redundant Measurement Noise Covariance Estimation

PubMed Central

Li, Zheng; Zhang, Hai; Zhou, Qifan; Che, Huan

2017-01-01

The main objective of the introduced study is to design an adaptive Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) tightly-coupled integration system that can provide more reliable navigation solutions by making full use of an adaptive Kalman filter (AKF) and satellite selection algorithm. To achieve this goal, we develop a novel redundant measurement noise covariance estimation (RMNCE) theorem, which adaptively estimates measurement noise properties by analyzing the difference sequences of system measurements. The proposed RMNCE approach is then applied to design both a modified weighted satellite selection algorithm and a type of adaptive unscented Kalman filter (UKF) to improve the performance of the tightly-coupled integration system. In addition, an adaptive measurement noise covariance expanding algorithm is developed to mitigate outliers when facing heavy multipath and other harsh situations. Both semi-physical simulation and field experiments were conducted to evaluate the performance of the proposed architecture and were compared with state-of-the-art algorithms. The results validate that the RMNCE provides a significant improvement in the measurement noise covariance estimation and the proposed architecture can improve the accuracy and reliability of the INS/GNSS tightly-coupled systems. The proposed architecture can effectively limit positioning errors under conditions of poor GNSS measurement quality and outperforms all the compared schemes. PMID:28872629

Quantum systems as embarrassed colleagues: what do tax evasion and state tomography have in common?

NASA Astrophysics Data System (ADS)

Ferrie, Chris; Blume-Kohout, Robin

2011-03-01

Quantum state estimation (a.k.a. ``tomography'') plays a key role in designing quantum information processors. As a problem, it resembles probability estimation - e.g. for classical coins or dice - but with some subtle and important discrepancies. We demonstrate an improved classical analogue that captures many of these differences: the ``noisy coin.'' Observations on noisy coins are unreliable - much like soliciting sensitive information such as ones tax preparation habits. So, like a quantum system, it cannot be sampled directly. Unlike standard coins or dice, whose worst-case estimation risk scales as 1 / N for all states, noisy coins (and quantum states) have a worst-case risk that scales as 1 /√{ N } and is overwhelmingly dominated by nearly-pure states. The resulting optimal estimation strategies for noisy coins are surprising and counterintuitive. We demonstrate some important consequences for quantum state estimation - in particular, that adaptive tomography can recover the 1 / N risk scaling of classical probability estimation.

Sequential reconstruction of driving-forces from nonlinear nonstationary dynamics

NASA Astrophysics Data System (ADS)

Güntürkün, Ulaş

2010-07-01

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

An adaptive transmission protocol for managing dynamic shared states in collaborative surgical simulation.

PubMed

Qin, J; Choi, K S; Ho, Simon S M; Heng, P A

2008-01-01

A force prediction algorithm is proposed to facilitate virtual-reality (VR) based collaborative surgical simulation by reducing the effect of network latencies. State regeneration is used to correct the estimated prediction. This algorithm is incorporated into an adaptive transmission protocol in which auxiliary features such as view synchronization and coupling control are equipped to ensure the system consistency. We implemented this protocol using multi-threaded technique on a cluster-based network architecture.

Genomic estimation of complex traits reveals ancient maize adaptation to temperate North America

USDA-ARS?s Scientific Manuscript database

By 4000 years ago, people had introduced maize to the southwestern United States; full agriculture was established quickly in the lowland deserts but delayed in the temperate highlands for 2000 years. We test if the earliest upland maize was adapted for early flowering, a characteristic of modern te...

Online Denoising Based on the Second-Order Adaptive Statistics Model.

PubMed

Yi, Sheng-Lun; Jin, Xue-Bo; Su, Ting-Li; Tang, Zhen-Yun; Wang, Fa-Fa; Xiang, Na; Kong, Jian-Lei

2017-07-20

Online denoising is motivated by real-time applications in the industrial process, where the data must be utilizable soon after it is collected. Since the noise in practical process is usually colored, it is quite a challenge for denoising techniques. In this paper, a novel online denoising method was proposed to achieve the processing of the practical measurement data with colored noise, and the characteristics of the colored noise were considered in the dynamic model via an adaptive parameter. The proposed method consists of two parts within a closed loop: the first one is to estimate the system state based on the second-order adaptive statistics model and the other is to update the adaptive parameter in the model using the Yule-Walker algorithm. Specifically, the state estimation process was implemented via the Kalman filter in a recursive way, and the online purpose was therefore attained. Experimental data in a reinforced concrete structure test was used to verify the effectiveness of the proposed method. Results show the proposed method not only dealt with the signals with colored noise, but also achieved a tradeoff between efficiency and accuracy.

Output feedback control for a class of nonlinear systems with actuator degradation and sensor noise.

PubMed

Ai, Weiqing; Lu, Zhenli; Li, Bin; Fei, Shumin

2016-11-01

This paper investigates the output feedback control problem of a class of nonlinear systems with sensor noise and actuator degradation. Firstly, by using the descriptor observer approach, the origin system is transformed into a descriptor system. On the basis of the descriptor system, a novel Proportional Derivative (PD) observer is developed to asymptotically estimate sensor noise and system state simultaneously. Then, by designing an adaptive law to estimate the effectiveness of actuator, an adaptive observer-based controller is constructed to ensure that system state can be regulated to the origin asymptotically. Finally, the design scheme is applied to address a flexible joint robot link problem. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Stochastic parameter estimation in nonlinear time-delayed vibratory systems with distributed delay

NASA Astrophysics Data System (ADS)

Torkamani, Shahab; Butcher, Eric A.

2013-07-01

The stochastic estimation of parameters and states in linear and nonlinear time-delayed vibratory systems with distributed delay is explored. The approach consists of first employing a continuous time approximation to approximate the delayed integro-differential system with a large set of ordinary differential equations having stochastic excitations. Then the problem of state and parameter estimation in the resulting stochastic ordinary differential system is represented as an optimal filtering problem using a state augmentation technique. By adapting the extended Kalman-Bucy filter to the augmented filtering problem, the unknown parameters of the time-delayed system are estimated from noise-corrupted, possibly incomplete measurements of the states. Similarly, the upper bound of the distributed delay can also be estimated by the proposed technique. As an illustrative example to a practical problem in vibrations, the parameter, delay upper bound, and state estimation from noise-corrupted measurements in a distributed force model widely used for modeling machine tool vibrations in the turning operation is investigated.

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

PubMed

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

2016-11-01

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

Fully anisotropic goal-oriented mesh adaptation for 3D steady Euler equations

NASA Astrophysics Data System (ADS)

Loseille, A.; Dervieux, A.; Alauzet, F.

2010-04-01

This paper studies the coupling between anisotropic mesh adaptation and goal-oriented error estimate. The former is very well suited to the control of the interpolation error. It is generally interpreted as a local geometric error estimate. On the contrary, the latter is preferred when studying approximation errors for PDEs. It generally involves non local error contributions. Consequently, a full and strong coupling between both is hard to achieve due to this apparent incompatibility. This paper shows how to achieve this coupling in three steps. First, a new a priori error estimate is proved in a formal framework adapted to goal-oriented mesh adaptation for output functionals. This estimate is based on a careful analysis of the contributions of the implicit error and of the interpolation error. Second, the error estimate is applied to the set of steady compressible Euler equations which are solved by a stabilized Galerkin finite element discretization. A goal-oriented error estimation is derived. It involves the interpolation error of the Euler fluxes weighted by the gradient of the adjoint state associated with the observed functional. Third, rewritten in the continuous mesh framework, the previous estimate is minimized on the set of continuous meshes thanks to a calculus of variations. The optimal continuous mesh is then derived analytically. Thus, it can be used as a metric tensor field to drive the mesh adaptation. From a numerical point of view, this method is completely automatic, intrinsically anisotropic, and does not depend on any a priori choice of variables to perform the adaptation. 3D examples of steady flows around supersonic and transsonic jets are presented to validate the current approach and to demonstrate its efficiency.

Observer-Based Adaptive Neural Network Control for Nonlinear Systems in Nonstrict-Feedback Form.

PubMed

Chen, Bing; Zhang, Huaguang; Lin, Chong

2016-01-01

This paper focuses on the problem of adaptive neural network (NN) control for a class of nonlinear nonstrict-feedback systems via output feedback. A novel adaptive NN backstepping output-feedback control approach is first proposed for nonlinear nonstrict-feedback systems. The monotonicity of system bounding functions and the structure character of radial basis function (RBF) NNs are used to overcome the difficulties that arise from nonstrict-feedback structure. A state observer is constructed to estimate the immeasurable state variables. By combining adaptive backstepping technique with approximation capability of radial basis function NNs, an output-feedback adaptive NN controller is designed through backstepping approach. It is shown that the proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. Two examples are used to illustrate the effectiveness of the proposed approach.

Adaptive neural network decentralized backstepping output-feedback control for nonlinear large-scale systems with time delays.

PubMed

Tong, Shao Cheng; Li, Yong Ming; Zhang, Hua-Guang

2011-07-01

In this paper, two adaptive neural network (NN) decentralized output feedback control approaches are proposed for a class of uncertain nonlinear large-scale systems with immeasurable states and unknown time delays. Using NNs to approximate the unknown nonlinear functions, an NN state observer is designed to estimate the immeasurable states. By combining the adaptive backstepping technique with decentralized control design principle, an adaptive NN decentralized output feedback control approach is developed. In order to overcome the problem of "explosion of complexity" inherent in the proposed control approach, the dynamic surface control (DSC) technique is introduced into the first adaptive NN decentralized control scheme, and a simplified adaptive NN decentralized output feedback DSC approach is developed. It is proved that the two proposed control approaches can guarantee that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded, and the observer errors and the tracking errors converge to a small neighborhood of the origin. Simulation results are provided to show the effectiveness of the proposed approaches.

Adaptive twisting sliding mode algorithm for hypersonic reentry vehicle attitude control based on finite-time observer.

PubMed

Guo, Zongyi; Chang, Jing; Guo, Jianguo; Zhou, Jun

2018-06-01

This paper focuses on the adaptive twisting sliding mode control for the Hypersonic Reentry Vehicles (HRVs) attitude tracking issue. The HRV attitude tracking model is transformed into the error dynamics in matched structure, whereas an unmeasurable state is redefined by lumping the existing unmatched disturbance with the angular rate. Hence, an adaptive finite-time observer is used to estimate the unknown state. Then, an adaptive twisting algorithm is proposed for systems subject to disturbances with unknown bounds. The stability of the proposed observer-based adaptive twisting approach is guaranteed, and the case of noisy measurement is analyzed. Also, the developed control law avoids the aggressive chattering phenomenon of the existing adaptive twisting approaches because the adaptive gains decrease close to the disturbance once the trajectories reach the sliding surface. Finally, numerical simulations on the attitude control of the HRV are conducted to verify the effectiveness and benefit of the proposed approach. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images

PubMed Central

Zhao, Haiying; Liu, Yong; Xie, Xiaojia; Liao, Yiyi; Liu, Xixi

2016-01-01

Visual odometry (VO) estimation from blurred image is a challenging problem in practical robot applications, and the blurred images will severely reduce the estimation accuracy of the VO. In this paper, we address the problem of visual odometry estimation from blurred images, and present an adaptive visual odometry estimation framework robust to blurred images. Our approach employs an objective measure of images, named small image gradient distribution (SIGD), to evaluate the blurring degree of the image, then an adaptive blurred image classification algorithm is proposed to recognize the blurred images, finally we propose an anti-blurred key-frame selection algorithm to enable the VO robust to blurred images. We also carried out varied comparable experiments to evaluate the performance of the VO algorithms with our anti-blur framework under varied blurred images, and the experimental results show that our approach can achieve superior performance comparing to the state-of-the-art methods under the condition with blurred images while not increasing too much computation cost to the original VO algorithms. PMID:27399704

Optimizing the learning rate for adaptive estimation of neural encoding models

PubMed Central

2018-01-01

Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains. PMID:29813069

Optimizing the learning rate for adaptive estimation of neural encoding models.

PubMed

Hsieh, Han-Lin; Shanechi, Maryam M

2018-05-01

Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains.

The experimental results of a self tuning adaptive controller using online frequency identification. [for Galileo spacecraft

NASA Technical Reports Server (NTRS)

Chiang, W.-W.; Cannon, R. H., Jr.

1985-01-01

A fourth-order laboratory dynamic system featuring very low structural damping and a noncolocated actuator-sensor pair has been used to test a novel real-time adaptive controller, implemented in a minicomputer, which consists of a state estimator, a set of state feedback gains, and a frequency-locked loop for real-time parameter identification. The adaptation algorithm employed can correct controller error and stabilize the system for more than 50 percent variation in the plant's natural frequency, compared with a 10 percent stability margin in frequency variation for a fixed gain controller having the same performance as the nominal plant condition. The very rapid convergence achievable by this adaptive system is demonstrated experimentally, and proven with simple, root-locus methods.

Channel Simulation in Quantum Metrology

NASA Astrophysics Data System (ADS)

Laurenza, Riccardo; Lupo, Cosmo; Spedalieri, Gaetana; Braunstein, Samuel L.; Pirandola, Stefano

2018-04-01

In this review we discuss how channel simulation can be used to simplify the most general protocols of quantum parameter estimation, where unlimited entanglement and adaptive joint operations may be employed. Whenever the unknown parameter encoded in a quantum channel is completely transferred in an environmental program state simulating the channel, the optimal adaptive estimation cannot beat the standard quantum limit. In this setting, we elucidate the crucial role of quantum teleportation as a primitive operation which allows one to completely reduce adaptive protocols over suitable teleportation-covariant channels and derive matching upper and lower bounds for parameter estimation. For these channels,wemay express the quantum Cramér Rao bound directly in terms of their Choi matrices. Our review considers both discrete- and continuous-variable systems, also presenting some new results for bosonic Gaussian channels using an alternative sub-optimal simulation. It is an open problem to design simulations for quantum channels that achieve the Heisenberg limit.

REVIEW: Internal models in sensorimotor integration: perspectives from adaptive control theory

NASA Astrophysics Data System (ADS)

Tin, Chung; Poon, Chi-Sang

2005-09-01

Internal models and adaptive controls are empirical and mathematical paradigms that have evolved separately to describe learning control processes in brain systems and engineering systems, respectively. This paper presents a comprehensive appraisal of the correlation between these paradigms with a view to forging a unified theoretical framework that may benefit both disciplines. It is suggested that the classic equilibrium-point theory of impedance control of arm movement is analogous to continuous gain-scheduling or high-gain adaptive control within or across movement trials, respectively, and that the recently proposed inverse internal model is akin to adaptive sliding control originally for robotic manipulator applications. Modular internal models' architecture for multiple motor tasks is a form of multi-model adaptive control. Stochastic methods, such as generalized predictive control, reinforcement learning, Bayesian learning and Hebbian feedback covariance learning, are reviewed and their possible relevance to motor control is discussed. Possible applicability of a Luenberger observer and an extended Kalman filter to state estimation problems—such as sensorimotor prediction or the resolution of vestibular sensory ambiguity—is also discussed. The important role played by vestibular system identification in postural control suggests an indirect adaptive control scheme whereby system states or parameters are explicitly estimated prior to the implementation of control. This interdisciplinary framework should facilitate the experimental elucidation of the mechanisms of internal models in sensorimotor systems and the reverse engineering of such neural mechanisms into novel brain-inspired adaptive control paradigms in future.

An Adaptive Orientation Estimation Method for Magnetic and Inertial Sensors in the Presence of Magnetic Disturbances

PubMed Central

Fan, Bingfei; Li, Qingguo; Wang, Chao; Liu, Tao

2017-01-01

Magnetic and inertial sensors have been widely used to estimate the orientation of human segments due to their low cost, compact size and light weight. However, the accuracy of the estimated orientation is easily affected by external factors, especially when the sensor is used in an environment with magnetic disturbances. In this paper, we propose an adaptive method to improve the accuracy of orientation estimations in the presence of magnetic disturbances. The method is based on existing gradient descent algorithms, and it is performed prior to sensor fusion algorithms. The proposed method includes stationary state detection and magnetic disturbance severity determination. The stationary state detection makes this method immune to magnetic disturbances in stationary state, while the magnetic disturbance severity determination helps to determine the credibility of magnetometer data under dynamic conditions, so as to mitigate the negative effect of the magnetic disturbances. The proposed method was validated through experiments performed on a customized three-axis instrumented gimbal with known orientations. The error of the proposed method and the original gradient descent algorithms were calculated and compared. Experimental results demonstrate that in stationary state, the proposed method is completely immune to magnetic disturbances, and in dynamic conditions, the error caused by magnetic disturbance is reduced by 51.2% compared with original MIMU gradient descent algorithm. PMID:28534858

Rapid Computation of Thermodynamic Properties over Multidimensional Nonbonded Parameter Spaces Using Adaptive Multistate Reweighting.

PubMed

Naden, Levi N; Shirts, Michael R

2016-04-12

We show how thermodynamic properties of molecular models can be computed over a large, multidimensional parameter space by combining multistate reweighting analysis with a linear basis function approach. This approach reduces the computational cost to estimate thermodynamic properties from molecular simulations for over 130,000 tested parameter combinations from over 1000 CPU years to tens of CPU days. This speed increase is achieved primarily by computing the potential energy as a linear combination of basis functions, computed from either modified simulation code or as the difference of energy between two reference states, which can be done without any simulation code modification. The thermodynamic properties are then estimated with the Multistate Bennett Acceptance Ratio (MBAR) as a function of multiple model parameters without the need to define a priori how the states are connected by a pathway. Instead, we adaptively sample a set of points in parameter space to create mutual configuration space overlap. The existence of regions of poor configuration space overlap are detected by analyzing the eigenvalues of the sampled states' overlap matrix. The configuration space overlap to sampled states is monitored alongside the mean and maximum uncertainty to determine convergence, as neither the uncertainty or the configuration space overlap alone is a sufficient metric of convergence. This adaptive sampling scheme is demonstrated by estimating with high precision the solvation free energies of charged particles of Lennard-Jones plus Coulomb functional form with charges between -2 and +2 and generally physical values of σij and ϵij in TIP3P water. We also compute entropy, enthalpy, and radial distribution functions of arbitrary unsampled parameter combinations using only the data from these sampled states and use the estimates of free energies over the entire space to examine the deviation of atomistic simulations from the Born approximation to the solvation free energy.

Robust partial integrated guidance and control for missiles via extended state observer.

PubMed

Wang, Qing; Ran, Maopeng; Dong, Chaoyang

2016-11-01

A novel extended state observer (ESO) based control is proposed for a class of nonlinear systems subject to multiple uncertainties, and then applied to partial integrated guidance and control (PIGC) design for a missile. The proposed control strategy incorporates both an ESO and an adaptive sliding mode control law. The multiple uncertainties are treated as an extended state of the plant, and then estimate them using the ESO and compensate for them in the control action, in real time. Based on the output of the ESO, the resulting adaptive sliding mode control law is inherently continuous and differentiable. Strict proof is given to show that the estimation error of the ESO can be arbitrarily small in a finite time. In addition, the adaptive sliding mode control law can achieve finite time convergence to a neighborhood of the origin, and the accurate expression of the convergent region is given. Finally, simulations are conducted on the planar missile-target engagement geometry. The effectiveness of the proposed control strategy in enhanced interception performance and improved robustness against multiple uncertainties are demonstrated. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

LQG control of a deformable mirror adaptive optics system with time-delayed measurements

NASA Astrophysics Data System (ADS)

Anderson, David J.

1991-12-01

This thesis proposes a linear quadratic Gaussian (LQG) control law for a ground-based deformable mirror adaptive optics system. The incoming image wavefront is distorted, primarily in phase, due to the turbulent effects of the earth's atmosphere. The adaptive optics system attempts to compensate for the distortion with a deformable mirror. A Hartman wavefront sensor measures the degree of distortion in the image wavefront. The measurements are input to a Kalman filter which estimates the system states. The state estimates are processed by a linear quadratic regulator which generates the appropriate control voltages to apply to the deformable mirror actuators. The dynamics model for the atmospheric phase distortion consists of 14 Zernike coefficient states; each modeled as a first-order linear time-invariant shaping filter driven by zero-mean white Gaussian noise. The dynamics of the deformable mirror are also model as 14 Zernike coefficients with first-order deterministic dynamics. A significant reduction in total wavefront phase distortion is achieved in the presence of time-delayed measurements. Wavefront sensor sampling rate is the major factor limiting system performance. The Multimode Simulation for Optimal Filter Evaluation (MSOFE) software is the performance evaluation tool of choice for this research.

Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions

PubMed Central

Liu, Weidong; Luo, Xi

2014-01-01

This paper proposes a new method for estimating sparse precision matrices in the high dimensional setting. It has been popular to study fast computation and adaptive procedures for this problem. We propose a novel approach, called Sparse Column-wise Inverse Operator, to address these two issues. We analyze an adaptive procedure based on cross validation, and establish its convergence rate under the Frobenius norm. The convergence rates under other matrix norms are also established. This method also enjoys the advantage of fast computation for large-scale problems, via a coordinate descent algorithm. Numerical merits are illustrated using both simulated and real datasets. In particular, it performs favorably on an HIV brain tissue dataset and an ADHD resting-state fMRI dataset. PMID:25750463

Practical extension of a Lake States tree height model

Treesearch

Don C. Bragg

2008-01-01

By adapting data from national and state champion lists and the predictions of an existing height model, an exponential function was developed to improvetree height estimation. As a case study, comparisons between the original and redesigned model were made with eastern white pine (Pinus strobus L.). Forexample, the heights...

Comparing Anisotropic Output-Based Grid Adaptation Methods by Decomposition

NASA Technical Reports Server (NTRS)

Park, Michael A.; Loseille, Adrien; Krakos, Joshua A.; Michal, Todd

2015-01-01

Anisotropic grid adaptation is examined by decomposing the steps of flow solution, ad- joint solution, error estimation, metric construction, and simplex grid adaptation. Multiple implementations of each of these steps are evaluated by comparison to each other and expected analytic results when available. For example, grids are adapted to analytic metric fields and grid measures are computed to illustrate the properties of multiple independent implementations of grid adaptation mechanics. Different implementations of each step in the adaptation process can be evaluated in a system where the other components of the adaptive cycle are fixed. Detailed examination of these properties allows comparison of different methods to identify the current state of the art and where further development should be targeted.

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

PubMed

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

2015-11-01

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

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.

Tracking objects in Earth orbit is fraught with complications. This is due to the large population of orbiting spacecraft and debris that continues to grow, passive (i.e. no direct communication) and data-sparse observations, and the presence of maneuvers and dynamics mismodeling. Accurate orbit determination in this environment requires an algorithm to capture both a system's state and its state dynamics in order to account for mismodelings. Previous studies by the authors yielded an algorithm called the Optimal Control Based Estimator (OCBE) - an algorithm that simultaneously estimates a system's state and optimal control policies that represent dynamic mismodeling in the system for an arbitrary orbit-observer setup. The stochastic properties of these estimated controls are then used to determine the presence of mismodelings (maneuver detection), as well as characterize and reconstruct the mismodelings. The purpose of this paper is to develop the OCBE into an accurate real-time orbit tracking and maneuver detection algorithm by automating the algorithm and removing its linear assumptions. This results in a nonlinear adaptive estimator. In its original form the OCBE had a parameter called the assumed dynamic uncertainty, which is selected by the user with each new measurement to reflect the level of dynamic mismodeling in the system. This human-in-the-loop approach precludes real-time application to orbit tracking problems due to their complexity. This paper focuses on the Adaptive OCBE, a version of the estimator where the assumed dynamic uncertainty is chosen automatically with each new measurement using maneuver detection results to ensure that state uncertainties are properly adjusted to account for all dynamic mismodelings. The paper also focuses on a nonlinear implementation of the estimator. Originally, the OCBE was derived from a nonlinear cost function then linearized about a nominal trajectory, which is assumed to be ballistic (i.e. the nominal optimal 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.

Image denoising in mixed Poisson-Gaussian noise.

PubMed

Luisier, Florian; Blu, Thierry; Unser, Michael

2011-03-01

We propose a general methodology (PURE-LET) to design and optimize a wide class of transform-domain thresholding algorithms for denoising images corrupted by mixed Poisson-Gaussian noise. We express the denoising process as a linear expansion of thresholds (LET) that we optimize by relying on a purely data-adaptive unbiased estimate of the mean-squared error (MSE), derived in a non-Bayesian framework (PURE: Poisson-Gaussian unbiased risk estimate). We provide a practical approximation of this theoretical MSE estimate for the tractable optimization of arbitrary transform-domain thresholding. We then propose a pointwise estimator for undecimated filterbank transforms, which consists of subband-adaptive thresholding functions with signal-dependent thresholds that are globally optimized in the image domain. We finally demonstrate the potential of the proposed approach through extensive comparisons with state-of-the-art techniques that are specifically tailored to the estimation of Poisson intensities. We also present denoising results obtained on real images of low-count fluorescence microscopy.

Adaptive control of nonlinear uncertain active suspension systems with prescribed performance.

PubMed

Huang, Yingbo; Na, Jing; Wu, Xing; Liu, Xiaoqin; Guo, Yu

2015-01-01

This paper proposes adaptive control designs for vehicle active suspension systems with unknown nonlinear dynamics (e.g., nonlinear spring and piece-wise linear damper dynamics). An adaptive control is first proposed to stabilize the vertical vehicle displacement and thus to improve the ride comfort and to guarantee other suspension requirements (e.g., road holding and suspension space limitation) concerning the vehicle safety and mechanical constraints. An augmented neural network is developed to online compensate for the unknown nonlinearities, and a novel adaptive law is developed to estimate both NN weights and uncertain model parameters (e.g., sprung mass), where the parameter estimation error is used as a leakage term superimposed on the classical adaptations. To further improve the control performance and simplify the parameter tuning, a prescribed performance function (PPF) characterizing the error convergence rate, maximum overshoot and steady-state error is used to propose another adaptive control. The stability for the closed-loop system is proved and particular performance requirements are analyzed. Simulations are included to illustrate the effectiveness of the proposed control schemes. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

White oak growth after 23 Years in a three-site provenance/progeny trial on a latitudinal gradient in Indiana

Treesearch

Yen-Ning Huang; Hao Zhang; Scott Rogers; Mark Coggeshall; Keith Woeste

2015-01-01

To increase the availability of improved, adapted white oak (Quercus alba L.) for midwestern United States landowners, we analyzed data from three 23-year-old provenance/progeny tests of 70 open-pollinated progenies from 17 provenances. Our goal was to estimate the heritability of height growth and range of adaptation and ultimately to determine...

Estimation of parameters and basic reproduction ratio for Japanese encephalitis transmission in the Philippines using sequential Monte Carlo filter

USDA-ARS?s Scientific Manuscript database

We developed a sequential Monte Carlo filter to estimate the states and the parameters in a stochastic model of Japanese Encephalitis (JE) spread in the Philippines. This method is particularly important for its adaptability to the availability of new incidence data. This method can also capture the...

Adaptable state based control system

NASA Technical Reports Server (NTRS)

Rasmussen, Robert D. (Inventor); Dvorak, Daniel L. (Inventor); Gostelow, Kim P. (Inventor); Starbird, Thomas W. (Inventor); Gat, Erann (Inventor); Chien, Steve Ankuo (Inventor); Keller, Robert M. (Inventor)

2004-01-01

An autonomous controller, comprised of a state knowledge manager, a control executor, hardware proxies and a statistical estimator collaborates with a goal elaborator, with which it shares common models of the behavior of the system and the controller. The elaborator uses the common models to generate from temporally indeterminate sets of goals, executable goals to be executed by the controller. The controller may be updated to operate in a different system or environment than that for which it was originally designed by the replacement of shared statistical models and by the instantiation of a new set of state variable objects derived from a state variable class. The adaptation of the controller does not require substantial modification of the goal elaborator for its application to the new system or environment.

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

PubMed

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

2018-04-01

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

Extended state observer based robust adaptive control on SE(3) for coupled spacecraft tracking maneuver with actuator saturation and misalignment

NASA Astrophysics Data System (ADS)

Zhang, Jianqiao; Ye, Dong; Sun, Zhaowei; Liu, Chuang

2018-02-01

This paper presents a robust adaptive controller integrated with an extended state observer (ESO) to solve coupled spacecraft tracking maneuver in the presence of model uncertainties, external disturbances, actuator uncertainties including magnitude deviation and misalignment, and even actuator saturation. More specifically, employing the exponential coordinates on the Lie group SE(3) to describe configuration tracking errors, the coupled six-degrees-of-freedom (6-DOF) dynamics are developed for spacecraft relative motion, in which a generic fully actuated thruster distribution is considered and the lumped disturbances are reconstructed by using anti-windup technique. Then, a novel ESO, developed via second order sliding mode (SOSM) technique and adding linear correction terms to improve the performance, is designed firstly to estimate the disturbances in finite time. Based on the estimated information, an adaptive fast terminal sliding mode (AFTSM) controller is developed to guarantee the almost global asymptotic stability of the resulting closed-loop system such that the trajectory can be tracked with all the aforementioned drawbacks addressed simultaneously. Finally, the effectiveness of the controller is illustrated through numerical examples.

Application of wavelet-based multi-model Kalman filters to real-time flood forecasting

NASA Astrophysics Data System (ADS)

Chou, Chien-Ming; Wang, Ru-Yih

2004-04-01

This paper presents the application of a multimodel method using a wavelet-based Kalman filter (WKF) bank to simultaneously estimate decomposed state variables and unknown parameters for real-time flood forecasting. Applying the Haar wavelet transform alters the state vector and input vector of the state space. In this way, an overall detail plus approximation describes each new state vector and input vector, which allows the WKF to simultaneously estimate and decompose state variables. The wavelet-based multimodel Kalman filter (WMKF) is a multimodel Kalman filter (MKF), in which the Kalman filter has been substituted for a WKF. The WMKF then obtains M estimated state vectors. Next, the M state-estimates, each of which is weighted by its possibility that is also determined on-line, are combined to form an optimal estimate. Validations conducted for the Wu-Tu watershed, a small watershed in Taiwan, have demonstrated that the method is effective because of the decomposition of wavelet transform, the adaptation of the time-varying Kalman filter and the characteristics of the multimodel method. Validation results also reveal that the resulting method enhances the accuracy of the runoff prediction of the rainfall-runoff process in the Wu-Tu watershed.

Reinforcement-learning-based dual-control methodology for complex nonlinear discrete-time systems with application to spark engine EGR operation.

PubMed

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

2008-08-01

A novel reinforcement-learning-based dual-control methodology adaptive neural network (NN) controller is developed to deliver a desired tracking performance for a class of complex feedback nonlinear discrete-time systems, which consists of a second-order nonlinear discrete-time system in nonstrict feedback form and an affine nonlinear discrete-time system, in the presence of bounded and unknown disturbances. For example, the exhaust gas recirculation (EGR) operation of a spark ignition (SI) engine is modeled by using such a complex nonlinear discrete-time system. A dual-controller approach is undertaken where primary adaptive critic NN controller is designed for the nonstrict feedback nonlinear discrete-time system whereas the secondary one for the affine nonlinear discrete-time system but the controllers together offer the desired performance. The primary adaptive critic NN controller includes an NN observer for estimating the states and output, an NN critic, and two action NNs for generating virtual control and actual control inputs for the nonstrict feedback nonlinear discrete-time system, whereas an additional critic NN and an action NN are included for the affine nonlinear discrete-time system by assuming the state availability. All NN weights adapt online towards minimization of a certain performance index, utilizing gradient-descent-based rule. Using Lyapunov theory, the uniformly ultimate boundedness (UUB) of the closed-loop tracking error, weight estimates, and observer estimates are shown. The adaptive critic NN controller performance is evaluated on an SI engine operating with high EGR levels where the controller objective is to reduce cyclic dispersion in heat release while minimizing fuel intake. Simulation and experimental results indicate that engine out emissions drop significantly at 20% EGR due to reduction in dispersion in heat release thus verifying the dual-control approach.

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.

Fuzzy Adaptive Output Feedback Control of Uncertain Nonlinear Systems With Prescribed Performance.

PubMed

Zhang, Jin-Xi; Yang, Guang-Hong

2018-05-01

This paper investigates the tracking control problem for a family of strict-feedback systems in the presence of unknown nonlinearities and immeasurable system states. A low-complexity adaptive fuzzy output feedback control scheme is proposed, based on a backstepping method. In the control design, a fuzzy adaptive state observer is first employed to estimate the unmeasured states. Then, a novel error transformation approach together with a new modification mechanism is introduced to guarantee the finite-time convergence of the output error to a predefined region and ensure the closed-loop stability. Compared with the existing methods, the main advantages of our approach are that: 1) without using extra command filters or auxiliary dynamic surface control techniques, the problem of explosion of complexity can still be addressed and 2) the design procedures are independent of the initial conditions. Finally, two practical examples are performed to further illustrate the above theoretic findings.

Adaptive estimation of a time-varying phase with coherent states: Smoothing can give an unbounded improvement over filtering

NASA Astrophysics Data System (ADS)

Laverick, Kiarn T.; Wiseman, Howard M.; Dinani, Hossein T.; Berry, Dominic W.

2018-04-01

The problem of measuring a time-varying phase, even when the statistics of the variation is known, is considerably harder than that of measuring a constant phase. In particular, the usual bounds on accuracy, such as the 1 /(4 n ¯) standard quantum limit with coherent states, do not apply. Here, by restricting to coherent states, we are able to analytically obtain the achievable accuracy, the equivalent of the standard quantum limit, for a wide class of phase variation. In particular, we consider the case where the phase has Gaussian statistics and a power-law spectrum equal to κp -1/|ω| p for large ω , for some p >1 . For coherent states with mean photon flux N , we give the quantum Cramér-Rao bound on the mean-square phase error as [psin(π /p ) ] -1(4N /κ ) -(p -1 )/p . Next, we consider whether the bound can be achieved by an adaptive homodyne measurement in the limit N /κ ≫1 , which allows the photocurrent to be linearized. Applying the optimal filtering for the resultant linear Gaussian system, we find the same scaling with N , but with a prefactor larger by a factor of p . By contrast, if we employ optimal smoothing we can exactly obtain the quantum Cramér-Rao bound. That is, contrary to previously considered (p =2 ) cases of phase estimation, here the improvement offered by smoothing over filtering is not limited to a factor of 2 but rather can be unbounded by a factor of p . We also study numerically the performance of these estimators for an adaptive measurement in the limit where N /κ is not large and find a more complicated picture.

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.

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

Sequential Monte Carlo filter for state estimation of LiFePO4 batteries based on an online updated model

NASA Astrophysics Data System (ADS)

Li, Jiahao; Klee Barillas, Joaquin; Guenther, Clemens; Danzer, Michael A.

2014-02-01

Battery state monitoring is one of the key techniques in battery management systems e.g. in electric vehicles. An accurate estimation can help to improve the system performance and to prolong the battery remaining useful life. Main challenges for the state estimation for LiFePO4 batteries are the flat characteristic of open-circuit-voltage over battery state of charge (SOC) and the existence of hysteresis phenomena. Classical estimation approaches like Kalman filtering show limitations to handle nonlinear and non-Gaussian error distribution problems. In addition, uncertainties in the battery model parameters must be taken into account to describe the battery degradation. In this paper, a novel model-based method combining a Sequential Monte Carlo filter with adaptive control to determine the cell SOC and its electric impedance is presented. The applicability of this dual estimator is verified using measurement data acquired from a commercial LiFePO4 cell. Due to a better handling of the hysteresis problem, results show the benefits of the proposed method against the estimation with an Extended Kalman filter.

Real-time stylistic prediction for whole-body human motions.

PubMed

Matsubara, Takamitsu; Hyon, Sang-Ho; Morimoto, Jun

2012-01-01

The ability to predict human motion is crucial in several contexts such as human tracking by computer vision and the synthesis of human-like computer graphics. Previous work has focused on off-line processes with well-segmented data; however, many applications such as robotics require real-time control with efficient computation. In this paper, we propose a novel approach called real-time stylistic prediction for whole-body human motions to satisfy these requirements. This approach uses a novel generative model to represent a whole-body human motion including rhythmic motion (e.g., walking) and discrete motion (e.g., jumping). The generative model is composed of a low-dimensional state (phase) dynamics and a two-factor observation model, allowing it to capture the diversity of motion styles in humans. A real-time adaptation algorithm was derived to estimate both state variables and style parameter of the model from non-stationary unlabeled sequential observations. Moreover, with a simple modification, the algorithm allows real-time adaptation even from incomplete (partial) observations. Based on the estimated state and style, a future motion sequence can be accurately predicted. In our implementation, it takes less than 15 ms for both adaptation and prediction at each observation. Our real-time stylistic prediction was evaluated for human walking, running, and jumping behaviors. Copyright © 2011 Elsevier Ltd. All rights reserved.

MULTISCALE ADAPTIVE SMOOTHING MODELS FOR THE HEMODYNAMIC RESPONSE FUNCTION IN FMRI*

PubMed Central

Wang, Jiaping; Zhu, Hongtu; Fan, Jianqing; Giovanello, Kelly; Lin, Weili

2012-01-01

In the event-related functional magnetic resonance imaging (fMRI) data analysis, there is an extensive interest in accurately and robustly estimating the hemodynamic response function (HRF) and its associated statistics (e.g., the magnitude and duration of the activation). Most methods to date are developed in the time domain and they have utilized almost exclusively the temporal information of fMRI data without accounting for the spatial information. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) in the frequency domain by integrating the spatial and temporal information to adaptively and accurately estimate HRFs pertaining to each stimulus sequence across all voxels in a three-dimensional (3D) volume. We use two sets of simulation studies and a real data set to examine the finite sample performance of MASM in estimating HRFs. Our real and simulated data analyses confirm that MASM outperforms several other state-of-art methods, such as the smooth finite impulse response (sFIR) model. PMID:24533041

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

NASA Technical Reports Server (NTRS)

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

1975-01-01

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

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

PubMed

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

2017-07-01

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

Genetic analyses, phenotypic adaptability and stability in sugarcane genotypes for commercial cultivation in Pernambuco.

PubMed

Dutra Filho, J A; Junior, T C; Simões Neto, D E

2015-10-05

In the present study, we assessed the agro-industrial performance of 22 sugarcane genotypes adaptable to edaphoclimatic conditions in production microregions in the State of Pernambuco, Brazil, and we recommended the commercial cultivation of select genotypes. The variables analyzed were as follows: sucrose percentage in cane juice, tonnage of saccharose per hectare (TPH), sugarcane tonnage per hectare (TCH), fiber, solid soluble contents, total recoverable sugar tonnage (ATR), and total recoverable sugar tonnage per hectare (ATR t/ha). A randomized block design with 4 repeats was used. Combined variance of the experiments, genetic parameter estimates, and environment stratification were analyzed. Phenotypic adaptability and stability were analyzed using the Annicchiarico and Wricke methods and analysis of variance. Genetic gain was estimated using the classic index and sum of ranks. Genotype selection was efficient for TPH, TCH, and ATR t/ha. Genotypes presented a great potential for improvement and a similar response pattern in Litoral Norte and Mata Sul microregions for TPH and TCH and Litoral Norte and Litoral Sul microregions for ATR t/ha. Genotypes SP78-4764, RB813804, and SP79-101 showed better productivity and phenotypic adaptability and stability, according to the Wricke and Annicchiarico methods. These genotypes can be recommended for cultivation in the sugarcane belt in the State of Pernambuco.

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.

Estimation of Faults in DC Electrical Power System

NASA Technical Reports Server (NTRS)

Gorinevsky, Dimitry; Boyd, Stephen; Poll, Scott

2009-01-01

This paper demonstrates a novel optimization-based approach to estimating fault states in a DC power system. Potential faults changing the circuit topology are included along with faulty measurements. Our approach can be considered as a relaxation of the mixed estimation problem. We develop a linear model of the circuit and pose a convex problem for estimating the faults and other hidden states. A sparse fault vector solution is computed by using 11 regularization. The solution is computed reliably and efficiently, and gives accurate diagnostics on the faults. We demonstrate a real-time implementation of the approach for an instrumented electrical power system testbed, the ADAPT testbed at NASA ARC. The estimates are computed in milliseconds on a PC. The approach performs well despite unmodeled transients and other modeling uncertainties present in the system.

On-board adaptive model for state of charge estimation of lithium-ion batteries based on Kalman filter with proportional integral-based error adjustment

NASA Astrophysics Data System (ADS)

Wei, Jingwen; Dong, Guangzhong; Chen, Zonghai

2017-10-01

With the rapid development of battery-powered electric vehicles, the lithium-ion battery plays a critical role in the reliability of vehicle system. In order to provide timely management and protection for battery systems, it is necessary to develop a reliable battery model and accurate battery parameters estimation to describe battery dynamic behaviors. Therefore, this paper focuses on an on-board adaptive model for state-of-charge (SOC) estimation of lithium-ion batteries. Firstly, a first-order equivalent circuit battery model is employed to describe battery dynamic characteristics. Then, the recursive least square algorithm and the off-line identification method are used to provide good initial values of model parameters to ensure filter stability and reduce the convergence time. Thirdly, an extended-Kalman-filter (EKF) is applied to on-line estimate battery SOC and model parameters. Considering that the EKF is essentially a first-order Taylor approximation of battery model, which contains inevitable model errors, thus, a proportional integral-based error adjustment technique is employed to improve the performance of EKF method and correct model parameters. Finally, the experimental results on lithium-ion batteries indicate that the proposed EKF with proportional integral-based error adjustment method can provide robust and accurate battery model and on-line parameter estimation.

MTPA control of mechanical sensorless IPMSM based on adaptive nonlinear control.

PubMed

Najjar-Khodabakhsh, Abbas; Soltani, Jafar

2016-03-01

In this paper, an adaptive nonlinear control scheme has been proposed for implementing maximum torque per ampere (MTPA) control strategy corresponding to interior permanent magnet synchronous motor (IPMSM) drive. This control scheme is developed in the rotor d-q axis reference frame using adaptive input-output state feedback linearization (AIOFL) method. The drive system control stability is supported by Lyapunov theory. The motor inductances are online estimated by an estimation law obtained by AIOFL. The estimation errors of these parameters are proved to be asymptotically converged to zero. Based on minimizing the motor current amplitude, the MTPA control strategy is performed by using the nonlinear optimization technique while considering the online reference torque. The motor reference torque is generated by a conventional rotor speed PI controller. By performing MTPA control strategy, the generated online motor d-q reference currents were used in AIOFL controller to obtain the SV-PWM reference voltages and the online estimation of the motor d-q inductances. In addition, the stator resistance is online estimated using a conventional PI controller. Moreover, the rotor position is detected using the online estimation of the stator flux and online estimation of the motor q-axis inductance. Simulation and experimental results obtained prove the effectiveness and the capability of the proposed control method. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Rao-Blackwellization for Adaptive Gaussian Sum Nonlinear Model Propagation

NASA Technical Reports Server (NTRS)

Semper, Sean R.; Crassidis, John L.; George, Jemin; Mukherjee, Siddharth; Singla, Puneet

2015-01-01

When dealing with imperfect data and general models of dynamic systems, the best estimate is always sought in the presence of uncertainty or unknown parameters. In many cases, as the first attempt, the Extended Kalman filter (EKF) provides sufficient solutions to handling issues arising from nonlinear and non-Gaussian estimation problems. But these issues may lead unacceptable performance and even divergence. In order to accurately capture the nonlinearities of most real-world dynamic systems, advanced filtering methods have been created to reduce filter divergence while enhancing performance. Approaches, such as Gaussian sum filtering, grid based Bayesian methods and particle filters are well-known examples of advanced methods used to represent and recursively reproduce an approximation to the state probability density function (pdf). Some of these filtering methods were conceptually developed years before their widespread uses were realized. Advanced nonlinear filtering methods currently benefit from the computing advancements in computational speeds, memory, and parallel processing. Grid based methods, multiple-model approaches and Gaussian sum filtering are numerical solutions that take advantage of different state coordinates or multiple-model methods that reduced the amount of approximations used. Choosing an efficient grid is very difficult for multi-dimensional state spaces, and oftentimes expensive computations must be done at each point. For the original Gaussian sum filter, a weighted sum of Gaussian density functions approximates the pdf but suffers at the update step for the individual component weight selections. In order to improve upon the original Gaussian sum filter, Ref. [2] introduces a weight update approach at the filter propagation stage instead of the measurement update stage. This weight update is performed by minimizing the integral square difference between the true forecast pdf and its Gaussian sum approximation. By adaptively updating each component weight during the nonlinear propagation stage an approximation of the true pdf can be successfully reconstructed. Particle filtering (PF) methods have gained popularity recently for solving nonlinear estimation problems due to their straightforward approach and the processing capabilities mentioned above. The basic concept behind PF is to represent any pdf as a set of random samples. As the number of samples increases, they will theoretically converge to the exact, equivalent representation of the desired pdf. When the estimated qth moment is needed, the samples are used for its construction allowing further analysis of the pdf characteristics. However, filter performance deteriorates as the dimension of the state vector increases. To overcome this problem Ref. [5] applies a marginalization technique for PF methods, decreasing complexity of the system to one linear and another nonlinear state estimation problem. The marginalization theory was originally developed by Rao and Blackwell independently. According to Ref. [6] it improves any given estimator under every convex loss function. The improvement comes from calculating a conditional expected value, often involving integrating out a supportive statistic. In other words, Rao-Blackwellization allows for smaller but separate computations to be carried out while reaching the main objective of the estimator. In the case of improving an estimator's variance, any supporting statistic can be removed and its variance determined. Next, any other information that dependents on the supporting statistic is found along with its respective variance. A new approach is developed here by utilizing the strengths of the adaptive Gaussian sum propagation in Ref. [2] and a marginalization approach used for PF methods found in Ref. [7]. In the following sections a modified filtering approach is presented based on a special state-space model within nonlinear systems to reduce the dimensionality of the optimization problem in Ref. [2]. First, the adaptive Gaussian sum propagation is explained and then the new marginalized adaptive Gaussian sum propagation is derived. Finally, an example simulation is presented.

Use of Frontal Lobe Hemodynamics as Reinforcement Signals to an Adaptive Controller

PubMed Central

DiStasio, Marcello M.; Francis, Joseph T.

2013-01-01

Decision-making ability in the frontal lobe (among other brain structures) relies on the assignment of value to states of the animal and its environment. Then higher valued states can be pursued and lower (or negative) valued states avoided. The same principle forms the basis for computational reinforcement learning controllers, which have been fruitfully applied both as models of value estimation in the brain, and as artificial controllers in their own right. This work shows how state desirability signals decoded from frontal lobe hemodynamics, as measured with near-infrared spectroscopy (NIRS), can be applied as reinforcers to an adaptable artificial learning agent in order to guide its acquisition of skills. A set of experiments carried out on an alert macaque demonstrate that both oxy- and deoxyhemoglobin concentrations in the frontal lobe show differences in response to both primarily and secondarily desirable (versus undesirable) stimuli. This difference allows a NIRS signal classifier to serve successfully as a reinforcer for an adaptive controller performing a virtual tool-retrieval task. The agent's adaptability allows its performance to exceed the limits of the NIRS classifier decoding accuracy. We also show that decoding state desirabilities is more accurate when using relative concentrations of both oxyhemoglobin and deoxyhemoglobin, rather than either species alone. PMID:23894500

Absolute phase estimation: adaptive local denoising and global unwrapping.

PubMed

Bioucas-Dias, Jose; Katkovnik, Vladimir; Astola, Jaakko; Egiazarian, Karen

2008-10-10

The paper attacks absolute phase estimation with a two-step approach: the first step applies an adaptive local denoising scheme to the modulo-2 pi noisy phase; the second step applies a robust phase unwrapping algorithm to the denoised modulo-2 pi phase obtained in the first step. The adaptive local modulo-2 pi phase denoising is a new algorithm based on local polynomial approximations. The zero-order and the first-order approximations of the phase are calculated in sliding windows of varying size. The zero-order approximation is used for pointwise adaptive window size selection, whereas the first-order approximation is used to filter the phase in the obtained windows. For phase unwrapping, we apply the recently introduced robust (in the sense of discontinuity preserving) PUMA unwrapping algorithm [IEEE Trans. Image Process.16, 698 (2007)] to the denoised wrapped phase. Simulations give evidence that the proposed algorithm yields state-of-the-art performance, enabling strong noise attenuation while preserving image details. (c) 2008 Optical Society of America

Stress inoculation training supported by physiology-driven adaptive virtual reality stimulation.

PubMed

Popović, Sinisa; Horvat, Marko; Kukolja, Davor; Dropuljić, Branimir; Cosić, Kresimir

2009-01-01

Significant proportion of psychological problems related to combat stress in recent large peacekeeping operations underscores importance of effective methods for strengthening the stress resistance of military personnel. Adaptive control of virtual reality (VR) stimulation, based on estimation of the subject's emotional state from physiological signals, may enhance existing stress inoculation training (SIT). Physiology-driven adaptive VR stimulation can tailor the progress of stressful stimuli delivery to the physiological characteristics of each individual, which is indicated for improvement in stress resistance. Therefore, following an overview of SIT and its applications in the military setting, generic concept of physiology-driven adaptive VR stimulation is presented in the paper. Toward the end of the paper, closed-loop adaptive control strategy applicable to SIT is outlined.

Monitoring is not enough: on the need for a model-based approach to migratory bird management

USGS Publications Warehouse

Nichols, J.D.; Bonney, Rick; Pashley, David N.; Cooper, Robert; Niles, Larry

2000-01-01

Informed management requires information about system state and about effects of potential management actions on system state. Population monitoring can provide the needed information about system state, as well as information that can be used to investigate effects of management actions. Three methods for investigating effects of management on bird populations are (1) retrospective analysis, (2) formal experimentation and constrained-design studies, and (3) adaptive management. Retrospective analyses provide weak inferences, regardless of the quality of the monitoring data. The active use of monitoring data in experimental or constrained-design studies or in adaptive management is recommended. Under both approaches, learning occurs via the comparison of estimates from the monitoring program with predictions from competing management models.

An adaptive model for vanadium redox flow battery and its application for online peak power estimation

NASA Astrophysics Data System (ADS)

Wei, Zhongbao; Meng, Shujuan; Tseng, King Jet; Lim, Tuti Mariana; Soong, Boon Hee; Skyllas-Kazacos, Maria

2017-03-01

An accurate battery model is the prerequisite for reliable state estimate of vanadium redox battery (VRB). As the battery model parameters are time varying with operating condition variation and battery aging, the common methods where model parameters are empirical or prescribed offline lacks accuracy and robustness. To address this issue, this paper proposes to use an online adaptive battery model to reproduce the VRB dynamics accurately. The model parameters are online identified with both the recursive least squares (RLS) and the extended Kalman filter (EKF). Performance comparison shows that the RLS is superior with respect to the modeling accuracy, convergence property, and computational complexity. Based on the online identified battery model, an adaptive peak power estimator which incorporates the constraints of voltage limit, SOC limit and design limit of current is proposed to fully exploit the potential of the VRB. Experiments are conducted on a lab-scale VRB system and the proposed peak power estimator is verified with a specifically designed "two-step verification" method. It is shown that different constraints dominate the allowable peak power at different stages of cycling. The influence of prediction time horizon selection on the peak power is also analyzed.

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.

Axioms of adaptivity

PubMed Central

Carstensen, C.; Feischl, M.; Page, M.; Praetorius, D.

2014-01-01

This paper aims first at a simultaneous axiomatic presentation of the proof of optimal convergence rates for adaptive finite element methods and second at some refinements of particular questions like the avoidance of (discrete) lower bounds, inexact solvers, inhomogeneous boundary data, or the use of equivalent error estimators. Solely four axioms guarantee the optimality in terms of the error estimators. Compared to the state of the art in the temporary literature, the improvements of this article can be summarized as follows: First, a general framework is presented which covers the existing literature on optimality of adaptive schemes. The abstract analysis covers linear as well as nonlinear problems and is independent of the underlying finite element or boundary element method. Second, efficiency of the error estimator is neither needed to prove convergence nor quasi-optimal convergence behavior of the error estimator. In this paper, efficiency exclusively characterizes the approximation classes involved in terms of the best-approximation error and data resolution and so the upper bound on the optimal marking parameters does not depend on the efficiency constant. Third, some general quasi-Galerkin orthogonality is not only sufficient, but also necessary for the R-linear convergence of the error estimator, which is a fundamental ingredient in the current quasi-optimality analysis due to Stevenson 2007. Finally, the general analysis allows for equivalent error estimators and inexact solvers as well as different non-homogeneous and mixed boundary conditions. PMID:25983390

Considerations about expected a posteriori estimation in adaptive testing: adaptive a priori, adaptive correction for bias, and adaptive integration interval.

PubMed

Raiche, Gilles; Blais, Jean-Guy

2009-01-01

In a computerized adaptive test, we would like to obtain an acceptable precision of the proficiency level estimate using an optimal number of items. Unfortunately, decreasing the number of items is accompanied by a certain degree of bias when the true proficiency level differs significantly from the a priori estimate. The authors suggest that it is possible to reduced the bias, and even the standard error of the estimate, by applying to each provisional estimation one or a combination of the following strategies: adaptive correction for bias proposed by Bock and Mislevy (1982), adaptive a priori estimate, and adaptive integration interval.

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

PubMed

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

2014-06-01

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

Geographic variation in opinions on climate change at state and local scales in the USA

NASA Astrophysics Data System (ADS)

Howe, Peter D.; Mildenberger, Matto; Marlon, Jennifer R.; Leiserowitz, Anthony

2015-06-01

Addressing climate change in the United States requires enactment of national, state and local mitigation and adaptation policies. The success of these initiatives depends on public opinion, policy support and behaviours at appropriate scales. Public opinion, however, is typically measured with national surveys that obscure geographic variability across regions, states and localities. Here we present independently validated high-resolution opinion estimates using a multilevel regression and poststratification model. The model accurately predicts climate change beliefs, risk perceptions and policy preferences at the state, congressional district, metropolitan and county levels, using a concise set of demographic and geographic predictors. The analysis finds substantial variation in public opinion across the nation. Nationally, 63% of Americans believe global warming is happening, but county-level estimates range from 43 to 80%, leading to a diversity of political environments for climate policy. These estimates provide an important new source of information for policymakers, educators and scientists to more effectively address the challenges of climate change.

Achieving quantum precision limit in adaptive qubit state tomography

NASA Astrophysics Data System (ADS)

Hou, Zhibo; Zhu, Huangjun; Xiang, Guo-Yong; Li, Chuan-Feng; Guo, Guang-Can

2016-02-01

The precision limit in quantum state tomography is of great interest not only to practical applications but also to foundational studies. However, little is known about this subject in the multiparameter setting even theoretically due to the subtle information trade-off among incompatible observables. In the case of a qubit, the theoretic precision limit was determined by Hayashi as well as Gill and Massar, but attaining the precision limit in experiments has remained a challenging task. Here we report the first experiment that achieves this precision limit in adaptive quantum state tomography on optical polarisation qubits. The two-step adaptive strategy used in our experiment is very easy to implement in practice. Yet it is surprisingly powerful in optimising most figures of merit of practical interest. Our study may have significant implications for multiparameter quantum estimation problems, such as quantum metrology. Meanwhile, it may promote our understanding about the complementarity principle and uncertainty relations from the information theoretic perspective.

Model Adaptation for Prognostics in a Particle Filtering Framework

NASA Technical Reports Server (NTRS)

Saha, Bhaskar; Goebel, Kai Frank

2011-01-01

One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.

The never ending road: improving, adapting and refining a needs-based model to estimate future general practitioner requirements in two Australian states.

PubMed

Laurence, Caroline O; Heywood, Troy; Bell, Janice; Atkinson, Kaye; Karnon, Jonathan

2018-03-27

Health workforce planning models have been developed to estimate the future health workforce requirements for a population whom they serve and have been used to inform policy decisions. To adapt and further develop a need-based GP workforce simulation model to incorporate current and estimated geographic distribution of patients and GPs. A need-based simulation model that estimates the supply of GPs and levels of services required in South Australia (SA) was adapted and applied to the Western Australian (WA) workforce. The main outcome measure was the differences in the number of full-time equivalent (FTE) GPs supplied and required from 2013 to 2033. The base scenario estimated a shortage of GPs in WA from 2019 onwards with a shortage of 493 FTE GPs in 2033, while for SA, estimates showed an oversupply over the projection period. The WA urban and rural models estimated an urban shortage of GPs over this period. A reduced international medical graduate recruitment scenario resulted in estimated shortfalls of GPs by 2033 for WA and SA. The WA-specific scenarios of lower population projections and registrar work value resulted in a reduced shortage of FTE GPs in 2033, while unfilled training places increased the shortfall of FTE GPs in 2033. The simulation model incorporates contextual differences to its structure that allows within and cross jurisdictional comparisons of workforce estimations. It also provides greater insights into the drivers of supply and demand and the impact of changes in workforce policy, promoting more informed decision-making.

Beaufort scale of wind force as adapted for use on forested areas of the northern Rocky Mountains

Treesearch

George M. Jemison

1934-01-01

The Beaufort scale of wind force, internationally employed by weather agencies, was not designed for use on mountainous and forested areas like those of the Rocky Mountains of northern Idaho and western Montana. The United States Forest Service has used it to estimate wind velocities in this region, but has found that in too many cases the resulting estimates were...

Adaptive approach for on-board impedance parameters and voltage estimation of lithium-ion batteries in electric vehicles

NASA Astrophysics Data System (ADS)

Farmann, Alexander; Waag, Wladislaw; Sauer, Dirk Uwe

2015-12-01

Robust algorithms using reduced order equivalent circuit model (ECM) for an accurate and reliable estimation of battery states in various applications become more popular. In this study, a novel adaptive, self-learning heuristic algorithm for on-board impedance parameters and voltage estimation of lithium-ion batteries (LIBs) in electric vehicles is introduced. The presented approach is verified using LIBs with different composition of chemistries (NMC/C, NMC/LTO, LFP/C) at different aging states. An impedance-based reduced order ECM incorporating ohmic resistance and a combination of a constant phase element and a resistance (so-called ZARC-element) is employed. Existing algorithms in vehicles are much more limited in the complexity of the ECMs. The algorithm is validated using seven day real vehicle data with high temperature variation including very low temperatures (from -20 °C to +30 °C) at different Depth-of-Discharges (DoDs). Two possibilities to approximate both ZARC-elements with finite number of RC-elements on-board are shown and the results of the voltage estimation are compared. Moreover, the current dependence of the charge-transfer resistance is considered by employing Butler-Volmer equation. Achieved results indicate that both models yield almost the same grade of accuracy.

Real-time assessment of mental workload using psychophysiological measures and artificial neural networks.

PubMed

Wilson, Glenn F; Russell, Christopher A

The functional state of the human operator is critical to optimal system performance. Degraded states of operator functioning can lead to errors and overall suboptimal system performance. Accurate assessment of operator functional state is crucial to the successful implementation of an adaptive aiding system. One method of determining operators' functional state is by monitoring their physiology. In the present study, artificial neural networks using physiological signals were used to continuously monitor, in real time, the functional state of 7 participants while they performed the Multi-Attribute Task Battery with two levels of task difficulty. Six channels of brain electrical activity and eye, heart and respiration measures were evaluated on line. The accuracy of the classifier was determined to test its utility as an on-line measure of operator state. The mean classification accuracies were 85%, 82%, and 86% for the baseline, low task difficulty, and high task difficulty conditions, respectively. The high levels of accuracy suggest that these procedures can be used to provide accurate estimates of operator functional state that can be used to provide adaptive aiding. The relative contribution of each of the 43 psychophysiological features was also determined. Actual or potential applications of this research include test and evaluation and adaptive aiding implementation.

Experimental joint quantum measurements with minimum uncertainty.

PubMed

Ringbauer, Martin; Biggerstaff, Devon N; Broome, Matthew A; Fedrizzi, Alessandro; Branciard, Cyril; White, Andrew G

2014-01-17

Quantum physics constrains the accuracy of joint measurements of incompatible observables. Here we test tight measurement-uncertainty relations using single photons. We implement two independent, idealized uncertainty-estimation methods, the three-state method and the weak-measurement method, and adapt them to realistic experimental conditions. Exceptional quantum state fidelities of up to 0.999 98(6) allow us to verge upon the fundamental limits of measurement uncertainty.

Stochastic Adaptive Estimation and Control.

DTIC Science & Technology

1994-10-26

Marcus, "Language Stability and Stabilizability of Discrete Event Dynamical Systems ," SIAM Journal on Control and Optimization, 31, September 1993...in the hierarchical control of flexible manufacturing systems ; in this problem, the model involves a hybrid process in continuous time whose state is...of the average cost control problem for discrete- time Markov processes. Our exposition covers from finite to Borel state and action spaces and

Real-Time Tracking of Selective Auditory Attention From M/EEG: A Bayesian Filtering Approach

PubMed Central

Miran, Sina; Akram, Sahar; Sheikhattar, Alireza; Simon, Jonathan Z.; Zhang, Tao; Babadi, Behtash

2018-01-01

Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, an ability which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). To produce robust results, these procedures require multiple trials for training purposes. Also, their decoding accuracy drops significantly when operating at high temporal resolutions. Thus, they are not well-suited for emerging real-time applications such as smart hearing aid devices or brain-computer interface systems, where training data might be limited and high temporal resolutions are desired. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the attentional state. Our proposed framework consists of three main modules: (1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, (2) Extracting reliable markers of the attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and (3) Devising a near real-time state-space estimator that translates the noisy and variable attention markers to robust and statistically interpretable estimates of the attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, ℓ1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our proposed framework using comprehensive simulations as well as application to experimentally acquired M/EEG data. Our results reveal that the proposed real-time algorithms perform nearly as accurately as the existing state-of-the-art offline techniques, while providing a significant degree of adaptivity, statistical robustness, and computational savings. PMID:29765298

Real-Time Tracking of Selective Auditory Attention From M/EEG: A Bayesian Filtering Approach.

PubMed

Miran, Sina; Akram, Sahar; Sheikhattar, Alireza; Simon, Jonathan Z; Zhang, Tao; Babadi, Behtash

2018-01-01

Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, an ability which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). To produce robust results, these procedures require multiple trials for training purposes. Also, their decoding accuracy drops significantly when operating at high temporal resolutions. Thus, they are not well-suited for emerging real-time applications such as smart hearing aid devices or brain-computer interface systems, where training data might be limited and high temporal resolutions are desired. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the attentional state. Our proposed framework consists of three main modules: (1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, (2) Extracting reliable markers of the attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and (3) Devising a near real-time state-space estimator that translates the noisy and variable attention markers to robust and statistically interpretable estimates of the attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, ℓ 1 -regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our proposed framework using comprehensive simulations as well as application to experimentally acquired M/EEG data. Our results reveal that the proposed real-time algorithms perform nearly as accurately as the existing state-of-the-art offline techniques, while providing a significant degree of adaptivity, statistical robustness, and computational savings.

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 application.

Multi-rate cubature Kalman filter based data fusion method with residual compensation to adapt to sampling rate discrepancy in attitude measurement system.

PubMed

Guo, Xiaoting; Sun, Changku; Wang, Peng

2017-08-01

This paper investigates the multi-rate inertial and vision data fusion problem in nonlinear attitude measurement systems, where the sampling rate of the inertial sensor is much faster than that of the vision sensor. To fully exploit the high frequency inertial data and obtain favorable fusion results, a multi-rate CKF (Cubature Kalman Filter) algorithm with estimated residual compensation is proposed in order to adapt to the problem of sampling rate discrepancy. During inter-sampling of slow observation data, observation noise can be regarded as infinite. The Kalman gain is unknown and approaches zero. The residual is also unknown. Therefore, the filter estimated state cannot be compensated. To obtain compensation at these moments, state error and residual formulas are modified when compared with the observation data available moments. Self-propagation equation of the state error is established to propagate the quantity from the moments with observation to the moments without observation. Besides, a multiplicative adjustment factor is introduced as Kalman gain, which acts on the residual. Then the filter estimated state can be compensated even when there are no visual observation data. The proposed method is tested and verified in a practical setup. Compared with multi-rate CKF without residual compensation and single-rate CKF, a significant improvement is obtained on attitude measurement by using the proposed multi-rate CKF with inter-sampling residual compensation. The experiment results with superior precision and reliability show the effectiveness of the proposed method.

KERNELHR: A program for estimating animal home ranges

USGS Publications Warehouse

Seaman, D.E.; Griffith, B.; Powell, R.A.

1998-01-01

Kernel methods are state of the art for estimating animal home-range area and utilization distribution (UD). The KERNELHR program was developed to provide researchers and managers a tool to implement this extremely flexible set of methods with many variants. KERNELHR runs interactively or from the command line on any personal computer (PC) running DOS. KERNELHR provides output of fixed and adaptive kernel home-range estimates, as well as density values in a format suitable for in-depth statistical and spatial analyses. An additional package of programs creates contour files for plotting in geographic information systems (GIS) and estimates core areas of ranges.

New charging strategy for lithium-ion batteries based on the integration of Taguchi method and state of charge estimation

NASA Astrophysics Data System (ADS)

Vo, Thanh Tu; Chen, Xiaopeng; Shen, Weixiang; Kapoor, Ajay

2015-01-01

In this paper, a new charging strategy of lithium-polymer batteries (LiPBs) has been proposed based on the integration of Taguchi method (TM) and state of charge estimation. The TM is applied to search an optimal charging current pattern. An adaptive switching gain sliding mode observer (ASGSMO) is adopted to estimate the SOC which controls and terminates the charging process. The experimental results demonstrate that the proposed charging strategy can successfully charge the same types of LiPBs with different capacities and cycle life. The proposed charging strategy also provides much shorter charging time, narrower temperature variation and slightly higher energy efficiency than the equivalent constant current constant voltage charging method.

Applied adaptive disturbance rejection using output redefinition on magnetic bearings

NASA Astrophysics Data System (ADS)

Matras, Alex Logan

Recent work has shown Adaptive Disturbance Rejection to be an effective technique for rejecting forces due to imbalance, runout and base motion disturbances on flywheels supported by magnetic bearings over a large span of frequencies. Often the applicability of some of the adaptive methods is limited because they require certain properties (such as almost-strict positive realness) that magnetic bearings do not possess. In this thesis, one method for adaptive disturbance rejection, called Adaptive Feedforward Cancellation (AFC), is modified to allow for a much wider range of frequencies to be rejected. This is accomplished by redefining the output of the original system to be the output from a reduced order state estimator instead. This can give a new system with an infinite gain margin. Additionally, the adaptation laws for the two disturbance rejection gains are slightly modified so that each adapts to a different signal in order to provide the best performance. A detailed model of a magnetic bearing is developed and computer simulations based on that model are performed to give an initial test of the new control law. A state-of-the-art magnetic bearing setup is then developed and used to implement the new control laws and determine their effectiveness. The results are successful and validate the new ideas that are presented.

Self-tuning control of attitude and momentum management for the Space Station

NASA Technical Reports Server (NTRS)

Shieh, L. S.; Sunkel, J. W.; Yuan, Z. Z.; Zhao, X. M.

1992-01-01

This paper presents a hybrid state-space self-tuning design methodology using dual-rate sampling for suboptimal digital adaptive control of attitude and momentum management for the Space Station. This new hybrid adaptive control scheme combines an on-line recursive estimation algorithm for indirectly identifying the parameters of a continuous-time system from the available fast-rate sampled data of the inputs and states and a controller synthesis algorithm for indirectly finding the slow-rate suboptimal digital controller from the designed optimal analog controller. The proposed method enables the development of digitally implementable control algorithms for the robust control of Space Station Freedom with unknown environmental disturbances and slowly time-varying dynamics.

Ensemble Data Assimilation Without Ensembles: Methodology and Application to Ocean Data Assimilation

NASA Technical Reports Server (NTRS)

Keppenne, Christian L.; Rienecker, Michele M.; Kovach, Robin M.; Vernieres, Guillaume

2013-01-01

Two methods to estimate background error covariances for data assimilation are introduced. While both share properties with the ensemble Kalman filter (EnKF), they differ from it in that they do not require the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The first method is referred-to as SAFE (Space Adaptive Forecast error Estimation) because it estimates error covariances from the spatial distribution of model variables within a single state vector. It can thus be thought of as sampling an ensemble in space. The second method, named FAST (Flow Adaptive error Statistics from a Time series), constructs an ensemble sampled from a moving window along a model trajectory. The underlying assumption in these methods is that forecast errors in data assimilation are primarily phase errors in space and/or time.

Toward Automatic Verification of Goal-Oriented Flow Simulations

NASA Technical Reports Server (NTRS)

Nemec, Marian; Aftosmis, Michael J.

2014-01-01

We demonstrate the power of adaptive mesh refinement with adjoint-based error estimates in verification of simulations governed by the steady Euler equations. The flow equations are discretized using a finite volume scheme on a Cartesian mesh with cut cells at the wall boundaries. The discretization error in selected simulation outputs is estimated using the method of adjoint-weighted residuals. Practical aspects of the implementation are emphasized, particularly in the formulation of the refinement criterion and the mesh adaptation strategy. Following a thorough code verification example, we demonstrate simulation verification of two- and three-dimensional problems. These involve an airfoil performance database, a pressure signature of a body in supersonic flow and a launch abort with strong jet interactions. The results show reliable estimates and automatic control of discretization error in all simulations at an affordable computational cost. Moreover, the approach remains effective even when theoretical assumptions, e.g., steady-state and solution smoothness, are relaxed.

Physiology-driven adaptive virtual reality stimulation for prevention and treatment of stress related disorders.

PubMed

Cosić, Kresimir; Popović, Sinisa; Kukolja, Davor; Horvat, Marko; Dropuljić, Branimir

2010-02-01

The significant proportion of severe psychological problems related to intensive stress in recent large peacekeeping operations underscores the importance of effective methods for strengthening the prevention and treatment of stress-related disorders. Adaptive control of virtual reality (VR) stimulation presented in this work, based on estimation of the person's emotional state from physiological signals, may enhance existing stress inoculation training (SIT). Physiology-driven adaptive VR stimulation can tailor the progress of stressful stimuli delivery to the physiological characteristics of each individual, which is indicated for improvement in stress resistance. Following an overview of physiology-driven adaptive VR stimulation, its major functional subsystems are described in more detail. A specific algorithm of stimuli delivery applicable to SIT is outlined.

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

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos

2016-12-01

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

Adaptive hybrid optimal quantum control for imprecisely characterized systems.

PubMed

Egger, D J; Wilhelm, F K

2014-06-20

Optimal quantum control theory carries a huge promise for quantum technology. Its experimental application, however, is often hindered by imprecise knowledge of the input variables, the quantum system's parameters. We show how to overcome this by adaptive hybrid optimal control, using a protocol named Ad-HOC. This protocol combines open- and closed-loop optimal control by first performing a gradient search towards a near-optimal control pulse and then an experimental fidelity estimation with a gradient-free method. For typical settings in solid-state quantum information processing, adaptive hybrid optimal control enhances gate fidelities by an order of magnitude, making optimal control theory applicable and useful.

Capacity Improvement of a Free Space Optical Satellite Uplink using Transmitter Power and Rate Adaptation

NASA Astrophysics Data System (ADS)

Gopal, Pooja; Jain, V. K.; Kar, Subrat

2017-12-01

Degradation due to atmospheric turbulence leads to significant outage in a free space optical satellite uplink with fixed transmitter parameters. If the channel state is known at the transmitter, then its parameters can be suitably changed, and there could be a considerable improvement in channel capacity. However, the extremely long link length of an Earth-to-Geostationary Earth Orbit (GEO) satellite link would render feedback of channel state from the receiver infeasible, before the channel changes. In this paper, a channel pre-estimation method at the transmitter is proposed, and the expression for capacity with transmitter power and rate adaptation is derived. The results are compared with that of the capacity with outage. It is observed that there can be an improvement by a factor of 1.66 in achievable average capacity per Hertz with the adaptive transmitter. Also, the outage probability is reduced from 18.02 % to almost 0.

A boundedness result for the direct heuristic dynamic programming.

PubMed

Liu, Feng; Sun, Jian; Si, Jennie; Guo, Wentao; Mei, Shengwei

2012-08-01

Approximate/adaptive dynamic programming (ADP) has been studied extensively in recent years for its potential scalability to solve large state and control space problems, including those involving continuous states and continuous controls. The applicability of ADP algorithms, especially the adaptive critic designs has been demonstrated in several case studies. Direct heuristic dynamic programming (direct HDP) is one of the ADP algorithms inspired by the adaptive critic designs. It has been shown applicable to industrial scale, realistic and complex control problems. In this paper, we provide a uniformly ultimately boundedness (UUB) result for the direct HDP learning controller under mild and intuitive conditions. By using a Lyapunov approach we show that the estimation errors of the learning parameters or the weights in the action and critic networks remain UUB. This result provides a useful controller convergence guarantee for the first time for the direct HDP design. Copyright © 2012 Elsevier Ltd. All rights reserved.

State estimation for distributed systems with sensing delay

NASA Astrophysics Data System (ADS)

Alexander, Harold L.

1991-08-01

Control of complex systems such as remote robotic vehicles requires combining data from many sensors where the data may often be delayed by sensory processing requirements. The number and variety of sensors make it desirable to distribute the computational burden of sensing and estimation among multiple processors. Classic Kalman filters do not lend themselves to distributed implementations or delayed measurement data. The alternative Kalman filter designs presented in this paper are adapted for delays in sensor data generation and for distribution of computation for sensing and estimation over a set of networked processors.

Adaptive Video Streaming Using Bandwidth Estimation for 3.5G Mobile Network

NASA Astrophysics Data System (ADS)

Nam, Hyeong-Min; Park, Chun-Su; Jung, Seung-Won; Ko, Sung-Jea

Currently deployed mobile networks including High Speed Downlink Packet Access (HSDPA) offer only best-effort Quality of Service (QoS). In wireless best effort networks, the bandwidth variation is a critical problem, especially, for mobile devices with small buffers. This is because the bandwidth variation leads to packet losses caused by buffer overflow as well as picture freezing due to high transmission delay or buffer underflow. In this paper, in order to provide seamless video streaming over HSDPA, we propose an efficient real-time video streaming method that consists of the available bandwidth (AB) estimation for the HSDPA network and the transmission rate control to prevent buffer overflows/underflows. In the proposed method, the client estimates the AB and the estimated AB is fed back to the server through real-time transport control protocol (RTCP) packets. Then, the server adaptively adjusts the transmission rate according to the estimated AB and the buffer state obtained from the RTCP feedback information. Experimental results show that the proposed method achieves seamless video streaming over the HSDPA network providing higher video quality and lower transmission delay.

Codon Usage Selection Can Bias Estimation of the Fraction of Adaptive Amino Acid Fixations.

PubMed

Matsumoto, Tomotaka; John, Anoop; Baeza-Centurion, Pablo; Li, Boyang; Akashi, Hiroshi

2016-06-01

A growing number of molecular evolutionary studies are estimating the proportion of adaptive amino acid substitutions (α) from comparisons of ratios of polymorphic and fixed DNA mutations. Here, we examine how violations of two of the model assumptions, neutral evolution of synonymous mutations and stationary base composition, affect α estimation. We simulated the evolution of coding sequences assuming weak selection on synonymous codon usage bias and neutral protein evolution, α = 0. We show that weak selection on synonymous mutations can give polymorphism/divergence ratios that yield α-hat (estimated α) considerably larger than its true value. Nonstationary evolution (changes in population size, selection, or mutation) can exacerbate such biases or, in some scenarios, give biases in the opposite direction, α-hat < α. These results demonstrate that two factors that appear to be prevalent among taxa, weak selection on synonymous mutations and non-steady-state nucleotide composition, should be considered when estimating α. Estimates of the proportion of adaptive amino acid fixations from large-scale analyses of Drosophila melanogaster polymorphism and divergence data are positively correlated with codon usage bias. Such patterns are consistent with α-hat inflation from weak selection on synonymous mutations and/or mutational changes within the examined gene trees. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Variational optical flow estimation based on stick tensor voting.

PubMed

Rashwan, Hatem A; Garcia, Miguel A; Puig, Domenec

2013-07-01

Variational optical flow techniques allow the estimation of flow fields from spatio-temporal derivatives. They are based on minimizing a functional that contains a data term and a regularization term. Recently, numerous approaches have been presented for improving the accuracy of the estimated flow fields. Among them, tensor voting has been shown to be particularly effective in the preservation of flow discontinuities. This paper presents an adaptation of the data term by using anisotropic stick tensor voting in order to gain robustness against noise and outliers with significantly lower computational cost than (full) tensor voting. In addition, an anisotropic complementary smoothness term depending on directional information estimated through stick tensor voting is utilized in order to preserve discontinuity capabilities of the estimated flow fields. Finally, a weighted non-local term that depends on both the estimated directional information and the occlusion state of pixels is integrated during the optimization process in order to denoise the final flow field. The proposed approach yields state-of-the-art results on the Middlebury benchmark.

Flatness-based adaptive fuzzy control of chaotic finance dynamics

NASA Astrophysics Data System (ADS)

Rigatos, G.; Siano, P.; Loia, V.; Tommasetti, A.; Troisi, O.

2017-11-01

A flatness-based adaptive fuzzy control is applied to the problem of stabilization of the dynamics of a chaotic finance system, describing interaction between the interest rate, the investment demand and the price exponent. By proving that the system is differentially flat and by applying differential flatness diffeomorphisms, its transformation to the linear canonical (Brunovsky) is performed. For the latter description of the system, the design of a stabilizing state feedback controller becomes possible. A first problem in the design of such a controller is that the dynamic model of the finance system is unknown and thus it has to be identified with the use neurofuzzy approximators. The estimated dynamics provided by the approximators is used in the computation of the control input, thus establishing an indirect adaptive control scheme. The learning rate of the approximators is chosen from the requirement the system's Lyapunov function to have always a negative first-order derivative. Another problem that has to be dealt with is that the control loop is implemented only with the use of output feedback. To estimate the non-measurable state vector elements of the finance system, a state observer is implemented in the control loop. The computation of the feedback control signal requires the solution of two algebraic Riccati equations at each iteration of the control algorithm. Lyapunov stability analysis demonstrates first that an H-infinity tracking performance criterion is satisfied. This signifies elevated robustness against modelling errors and external perturbations. Moreover, the global asymptotic stability is proven for the control loop.

Energy-Efficient Transmissions for Remote Wireless Sensor Networks: An Integrated HAP/Satellite Architecture for Emergency Scenarios

PubMed Central

Dong, Feihong; Li, Hongjun; Gong, Xiangwu; Liu, Quan; Wang, Jingchao

2015-01-01

A typical application scenario of remote wireless sensor networks (WSNs) is identified as an emergency scenario. One of the greatest design challenges for communications in emergency scenarios is energy-efficient transmission, due to scarce electrical energy in large-scale natural and man-made disasters. Integrated high altitude platform (HAP)/satellite networks are expected to optimally meet emergency communication requirements. In this paper, a novel integrated HAP/satellite (IHS) architecture is proposed, and three segments of the architecture are investigated in detail. The concept of link-state advertisement (LSA) is designed in a slow flat Rician fading channel. The LSA is received and processed by the terminal to estimate the link state information, which can significantly reduce the energy consumption at the terminal end. Furthermore, the transmission power requirements of the HAPs and terminals are derived using the gradient descent and differential equation methods. The energy consumption is modeled at both the source and system level. An innovative and adaptive algorithm is given for the energy-efficient path selection. The simulation results validate the effectiveness of the proposed adaptive algorithm. It is shown that the proposed adaptive algorithm can significantly improve energy efficiency when combined with the LSA and the energy consumption estimation. PMID:26404292

Energy-Efficient Transmissions for Remote Wireless Sensor Networks: An Integrated HAP/Satellite Architecture for Emergency Scenarios.

PubMed

Dong, Feihong; Li, Hongjun; Gong, Xiangwu; Liu, Quan; Wang, Jingchao

2015-09-03

A typical application scenario of remote wireless sensor networks (WSNs) is identified as an emergency scenario. One of the greatest design challenges for communications in emergency scenarios is energy-efficient transmission, due to scarce electrical energy in large-scale natural and man-made disasters. Integrated high altitude platform (HAP)/satellite networks are expected to optimally meet emergency communication requirements. In this paper, a novel integrated HAP/satellite (IHS) architecture is proposed, and three segments of the architecture are investigated in detail. The concept of link-state advertisement (LSA) is designed in a slow flat Rician fading channel. The LSA is received and processed by the terminal to estimate the link state information, which can significantly reduce the energy consumption at the terminal end. Furthermore, the transmission power requirements of the HAPs and terminals are derived using the gradient descent and differential equation methods. The energy consumption is modeled at both the source and system level. An innovative and adaptive algorithm is given for the energy-efficient path selection. The simulation results validate the effectiveness of the proposed adaptive algorithm. It is shown that the proposed adaptive algorithm can significantly improve energy efficiency when combined with the LSA and the energy consumption estimation.

Accelerating the Convergence of Replica Exchange Simulations Using Gibbs Sampling and Adaptive Temperature Sets

DOE PAGES

Vogel, Thomas; Perez, Danny

2015-08-28

We recently introduced a novel replica-exchange scheme in which an individual replica can sample from states encountered by other replicas at any previous time by way of a global configuration database, enabling the fast propagation of relevant states through the whole ensemble of replicas. This mechanism depends on the knowledge of global thermodynamic functions which are measured during the simulation and not coupled to the heat bath temperatures driving the individual simulations. Therefore, this setup also allows for a continuous adaptation of the temperature set. In this paper, we will review the new scheme and demonstrate its capability. Furthermore, themore » method is particularly useful for the fast and reliable estimation of the microcanonical temperature T(U) or, equivalently, of the density of states g(U) over a wide range of energies.« less

Multi-category micro-milling tool wear monitoring with continuous hidden Markov models

NASA Astrophysics Data System (ADS)

Zhu, Kunpeng; Wong, Yoke San; Hong, Geok Soon

2009-02-01

In-process monitoring of tool conditions is important in micro-machining due to the high precision requirement and high tool wear rate. Tool condition monitoring in micro-machining poses new challenges compared to conventional machining. In this paper, a multi-category classification approach is proposed for tool flank wear state identification in micro-milling. Continuous Hidden Markov models (HMMs) are adapted for modeling of the tool wear process in micro-milling, and estimation of the tool wear state given the cutting force features. For a noise-robust approach, the HMM outputs are connected via a medium filter to minimize the tool state before entry into the next state due to high noise level. A detailed study on the selection of HMM structures for tool condition monitoring (TCM) is presented. Case studies on the tool state estimation in the micro-milling of pure copper and steel demonstrate the effectiveness and potential of these methods.

Spectrally-Temporally Adapted Spectrally Modulated Spectrally Encoded (SMSE) Waveform Design for Coexistent CR-Based SDR Applications

DTIC Science & Technology

2010-03-01

uses all available resources in some optimized manner. By further exploiting the design flexibility and computational efficiency of Orthogonal Frequency...in the following sections. 3.2.1 Estimation of PU Signal Statistics. The Estimate PU Signal Statis- tics function of Fig 3.4 is used to compute the...consecutive PU transmissions, and 4) the probability of transitioning from one transmission state to another. These statistics are then used to compute the

Theoretical Discussion of Electron Transport Rate Constant at TCNQ / Ge and TiO2 System

NASA Astrophysics Data System (ADS)

Al-agealy, Hadi J. M.; Alshafaay, B.; Hassooni, Mohsin A.; Ashwiekh, Ahmed M.; Sadoon, Abbas K.; Majeed, Raad H.; Ghadhban, Rawnaq Q.; Mahdi, Shatha H.

2018-05-01

We have been studying and estimation the electronic transport constant at TCNQ / Ge and Tio2 interface by means of tunneling potential (TP), transport energy reorientation (TER), driving transition energy DTE and coupling coefficient constant. A simple quantum model for the transition processes was adapted to estimation and analysis depending on the quantum state for donor state |α D > and acceptor stated |α A > and assuming continuum levels of the system. Evaluation results were performed for the surfaces of Ge and Tio2 as best as for multilayer TCNQ. The results show an electronic transfer feature for electronic TCNQ density of states and a semiconductor behavior. The electronic rate constant result for both systems shows a good tool to election system in applied devices. All these results indicate the

A method to estimate the population level of Aceria litchii (Prostigmata: Eriophyidae) and a study of the population dynamics of this species and its predators on litchi trees in southern Brazil.

PubMed

De Azevedo, Letícia Henrique; Maeda, Enzo Yuji; Inomoto, Mário Massayuki; De Moraes, Gilberto José

2014-02-01

Litchi (Litchi chinensis Sonnerat) is native to Southeast Asia, where most of the world cultivation of this crop is done. Its commercial cultivation in Brazil is recent and concentrated in the state of São Paulo. This crop has been severely damaged in Asia and Brazil by the litchi erineum mite, Aceria litchii (Keifer) (Eriophyidae). The objectives of this study were the adaptation of a method to estimate the density of A. litchii, an evaluation of the population dynamics of this pest and of its associated predators in the state of São Paulo, and an estimation of its injury levels to litchi trees. To estimate the density of A. litchii, an adaptation of a method commonly used to evaluate nematode densities in plant roots was performed. This method was shown to be adequate for the estimation of the number of A. litchii, and it might also be useful for similar evaluations of other erineum forming mites. Field samples to determine the pest population dynamics were collected monthly from August 2011 to July 2012. Sampled leaves were examined under a stereomicroscope for removal of predators and subsequent extraction ofA. litchii by the adapted method. A. litchii reached maximum densities in November 2011 and June 2012, being found at low densities between January and March 2012. The pattern of variation of A. litchii injury levels was similar to that of the density of A. litchii. The main predatory mite co-occurring with A. litchii was the phytoseiid Phytoseius intermedius Evans and McFarlane. However, high injury levels due toA. litchii suggest that the predator was unable to prevent visible damages to the trees, indicating that control activities should be adopted by growers.

Adaptive multitaper time-frequency spectrum estimation

NASA Astrophysics Data System (ADS)

Pitton, James W.

1999-11-01

In earlier work, Thomson's adaptive multitaper spectrum estimation method was extended to the nonstationary case. This paper reviews the time-frequency multitaper method and the adaptive procedure, and explores some properties of the eigenvalues and eigenvectors. The variance of the adaptive estimator is used to construct an adaptive smoother, which is used to form a high resolution estimate. An F-test for detecting and removing sinusoidal components in the time-frequency spectrum is also given.

Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics.

PubMed

Wang, Huanqing; Liu, Peter Xiaoping; Li, Shuai; Wang, Ding

2017-08-29

This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.

United States Air Force Graduate Student Research Program. Program Technical rept. Volume 2

DTIC Science & Technology

1988-12-01

Applications William Geisler xxiii 13 Stability of Jets Under the Supercritical David Graham State 14 In-Plane Fracture in 2-D Carbon-Carbon Gary Griesheim...Dr. Susan Collins *** Engineering and Services Center 19 Investigation of Sorption Kinetics Mark Brusseau 20 Estimation of Jet Fuel Contamination in...Development 47 A Study of Sky Backgrounds and Sub-Visual Eric Schmidt Cirrus 48 Adaptive Array Architectures with Low- Tien Tran Sensitivity to

Adaptive estimation of a time-varying phase with a power-law spectrum via continuous squeezed states

NASA Astrophysics Data System (ADS)

Dinani, Hossein T.; Berry, Dominic W.

2017-06-01

When measuring a time-varying phase, the standard quantum limit and Heisenberg limit as usually defined, for a constant phase, do not apply. If the phase has Gaussian statistics and a power-law spectrum 1 /|ω| p with p >1 , then the generalized standard quantum limit and Heisenberg limit have recently been found to have scalings of 1 /N(p -1 )/p and 1 /N2 (p -1 )/(p +1 ) , respectively, where N is the mean photon flux. We show that this Heisenberg scaling can be achieved via adaptive measurements on squeezed states. We predict the experimental parameters analytically, and test them with numerical simulations. Previous work had considered the special case of p =2 .

Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks.

PubMed

Wang, Leimin; Shen, Yi; Zhang, Guodong

Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.

Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm

NASA Astrophysics Data System (ADS)

Song, Huijie; Dong, Shaowu; Wu, Wenjun; Jiang, Meng; Wang, Weixiong

2018-06-01

The abnormal frequencies of an atomic clock mainly include frequency jump and frequency drift jump. Atomic clock frequency anomaly detection is a key technique in time-keeping. The Kalman filter algorithm, as a linear optimal algorithm, has been widely used in real-time detection for abnormal frequency. In order to obtain an optimal state estimation, the observation model and dynamic model of the Kalman filter algorithm should satisfy Gaussian white noise conditions. The detection performance is degraded if anomalies affect the observation model or dynamic model. The idea of the adaptive Kalman filter algorithm, applied to clock frequency anomaly detection, uses the residuals given by the prediction for building ‘an adaptive factor’ the prediction state covariance matrix is real-time corrected by the adaptive factor. The results show that the model error is reduced and the detection performance is improved. The effectiveness of the algorithm is verified by the frequency jump simulation, the frequency drift jump simulation and the measured data of the atomic clock by using the chi-square test.

Climate change damages to Alaska public infrastructure and the economics of proactive adaptation

PubMed Central

Melvin, April M.; Larsen, Peter; Boehlert, Brent; Neumann, James E.; Chinowsky, Paul; Espinet, Xavier; Martinich, Jeremy; Baumann, Matthew S.; Rennels, Lisa; Bothner, Alexandra; Nicolsky, Dmitry J.; Marchenko, Sergey S.

2017-01-01

Climate change in the circumpolar region is causing dramatic environmental change that is increasing the vulnerability of infrastructure. We quantified the economic impacts of climate change on Alaska public infrastructure under relatively high and low climate forcing scenarios [representative concentration pathway 8.5 (RCP8.5) and RCP4.5] using an infrastructure model modified to account for unique climate impacts at northern latitudes, including near-surface permafrost thaw. Additionally, we evaluated how proactive adaptation influenced economic impacts on select infrastructure types and developed first-order estimates of potential land losses associated with coastal erosion and lengthening of the coastal ice-free season for 12 communities. Cumulative estimated expenses from climate-related damage to infrastructure without adaptation measures (hereafter damages) from 2015 to 2099 totaled $5.5 billion (2015 dollars, 3% discount) for RCP8.5 and $4.2 billion for RCP4.5, suggesting that reducing greenhouse gas emissions could lessen damages by $1.3 billion this century. The distribution of damages varied across the state, with the largest damages projected for the interior and southcentral Alaska. The largest source of damages was road flooding caused by increased precipitation followed by damages to buildings associated with near-surface permafrost thaw. Smaller damages were observed for airports, railroads, and pipelines. Proactive adaptation reduced total projected cumulative expenditures to $2.9 billion for RCP8.5 and $2.3 billion for RCP4.5. For road flooding, adaptation provided an annual savings of 80–100% across four study eras. For nearly all infrastructure types and time periods evaluated, damages and adaptation costs were larger for RCP8.5 than RCP4.5. Estimated coastal erosion losses were also larger for RCP8.5. PMID:28028223

Climate change damages to Alaska public infrastructure and the economics of proactive adaptation.

PubMed

Melvin, April M; Larsen, Peter; Boehlert, Brent; Neumann, James E; Chinowsky, Paul; Espinet, Xavier; Martinich, Jeremy; Baumann, Matthew S; Rennels, Lisa; Bothner, Alexandra; Nicolsky, Dmitry J; Marchenko, Sergey S

2017-01-10

Climate change in the circumpolar region is causing dramatic environmental change that is increasing the vulnerability of infrastructure. We quantified the economic impacts of climate change on Alaska public infrastructure under relatively high and low climate forcing scenarios [representative concentration pathway 8.5 (RCP8.5) and RCP4.5] using an infrastructure model modified to account for unique climate impacts at northern latitudes, including near-surface permafrost thaw. Additionally, we evaluated how proactive adaptation influenced economic impacts on select infrastructure types and developed first-order estimates of potential land losses associated with coastal erosion and lengthening of the coastal ice-free season for 12 communities. Cumulative estimated expenses from climate-related damage to infrastructure without adaptation measures (hereafter damages) from 2015 to 2099 totaled $5.5 billion (2015 dollars, 3% discount) for RCP8.5 and $4.2 billion for RCP4.5, suggesting that reducing greenhouse gas emissions could lessen damages by $1.3 billion this century. The distribution of damages varied across the state, with the largest damages projected for the interior and southcentral Alaska. The largest source of damages was road flooding caused by increased precipitation followed by damages to buildings associated with near-surface permafrost thaw. Smaller damages were observed for airports, railroads, and pipelines. Proactive adaptation reduced total projected cumulative expenditures to $2.9 billion for RCP8.5 and $2.3 billion for RCP4.5. For road flooding, adaptation provided an annual savings of 80-100% across four study eras. For nearly all infrastructure types and time periods evaluated, damages and adaptation costs were larger for RCP8.5 than RCP4.5. Estimated coastal erosion losses were also larger for RCP8.5.

A Self-Organizing State-Space-Model Approach for Parameter Estimation in Hodgkin-Huxley-Type Models of Single Neurons

PubMed Central

Vavoulis, Dimitrios V.; Straub, Volko A.; Aston, John A. D.; Feng, Jianfeng

2012-01-01

Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm), often in combination with a local search method (such as gradient descent) in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a potentially useful tool in the construction of biophysical neuron models. PMID:22396632

Genomic estimation of complex traits reveals ancient maize adaptation to temperate North America.

PubMed

Swarts, Kelly; Gutaker, Rafal M; Benz, Bruce; Blake, Michael; Bukowski, Robert; Holland, James; Kruse-Peeples, Melissa; Lepak, Nicholas; Prim, Lynda; Romay, M Cinta; Ross-Ibarra, Jeffrey; Sanchez-Gonzalez, Jose de Jesus; Schmidt, Chris; Schuenemann, Verena J; Krause, Johannes; Matson, R G; Weigel, Detlef; Buckler, Edward S; Burbano, Hernán A

2017-08-04

By 4000 years ago, people had introduced maize to the southwestern United States; full agriculture was established quickly in the lowland deserts but delayed in the temperate highlands for 2000 years. We test if the earliest upland maize was adapted for early flowering, a characteristic of modern temperate maize. We sequenced fifteen 1900-year-old maize cobs from Turkey Pen Shelter in the temperate Southwest. Indirectly validated genomic models predicted that Turkey Pen maize was marginally adapted with respect to flowering, as well as short, tillering, and segregating for yellow kernel color. Temperate adaptation drove modern population differentiation and was selected in situ from ancient standing variation. Validated prediction of polygenic traits improves our understanding of ancient phenotypes and the dynamics of environmental adaptation. Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Attractive manifold-based adaptive solar attitude control of satellites in elliptic orbits

NASA Astrophysics Data System (ADS)

Lee, Keum W.; Singh, Sahjendra N.

2011-01-01

The paper presents a novel noncertainty-equivalent adaptive (NCEA) control system for the pitch attitude control of satellites in elliptic orbits using solar radiation pressure (SRP). The satellite is equipped with two identical solar flaps to produce control moments. The adaptive law is based on the attractive manifold design using filtered signals for synthesis, which is a modification of the immersion and invariance (I&I) method. The control system has a modular controller-estimator structure and has separate tunable gains. A special feature of this NCEA law is that the trajectories of the satellite converge to a manifold in an extended state space, and the adaptive law recovers the performance of a deterministic controller. This recovery of performance cannot be obtained with certainty-equivalent adaptive (CEA) laws. Simulation results are presented which show that the NCEA law accomplishes precise attitude control of the satellite in an elliptic orbit, despite large parameter uncertainties.

Probability based remaining capacity estimation using data-driven and neural network model

NASA Astrophysics Data System (ADS)

Wang, Yujie; Yang, Duo; Zhang, Xu; Chen, Zonghai

2016-05-01

Since large numbers of lithium-ion batteries are composed in pack and the batteries are complex electrochemical devices, their monitoring and safety concerns are key issues for the applications of battery technology. An accurate estimation of battery remaining capacity is crucial for optimization of the vehicle control, preventing battery from over-charging and over-discharging and ensuring the safety during its service life. The remaining capacity estimation of a battery includes the estimation of state-of-charge (SOC) and state-of-energy (SOE). In this work, a probability based adaptive estimator is presented to obtain accurate and reliable estimation results for both SOC and SOE. For the SOC estimation, an n ordered RC equivalent circuit model is employed by combining an electrochemical model to obtain more accurate voltage prediction results. For the SOE estimation, a sliding window neural network model is proposed to investigate the relationship between the terminal voltage and the model inputs. To verify the accuracy and robustness of the proposed model and estimation algorithm, experiments under different dynamic operation current profiles are performed on the commercial 1665130-type lithium-ion batteries. The results illustrate that accurate and robust estimation can be obtained by the proposed method.

Remote sensing in Iowa agriculture. [cropland inventory, soils, forestland, and crop diseases

NASA Technical Reports Server (NTRS)

Mahlstede, J. P. (Principal Investigator); Carlson, R. E.

1973-01-01

The author has identified the following significant results. Results include the estimation of forested and crop vegetation acreages using the ERTS-1 imagery. The methods used to achieve these estimates still require refinement, but the results appear promising. Practical applications would be directed toward achieving current land use inventories of these natural resources. This data is presently collected by sampling type surveys. If ERTS-1 can observe this and area estimates can be determined accurately, then a step forward has been achieved. Cost benefit relationship will have to be favorable. Problems still exist in these estimation techniques due to the diversity of the scene observed in the ERTS-1 imagery covering other part of Iowa. This is due to influence of topography and soils upon the adaptability of the vegetation to specific areas of the state. The state mosaic produced from ERTS-1 imagery shows these patterns very well. Research directed to acreage estimates is continuing.

On state-of-charge determination for lithium-ion batteries

NASA Astrophysics Data System (ADS)

Li, Zhe; Huang, Jun; Liaw, Bor Yann; Zhang, Jianbo

2017-04-01

Accurate estimation of state-of-charge (SOC) of a battery through its life remains challenging in battery research. Although improved precisions continue to be reported at times, almost all are based on regression methods empirically, while the accuracy is often not properly addressed. Here, a comprehensive review is set to address such issues, from fundamental principles that are supposed to define SOC to methodologies to estimate SOC for practical use. It covers topics from calibration, regression (including modeling methods) to validation in terms of precision and accuracy. At the end, we intend to answer the following questions: 1) can SOC estimation be self-adaptive without bias? 2) Why Ah-counting is a necessity in almost all battery-model-assisted regression methods? 3) How to establish a consistent framework of coupling in multi-physics battery models? 4) To assess the accuracy in SOC estimation, statistical methods should be employed to analyze factors that contribute to the uncertainty. We hope, through this proper discussion of the principles, accurate SOC estimation can be widely achieved.

Certainty Equivalence M-MRAC for Systems with Unmatched Uncertainties

NASA Technical Reports Server (NTRS)

Stepanyan, Vahram; Krishnakumar, Kalmanje

2012-01-01

The paper presents a certainty equivalence state feedback indirect adaptive control design method for the systems of any relative degree with unmatched uncertainties. The approach is based on the parameter identification (estimation) model, which is completely separated from the control design and is capable of producing parameter estimates as fast as the computing power allows without generating high frequency oscillations. It is shown that the system's input and output tracking errors can be systematically decreased by the proper choice of the design parameters.

Software Testbed for Developing and Evaluating Integrated Autonomous Subsystems

NASA Technical Reports Server (NTRS)

Ong, James; Remolina, Emilio; Prompt, Axel; Robinson, Peter; Sweet, Adam; Nishikawa, David

2015-01-01

To implement fault tolerant autonomy in future space systems, it will be necessary to integrate planning, adaptive control, and state estimation subsystems. However, integrating these subsystems is difficult, time-consuming, and error-prone. This paper describes Intelliface/ADAPT, a software testbed that helps researchers develop and test alternative strategies for integrating planning, execution, and diagnosis subsystems more quickly and easily. The testbed's architecture, graphical data displays, and implementations of the integrated subsystems support easy plug and play of alternate components to support research and development in fault-tolerant control of autonomous vehicles and operations support systems. Intelliface/ADAPT controls NASA's Advanced Diagnostics and Prognostics Testbed (ADAPT), which comprises batteries, electrical loads (fans, pumps, and lights), relays, circuit breakers, invertors, and sensors. During plan execution, an experimentor can inject faults into the ADAPT testbed by tripping circuit breakers, changing fan speed settings, and closing valves to restrict fluid flow. The diagnostic subsystem, based on NASA's Hybrid Diagnosis Engine (HyDE), detects and isolates these faults to determine the new state of the plant, ADAPT. Intelliface/ADAPT then updates its model of the ADAPT system's resources and determines whether the current plan can be executed using the reduced resources. If not, the planning subsystem generates a new plan that reschedules tasks, reconfigures ADAPT, and reassigns the use of ADAPT resources as needed to work around the fault. The resource model, planning domain model, and planning goals are expressed using NASA's Action Notation Modeling Language (ANML). Parts of the ANML model are generated automatically, and other parts are constructed by hand using the Planning Model Integrated Development Environment, a visual Eclipse-based IDE that accelerates ANML model development. Because native ANML planners are currently under development and not yet sufficiently capable, the ANML model is translated into the New Domain Definition Language (NDDL) and sent to NASA's EUROPA planning system for plan generation. The adaptive controller executes the new plan, using augmented, hierarchical finite state machines to select and sequence actions based on the state of the ADAPT system. Real-time sensor data, commands, and plans are displayed in information-dense arrays of timelines and graphs that zoom and scroll in unison. A dynamic schematic display uses color to show the real-time fault state and utilization of the system components and resources. An execution manager coordinates the activities of the other subsystems. The subsystems are integrated using the Internet Communications Engine (ICE). an object-oriented toolkit for building distributed applications.

Supersensitive ancilla-based adaptive quantum phase estimation

NASA Astrophysics Data System (ADS)

Larson, Walker; Saleh, Bahaa E. A.

2017-10-01

The supersensitivity attained in quantum phase estimation is known to be compromised in the presence of decoherence. This is particularly patent at blind spots—phase values at which sensitivity is totally lost. One remedy is to use a precisely known reference phase to shift the operation point to a less vulnerable phase value. Since this is not always feasible, we present here an alternative approach based on combining the probe with an ancillary degree of freedom containing adjustable parameters to create an entangled quantum state of higher dimension. We validate this concept by simulating a configuration of a Mach-Zehnder interferometer with a two-photon probe and a polarization ancilla of adjustable parameters, entangled at a polarizing beam splitter. At the interferometer output, the photons are measured after an adjustable unitary transformation in the polarization subspace. Through calculation of the Fisher information and simulation of an estimation procedure, we show that optimizing the adjustable polarization parameters using an adaptive measurement process provides globally supersensitive unbiased phase estimates for a range of decoherence levels, without prior information or a reference phase.

A Variable Step-Size Proportionate Affine Projection Algorithm for Identification of Sparse Impulse Response

NASA Astrophysics Data System (ADS)

Liu, Ligang; Fukumoto, Masahiro; Saiki, Sachio; Zhang, Shiyong

2009-12-01

Proportionate adaptive algorithms have been proposed recently to accelerate convergence for the identification of sparse impulse response. When the excitation signal is colored, especially the speech, the convergence performance of proportionate NLMS algorithms demonstrate slow convergence speed. The proportionate affine projection algorithm (PAPA) is expected to solve this problem by using more information in the input signals. However, its steady-state performance is limited by the constant step-size parameter. In this article we propose a variable step-size PAPA by canceling the a posteriori estimation error. This can result in high convergence speed using a large step size when the identification error is large, and can then considerably decrease the steady-state misalignment using a small step size after the adaptive filter has converged. Simulation results show that the proposed approach can greatly improve the steady-state misalignment without sacrificing the fast convergence of PAPA.

A hyper-temporal remote sensing protocol for detecting ecosystem disturbance, classifying ecological state, and assessing soil resilience

USDA-ARS?s Scientific Manuscript database

Hyper-temporal remote sensing is capable of detecting detailed information on vegetation dynamics relating to plant functional types (PFT), a useful proxy for estimating soil physical and chemical properties. A central concept of PFT is that plant morphological and physiological adaptations are link...

Teacher Burnout: Is It Real? Can We Prevent It?

ERIC Educational Resources Information Center

Terry, Paul M.

Excessive, prolonged stress can alter the body's normal physiologic function. If adequate coping mechanisms are not instituted, this extreme state can lead to burnout. Burnout can be experienced with physical, intellectual, social, psycho-emotional, and spiritual adaptations. It has been estimated that up to 40 percent of U.S. teachers will not be…

Effects of phase vector and history extension on prediction power of adaptive-network based fuzzy inference system (ANFIS) model for a real scale anaerobic wastewater treatment plant operating under unsteady state.

PubMed

Perendeci, Altinay; Arslan, Sever; Tanyolaç, Abdurrahman; Celebi, Serdar S

2009-10-01

A conceptual neural fuzzy model based on adaptive-network based fuzzy inference system, ANFIS, was proposed using available input on-line and off-line operational variables for a sugar factory anaerobic wastewater treatment plant operating under unsteady state to estimate the effluent chemical oxygen demand, COD. The predictive power of the developed model was improved as a new approach by adding the phase vector and the recent values of COD up to 5-10 days, longer than overall retention time of wastewater in the system. History of last 10 days for COD effluent with two-valued phase vector in the input variable matrix including all parameters had more predictive power. History of 7 days with two-valued phase vector in the matrix comprised of only on-line variables yielded fairly well estimations. The developed ANFIS model with phase vector and history extension has been able to adequately represent the behavior of the treatment system.

Application of Bayesian techniques to model the burden of human salmonellosis attributable to U.S. food commodities at the point of processing: adaptation of a Danish model.

PubMed

Guo, Chuanfa; Hoekstra, Robert M; Schroeder, Carl M; Pires, Sara Monteiro; Ong, Kanyin Liane; Hartnett, Emma; Naugle, Alecia; Harman, Jane; Bennett, Patricia; Cieslak, Paul; Scallan, Elaine; Rose, Bonnie; Holt, Kristin G; Kissler, Bonnie; Mbandi, Evelyne; Roodsari, Reza; Angulo, Frederick J; Cole, Dana

2011-04-01

Mathematical models that estimate the proportion of foodborne illnesses attributable to food commodities at specific points in the food chain may be useful to risk managers and policy makers to formulate public health goals, prioritize interventions, and document the effectiveness of mitigations aimed at reducing illness. Using human surveillance data on laboratory-confirmed Salmonella infections from the Centers for Disease Control and Prevention and Salmonella testing data from U.S. Department of Agriculture Food Safety and Inspection Service's regulatory programs, we developed a point-of-processing foodborne illness attribution model by adapting the Hald Salmonella Bayesian source attribution model. Key model outputs include estimates of the relative proportions of domestically acquired sporadic human Salmonella infections resulting from contamination of raw meat, poultry, and egg products processed in the United States from 1998 through 2003. The current model estimates the relative contribution of chicken (48%), ground beef (28%), turkey (17%), egg products (6%), intact beef (1%), and pork (<1%) across 109 Salmonella serotypes found in food commodities at point of processing. While interpretation of the attribution estimates is constrained by data inputs, the adapted model shows promise and may serve as a basis for a common approach to attribution of human salmonellosis and food safety decision-making in more than one country. © Mary Ann Liebert, Inc.

Application of Bayesian Techniques to Model the Burden of Human Salmonellosis Attributable to U.S. Food Commodities at the Point of Processing: Adaptation of a Danish Model

PubMed Central

Guo, Chuanfa; Hoekstra, Robert M.; Schroeder, Carl M.; Pires, Sara Monteiro; Ong, Kanyin Liane; Hartnett, Emma; Naugle, Alecia; Harman, Jane; Bennett, Patricia; Cieslak, Paul; Scallan, Elaine; Rose, Bonnie; Holt, Kristin G.; Kissler, Bonnie; Mbandi, Evelyne; Roodsari, Reza; Angulo, Frederick J.

2011-01-01

Abstract Mathematical models that estimate the proportion of foodborne illnesses attributable to food commodities at specific points in the food chain may be useful to risk managers and policy makers to formulate public health goals, prioritize interventions, and document the effectiveness of mitigations aimed at reducing illness. Using human surveillance data on laboratory-confirmed Salmonella infections from the Centers for Disease Control and Prevention and Salmonella testing data from U.S. Department of Agriculture Food Safety and Inspection Service's regulatory programs, we developed a point-of-processing foodborne illness attribution model by adapting the Hald Salmonella Bayesian source attribution model. Key model outputs include estimates of the relative proportions of domestically acquired sporadic human Salmonella infections resulting from contamination of raw meat, poultry, and egg products processed in the United States from 1998 through 2003. The current model estimates the relative contribution of chicken (48%), ground beef (28%), turkey (17%), egg products (6%), intact beef (1%), and pork (<1%) across 109 Salmonella serotypes found in food commodities at point of processing. While interpretation of the attribution estimates is constrained by data inputs, the adapted model shows promise and may serve as a basis for a common approach to attribution of human salmonellosis and food safety decision-making in more than one country. PMID:21235394

On-Board Event-Based State Estimation for Trajectory Approaching and Tracking of a Vehicle

PubMed Central

Martínez-Rey, Miguel; Espinosa, Felipe; Gardel, Alfredo; Santos, Carlos

2015-01-01

For the problem of pose estimation of an autonomous vehicle using networked external sensors, the processing capacity and battery consumption of these sensors, as well as the communication channel load should be optimized. Here, we report an event-based state estimator (EBSE) consisting of an unscented Kalman filter that uses a triggering mechanism based on the estimation error covariance matrix to request measurements from the external sensors. This EBSE generates the events of the estimator module on-board the vehicle and, thus, allows the sensors to remain in stand-by mode until an event is generated. The proposed algorithm requests a measurement every time the estimation distance root mean squared error (DRMS) value, obtained from the estimator's covariance matrix, exceeds a threshold value. This triggering threshold can be adapted to the vehicle's working conditions rendering the estimator even more efficient. An example of the use of the proposed EBSE is given, where the autonomous vehicle must approach and follow a reference trajectory. By making the threshold a function of the distance to the reference location, the estimator can halve the use of the sensors with a negligible deterioration in the performance of the approaching maneuver. PMID:26102489

An introduction of component fusion extend Kalman filtering method

NASA Astrophysics Data System (ADS)

Geng, Yue; Lei, Xusheng

2018-05-01

In this paper, the Component Fusion Extend Kalman Filtering (CFEKF) algorithm is proposed. Assuming each component of error propagation are independent with Gaussian distribution. The CFEKF can be obtained through the maximum likelihood of propagation error, which can adjust the state transition matrix and the measured matrix adaptively. With minimize linearization error, CFEKF can an effectively improve the estimation accuracy of nonlinear system state. The computation of CFEKF is similar to EKF which is easy for application.

Rotor Position Sensorless Control and Its Parameter Sensitivity of Permanent Magnet Motor Based on Model Reference Adaptive System

NASA Astrophysics Data System (ADS)

Ohara, Masaki; Noguchi, Toshihiko

This paper describes a new method for a rotor position sensorless control of a surface permanent magnet synchronous motor based on a model reference adaptive system (MRAS). This method features the MRAS in a current control loop to estimate a rotor speed and position by using only current sensors. This method as well as almost all the conventional methods incorporates a mathematical model of the motor, which consists of parameters such as winding resistances, inductances, and an induced voltage constant. Hence, the important thing is to investigate how the deviation of these parameters affects the estimated rotor position. First, this paper proposes a structure of the sensorless control applied in the current control loop. Next, it proves the stability of the proposed method when motor parameters deviate from the nominal values, and derives the relationship between the estimated position and the deviation of the parameters in a steady state. Finally, some experimental results are presented to show performance and effectiveness of the proposed method.

Modern control concepts in hydrology

NASA Technical Reports Server (NTRS)

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

1974-01-01

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

Autonomous Component Health Management with Failed Component Detection, Identification, and Avoidance

NASA Technical Reports Server (NTRS)

Davis, Robert N.; Polites, Michael E.; Trevino, Luis C.

2004-01-01

This paper details a novel scheme for autonomous component health management (ACHM) with failed actuator detection and failed sensor detection, identification, and avoidance. This new scheme has features that far exceed the performance of systems with triple-redundant sensing and voting, yet requires fewer sensors and could be applied to any system with redundant sensing. Relevant background to the ACHM scheme is provided, and the simulation results for the application of that scheme to a single-axis spacecraft attitude control system with a 3rd order plant and dual-redundant measurement of system states are presented. ACHM fulfills key functions needed by an integrated vehicle health monitoring (IVHM) system. It is: autonomous; adaptive; works in realtime; provides optimal state estimation; identifies failed components; avoids failed components; reconfigures for multiple failures; reconfigures for intermittent failures; works for hard-over, soft, and zero-output failures; and works for both open- and closed-loop systems. The ACHM scheme combines a prefilter that generates preliminary state estimates, detects and identifies failed sensors and actuators, and avoids the use of failed sensors in state estimation with a fixed-gain Kalman filter that generates optimal state estimates and provides model-based state estimates that comprise an integral part of the failure detection logic. The results show that ACHM successfully isolates multiple persistent and intermittent hard-over, soft, and zero-output failures. It is now ready to be tested on a computer model of an actual system.

Adaptive Neural Networks Prescribed Performance Control Design for Switched Interconnected Uncertain Nonlinear Systems.

PubMed

Li, Yongming; Tong, Shaocheng

2017-06-28

In this paper, an adaptive neural networks (NNs)-based decentralized control scheme with the prescribed performance is proposed for uncertain switched nonstrict-feedback interconnected nonlinear systems. It is assumed that nonlinear interconnected terms and nonlinear functions of the concerned systems are unknown, and also the switching signals are unknown and arbitrary. A linear state estimator is constructed to solve the problem of unmeasured states. The NNs are employed to approximate unknown interconnected terms and nonlinear functions. A new output feedback decentralized control scheme is developed by using the adaptive backstepping design technique. The control design problem of nonlinear interconnected switched systems with unknown switching signals can be solved by the proposed scheme, and only a tuning parameter is needed for each subsystem. The proposed scheme can ensure that all variables of the control systems are semi-globally uniformly ultimately bounded and the tracking errors converge to a small residual set with the prescribed performance bound. The effectiveness of the proposed control approach is verified by some simulation results.

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%.

On-line self-learning time forward voltage prognosis for lithium-ion batteries using adaptive neuro-fuzzy inference system

NASA Astrophysics Data System (ADS)

Fleischer, Christian; Waag, Wladislaw; Bai, Ziou; Sauer, Dirk Uwe

2013-12-01

The battery management system (BMS) of a battery-electric road vehicle must ensure an optimal operation of the electrochemical storage system to guarantee for durability and reliability. In particular, the BMS must provide precise information about the battery's state-of-functionality, i.e. how much dis-/charging power can the battery accept at current state and condition while at the same time preventing it from operating outside its safe operating area. These critical limits have to be calculated in a predictive manner, which serve as a significant input factor for the supervising vehicle energy management (VEM). The VEM must provide enough power to the vehicle's drivetrain for certain tasks and especially in critical driving situations. Therefore, this paper describes a new approach which can be used for state-of-available-power estimation with respect to lowest/highest cell voltage prediction using an adaptive neuro-fuzzy inference system (ANFIS). The estimated voltage for a given time frame in the future is directly compared with the actual voltage, verifying the effectiveness and accuracy of a relative voltage prediction error of less than 1%. Moreover, the real-time operating capability of the proposed algorithm was verified on a battery test bench while running on a real-time system performing voltage prediction.

Standing stability enhancement with an intelligent powered transfemoral prosthesis.

PubMed

Lawson, Brian Edward; Varol, Huseyin Atakan; Goldfarb, Michael

2011-09-01

The authors have developed a ground-adaptive standing controller for a powered knee and ankle prosthesis which is intended to enhance the standing stability of transfemoral amputees. The finite-state-based controller includes a ground-searching phase, a slope estimation phase, and a joint impedance modulation phase, which together enable the prosthesis to quickly conform to the ground and provide stabilizing assistance to the user. In order to assess the efficacy of the ground-adaptive standing controller, the control approach was implemented on a powered knee and ankle prosthesis, and experimental data were collected on an amputee subject for a variety of standing conditions. Results indicate that the controller can estimate the ground slope within ±1° over a range of ±15°, and that it can provide appropriate joint impedances for standing on slopes within this range.

Practical Considerations about Expected A Posteriori Estimation in Adaptive Testing: Adaptive A Priori, Adaptive Correction for Bias, and Adaptive Integration Interval.

ERIC Educational Resources Information Center

Raiche, Gilles; Blais, Jean-Guy

In a computerized adaptive test (CAT), it would be desirable to obtain an acceptable precision of the proficiency level estimate using an optimal number of items. Decreasing the number of items is accompanied, however, by a certain degree of bias when the true proficiency level differs significantly from the a priori estimate. G. Raiche (2000) has…

Epidemiological burden of postmenopausal osteoporosis in Italy from 2010 to 2020: estimations from a disease model.

PubMed

Piscitelli, P; Brandi, M; Cawston, H; Gauthier, A; Kanis, J A; Compston, J; Borgström, F; Cooper, C; McCloskey, E

2014-11-01

The article describes the adaptation of a model to estimate the burden of postmenopausal osteoporosis in women aged 50 years and over in Italy between 2010 and 2020. For this purpose, a validated postmenopausal osteoporosis disease model developed for Sweden was adapted to Italy. For each year of the study, the 'incident cohort' (women experiencing a first osteoporotic fracture) was identified and run through a Markov model using 1-year cycles until 2020. Health states were based on the number of fractures and deaths. Fracture by site (hip, clinical vertebral, non-hip non-vertebral) was tracked for each health state. Transition probabilities reflected fracture site-specific risk of death and subsequent fractures. Model inputs specific to Italy included population size and life tables from 1970 to 2020, incidence of hip fracture and BMD by age in the general population (mean and standard deviation). The model estimated that the number of postmenopausal osteoporotic women would increase from 3.3 million to 3.7 million between 2010 and 2020 (+14.3%). Assuming unchanged incidence rates by age group over time, the model predicted the overall number of osteoporotic fractures to increase from 285.0 to 335.8 thousand fractures between 2010 and 2020 (+17.8%). The estimated expected increases in hip, vertebral and non-hip non-vertebral fractures were 22.3, 17.2 and 16.3%, respectively. Due to demographic changes, the burden of fractures is expected to increase markedly by 2020.

J-Adaptive estimation with estimated noise statistics. [for orbit determination

NASA Technical Reports Server (NTRS)

Jazwinski, A. H.; Hipkins, C.

1975-01-01

The J-Adaptive estimator described by Jazwinski and Hipkins (1972) is extended to include the simultaneous estimation of the statistics of the unmodeled system accelerations. With the aid of simulations it is demonstrated that the J-Adaptive estimator with estimated noise statistics can automatically estimate satellite orbits to an accuracy comparable with the data noise levels, when excellent, continuous tracking coverage is available. Such tracking coverage will be available from satellite-to-satellite tracking.

Adaptive Window Zero-Crossing-Based Instantaneous Frequency Estimation

NASA Astrophysics Data System (ADS)

Sekhar, S. Chandra; Sreenivas, TV

2004-12-01

We address the problem of estimating instantaneous frequency (IF) of a real-valued constant amplitude time-varying sinusoid. Estimation of polynomial IF is formulated using the zero-crossings of the signal. We propose an algorithm to estimate nonpolynomial IF by local approximation using a low-order polynomial, over a short segment of the signal. This involves the choice of window length to minimize the mean square error (MSE). The optimal window length found by directly minimizing the MSE is a function of the higher-order derivatives of the IF which are not available a priori. However, an optimum solution is formulated using an adaptive window technique based on the concept of intersection of confidence intervals. The adaptive algorithm enables minimum MSE-IF (MMSE-IF) estimation without requiring a priori information about the IF. Simulation results show that the adaptive window zero-crossing-based IF estimation method is superior to fixed window methods and is also better than adaptive spectrogram and adaptive Wigner-Ville distribution (WVD)-based IF estimators for different signal-to-noise ratio (SNR).

Transition-Tempered Metadynamics: Robust, Convergent Metadynamics via On-the-Fly Transition Barrier Estimation.

PubMed

Dama, James F; Rotskoff, Grant; Parrinello, Michele; Voth, Gregory A

2014-09-09

Well-tempered metadynamics has proven to be a practical and efficient adaptive enhanced sampling method for the computational study of biomolecular and materials systems. However, choosing its tunable parameter can be challenging and requires balancing a trade-off between fast escape from local metastable states and fast convergence of an overall free energy estimate. In this article, we present a new smoothly convergent variant of metadynamics, transition-tempered metadynamics, that removes that trade-off and is more robust to changes in its own single tunable parameter, resulting in substantial speed and accuracy improvements. The new method is specifically designed to study state-to-state transitions in which the states of greatest interest are known ahead of time, but transition mechanisms are not. The design is guided by a picture of adaptive enhanced sampling as a means to increase dynamical connectivity of a model's state space until percolation between all points of interest is reached, and it uses the degree of dynamical percolation to automatically tune the convergence rate. We apply the new method to Brownian dynamics on 48 random 1D surfaces, blocked alanine dipeptide in vacuo, and aqueous myoglobin, finding that transition-tempered metadynamics substantially and reproducibly improves upon well-tempered metadynamics in terms of first barrier crossing rate, convergence rate, and robustness to the choice of tuning parameter. Moreover, the trade-off between first barrier crossing rate and convergence rate is eliminated: the new method drives escape from an initial metastable state as fast as metadynamics without tempering, regardless of tuning.

Adaptive EMG noise reduction in ECG signals using noise level approximation

NASA Astrophysics Data System (ADS)

Marouf, Mohamed; Saranovac, Lazar

2017-12-01

In this paper the usage of noise level approximation for adaptive Electromyogram (EMG) noise reduction in the Electrocardiogram (ECG) signals is introduced. To achieve the adequate adaptiveness, a translation-invariant noise level approximation is employed. The approximation is done in the form of a guiding signal extracted as an estimation of the signal quality vs. EMG noise. The noise reduction framework is based on a bank of low pass filters. So, the adaptive noise reduction is achieved by selecting the appropriate filter with respect to the guiding signal aiming to obtain the best trade-off between the signal distortion caused by filtering and the signal readability. For the evaluation purposes; both real EMG and artificial noises are used. The tested ECG signals are from the MIT-BIH Arrhythmia Database Directory, while both real and artificial records of EMG noise are added and used in the evaluation process. Firstly, comparison with state of the art methods is conducted to verify the performance of the proposed approach in terms of noise cancellation while preserving the QRS complex waves. Additionally, the signal to noise ratio improvement after the adaptive noise reduction is computed and presented for the proposed method. Finally, the impact of adaptive noise reduction method on QRS complexes detection was studied. The tested signals are delineated using a state of the art method, and the QRS detection improvement for different SNR is presented.

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-zero mean error that increases when gyro bias is increased. The second method is shown to not exhibit any steady state error in the tested scenarios that is inherent to its design. The second method can correct for attitude errors that arise from both integration error and gyro bias states, but it suffers from lack of attitude error observability. The attitude errors are shown to be more observable in wind, but increased integration error in wind outweighs the increase in attitude corrections that such increased observability brings, resulting in larger attitude errors in wind. Overall, this work highlights many technical deficiencies of both of these methods of state estimation that could be improved upon in the future to enhance state estimation for small UAVs in windy conditions.

Has climate change shifted US maize planting times?

NASA Astrophysics Data System (ADS)

Butler, E.; Stine, A.; Huybers, P.

2012-12-01

Global warming has been accompanied by an earlier onset of spring phenological events across a range of ecosystems. However, the degree to which humans have adapted planting schedules to a changing climate remains an open question; the leading hypotheses for earlier planting are improved hardiness of cultivars and farming equipment. Here we examine the relationship between historical temperature and precipitation from 549 weather stations from the United States Historical Climatology Network (USHCN) with planting schedules from 20 states in the United States Department of Agriculture/National Agriculture Statistics Service (USDA/NASS) database. We construct an empirical model to relate yearly weather conditions to predict planting dates and compare this to the spatial distribution of climate conditions and mean planting times. Evidence for a relationship between climate and planting schedules indicates that planting schedules for US maize have been adapted to yearly variations and overall changes in climatology. As one might expect, hotter temperatures lead to earlier plantings while greater precipitation leads to later planting. These findings serve to indicate extant adaptation between US farmers and climate change, and will aid in forecasting future shifts to planting schedules as climate continues to change. Furthermore, the statistical model should also be useful for estimating planting times for states and years for which records do not otherwise exist.

Local error estimates for adaptive simulation of the Reaction–Diffusion Master Equation via operator splitting

PubMed Central

Hellander, Andreas; Lawson, Michael J; Drawert, Brian; Petzold, Linda

2015-01-01

The efficiency of exact simulation methods for the reaction-diffusion master equation (RDME) is severely limited by the large number of diffusion events if the mesh is fine or if diffusion constants are large. Furthermore, inherent properties of exact kinetic-Monte Carlo simulation methods limit the efficiency of parallel implementations. Several approximate and hybrid methods have appeared that enable more efficient simulation of the RDME. A common feature to most of them is that they rely on splitting the system into its reaction and diffusion parts and updating them sequentially over a discrete timestep. This use of operator splitting enables more efficient simulation but it comes at the price of a temporal discretization error that depends on the size of the timestep. So far, existing methods have not attempted to estimate or control this error in a systematic manner. This makes the solvers hard to use for practitioners since they must guess an appropriate timestep. It also makes the solvers potentially less efficient than if the timesteps are adapted to control the error. Here, we derive estimates of the local error and propose a strategy to adaptively select the timestep when the RDME is simulated via a first order operator splitting. While the strategy is general and applicable to a wide range of approximate and hybrid methods, we exemplify it here by extending a previously published approximate method, the Diffusive Finite-State Projection (DFSP) method, to incorporate temporal adaptivity. PMID:26865735

Quantifying the impact of adaptive traffic control systems on crash frequency and severity: Evidence from Oakland County, Michigan.

PubMed

Fink, Joshua; Kwigizile, Valerian; Oh, Jun-Seok

2016-06-01

Despite seeing widespread usage worldwide, adaptive traffic control systems have experienced relatively little use in the United States. Of the systems used, the Sydney Coordinated Adaptive Traffic System (SCATS) is the most popular in America. Safety benefits of these systems are not as well understood nor as commonly documented. This study investigates the safety benefits of adaptive traffic control systems by using the large SCATS-based system in Oakland County, MI known as FAST-TRAC. This study uses data from FAST-TRAC-controlled intersections in Oakland County and compares a wide variety of geometric, traffic, and crash characteristics to similar intersections in metropolitan areas elsewhere in Michigan. Data from 498 signalized intersections are used to conduct a cross-sectional analysis. Negative binomial models are used to estimate models for three dependent crash variables. Multinomial logit models are used to estimate an injury severity model. A variable tracking the presence of FAST-TRAC controllers at intersections is used in all models to determine if a SCATS-based system has an impact on crash occurrences or crash severity. Estimates show that the presence of SCATS-based controllers at intersections is likely to reduce angle crashes by up to 19.3%. Severity results show a statistically significant increase in non-serious injuries, but not a significant reduction in incapacitating injuries or fatal accidents. Copyright © 2016 Elsevier Ltd and National Safety Council. All rights reserved.

Local error estimates for adaptive simulation of the Reaction-Diffusion Master Equation via operator splitting.

PubMed

Hellander, Andreas; Lawson, Michael J; Drawert, Brian; Petzold, Linda

2014-06-01

The efficiency of exact simulation methods for the reaction-diffusion master equation (RDME) is severely limited by the large number of diffusion events if the mesh is fine or if diffusion constants are large. Furthermore, inherent properties of exact kinetic-Monte Carlo simulation methods limit the efficiency of parallel implementations. Several approximate and hybrid methods have appeared that enable more efficient simulation of the RDME. A common feature to most of them is that they rely on splitting the system into its reaction and diffusion parts and updating them sequentially over a discrete timestep. This use of operator splitting enables more efficient simulation but it comes at the price of a temporal discretization error that depends on the size of the timestep. So far, existing methods have not attempted to estimate or control this error in a systematic manner. This makes the solvers hard to use for practitioners since they must guess an appropriate timestep. It also makes the solvers potentially less efficient than if the timesteps are adapted to control the error. Here, we derive estimates of the local error and propose a strategy to adaptively select the timestep when the RDME is simulated via a first order operator splitting. While the strategy is general and applicable to a wide range of approximate and hybrid methods, we exemplify it here by extending a previously published approximate method, the Diffusive Finite-State Projection (DFSP) method, to incorporate temporal adaptivity.

Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics.

PubMed

Li, Dongming; Sun, Changming; Yang, Jinhua; Liu, Huan; Peng, Jiaqi; Zhang, Lijuan

2017-04-06

An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image restoration algorithm via maximum likelihood estimation. Our proposed algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed algorithm. Experimental results show that our algorithm produces accurate AO image restoration results and outperforms the current state-of-the-art blind deconvolution methods.

Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics

PubMed Central

Li, Dongming; Sun, Changming; Yang, Jinhua; Liu, Huan; Peng, Jiaqi; Zhang, Lijuan

2017-01-01

An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image restoration algorithm via maximum likelihood estimation. Our proposed algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed algorithm. Experimental results show that our algorithm produces accurate AO image restoration results and outperforms the current state-of-the-art blind deconvolution methods. PMID:28383503

Modeling GHG Emissions and Carbon Changes in Agricultural and Forest Systems to Guide Mitigation and Adaptation: Synthesis and Future Needs

USDA-ARS?s Scientific Manuscript database

Agricultural production systems and land use change for agriculture and forestry are important sources of anthropogenic greenhouse gas (GHG) emissions. Recent commitments by the European Union, the United States, and China to reduce GHG emissions highlight the need to improve estimates of current em...

Moving-Bank Multiple Model Adaptive Estimation and Control Applied to a Large Flexible Space Structure

DTIC Science & Technology

1990-12-01

was determined from the difference between the 24-state matrix product, HtP (t’)HT, and the six-state matrix product, HfPf (tT)HT’. For this...The true position for node 7, which represents the rigid body position of the structure, is not damped and can be interpreted as a rigid body...application, considering the same issues as explored in this research. Continue with a physical interpretation of the structure positions for determining the

Processing and comparison of two weighing lysimeters at the Rietholzbach catchment

NASA Astrophysics Data System (ADS)

Ruth, Conall; Michel, Dominik; Hirschi, Martin; Seneviratne, Sonia I.

2017-04-01

Weighing lysimeters are a well-established means of accurately obtaining local-scale estimates of actual evapotranspiration and seepage within soils. Current state-of-the-art devices have very high temporal resolutions and weighing precisions, and can also be used to estimate precipitation. These, however, require complex filtering to first remove noise (e.g. resulting from wind influence) from the mass measurements. At the Rietholzbach research catchment in northeastern Switzerland, two weighing lysimeters are in operation. One is a recently-installed state-of-the-art mini-lysimeter with a pump-controlled lower boundary; the other is a large free-drainage lysimeter in operation since 1976. To determine the optimal processing approach for the mini-lysimeter, a number of reported approaches were applied, with the resulting evapotranspiration and precipitation records being compared to those of the large lysimeter and a tipping bucket, respectively. Out of those examined, we found the Adaptive-Window and Adaptive-Threshold (AWAT) filter and a similar, non-adaptive approach, to perform best. Using the AWAT-filtered mini-lysimeter data as a reference, additional, retrospectively-applicable processing steps for the large lysimeter were then investigated. Those found to be most beneficial were the application of a three-point (10-min) moving mean to the mass measurements, and the setting-to-zero of estimated evapotranspiration and condensation in hours with greater-than-zero reference tipping bucket precipitation recordings. A comparison of lysimeter mass increases associated with precipitation revealed that the large lysimeter experiences a previously unknown under-catch of 11.1% (for liquid precipitation). Daily seepage measurements were found to be generally greater from the mini-lysimeter, probably reflecting the reduced input of water to the large lysimeter due to this under-catch.

Adaptive output feedback control of flexible-joint robots using neural networks: dynamic surface design approach.

PubMed

Yoo, Sung Jin; Park, Jin Bae; Choi, Yoon Ho

2008-10-01

In this paper, we propose a new robust output feedback control approach for flexible-joint electrically driven (FJED) robots via the observer dynamic surface design technique. The proposed method only requires position measurements of the FJED robots. To estimate the link and actuator velocity information of the FJED robots with model uncertainties, we develop an adaptive observer using self-recurrent wavelet neural networks (SRWNNs). The SRWNNs are used to approximate model uncertainties in both robot (link) dynamics and actuator dynamics, and all their weights are trained online. Based on the designed observer, the link position tracking controller using the estimated states is induced from the dynamic surface design procedure. Therefore, the proposed controller can be designed more simply than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop adaptive system are uniformly ultimately bounded. Finally, the simulation results on a three-link FJED robot are presented to validate the good position tracking performance and robustness of the proposed control system against payload uncertainties and external disturbances.

Dual Extended Kalman Filter for the Identification of Time-Varying Human Manual Control Behavior

NASA Technical Reports Server (NTRS)

Popovici, Alexandru; Zaal, Peter M. T.; Pool, Daan M.

2017-01-01

A Dual Extended Kalman Filter was implemented for the identification of time-varying human manual control behavior. Two filters that run concurrently were used, a state filter that estimates the equalization dynamics, and a parameter filter that estimates the neuromuscular parameters and time delay. Time-varying parameters were modeled as a random walk. The filter successfully estimated time-varying human control behavior in both simulated and experimental data. Simple guidelines are proposed for the tuning of the process and measurement covariance matrices and the initial parameter estimates. The tuning was performed on simulation data, and when applied on experimental data, only an increase in measurement process noise power was required in order for the filter to converge and estimate all parameters. A sensitivity analysis to initial parameter estimates showed that the filter is more sensitive to poor initial choices of neuromuscular parameters than equalization parameters, and bad choices for initial parameters can result in divergence, slow convergence, or parameter estimates that do not have a real physical interpretation. The promising results when applied to experimental data, together with its simple tuning and low dimension of the state-space, make the use of the Dual Extended Kalman Filter a viable option for identifying time-varying human control parameters in manual tracking tasks, which could be used in real-time human state monitoring and adaptive human-vehicle haptic interfaces.

Stress estimation in reservoirs using an integrated inverse method

NASA Astrophysics Data System (ADS)

Mazuyer, Antoine; Cupillard, Paul; Giot, Richard; Conin, Marianne; Leroy, Yves; Thore, Pierre

2018-05-01

Estimating the stress in reservoirs and their surroundings prior to the production is a key issue for reservoir management planning. In this study, we propose an integrated inverse method to estimate such initial stress state. The 3D stress state is constructed with the displacement-based finite element method assuming linear isotropic elasticity and small perturbations in the current geometry of the geological structures. The Neumann boundary conditions are defined as piecewise linear functions of depth. The discontinuous functions are determined with the CMA-ES (Covariance Matrix Adaptation Evolution Strategy) optimization algorithm to fit wellbore stress data deduced from leak-off tests and breakouts. The disregard of the geological history and the simplified rheological assumptions mean that only the stress field, statically admissible and matching the wellbore data should be exploited. The spatial domain of validity of this statement is assessed by comparing the stress estimations for a synthetic folded structure of finite amplitude with a history constructed assuming a viscous response.

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

A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve

NASA Astrophysics Data System (ADS)

Yang, Duo; Zhang, Xu; Pan, Rui; Wang, Yujie; Chen, Zonghai

2018-04-01

The state-of-health (SOH) estimation is always a crucial issue for lithium-ion batteries. In order to provide an accurate and reliable SOH estimation, a novel Gaussian process regression (GPR) model based on charging curve is proposed in this paper. Different from other researches where SOH is commonly estimated by cycle life, in this work four specific parameters extracted from charging curves are used as inputs of the GPR model instead of cycle numbers. These parameters can reflect the battery aging phenomenon from different angles. The grey relational analysis method is applied to analyze the relational grade between selected features and SOH. On the other hand, some adjustments are made in the proposed GPR model. Covariance function design and the similarity measurement of input variables are modified so as to improve the SOH estimate accuracy and adapt to the case of multidimensional input. Several aging data from NASA data repository are used for demonstrating the estimation effect by the proposed method. Results show that the proposed method has high SOH estimation accuracy. Besides, a battery with dynamic discharging profile is used to verify the robustness and reliability of this method.

Adaptive Filtering Using Recurrent Neural Networks

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.

2005-01-01

A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

Dynamic Filtering Improves Attentional State Prediction with fNIRS

NASA Technical Reports Server (NTRS)

Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.

2016-01-01

Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% +/- 6% versus 72% +/- 15%).

Cost calculator methods for estimating casework time in child welfare services: A promising approach for use in implementation of evidence-based practices and other service innovations.

PubMed

Holmes, Lisa; Landsverk, John; Ward, Harriet; Rolls-Reutz, Jennifer; Saldana, Lisa; Wulczyn, Fred; Chamberlain, Patricia

2014-04-01

Estimating costs in child welfare services is critical as new service models are incorporated into routine practice. This paper describes a unit costing estimation system developed in England (cost calculator) together with a pilot test of its utility in the United States where unit costs are routinely available for health services but not for child welfare services. The cost calculator approach uses a unified conceptual model that focuses on eight core child welfare processes. Comparison of these core processes in England and in four counties in the United States suggests that the underlying child welfare processes generated from England were perceived as very similar by child welfare staff in California county systems with some exceptions in the review and legal processes. Overall, the adaptation of the cost calculator for use in the United States child welfare systems appears promising. The paper also compares the cost calculator approach to the workload approach widely used in the United States and concludes that there are distinct differences between the two approaches with some possible advantages to the use of the cost calculator approach, especially in the use of this method for estimating child welfare costs in relation to the incorporation of evidence-based interventions into routine practice.

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.

An Adaptive Nonlinear Basal-Bolus Calculator for Patients With Type 1 Diabetes

PubMed Central

Boiroux, Dimitri; Aradóttir, Tinna Björk; Nørgaard, Kirsten; Poulsen, Niels Kjølstad; Madsen, Henrik; Jørgensen, John Bagterp

2016-01-01

Background: Bolus calculators help patients with type 1 diabetes to mitigate the effect of meals on their blood glucose by administering a large amount of insulin at mealtime. Intraindividual changes in patients physiology and nonlinearity in insulin-glucose dynamics pose a challenge to the accuracy of such calculators. Method: We propose a method based on a continuous-discrete unscented Kalman filter to continuously track the postprandial glucose dynamics and the insulin sensitivity. We augment the Medtronic Virtual Patient (MVP) model to simulate noise-corrupted data from a continuous glucose monitor (CGM). The basal rate is determined by calculating the steady state of the model and is adjusted once a day before breakfast. The bolus size is determined by optimizing the postprandial glucose values based on an estimate of the insulin sensitivity and states, as well as the announced meal size. Following meal announcements, the meal compartment and the meal time constant are estimated, otherwise insulin sensitivity is estimated. Results: We compare the performance of a conventional linear bolus calculator with the proposed bolus calculator. The proposed basal-bolus calculator significantly improves the time spent in glucose target (P < .01) compared to the conventional bolus calculator. Conclusion: An adaptive nonlinear basal-bolus calculator can efficiently compensate for physiological changes. Further clinical studies will be needed to validate the results. PMID:27613658

Health risks of climate change: an assessment of uncertainties and its implications for adaptation policies.

PubMed

Wardekker, J Arjan; de Jong, Arie; van Bree, Leendert; Turkenburg, Wim C; van der Sluijs, Jeroen P

2012-09-19

Projections of health risks of climate change are surrounded with uncertainties in knowledge. Understanding of these uncertainties will help the selection of appropriate adaptation policies. We made an inventory of conceivable health impacts of climate change, explored the type and level of uncertainty for each impact, and discussed its implications for adaptation policy. A questionnaire-based expert elicitation was performed using an ordinal scoring scale. Experts were asked to indicate the level of precision with which health risks can be estimated, given the present state of knowledge. We assessed the individual scores, the expertise-weighted descriptive statistics, and the argumentation given for each score. Suggestions were made for how dealing with uncertainties could be taken into account in climate change adaptation policy strategies. The results showed that the direction of change could be indicated for most anticipated health effects. For several potential effects, too little knowledge exists to indicate whether any impact will occur, or whether the impact will be positive or negative. For several effects, rough 'order-of-magnitude' estimates were considered possible. Factors limiting health impact quantification include: lack of data, multi-causality, unknown impacts considering a high-quality health system, complex cause-effect relations leading to multi-directional impacts, possible changes of present-day response-relations, and difficulties in predicting local climate impacts. Participants considered heat-related mortality and non-endemic vector-borne diseases particularly relevant for climate change adaptation. For possible climate related health impacts characterised by ignorance, adaptation policies that focus on enhancing the health system's and society's capability of dealing with possible future changes, uncertainties and surprises (e.g. through resilience, flexibility, and adaptive capacity) are most appropriate. For climate related health effects for which rough risk estimates are available, 'robust decision-making' is recommended. For health effects with limited societal and policy relevance, we recommend focusing on no-regret measures. For highly relevant health effects, precautionary measures can be considered. This study indicated that analysing and characterising uncertainty by means of a typology can be a very useful approach for selection and prioritization of preferred adaptation policies to reduce future climate related health risks.

AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal

PubMed Central

Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang

2015-01-01

An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal. PMID:26512665

AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal.

PubMed

Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang

2015-10-23

An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal.

Observer-Based Adaptive Fault-Tolerant Tracking Control of Nonlinear Nonstrict-Feedback Systems.

PubMed

Wu, Chengwei; Liu, Jianxing; Xiong, Yongyang; Wu, Ligang

2017-06-28

This paper studies an output-based adaptive fault-tolerant control problem for nonlinear systems with nonstrict-feedback form. Neural networks are utilized to identify the unknown nonlinear characteristics in the system. An observer and a general fault model are constructed to estimate the unavailable states and describe the fault, respectively. Adaptive parameters are constructed to overcome the difficulties in the design process for nonstrict-feedback systems. Meanwhile, dynamic surface control technique is introduced to avoid the problem of ''explosion of complexity''. Furthermore, based on adaptive backstepping control method, an output-based adaptive neural tracking control strategy is developed for the considered system against actuator fault, which can ensure that all the signals in the resulting closed-loop system are bounded, and the system output signal can be regulated to follow the response of the given reference signal with a small error. Finally, the simulation results are provided to validate the effectiveness of the control strategy proposed in this paper.

Adapting to rates versus amounts of climate change: a case of adaptation to sea-level rise

NASA Astrophysics Data System (ADS)

Shayegh, Soheil; Moreno-Cruz, Juan; Caldeira, Ken

2016-10-01

Adaptation is the process of adjusting to climate change in order to moderate harm or exploit beneficial opportunities associated with it. Most adaptation strategies are designed to adjust to a new climate state. However, despite our best efforts to curtail greenhouse gas emissions, climate is likely to continue changing far into the future. Here, we show how considering rates of change affects the projected optimal adaptation strategy. We ground our discussion with an example of optimal investment in the face of continued sea-level rise, presenting a quantitative model that illustrates the interplay among physical and economic factors governing coastal development decisions such as rate of sea-level rise, land slope, discount rate, and depreciation rate. This model shows that the determination of optimal investment strategies depends on taking into account future rates of sea-level rise, as well as social and political constraints. This general approach also applies to the development of improved strategies to adapt to ongoing trends in temperature, precipitation, and other climate variables. Adaptation to some amount of change instead of adaptation to ongoing rates of change may produce inaccurate estimates of damages to the social systems and their ability to respond to external pressures.

Demosaicking algorithm for the Kodak-RGBW color filter array

NASA Astrophysics Data System (ADS)

Rafinazari, M.; Dubois, E.

2015-01-01

Digital cameras capture images through different Color Filter Arrays and then reconstruct the full color image. Each CFA pixel only captures one primary color component; the other primary components will be estimated using information from neighboring pixels. During the demosaicking algorithm, the two unknown color components will be estimated at each pixel location. Most of the demosaicking algorithms use the RGB Bayer CFA pattern with Red, Green and Blue filters. The least-Squares Luma-Chroma demultiplexing method is a state of the art demosaicking method for the Bayer CFA. In this paper we develop a new demosaicking algorithm using the Kodak-RGBW CFA. This particular CFA reduces noise and improves the quality of the reconstructed images by adding white pixels. We have applied non-adaptive and adaptive demosaicking method using the Kodak-RGBW CFA on the standard Kodak image dataset and the results have been compared with previous work.

Dynamic Model Improves Agronomic and Environmental Outcomes for Maize Nitrogen Management over Static Approach.

PubMed

Sela, Shai; van Es, Harold M; Moebius-Clune, Bianca N; Marjerison, Rebecca; Moebius-Clune, Daniel; Schindelbeck, Robert; Severson, Keith; Young, Eric

2017-03-01

Large temporal and spatial variability in soil nitrogen (N) availability leads many farmers across the United States to over-apply N fertilizers in maize ( L.) production environments, often resulting in large environmental N losses. Static Stanford-type N recommendation tools are typically promoted in the United States, but new dynamic model-based decision tools allow for highly adaptive N recommendations that account for specific production environments and conditions. This study compares the Corn N Calculator (CNC), a static N recommendation tool for New York, to Adapt-N, a dynamic simulation tool that combines soil, crop, and management information with real-time weather data to estimate optimum N application rates for maize. The efficiency of the two tools in predicting the Economically Optimum N Rate (EONR) is compared using field data from 14 multiple N-rate trials conducted in New York during the years 2011 through 2015. The CNC tool was used with both realistic grower-estimated potential yields and those extracted from the CNC default database, which were found to be unrealistically low when compared with field data. By accounting for weather and site-specific conditions, the Adapt-N tool was found to increase the farmer profits and significantly improve the prediction of the EONR (RMSE = 34 kg ha). Furthermore, using a dynamic instead of a static approach led to reduced N application rates, which in turn resulted in substantially lower simulated environmental N losses. This study shows that better N management through a dynamic decision tool such as Adapt-N can help reduce environmental impacts while sustaining farm economic viability. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Sensorimotor adaptation of point-to-point arm movements after spaceflight: the role of internal representation of gravity force in trajectory planning.

PubMed

Gaveau, Jérémie; Paizis, Christos; Berret, Bastien; Pozzo, Thierry; Papaxanthis, Charalambos

2011-08-01

After an exposure to weightlessness, the central nervous system operates under new dynamic and sensory contexts. To find optimal solutions for rapid adaptation, cosmonauts have to decide whether parameters from the world or their body have changed and to estimate their properties. Here, we investigated sensorimotor adaptation after a spaceflight of 10 days. Five cosmonauts performed forward point-to-point arm movements in the sagittal plane 40 days before and 24 and 72 h after the spaceflight. We found that, whereas the shape of hand velocity profiles remained unaffected after the spaceflight, hand path curvature significantly increased 1 day after landing and returned to the preflight level on the third day. Control experiments, carried out by 10 subjects under normal gravity conditions, showed that loading the arm with varying loads (from 0.3 to 1.350 kg) did not affect path curvature. Therefore, changes in path curvature after spaceflight cannot be the outcome of a control process based on the subjective feeling that arm inertia was increased. By performing optimal control simulations, we found that arm kinematics after exposure to microgravity corresponded to a planning process that overestimated the gravity level and optimized movements in a hypergravity environment (∼1.4 g). With time and practice, the sensorimotor system was recalibrated to Earth's gravity conditions, and cosmonauts progressively generated accurate estimations of the body state, gravity level, and sensory consequences of the motor commands (72 h). These observations provide novel insights into how the central nervous system evaluates body (inertia) and environmental (gravity) states during sensorimotor adaptation of point-to-point arm movements after an exposure to weightlessness.

Frequency-domain beamformers using conjugate gradient techniques for speech enhancement.

PubMed

Zhao, Shengkui; Jones, Douglas L; Khoo, Suiyang; Man, Zhihong

2014-09-01

A multiple-iteration constrained conjugate gradient (MICCG) algorithm and a single-iteration constrained conjugate gradient (SICCG) algorithm are proposed to realize the widely used frequency-domain minimum-variance-distortionless-response (MVDR) beamformers and the resulting algorithms are applied to speech enhancement. The algorithms are derived based on the Lagrange method and the conjugate gradient techniques. The implementations of the algorithms avoid any form of explicit or implicit autocorrelation matrix inversion. Theoretical analysis establishes formal convergence of the algorithms. Specifically, the MICCG algorithm is developed based on a block adaptation approach and it generates a finite sequence of estimates that converge to the MVDR solution. For limited data records, the estimates of the MICCG algorithm are better than the conventional estimators and equivalent to the auxiliary vector algorithms. The SICCG algorithm is developed based on a continuous adaptation approach with a sample-by-sample updating procedure and the estimates asymptotically converge to the MVDR solution. An illustrative example using synthetic data from a uniform linear array is studied and an evaluation on real data recorded by an acoustic vector sensor array is demonstrated. Performance of the MICCG algorithm and the SICCG algorithm are compared with the state-of-the-art approaches.

Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach

NASA Astrophysics Data System (ADS)

Chen, Chaochao; Vachtsevanos, George; Orchard, Marcos E.

2012-04-01

Machine prognosis can be considered as the generation of long-term predictions that describe the evolution in time of a fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component/subsystem so that timely maintenance can be performed to avoid catastrophic failures. This paper proposes an integrated RUL prediction method using adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which forecasts the time evolution of the fault indicator and estimates the probability density function (pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter as a model describing the fault progression. The high-order particle filter is used to estimate the current state and carry out p-step-ahead predictions via a set of particles. These predictions are used to estimate the RUL pdf. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results demonstrate that it outperforms both the conventional ANFIS predictor and the particle-filter-based predictor where the fault growth model is a first-order model that is trained via the ANFIS.

Preliminary Exploration of Adaptive State Predictor Based Human Operator Modeling

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.; Gregory, Irene M.

2012-01-01

Control-theoretic modeling of the human operator dynamic behavior in manual control tasks has a long and rich history. In the last two decades, there has been a renewed interest in modeling the human operator. There has also been significant work on techniques used to identify the pilot model of a given structure. The purpose of this research is to attempt to go beyond pilot identification based on collected experimental data and to develop a predictor of pilot behavior. An experiment was conducted to quantify the effects of changing aircraft dynamics on an operator s ability to track a signal in order to eventually model a pilot adapting to changing aircraft dynamics. A gradient descent estimator and a least squares estimator with exponential forgetting used these data to predict pilot stick input. The results indicate that individual pilot characteristics and vehicle dynamics did not affect the accuracy of either estimator method to estimate pilot stick input. These methods also were able to predict pilot stick input during changing aircraft dynamics and they may have the capability to detect a change in a subject due to workload, engagement, etc., or the effects of changes in vehicle dynamics on the pilot.

Improved remote gaze estimation using corneal reflection-adaptive geometric transforms

NASA Astrophysics Data System (ADS)

Ma, Chunfei; Baek, Seung-Jin; Choi, Kang-A.; Ko, Sung-Jea

2014-05-01

Recently, the remote gaze estimation (RGE) technique has been widely applied to consumer devices as a more natural interface. In general, the conventional RGE method estimates a user's point of gaze using a geometric transform, which represents the relationship between several infrared (IR) light sources and their corresponding corneal reflections (CRs) in the eye image. Among various methods, the homography normalization (HN) method achieves state-of-the-art performance. However, the geometric transform of the HN method requiring four CRs is infeasible for the case when fewer than four CRs are available. To solve this problem, this paper proposes a new RGE method based on three alternative geometric transforms, which are adaptive to the number of CRs. Unlike the HN method, the proposed method not only can operate with two or three CRs, but can also provide superior accuracy. To further enhance the performance, an effective error correction method is also proposed. By combining the introduced transforms with the error-correction method, the proposed method not only provides high accuracy and robustness for gaze estimation, but also allows for a more flexible system setup with a different number of IR light sources. Experimental results demonstrate the effectiveness of the proposed method.

Noncanonical Photodynamics of the Orange/Green Cyanobacteriochrome Power Sensor NpF2164g7 from the PtxD Phototaxis Regulator of Nostoc punctiforme.

PubMed

Kirpich, Julia S; Chang, Che-Wei; Madsen, Dorte; Gottlieb, Sean M; Martin, Shelley S; Rockwell, Nathan C; Lagarias, J Clark; Larsen, Delmar S

2018-05-08

Forward and reverse primary (<10 ns) and secondary (>10 ns) photodynamics of cyanobacteriochrome (CBCR) NpF2164g7 were characterized by global analysis of ultrafast broadband transient absorption measurements. NpF2164g7 is the most C-terminal bilin-binding GAF domain in the Nostoc punctiforme phototaxis sensor PtxD (locus Npun_F2164). Although a member of the canonical red/green CBCR subfamily phylogenetically, NpF2164g7 exhibits an orange-absorbing 15Z P o dark-adapted state instead of the typical red-absorbing 15Z P r dark-adapted state characteristic of this subfamily. The green-absorbing 15E P g photoproduct of NpF2164g7 is unstable, allowing this CBCR domain to function as a power sensor. Photoexcitation of the 15Z P o state triggers inhomogeneous excited-state dynamics with three spectrally and temporally distinguishable pathways to generate the light-adapted 15E P g state in high yield (estimated at 25-30%). Although observed in other CBCR domains, the inhomogeneity in NpF2164g7 extends far into secondary relaxation dynamics (10 ns -1 ms) through to formation of 15E P g . In the reverse direction, the primary dynamics after photoexcitation of 15E P g are qualitatively similar to those of other red/green CBCRs, but secondary dynamics involve a "pre-equilibrium" step before regenerating 15Z P o . The anomalous photodynamics of NpF2164g7 may reflect an evolutionary adaptation of CBCR sensors that function as broadband light intensity sensors.

Sideslip estimation for articulated heavy vehicles at the limits of adhesion

NASA Astrophysics Data System (ADS)

Morrison, Graeme; Cebon, David

2016-11-01

Various active safety systems proposed for articulated heavy goods vehicles (HGVs) require an accurate estimate of vehicle sideslip angle. However in contrast to passenger cars, there has been minimal published research on sideslip estimation for articulated HGVs. State-of-the-art observers, which rely on linear vehicle models, perform poorly when manoeuvring near the limits of tyre adhesion. This paper investigates three nonlinear Kalman filters (KFs) for estimating the tractor sideslip angle of a tractor-semitrailer. These are compared to the current state-of-the-art, through computer simulations and vehicle test data. An unscented KF using a 5 degrees-of-freedom single-track vehicle model with linear adaptive tyres is found to substantially outperform the state-of-the-art linear KF across a range of test manoeuvres on different surfaces, both at constant speed and during emergency braking. Robustness of the observer to parameter uncertainty is also demonstrated. Data supporting this research can be accessed at http://dx.doi.org/10.17863/CAM.1234

Merchandizer: Adapting USLYCOWG to the Marketplace

Treesearch

1984-01-01

USLYCOWG is a computer model that estimates yields in cubic feet per acre from unthinned slash and loblolly pine plantations established on cutover sites in the West Gulf states. However, most stumpage in the West Gulf region is sold in other volumetric units, such as cords, tons, and board feet, or by weight. A series of computer routines has been developed that...

Predicting Potential Changes in Suitable Habitat and Distribution by 2100 for Tree Species of the Eastern United States

Treesearch

Louis R Iverson; Anantha M. Prasad; Mark W. Schwartz; Mark W. Schwartz

2005-01-01

We predict current distribution and abundance for tree species present in eastern North America, and subsequently estimate potential suitable habitat for those species under a changed climate with 2 x CO2. We used a series of statistical models (i.e., Regression Tree Analysis (RTA), Multivariate Adaptive Regression Splines (MARS), Bagging Trees (...

Consensus-based distributed cooperative learning from closed-loop neural control systems.

PubMed

Chen, Weisheng; Hua, Shaoyong; Zhang, Huaguang

2015-02-01

In this paper, the neural tracking problem is addressed for a group of uncertain nonlinear systems where the system structures are identical but the reference signals are different. This paper focuses on studying the learning capability of neural networks (NNs) during the control process. First, we propose a novel control scheme called distributed cooperative learning (DCL) control scheme, by establishing the communication topology among adaptive laws of NN weights to share their learned knowledge online. It is further proved that if the communication topology is undirected and connected, all estimated weights of NNs can converge to small neighborhoods around their optimal values over a domain consisting of the union of all state orbits. Second, as a corollary it is shown that the conclusion on the deterministic learning still holds in the decentralized adaptive neural control scheme where, however, the estimated weights of NNs just converge to small neighborhoods of the optimal values along their own state orbits. Thus, the learned controllers obtained by DCL scheme have the better generalization capability than ones obtained by decentralized learning method. A simulation example is provided to verify the effectiveness and advantages of the control schemes proposed in this paper.

Integrating Systems Health Management with Adaptive Controls for a Utility-Scale Wind Turbine

NASA Technical Reports Server (NTRS)

Frost, Susan A.; Goebel, Kai; Trinh, Khanh V.; Balas, Mark J.; Frost, Alan M.

2011-01-01

Increasing turbine up-time and reducing maintenance costs are key technology drivers for wind turbine operators. Components within wind turbines are subject to considerable stresses due to unpredictable environmental conditions resulting from rapidly changing local dynamics. Systems health management has the aim to assess the state-of-health of components within a wind turbine, to estimate remaining life, and to aid in autonomous decision-making to minimize damage. Advanced adaptive controls can provide the mechanism to enable optimized operations that also provide the enabling technology for Systems Health Management goals. The work reported herein explores the integration of condition monitoring of wind turbine blades with contingency management and adaptive controls. Results are demonstrated using a high fidelity simulator of a utility-scale wind turbine.

Adaptive Distributed Video Coding with Correlation Estimation using Expectation Propagation

PubMed Central

Cui, Lijuan; Wang, Shuang; Jiang, Xiaoqian; Cheng, Samuel

2013-01-01

Distributed video coding (DVC) is rapidly increasing in popularity by the way of shifting the complexity from encoder to decoder, whereas no compression performance degrades, at least in theory. In contrast with conventional video codecs, the inter-frame correlation in DVC is explored at decoder based on the received syndromes of Wyner-Ziv (WZ) frame and side information (SI) frame generated from other frames available only at decoder. However, the ultimate decoding performances of DVC are based on the assumption that the perfect knowledge of correlation statistic between WZ and SI frames should be available at decoder. Therefore, the ability of obtaining a good statistical correlation estimate is becoming increasingly important in practical DVC implementations. Generally, the existing correlation estimation methods in DVC can be classified into two main types: pre-estimation where estimation starts before decoding and on-the-fly (OTF) estimation where estimation can be refined iteratively during decoding. As potential changes between frames might be unpredictable or dynamical, OTF estimation methods usually outperforms pre-estimation techniques with the cost of increased decoding complexity (e.g., sampling methods). In this paper, we propose a low complexity adaptive DVC scheme using expectation propagation (EP), where correlation estimation is performed OTF as it is carried out jointly with decoding of the factor graph-based DVC code. Among different approximate inference methods, EP generally offers better tradeoff between accuracy and complexity. Experimental results show that our proposed scheme outperforms the benchmark state-of-the-art DISCOVER codec and other cases without correlation tracking, and achieves comparable decoding performance but with significantly low complexity comparing with sampling method. PMID:23750314

Adaptive distributed video coding with correlation estimation using expectation propagation

NASA Astrophysics Data System (ADS)

Cui, Lijuan; Wang, Shuang; Jiang, Xiaoqian; Cheng, Samuel

2012-10-01

Distributed video coding (DVC) is rapidly increasing in popularity by the way of shifting the complexity from encoder to decoder, whereas no compression performance degrades, at least in theory. In contrast with conventional video codecs, the inter-frame correlation in DVC is explored at decoder based on the received syndromes of Wyner-Ziv (WZ) frame and side information (SI) frame generated from other frames available only at decoder. However, the ultimate decoding performances of DVC are based on the assumption that the perfect knowledge of correlation statistic between WZ and SI frames should be available at decoder. Therefore, the ability of obtaining a good statistical correlation estimate is becoming increasingly important in practical DVC implementations. Generally, the existing correlation estimation methods in DVC can be classified into two main types: pre-estimation where estimation starts before decoding and on-the-fly (OTF) estimation where estimation can be refined iteratively during decoding. As potential changes between frames might be unpredictable or dynamical, OTF estimation methods usually outperforms pre-estimation techniques with the cost of increased decoding complexity (e.g., sampling methods). In this paper, we propose a low complexity adaptive DVC scheme using expectation propagation (EP), where correlation estimation is performed OTF as it is carried out jointly with decoding of the factor graph-based DVC code. Among different approximate inference methods, EP generally offers better tradeoff between accuracy and complexity. Experimental results show that our proposed scheme outperforms the benchmark state-of-the-art DISCOVER codec and other cases without correlation tracking, and achieves comparable decoding performance but with significantly low complexity comparing with sampling method.

Adaptive Distributed Video Coding with Correlation Estimation using Expectation Propagation.

PubMed

Cui, Lijuan; Wang, Shuang; Jiang, Xiaoqian; Cheng, Samuel

2012-10-15

Distributed video coding (DVC) is rapidly increasing in popularity by the way of shifting the complexity from encoder to decoder, whereas no compression performance degrades, at least in theory. In contrast with conventional video codecs, the inter-frame correlation in DVC is explored at decoder based on the received syndromes of Wyner-Ziv (WZ) frame and side information (SI) frame generated from other frames available only at decoder. However, the ultimate decoding performances of DVC are based on the assumption that the perfect knowledge of correlation statistic between WZ and SI frames should be available at decoder. Therefore, the ability of obtaining a good statistical correlation estimate is becoming increasingly important in practical DVC implementations. Generally, the existing correlation estimation methods in DVC can be classified into two main types: pre-estimation where estimation starts before decoding and on-the-fly (OTF) estimation where estimation can be refined iteratively during decoding. As potential changes between frames might be unpredictable or dynamical, OTF estimation methods usually outperforms pre-estimation techniques with the cost of increased decoding complexity (e.g., sampling methods). In this paper, we propose a low complexity adaptive DVC scheme using expectation propagation (EP), where correlation estimation is performed OTF as it is carried out jointly with decoding of the factor graph-based DVC code. Among different approximate inference methods, EP generally offers better tradeoff between accuracy and complexity. Experimental results show that our proposed scheme outperforms the benchmark state-of-the-art DISCOVER codec and other cases without correlation tracking, and achieves comparable decoding performance but with significantly low complexity comparing with sampling method.

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 results highlight the need to improve access to care and increase coverage and equity of pneumonia preventing vaccines in states with high pneumonia burden. PMID:26086700

J-adaptive estimation with estimated noise statistics

NASA Technical Reports Server (NTRS)

Jazwinski, A. H.; Hipkins, C.

1973-01-01

The J-adaptive sequential estimator is extended to include simultaneous estimation of the noise statistics in a model for system dynamics. This extension completely automates the estimator, eliminating the requirement of an analyst in the loop. Simulations in satellite orbit determination demonstrate the efficacy of the sequential estimation algorithm.

[The readiness of the young teacher for the job].

PubMed

Ruskova, R

1992-01-01

The study aims at studying the professional readiness of the young teachers concerning their psychic state. It includes subjective-individual determinants--attitude to the profession, professional choice and steadiness, professional skills and satisfaction. The investigation is part of a broad complex study. The method used is directed first of all to self-estimation of the teacher concerning the structural system of the pedagogic activity which embraces supplementary questionnaire, revealing the motivation side of the scales for self-estimation. The subject of the examination are teachers from primary schools with length of service one to five years--time for completing their adaptation to the profession. The investigation includes 40 teachers from the cities of Sofia and Burgas. A general conclusion could be made, that there is professional readiness of the young teacher to be up to the requirements. His/her self-estimation corresponds to the adaptive behaviour and the choice of profession has a considerable effect on the professional steadiness. The general low satisfaction is not a sign for dysadaptation, but this low level presupposes lack of stimuli for personal development and perfection.

A TWO-STATE MIXED HIDDEN MARKOV MODEL FOR RISKY TEENAGE DRIVING BEHAVIOR

PubMed Central

Jackson, John C.; Albert, Paul S.; Zhang, Zhiwei

2016-01-01

This paper proposes a joint model for longitudinal binary and count outcomes. We apply the model to a unique longitudinal study of teen driving where risky driving behavior and the occurrence of crashes or near crashes are measured prospectively over the first 18 months of licensure. Of scientific interest is relating the two processes and predicting crash and near crash outcomes. We propose a two-state mixed hidden Markov model whereby the hidden state characterizes the mean for the joint longitudinal crash/near crash outcomes and elevated g-force events which are a proxy for risky driving. Heterogeneity is introduced in both the conditional model for the count outcomes and the hidden process using a shared random effect. An estimation procedure is presented using the forward–backward algorithm along with adaptive Gaussian quadrature to perform numerical integration. The estimation procedure readily yields hidden state probabilities as well as providing for a broad class of predictors. PMID:27766124

Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression

PubMed Central

Liu, Yu-Ying; Li, Shuang; Li, Fuxin; Song, Le; Rehg, James M.

2016-01-01

The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time. However, the lack of an efficient parameter learning algorithm for CT-HMM restricts its use to very small models or requires unrealistic constraints on the state transitions. In this paper, we present the first complete characterization of efficient EM-based learning methods for CT-HMM models. We demonstrate that the learning problem consists of two challenges: the estimation of posterior state probabilities and the computation of end-state conditioned statistics. We solve the first challenge by reformulating the estimation problem in terms of an equivalent discrete time-inhomogeneous hidden Markov model. The second challenge is addressed by adapting three approaches from the continuous time Markov chain literature to the CT-HMM domain. We demonstrate the use of CT-HMMs with more than 100 states to visualize and predict disease progression using a glaucoma dataset and an Alzheimer’s disease dataset. PMID:27019571

Combining facial dynamics with appearance for age estimation.

PubMed

Dibeklioglu, Hamdi; Alnajar, Fares; Ali Salah, Albert; Gevers, Theo

2015-06-01

Estimating the age of a human from the captured images of his/her face is a challenging problem. In general, the existing approaches to this problem use appearance features only. In this paper, we show that in addition to appearance information, facial dynamics can be leveraged in age estimation. We propose a method to extract and use dynamic features for age estimation, using a person's smile. Our approach is tested on a large, gender-balanced database with 400 subjects, with an age range between 8 and 76. In addition, we introduce a new database on posed disgust expressions with 324 subjects in the same age range, and evaluate the reliability of the proposed approach when used with another expression. State-of-the-art appearance-based age estimation methods from the literature are implemented as baseline. We demonstrate that for each of these methods, the addition of the proposed dynamic features results in statistically significant improvement. We further propose a novel hierarchical age estimation architecture based on adaptive age grouping. We test our approach extensively, including an exploration of spontaneous versus posed smile dynamics, and gender-specific age estimation. We show that using spontaneity information reduces the mean absolute error by up to 21%, advancing the state of the art for facial age estimation.

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.

Adaptive vehicle motion estimation and prediction

NASA Astrophysics Data System (ADS)

Zhao, Liang; Thorpe, Chuck E.

1999-01-01

Accurate motion estimation and reliable maneuver prediction enable an automated car to react quickly and correctly to the rapid maneuvers of the other vehicles, and so allow safe and efficient navigation. In this paper, we present a car tracking system which provides motion estimation, maneuver prediction and detection of the tracked car. The three strategies employed - adaptive motion modeling, adaptive data sampling, and adaptive model switching probabilities - result in an adaptive interacting multiple model algorithm (AIMM). The experimental results on simulated and real data demonstrate that our tracking system is reliable, flexible, and robust. The adaptive tracking makes the system intelligent and useful in various autonomous driving tasks.

Context-aware adaptive spelling in motor imagery BCI

NASA Astrophysics Data System (ADS)

Perdikis, S.; Leeb, R.; Millán, J. d. R.

2016-06-01

Objective. This work presents a first motor imagery-based, adaptive brain-computer interface (BCI) speller, which is able to exploit application-derived context for improved, simultaneous classifier adaptation and spelling. Online spelling experiments with ten able-bodied users evaluate the ability of our scheme, first, to alleviate non-stationarity of brain signals for restoring the subject’s performances, second, to guide naive users into BCI control avoiding initial offline BCI calibration and, third, to outperform regular unsupervised adaptation. Approach. Our co-adaptive framework combines the BrainTree speller with smooth-batch linear discriminant analysis adaptation. The latter enjoys contextual assistance through BrainTree’s language model to improve online expectation-maximization maximum-likelihood estimation. Main results. Our results verify the possibility to restore single-sample classification and BCI command accuracy, as well as spelling speed for expert users. Most importantly, context-aware adaptation performs significantly better than its unsupervised equivalent and similar to the supervised one. Although no significant differences are found with respect to the state-of-the-art PMean approach, the proposed algorithm is shown to be advantageous for 30% of the users. Significance. We demonstrate the possibility to circumvent supervised BCI recalibration, saving time without compromising the adaptation quality. On the other hand, we show that this type of classifier adaptation is not as efficient for BCI training purposes.

Context-aware adaptive spelling in motor imagery BCI.

PubMed

Perdikis, S; Leeb, R; Millán, J D R

2016-06-01

This work presents a first motor imagery-based, adaptive brain-computer interface (BCI) speller, which is able to exploit application-derived context for improved, simultaneous classifier adaptation and spelling. Online spelling experiments with ten able-bodied users evaluate the ability of our scheme, first, to alleviate non-stationarity of brain signals for restoring the subject's performances, second, to guide naive users into BCI control avoiding initial offline BCI calibration and, third, to outperform regular unsupervised adaptation. Our co-adaptive framework combines the BrainTree speller with smooth-batch linear discriminant analysis adaptation. The latter enjoys contextual assistance through BrainTree's language model to improve online expectation-maximization maximum-likelihood estimation. Our results verify the possibility to restore single-sample classification and BCI command accuracy, as well as spelling speed for expert users. Most importantly, context-aware adaptation performs significantly better than its unsupervised equivalent and similar to the supervised one. Although no significant differences are found with respect to the state-of-the-art PMean approach, the proposed algorithm is shown to be advantageous for 30% of the users. We demonstrate the possibility to circumvent supervised BCI recalibration, saving time without compromising the adaptation quality. On the other hand, we show that this type of classifier adaptation is not as efficient for BCI training purposes.

Adaptive Sampling approach to environmental site characterization at Joliet Army Ammunition Plant: Phase 2 demonstration

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

Bujewski, G.E.; Johnson, R.L.

1996-04-01

Adaptive sampling programs provide real opportunities to save considerable time and money when characterizing hazardous waste sites. This Strategic Environmental Research and Development Program (SERDP) project demonstrated two decision-support technologies, SitePlanner{trademark} and Plume{trademark}, that can facilitate the design and deployment of an adaptive sampling program. A demonstration took place at Joliet Army Ammunition Plant (JAAP), and was unique in that it was tightly coupled with ongoing Army characterization work at the facility, with close scrutiny by both state and federal regulators. The demonstration was conducted in partnership with the Army Environmental Center`s (AEC) Installation Restoration Program and AEC`s Technology Developmentmore » Program. AEC supported researchers from Tufts University who demonstrated innovative field analytical techniques for the analysis of TNT and DNT. SitePlanner{trademark} is an object-oriented database specifically designed for site characterization that provides an effective way to compile, integrate, manage and display site characterization data as it is being generated. Plume{trademark} uses a combination of Bayesian analysis and geostatistics to provide technical staff with the ability to quantitatively merge soft and hard information for an estimate of the extent of contamination. Plume{trademark} provides an estimate of contamination extent, measures the uncertainty associated with the estimate, determines the value of additional sampling, and locates additional samples so that their value is maximized.« less

Performance Enhancement for a GPS Vector-Tracking Loop Utilizing an Adaptive Iterated Extended Kalman Filter

PubMed Central

Chen, Xiyuan; Wang, Xiying; Xu, Yuan

2014-01-01

This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively. PMID:25502124

Performance enhancement for a GPS vector-tracking loop utilizing an adaptive iterated extended Kalman filter.

PubMed

Chen, Xiyuan; Wang, Xiying; Xu, Yuan

2014-12-09

This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively.

New method for propagating the square root covariance matrix in triangular form. [using Kalman-Bucy filter

NASA Technical Reports Server (NTRS)

Choe, C. Y.; Tapley, B. D.

1975-01-01

A method proposed by Potter of applying the Kalman-Bucy filter to the problem of estimating the state of a dynamic system is described, in which the square root of the state error covariance matrix is used to process the observations. A new technique which propagates the covariance square root matrix in lower triangular form is given for the discrete observation case. The technique is faster than previously proposed algorithms and is well-adapted for use with the Carlson square root measurement algorithm.

On Multi-Dimensional Unstructured Mesh Adaption

NASA Technical Reports Server (NTRS)

Wood, William A.; Kleb, William L.

1999-01-01

Anisotropic unstructured mesh adaption is developed for a truly multi-dimensional upwind fluctuation splitting scheme, as applied to scalar advection-diffusion. The adaption is performed locally using edge swapping, point insertion/deletion, and nodal displacements. Comparisons are made versus the current state of the art for aggressive anisotropic unstructured adaption, which is based on a posteriori error estimates. Demonstration of both schemes to model problems, with features representative of compressible gas dynamics, show the present method to be superior to the a posteriori adaption for linear advection. The performance of the two methods is more similar when applied to nonlinear advection, with a difference in the treatment of shocks. The a posteriori adaption can excessively cluster points to a shock, while the present multi-dimensional scheme tends to merely align with a shock, using fewer nodes. As a consequence of this alignment tendency, an implementation of eigenvalue limiting for the suppression of expansion shocks is developed for the multi-dimensional distribution scheme. The differences in the treatment of shocks by the adaption schemes, along with the inherently low levels of artificial dissipation in the fluctuation splitting solver, suggest the present method is a strong candidate for applications to compressible gas dynamics.

Kalman filter control of a model of spatiotemporal cortical dynamics

PubMed Central

Schiff, Steven J; Sauer, Tim

2007-01-01

Recent advances in Kalman filtering to estimate system state and parameters in nonlinear systems have offered the potential to apply such approaches to spatiotemporal nonlinear systems. We here adapt the nonlinear method of unscented Kalman filtering to observe the state and estimate parameters in a computational spatiotemporal excitable system that serves as a model for cerebral cortex. We demonstrate the ability to track spiral wave dynamics, and to use an observer system to calculate control signals delivered through applied electrical fields. We demonstrate how this strategy can control the frequency of such a system, or quench the wave patterns, while minimizing the energy required for such results. These findings are readily testable in experimental applications, and have the potential to be applied to the treatment of human disease. PMID:18310806

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 effects of severe events on the grid. The power grid continues to grow and the number of measurements is increasing at an accelerated rate due to the variety of smart grid devices being introduced. A parallel state estimation implementation will have better performance than traditional, sequential state estimation by utilizing the power of high performance computing (HPC). This increased performance positions parallel state estimators as valuable tools for operating the increasingly more complex power grid.« less

Type-2 fuzzy logic control based MRAS speed estimator for speed sensorless direct torque and flux control of an induction motor drive.

PubMed

Ramesh, Tejavathu; Kumar Panda, Anup; Shiva Kumar, S

2015-07-01

In this research study, a model reference adaptive system (MRAS) speed estimator for speed sensorless direct torque and flux control (DTFC) of an induction motor drive (IMD) using two adaptation mechanism schemes are proposed to replace the conventional proportional integral controller (PIC). The first adaptation mechanism scheme is based on Type-1 fuzzy logic controller (T1FLC), which is used to achieve high performance sensorless drive in both transient as well as steady state conditions. However, the Type-1 fuzzy sets are certain and unable to work effectively when higher degree of uncertainties presents in the system which can be caused by sudden change in speed or different load disturbances, process noise etc. Therefore, a new Type-2 fuzzy logic controller (T2FLC) based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties and improves the performance and also robust to various load torque and sudden change in speed conditions, respectively. The detailed performances of various adaptation mechanism schemes are carried out in a MATLAB/Simulink environment with a speed sensor and speed sensorless modes of operation when an IMD is operating under different operating conditions, such as, no-load, load and sudden change in speed, respectively. To validate the different control approaches, the system also implemented on real-time system and adequate results are reported for its validation. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A proposed Kalman filter algorithm for estimation of unmeasured output variables for an F100 turbofan engine

NASA Technical Reports Server (NTRS)

Alag, Gurbux S.; Gilyard, Glenn B.

1990-01-01

To develop advanced control systems for optimizing aircraft engine performance, unmeasurable output variables must be estimated. The estimation has to be done in an uncertain environment and be adaptable to varying degrees of modeling errors and other variations in engine behavior over its operational life cycle. This paper represented an approach to estimate unmeasured output variables by explicitly modeling the effects of off-nominal engine behavior as biases on the measurable output variables. A state variable model accommodating off-nominal behavior is developed for the engine, and Kalman filter concepts are used to estimate the required variables. Results are presented from nonlinear engine simulation studies as well as the application of the estimation algorithm on actual flight data. The formulation presented has a wide range of application since it is not restricted or tailored to the particular application described.

Adaptive Modal Identification for Flutter Suppression Control

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Drew, Michael; Swei, Sean S.

2016-01-01

In this paper, we will develop an adaptive modal identification method for identifying the frequencies and damping of a flutter mode based on model-reference adaptive control (MRAC) and least-squares methods. The least-squares parameter estimation will achieve parameter convergence in the presence of persistent excitation whereas the MRAC parameter estimation does not guarantee parameter convergence. Two adaptive flutter suppression control approaches are developed: one based on MRAC and the other based on the least-squares method. The MRAC flutter suppression control is designed as an integral part of the parameter estimation where the feedback signal is used to estimate the modal information. On the other hand, the separation principle of control and estimation is applied to the least-squares method. The least-squares modal identification is used to perform parameter estimation.

Differential flatness properties and multivariable adaptive control of ovarian system dynamics

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos

2016-12-01

The ovarian system exhibits nonlinear dynamics which is modeled by a set of coupled nonlinear differential equations. The paper proposes adaptive fuzzy control based on differential flatness theory for the complex dynamics of the ovarian system. It is proven that the dynamic model of the ovarian system, having as state variables the LH and the FSH hormones and their derivatives, is a differentially flat one. This means that all its state variables and its control inputs can be described as differential functions of the flat output. By exploiting differential flatness properties the system's dynamic model is written in the multivariable linear canonical (Brunovsky) form, for which the design of a state feedback controller becomes possible. After this transformation, the new control inputs of the system contain unknown nonlinear parts, which are identified with the use of neurofuzzy approximators. The learning procedure for these estimators is determined by the requirement the first derivative of the closed-loop's Lyapunov function to be a negative one. Moreover, Lyapunov stability analysis shows that H-infinity tracking performance is succeeded for the feedback control loop and this assures improved robustness to the aforementioned model uncertainty as well as to external perturbations. The efficiency of the proposed adaptive fuzzy control scheme is confirmed through simulation experiments.

Adaptive Resampling Particle Filters for GPS Carrier-Phase Navigation and Collision Avoidance System

NASA Astrophysics Data System (ADS)

Hwang, Soon Sik

This dissertation addresses three problems: 1) adaptive resampling technique (ART) for Particle Filters, 2) precise relative positioning using Global Positioning System (GPS) Carrier-Phase (CP) measurements applied to nonlinear integer resolution problem for GPS CP navigation using Particle Filters, and 3) collision detection system based on GPS CP broadcasts. First, Monte Carlo filters, called Particle Filters (PF), are widely used where the system is non-linear and non-Gaussian. In real-time applications, their estimation accuracies and efficiencies are significantly affected by the number of particles and the scheduling of relocating weights and samples, the so-called resampling step. In this dissertation, the appropriate number of particles is estimated adaptively such that the error of the sample mean and variance stay in bounds. These bounds are given by the confidence interval of a normal probability distribution for a multi-variate state. Two required number of samples maintaining the mean and variance error within the bounds are derived. The time of resampling is determined when the required sample number for the variance error crosses the required sample number for the mean error. Second, the PF using GPS CP measurements with adaptive resampling is applied to precise relative navigation between two GPS antennas. In order to make use of CP measurements for navigation, the unknown number of cycles between GPS antennas, the so called integer ambiguity, should be resolved. The PF is applied to this integer ambiguity resolution problem where the relative navigation states estimation involves nonlinear observations and nonlinear dynamics equation. Using the PF, the probability density function of the states is estimated by sampling from the position and velocity space and the integer ambiguities are resolved without using the usual hypothesis tests to search for the integer ambiguity. The ART manages the number of position samples and the frequency of the resampling step for real-time kinematics GPS navigation. The experimental results demonstrate the performance of the ART and the insensitivity of the proposed approach to GPS CP cycle-slips. Third, the GPS has great potential for the development of new collision avoidance systems and is being considered for the next generation Traffic alert and Collision Avoidance System (TCAS). The current TCAS equipment, is capable of broadcasting GPS code information to nearby airplanes, and also, the collision avoidance system using the navigation information based on GPS code has been studied by researchers. In this dissertation, the aircraft collision detection system using GPS CP information is addressed. The PF with position samples is employed for the CP based relative position estimation problem and the same algorithm can be used to determine the vehicle attitude if multiple GPS antennas are used. For a reliable and enhanced collision avoidance system, three dimensional trajectories are projected using the estimates of the relative position, velocity, and the attitude. It is shown that the performance of GPS CP based collision detecting algorithm meets the accuracy requirements for a precise approach of flight for auto landing with significantly less unnecessary collision false alarms and no miss alarms.

Dynamic filtering improves attentional state prediction with fNIRS

PubMed Central

Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.

2016-01-01

Brain activity can predict a person’s level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise – thereby increasing such state prediction accuracy – remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% ± 6% versus 72% ± 15%). PMID:27231602

High-Level Connectionist Models

DTIC Science & Technology

1993-10-01

subject of intense research since Axelrod (1984) showed that two agents engaged in a prisoner’s dilemma ( Poundstone , 1992) can evolve into mutually...The various parameter values for the program are set as described above unless otherwise noted. 4.1 Williams ’ Trigger Problem As an initial test...M. P. Vecchi. Optimization by simulated annealing. Sci- ence, 220:671-680, 1983. [39] R. J. Williams . Adaptive State Representation and Estimation

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

NASA Astrophysics Data System (ADS)

Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

2013-09-01

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

Modified Multiple Model Adaptive Estimation (M3AE) for Simultaneous Parameter and State Estimation

DTIC Science & Technology

1998-03-01

Contents Page Dedication : iv Acknowledgments v Table Of Contents vi List of Figures . . ; x List of Tables xv Abstract xvii Chapter 1 ...INTRODUCTION 1 1.1 Overview 1 1.2 Background 7 1.2.1 The Chi-Square Test 9 1.2.2 Generalized Likelihood Ratio (GLR) Testing 10 1.2.3 Multiple...M3AE Covariance Analysis 115 4.1.3 Simulations and Performance Analysis 121 4.1.3.1 Test Case 1 : aT = 32.0 124 4.1.3.2 Test Case 2: aT = 37.89, and

A MAP-based image interpolation method via Viterbi decoding of Markov chains of interpolation functions.

PubMed

Vedadi, Farhang; Shirani, Shahram

2014-01-01

A new method of image resolution up-conversion (image interpolation) based on maximum a posteriori sequence estimation is proposed. Instead of making a hard decision about the value of each missing pixel, we estimate the missing pixels in groups. At each missing pixel of the high resolution (HR) image, we consider an ensemble of candidate interpolation methods (interpolation functions). The interpolation functions are interpreted as states of a Markov model. In other words, the proposed method undergoes state transitions from one missing pixel position to the next. Accordingly, the interpolation problem is translated to the problem of estimating the optimal sequence of interpolation functions corresponding to the sequence of missing HR pixel positions. We derive a parameter-free probabilistic model for this to-be-estimated sequence of interpolation functions. Then, we solve the estimation problem using a trellis representation and the Viterbi algorithm. Using directional interpolation functions and sequence estimation techniques, we classify the new algorithm as an adaptive directional interpolation using soft-decision estimation techniques. Experimental results show that the proposed algorithm yields images with higher or comparable peak signal-to-noise ratios compared with some benchmark interpolation methods in the literature while being efficient in terms of implementation and complexity considerations.

ZZ-Type a posteriori error estimators for adaptive boundary element methods on a curve☆

PubMed Central

Feischl, Michael; Führer, Thomas; Karkulik, Michael; Praetorius, Dirk

2014-01-01

In the context of the adaptive finite element method (FEM), ZZ-error estimators named after Zienkiewicz and Zhu (1987) [52] are mathematically well-established and widely used in practice. In this work, we propose and analyze ZZ-type error estimators for the adaptive boundary element method (BEM). We consider weakly singular and hyper-singular integral equations and prove, in particular, convergence of the related adaptive mesh-refining algorithms. Throughout, the theoretical findings are underlined by numerical experiments. PMID:24748725

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

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

Reinforcement learning neural-network-based controller for nonlinear discrete-time systems with input constraints.

PubMed

He, Pingan; Jagannathan, S

2007-04-01

A novel adaptive-critic-based neural network (NN) controller in discrete time is designed to deliver a desired tracking performance for a class of nonlinear systems in the presence of actuator constraints. The constraints of the actuator are treated in the controller design as the saturation nonlinearity. The adaptive critic NN controller architecture based on state feedback includes two NNs: the critic NN is used to approximate the "strategic" utility function, whereas the action NN is employed to minimize both the strategic utility function and the unknown nonlinear dynamic estimation errors. The critic and action NN weight updates are derived by minimizing certain quadratic performance indexes. Using the Lyapunov approach and with novel weight updates, the uniformly ultimate boundedness of the closed-loop tracking error and weight estimates is shown in the presence of NN approximation errors and bounded unknown disturbances. The proposed NN controller works in the presence of multiple nonlinearities, unlike other schemes that normally approximate one nonlinearity. Moreover, the adaptive critic NN controller does not require an explicit offline training phase, and the NN weights can be initialized at zero or random. Simulation results justify the theoretical analysis.

Motion adaptive Kalman filter for super-resolution

NASA Astrophysics Data System (ADS)

Richter, Martin; Nasse, Fabian; Schröder, Hartmut

2011-01-01

Superresolution is a sophisticated strategy to enhance image quality of both low and high resolution video, performing tasks like artifact reduction, scaling and sharpness enhancement in one algorithm, all of them reconstructing high frequency components (above Nyquist frequency) in some way. Especially recursive superresolution algorithms can fulfill high quality aspects because they control the video output using a feed-back loop and adapt the result in the next iteration. In addition to excellent output quality, temporal recursive methods are very hardware efficient and therefore even attractive for real-time video processing. A very promising approach is the utilization of Kalman filters as proposed by Farsiu et al. Reliable motion estimation is crucial for the performance of superresolution. Therefore, robust global motion models are mainly used, but this also limits the application of superresolution algorithm. Thus, handling sequences with complex object motion is essential for a wider field of application. Hence, this paper proposes improvements by extending the Kalman filter approach using motion adaptive variance estimation and segmentation techniques. Experiments confirm the potential of our proposal for ideal and real video sequences with complex motion and further compare its performance to state-of-the-art methods like trainable filters.

Performance Enhancement of Pharmacokinetic Diffuse Fluorescence Tomography by Use of Adaptive Extended Kalman Filtering.

PubMed

Wang, Xin; Wu, Linhui; Yi, Xi; Zhang, Yanqi; Zhang, Limin; Zhao, Huijuan; Gao, Feng

2015-01-01

Due to both the physiological and morphological differences in the vascularization between healthy and diseased tissues, pharmacokinetic diffuse fluorescence tomography (DFT) can provide contrast-enhanced and comprehensive information for tumor diagnosis and staging. In this regime, the extended Kalman filtering (EKF) based method shows numerous advantages including accurate modeling, online estimation of multiparameters, and universal applicability to any optical fluorophore. Nevertheless the performance of the conventional EKF highly hinges on the exact and inaccessible prior knowledge about the initial values. To address the above issues, an adaptive-EKF scheme is proposed based on a two-compartmental model for the enhancement, which utilizes a variable forgetting-factor to compensate the inaccuracy of the initial states and emphasize the effect of the current data. It is demonstrated using two-dimensional simulative investigations on a circular domain that the proposed adaptive-EKF can obtain preferable estimation of the pharmacokinetic-rates to the conventional-EKF and the enhanced-EKF in terms of quantitativeness, noise robustness, and initialization independence. Further three-dimensional numerical experiments on a digital mouse model validate the efficacy of the method as applied in realistic biological systems.

Multiple testing with discrete data: Proportion of true null hypotheses and two adaptive FDR procedures.

PubMed

Chen, Xiongzhi; Doerge, Rebecca W; Heyse, Joseph F

2018-05-11

We consider multiple testing with false discovery rate (FDR) control when p values have discrete and heterogeneous null distributions. We propose a new estimator of the proportion of true null hypotheses and demonstrate that it is less upwardly biased than Storey's estimator and two other estimators. The new estimator induces two adaptive procedures, that is, an adaptive Benjamini-Hochberg (BH) procedure and an adaptive Benjamini-Hochberg-Heyse (BHH) procedure. We prove that the adaptive BH (aBH) procedure is conservative nonasymptotically. Through simulation studies, we show that these procedures are usually more powerful than their nonadaptive counterparts and that the adaptive BHH procedure is usually more powerful than the aBH procedure and a procedure based on randomized p-value. The adaptive procedures are applied to a study of HIV vaccine efficacy, where they identify more differentially polymorphic positions than the BH procedure at the same FDR level. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

High precision tracking control of a servo gantry with dynamic friction compensation.

PubMed

Zhang, Yangming; Yan, Peng; Zhang, Zhen

2016-05-01

This paper is concerned with the tracking control problem of a voice coil motor (VCM) actuated servo gantry system. By utilizing an adaptive control technique combined with a sliding mode approach, an adaptive sliding mode control (ASMC) law with friction compensation scheme is proposed in presence of both frictions and external disturbances. Based on the LuGre dynamic friction model, a dual-observer structure is used to estimate the unmeasurable friction state, and an adaptive control law is synthesized to effectively handle the unknown friction model parameters as well as the bound of the disturbances. Moreover, the proposed control law is also implemented on a VCM servo gantry system for motion tracking. Simulations and experimental results demonstrate good tracking performance, which outperform traditional control approaches. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Scale-adaptive compressive tracking with feature integration

NASA Astrophysics Data System (ADS)

Liu, Wei; Li, Jicheng; Chen, Xiao; Li, Shuxin

2016-05-01

Numerous tracking-by-detection methods have been proposed for robust visual tracking, among which compressive tracking (CT) has obtained some promising results. A scale-adaptive CT method based on multifeature integration is presented to improve the robustness and accuracy of CT. We introduce a keypoint-based model to achieve the accurate scale estimation, which can additionally give a prior location of the target. Furthermore, by the high efficiency of data-independent random projection matrix, multiple features are integrated into an effective appearance model to construct the naïve Bayes classifier. At last, an adaptive update scheme is proposed to update the classifier conservatively. Experiments on various challenging sequences demonstrate substantial improvements by our proposed tracker over CT and other state-of-the-art trackers in terms of dealing with scale variation, abrupt motion, deformation, and illumination changes.

Adjoint-Based Algorithms for Adaptation and Design Optimizations on Unstructured Grids

NASA Technical Reports Server (NTRS)

Nielsen, Eric J.

2006-01-01

Schemes based on discrete adjoint algorithms present several exciting opportunities for significantly advancing the current state of the art in computational fluid dynamics. Such methods provide an extremely efficient means for obtaining discretely consistent sensitivity information for hundreds of design variables, opening the door to rigorous, automated design optimization of complex aerospace configuration using the Navier-Stokes equation. Moreover, the discrete adjoint formulation provides a mathematically rigorous foundation for mesh adaptation and systematic reduction of spatial discretization error. Error estimates are also an inherent by-product of an adjoint-based approach, valuable information that is virtually non-existent in today's large-scale CFD simulations. An overview of the adjoint-based algorithm work at NASA Langley Research Center is presented, with examples demonstrating the potential impact on complex computational problems related to design optimization as well as mesh adaptation.

On Time Delay Margin Estimation for Adaptive Control and Optimal Control Modification

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2011-01-01

This paper presents methods for estimating time delay margin for adaptive control of input delay systems with almost linear structured uncertainty. The bounded linear stability analysis method seeks to represent an adaptive law by a locally bounded linear approximation within a small time window. The time delay margin of this input delay system represents a local stability measure and is computed analytically by three methods: Pade approximation, Lyapunov-Krasovskii method, and the matrix measure method. These methods are applied to the standard model-reference adaptive control, s-modification adaptive law, and optimal control modification adaptive law. The windowing analysis results in non-unique estimates of the time delay margin since it is dependent on the length of a time window and parameters which vary from one time window to the next. The optimal control modification adaptive law overcomes this limitation in that, as the adaptive gain tends to infinity and if the matched uncertainty is linear, then the closed-loop input delay system tends to a LTI system. A lower bound of the time delay margin of this system can then be estimated uniquely without the need for the windowing analysis. Simulation results demonstrates the feasibility of the bounded linear stability method for time delay margin estimation.

Discriminative clustering on manifold for adaptive transductive classification.

PubMed

Zhang, Zhao; Jia, Lei; Zhang, Min; Li, Bing; Zhang, Li; Li, Fanzhang

2017-10-01

In this paper, we mainly propose a novel adaptive transductive label propagation approach by joint discriminative clustering on manifolds for representing and classifying high-dimensional data. Our framework seamlessly combines the unsupervised manifold learning, discriminative clustering and adaptive classification into a unified model. Also, our method incorporates the adaptive graph weight construction with label propagation. Specifically, our method is capable of propagating label information using adaptive weights over low-dimensional manifold features, which is different from most existing studies that usually predict the labels and construct the weights in the original Euclidean space. For transductive classification by our formulation, we first perform the joint discriminative K-means clustering and manifold learning to capture the low-dimensional nonlinear manifolds. Then, we construct the adaptive weights over the learnt manifold features, where the adaptive weights are calculated through performing the joint minimization of the reconstruction errors over features and soft labels so that the graph weights can be joint-optimal for data representation and classification. Using the adaptive weights, we can easily estimate the unknown labels of samples. After that, our method returns the updated weights for further updating the manifold features. Extensive simulations on image classification and segmentation show that our proposed algorithm can deliver the state-of-the-art performance on several public datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Application of Parallel Adjoint-Based Error Estimation and Anisotropic Grid Adaptation for Three-Dimensional Aerospace Configurations

NASA Technical Reports Server (NTRS)

Lee-Rausch, E. M.; Park, M. A.; Jones, W. T.; Hammond, D. P.; Nielsen, E. J.

2005-01-01

This paper demonstrates the extension of error estimation and adaptation methods to parallel computations enabling larger, more realistic aerospace applications and the quantification of discretization errors for complex 3-D solutions. Results were shown for an inviscid sonic-boom prediction about a double-cone configuration and a wing/body segmented leading edge (SLE) configuration where the output function of the adjoint was pressure integrated over a part of the cylinder in the near field. After multiple cycles of error estimation and surface/field adaptation, a significant improvement in the inviscid solution for the sonic boom signature of the double cone was observed. Although the double-cone adaptation was initiated from a very coarse mesh, the near-field pressure signature from the final adapted mesh compared very well with the wind-tunnel data which illustrates that the adjoint-based error estimation and adaptation process requires no a priori refinement of the mesh. Similarly, the near-field pressure signature for the SLE wing/body sonic boom configuration showed a significant improvement from the initial coarse mesh to the final adapted mesh in comparison with the wind tunnel results. Error estimation and field adaptation results were also presented for the viscous transonic drag prediction of the DLR-F6 wing/body configuration, and results were compared to a series of globally refined meshes. Two of these globally refined meshes were used as a starting point for the error estimation and field-adaptation process where the output function for the adjoint was the total drag. The field-adapted results showed an improvement in the prediction of the drag in comparison with the finest globally refined mesh and a reduction in the estimate of the remaining drag error. The adjoint-based adaptation parameter showed a need for increased resolution in the surface of the wing/body as well as a need for wake resolution downstream of the fuselage and wing trailing edge in order to achieve the requested drag tolerance. Although further adaptation was required to meet the requested tolerance, no further cycles were computed in order to avoid large discrepancies between the surface mesh spacing and the refined field spacing.

Nonlinear adaptive control system design with asymptotically stable parameter estimation error

NASA Astrophysics Data System (ADS)

Mishkov, Rumen; Darmonski, Stanislav

2018-01-01

The paper presents a new general method for nonlinear adaptive system design with asymptotic stability of the parameter estimation error. The advantages of the approach include asymptotic unknown parameter estimation without persistent excitation and capability to directly control the estimates transient response time. The method proposed modifies the basic parameter estimation dynamics designed via a known nonlinear adaptive control approach. The modification is based on the generalised prediction error, a priori constraints with a hierarchical parameter projection algorithm, and the stable data accumulation concepts. The data accumulation principle is the main tool for achieving asymptotic unknown parameter estimation. It relies on the parametric identifiability system property introduced. Necessary and sufficient conditions for exponential stability of the data accumulation dynamics are derived. The approach is applied in a nonlinear adaptive speed tracking vector control of a three-phase induction motor.

An adaptive displacement estimation algorithm for improved reconstruction of thermal strain.

PubMed

Ding, Xuan; Dutta, Debaditya; Mahmoud, Ahmed M; Tillman, Bryan; Leers, Steven A; Kim, Kang

2015-01-01

Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas' estimator and time-shift estimators such as normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas' estimator is limited by phase-wrapping and NXcorr performs poorly when the SNR is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas' estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas' estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas' estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI showed that the adaptive displacement estimator was less biased than either Loupas' estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7 to 350% and the spatial accuracy by 1.2 to 23.0% (P < 0.001). An ex vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and resulted in improved strain reconstruction.

An Adaptive Displacement Estimation Algorithm for Improved Reconstruction of Thermal Strain

PubMed Central

Ding, Xuan; Dutta, Debaditya; Mahmoud, Ahmed M.; Tillman, Bryan; Leers, Steven A.; Kim, Kang

2014-01-01

Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas’ estimator and time-shift estimators like normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas’ estimator is limited by phase-wrapping and NXcorr performs poorly when the signal-to-noise ratio (SNR) is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas’ estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex-vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas’ estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas’ estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI using Field II showed that the adaptive displacement estimator was less biased than either Loupas’ estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7–350% and the spatial accuracy by 1.2–23.0% (p < 0.001). An ex-vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and results in improved strain reconstruction. PMID:25585398

Effects of Estimation Bias on Multiple-Category Classification with an IRT-Based Adaptive Classification Procedure

ERIC Educational Resources Information Center

Yang, Xiangdong; Poggio, John C.; Glasnapp, Douglas R.

2006-01-01

The effects of five ability estimators, that is, maximum likelihood estimator, weighted likelihood estimator, maximum a posteriori, expected a posteriori, and Owen's sequential estimator, on the performances of the item response theory-based adaptive classification procedure on multiple categories were studied via simulations. The following…

Background Error Covariance Estimation using Information from a Single Model Trajectory with Application to Ocean Data Assimilation into the GEOS-5 Coupled Model

NASA Technical Reports Server (NTRS)

Keppenne, Christian L.; Rienecker, Michele M.; Kovach, Robin M.; Vernieres, Guillaume; Koster, Randal D. (Editor)

2014-01-01

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory. SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Background Error Covariance Estimation Using Information from a Single Model Trajectory with Application to Ocean Data Assimilation

NASA Technical Reports Server (NTRS)

Keppenne, Christian L.; Rienecker, Michele; Kovach, Robin M.; Vernieres, Guillaume

2014-01-01

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory.SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input-output direct feed-through term.

PubMed

Tsai, Jason S-H; Hsu, Wen-Teng; Lin, Long-Guei; Guo, Shu-Mei; Tann, Joseph W

2014-01-01

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input-output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Parameter estimation for stiff deterministic dynamical systems via ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Arnold, Andrea; Calvetti, Daniela; Somersalo, Erkki

2014-10-01

A commonly encountered problem in numerous areas of applications is to estimate the unknown coefficients of a dynamical system from direct or indirect observations at discrete times of some of the components of the state vector. A related problem is to estimate unobserved components of the state. An egregious example of such a problem is provided by metabolic models, in which the numerous model parameters and the concentrations of the metabolites in tissue are to be estimated from concentration data in the blood. A popular method for addressing similar questions in stochastic and turbulent dynamics is the ensemble Kalman filter (EnKF), a particle-based filtering method that generalizes classical Kalman filtering. In this work, we adapt the EnKF algorithm for deterministic systems in which the numerical approximation error is interpreted as a stochastic drift with variance based on classical error estimates of numerical integrators. This approach, which is particularly suitable for stiff systems where the stiffness may depend on the parameters, allows us to effectively exploit the parallel nature of particle methods. Moreover, we demonstrate how spatial prior information about the state vector, which helps the stability of the computed solution, can be incorporated into the filter. The viability of the approach is shown by computed examples, including a metabolic system modeling an ischemic episode in skeletal muscle, with a high number of unknown parameters.

Adaptive State Predictor Based Human Operator Modeling on Longitudinal and Lateral Control

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.; Gregory, Irene M.; Hempley, Lucas E.

2015-01-01

Control-theoretic modeling of the human operator dynamic behavior in manual control tasks has a long and rich history. In the last two decades, there has been a renewed interest in modeling the human operator. There has also been significant work on techniques used to identify the pilot model of a given structure. The purpose of this research is to attempt to go beyond pilot identification based on collected experimental data and to develop a predictor of pilot behavior. An experiment was conducted to categorize these interactions of the pilot with an adaptive controller compensating during control surface failures. A general linear in-parameter model structure is used to represent a pilot. Three different estimation methods are explored. A gradient descent estimator (GDE), a least squares estimator with exponential forgetting (LSEEF), and a least squares estimator with bounded gain forgetting (LSEBGF) used the experiment data to predict pilot stick input. Previous results have found that the GDE and LSEEF methods are fairly accurate in predicting longitudinal stick input from commanded pitch. This paper discusses the accuracy of each of the three methods - GDE, LSEEF, and LSEBGF - to predict both pilot longitudinal and lateral stick input from the flight director's commanded pitch and bank attitudes.

Eigenvector method for umbrella sampling enables error analysis

PubMed Central

Thiede, Erik H.; Van Koten, Brian; Weare, Jonathan; Dinner, Aaron R.

2016-01-01

Umbrella sampling efficiently yields equilibrium averages that depend on exploring rare states of a model by biasing simulations to windows of coordinate values and then combining the resulting data with physical weighting. Here, we introduce a mathematical framework that casts the step of combining the data as an eigenproblem. The advantage to this approach is that it facilitates error analysis. We discuss how the error scales with the number of windows. Then, we derive a central limit theorem for averages that are obtained from umbrella sampling. The central limit theorem suggests an estimator of the error contributions from individual windows, and we develop a simple and computationally inexpensive procedure for implementing it. We demonstrate this estimator for simulations of the alanine dipeptide and show that it emphasizes low free energy pathways between stable states in comparison to existing approaches for assessing error contributions. Our work suggests the possibility of using the estimator and, more generally, the eigenvector method for umbrella sampling to guide adaptation of the simulation parameters to accelerate convergence. PMID:27586912

A new chaotic communication scheme based on adaptive synchronization.

PubMed

Xiang-Jun, Wu

2006-12-01

A new chaotic communication scheme using adaptive synchronization technique of two unified chaotic systems is proposed. Different from the existing secure communication methods, the transmitted signal is modulated into the parameter of chaotic systems. The adaptive synchronization technique is used to synchronize two identical chaotic systems embedded in the transmitter and the receiver. It is assumed that the parameter of the receiver system is unknown. Based on the Lyapunov stability theory, an adaptive control law is derived to make the states of two identical unified chaotic systems with unknown system parameters asymptotically synchronized; thus the parameter of the receiver system is identified. Then the recovery of the original information signal in the receiver is successfully achieved on the basis of the estimated parameter. It is noticed that the time required for recovering the information signal and the accuracy of the recovered signal very sensitively depends on the frequency of the information signal. Numerical results have verified the effectiveness of the proposed scheme.

An adaptive optics system for solid-state laser systems used in inertial confinement fusion

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

Salmon, J.T.; Bliss, E.S.; Byrd, J.L.

1995-09-17

Using adaptive optics the authors have obtained nearly diffraction-limited 5 kJ, 3 nsec output pulses at 1.053 {micro}m from the Beamlet demonstration system for the National Ignition Facility (NIF). The peak Strehl ratio was improved from 0.009 to 0.50, as estimated from measured wavefront errors. They have also measured the relaxation of the thermally induced aberrations in the main beam line over a period of 4.5 hours. Peak-to-valley aberrations range from 6.8 waves at 1.053 {micro}m within 30 minutes after a full system shot to 3.9 waves after 4.5 hours. The adaptive optics system must have enough range to correctmore » accumulated thermal aberrations from several shots in addition to the immediate shot-induced error. Accumulated wavefront errors in the beam line will affect both the design of the adaptive optics system for NIF and the performance of that system.« less

Virtual reality adaptive stimulation of limbic networks in the mental readiness training.

PubMed

Cosić, Kresimir; Popović, Sinisa; Kostović, Ivica; Judas, Milos

2010-01-01

A significant proportion of severe psychological problems in recent large-scale peacekeeping operations underscores the importance of effective methods for strengthening the stress resilience. Virtual reality (VR) adaptive stimulation, based on the estimation of the participant's emotional state from physiological signals, may enhance the mental readiness training (MRT). Understanding neurobiological mechanisms by which the MRT based on VR adaptive stimulation can affect the resilience to stress is important for practical application in the stress resilience management. After the delivery of a traumatic audio-visual stimulus in the VR, the cascade of events occurs in the brain, which evokes various physiological manifestations. In addition to the "limbic" emotional and visceral brain circuitry, other large-scale sensory, cognitive, and memory brain networks participate with less known impact in this physiological response. The MRT based on VR adaptive stimulation may strengthen the stress resilience through targeted brain-body interactions. Integrated interdisciplinary efforts, which would integrate the brain imaging and the proposed approach, may contribute to clarifying the neurobiological foundation of the resilience to stress.

Design and analysis of adaptive Super-Twisting sliding mode control for a microgyroscope.

PubMed

Feng, Zhilin; Fei, Juntao

2018-01-01

This paper proposes a novel adaptive Super-Twisting sliding mode control for a microgyroscope under unknown model uncertainties and external disturbances. In order to improve the convergence rate of reaching the sliding surface and the accuracy of regulating and trajectory tracking, a high order Super-Twisting sliding mode control strategy is employed, which not only can combine the advantages of the traditional sliding mode control with the Super-Twisting sliding mode control, but also guarantee that the designed control system can reach the sliding surface and equilibrium point in a shorter finite time from any initial state and avoid chattering problems. In consideration of unknown parameters of micro gyroscope system, an adaptive algorithm based on Lyapunov stability theory is designed to estimate the unknown parameters and angular velocity of microgyroscope. Finally, the effectiveness of the proposed scheme is demonstrated by simulation results. The comparative study between adaptive Super-Twisting sliding mode control and conventional sliding mode control demonstrate the superiority of the proposed method.

How Big Is Big Enough? Sample Size Requirements for CAST Item Parameter Estimation

ERIC Educational Resources Information Center

Chuah, Siang Chee; Drasgow, Fritz; Luecht, Richard

2006-01-01

Adaptive tests offer the advantages of reduced test length and increased accuracy in ability estimation. However, adaptive tests require large pools of precalibrated items. This study looks at the development of an item pool for 1 type of adaptive administration: the computer-adaptive sequential test. An important issue is the sample size required…

Adaptive robust motion trajectory tracking control of pneumatic cylinders with LuGre model-based friction compensation

NASA Astrophysics Data System (ADS)

Meng, Deyuan; Tao, Guoliang; Liu, Hao; Zhu, Xiaocong

2014-07-01

Friction compensation is particularly important for motion trajectory tracking control of pneumatic cylinders at low speed movement. However, most of the existing model-based friction compensation schemes use simple classical models, which are not enough to address applications with high-accuracy position requirements. Furthermore, the friction force in the cylinder is time-varying, and there exist rather severe unmodelled dynamics and unknown disturbances in the pneumatic system. To deal with these problems effectively, an adaptive robust controller with LuGre model-based dynamic friction compensation is constructed. The proposed controller employs on-line recursive least squares estimation (RLSE) to reduce the extent of parametric uncertainties, and utilizes the sliding mode control method to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. In addition, in order to realize LuGre model-based friction compensation, the modified dual-observer structure for estimating immeasurable friction internal state is developed. Therefore, a prescribed motion tracking transient performance and final tracking accuracy can be guaranteed. Since the system model uncertainties are unmatched, the recursive backstepping design technology is applied. In order to solve the conflicts between the sliding mode control design and the adaptive control design, the projection mapping is used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Finally, the proposed controller is tested for tracking sinusoidal trajectories and smooth square trajectory under different loads and sudden disturbance. The testing results demonstrate that the achievable performance of the proposed controller is excellent and is much better than most other studies in literature. Especially when a 0.5 Hz sinusoidal trajectory is tracked, the maximum tracking error is 0.96 mm and the average tracking error is 0.45 mm. This paper constructs an adaptive robust controller which can compensate the friction force in the cylinder.

Trial-by-Trial Motor Cortical Correlates of a Rapidly Adapting Visuomotor Internal Model

PubMed Central

Ryu, Stephen I.

2017-01-01

Accurate motor control is mediated by internal models of how neural activity generates movement. We examined neural correlates of an adapting internal model of visuomotor gain in motor cortex while two macaques performed a reaching task in which the gain scaling between the hand and a presented cursor was varied. Previous studies of cortical changes during visuomotor adaptation focused on preparatory and perimovement epochs and analyzed trial-averaged neural data. Here, we recorded simultaneous neural population activity using multielectrode arrays and focused our analysis on neural differences in the period before the target appeared. We found that we could estimate the monkey's internal model of the gain using the neural population state during this pretarget epoch. This neural correlate depended on the gain experienced during recent trials and it predicted the speed of the subsequent reach. To explore the utility of this internal model estimate for brain–machine interfaces, we performed an offline analysis showing that it can be used to compensate for upcoming reach extent errors. Together, these results demonstrate that pretarget neural activity in motor cortex reflects the monkey's internal model of visuomotor gain on single trials and can potentially be used to improve neural prostheses. SIGNIFICANCE STATEMENT When generating movement commands, the brain is believed to use internal models of the relationship between neural activity and the body's movement. Visuomotor adaptation tasks have revealed neural correlates of these computations in multiple brain areas during movement preparation and execution. Here, we describe motor cortical changes in a visuomotor gain change task even before a specific movement is cued. We were able to estimate the gain internal model from these pretarget neural correlates and relate it to single-trial behavior. This is an important step toward understanding the sensorimotor system's algorithms for updating its internal models after specific movements and errors. Furthermore, the ability to estimate the internal model before movement could improve motor neural prostheses being developed for people with paralysis. PMID:28087767

Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method

PubMed Central

Cao, Youfang; Liang, Jie

2013-01-01

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape. PMID:23862966

Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method

NASA Astrophysics Data System (ADS)

Cao, Youfang; Liang, Jie

2013-07-01

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.

Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method.

PubMed

Cao, Youfang; Liang, Jie

2013-07-14

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.

Effects of measurement unobservability on neural extended Kalman filter tracking

NASA Astrophysics Data System (ADS)

Stubberud, Stephen C.; Kramer, Kathleen A.

2009-05-01

An important component of tracking fusion systems is the ability to fuse various sensors into a coherent picture of the scene. When multiple sensor systems are being used in an operational setting, the types of data vary. A significant but often overlooked concern of multiple sensors is the incorporation of measurements that are unobservable. An unobservable measurement is one that may provide information about the state, but cannot recreate a full target state. A line of bearing measurement, for example, cannot provide complete position information. Often, such measurements come from passive sensors such as a passive sonar array or an electronic surveillance measure (ESM) system. Unobservable measurements will, over time, result in the measurement uncertainty to grow without bound. While some tracking implementations have triggers to protect against the detrimental effects, many maneuver tracking algorithms avoid discussing this implementation issue. One maneuver tracking technique is the neural extended Kalman filter (NEKF). The NEKF is an adaptive estimation algorithm that estimates the target track as it trains a neural network on line to reduce the error between the a priori target motion model and the actual target dynamics. The weights of neural network are trained in a similar method to the state estimation/parameter estimation Kalman filter techniques. The NEKF has been shown to improve target tracking accuracy through maneuvers and has been use to predict target behavior using the new model that consists of the a priori model and the neural network. The key to the on-line adaptation of the NEKF is the fact that the neural network is trained using the same residuals as the Kalman filter for the tracker. The neural network weights are treated as augmented states to the target track. Through the state-coupling function, the weights are coupled to the target states. Thus, if the measurements cause the states of the target track to be unobservable, then the weights of the neural network have unobservable modes as well. In recent analysis, the NEKF was shown to have a significantly larger growth in the eigenvalues of the error covariance matrix than the standard EKF tracker when the measurements were purely bearings-only. This caused detrimental effects to the ability of the NEKF to model the target dynamics. In this work, the analysis is expanded to determine the detrimental effects of bearings-only measurements of various uncertainties on the performance of the NEKF when these unobservable measurements are interlaced with completely observable measurements. This analysis provides the ability to put implementation limitations on the NEKF when bearings-only sensors are present.

Estimating a WTP-based value of a QALY: the 'chained' approach.

PubMed

Robinson, Angela; Gyrd-Hansen, Dorte; Bacon, Philomena; Baker, Rachel; Pennington, Mark; Donaldson, Cam

2013-09-01

A major issue in health economic evaluation is that of the value to place on a quality adjusted life year (QALY), commonly used as a measure of health care effectiveness across Europe. This critical policy issue is reflected in the growing interest across Europe in development of more sound methods to elicit such a value. EuroVaQ was a collaboration of researchers from 9 European countries, the main aim being to develop more robust methods to determine the monetary value of a QALY based on surveys of the general public. The 'chained' approach of deriving a societal willingness-to-pay (WTP) based monetary value of a QALY used the following basic procedure. First, utility values were elicited for health states using the standard gamble (SG) and time trade off (TTO) methods. Second, a monetary value to avoid some risk/duration of that health state was elicited and the implied WTP per QALY estimated. We developed within EuroVaQ an adaptation to the 'chained approach' that attempts to overcome problems documented previously (in particular the tendency to arrive at exceedingly high WTP per QALY values). The survey was administered via Internet panels in each participating country and almost 22,000 responses achieved. Estimates of the value of a QALY varied across question and were, if anything, on the low side with the (trimmed) 'all country' mean WTP per QALY ranging from $18,247 to $34,097. Untrimmed means were considerably higher and medians considerably lower in each case. We conclude that the adaptation to the chained approach described here is a potentially useful technique for estimating WTP per QALY. A number of methodological challenges do still exist, however, and there is scope for further refinement. Copyright © 2013 Elsevier Ltd. All rights reserved.

Improved recovery of the hemodynamic response in Diffuse Optical Imaging using short optode separations and state-space modeling

PubMed Central

Gagnon, Louis; Perdue, Katherine; Greve, Douglas N.; Goldenholz, Daniel; Kaskhedikar, Gayatri; Boas, David A.

2011-01-01

Diffuse Optical Imaging (DOI) allows the recovery of the hemodynamic response associated with evoked brain activity. The signal is contaminated with systemic physiological interference which occurs in the superficial layers of the head as well as in the brain tissue. The back-reflection geometry of the measurement makes the DOI signal strongly contaminated by systemic interference occurring in the superficial layers. A recent development has been the use of signals from small source-detector separation (1 cm) optodes as regressors. Since those additional measurements are mainly sensitive to superficial layers in adult humans, they help in removing the systemic interference present in longer separation measurements (3 cm). Encouraged by those findings, we developed a dynamic estimation procedure to remove global interference using small optode separations and to estimate simultaneously the hemodynamic response. The algorithm was tested by recovering a simulated synthetic hemodynamic response added over baseline DOI data acquired from 6 human subjects at rest. The performance of the algorithm was quantified by the Pearson R2 coefficient and the mean square error (MSE) between the recovered and the simulated hemodynamic responses. Our dynamic estimator was also compared with a static estimator and the traditional adaptive filtering method. We observed a significant improvement (two-tailed paired t-test, p < 0.05) in both HbO and HbR recovery using our Kalman filter dynamic estimator compared to the traditional adaptive filter, the static estimator and the standard GLM technique. PMID:21385616

Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

PubMed

Mahmoudi, Babak; Pohlmeyer, Eric A; Prins, Noeline W; Geng, Shijia; Sanchez, Justin C

2013-12-01

Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

Modeling Speed-Accuracy Tradeoff in Adaptive System for Practicing Estimation

ERIC Educational Resources Information Center

Nižnan, Juraj

2015-01-01

Estimation is useful in situations where an exact answer is not as important as a quick answer that is good enough. A web-based adaptive system for practicing estimates is currently being developed. We propose a simple model for estimating student's latent skill of estimation. This model combines a continuous measure of correctness and response…

Object tracking algorithm based on the color histogram probability distribution

NASA Astrophysics Data System (ADS)

Li, Ning; Lu, Tongwei; Zhang, Yanduo

2018-04-01

In order to resolve tracking failure resulted from target's being occlusion and follower jamming caused by objects similar to target in the background, reduce the influence of light intensity. This paper change HSV and YCbCr color channel correction the update center of the target, continuously updated image threshold self-adaptive target detection effect, Clustering the initial obstacles is roughly range, shorten the threshold range, maximum to detect the target. In order to improve the accuracy of detector, this paper increased the Kalman filter to estimate the target state area. The direction predictor based on the Markov model is added to realize the target state estimation under the condition of background color interference and enhance the ability of the detector to identify similar objects. The experimental results show that the improved algorithm more accurate and faster speed of processing.

On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models. Part 1. Requirements, critical review of methods and modeling

NASA Astrophysics Data System (ADS)

Fleischer, Christian; Waag, Wladislaw; Heyn, Hans-Martin; Sauer, Dirk Uwe

2014-08-01

Lithium-ion battery systems employed in high power demanding systems such as electric vehicles require a sophisticated monitoring system to ensure safe and reliable operation. Three major states of the battery are of special interest and need to be constantly monitored, these include: battery state of charge (SoC), battery state of health (capcity fade determination, SoH), and state of function (power fade determination, SoF). In a series of two papers, we propose a system of algorithms based on a weighted recursive least quadratic squares parameter estimator, that is able to determine the battery impedance and diffusion parameters for accurate state estimation. The functionality was proven on different battery chemistries with different aging conditions. The first paper investigates the general requirements on BMS for HEV/EV applications. In parallel, the commonly used methods for battery monitoring are reviewed to elaborate their strength and weaknesses in terms of the identified requirements for on-line applications. Special emphasis will be placed on real-time capability and memory optimized code for cost-sensitive industrial or automotive applications in which low-cost microcontrollers must be used. Therefore, a battery model is presented which includes the influence of the Butler-Volmer kinetics on the charge-transfer process. Lastly, the mass transport process inside the battery is modeled in a novel state-space representation.

An hp-adaptivity and error estimation for hyperbolic conservation laws

NASA Technical Reports Server (NTRS)

Bey, Kim S.

1995-01-01

This paper presents an hp-adaptive discontinuous Galerkin method for linear hyperbolic conservation laws. A priori and a posteriori error estimates are derived in mesh-dependent norms which reflect the dependence of the approximate solution on the element size (h) and the degree (p) of the local polynomial approximation. The a posteriori error estimate, based on the element residual method, provides bounds on the actual global error in the approximate solution. The adaptive strategy is designed to deliver an approximate solution with the specified level of error in three steps. The a posteriori estimate is used to assess the accuracy of a given approximate solution and the a priori estimate is used to predict the mesh refinements and polynomial enrichment needed to deliver the desired solution. Numerical examples demonstrate the reliability of the a posteriori error estimates and the effectiveness of the hp-adaptive strategy.

Adaptive torque estimation of robot joint with harmonic drive transmission

NASA Astrophysics Data System (ADS)

Shi, Zhiguo; Li, Yuankai; Liu, Guangjun

2017-11-01

Robot joint torque estimation using input and output position measurements is a promising technique, but the result may be affected by the load variation of the joint. In this paper, a torque estimation method with adaptive robustness and optimality adjustment according to load variation is proposed for robot joint with harmonic drive transmission. Based on a harmonic drive model and a redundant adaptive robust Kalman filter (RARKF), the proposed approach can adapt torque estimation filtering optimality and robustness to the load variation by self-tuning the filtering gain and self-switching the filtering mode between optimal and robust. The redundant factor of RARKF is designed as a function of the motor current for tolerating the modeling error and load-dependent filtering mode switching. The proposed joint torque estimation method has been experimentally studied in comparison with a commercial torque sensor and two representative filtering methods. The results have demonstrated the effectiveness of the proposed torque estimation technique.

Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach

PubMed Central

Xie, Weihong; Yu, Yang

2017-01-01

Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively “switch” from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly. PMID:29124062

Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach.

PubMed

Liang, Fan; Xie, Weihong; Yu, Yang

2017-01-01

Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively "switch" from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly.

Analysis technique for controlling system wavefront error with active/adaptive optics

NASA Astrophysics Data System (ADS)

Genberg, Victor L.; Michels, Gregory J.

2017-08-01

The ultimate goal of an active mirror system is to control system level wavefront error (WFE). In the past, the use of this technique was limited by the difficulty of obtaining a linear optics model. In this paper, an automated method for controlling system level WFE using a linear optics model is presented. An error estimate is included in the analysis output for both surface error disturbance fitting and actuator influence function fitting. To control adaptive optics, the technique has been extended to write system WFE in state space matrix form. The technique is demonstrated by example with SigFit, a commercially available tool integrating mechanical analysis with optical analysis.

Concept and evaluation of food craving: unidimensional scales based on the Trait and the State Food Craving Questionnaire.

PubMed

Maranhão, Mara Fernandes; Estella, Nara Mendes; Cogo-Moreira, Hugo; Schmidt, Ulrike; Campbell, Iain C; Claudino, Angélica Medeiros

2018-01-01

"Craving" is a motivational state that promotes an intense desire related to consummatory behaviors. Despite growing interest in the concept of food craving, there is a lack of available instruments to assess it in Brazilian Portuguese. The objectives were to translate and adapt the Trait and the State Food Craving Questionnaire (FCQ-T and FCQ-S) to Brazilian Portuguese and to evaluate the psychometric properties of these versions.The FCQ-T and FCQ-S were translated and adapted to Brazilian Portuguese and administered to students at the Federal University of São Paulo. Both questionnaires in their original models were examined considering different estimators (frequentist and bayesian). The goodness of fit underlying the items from both scales was assessed through the following fit indices: χ2, WRMR residual, comparative fit index, Tucker-Lewis index and RMSEA. Data from 314 participants were included in the analyses. Poor fit indices were obtained for both of the original questionnaires regardless of the estimator used and original structural model. Thus, three eating disorder experts reviewed the content of the instruments and selected the items which were considered to assess the core aspects of the craving construct. The new and reduced models (questionnaires) generated good fit indices. Our abbreviated versions of FCQ-S and FCQ-T considerably diverge from the conceptual framework of the original questionnaires. Based on the results of this study, we propose a possible alternative, i.e., to assess craving for food as a unidimensional construct.

Particle filter based hybrid prognostics for health monitoring of uncertain systems in bond graph framework

NASA Astrophysics Data System (ADS)

Jha, Mayank Shekhar; Dauphin-Tanguy, G.; Ould-Bouamama, B.

2016-06-01

The paper's main objective is to address the problem of health monitoring of system parameters in Bond Graph (BG) modeling framework, by exploiting its structural and causal properties. The system in feedback control loop is considered uncertain globally. Parametric uncertainty is modeled in interval form. The system parameter is undergoing degradation (prognostic candidate) and its degradation model is assumed to be known a priori. The detection of degradation commencement is done in a passive manner which involves interval valued robust adaptive thresholds over the nominal part of the uncertain BG-derived interval valued analytical redundancy relations (I-ARRs). The latter forms an efficient diagnostic module. The prognostics problem is cast as joint state-parameter estimation problem, a hybrid prognostic approach, wherein the fault model is constructed by considering the statistical degradation model of the system parameter (prognostic candidate). The observation equation is constructed from nominal part of the I-ARR. Using particle filter (PF) algorithms; the estimation of state of health (state of prognostic candidate) and associated hidden time-varying degradation progression parameters is achieved in probabilistic terms. A simplified variance adaptation scheme is proposed. Associated uncertainties which arise out of noisy measurements, parametric degradation process, environmental conditions etc. are effectively managed by PF. This allows the production of effective predictions of the remaining useful life of the prognostic candidate with suitable confidence bounds. The effectiveness of the novel methodology is demonstrated through simulations and experiments on a mechatronic system.

Thermodynamic properties of solvated peptides from selective integrated tempering sampling with a new weighting factor estimation algorithm

NASA Astrophysics Data System (ADS)

Shen, Lin; Xie, Liangxu; Yang, Mingjun

2017-04-01

Conformational sampling under rugged energy landscape is always a challenge in computer simulations. The recently developed integrated tempering sampling, together with its selective variant (SITS), emerges to be a powerful tool in exploring the free energy landscape or functional motions of various systems. The estimation of weighting factors constitutes a critical step in these methods and requires accurate calculation of partition function ratio between different thermodynamic states. In this work, we propose a new adaptive update algorithm to compute the weighting factors based on the weighted histogram analysis method (WHAM). The adaptive-WHAM algorithm with SITS is then applied to study the thermodynamic properties of several representative peptide systems solvated in an explicit water box. The performance of the new algorithm is validated in simulations of these solvated peptide systems. We anticipate more applications of this coupled optimisation and production algorithm to other complicated systems such as the biochemical reactions in solution.

Meal Detection in Patients With Type 1 Diabetes: A New Module for the Multivariable Adaptive Artificial Pancreas Control System.

PubMed

Turksoy, Kamuran; Samadi, Sediqeh; Feng, Jianyuan; Littlejohn, Elizabeth; Quinn, Laurie; Cinar, Ali

2016-01-01

A novel meal-detection algorithm is developed based on continuous glucose measurements. Bergman's minimal model is modified and used in an unscented Kalman filter for state estimations. The estimated rate of appearance of glucose is used for meal detection. Data from nine subjects are used to assess the performance of the algorithm. The results indicate that the proposed algorithm works successfully with high accuracy. The average change in glucose levels between the meals and the detection points is 16(±9.42) [mg/dl] for 61 successfully detected meals and snacks. The algorithm is developed as a new module of an integrated multivariable adaptive artificial pancreas control system. Meal detection with the proposed method is used to administer insulin boluses and prevent most of postprandial hyperglycemia without any manual meal announcements. A novel meal bolus calculation method is proposed and tested with the UVA/Padova simulator. The results indicate significant reduction in hyperglycemia.

Adjoint-Based, Three-Dimensional Error Prediction and Grid Adaptation

NASA Technical Reports Server (NTRS)

Park, Michael A.

2002-01-01

Engineering computational fluid dynamics (CFD) analysis and design applications focus on output functions (e.g., lift, drag). Errors in these output functions are generally unknown and conservatively accurate solutions may be computed. Computable error estimates can offer the possibility to minimize computational work for a prescribed error tolerance. Such an estimate can be computed by solving the flow equations and the linear adjoint problem for the functional of interest. The computational mesh can be modified to minimize the uncertainty of a computed error estimate. This robust mesh-adaptation procedure automatically terminates when the simulation is within a user specified error tolerance. This procedure for estimating and adapting to error in a functional is demonstrated for three-dimensional Euler problems. An adaptive mesh procedure that links to a Computer Aided Design (CAD) surface representation is demonstrated for wing, wing-body, and extruded high lift airfoil configurations. The error estimation and adaptation procedure yielded corrected functions that are as accurate as functions calculated on uniformly refined grids with ten times as many grid points.

Just-in-time adaptive classifiers-part II: designing the classifier.

PubMed

Alippi, Cesare; Roveri, Manuel

2008-12-01

Aging effects, environmental changes, thermal drifts, and soft and hard faults affect physical systems by changing their nature and behavior over time. To cope with a process evolution adaptive solutions must be envisaged to track its dynamics; in this direction, adaptive classifiers are generally designed by assuming the stationary hypothesis for the process generating the data with very few results addressing nonstationary environments. This paper proposes a methodology based on k-nearest neighbor (NN) classifiers for designing adaptive classification systems able to react to changing conditions just-in-time (JIT), i.e., exactly when it is needed. k-NN classifiers have been selected for their computational-free training phase, the possibility to easily estimate the model complexity k and keep under control the computational complexity of the classifier through suitable data reduction mechanisms. A JIT classifier requires a temporal detection of a (possible) process deviation (aspect tackled in a companion paper) followed by an adaptive management of the knowledge base (KB) of the classifier to cope with the process change. The novelty of the proposed approach resides in the general framework supporting the real-time update of the KB of the classification system in response to novel information coming from the process both in stationary conditions (accuracy improvement) and in nonstationary ones (process tracking) and in providing a suitable estimate of k. It is shown that the classification system grants consistency once the change targets the process generating the data in a new stationary state, as it is the case in many real applications.

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

PubMed

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

2009-10-01

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

Adaptive estimation of the log fluctuating conductivity from tracer data at the Cape Cod Site

USGS Publications Warehouse

Deng, F.W.; Cushman, J.H.; Delleur, J.W.

1993-01-01

An adaptive estimation scheme is used to obtain the integral scale and variance of the log-fluctuating conductivity at the Cape Cod site based on the fast Fourier transform/stochastic model of Deng et al. (1993) and a Kalmanlike filter. The filter incorporates prior estimates of the unknown parameters with tracer moment data to adaptively obtain improved estimates as the tracer evolves. The results show that significant improvement in the prior estimates of the conductivity can lead to substantial improvement in the ability to predict plume movement. The structure of the covariance function of the log-fluctuating conductivity can be identified from the robustness of the estimation. Both the longitudinal and transverse spatial moment data are important to the estimation.

Set-free Markov state model building

NASA Astrophysics Data System (ADS)

Weber, Marcus; Fackeldey, Konstantin; Schütte, Christof

2017-03-01

Molecular dynamics (MD) simulations face challenging problems since the time scales of interest often are much longer than what is possible to simulate; and even if sufficiently long simulations are possible the complex nature of the resulting simulation data makes interpretation difficult. Markov State Models (MSMs) help to overcome these problems by making experimentally relevant time scales accessible via coarse grained representations that also allow for convenient interpretation. However, standard set-based MSMs exhibit some caveats limiting their approximation quality and statistical significance. One of the main caveats results from the fact that typical MD trajectories repeatedly re-cross the boundary between the sets used to build the MSM which causes statistical bias in estimating the transition probabilities between these sets. In this article, we present a set-free approach to MSM building utilizing smooth overlapping ansatz functions instead of sets and an adaptive refinement approach. This kind of meshless discretization helps to overcome the recrossing problem and yields an adaptive refinement procedure that allows us to improve the quality of the model while exploring state space and inserting new ansatz functions into the MSM.

Classification of Reactor Facility Operational State Using SPRT Methods with Radiation Sensor Networks

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

Ramirez Aviles, Camila A.; Rao, Nageswara S.

We consider the problem of inferring the operational state of a reactor facility by using measurements from a radiation sensor network, which is deployed around the facility’s ventilation stack. The radiation emissions from the stack decay with distance, and the corresponding measurements are inherently random with parameters determined by radiation intensity levels at the sensor locations. We fuse measurements from network sensors to estimate the intensity at the stack, and use this estimate in a one-sided Sequential Probability Ratio Test (SPRT) to infer the on/off state of the reactor facility. We demonstrate the superior performance of this method over conventionalmore » majority vote fusers and individual sensors using (i) test measurements from a network of NaI sensors, and (ii) emulated measurements using radioactive effluents collected at a reactor facility stack. We analytically quantify the performance improvements of individual sensors and their networks with adaptive thresholds over those with fixed ones, by using the packing number of the radiation intensity space.« less

Multi-frequency Phase Unwrap from Noisy Data: Adaptive Least Squares Approach

NASA Astrophysics Data System (ADS)

Katkovnik, Vladimir; Bioucas-Dias, José

2010-04-01

Multiple frequency interferometry is, basically, a phase acquisition strategy aimed at reducing or eliminating the ambiguity of the wrapped phase observations or, equivalently, reducing or eliminating the fringe ambiguity order. In multiple frequency interferometry, the phase measurements are acquired at different frequencies (or wavelengths) and recorded using the corresponding sensors (measurement channels). Assuming that the absolute phase to be reconstructed is piece-wise smooth, we use a nonparametric regression technique for the phase reconstruction. The nonparametric estimates are derived from a local least squares criterion, which, when applied to the multifrequency data, yields denoised (filtered) phase estimates with extended ambiguity (periodized), compared with the phase ambiguities inherent to each measurement frequency. The filtering algorithm is based on local polynomial (LPA) approximation for design of nonlinear filters (estimators) and adaptation of these filters to unknown smoothness of the spatially varying absolute phase [9]. For phase unwrapping, from filtered periodized data, we apply the recently introduced robust (in the sense of discontinuity preserving) PUMA unwrapping algorithm [1]. Simulations give evidence that the proposed algorithm yields state-of-the-art performance for continuous as well as for discontinues phase surfaces, enabling phase unwrapping in extraordinary difficult situations when all other algorithms fail.

Adaptive management of flows from dams: a win-win framework for water users

USGS Publications Warehouse

Irwin, Elise R.

2013-01-01

Alabama is blessed with more than 77,000 miles of rivers and streams that carve through the terrestrial landscape of the state. When you think about it, every road you drive on crosses a river and many of our major cities are located on the bank of a river. In fact, Alabama's capital cities - Cahawba (Dallas County; 1820-1826), Tuscaloosa (Tuscaloosa County; 1826-1846), and Montgomery County; 1846-present) - were all located on major rivers. It is estimated by the U.S. Geological Survey that 10 percent of the freshwater resources in the continental United States flows through Alabama. When you look at a map of its hydrology, the state is blue!

Accurate Inventories Of Irrigated Land

NASA Technical Reports Server (NTRS)

Wall, S.; Thomas, R.; Brown, C.

1992-01-01

System for taking land-use inventories overcomes two problems in estimating extent of irrigated land: only small portion of large state surveyed in given year, and aerial photographs made on 1 day out of year do not provide adequate picture of areas growing more than one crop per year. Developed for state of California as guide to controlling, protecting, conserving, and distributing water within state. Adapted to any large area in which large amounts of irrigation water needed for agriculture. Combination of satellite images, aerial photography, and ground surveys yields data for computer analysis. Analyst also consults agricultural statistics, current farm reports, weather reports, and maps. These information sources aid in interpreting patterns, colors, textures, and shapes on Landsat-images.

Identification of the focal plane wavefront control system using E-M algorithm

NASA Astrophysics Data System (ADS)

Sun, He; Kasdin, N. Jeremy; Vanderbei, Robert

2017-09-01

In a typical focal plane wavefront control (FPWC) system, such as the adaptive optics system of NASA's WFIRST mission, the efficient controllers and estimators in use are usually model-based. As a result, the modeling accuracy of the system influences the ultimate performance of the control and estimation. Currently, a linear state space model is used and calculated based on lab measurements using Fourier optics. Although the physical model is clearly defined, it is usually biased due to incorrect distance measurements, imperfect diagnoses of the optical aberrations, and our lack of knowledge of the deformable mirrors (actuator gains and influence functions). In this paper, we present a new approach for measuring/estimating the linear state space model of a FPWC system using the expectation-maximization (E-M) algorithm. Simulation and lab results in the Princeton's High Contrast Imaging Lab (HCIL) show that the E-M algorithm can well handle both the amplitude and phase errors and accurately recover the system. Using the recovered state space model, the controller creates dark holes with faster speed. The final accuracy of the model depends on the amount of data used for learning.

The role of population monitoring in the management of North American waterfowl

USGS Publications Warehouse

Nichols, J.D.; Williams, B.K.; Johnson, F.A.

2000-01-01

Despite the effort and expense devoted to large-scale monitoring programs, few existing programs have been designed with specific objectives in mind and few permit strong inferences about the dynamics of monitored systems. The waterfowl population monitoring programs of the U.S. Fish and Wildlife Service, Canadian Wildlife Service and state and provincial agencies provide a nice example with respect to program objectives, design and implementation. The May Breeding Grounds Survey provides an estimate of system state (population size) that serves two primary purposes in the adaptive management process: identifying the appropriate time-specific management actions and updating the information state (model weights) by providing a basis for evaluating predictions of competing models. Other waterfowl monitoring programs (e.g., banding program, hunter questionnaire survey, parts collection survey, winter survey) provide estimates of vital rates (rates of survival, reproduction and movement) associated with system dynamics and variables associated with management objectives (e.g., harvest). The reliability of estimates resulting from monitoring programs depends strongly on whether considerations about spatial variation and detection probability have been adequately incorporated into program design and implementation. Certain waterfowl surveys again provide nice examples of monitoring programs that incorporate these considerations.

Neural network based adaptive control for nonlinear dynamic regimes

NASA Astrophysics Data System (ADS)

Shin, Yoonghyun

Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named 'composite model reference adaptive control' is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of 'pseudo-control hedging' techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.

Phase unwrapping algorithm using polynomial phase approximation and linear Kalman filter.

PubMed

Kulkarni, Rishikesh; Rastogi, Pramod

2018-02-01

A noise-robust phase unwrapping algorithm is proposed based on state space analysis and polynomial phase approximation using wrapped phase measurement. The true phase is approximated as a two-dimensional first order polynomial function within a small sized window around each pixel. The estimates of polynomial coefficients provide the measurement of phase and local fringe frequencies. A state space representation of spatial phase evolution and the wrapped phase measurement is considered with the state vector consisting of polynomial coefficients as its elements. Instead of using the traditional nonlinear Kalman filter for the purpose of state estimation, we propose to use the linear Kalman filter operating directly with the wrapped phase measurement. The adaptive window width is selected at each pixel based on the local fringe density to strike a balance between the computation time and the noise robustness. In order to retrieve the unwrapped phase, either a line-scanning approach or a quality guided strategy of pixel selection is used depending on the underlying continuous or discontinuous phase distribution, respectively. Simulation and experimental results are provided to demonstrate the applicability of the proposed method.

Mitochondrial phylogenomics of Hemiptera reveals adaptive innovations driving the diversification of true bugs

PubMed Central

Li, Hu; Leavengood, John M.; Chapman, Eric G.; Burkhardt, Daniel; Song, Fan; Jiang, Pei; Liu, Jinpeng; Cai, Wanzhi

2017-01-01

Hemiptera, the largest non-holometabolous order of insects, represents approximately 7% of metazoan diversity. With extraordinary life histories and highly specialized morphological adaptations, hemipterans have exploited diverse habitats and food sources through approximately 300 Myr of evolution. To elucidate the phylogeny and evolutionary history of Hemiptera, we carried out the most comprehensive mitogenomics analysis on the richest taxon sampling to date covering all the suborders and infraorders, including 34 newly sequenced and 94 published mitogenomes. With optimized branch length and sequence heterogeneity, Bayesian analyses using a site-heterogeneous mixture model resolved the higher-level hemipteran phylogeny as (Sternorrhyncha, (Auchenorrhyncha, (Coleorrhyncha, Heteroptera))). Ancestral character state reconstruction and divergence time estimation suggest that the success of true bugs (Heteroptera) is probably due to angiosperm coevolution, but key adaptive innovations (e.g. prognathous mouthpart, predatory behaviour, and haemelytron) facilitated multiple independent shifts among diverse feeding habits and multiple independent colonizations of aquatic habitats. PMID:28878063

Distributed Adaptive Fuzzy Control for Nonlinear Multiagent Systems Via Sliding Mode Observers.

PubMed

Shen, Qikun; Shi, Peng; Shi, Yan

2016-12-01

In this paper, the problem of distributed adaptive fuzzy control is investigated for high-order uncertain nonlinear multiagent systems on directed graph with a fixed topology. It is assumed that only the outputs of each follower and its neighbors are available in the design of its distributed controllers. Equivalent output injection sliding mode observers are proposed for each follower to estimate the states of itself and its neighbors, and an observer-based distributed adaptive controller is designed for each follower to guarantee that it asymptotically synchronizes to a leader with tracking errors being semi-globally uniform ultimate bounded, in which fuzzy logic systems are utilized to approximate unknown functions. Based on algebraic graph theory and Lyapunov function approach, using Filippov-framework, the closed-loop system stability analysis is conducted. Finally, numerical simulations are provided to illustrate the effectiveness and potential of the developed design techniques.

Modelling Errors in Automatic Speech Recognition for Dysarthric Speakers

NASA Astrophysics Data System (ADS)

Caballero Morales, Santiago Omar; Cox, Stephen J.

2009-12-01

Dysarthria is a motor speech disorder characterized by weakness, paralysis, or poor coordination of the muscles responsible for speech. Although automatic speech recognition (ASR) systems have been developed for disordered speech, factors such as low intelligibility and limited phonemic repertoire decrease speech recognition accuracy, making conventional speaker adaptation algorithms perform poorly on dysarthric speakers. In this work, rather than adapting the acoustic models, we model the errors made by the speaker and attempt to correct them. For this task, two techniques have been developed: (1) a set of "metamodels" that incorporate a model of the speaker's phonetic confusion matrix into the ASR process; (2) a cascade of weighted finite-state transducers at the confusion matrix, word, and language levels. Both techniques attempt to correct the errors made at the phonetic level and make use of a language model to find the best estimate of the correct word sequence. Our experiments show that both techniques outperform standard adaptation techniques.

Adaptive Event-Triggered Control Based on Heuristic Dynamic Programming for Nonlinear Discrete-Time Systems.

PubMed

Dong, Lu; Zhong, Xiangnan; Sun, Changyin; He, Haibo

2017-07-01

This paper presents the design of a novel adaptive event-triggered control method based on the heuristic dynamic programming (HDP) technique for nonlinear discrete-time systems with unknown system dynamics. In the proposed method, the control law is only updated when the event-triggered condition is violated. Compared with the periodic updates in the traditional adaptive dynamic programming (ADP) control, the proposed method can reduce the computation and transmission cost. An actor-critic framework is used to learn the optimal event-triggered control law and the value function. Furthermore, a model network is designed to estimate the system state vector. The main contribution of this paper is to design a new trigger threshold for discrete-time systems. A detailed Lyapunov stability analysis shows that our proposed event-triggered controller can asymptotically stabilize the discrete-time systems. Finally, we test our method on two different discrete-time systems, and the simulation results are included.

Weak and Dynamic GNSS Signal Tracking Strategies for Flight Missions in the Space Service Volume

PubMed Central

Jing, Shuai; Zhan, Xingqun; Liu, Baoyu; Chen, Maolin

2016-01-01

Weak-signal and high-dynamics are of two primary concerns of space navigation using GNSS (Global Navigation Satellite System) in the space service volume (SSV). The paper firstly defines a reference assumption third-order phase-locked loop (PLL) as the baseline of an onboard GNSS receiver, and proves the incompetence of this conventional architecture. Then an adaptive four-state Kalman filter (KF)-based algorithm is introduced to realize the optimization of loop noise bandwidth, which can adaptively regulate its filter gain according to the received signal power and line-of-sight (LOS) dynamics. To overcome the matter of losing lock in weak-signal and high-dynamic environments, an open loop tracking strategy aided by an inertial navigation system (INS) is recommended, and the traditional maximum likelihood estimation (MLE) method is modified in a non-coherent way by reconstructing the likelihood cost function. Furthermore, a typical mission with combined orbital maneuvering and non-maneuvering arcs is taken as a destination object to test the two proposed strategies. Finally, the experiment based on computer simulation identifies the effectiveness of an adaptive four-state KF-based strategy under non-maneuvering conditions and the virtue of INS-assisted methods under maneuvering conditions. PMID:27598164

Decentralized Adaptive Neural Output-Feedback DSC for Switched Large-Scale Nonlinear Systems.

PubMed

Lijun Long; Jun Zhao

2017-04-01

In this paper, for a class of switched large-scale uncertain nonlinear systems with unknown control coefficients and unmeasurable states, a switched-dynamic-surface-based decentralized adaptive neural output-feedback control approach is developed. The approach proposed extends the classical dynamic surface control (DSC) technique for nonswitched version to switched version by designing switched first-order filters, which overcomes the problem of multiple "explosion of complexity." Also, a dual common coordinates transformation of all subsystems is exploited to avoid individual coordinate transformations for subsystems that are required when applying the backstepping recursive design scheme. Nussbaum-type functions are utilized to handle the unknown control coefficients, and a switched neural network observer is constructed to estimate the unmeasurable states. Combining with the average dwell time method and backstepping and the DSC technique, decentralized adaptive neural controllers of subsystems are explicitly designed. It is proved that the approach provided can guarantee the semiglobal uniformly ultimately boundedness for all the signals in the closed-loop system under a class of switching signals with average dwell time, and the tracking errors to a small neighborhood of the origin. A two inverted pendulums system is provided to demonstrate the effectiveness of the method proposed.

Weak and Dynamic GNSS Signal Tracking Strategies for Flight Missions in the Space Service Volume.

PubMed

Jing, Shuai; Zhan, Xingqun; Liu, Baoyu; Chen, Maolin

2016-09-02

Weak-signal and high-dynamics are of two primary concerns of space navigation using GNSS (Global Navigation Satellite System) in the space service volume (SSV). The paper firstly defines a reference assumption third-order phase-locked loop (PLL) as the baseline of an onboard GNSS receiver, and proves the incompetence of this conventional architecture. Then an adaptive four-state Kalman filter (KF)-based algorithm is introduced to realize the optimization of loop noise bandwidth, which can adaptively regulate its filter gain according to the received signal power and line-of-sight (LOS) dynamics. To overcome the matter of losing lock in weak-signal and high-dynamic environments, an open loop tracking strategy aided by an inertial navigation system (INS) is recommended, and the traditional maximum likelihood estimation (MLE) method is modified in a non-coherent way by reconstructing the likelihood cost function. Furthermore, a typical mission with combined orbital maneuvering and non-maneuvering arcs is taken as a destination object to test the two proposed strategies. Finally, the experiment based on computer simulation identifies the effectiveness of an adaptive four-state KF-based strategy under non-maneuvering conditions and the virtue of INS-assisted methods under maneuvering conditions.

Statistical Indexes for Monitoring Item Behavior under Computer Adaptive Testing Environment.

ERIC Educational Resources Information Center

Zhu, Renbang; Yu, Feng; Liu, Su

A computerized adaptive test (CAT) administration usually requires a large supply of items with accurately estimated psychometric properties, such as item response theory (IRT) parameter estimates, to ensure the precision of examinee ability estimation. However, an estimated IRT model of a given item in any given pool does not always correctly…

A Methodology for Forecasting Damage & Economic Consequences to Floods: Building on the National Flood Interoperability Experiment (NFIE)

NASA Astrophysics Data System (ADS)

Tootle, G. A.; Gutenson, J. L.; Zhu, L.; Ernest, A. N. S.; Oubeidillah, A.; Zhang, X.

2015-12-01

The National Flood Interoperability Experiment (NFIE) held June 3-July 17, 2015 at the National Water Center (NWC) in Tuscaloosa, Alabama sought to demonstrate an increase in flood predictive capacity for the coterminous United States (CONUS). Accordingly, NFIE-derived technologies and workflows offer the ability to forecast flood damage and economic consequence estimates that coincide with the hydrologic and hydraulic estimations these physics-based models generate. A model providing an accurate prediction of damage and economic consequences is a valuable asset when allocating funding for disaster response, recovery, and relief. Damage prediction and economic consequence assessment also offer an adaptation planning mechanism for defending particularly valuable or vulnerable structures. The NFIE, held at the NWC on The University of Alabama (UA) campus led to the development of this large scale flow and inundation forecasting framework. Currently, the system can produce 15-hour lead-time forecasts for the entire coterminous United States (CONUS). A concept which is anticipated to become operational as of May 2016 within the NWC. The processing of such a large-scale, fine resolution model is accomplished in a parallel computing environment using large supercomputing clusters. Traditionally, flood damage and economic consequence assessment is calculated in a desktop computing environment with a ménage of meteorology, hydrology, hydraulic, and damage assessment tools. In the United States, there are a range of these flood damage/ economic consequence assessment software's available to local, state, and federal emergency management agencies. Among the more commonly used and freely accessible models are the Hydrologic Engineering Center's Flood Damage Reduction Analysis (HEC-FDA), Flood Impact Assessment (HEC-FIA), and Federal Emergency Management Agency's (FEMA's) United States Multi-Hazard (Hazus-MH). All of which exist only in a desktop environment. With this, authors submit an initial framework for estimating damage and economic consequences to floods using flow and inundation products from the NFIE framework. This adaptive system utilizes existing nationwide datasets describing location and use of structures and can take assimilate a range of data resolutions.

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 in the eastern United States. Environ Health Perspect 122:10–16; http://dx.doi.org/10.1289/ehp.1306670 PMID:24192064

Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.

PubMed

Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu

2015-08-01

This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm.

Bounded Linear Stability Analysis - A Time Delay Margin Estimation Approach for Adaptive Control

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Ishihara, Abraham K.; Krishnakumar, Kalmanje Srinlvas; Bakhtiari-Nejad, Maryam

2009-01-01

This paper presents a method for estimating time delay margin for model-reference adaptive control of systems with almost linear structured uncertainty. The bounded linear stability analysis method seeks to represent the conventional model-reference adaptive law by a locally bounded linear approximation within a small time window using the comparison lemma. The locally bounded linear approximation of the combined adaptive system is cast in a form of an input-time-delay differential equation over a small time window. The time delay margin of this system represents a local stability measure and is computed analytically by a matrix measure method, which provides a simple analytical technique for estimating an upper bound of time delay margin. Based on simulation results for a scalar model-reference adaptive control system, both the bounded linear stability method and the matrix measure method are seen to provide a reasonably accurate and yet not too conservative time delay margin estimation.

Is adaptation of the word accentuation test of premorbid intelligence necessary for use among older, Spanish-speaking immigrants in the United States?

PubMed

Schrauf, Robert W; Weintraub, Sandra; Navarro, Ellen

2006-05-01

Adaptations of the National Adult Reading Test (NART) for assessing premorbid intelligence in languages other than English requires (a) generating word-items that are rare and do not follow grapheme-to-phoneme mappings common in that language, and (b) subsequent validation against a cognitive battery normed on the population of interest. Such tests exist for Italy, France, Spain, and Argentina, all normed against national versions of the Wechsler Adult Intelligence Scale. Given the varieties of Spanish spoken in the United States, the adaptation of the Spanish Word Accentuation Test (WAT) requires re-validating the original word list, plus possible new items, against a cognitive battery that has been normed on Spanish-speakers from many countries. This study reports the generation of 55 additional words and revalidation in a sample of 80 older, Spanish-dominant immigrants. The Batería Woodcock-Muñoz Revisada (BWM-R), normed on Spanish speakers from six countries and five U.S. states, was used to establish criterion validity. The original WAT word list accounted for 77% of the variance in the BWM-R and 58% of the variance in Ravens Colored Progressive Matrices, suggesting that the unmodified list possesses adequate predictive validity as an indicator of intelligence. Regression equations are provided for estimating BWM-R and Ravens scores from WAT scores.

Mission Data System Java Edition Version 7

NASA Technical Reports Server (NTRS)

Reinholtz, William K.; Wagner, David A.

2013-01-01

The Mission Data System framework defines closed-loop control system abstractions from State Analysis including interfaces for state variables, goals, estimators, and controllers that can be adapted to implement a goal-oriented control system. The framework further provides an execution environment that includes a goal scheduler, execution engine, and fault monitor that support the expression of goal network activity plans. Using these frameworks, adapters can build a goal-oriented control system where activity coordination is verified before execution begins (plan time), and continually during execution. Plan failures including violations of safety constraints expressed in the plan can be handled through automatic re-planning. This version optimizes a number of key interfaces and features to minimize dependencies, performance overhead, and improve reliability. Fault diagnosis and real-time projection capabilities are incorporated. This version enhances earlier versions primarily through optimizations and quality improvements that raise the technology readiness level. Goals explicitly constrain system states over explicit time intervals to eliminate ambiguity about intent, as compared to command-oriented control that only implies persistent intent until another command is sent. A goal network scheduling and verification process ensures that all goals in the plan are achievable before starting execution. Goal failures at runtime can be detected (including predicted failures) and handled by adapted response logic. Responses can include plan repairs (try an alternate tactic to achieve the same goal), goal shedding, ignoring the fault, cancelling the plan, or safing the system.

Comparison of different estimation techniques for biomass concentration in large scale yeast fermentation.

PubMed

Hocalar, A; Türker, M; Karakuzu, C; Yüzgeç, U

2011-04-01

In this study, previously developed five different state estimation methods are examined and compared for estimation of biomass concentrations at a production scale fed-batch bioprocess. These methods are i. estimation based on kinetic model of overflow metabolism; ii. estimation based on metabolic black-box model; iii. estimation based on observer; iv. estimation based on artificial neural network; v. estimation based on differential evaluation. Biomass concentrations are estimated from available measurements and compared with experimental data obtained from large scale fermentations. The advantages and disadvantages of the presented techniques are discussed with regard to accuracy, reproducibility, number of primary measurements required and adaptation to different working conditions. Among the various techniques, the metabolic black-box method seems to have advantages although the number of measurements required is more than that for the other methods. However, the required extra measurements are based on commonly employed instruments in an industrial environment. This method is used for developing a model based control of fed-batch yeast fermentations. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Designing hydrologic monitoring networks to maximize predictability of hydrologic conditions in a data assimilation system: a case study from South Florida, U.S.A

NASA Astrophysics Data System (ADS)

Flores, A. N.; Pathak, C. S.; Senarath, S. U.; Bras, R. L.

2009-12-01

Robust hydrologic monitoring networks represent a critical element of decision support systems for effective water resource planning and management. Moreover, process representation within hydrologic simulation models is steadily improving, while at the same time computational costs are decreasing due to, for instance, readily available high performance computing resources. The ability to leverage these increasingly complex models together with the data from these monitoring networks to provide accurate and timely estimates of relevant hydrologic variables within a multiple-use, managed water resources system would substantially enhance the information available to resource decision makers. Numerical data assimilation techniques provide mathematical frameworks through which uncertain model predictions can be constrained to observational data to compensate for uncertainties in the model forcings and parameters. In ensemble-based data assimilation techniques such as the ensemble Kalman Filter (EnKF), information in observed variables such as canal, marsh and groundwater stages are propagated back to the model states in a manner related to: (1) the degree of certainty in the model state estimates and observations, and (2) the cross-correlation between the model states and the observable outputs of the model. However, the ultimate degree to which hydrologic conditions can be accurately predicted in an area of interest is controlled, in part, by the configuration of the monitoring network itself. In this proof-of-concept study we developed an approach by which the design of an existing hydrologic monitoring network is adapted to iteratively improve the predictions of hydrologic conditions within an area of the South Florida Water Management District (SFWMD). The objective of the network design is to minimize prediction errors of key hydrologic states and fluxes produced by the spatially distributed Regional Simulation Model (RSM), developed specifically to simulate the hydrologic conditions in several intensively managed and hydrologically complex watersheds within the SFWMD system. In a series of synthetic experiments RSM is used to generate the notionally true hydrologic state and the relevant observational data. The EnKF is then used as the mechanism to fuse RSM hydrologic estimates with data from the candidate network. The performance of the candidate network is measured by the prediction errors of the EnKF estimates of hydrologic states, relative to the notionally true scenario. The candidate network is then adapted by relocating existing observational sites to unobserved areas where predictions of local hydrologic conditions are most uncertain and the EnKF procedure repeated. Iteration of the monitoring network continues until further improvements in EnKF-based predictions of hydrologic conditions are negligible.

The United States Marine Corps Reconnaissance Reserve: Adaptation and Integration for the Future

DTIC Science & Technology

2012-05-01

per response, including the time for reviewing instructions, searching data sources, gathering and maintaining the data needed, and completing and...reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of infonnation, including...PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (00-MM-YYYY) 12. REPORT TYPE 3. DATES COVERED (From- To) 01-05-2012 Master of

Operator State Estimation for Adaptive Aiding in Uninhabited Combat Air Vehicles

DTIC Science & Technology

2005-09-01

1992). Van Boxtel, A., W. Waterink, and I.J.T. Veldhuizen . “Tonic Facial EMG Activity As An Index of Mental Effort: Effects of Work Rate, Time-On...the ‘normal’ functioning of brain activity (Beaumont, Burov, Carter, Cheuvront, Sawka, Wilson, Van Orden, Hockey, Balkin and Gundel, 2004). For...by the sympathetic nervous system. Electromyographic activity has been shown to predict arousal accurately ( Veldhuizen , Gaillard, and de Vries, 2003

Progress in adapting k-NN methods for forest mapping and estimation using the new annual Forest Inventory and Analysis data

Treesearch

Reija Haapanen; Kimmo Lehtinen; Jukka Miettinen; Marvin E. Bauer; Alan R. Ek

2002-01-01

The k-nearest neighbor (k-NN) method has been undergoing development and testing for applications with USDA Forest Service Forest Inventory and Analysis (FIA) data in Minnesota since 1997. Research began using the 1987-1990 FIA inventory of the state, the then standard 10-point cluster plots, and Landsat TM imagery. In the past year, research has moved to examine...

Adaptive kernel function using line transect sampling

NASA Astrophysics Data System (ADS)

Albadareen, Baker; Ismail, Noriszura

2018-04-01

The estimation of f(0) is crucial in the line transect method which is used for estimating population abundance in wildlife survey's. The classical kernel estimator of f(0) has a high negative bias. Our study proposes an adaptation in the kernel function which is shown to be more efficient than the usual kernel estimator. A simulation study is adopted to compare the performance of the proposed estimators with the classical kernel estimators.

Application of adaptive cluster sampling to low-density populations of freshwater mussels

USGS Publications Warehouse

Smith, D.R.; Villella, R.F.; Lemarie, D.P.

2003-01-01

Freshwater mussels appear to be promising candidates for adaptive cluster sampling because they are benthic macroinvertebrates that cluster spatially and are frequently found at low densities. We applied adaptive cluster sampling to estimate density of freshwater mussels at 24 sites along the Cacapon River, WV, where a preliminary timed search indicated that mussels were present at low density. Adaptive cluster sampling increased yield of individual mussels and detection of uncommon species; however, it did not improve precision of density estimates. Because finding uncommon species, collecting individuals of those species, and estimating their densities are important conservation activities, additional research is warranted on application of adaptive cluster sampling to freshwater mussels. However, at this time we do not recommend routine application of adaptive cluster sampling to freshwater mussel populations. The ultimate, and currently unanswered, question is how to tell when adaptive cluster sampling should be used, i.e., when is a population sufficiently rare and clustered for adaptive cluster sampling to be efficient and practical? A cost-effective procedure needs to be developed to identify biological populations for which adaptive cluster sampling is appropriate.

Adaptation, plant evolution, and the fossil record

NASA Technical Reports Server (NTRS)

Knoll, A. H.; Niklas, K. J.

1987-01-01

The importance of adaptation in determining patterns of evolution has become an important focus of debate in evolutionary biology. As it pertains to paleobotany, the issue is whether or not adaptive evolution mediated by natural selection is sufficient to explain the stratigraphic distributions of taxa and character states observed in the plant fossil record. One means of addressing this question is the functional evaluation of stratigraphic series of plant organs set in the context of paleoenvironmental change and temporal patterns of floral composition within environments. For certain organ systems, quantitative estimates of biophysical performance can be made on the basis of structures preserved in the fossil record. Performance estimates for plants separated in time or space can be compared directly. Implicit in different hypotheses of the forces that shape the evolutionary record (e.g. adaptation, mass extinction, rapid environmental change, chance) are predictions about stratigraphic and paleoenvironmental trends in the efficacy of functional performance. Existing data suggest that following the evolution of a significant structural innovation, adaptation for improved functional performance can be a major determinant of evolutionary changes in plants; however, there are structural and development limits to functional improvement, and once these are reached, the structure in question may no longer figure strongly in selection until and unless a new innovation evolves. The Silurian-Devonian paleobotanical record is consistent with the hypothesis that the succession of lowland floodplain dominants preserved in the fossil record of this interval was determined principally by the repeated evolution of new taxa that rose to ecological importance because of competitive advantages conferred by improved biophysical performance. This does not seem to be equally true for Carboniferous-Jurassic dominants of swamp and lowland floodplain environments. In these cases, environmental disruption appears to have been a major factor in shaping the fossil record. This does not mean that continuing adaptation was not important during this interval, but it may indicate that adaptive evolution was strongest in environments other than those best represented in the paleobotanical record.

Adaptation, plant evolution, and the fossil record.

PubMed

Knoll, A H; Niklas, K J

1987-01-01

The importance of adaptation in determining patterns of evolution has become an important focus of debate in evolutionary biology. As it pertains to paleobotany, the issue is whether or not adaptive evolution mediated by natural selection is sufficient to explain the stratigraphic distributions of taxa and character states observed in the plant fossil record. One means of addressing this question is the functional evaluation of stratigraphic series of plant organs set in the context of paleoenvironmental change and temporal patterns of floral composition within environments. For certain organ systems, quantitative estimates of biophysical performance can be made on the basis of structures preserved in the fossil record. Performance estimates for plants separated in time or space can be compared directly. Implicit in different hypotheses of the forces that shape the evolutionary record (e.g. adaptation, mass extinction, rapid environmental change, chance) are predictions about stratigraphic and paleoenvironmental trends in the efficacy of functional performance. Existing data suggest that following the evolution of a significant structural innovation, adaptation for improved functional performance can be a major determinant of evolutionary changes in plants; however, there are structural and development limits to functional improvement, and once these are reached, the structure in question may no longer figure strongly in selection until and unless a new innovation evolves. The Silurian-Devonian paleobotanical record is consistent with the hypothesis that the succession of lowland floodplain dominants preserved in the fossil record of this interval was determined principally by the repeated evolution of new taxa that rose to ecological importance because of competitive advantages conferred by improved biophysical performance. This does not seem to be equally true for Carboniferous-Jurassic dominants of swamp and lowland floodplain environments. In these cases, environmental disruption appears to have been a major factor in shaping the fossil record. This does not mean that continuing adaptation was not important during this interval, but it may indicate that adaptive evolution was strongest in environments other than those best represented in the paleobotanical record.

Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots

PubMed Central

Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

2015-01-01

Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models, which have hitherto been the state of the art, to model a subset of similar walking behaviors in walking robots. PMID:26441629

Sequential causal inference: Application to randomized trials of adaptive treatment strategies

PubMed Central

Dawson, Ree; Lavori, Philip W.

2009-01-01

SUMMARY Clinical trials that randomize subjects to decision algorithms, which adapt treatments over time according to individual response, have gained considerable interest as investigators seek designs that directly inform clinical decision making. We consider designs in which subjects are randomized sequentially at decision points, among adaptive treatment options under evaluation. We present a sequential method to estimate the comparative effects of the randomized adaptive treatments, which are formalized as adaptive treatment strategies. Our causal estimators are derived using Bayesian predictive inference. We use analytical and empirical calculations to compare the predictive estimators to (i) the ‘standard’ approach that allocates the sequentially obtained data to separate strategy-specific groups as would arise from randomizing subjects at baseline; (ii) the semi-parametric approach of marginal mean models that, under appropriate experimental conditions, provides the same sequential estimator of causal differences as the proposed approach. Simulation studies demonstrate that sequential causal inference offers substantial efficiency gains over the standard approach to comparing treatments, because the predictive estimators can take advantage of the monotone structure of shared data among adaptive strategies. We further demonstrate that the semi-parametric asymptotic variances, which are marginal ‘one-step’ estimators, may exhibit significant bias, in contrast to the predictive variances. We show that the conditions under which the sequential method is attractive relative to the other two approaches are those most likely to occur in real studies. PMID:17914714

On valuing information in adaptive-management models.

PubMed

Moore, Alana L; McCarthy, Michael A

2010-08-01

Active adaptive management looks at the benefit of using strategies that may be suboptimal in the near term but may provide additional information that will facilitate better management in the future. In many adaptive-management problems that have been studied, the optimal active and passive policies (accounting for learning when designing policies and designing policy on the basis of current best information, respectively) are very similar. This seems paradoxical; when faced with uncertainty about the best course of action, managers should spend very little effort on actively designing programs to learn about the system they are managing. We considered two possible reasons why active and passive adaptive solutions are often similar. First, the benefits of learning are often confined to the particular case study in the modeled scenario, whereas in reality information gained from local studies is often applied more broadly. Second, management objectives that incorporate the variance of an estimate may place greater emphasis on learning than more commonly used objectives that aim to maximize an expected value. We explored these issues in a case study of Merri Creek, Melbourne, Australia, in which the aim was to choose between two options for revegetation. We explicitly incorporated monitoring costs in the model. The value of the terminal rewards and the choice of objective both influenced the difference between active and passive adaptive solutions. Explicitly considering the cost of monitoring provided a different perspective on how the terminal reward and management objective affected learning. The states for which it was optimal to monitor did not always coincide with the states in which active and passive adaptive management differed. Our results emphasize that spending resources on monitoring is only optimal when the expected benefits of the options being considered are similar and when the pay-off for learning about their benefits is large.

Free energy calculations: an efficient adaptive biasing potential method.

PubMed

Dickson, Bradley M; Legoll, Frédéric; Lelièvre, Tony; Stoltz, Gabriel; Fleurat-Lessard, Paul

2010-05-06

We develop an efficient sampling and free energy calculation technique within the adaptive biasing potential (ABP) framework. By mollifying the density of states we obtain an approximate free energy and an adaptive bias potential that is computed directly from the population along the coordinates of the free energy. Because of the mollifier, the bias potential is "nonlocal", and its gradient admits a simple analytic expression. A single observation of the reaction coordinate can thus be used to update the approximate free energy at every point within a neighborhood of the observation. This greatly reduces the equilibration time of the adaptive bias potential. This approximation introduces two parameters: strength of mollification and the zero of energy of the bias potential. While we observe that the approximate free energy is a very good estimate of the actual free energy for a large range of mollification strength, we demonstrate that the errors associated with the mollification may be removed via deconvolution. The zero of energy of the bias potential, which is easy to choose, influences the speed of convergence but not the limiting accuracy. This method is simple to apply to free energy or mean force computation in multiple dimensions and does not involve second derivatives of the reaction coordinates, matrix manipulations nor on-the-fly adaptation of parameters. For the alanine dipeptide test case, the new method is found to gain as much as a factor of 10 in efficiency as compared to two basic implementations of the adaptive biasing force methods, and it is shown to be as efficient as well-tempered metadynamics with the postprocess deconvolution giving a clear advantage to the mollified density of states method.

Motivations and Barriers for Policymakers to Developing State Adaptation Plans

NASA Astrophysics Data System (ADS)

Miller, R.; Sylak-Glassman, E.

2016-12-01

Current approaches for developing high-quality adaptation plan require significant resources. In recent years, communities have grown to embrace adaptive plans across multiple forms, including adaptive capacity assessments, resilience strategies, and vulnerability assessments. Across the United States, as of this writing, 14 states have established adaptation plans, with another 8 states having begun the process. Given the high resources requirements and increasing interest in the development of adaptation plans, we aim to examine patterns behind the establishment of resilience plans at the state level. We examine demographic, financial, political, and physical characteristics associated with different states in an effort to explore the reasoning behind investing in the development of adaptation plans. This analysis considers quantitative and qualitative factors, including recent elections for political parties, politicians' climate-related statements and campaign promises, demographics, budgets, and regional climate threats. The analysis aims to identify motivations for state leadership taking action to develop adaptation plans. Results from the analysis seek to identify the primary drivers and barriers associated with state-wide resilience planning. These results could inform the design of scientific communication tools or approaches to aid future adaptation responses to climate change.

Adaptive control strategies for flexible robotic arm

NASA Technical Reports Server (NTRS)

Bialasiewicz, Jan T.

1993-01-01

The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity if not unstable closed-loop behavior. Therefore a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response.

Adaptive super twisting vibration control of a flexible spacecraft with state rate estimation

NASA Astrophysics Data System (ADS)

Malekzadeh, Maryam; Karimpour, Hossein

2018-05-01

The robust attitude and vibration control of a flexible spacecraft trying to perform accurate maneuvers in spite of various sources of uncertainty is addressed here. Difficulties for achieving precise and stable pointing arise from noisy onboard sensors, parameters indeterminacy, outer disturbances as well as un-modeled or hidden dynamics interactions. Based on high-order sliding-mode methods, the non-minimum phase nature of the problem is dealt with through output redefinition. An adaptive super-twisting algorithm (ASTA) is incorporated with its observer counterpart on the system under consideration to get reliable attitude and vibration control in the presence of sensor noise and momentum coupling. The closed-loop efficiency is verified through simulations under various indeterminate situations and got compared to other methods.

Real-Time Feedback Control of Flow-Induced Cavity Tones. Part 2; Adaptive Control

NASA Technical Reports Server (NTRS)

Kegerise, M. A.; Cabell, R. H.; Cattafesta, L. N., III

2006-01-01

An adaptive generalized predictive control (GPC) algorithm was formulated and applied to the cavity flow-tone problem. The algorithm employs gradient descent to update the GPC coefficients at each time step. Past input-output data and an estimate of the open-loop pulse response sequence are all that is needed to implement the algorithm for application at fixed Mach numbers. Transient measurements made during controller adaptation revealed that the controller coefficients converged to a steady state in the mean, and this implies that adaptation can be turned off at some point with no degradation in control performance. When converged, the control algorithm demonstrated multiple Rossiter mode suppression at fixed Mach numbers ranging from 0.275 to 0.38. However, as in the case of fixed-gain GPC, the adaptive GPC performance was limited by spillover in sidebands around the suppressed Rossiter modes. The algorithm was also able to maintain suppression of multiple cavity tones as the freestream Mach number was varied over a modest range (0.275 to 0.29). Beyond this range, stable operation of the control algorithm was not possible due to the fixed plant model in the algorithm.

Robust adaptive precision motion control of hydraulic actuators with valve dead-zone compensation.

PubMed

Deng, Wenxiang; Yao, Jianyong; Ma, Dawei

2017-09-01

This paper addresses the high performance motion control of hydraulic actuators with parametric uncertainties, unmodeled disturbances and unknown valve dead-zone. By constructing a smooth dead-zone inverse, a robust adaptive controller is proposed via backstepping method, in which adaptive law is synthesized to deal with parametric uncertainties and a continuous nonlinear robust control law to suppress unmodeled disturbances. Since the unknown dead-zone parameters can be estimated by adaptive law and then the effect of dead-zone can be compensated effectively via inverse operation, improved tracking performance can be expected. In addition, the disturbance upper bounds can also be updated online by adaptive laws, which increases the controller operability in practice. The Lyapunov based stability analysis shows that excellent asymptotic output tracking with zero steady-state error can be achieved by the developed controller even in the presence of unmodeled disturbance and unknown valve dead-zone. Finally, the proposed control strategy is experimentally tested on a servovalve controlled hydraulic actuation system subjected to an artificial valve dead-zone. Comparative experimental results are obtained to illustrate the effectiveness of the proposed control scheme. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A frequency-domain estimator for use in adaptive control systems

NASA Technical Reports Server (NTRS)

Lamaire, Richard O.; Valavani, Lena; Athans, Michael; Stein, Gunter

1991-01-01

This paper presents a frequency-domain estimator that can identify both a parametrized nominal model of a plant as well as a frequency-domain bounding function on the modeling error associated with this nominal model. This estimator, which we call a robust estimator, can be used in conjunction with a robust control-law redesign algorithm to form a robust adaptive controller.

Adaptive statistical pattern classifiers for remotely sensed data

NASA Technical Reports Server (NTRS)

Gonzalez, R. C.; Pace, M. O.; Raulston, H. S.

1975-01-01

A technique for the adaptive estimation of nonstationary statistics necessary for Bayesian classification is developed. The basic approach to the adaptive estimation procedure consists of two steps: (1) an optimal stochastic approximation of the parameters of interest and (2) a projection of the parameters in time or position. A divergence criterion is developed to monitor algorithm performance. Comparative results of adaptive and nonadaptive classifier tests are presented for simulated four dimensional spectral scan data.

RAD-ADAPT: Software for modelling clonogenic assay data in radiation biology.

PubMed

Zhang, Yaping; Hu, Kaiqiang; Beumer, Jan H; Bakkenist, Christopher J; D'Argenio, David Z

2017-04-01

We present a comprehensive software program, RAD-ADAPT, for the quantitative analysis of clonogenic assays in radiation biology. Two commonly used models for clonogenic assay analysis, the linear-quadratic model and single-hit multi-target model, are included in the software. RAD-ADAPT uses maximum likelihood estimation method to obtain parameter estimates with the assumption that cell colony count data follow a Poisson distribution. The program has an intuitive interface, generates model prediction plots, tabulates model parameter estimates, and allows automatic statistical comparison of parameters between different groups. The RAD-ADAPT interface is written using the statistical software R and the underlying computations are accomplished by the ADAPT software system for pharmacokinetic/pharmacodynamic systems analysis. The use of RAD-ADAPT is demonstrated using an example that examines the impact of pharmacologic ATM and ATR kinase inhibition on human lung cancer cell line A549 after ionizing radiation. Copyright © 2017 Elsevier B.V. All rights reserved.

Adaptive Management of Bull Trout Populations in the Lemhi Basin

USGS Publications Warehouse

Peterson, James T.; Tyre, Andrew J.; Converse, Sarah J.; Bogich, Tiffany L.; Miller, Damien; Post van der Burg, Max; Thomas, Carmen; Thompson, Ralph J.; Wood, Jeri; Brewer, Donna; Runge, Michael C.

2011-01-01

The bull trout Salvelinus confluentus, a stream-living salmonid distributed in drainages of the northwestern United States, is listed as threatened under the Endangered Species Act because of rangewide declines. One proposed recovery action is the reconnection of tributaries in the Lemhi Basin. Past water use policies in this core area disconnected headwater spawning sites from downstream habitat and have led to the loss of migratory life history forms. We developed an adaptive management framework to analyze which types of streams should be prioritized for reconnection under a proposed Habitat Conservation Plan. We developed a Stochastic Dynamic Program that identified optimal policies over time under four different assumptions about the nature of the migratory behavior and the effects of brook trout Salvelinus fontinalis on subpopulations of bull trout. In general, given the current state of the system and the uncertainties about the dynamics, the optimal policy would be to connect streams that are currently occupied by bull trout. We also estimated the value of information as the difference between absolute certainty about which of our four assumptions were correct, and a model averaged optimization assuming no knowledge. Overall there is little to be gained by learning about the dynamics of the system in its current state, although in other parts of the state space reducing uncertainties about the system would be very valuable. We also conducted a sensitivity analysis; the optimal decision at the current state does not change even when parameter values are changed up to 75% of the baseline values. Overall, the exercise demonstrates that it is possible to apply adaptive management principles to threatened and endangered species, but logistical and data availability constraints make detailed analyses difficult.

An Analysis of a Finite Element Method for Convection-Diffusion Problems. Part II. A Posteriori Error Estimates and Adaptivity.

DTIC Science & Technology

1983-03-01

AN ANALYSIS OF A FINITE ELEMENT METHOD FOR CONVECTION- DIFFUSION PROBLEMS PART II: A POSTERIORI ERROR ESTIMATES AND ADAPTIVITY by W. G. Szymczak Y 6a...PERIOD COVERED AN ANALYSIS OF A FINITE ELEMENT METHOD FOR final life of the contract CONVECTION- DIFFUSION PROBLEM S. Part II: A POSTERIORI ERROR ...Element Method for Convection- Diffusion Problems. Part II: A Posteriori Error Estimates and Adaptivity W. G. Szvmczak and I. Babu~ka# Laboratory for

Anti-inflammatory salicylate treatment alters the metabolic adaptations to lactation in dairy cattle

PubMed Central

Farney, Jaymelynn K.; Mamedova, Laman K.; Coetzee, Johann F.; KuKanich, Butch; Sordillo, Lorraine M.; Stoakes, Sara K.; Minton, J. Ernest; Hollis, Larry C.

2013-01-01

Adapting to the lactating state requires metabolic adjustments in multiple tissues, especially in the dairy cow, which must meet glucose demands that can exceed 5 kg/day in the face of negligible gastrointestinal glucose absorption. These challenges are met through the process of homeorhesis, the alteration of metabolic setpoints to adapt to a shift in physiological state. To investigate the role of inflammation-associated pathways in these homeorhetic adaptations, we treated cows with the nonsteroidal anti-inflammatory drug sodium salicylate (SS) for the first 7 days of lactation. Administration of SS decreased liver TNF-α mRNA and marginally decreased plasma TNF-α concentration, but plasma eicosanoids and liver NF-κB activity were unaltered during treatment. Despite the mild impact on these inflammatory markers, SS clearly altered metabolic function. Plasma glucose concentration was decreased by SS, but this was not explained by a shift in hepatic gluconeogenic gene expression or by altered milk lactose secretion. Insulin concentrations decreased in SS-treated cows on day 7 compared with controls, which was consistent with the decline in plasma glucose concentration. The revised quantitative insulin sensitivity check index (RQUICKI) was then used to assess whether altered insulin sensitivity may have influenced glucose utilization rate with SS. The RQUICKI estimate of insulin sensitivity was significantly elevated by SS on day 7, coincident with the decline in plasma glucose concentration. Salicylate prevented postpartum insulin resistance, likely causing excessive glucose utilization in peripheral tissues and hypoglycemia. These results represent the first evidence that inflammation-associated pathways are involved in homeorhetic adaptations to lactation. PMID:23678026

Empirical and numerical investigation of mass movements - data fusion and analysis

NASA Astrophysics Data System (ADS)

Schmalz, Thilo; Eichhorn, Andreas; Buhl, Volker; Tinkhof, Kurt Mair Am; Preh, Alexander; Tentschert, Ewald-Hans; Zangerl, Christian

2010-05-01

Increasing settlement activities of people in mountanious regions and the appearance of extreme climatic conditions motivate the investigation of landslides. Within the last few years a significant rising of disastrous slides could be registered which generated a broad public interest and the request for security measures. The FWF (Austrian Science Fund) funded project ‘KASIP' (Knowledge-based Alarm System with Identified Deformation Predictor) deals with the development of a new type of alarm system based on calibrated numerical slope models for the realistic calculation of failure scenarios. In KASIP, calibration is the optimal adaptation of a numerical model to available monitoring data by least-squares techniques (e.g. adaptive Kalman-filtering). Adaptation means the determination of a priori uncertain physical parameters like the strength of the geological structure. The object of our studies in KASIP is the landslide ‘Steinlehnen' near Innsbruck (Northern Tyrol, Austria). The first part of the presentation is focussed on the determination of geometrical surface-information. This also includes the description of the monitoring system for the collection of the displacement data and filter approaches for the estimation of the slopes kinematic behaviour. The necessity of continous monitoring and the effect of data gaps for reliable filter results and the prediction of the future state is discussed. The second part of the presentation is more focussed on the numerical modelling of the slope by FD- (Finite Difference-) methods and the development of the adaptive Kalman-filter. The realisation of the numerical slope model is developed by FLAC3D (software company HCItasca Ltd.). The model contains different geomechanical approaches (like Mohr-Coulomb) and enables the calculation of great deformations and the failure of the slope. Stability parameters (like the factor-of-safety FS) allow the evaluation of the current state of the slope. Until now, the adaptation of relevant material parameters is often performed by trial and error methods. This common method shall be improved by adaptive Kalman-filtering methods. In contrast to trial and error, Kalman-filtering also considers stochastical information of the input data. Especially the estimation of strength parameters (cohesion c, angle of internal friction phi) in a dynamic consideration of the slope is discussed. Problems with conditioning and numerical stability of the filter matrices, memory overflow and computing time are outlined. It is shown that the Kalman-filter is in principle suitable for an semi-automated adaptation process and obtains realistic values for the unknown material parameters.

Adaptive sampling in research on risk-related behaviors.

PubMed

Thompson, Steven K; Collins, Linda M

2002-11-01

This article introduces adaptive sampling designs to substance use researchers. Adaptive sampling is particularly useful when the population of interest is rare, unevenly distributed, hidden, or hard to reach. Examples of such populations are injection drug users, individuals at high risk for HIV/AIDS, and young adolescents who are nicotine dependent. In conventional sampling, the sampling design is based entirely on a priori information, and is fixed before the study begins. By contrast, in adaptive sampling, the sampling design adapts based on observations made during the survey; for example, drug users may be asked to refer other drug users to the researcher. In the present article several adaptive sampling designs are discussed. Link-tracing designs such as snowball sampling, random walk methods, and network sampling are described, along with adaptive allocation and adaptive cluster sampling. It is stressed that special estimation procedures taking the sampling design into account are needed when adaptive sampling has been used. These procedures yield estimates that are considerably better than conventional estimates. For rare and clustered populations adaptive designs can give substantial gains in efficiency over conventional designs, and for hidden populations link-tracing and other adaptive procedures may provide the only practical way to obtain a sample large enough for the study objectives.

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.

High-order tracking differentiator based adaptive neural control of a flexible air-breathing hypersonic vehicle subject to actuators constraints.

PubMed

Bu, Xiangwei; Wu, Xiaoyan; Tian, Mingyan; Huang, Jiaqi; Zhang, Rui; Ma, Zhen

2015-09-01

In this paper, an adaptive neural controller is exploited for a constrained flexible air-breathing hypersonic vehicle (FAHV) based on high-order tracking differentiator (HTD). By utilizing functional decomposition methodology, the dynamic model is reasonably decomposed into the respective velocity subsystem and altitude subsystem. For the velocity subsystem, a dynamic inversion based neural controller is constructed. By introducing the HTD to adaptively estimate the newly defined states generated in the process of model transformation, a novel neural based altitude controller that is quite simpler than the ones derived from back-stepping is addressed based on the normal output-feedback form instead of the strict-feedback formulation. Based on minimal-learning parameter scheme, only two neural networks with two adaptive parameters are needed for neural approximation. Especially, a novel auxiliary system is explored to deal with the problem of control inputs constraints. Finally, simulation results are presented to test the effectiveness of the proposed control strategy in the presence of system uncertainties and actuators constraints. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Motion-adaptive spatio-temporal regularization for accelerated dynamic MRI.

PubMed

Asif, M Salman; Hamilton, Lei; Brummer, Marijn; Romberg, Justin

2013-09-01

Accelerated magnetic resonance imaging techniques reduce signal acquisition time by undersampling k-space. A fundamental problem in accelerated magnetic resonance imaging is the recovery of quality images from undersampled k-space data. Current state-of-the-art recovery algorithms exploit the spatial and temporal structures in underlying images to improve the reconstruction quality. In recent years, compressed sensing theory has helped formulate mathematical principles and conditions that ensure recovery of (structured) sparse signals from undersampled, incoherent measurements. In this article, a new recovery algorithm, motion-adaptive spatio-temporal regularization, is presented that uses spatial and temporal structured sparsity of MR images in the compressed sensing framework to recover dynamic MR images from highly undersampled k-space data. In contrast to existing algorithms, our proposed algorithm models temporal sparsity using motion-adaptive linear transformations between neighboring images. The efficiency of motion-adaptive spatio-temporal regularization is demonstrated with experiments on cardiac magnetic resonance imaging for a range of reduction factors. Results are also compared with k-t FOCUSS with motion estimation and compensation-another recently proposed recovery algorithm for dynamic magnetic resonance imaging. . Copyright © 2012 Wiley Periodicals, Inc.

Using Emotion as Information in Future-Oriented Cognition: Individual Differences in the Context of State Negative Affect

PubMed Central

Marroquín, Brett; Boyle, Chloe C.; Nolen-Hoeksema, Susan; Stanton, Annette L.

2016-01-01

Predictions about the future are susceptible to mood-congruent influences of emotional state. However, recent work suggests individuals also differ in the degree to which they incorporate emotion into cognition. This study examined the role of such individual differences in the context of state negative emotion. We examined whether trait tendencies to use negative or positive emotion as information affect individuals' predictions of what will happen in the future (likelihood estimation) and how events will feel (affective forecasting), and whether trait influences depend on emotional state. Participants (N=119) reported on tendencies to use emotion as information (“following feelings”), underwent an emotion induction (negative versus neutral), and made likelihood estimates and affective forecasts for future events. Views of the future were predicted by both emotional state and individual differences in following feelings. Whereas following negative feelings affected most future-oriented cognition across emotional states, following positive feelings specifically buffered individuals' views of the future in the negative emotion condition, and specifically for positive future events, a category of future-event prediction especially important in psychological health. Individual differences may confer predisposition toward optimistic or pessimistic expectations of the future in the context of acute negative emotion, with implications for adaptive and maladaptive functioning. PMID:27041783

Psychological state estimation from physiological recordings during robot-assisted gait rehabilitation.

PubMed

Koenig, Alexander; Omlin, Ximena; Zimmerli, Lukas; Sapa, Mark; Krewer, Carmen; Bolliger, Marc; Müller, Friedemann; Riener, Robert

2011-01-01

Robot-assisted treadmill training is an established intervention used to improve walking ability in patients with neurological disorders. Although it has been shown that attention to the task is a key factor for successful rehabilitation, the psychological state of patients during robot-assisted gait therapy is often neglected. We presented 17 nondisabled subjects and 10 patients with neurological disorders a virtual-reality task with varying difficulty levels to induce feelings of being bored, excited, and overstressed. We developed an approach to automatically estimate and classify a patient's psychological state, i.e., his or her mental engagement, in real time during gait training. We used psychophysiological measurements to obtain an objective measure of the current psychological state. Automatic classification was performed by a neural network. We found that heart rate, skin conductance responses, and skin temperature can be used as markers for psychological states in the presence of physical effort induced by walking. The classifier achieved a classification error of 1.4% for nondisabled subjects and 2.1% for patients with neurological disorders. Using our new method, we processed the psychological state data in real time. Our method is a first step toward real-time auto-adaptive gait training with potential to improve rehabilitation results by optimally challenging patients at all times during exercise.

Output Feedback-Based Boundary Control of Uncertain Coupled Semilinear Parabolic PDE Using Neurodynamic Programming.

PubMed

Talaei, Behzad; Jagannathan, Sarangapani; Singler, John

2018-04-01

In this paper, neurodynamic programming-based output feedback boundary control of distributed parameter systems governed by uncertain coupled semilinear parabolic partial differential equations (PDEs) under Neumann or Dirichlet boundary control conditions is introduced. First, Hamilton-Jacobi-Bellman (HJB) equation is formulated in the original PDE domain and the optimal control policy is derived using the value functional as the solution of the HJB equation. Subsequently, a novel observer is developed to estimate the system states given the uncertain nonlinearity in PDE dynamics and measured outputs. Consequently, the suboptimal boundary control policy is obtained by forward-in-time estimation of the value functional using a neural network (NN)-based online approximator and estimated state vector obtained from the NN observer. Novel adaptive tuning laws in continuous time are proposed for learning the value functional online to satisfy the HJB equation along system trajectories while ensuring the closed-loop stability. Local uniformly ultimate boundedness of the closed-loop system is verified by using Lyapunov theory. The performance of the proposed controller is verified via simulation on an unstable coupled diffusion reaction process.

Construction of a Computerized Adaptive Testing Version of the Quebec Adaptive Behavior Scale.

ERIC Educational Resources Information Center

Tasse, Marc J.; And Others

Multilog (Thissen, 1991) was used to estimate parameters of 225 items from the Quebec Adaptive Behavior Scale (QABS). A database containing actual data from 2,439 subjects was used for the parameterization procedures. The two-parameter-logistic model was used in estimating item parameters and in the testing strategy. MicroCAT (Assessment Systems…

New methods for state estimation and adaptive observation of environmental flow systems leveraging coordinated swarms of sensor vehicles

NASA Astrophysics Data System (ADS)

Bewley, Thomas

2015-11-01

Accurate long-term forecasts of the path and intensity of hurricanes are imperative to protect property and save lives. Accurate estimations and forecasts of the spread of large-scale contaminant plumes, such as those from Deepwater Horizon, Fukushima, and recent volcanic eruptions in Iceland, are essential for assessing environment impact, coordinating remediation efforts, and in certain cases moving folks out of harm's way. The challenges in estimating and forecasting such systems include: (a) environmental flow modeling, (b) high-performance real-time computing, (c) assimilating measured data into numerical simulations, and (d) acquiring in-situ data, beyond what can be measured from satellites, that is maximally relevant for reducing forecast uncertainty. This talk will focus on new techniques for addressing (c) and (d), namely, data assimilation and adaptive observation, in both hurricanes and large-scale environmental plumes. In particular, we will present a new technique for the energy-efficient coordination of swarms of sensor-laden balloons for persistent, in-situ, distributed, real-time measurement of developing hurricanes, leveraging buoyancy control only (coupled with the predictable and strongly stratified flowfield within the hurricane). Animations of these results are available at http://flowcontrol.ucsd.edu/3dhurricane.mp4 and http://flowcontrol.ucsd.edu/katrina.mp4. We also will survey our unique hybridization of the venerable Ensemble Kalman and Variational approaches to large-scale data assimilation in environmental flow systems, and how essentially the dual of this hybrid approach may be used to solve the adaptive observation problem in a uniquely effective and rigorous fashion.

Multichannel Speech Enhancement Based on Generalized Gamma Prior Distribution with Its Online Adaptive Estimation

NASA Astrophysics Data System (ADS)

Dat, Tran Huy; Takeda, Kazuya; Itakura, Fumitada

We present a multichannel speech enhancement method based on MAP speech spectral magnitude estimation using a generalized gamma model of speech prior distribution, where the model parameters are adapted from actual noisy speech in a frame-by-frame manner. The utilization of a more general prior distribution with its online adaptive estimation is shown to be effective for speech spectral estimation in noisy environments. Furthermore, the multi-channel information in terms of cross-channel statistics are shown to be useful to better adapt the prior distribution parameters to the actual observation, resulting in better performance of speech enhancement algorithm. We tested the proposed algorithm in an in-car speech database and obtained significant improvements of the speech recognition performance, particularly under non-stationary noise conditions such as music, air-conditioner and open window.

Adaptive channel estimation for soft decision decoding over non-Gaussian optical channel

NASA Astrophysics Data System (ADS)

Xiang, Jing-song; Miao, Tao-tao; Huang, Sheng; Liu, Huan-lin

2016-10-01

An adaptive priori likelihood ratio (LLR) estimation method is proposed over non-Gaussian channel in the intensity modulation/direct detection (IM/DD) optical communication systems. Using the nonparametric histogram and the weighted least square linear fitting in the tail regions, the LLR is estimated and used for the soft decision decoding of the low-density parity-check (LDPC) codes. This method can adapt well to the three main kinds of intensity modulation/direct detection (IM/DD) optical channel, i.e., the chi-square channel, the Webb-Gaussian channel and the additive white Gaussian noise (AWGN) channel. The performance penalty of channel estimation is neglected.

Estimating the Relevance of World Disturbances to Explain Savings, Interference and Long-Term Motor Adaptation Effects

PubMed Central

Berniker, Max; Kording, Konrad P.

2011-01-01

Recent studies suggest that motor adaptation is the result of multiple, perhaps linear processes each with distinct time scales. While these models are consistent with some motor phenomena, they can neither explain the relatively fast re-adaptation after a long washout period, nor savings on a subsequent day. Here we examined if these effects can be explained if we assume that the CNS stores and retrieves movement parameters based on their possible relevance. We formalize this idea with a model that infers not only the sources of potential motor errors, but also their relevance to the current motor circumstances. In our model adaptation is the process of re-estimating parameters that represent the body and the world. The likelihood of a world parameter being relevant is then based on the mismatch between an observed movement and that predicted when not compensating for the estimated world disturbance. As such, adapting to large motor errors in a laboratory setting should alert subjects that disturbances are being imposed on them, even after motor performance has returned to baseline. Estimates of this external disturbance should be relevant both now and in future laboratory settings. Estimated properties of our bodies on the other hand should always be relevant. Our model demonstrates savings, interference, spontaneous rebound and differences between adaptation to sudden and gradual disturbances. We suggest that many issues concerning savings and interference can be understood when adaptation is conditioned on the relevance of parameters. PMID:21998574

Modeling phenotypic metabolic adaptations of Mycobacterium tuberculosis H37Rv under hypoxia.

PubMed

Fang, Xin; Wallqvist, Anders; Reifman, Jaques

2012-01-01

The ability to adapt to different conditions is key for Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), to successfully infect human hosts. Adaptations allow the organism to evade the host immune responses during acute infections and persist for an extended period of time during the latent infectious stage. In latently infected individuals, estimated to include one-third of the human population, the organism exists in a variety of metabolic states, which impedes the development of a simple strategy for controlling or eradicating this disease. Direct knowledge of the metabolic states of M. tuberculosis in patients would aid in the management of the disease as well as in forming the basis for developing new drugs and designing more efficacious drug cocktails. Here, we propose an in silico approach to create state-specific models based on readily available gene expression data. The coupling of differential gene expression data with a metabolic network model allowed us to characterize the metabolic adaptations of M. tuberculosis H37Rv to hypoxia. Given the microarray data for the alterations in gene expression, our model predicted reduced oxygen uptake, ATP production changes, and a global change from an oxidative to a reductive tricarboxylic acid (TCA) program. Alterations in the biomass composition indicated an increase in the cell wall metabolites required for cell-wall growth, as well as heightened accumulation of triacylglycerol in preparation for a low-nutrient, low metabolic activity life style. In contrast, the gene expression program in the deletion mutant of dosR, which encodes the immediate hypoxic response regulator, failed to adapt to low-oxygen stress. Our predictions were compatible with recent experimental observations of M. tuberculosis activity under hypoxic and anaerobic conditions. Importantly, alterations in the flow and accumulation of a particular metabolite were not necessarily directly linked to differential gene expression of the enzymes catalyzing the related metabolic reactions.

Vision System for Coarsely Estimating Motion Parameters for Unknown Fast Moving Objects in Space

PubMed Central

Chen, Min; Hashimoto, Koichi

2017-01-01

Motivated by biological interests in analyzing navigation behaviors of flying animals, we attempt to build a system measuring their motion states. To do this, in this paper, we build a vision system to detect unknown fast moving objects within a given space, calculating their motion parameters represented by positions and poses. We proposed a novel method to detect reliable interest points from images of moving objects, which can be hardly detected by general purpose interest point detectors. 3D points reconstructed using these interest points are then grouped and maintained for detected objects, according to a careful schedule, considering appearance and perspective changes. In the estimation step, a method is introduced to adapt the robust estimation procedure used for dense point set to the case for sparse set, reducing the potential risk of greatly biased estimation. Experiments are conducted against real scenes, showing the capability of the system of detecting multiple unknown moving objects and estimating their positions and poses. PMID:29206189

An Adaptive Kalman Filter using a Simple Residual Tuning Method

NASA Technical Reports Server (NTRS)

Harman, Richard R.

1999-01-01

One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.

System Identification for Nonlinear Control Using Neural Networks

NASA Technical Reports Server (NTRS)

Stengel, Robert F.; Linse, Dennis J.

1990-01-01

An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique.

Convergent Cross Mapping: Basic concept, influence of estimation parameters and practical application.

PubMed

Schiecke, Karin; Pester, Britta; Feucht, Martha; Leistritz, Lutz; Witte, Herbert

2015-01-01

In neuroscience, data are typically generated from neural network activity. Complex interactions between measured time series are involved, and nothing or only little is known about the underlying dynamic system. Convergent Cross Mapping (CCM) provides the possibility to investigate nonlinear causal interactions between time series by using nonlinear state space reconstruction. Aim of this study is to investigate the general applicability, and to show potentials and limitation of CCM. Influence of estimation parameters could be demonstrated by means of simulated data, whereas interval-based application of CCM on real data could be adapted for the investigation of interactions between heart rate and specific EEG components of children with temporal lobe epilepsy.

Model Based Optimal Control, Estimation, and Validation of Lithium-Ion Batteries

NASA Astrophysics Data System (ADS)

Perez, Hector Eduardo

This dissertation focuses on developing and experimentally validating model based control techniques to enhance the operation of lithium ion batteries, safely. An overview of the contributions to address the challenges that arise are provided below. Chapter 1: This chapter provides an introduction to battery fundamentals, models, and control and estimation techniques. Additionally, it provides motivation for the contributions of this dissertation. Chapter 2: This chapter examines reference governor (RG) methods for satisfying state constraints in Li-ion batteries. Mathematically, these constraints are formulated from a first principles electrochemical model. Consequently, the constraints explicitly model specific degradation mechanisms, such as lithium plating, lithium depletion, and overheating. This contrasts with the present paradigm of limiting measured voltage, current, and/or temperature. The critical challenges, however, are that (i) the electrochemical states evolve according to a system of nonlinear partial differential equations, and (ii) the states are not physically measurable. Assuming available state and parameter estimates, this chapter develops RGs for electrochemical battery models. The results demonstrate how electrochemical model state information can be utilized to ensure safe operation, while simultaneously enhancing energy capacity, power, and charge speeds in Li-ion batteries. Chapter 3: Complex multi-partial differential equation (PDE) electrochemical battery models are characterized by parameters that are often difficult to measure or identify. This parametric uncertainty influences the state estimates of electrochemical model-based observers for applications such as state-of-charge (SOC) estimation. This chapter develops two sensitivity-based interval observers that map bounded parameter uncertainty to state estimation intervals, within the context of electrochemical PDE models and SOC estimation. Theoretically, this chapter extends the notion of interval observers to PDE models using a sensitivity-based approach. Practically, this chapter quantifies the sensitivity of battery state estimates to parameter variations, enabling robust battery management schemes. The effectiveness of the proposed sensitivity-based interval observers is verified via a numerical study for the range of uncertain parameters. Chapter 4: This chapter seeks to derive insight on battery charging control using electrochemistry models. Directly using full order complex multi-partial differential equation (PDE) electrochemical battery models is difficult and sometimes impossible to implement. This chapter develops an approach for obtaining optimal charge control schemes, while ensuring safety through constraint satisfaction. An optimal charge control problem is mathematically formulated via a coupled reduced order electrochemical-thermal model which conserves key electrochemical and thermal state information. The Legendre-Gauss-Radau (LGR) pseudo-spectral method with adaptive multi-mesh-interval collocation is employed to solve the resulting nonlinear multi-state optimal control problem. Minimum time charge protocols are analyzed in detail subject to solid and electrolyte phase concentration constraints, as well as temperature constraints. The optimization scheme is examined using different input current bounds, and an insight on battery design for fast charging is provided. Experimental results are provided to compare the tradeoffs between an electrochemical-thermal model based optimal charge protocol and a traditional charge protocol. Chapter 5: Fast and safe charging protocols are crucial for enhancing the practicality of batteries, especially for mobile applications such as smartphones and electric vehicles. This chapter proposes an innovative approach to devising optimally health-conscious fast-safe charge protocols. A multi-objective optimal control problem is mathematically formulated via a coupled electro-thermal-aging battery model, where electrical and aging sub-models depend upon the core temperature captured by a two-state thermal sub-model. The Legendre-Gauss-Radau (LGR) pseudo-spectral method with adaptive multi-mesh-interval collocation is employed to solve the resulting highly nonlinear six-state optimal control problem. Charge time and health degradation are therefore optimally traded off, subject to both electrical and thermal constraints. Minimum-time, minimum-aging, and balanced charge scenarios are examined in detail. Sensitivities to the upper voltage bound, ambient temperature, and cooling convection resistance are investigated as well. Experimental results are provided to compare the tradeoffs between a balanced and traditional charge protocol. Chapter 6: This chapter provides concluding remarks on the findings of this dissertation and a discussion of future work.

Accounting for adaptation and intensity in projecting heat wave-related mortality.

PubMed

Wang, Yan; Nordio, Francesco; Nairn, John; Zanobetti, Antonella; Schwartz, Joel D

2018-02-01

How adaptation and intensity of heat waves affect heat wave-related mortality is unclear, making health projections difficult. We estimated the effect of heat waves, the effect of the intensity of heat waves, and adaptation on mortality in 209 U.S. cities with 168 million people during 1962-2006. We improved the standard time-series models by incorporating the intensity of heat waves using excess heat factor (EHF) and estimating adaptation empirically using interactions with yearly mean summer temperature (MST). We combined the epidemiological estimates for heat wave, intensity, and adaptation with the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model dataset to project heat wave-related mortality by 2050. The effect of heat waves increased with its intensity. Adaptation to heat waves occurred, which was shown by the decreasing effect of heat waves with MST. However, adaptation was lessened as MST increased. Ignoring adaptation in projections would result in a substantial overestimate of the projected heat wave-related mortality (by 277-747% in 2050). Incorporating the empirically estimated adaptation into projections would result in little change in the projected heat wave-related mortality between 2006 and 2050. This differs regionally, however, with increasing mortality over time for cities in the southern and western U.S. but decreasing mortality over time for the north. Accounting for adaptation is important to reduce bias in the projections of heat wave-related mortality. The finding that the southern and western U.S. are the areas that face increasing heat-related deaths is novel, and indicates that more regional adaptation strategies are needed. Copyright © 2017 Elsevier Inc. All rights reserved.

Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering

PubMed Central

Carmena, Jose M.

2016-01-01

Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter initialization. Finally, the architecture extended control to tasks beyond those used for CLDA training. These results have significant implications towards the development of clinically-viable neuroprosthetics. PMID:27035820

Gain-adaptive vector quantization for medium-rate speech coding

NASA Technical Reports Server (NTRS)

Chen, J.-H.; Gersho, A.

1985-01-01

A class of adaptive vector quantizers (VQs) that can dynamically adjust the 'gain' of codevectors according to the input signal level is introduced. The encoder uses a gain estimator to determine a suitable normalization of each input vector prior to VQ coding. The normalized vectors have reduced dynamic range and can then be more efficiently coded. At the receiver, the VQ decoder output is multiplied by the estimated gain. Both forward and backward adaptation are considered and several different gain estimators are compared and evaluated. An approach to optimizing the design of gain estimators is introduced. Some of the more obvious techniques for achieving gain adaptation are substantially less effective than the use of optimized gain estimators. A novel design technique that is needed to generate the appropriate gain-normalized codebook for the vector quantizer is introduced. Experimental results show that a significant gain in segmental SNR can be obtained over nonadaptive VQ with a negligible increase in complexity.

Trial-by-Trial Motor Cortical Correlates of a Rapidly Adapting Visuomotor Internal Model.

PubMed

Stavisky, Sergey D; Kao, Jonathan C; Ryu, Stephen I; Shenoy, Krishna V

2017-02-15

Accurate motor control is mediated by internal models of how neural activity generates movement. We examined neural correlates of an adapting internal model of visuomotor gain in motor cortex while two macaques performed a reaching task in which the gain scaling between the hand and a presented cursor was varied. Previous studies of cortical changes during visuomotor adaptation focused on preparatory and perimovement epochs and analyzed trial-averaged neural data. Here, we recorded simultaneous neural population activity using multielectrode arrays and focused our analysis on neural differences in the period before the target appeared. We found that we could estimate the monkey's internal model of the gain using the neural population state during this pretarget epoch. This neural correlate depended on the gain experienced during recent trials and it predicted the speed of the subsequent reach. To explore the utility of this internal model estimate for brain-machine interfaces, we performed an offline analysis showing that it can be used to compensate for upcoming reach extent errors. Together, these results demonstrate that pretarget neural activity in motor cortex reflects the monkey's internal model of visuomotor gain on single trials and can potentially be used to improve neural prostheses. SIGNIFICANCE STATEMENT When generating movement commands, the brain is believed to use internal models of the relationship between neural activity and the body's movement. Visuomotor adaptation tasks have revealed neural correlates of these computations in multiple brain areas during movement preparation and execution. Here, we describe motor cortical changes in a visuomotor gain change task even before a specific movement is cued. We were able to estimate the gain internal model from these pretarget neural correlates and relate it to single-trial behavior. This is an important step toward understanding the sensorimotor system's algorithms for updating its internal models after specific movements and errors. Furthermore, the ability to estimate the internal model before movement could improve motor neural prostheses being developed for people with paralysis. Copyright © 2017 the authors 0270-6474/17/371721-12$15.00/0.

Real-time prediction of respiratory motion based on a local dynamic model in an augmented space

NASA Astrophysics Data System (ADS)

Hong, S.-M.; Jung, B.-H.; Ruan, D.

2011-03-01

Motion-adaptive radiotherapy aims to deliver ablative radiation dose to the tumor target with minimal normal tissue exposure, by accounting for real-time target movement. In practice, prediction is usually necessary to compensate for system latency induced by measurement, communication and control. This work focuses on predicting respiratory motion, which is most dominant for thoracic and abdominal tumors. We develop and investigate the use of a local dynamic model in an augmented space, motivated by the observation that respiratory movement exhibits a locally circular pattern in a plane augmented with a delayed axis. By including the angular velocity as part of the system state, the proposed dynamic model effectively captures the natural evolution of respiratory motion. The first-order extended Kalman filter is used to propagate and update the state estimate. The target location is predicted by evaluating the local dynamic model equations at the required prediction length. This method is complementary to existing work in that (1) the local circular motion model characterizes 'turning', overcoming the limitation of linear motion models; (2) it uses a natural state representation including the local angular velocity and updates the state estimate systematically, offering explicit physical interpretations; (3) it relies on a parametric model and is much less data-satiate than the typical adaptive semiparametric or nonparametric method. We tested the performance of the proposed method with ten RPM traces, using the normalized root mean squared difference between the predicted value and the retrospective observation as the error metric. Its performance was compared with predictors based on the linear model, the interacting multiple linear models and the kernel density estimator for various combinations of prediction lengths and observation rates. The local dynamic model based approach provides the best performance for short to medium prediction lengths under relatively low observation rate. Sensitivity analysis indicates its robustness toward the choice of parameters. Its simplicity, robustness and low computation cost makes the proposed local dynamic model an attractive tool for real-time prediction with system latencies below 0.4 s.

Real-time prediction of respiratory motion based on a local dynamic model in an augmented space.

PubMed

Hong, S-M; Jung, B-H; Ruan, D

2011-03-21

Motion-adaptive radiotherapy aims to deliver ablative radiation dose to the tumor target with minimal normal tissue exposure, by accounting for real-time target movement. In practice, prediction is usually necessary to compensate for system latency induced by measurement, communication and control. This work focuses on predicting respiratory motion, which is most dominant for thoracic and abdominal tumors. We develop and investigate the use of a local dynamic model in an augmented space, motivated by the observation that respiratory movement exhibits a locally circular pattern in a plane augmented with a delayed axis. By including the angular velocity as part of the system state, the proposed dynamic model effectively captures the natural evolution of respiratory motion. The first-order extended Kalman filter is used to propagate and update the state estimate. The target location is predicted by evaluating the local dynamic model equations at the required prediction length. This method is complementary to existing work in that (1) the local circular motion model characterizes 'turning', overcoming the limitation of linear motion models; (2) it uses a natural state representation including the local angular velocity and updates the state estimate systematically, offering explicit physical interpretations; (3) it relies on a parametric model and is much less data-satiate than the typical adaptive semiparametric or nonparametric method. We tested the performance of the proposed method with ten RPM traces, using the normalized root mean squared difference between the predicted value and the retrospective observation as the error metric. Its performance was compared with predictors based on the linear model, the interacting multiple linear models and the kernel density estimator for various combinations of prediction lengths and observation rates. The local dynamic model based approach provides the best performance for short to medium prediction lengths under relatively low observation rate. Sensitivity analysis indicates its robustness toward the choice of parameters. Its simplicity, robustness and low computation cost makes the proposed local dynamic model an attractive tool for real-time prediction with system latencies below 0.4 s.

Adaptive Importance Sampling for Control and Inference

NASA Astrophysics Data System (ADS)

Kappen, H. J.; Ruiz, H. C.

2016-03-01

Path integral (PI) control problems are a restricted class of non-linear control problems that can be solved formally as a Feynman-Kac PI and can be estimated using Monte Carlo sampling. In this contribution we review PI control theory in the finite horizon case. We subsequently focus on the problem how to compute and represent control solutions. We review the most commonly used methods in robotics and control. Within the PI theory, the question of how to compute becomes the question of importance sampling. Efficient importance samplers are state feedback controllers and the use of these requires an efficient representation. Learning and representing effective state-feedback controllers for non-linear stochastic control problems is a very challenging, and largely unsolved, problem. We show how to learn and represent such controllers using ideas from the cross entropy method. We derive a gradient descent method that allows to learn feed-back controllers using an arbitrary parametrisation. We refer to this method as the path integral cross entropy method or PICE. We illustrate this method for some simple examples. The PI control methods can be used to estimate the posterior distribution in latent state models. In neuroscience these problems arise when estimating connectivity from neural recording data using EM. We demonstrate the PI control method as an accurate alternative to particle filtering.

Mitigation, adaptation, and climate change: results from recent research on US timber markets.

Treesearch

Brent Sohngen; Ralph Alig

2000-01-01

This paper reviews recent studies that have addressed how US timber markets may adapt to climate change, and how US forests could be used to mitigate potential climate change. The studies are discussed in light of the ecological and economic assumptions used to estimate adaptation. Estimates of both economic impacts and carbon sequestration costs depend heavily on the...

Personalized State-space Modeling of Glucose Dynamics for Type 1 Diabetes Using Continuously Monitored Glucose, Insulin Dose, and Meal Intake: An Extended Kalman Filter Approach.

PubMed

Wang, Qian; Molenaar, Peter; Harsh, Saurabh; Freeman, Kenneth; Xie, Jinyu; Gold, Carol; Rovine, Mike; Ulbrecht, Jan

2014-03-01

An essential component of any artificial pancreas is on the prediction of blood glucose levels as a function of exogenous and endogenous perturbations such as insulin dose, meal intake, and physical activity and emotional tone under natural living conditions. In this article, we present a new data-driven state-space dynamic model with time-varying coefficients that are used to explicitly quantify the time-varying patient-specific effects of insulin dose and meal intake on blood glucose fluctuations. Using the 3-variate time series of glucose level, insulin dose, and meal intake of an individual type 1 diabetic subject, we apply an extended Kalman filter (EKF) to estimate time-varying coefficients of the patient-specific state-space model. We evaluate our empirical modeling using (1) the FDA-approved UVa/Padova simulator with 30 virtual patients and (2) clinical data of 5 type 1 diabetic patients under natural living conditions. Compared to a forgetting-factor-based recursive ARX model of the same order, the EKF model predictions have higher fit, and significantly better temporal gain and J index and thus are superior in early detection of upward and downward trends in glucose. The EKF based state-space model developed in this article is particularly suitable for model-based state-feedback control designs since the Kalman filter estimates the state variable of the glucose dynamics based on the measured glucose time series. In addition, since the model parameters are estimated in real time, this model is also suitable for adaptive control. © 2014 Diabetes Technology Society.

Assessing Continuous Operator Workload With a Hybrid Scaffolded Neuroergonomic Modeling Approach.

PubMed

Borghetti, Brett J; Giametta, Joseph J; Rusnock, Christina F

2017-02-01

We aimed to predict operator workload from neurological data using statistical learning methods to fit neurological-to-state-assessment models. Adaptive systems require real-time mental workload assessment to perform dynamic task allocations or operator augmentation as workload issues arise. Neuroergonomic measures have great potential for informing adaptive systems, and we combine these measures with models of task demand as well as information about critical events and performance to clarify the inherent ambiguity of interpretation. We use machine learning algorithms on electroencephalogram (EEG) input to infer operator workload based upon Improved Performance Research Integration Tool workload model estimates. Cross-participant models predict workload of other participants, statistically distinguishing between 62% of the workload changes. Machine learning models trained from Monte Carlo resampled workload profiles can be used in place of deterministic workload profiles for cross-participant modeling without incurring a significant decrease in machine learning model performance, suggesting that stochastic models can be used when limited training data are available. We employed a novel temporary scaffold of simulation-generated workload profile truth data during the model-fitting process. A continuous workload profile serves as the target to train our statistical machine learning models. Once trained, the workload profile scaffolding is removed and the trained model is used directly on neurophysiological data in future operator state assessments. These modeling techniques demonstrate how to use neuroergonomic methods to develop operator state assessments, which can be employed in adaptive systems.

Adaptive Sequential Monte Carlo for Multiple Changepoint Analysis

DOE PAGES

Heard, Nicholas A.; Turcotte, Melissa J. M.

2016-05-21

Process monitoring and control requires detection of structural changes in a data stream in real time. This paper introduces an efficient sequential Monte Carlo algorithm designed for learning unknown changepoints in continuous time. The method is intuitively simple: new changepoints for the latest window of data are proposed by conditioning only on data observed since the most recent estimated changepoint, as these observations carry most of the information about the current state of the process. The proposed method shows improved performance over the current state of the art. Another advantage of the proposed algorithm is that it can be mademore » adaptive, varying the number of particles according to the apparent local complexity of the target changepoint probability distribution. This saves valuable computing time when changes in the changepoint distribution are negligible, and enables re-balancing of the importance weights of existing particles when a significant change in the target distribution is encountered. The plain and adaptive versions of the method are illustrated using the canonical continuous time changepoint problem of inferring the intensity of an inhomogeneous Poisson process, although the method is generally applicable to any changepoint problem. Performance is demonstrated using both conjugate and non-conjugate Bayesian models for the intensity. Lastly, appendices to the article are available online, illustrating the method on other models and applications.« less

Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure.

PubMed

Fei, Juntao; Lu, Cheng

2018-04-01

In this paper, an adaptive sliding mode control system using a double loop recurrent neural network (DLRNN) structure is proposed for a class of nonlinear dynamic systems. A new three-layer RNN is proposed to approximate unknown dynamics with two different kinds of feedback loops where the firing weights and output signal calculated in the last step are stored and used as the feedback signals in each feedback loop. Since the new structure has combined the advantages of internal feedback NN and external feedback NN, it can acquire the internal state information while the output signal is also captured, thus the new designed DLRNN can achieve better approximation performance compared with the regular NNs without feedback loops or the regular RNNs with a single feedback loop. The new proposed DLRNN structure is employed in an equivalent controller to approximate the unknown nonlinear system dynamics, and the parameters of the DLRNN are updated online by adaptive laws to get favorable approximation performance. To investigate the effectiveness of the proposed controller, the designed adaptive sliding mode controller with the DLRNN is applied to a -axis microelectromechanical system gyroscope to control the vibrating dynamics of the proof mass. Simulation results demonstrate that the proposed methodology can achieve good tracking property, and the comparisons of the approximation performance between radial basis function NN, RNN, and DLRNN show that the DLRNN can accurately estimate the unknown dynamics with a fast speed while the internal states of DLRNN are more stable.

Heli/SITAN: A Terrain Referenced Navigation algorithm for helicopters

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

Hollowell, J.

1990-01-01

Heli/SITAN is a Terrain Referenced Navigation (TRN) algorithm that utilizes radar altimeter ground clearance measurements in combination with a conventional navigation system and a stored digital terrain elevation map to accurately estimate a helicopter's position. Multiple Model Adaptive Estimation (MMAE) techniques are employed using a bank of single state Kalman filters to ensure that reliable position estimates are obtained even in the face of large initial position errors. A real-time implementation of the algorithm was tested aboard a US Army UH-1 helicopter equipped with a Singer-Kearfott Doppler Velocity Sensor (DVS) and a Litton LR-80 strapdown Attitude and Heading Reference Systemmore » (AHRS). The median radial error of the position fixes provided in real-time by this implementation was less than 50 m for a variety of mission profiles. 6 refs., 7 figs.« less

An algorithm of adaptive scale object tracking in occlusion

NASA Astrophysics Data System (ADS)

Zhao, Congmei

2017-05-01

Although the correlation filter-based trackers achieve the competitive results both on accuracy and robustness, there are still some problems in handling scale variations, object occlusion, fast motions and so on. In this paper, a multi-scale kernel correlation filter algorithm based on random fern detector was proposed. The tracking task was decomposed into the target scale estimation and the translation estimation. At the same time, the Color Names features and HOG features were fused in response level to further improve the overall tracking performance of the algorithm. In addition, an online random fern classifier was trained to re-obtain the target after the target was lost. By comparing with some algorithms such as KCF, DSST, TLD, MIL, CT and CSK, experimental results show that the proposed approach could estimate the object state accurately and handle the object occlusion effectively.

Theory of psychological adaptive modes.

PubMed

Lehti, Juha

2016-05-01

When an individual is facing a stressor and normal stress-response mechanism cannot guarantee sufficient adaptation, special emotional states, adaptive modes, are activated (for example a depressive reaction). Adaptive modes are involuntary states of mind, they are of comprehensive nature, they interfere with normal functioning, and they cannot be repressed or controlled the same way as many emotions. Their transformational nature differentiates them from other emotional states. The object of the adaptive mode is to optimize the problem-solving abilities according to the situation that has provoked the mode. Cognitions and emotions during the adaptive mode are different than in a normal mental state. These altered cognitions and emotional reactions guide the individual to use the correct coping skills in order to deal with the stressor. Successful adaptation will cause the adaptive mode to fade off since the adaptive mode is no longer necessary, and the process as a whole will lead to raised well-being. However, if the adaptation process is inadequate, then the transformation period is prolonged, and the adaptive mode will turn into a dysfunctional state. Many psychiatric disorders are such maladaptive processes. The maladaptive processes can be turned into functional ones by using adaptive skills that are used in functional adaptive processes. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

Verbanis, E.; Martin, A.; Rosset, D.

Imperfections in experimental measurement schemes can lead to falsely identifying, or over estimating, entanglement in a quantum system. A recent solution to this is to define schemes that are robust to measurement imperfections—measurement-device-independent entanglement witness (MDI-EW). This approach can be adapted to witness all entangled qubit states for a wide range of physical systems and does not depend on detection efficiencies or classical communication between devices. In this paper, we extend the theory to remove the necessity of prior knowledge about the two-qubit states to be witnessed. Moreover, we tested this model via a novel experimental implementation for MDI-EW thatmore » significantly reduces the experimental complexity. Finally, by applying it to a bipartite Werner state, we demonstrate the robustness of this approach against noise by witnessing entanglement down to an entangled state fraction close to 0.4.« less

A new anisotropic mesh adaptation method based upon hierarchical a posteriori error estimates

NASA Astrophysics Data System (ADS)

Huang, Weizhang; Kamenski, Lennard; Lang, Jens

2010-03-01

A new anisotropic mesh adaptation strategy for finite element solution of elliptic differential equations is presented. It generates anisotropic adaptive meshes as quasi-uniform ones in some metric space, with the metric tensor being computed based on hierarchical a posteriori error estimates. A global hierarchical error estimate is employed in this study to obtain reliable directional information of the solution. Instead of solving the global error problem exactly, which is costly in general, we solve it iteratively using the symmetric Gauß-Seidel method. Numerical results show that a few GS iterations are sufficient for obtaining a reasonably good approximation to the error for use in anisotropic mesh adaptation. The new method is compared with several strategies using local error estimators or recovered Hessians. Numerical results are presented for a selection of test examples and a mathematical model for heat conduction in a thermal battery with large orthotropic jumps in the material coefficients.

Model reference adaptive control (MRAC)-based parameter identification applied to surface-mounted permanent magnet synchronous motor

NASA Astrophysics Data System (ADS)

Zhong, Chongquan; Lin, Yaoyao

2017-11-01

In this work, a model reference adaptive control-based estimated algorithm is proposed for online multi-parameter identification of surface-mounted permanent magnet synchronous machines. By taking the dq-axis equations of a practical motor as the reference model and the dq-axis estimation equations as the adjustable model, a standard model-reference-adaptive-system-based estimator was established. Additionally, the Popov hyperstability principle was used in the design of the adaptive law to guarantee accurate convergence. In order to reduce the oscillation of identification result, this work introduces a first-order low-pass digital filter to improve precision regarding the parameter estimation. The proposed scheme was then applied to an SPM synchronous motor control system without any additional circuits and implemented using a DSP TMS320LF2812. For analysis, the experimental results reveal the effectiveness of the proposed method.

Adjoint-Based Mesh Adaptation for the Sonic Boom Signature Loudness

NASA Technical Reports Server (NTRS)

Rallabhandi, Sriram K.; Park, Michael A.

2017-01-01

The mesh adaptation functionality of FUN3D is utilized to obtain a mesh optimized to calculate sonic boom ground signature loudness. During this process, the coupling between the discrete-adjoints of the computational fluid dynamics tool FUN3D and the atmospheric propagation tool sBOOM is exploited to form the error estimate. This new mesh adaptation methodology will allow generation of suitable meshes adapted to reduce the estimated errors in the ground loudness, which is an optimization metric employed in supersonic aircraft design. This new output-based adaptation could allow new insights into meshing for sonic boom analysis and design, and complements existing output-based adaptation techniques such as adaptation to reduce estimated errors in off-body pressure functional. This effort could also have implications for other coupled multidisciplinary adjoint capabilities (e.g., aeroelasticity) as well as inclusion of propagation specific parameters such as prevailing winds or non-standard atmospheric conditions. Results are discussed in the context of existing methods and appropriate conclusions are drawn as to the efficacy and efficiency of the developed capability.

Simple robust control laws for robot manipulators. Part 2: Adaptive case

NASA Technical Reports Server (NTRS)

Bayard, D. S.; Wen, J. T.

1987-01-01

A new class of asymptotically stable adaptive control laws is introduced for application to the robotic manipulator. Unlike most applications of adaptive control theory to robotic manipulators, this analysis addresses the nonlinear dynamics directly without approximation, linearization, or ad hoc assumptions, and utilizes a parameterization based on physical (time-invariant) quantities. This approach is made possible by using energy-like Lyapunov functions which retain the nonlinear character and structure of the dynamics, rather than simple quadratic forms which are ubiquitous to the adaptive control literature, and which have bound the theory tightly to linear systems with unknown parameters. It is a unique feature of these results that the adaptive forms arise by straightforward certainty equivalence adaptation of their nonadaptive counterparts found in the companion to this paper (i.e., by replacing unknown quantities by their estimates) and that this simple approach leads to asymptotically stable closed-loop adaptive systems. Furthermore, it is emphasized that this approach does not require convergence of the parameter estimates (i.e., via persistent excitation), invertibility of the mass matrix estimate, or measurement of the joint accelerations.

An Expert System For Multispectral Threat Assessment And Response

NASA Astrophysics Data System (ADS)

Steinberg, Alan N.

1987-05-01

A concept has been defined for an automatic system to manage the self-defense of a combat aircraft. Distinctive new features of this concept include: a. the flexible prioritization of tasks and coordinated use of sensor, countermeasures, flight systems and weapons assets by means of an automated planning function; b. the integration of state-of-the-art data fusion algorithms with event prediction processing; c. the use of advanced Artificial Intelligence tools to emulate the decision processes of tactical EW experts. Threat Assessment functions (a) estimate threat identity, lethality and intent on the basis of multi-spectral sensor data, and (b) predict the time to critical events in threat engagements (e.g., target acquisition, tracking, weapon launch, impact). Response Management functions (a) select candidate responses to reported threat situations; (b) estimate the effects of candidate actions on survival; and (c) coordinate the assignment of sensors, weapons and countermeasures with the flight plan. The system employs Finite State Models to represent current engagements and to predict subsequent events. Each state in a model is associated with a set of observable features, allowing interpretation of sensor data and adaptive use of sensor assets. Defined conditions on state transitions allow prediction of times to critical future states and are used in planning self-defensive responses, which are designed either to impede a particular state transition or to force a transition to a lower threat state.

Adaptive Environmental Source Localization and Tracking with Unknown Permittivity and Path Loss Coefficients †

PubMed Central

Fidan, Barış; Umay, Ilknur

2015-01-01

Accurate signal-source and signal-reflector target localization tasks via mobile sensory units and wireless sensor networks (WSNs), including those for environmental monitoring via sensory UAVs, require precise knowledge of specific signal propagation properties of the environment, which are permittivity and path loss coefficients for the electromagnetic signal case. Thus, accurate estimation of these coefficients has significant importance for the accuracy of location estimates. In this paper, we propose a geometric cooperative technique to instantaneously estimate such coefficients, with details provided for received signal strength (RSS) and time-of-flight (TOF)-based range sensors. The proposed technique is integrated to a recursive least squares (RLS)-based adaptive localization scheme and an adaptive motion control law, to construct adaptive target localization and adaptive target tracking algorithms, respectively, that are robust to uncertainties in aforementioned environmental signal propagation coefficients. The efficiency of the proposed adaptive localization and tracking techniques are both mathematically analysed and verified via simulation experiments. PMID:26690441

Distributed estimation for adaptive sensor selection in wireless sensor networks

NASA Astrophysics Data System (ADS)

Mahmoud, Magdi S.; Hassan Hamid, Matasm M.

2014-05-01

Wireless sensor networks (WSNs) are usually deployed for monitoring systems with the distributed detection and estimation of sensors. Sensor selection in WSNs is considered for target tracking. A distributed estimation scenario is considered based on the extended information filter. A cost function using the geometrical dilution of precision measure is derived for active sensor selection. A consensus-based estimation method is proposed in this paper for heterogeneous WSNs with two types of sensors. The convergence properties of the proposed estimators are analyzed under time-varying inputs. Accordingly, a new adaptive sensor selection (ASS) algorithm is presented in which the number of active sensors is adaptively determined based on the absolute local innovations vector. Simulation results show that the tracking accuracy of the ASS is comparable to that of the other algorithms.

An Adaptive Low-Cost GNSS/MEMS-IMU Tightly-Coupled Integration System with Aiding Measurement in a GNSS Signal-Challenged Environment

PubMed Central

Zhou, Qifan; Zhang, Hai; Li, You; Li, Zheng

2015-01-01

The main aim of this paper is to develop a low-cost GNSS/MEMS-IMU tightly-coupled integration system with aiding information that can provide reliable position solutions when the GNSS signal is challenged such that less than four satellites are visible in a harsh environment. To achieve this goal, we introduce an adaptive tightly-coupled integration system with height and heading aiding (ATCA). This approach adopts a novel redundant measurement noise estimation method for an adaptive Kalman filter application and also augments external measurements in the filter to aid the position solutions, as well as uses different filters to deal with various situations. On the one hand, the adaptive Kalman filter makes use of the redundant measurement system’s difference sequence to estimate and tune noise variance instead of employing a traditional innovation sequence to avoid coupling with the state vector error. On the other hand, this method uses the external height and heading angle as auxiliary references and establishes a model for the measurement equation in the filter. In the meantime, it also changes the effective filter online based on the number of tracked satellites. These measures have increasingly enhanced the position constraints and the system observability, improved the computational efficiency and have led to a good result. Both simulated and practical experiments have been carried out, and the results demonstrate that the proposed method is effective at limiting the system errors when there are less than four visible satellites, providing a satisfactory navigation solution. PMID:26393605

An Adaptive Low-Cost GNSS/MEMS-IMU Tightly-Coupled Integration System with Aiding Measurement in a GNSS Signal-Challenged Environment.

PubMed

Zhou, Qifan; Zhang, Hai; Li, You; Li, Zheng

2015-09-18

The main aim of this paper is to develop a low-cost GNSS/MEMS-IMU tightly-coupled integration system with aiding information that can provide reliable position solutions when the GNSS signal is challenged such that less than four satellites are visible in a harsh environment. To achieve this goal, we introduce an adaptive tightly-coupled integration system with height and heading aiding (ATCA). This approach adopts a novel redundant measurement noise estimation method for an adaptive Kalman filter application and also augments external measurements in the filter to aid the position solutions, as well as uses different filters to deal with various situations. On the one hand, the adaptive Kalman filter makes use of the redundant measurement system's difference sequence to estimate and tune noise variance instead of employing a traditional innovation sequence to avoid coupling with the state vector error. On the other hand, this method uses the external height and heading angle as auxiliary references and establishes a model for the measurement equation in the filter. In the meantime, it also changes the effective filter online based on the number of tracked satellites. These measures have increasingly enhanced the position constraints and the system observability, improved the computational efficiency and have led to a good result. Both simulated and practical experiments have been carried out, and the results demonstrate that the proposed method is effective at limiting the system errors when there are less than four visible satellites, providing a satisfactory navigation solution.

Design of Robust Adaptive Unbalance Response Controllers for Rotors with Magnetic Bearings

NASA Technical Reports Server (NTRS)

Knospe, Carl R.; Tamer, Samir M.; Fedigan, Stephen J.

1996-01-01

Experimental results have recently demonstrated that an adaptive open loop control strategy can be highly effective in the suppression of unbalance induced vibration on rotors supported in active magnetic bearings. This algorithm, however, relies upon a predetermined gain matrix. Typically, this matrix is determined by an optimal control formulation resulting in the choice of the pseudo-inverse of the nominal influence coefficient matrix as the gain matrix. This solution may result in problems with stability and performance robustness since the estimated influence coefficient matrix is not equal to the actual influence coefficient matrix. Recently, analysis tools have been developed to examine the robustness of this control algorithm with respect to structured uncertainty. Herein, these tools are extended to produce a design procedure for determining the adaptive law's gain matrix. The resulting control algorithm has a guaranteed convergence rate and steady state performance in spite of the uncertainty in the rotor system. Several examples are presented which demonstrate the effectiveness of this approach and its advantages over the standard optimal control formulation.

Data Assimilation Methods on a Non-conservative Adaptive Mesh

NASA Astrophysics Data System (ADS)

Guider, Colin Thomas; Rabatel, Matthias; Carrassi, Alberto; Jones, Christopher K. R. T.

2017-04-01

Adaptive mesh methods are used to model a wide variety of physical phenomena. Some of these models, in particular those of sea ice movement, are particularly interesting in that they use a remeshing process to remove and insert mesh points at various points in their evolution. This presents a challenge in developing compatible data assimilation schemes, as the dimension of the state space we wish to estimate can change over time when these remeshings occur. In this work, we first describe a remeshing scheme for an adaptive mesh in one dimension. We then develop advanced data assimilation methods that are appropriate for such a moving and remeshed grid. We hope to extend these techniques to two-dimensional models, like the Lagrangian sea ice model neXtSIM te{ns}. \\bibitem{ns} P. Rampal, S. Bouillon, E. Ólason, and M. Morlighem. ne{X}t{SIM}: a new {L}agrangian sea ice model. {The Cryosphere}, 10 (3): 1055-1073, 2016.

Formation tracker design of multiple mobile robots with wheel perturbations: adaptive output-feedback approach

NASA Astrophysics Data System (ADS)

Yoo, Sung Jin

2016-11-01

This paper presents a theoretical design approach for output-feedback formation tracking of multiple mobile robots under wheel perturbations. It is assumed that these perturbations are unknown and the linear and angular velocities of the robots are unmeasurable. First, adaptive state observers for estimating unmeasurable velocities of the robots are developed under the robots' kinematics and dynamics including wheel perturbation effects. Then, we derive a virtual-structure-based formation tracker scheme according to the observer dynamic surface design procedure. The main difficulty of the output-feedback control design is to manage the coupling problems between unmeasurable velocities and unknown wheel perturbation effects. These problems are avoided by using the adaptive technique and the function approximation property based on fuzzy logic systems. From the Lyapunov stability analysis, it is shown that point tracking errors of each robot and synchronisation errors for the desired formation converge to an adjustable neighbourhood of the origin, while all signals in the controlled closed-loop system are semiglobally uniformly ultimately bounded.

ADAPTING A PARENT-COMPLETED, SOCIOEMOTIONAL QUESTIONNAIRE IN CHINA: THE AGES & STAGES QUESTIONNAIRES: SOCIAL-EMOTIONAL.

PubMed

Bian, Xiaoyan; Xie, Huichao; Squires, Jane; Chen, Chieh-Yu

2017-03-01

The Ages & Stages Questionnaire: Social-Emotional (ASQ:SE; Squires, Bricker, & Twombly, 2002a), developed in the United States, was translated and adapted for use in China. Lack of valid and reliable instruments for identifying social and emotional delays in young children is a worldwide issue. Professionals in China have recently focused efforts on developing methods for early identification of social, emotional, and behavioral issues in the birth-to-5 population. Following the guidelines of the International Test Commission, the ASQ:SE was translated into Simplified Chinese (ASQ:SE-C) to collect a normative sample of 2,528 children across China. Data were analyzed to evaluate the psychometric properties of the ASQ:SE-C, using both classical test theory and item response theory, including generating cutoff points appropriate for the Chinese sample. A panel of Chinese experts was surveyed to assess face validity and estimated utility of the newly adapted tool. Discussions of research findings and implications for future studies are provided. © 2017 Michigan Association for Infant Mental Health.

AMOBH: Adaptive Multiobjective Black Hole Algorithm.

PubMed

Wu, Chong; Wu, Tao; Fu, Kaiyuan; Zhu, Yuan; Li, Yongbo; He, Wangyong; Tang, Shengwen

2017-01-01

This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called "adaptive multiobjective black hole algorithm" (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases.

On-line estimation of error covariance parameters for atmospheric data assimilation

NASA Technical Reports Server (NTRS)

Dee, Dick P.

1995-01-01

A simple scheme is presented for on-line estimation of covariance parameters in statistical data assimilation systems. The scheme is based on a maximum-likelihood approach in which estimates are produced on the basis of a single batch of simultaneous observations. Simple-sample covariance estimation is reasonable as long as the number of available observations exceeds the number of tunable parameters by two or three orders of magnitude. Not much is known at present about model error associated with actual forecast systems. Our scheme can be used to estimate some important statistical model error parameters such as regionally averaged variances or characteristic correlation length scales. The advantage of the single-sample approach is that it does not rely on any assumptions about the temporal behavior of the covariance parameters: time-dependent parameter estimates can be continuously adjusted on the basis of current observations. This is of practical importance since it is likely to be the case that both model error and observation error strongly depend on the actual state of the atmosphere. The single-sample estimation scheme can be incorporated into any four-dimensional statistical data assimilation system that involves explicit calculation of forecast error covariances, including optimal interpolation (OI) and the simplified Kalman filter (SKF). The computational cost of the scheme is high but not prohibitive; on-line estimation of one or two covariance parameters in each analysis box of an operational bozed-OI system is currently feasible. A number of numerical experiments performed with an adaptive SKF and an adaptive version of OI, using a linear two-dimensional shallow-water model and artificially generated model error are described. The performance of the nonadaptive versions of these methods turns out to depend rather strongly on correct specification of model error parameters. These parameters are estimated under a variety of conditions, including uniformly distributed model error and time-dependent model error statistics.

Performance Analysis on Carrier Phase-Based Tightly-Coupled GPS/BDS/INS Integration in GNSS Degraded and Denied Environments

PubMed Central

Han, Houzeng; Wang, Jian; Wang, Jinling; Tan, Xinglong

2015-01-01

The integration of Global Navigation Satellite Systems (GNSS) carrier phases with Inertial Navigation System (INS) measurements is essential to provide accurate and continuous position, velocity and attitude information, however it is necessary to fix ambiguities rapidly and reliably to obtain high accuracy navigation solutions. In this paper, we present the notion of combining the Global Positioning System (GPS), the BeiDou Navigation Satellite System (BDS) and low-cost micro-electro-mechanical sensors (MEMS) inertial systems for reliable navigation. An adaptive multipath factor-based tightly-coupled (TC) GPS/BDS/INS integration algorithm is presented and the overall performance of the integrated system is illustrated. A twenty seven states TC GPS/BDS/INS model is adopted with an extended Kalman filter (EKF), which is carried out by directly fusing ambiguity fixed double-difference (DD) carrier phase measurements with the INS predicted pseudoranges to estimate the error states. The INS-aided integer ambiguity resolution (AR) strategy is developed by using a dynamic model, a two-step estimation procedure is applied with adaptively estimated covariance matrix to further improve the AR performance. A field vehicular test was carried out to demonstrate the positioning performance of the combined system. The results show the TC GPS/BDS/INS system significantly improves the single-epoch AR reliability as compared to that of GPS/BDS-only or single satellite navigation system integrated strategy, especially for high cut-off elevations. The AR performance is also significantly improved for the combined system with adaptive covariance matrix in the presence of low elevation multipath related to the GNSS-only case. A total of fifteen simulated outage tests also show that the time to relock of the GPS/BDS signals is shortened, which improves the system availability. The results also indicate that TC integration system achieves a few centimeters accuracy in positioning based on the comparison analysis and covariance analysis, even in harsh environments (e.g., in urban canyons), thus we can see the advantage of positioning at high cut-off elevations that the combined GPS/BDS brings. PMID:25875191

Performance analysis on carrier phase-based tightly-coupled GPS/BDS/INS integration in GNSS degraded and denied environments.

PubMed

Han, Houzeng; Wang, Jian; Wang, Jinling; Tan, Xinglong

2015-04-14

The integration of Global Navigation Satellite Systems (GNSS) carrier phases with Inertial Navigation System (INS) measurements is essential to provide accurate and continuous position, velocity and attitude information, however it is necessary to fix ambiguities rapidly and reliably to obtain high accuracy navigation solutions. In this paper, we present the notion of combining the Global Positioning System (GPS), the BeiDou Navigation Satellite System (BDS) and low-cost micro-electro-mechanical sensors (MEMS) inertial systems for reliable navigation. An adaptive multipath factor-based tightly-coupled (TC) GPS/BDS/INS integration algorithm is presented and the overall performance of the integrated system is illustrated. A twenty seven states TC GPS/BDS/INS model is adopted with an extended Kalman filter (EKF), which is carried out by directly fusing ambiguity fixed double-difference (DD) carrier phase measurements with the INS predicted pseudoranges to estimate the error states. The INS-aided integer ambiguity resolution (AR) strategy is developed by using a dynamic model, a two-step estimation procedure is applied with adaptively estimated covariance matrix to further improve the AR performance. A field vehicular test was carried out to demonstrate the positioning performance of the combined system. The results show the TC GPS/BDS/INS system significantly improves the single-epoch AR reliability as compared to that of GPS/BDS-only or single satellite navigation system integrated strategy, especially for high cut-off elevations. The AR performance is also significantly improved for the combined system with adaptive covariance matrix in the presence of low elevation multipath related to the GNSS-only case. A total of fifteen simulated outage tests also show that the time to relock of the GPS/BDS signals is shortened, which improves the system availability. The results also indicate that TC integration system achieves a few centimeters accuracy in positioning based on the comparison analysis and covariance analysis, even in harsh environments (e.g., in urban canyons), thus we can see the advantage of positioning at high cut-off elevations that the combined GPS/BDS brings.

Neural self-tuning adaptive control of non-minimum phase system

NASA Technical Reports Server (NTRS)

Ho, Long T.; Bialasiewicz, Jan T.; Ho, Hai T.

1993-01-01

The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity, if not unstable, closed-loop behavior. Therefore, a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response.

M-MRAC Backstepping for Systems with Unknown Virtual Control Coefficients

NASA Technical Reports Server (NTRS)

Stepanyan, Vahram; Krishnakumar, Kalmanje

2015-01-01

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

The Cross-Validation of the United States Air Force Submaximal Cycle Ergometer Test to Estimate Aerobic Capacity

DTIC Science & Technology

1994-06-01

the University of Florida. When body composition variables were included in the regression model, such as % body fat and fet free mass, as well as the...maximal oxygen intake . JAMA 203:201-210, 1968. 2. Sharp, J.R. The new Air Force fitness test: A field trial assessing effectiveness and safety...more muscle mass and less fat than the female counterpart. However males and females appear to adapt equally to training (53,55). Also men have a larger

Motion Coordination and Adaptation Using Deception and Human Interactions

DTIC Science & Technology

2016-11-18

evolves an interface (front) by embedding it as a hyper - surface in a higher dimension, where time is the augmented dimension. Automatic handling of...the open- loop system trajectories. The results are depicted in Fig. 32. From the shape of the value function in Fig. 32(b) it is seen that the value is...estimate of the value function only over the area of the state space visited by the sampled (open- loop ) trajectories. In that sense, the areas not

Crash avoidance potential of four passenger vehicle technologies.

PubMed

Jermakian, Jessica S

2011-05-01

The objective was to update estimates of maximum potential crash reductions in the United States associated with each of four crash avoidance technologies: side view assist, forward collision warning/mitigation, lane departure warning/prevention, and adaptive headlights. Compared with previous estimates (Farmer, 2008), estimates in this study attempted to account for known limitations of current systems. Crash records were extracted from the 2004-08 files of the National Automotive Sampling System General Estimates System (NASS GES) and the Fatality Analysis Reporting System (FARS). Crash descriptors such as vehicle damage location, road characteristics, time of day, and precrash maneuvers were reviewed to determine whether the information or action provided by each technology potentially could have prevented or mitigated the crash. Of the four crash avoidance technologies, forward collision warning/mitigation had the greatest potential for preventing crashes of any severity; the technology is potentially applicable to 1.2 million crashes in the United States each year, including 66,000 serious and moderate injury crashes and 879 fatal crashes. Lane departure warning/prevention systems appeared relevant to 179,000 crashes per year. Side view assist and adaptive headlights could prevent 395,000 and 142,000 crashes per year, respectively. Lane departure warning/prevention was relevant to the most fatal crashes, up to 7500 fatal crashes per year. A combination of all four current technologies potentially could prevent or mitigate (without double counting) up to 1,866,000 crashes each year, including 149,000 serious and moderate injury crashes and 10,238 fatal crashes. If forward collision warning were extended to detect objects, pedestrians, and bicyclists, it would be relevant to an additional 3868 unique fatal crashes. There is great potential effectiveness for vehicle-based crash avoidance systems. However, it is yet to be determined how drivers will interact with the systems. The actual effectiveness of these systems will not be known until sufficient real-world experience has been gained. Copyright © 2010 Elsevier Ltd. All rights reserved.

Light-based Modeling and Control of Circadian Rhythm

DTIC Science & Technology

2016-08-29

the foundation of the full research. 1. Circadian phase estimation and control: Demonstrate the applicability of the adaptive notch filter (ANF) to...the adaptive notch filter (ANF) to extract circadian phase from noisy Drosophila locomotive activity measurements and the efficacy of using the ANF...full research. 1. Circadian phase estimation and control: Demonstrate the applicability of the adaptive notch filter (ANF) to extract circadian

Self-adaptive signals separation for non-contact heart rate estimation from facial video in realistic environments.

PubMed

Liu, Xuenan; Yang, Xuezhi; Jin, Jing; Li, Jiangshan

2018-06-05

Recent researches indicate that facial epidermis color varies with the rhythm of heat beats. It can be captured by consumer-level cameras and, astonishingly, be adopted to estimate heart rate (HR). The HR estimated remains not as precise as required in practical environment where illumination interference, facial expressions, or motion artifacts are involved, though numerous methods have been proposed in the last few years. A novel algorithm is proposed to make non-contact HR estimation technique more robust. First, the face of subject is detected and tracked to follow the head movement. The facial region then falls into several blocks, and the chrominance feature of each block is extracted to establish raw HR sub-signal. Self-adaptive signals separation (SASS) is performed to separate the noiseless HR sub-signals from raw sub-signals. On that basis, the noiseless sub-signals full of HR information are selected using weight-based scheme to establish the holistic HR signal, from which average HR is computed adopting wavelet transform and data filter. Forty subjects take part in our experiments, whose facial videos are recorded by a normal webcam with the frame rate of 30 fps under ambient lighting conditions. The average HR estimated by our method correlates strongly with ground truth measurements, as indicated in experimental results measured in static scenario with the Pearson's correlation r=0.980 and dynamic scenario with the Pearson's correlation r=0.897. Our method, compared to the newest method, decreases the error rate by 38.63% and increases the Pearson's correlation by 15.59%, indicating that our method evidently outperforms state-of-the-art non-contact HR estimation methods in realistic environments. © 2018 Institute of Physics and Engineering in Medicine.

Reduction of uncertainty for estimating runoff with the NRCS CN model by the adaptation to local climatic conditions

NASA Astrophysics Data System (ADS)

Durán-Barroso, Pablo; González, Javier; Valdés, Juan B.

2016-04-01

Rainfall-runoff quantification is one of the most important tasks in both engineering and watershed management as it allows to identify, forecast and explain watershed response. For that purpose, the Natural Resources Conservation Service Curve Number method (NRCS CN) is the conceptual lumped model more recognized in the field of rainfall-runoff estimation. Furthermore, there is still an ongoing discussion about the procedure to determine the portion of rainfall retained in the watershed before runoff is generated, called as initial abstractions. This concept is computed as a ratio (λ) of the soil potential maximum retention S of the watershed. Initially, this ratio was assumed to be 0.2, but later it has been proposed to be modified to 0.05. However, the actual procedures to convert NRCS CN model parameters obtained under a different hypothesis about λ do not incorporate any adaptation of climatic conditions of each watershed. By this reason, we propose a new simple method for computing model parameters which is adapted to local conditions taking into account regional patterns of climate conditions. After checking the goodness of this procedure against the actual ones in 34 different watersheds located in Ohio and Texas (United States), we concluded that this novel methodology represents the most accurate and efficient alternative to refit the initial abstraction ratio.

Application of Avco data analysis and prediction techniques (ADAPT) to prediction of sunspot activity

NASA Technical Reports Server (NTRS)

Hunter, H. E.; Amato, R. A.

1972-01-01

The results are presented of the application of Avco Data Analysis and Prediction Techniques (ADAPT) to derivation of new algorithms for the prediction of future sunspot activity. The ADAPT derived algorithms show a factor of 2 to 3 reduction in the expected 2-sigma errors in the estimates of the 81-day running average of the Zurich sunspot numbers. The report presents: (1) the best estimates for sunspot cycles 20 and 21, (2) a comparison of the ADAPT performance with conventional techniques, and (3) specific approaches to further reduction in the errors of estimated sunspot activity and to recovery of earlier sunspot historical data. The ADAPT programs are used both to derive regression algorithm for prediction of the entire 11-year sunspot cycle from the preceding two cycles and to derive extrapolation algorithms for extrapolating a given sunspot cycle based on any available portion of the cycle.

An Adaptive Kalman Filter Using a Simple Residual Tuning Method

NASA Technical Reports Server (NTRS)

Harman, Richard R.

1999-01-01

One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. A. H. Jazwinski developed a specialized version of this technique for estimation of process noise. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.

The adaptive observer. [liapunov synthesis, single-input single-output, and reduced observers

NASA Technical Reports Server (NTRS)

Carroll, R. L.

1973-01-01

The simple generation of state from available measurements, for use in systems for which the criteria defining the acceptable state behavior mandates a control that is dependent upon unavailable measurement is described as an adaptive means for determining the state of a linear time invariant differential system having unknown parameters. A single input output adaptive observer and the reduced adaptive observer is developed. The basic ideas for both the adaptive observer and the nonadaptive observer are examined. A survey of the Liapunov synthesis technique is taken, and the technique is applied to adaptive algorithm for the adaptive observer.

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

Vogel, Thomas; Perez, Danny

We recently introduced a novel replica-exchange scheme in which an individual replica can sample from states encountered by other replicas at any previous time by way of a global configuration database, enabling the fast propagation of relevant states through the whole ensemble of replicas. This mechanism depends on the knowledge of global thermodynamic functions which are measured during the simulation and not coupled to the heat bath temperatures driving the individual simulations. Therefore, this setup also allows for a continuous adaptation of the temperature set. In this paper, we will review the new scheme and demonstrate its capability. Furthermore, themore » method is particularly useful for the fast and reliable estimation of the microcanonical temperature T(U) or, equivalently, of the density of states g(U) over a wide range of energies.« less

Robust passive control for a class of uncertain neutral systems based on sliding mode observer.

PubMed

Liu, Zhen; Zhao, Lin; Kao, Yonggui; Gao, Cunchen

2017-01-01

The passivity-based sliding mode control (SMC) problem for a class of uncertain neutral systems with unmeasured states is investigated. Firstly, a particular non-fragile state observer is designed to generate the estimations of the system states, based upon which a novel integral-type sliding surface function is established for the control process. Secondly, a new sufficient condition for robust asymptotic stability and passivity of the resultant sliding mode dynamics (SMDs) is obtained in terms of linear matrix inequalities (LMIs). Thirdly, the finite-time reachability of the predesigned sliding surface is ensured by resorting to a novel adaptive SMC law. Finally, the validity and superiority of the scheme are justified via several examples. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A Simple Method for Deriving the Confidence Regions for the Penalized Cox’s Model via the Minimand Perturbation†

PubMed Central

Lin, Chen-Yen; Halabi, Susan

2017-01-01

We propose a minimand perturbation method to derive the confidence regions for the regularized estimators for the Cox’s proportional hazards model. Although the regularized estimation procedure produces a more stable point estimate, it remains challenging to provide an interval estimator or an analytic variance estimator for the associated point estimate. Based on the sandwich formula, the current variance estimator provides a simple approximation, but its finite sample performance is not entirely satisfactory. Besides, the sandwich formula can only provide variance estimates for the non-zero coefficients. In this article, we present a generic description for the perturbation method and then introduce a computation algorithm using the adaptive least absolute shrinkage and selection operator (LASSO) penalty. Through simulation studies, we demonstrate that our method can better approximate the limiting distribution of the adaptive LASSO estimator and produces more accurate inference compared with the sandwich formula. The simulation results also indicate the possibility of extending the applications to the adaptive elastic-net penalty. We further demonstrate our method using data from a phase III clinical trial in prostate cancer. PMID:29326496

A Simple Method for Deriving the Confidence Regions for the Penalized Cox's Model via the Minimand Perturbation.

PubMed

Lin, Chen-Yen; Halabi, Susan

2017-01-01

We propose a minimand perturbation method to derive the confidence regions for the regularized estimators for the Cox's proportional hazards model. Although the regularized estimation procedure produces a more stable point estimate, it remains challenging to provide an interval estimator or an analytic variance estimator for the associated point estimate. Based on the sandwich formula, the current variance estimator provides a simple approximation, but its finite sample performance is not entirely satisfactory. Besides, the sandwich formula can only provide variance estimates for the non-zero coefficients. In this article, we present a generic description for the perturbation method and then introduce a computation algorithm using the adaptive least absolute shrinkage and selection operator (LASSO) penalty. Through simulation studies, we demonstrate that our method can better approximate the limiting distribution of the adaptive LASSO estimator and produces more accurate inference compared with the sandwich formula. The simulation results also indicate the possibility of extending the applications to the adaptive elastic-net penalty. We further demonstrate our method using data from a phase III clinical trial in prostate cancer.

Accurate Attitude Estimation Using ARS under Conditions of Vehicle Movement Based on Disturbance Acceleration Adaptive Estimation and Correction

PubMed Central

Xing, Li; Hang, Yijun; Xiong, Zhi; Liu, Jianye; Wan, Zhong

2016-01-01

This paper describes a disturbance acceleration adaptive estimate and correction approach for an attitude reference system (ARS) so as to improve the attitude estimate precision under vehicle movement conditions. The proposed approach depends on a Kalman filter, where the attitude error, the gyroscope zero offset error and the disturbance acceleration error are estimated. By switching the filter decay coefficient of the disturbance acceleration model in different acceleration modes, the disturbance acceleration is adaptively estimated and corrected, and then the attitude estimate precision is improved. The filter was tested in three different disturbance acceleration modes (non-acceleration, vibration-acceleration and sustained-acceleration mode, respectively) by digital simulation. Moreover, the proposed approach was tested in a kinematic vehicle experiment as well. Using the designed simulations and kinematic vehicle experiments, it has been shown that the disturbance acceleration of each mode can be accurately estimated and corrected. Moreover, compared with the complementary filter, the experimental results have explicitly demonstrated the proposed approach further improves the attitude estimate precision under vehicle movement conditions. PMID:27754469

Accurate Attitude Estimation Using ARS under Conditions of Vehicle Movement Based on Disturbance Acceleration Adaptive Estimation and Correction.

PubMed

Xing, Li; Hang, Yijun; Xiong, Zhi; Liu, Jianye; Wan, Zhong

2016-10-16

This paper describes a disturbance acceleration adaptive estimate and correction approach for an attitude reference system (ARS) so as to improve the attitude estimate precision under vehicle movement conditions. The proposed approach depends on a Kalman filter, where the attitude error, the gyroscope zero offset error and the disturbance acceleration error are estimated. By switching the filter decay coefficient of the disturbance acceleration model in different acceleration modes, the disturbance acceleration is adaptively estimated and corrected, and then the attitude estimate precision is improved. The filter was tested in three different disturbance acceleration modes (non-acceleration, vibration-acceleration and sustained-acceleration mode, respectively) by digital simulation. Moreover, the proposed approach was tested in a kinematic vehicle experiment as well. Using the designed simulations and kinematic vehicle experiments, it has been shown that the disturbance acceleration of each mode can be accurately estimated and corrected. Moreover, compared with the complementary filter, the experimental results have explicitly demonstrated the proposed approach further improves the attitude estimate precision under vehicle movement conditions.

Subsonic flight test evaluation of a propulsion system parameter estimation process for the F100 engine

NASA Technical Reports Server (NTRS)

Orme, John S.; Gilyard, Glenn B.

1992-01-01

Integrated engine-airframe optimal control technology may significantly improve aircraft performance. This technology requires a reliable and accurate parameter estimator to predict unmeasured variables. To develop this technology base, NASA Dryden Flight Research Facility (Edwards, CA), McDonnell Aircraft Company (St. Louis, MO), and Pratt & Whitney (West Palm Beach, FL) have developed and flight-tested an adaptive performance seeking control system which optimizes the quasi-steady-state performance of the F-15 propulsion system. This paper presents flight and ground test evaluations of the propulsion system parameter estimation process used by the performance seeking control system. The estimator consists of a compact propulsion system model and an extended Kalman filter. The extended Laman filter estimates five engine component deviation parameters from measured inputs. The compact model uses measurements and Kalman-filter estimates as inputs to predict unmeasured propulsion parameters such as net propulsive force and fan stall margin. The ability to track trends and estimate absolute values of propulsion system parameters was demonstrated. For example, thrust stand results show a good correlation, especially in trends, between the performance seeking control estimated and measured thrust.

The Independent Evolution Method Is Not a Viable Phylogenetic Comparative Method

PubMed Central

2015-01-01

Phylogenetic comparative methods (PCMs) use data on species traits and phylogenetic relationships to shed light on evolutionary questions. Recently, Smaers and Vinicius suggested a new PCM, Independent Evolution (IE), which purportedly employs a novel model of evolution based on Felsenstein’s Adaptive Peak Model. The authors found that IE improves upon previous PCMs by producing more accurate estimates of ancestral states, as well as separate estimates of evolutionary rates for each branch of a phylogenetic tree. Here, we document substantial theoretical and computational issues with IE. When data are simulated under a simple Brownian motion model of evolution, IE produces severely biased estimates of ancestral states and changes along individual branches. We show that these branch-specific changes are essentially ancestor-descendant or “directional” contrasts, and draw parallels between IE and previous PCMs such as “minimum evolution”. Additionally, while comparisons of branch-specific changes between variables have been interpreted as reflecting the relative strength of selection on those traits, we demonstrate through simulations that regressing IE estimated branch-specific changes against one another gives a biased estimate of the scaling relationship between these variables, and provides no advantages or insights beyond established PCMs such as phylogenetically independent contrasts. In light of our findings, we discuss the results of previous papers that employed IE. We conclude that Independent Evolution is not a viable PCM, and should not be used in comparative analyses. PMID:26683838

Methodologies for Adaptive Flight Envelope Estimation and Protection

NASA Technical Reports Server (NTRS)

Tang, Liang; Roemer, Michael; Ge, Jianhua; Crassidis, Agamemnon; Prasad, J. V. R.; Belcastro, Christine

2009-01-01

This paper reports the latest development of several techniques for adaptive flight envelope estimation and protection system for aircraft under damage upset conditions. Through the integration of advanced fault detection algorithms, real-time system identification of the damage/faulted aircraft and flight envelop estimation, real-time decision support can be executed autonomously for improving damage tolerance and flight recoverability. Particularly, a bank of adaptive nonlinear fault detection and isolation estimators were developed for flight control actuator faults; a real-time system identification method was developed for assessing the dynamics and performance limitation of impaired aircraft; online learning neural networks were used to approximate selected aircraft dynamics which were then inverted to estimate command margins. As off-line training of network weights is not required, the method has the advantage of adapting to varying flight conditions and different vehicle configurations. The key benefit of the envelope estimation and protection system is that it allows the aircraft to fly close to its limit boundary by constantly updating the controller command limits during flight. The developed techniques were demonstrated on NASA s Generic Transport Model (GTM) simulation environments with simulated actuator faults. Simulation results and remarks on future work are presented.

Methods for estimating the amount of vernal pool habitat in the northeastern United States

USGS Publications Warehouse

Van Meter, R.; Bailey, L.L.; Grant, E.H.C.

2008-01-01

The loss of small, seasonal wetlands is a major concern for a variety of state, local, and federal organizations in the northeastern U.S. Identifying and estimating the number of vernal pools within a given region is critical to developing long-term conservation and management strategies for these unique habitats and their faunal communities. We use three probabilistic sampling methods (simple random sampling, adaptive cluster sampling, and the dual frame method) to estimate the number of vernal pools on protected, forested lands. Overall, these methods yielded similar values of vernal pool abundance for each study area, and suggest that photographic interpretation alone may grossly underestimate the number of vernal pools in forested habitats. We compare the relative efficiency of each method and discuss ways of improving precision. Acknowledging that the objectives of a study or monitoring program ultimately determine which sampling designs are most appropriate, we recommend that some type of probabilistic sampling method be applied. We view the dual-frame method as an especially useful way of combining incomplete remote sensing methods, such as aerial photograph interpretation, with a probabilistic sample of the entire area of interest to provide more robust estimates of the number of vernal pools and a more representative sample of existing vernal pool habitats.

Pitch-Learning Algorithm For Speech Encoders

NASA Technical Reports Server (NTRS)

Bhaskar, B. R. Udaya

1988-01-01

Adaptive algorithm detects and corrects errors in sequence of estimates of pitch period of speech. Algorithm operates in conjunction with techniques used to estimate pitch period. Used in such parametric and hybrid speech coders as linear predictive coders and adaptive predictive coders.

Stability and error estimation for Component Adaptive Grid methods

NASA Technical Reports Server (NTRS)

Oliger, Joseph; Zhu, Xiaolei

1994-01-01

Component adaptive grid (CAG) methods for solving hyperbolic partial differential equations (PDE's) are discussed in this paper. Applying recent stability results for a class of numerical methods on uniform grids. The convergence of these methods for linear problems on component adaptive grids is established here. Furthermore, the computational error can be estimated on CAG's using the stability results. Using these estimates, the error can be controlled on CAG's. Thus, the solution can be computed efficiently on CAG's within a given error tolerance. Computational results for time dependent linear problems in one and two space dimensions are presented.

Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons.

PubMed

Nicola, Wilten; Campbell, Sue Ann

2013-01-01

We analytically derive mean-field models for all-to-all coupled networks of heterogeneous, adapting, two-dimensional integrate and fire neurons. The class of models we consider includes the Izhikevich, adaptive exponential and quartic integrate and fire models. The heterogeneity in the parameters leads to different moment closure assumptions that can be made in the derivation of the mean-field model from the population density equation for the large network. Three different moment closure assumptions lead to three different mean-field systems. These systems can be used for distinct purposes such as bifurcation analysis of the large networks, prediction of steady state firing rate distributions, parameter estimation for actual neurons and faster exploration of the parameter space. We use the mean-field systems to analyze adaptation induced bursting under realistic sources of heterogeneity in multiple parameters. Our analysis demonstrates that the presence of heterogeneity causes the Hopf bifurcation associated with the emergence of bursting to change from sub-critical to super-critical. This is confirmed with numerical simulations of the full network for biologically reasonable parameter values. This change decreases the plausibility of adaptation being the cause of bursting in hippocampal area CA3, an area with a sizable population of heavily coupled, strongly adapting neurons.

Adaptive nonsingular fast terminal sliding-mode control for the tracking problem of uncertain dynamical systems.

PubMed

Boukattaya, Mohamed; Mezghani, Neila; Damak, Tarak

2018-06-01

In this paper, robust and adaptive nonsingular fast terminal sliding-mode (NFTSM) control schemes for the trajectory tracking problem are proposed with known or unknown upper bound of the system uncertainty and external disturbances. The developed controllers take the advantage of the NFTSM theory to ensure fast convergence rate, singularity avoidance, and robustness against uncertainties and external disturbances. First, a robust NFTSM controller is proposed which guarantees that sliding surface and equilibrium point can be reached in a short finite-time from any initial state. Then, in order to cope with the unknown upper bound of the system uncertainty which may be occurring in practical applications, a new adaptive NFTSM algorithm is developed. One feature of the proposed control law is their adaptation techniques where the prior knowledge of parameters uncertainty and disturbances is not needed. However, the adaptive tuning law can estimate the upper bound of these uncertainties using only position and velocity measurements. Moreover, the proposed controller eliminates the chattering effect without losing the robustness property and the precision. Stability analysis is performed using the Lyapunov stability theory, and simulation studies are conducted to verify the effectiveness of the developed control schemes. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons

PubMed Central

Nicola, Wilten; Campbell, Sue Ann

2013-01-01

We analytically derive mean-field models for all-to-all coupled networks of heterogeneous, adapting, two-dimensional integrate and fire neurons. The class of models we consider includes the Izhikevich, adaptive exponential and quartic integrate and fire models. The heterogeneity in the parameters leads to different moment closure assumptions that can be made in the derivation of the mean-field model from the population density equation for the large network. Three different moment closure assumptions lead to three different mean-field systems. These systems can be used for distinct purposes such as bifurcation analysis of the large networks, prediction of steady state firing rate distributions, parameter estimation for actual neurons and faster exploration of the parameter space. We use the mean-field systems to analyze adaptation induced bursting under realistic sources of heterogeneity in multiple parameters. Our analysis demonstrates that the presence of heterogeneity causes the Hopf bifurcation associated with the emergence of bursting to change from sub-critical to super-critical. This is confirmed with numerical simulations of the full network for biologically reasonable parameter values. This change decreases the plausibility of adaptation being the cause of bursting in hippocampal area CA3, an area with a sizable population of heavily coupled, strongly adapting neurons. PMID:24416013

Sinusoidal synthesis based adaptive tracking for rotating machinery fault detection

NASA Astrophysics Data System (ADS)

Li, Gang; McDonald, Geoff L.; Zhao, Qing

2017-01-01

This paper presents a novel Sinusoidal Synthesis Based Adaptive Tracking (SSBAT) technique for vibration-based rotating machinery fault detection. The proposed SSBAT algorithm is an adaptive time series technique that makes use of both frequency and time domain information of vibration signals. Such information is incorporated in a time varying dynamic model. Signal tracking is then realized by applying adaptive sinusoidal synthesis to the vibration signal. A modified Least-Squares (LS) method is adopted to estimate the model parameters. In addition to tracking, the proposed vibration synthesis model is mainly used as a linear time-varying predictor. The health condition of the rotating machine is monitored by checking the residual between the predicted and measured signal. The SSBAT method takes advantage of the sinusoidal nature of vibration signals and transfers the nonlinear problem into a linear adaptive problem in the time domain based on a state-space realization. It has low computation burden and does not need a priori knowledge of the machine under the no-fault condition which makes the algorithm ideal for on-line fault detection. The method is validated using both numerical simulation and practical application data. Meanwhile, the fault detection results are compared with the commonly adopted autoregressive (AR) and autoregressive Minimum Entropy Deconvolution (ARMED) method to verify the feasibility and performance of the SSBAT method.

A new adaptive algorithm for automated feature extraction in exponentially damped signals for health monitoring of smart structures

NASA Astrophysics Data System (ADS)

Qarib, Hossein; Adeli, Hojjat

2015-12-01

In this paper authors introduce a new adaptive signal processing technique for feature extraction and parameter estimation in noisy exponentially damped signals. The iterative 3-stage method is based on the adroit integration of the strengths of parametric and nonparametric methods such as multiple signal categorization, matrix pencil, and empirical mode decomposition algorithms. The first stage is a new adaptive filtration or noise removal scheme. The second stage is a hybrid parametric-nonparametric signal parameter estimation technique based on an output-only system identification technique. The third stage is optimization of estimated parameters using a combination of the primal-dual path-following interior point algorithm and genetic algorithm. The methodology is evaluated using a synthetic signal and a signal obtained experimentally from transverse vibrations of a steel cantilever beam. The method is successful in estimating the frequencies accurately. Further, it estimates the damping exponents. The proposed adaptive filtration method does not include any frequency domain manipulation. Consequently, the time domain signal is not affected as a result of frequency domain and inverse transformations.

An a-posteriori finite element error estimator for adaptive grid computation of viscous incompressible flows

NASA Astrophysics Data System (ADS)

Wu, Heng

2000-10-01

In this thesis, an a-posteriori error estimator is presented and employed for solving viscous incompressible flow problems. In an effort to detect local flow features, such as vortices and separation, and to resolve flow details precisely, a velocity angle error estimator e theta which is based on the spatial derivative of velocity direction fields is designed and constructed. The a-posteriori error estimator corresponds to the antisymmetric part of the deformation-rate-tensor, and it is sensitive to the second derivative of the velocity angle field. Rationality discussions reveal that the velocity angle error estimator is a curvature error estimator, and its value reflects the accuracy of streamline curves. It is also found that the velocity angle error estimator contains the nonlinear convective term of the Navier-Stokes equations, and it identifies and computes the direction difference when the convective acceleration direction and the flow velocity direction have a disparity. Through benchmarking computed variables with the analytic solution of Kovasznay flow or the finest grid of cavity flow, it is demonstrated that the velocity angle error estimator has a better performance than the strain error estimator. The benchmarking work also shows that the computed profile obtained by using etheta can achieve the best matching outcome with the true theta field, and that it is asymptotic to the true theta variation field, with a promise of fewer unknowns. Unstructured grids are adapted by employing local cell division as well as unrefinement of transition cells. Using element class and node class can efficiently construct a hierarchical data structure which provides cell and node inter-reference at each adaptive level. Employing element pointers and node pointers can dynamically maintain the connection of adjacent elements and adjacent nodes, and thus avoids time-consuming search processes. The adaptive scheme is applied to viscous incompressible flow at different Reynolds numbers. It is found that the velocity angle error estimator can detect most flow characteristics and produce dense grids in the regions where flow velocity directions have abrupt changes. In addition, the e theta estimator makes the derivative error dilutely distribute in the whole computational domain and also allows the refinement to be conducted at regions of high error. Through comparison of the velocity angle error across the interface with neighbouring cells, it is verified that the adaptive scheme in using etheta provides an optimum mesh which can clearly resolve local flow features in a precise way. The adaptive results justify the applicability of the etheta estimator and prove that this error estimator is a valuable adaptive indicator for the automatic refinement of unstructured grids.

Adaptive Monte Carlo methods

NASA Astrophysics Data System (ADS)

Fasnacht, Marc

We develop adaptive Monte Carlo methods for the calculation of the free energy as a function of a parameter of interest. The methods presented are particularly well-suited for systems with complex energy landscapes, where standard sampling techniques have difficulties. The Adaptive Histogram Method uses a biasing potential derived from histograms recorded during the simulation to achieve uniform sampling in the parameter of interest. The Adaptive Integration method directly calculates an estimate of the free energy from the average derivative of the Hamiltonian with respect to the parameter of interest and uses it as a biasing potential. We compare both methods to a state of the art method, and demonstrate that they compare favorably for the calculation of potentials of mean force of dense Lennard-Jones fluids. We use the Adaptive Integration Method to calculate accurate potentials of mean force for different types of simple particles in a Lennard-Jones fluid. Our approach allows us to separate the contributions of the solvent to the potential of mean force from the effect of the direct interaction between the particles. With contributions of the solvent determined, we can find the potential of mean force directly for any other direct interaction without additional simulations. We also test the accuracy of the Adaptive Integration Method on a thermodynamic cycle, which allows us to perform a consistency check between potentials of mean force and chemical potentials calculated using the Adaptive Integration Method. The results demonstrate a high degree of consistency of the method.

Gradual and contingent evolutionary emergence of leaf mimicry in butterfly wing patterns.

PubMed

Suzuki, Takao K; Tomita, Shuichiro; Sezutsu, Hideki

2014-11-25

Special resemblance of animals to natural objects such as leaves provides a representative example of evolutionary adaptation. The existence of such sophisticated features challenges our understanding of how complex adaptive phenotypes evolved. Leaf mimicry typically consists of several pattern elements, the spatial arrangement of which generates the leaf venation-like appearance. However, the process by which leaf patterns evolved remains unclear. In this study we show the evolutionary origin and process for the leaf pattern in Kallima (Nymphalidae) butterflies. Using comparative morphological analyses, we reveal that the wing patterns of Kallima and 45 closely related species share the same ground plan, suggesting that the pattern elements of leaf mimicry have been inherited across species with lineage-specific changes of their character states. On the basis of these analyses, phylogenetic comparative methods estimated past states of the pattern elements and enabled reconstruction of the wing patterns of the most recent common ancestor. This analysis shows that the leaf pattern has evolved through several intermediate patterns. Further, we use Bayesian statistical methods to estimate the temporal order of character-state changes in the pattern elements by which leaf mimesis evolved, and show that the pattern elements changed their spatial arrangement (e.g., from a curved line to a straight line) in a stepwise manner and finally establish a close resemblance to a leaf venation-like appearance. Our study provides the first evidence for stepwise and contingent evolution of leaf mimicry.　 Leaf mimicry patterns evolved in a gradual, rather than a sudden, manner from a non-mimetic ancestor. Through a lineage of Kallima butterflies, the leaf patterns evolutionarily originated through temporal accumulation of orchestrated changes in multiple pattern elements.

[The peptide correction of neurotic disorders among professional truck-drivers].

PubMed

Bashkireva, A S; Artamonova, V G

2012-01-01

This study was designed to estimate the neurobehavioral status and to compare the prevalence of psychoadaptive disorders among lorry-drivers (experimental group) and metal craftsmen (control group) in connection with their age, length of service, occupational hazards, work schedule and sociodemographic characteristics. 150 male lorry-drivers (mean age 43.3 +/- 0.9) and 150 male metal craftsmen (mean age 42.8 +/- 0.9) were examined using a clinical questionnaire to identify, estimate and compare neurotic states. The study comprised 3 groups: 1st--subjects with stable psychic adaptation, 2nd--subjects with unstable psychic adaptation, a risk group, 3rd--subjects with stable psychic disadaptation, i.e. with some borderline mental disorders (BMD). Significant differences in the prevalence of psychic adaptation and disadaptation among groups under study were found. The predominance of the 2nd and 3rd groups among lorry-drivers in comparison with control group was found. The results showed that social and demographic characteristics had no significant influence neither in experimental nor in control groups (p > 0.1). Variability of psychoemotional imbalance levels among lorry-drivers was found to be due to a combination of the following factors: occupational exposure and their work schedule, while in control group--to the age of metal craftsmen. Comparative analysis of neurobehavioral disorders revealed the predominance of the asthenic symptoms, anxious and depressive manifestations, hysterical reactions among lorry-drivers, and the vegetative disorders only in control group. The results thus obtained support the hypothesis of occupational hazards and long driving experience being the risk factors for the development of BMD. The application of bioregulating peptides was found to restore the organism adaptive potential, improved psychoemotional indices, intensified resistance to work stress and reduced occupational risk of borderline mental disorders (p < 0.001-0.05). The best effect was obtained in case of combined application of several cytogens (pinealon and vezugen), which were optimally selected regarding the effect of each adverse occupational factor on a target organ or system. The employed parameters of psychoemotional state were rather informative for assessing the peptidergic properties of cytogens in occupational medicine and geriatrics.

Interior Noise Reduction by Adaptive Feedback Vibration Control

NASA Technical Reports Server (NTRS)

Lim, Tae W.

1998-01-01

The objective of this project is to investigate the possible use of adaptive digital filtering techniques in simultaneous, multiple-mode identification of the modal parameters of a vibrating structure in real-time. It is intended that the results obtained from this project will be used for state estimation needed in adaptive structural acoustics control. The work done in this project is basically an extension of the work on real-time single mode identification, which was performed successfully using a digital signal processor (DSP) at NASA, Langley. Initially, in this investigation the single mode identification work was duplicated on a different processor, namely the Texas Instruments TMS32OC40 DSP. The system identification results for the single mode case were very good. Then an algorithm for simultaneous two mode identification was developed and tested using analytical simulation. When it successfully performed the expected tasks, it was implemented in real-time on the DSP system to identify the first two modes of vibration of a cantilever aluminum beam. The results of the simultaneous two mode case were good but some problems were identified related to frequency warping and spurious mode identification. The frequency warping problem was found to be due to the bilinear transformation used in the algorithm to convert the system transfer function from the continuous-time domain to the discrete-time domain. An alternative approach was developed to rectify the problem. The spurious mode identification problem was found to be associated with high sampling rates. Noise in the signal is suspected to be the cause of this problem but further investigation will be needed to clarify the cause. For simultaneous identification of more than two modes, it was found that theoretically an adaptive digital filter can be designed to identify the required number of modes, but the algebra became very complex which made it impossible to implement in the DSP system used in this study. The on-line identification algorithm developed in this research will be useful in constructing a state estimator for feedback vibration control.

System health monitoring using multiple-model adaptive estimation techniques

NASA Astrophysics Data System (ADS)

Sifford, Stanley Ryan

Monitoring system health for fault detection and diagnosis by tracking system parameters concurrently with state estimates is approached using a new multiple-model adaptive estimation (MMAE) method. This novel method is called GRid-based Adaptive Parameter Estimation (GRAPE). GRAPE expands existing MMAE methods by using new techniques to sample the parameter space. GRAPE expands on MMAE with the hypothesis that sample models can be applied and resampled without relying on a predefined set of models. GRAPE is initially implemented in a linear framework using Kalman filter models. A more generalized GRAPE formulation is presented using extended Kalman filter (EKF) models to represent nonlinear systems. GRAPE can handle both time invariant and time varying systems as it is designed to track parameter changes. Two techniques are presented to generate parameter samples for the parallel filter models. The first approach is called selected grid-based stratification (SGBS). SGBS divides the parameter space into equally spaced strata. The second approach uses Latin Hypercube Sampling (LHS) to determine the parameter locations and minimize the total number of required models. LHS is particularly useful when the parameter dimensions grow. Adding more parameters does not require the model count to increase for LHS. Each resample is independent of the prior sample set other than the location of the parameter estimate. SGBS and LHS can be used for both the initial sample and subsequent resamples. Furthermore, resamples are not required to use the same technique. Both techniques are demonstrated for both linear and nonlinear frameworks. The GRAPE framework further formalizes the parameter tracking process through a general approach for nonlinear systems. These additional methods allow GRAPE to either narrow the focus to converged values within a parameter range or expand the range in the appropriate direction to track the parameters outside the current parameter range boundary. Customizable rules define the specific resample behavior when the GRAPE parameter estimates converge. Convergence itself is determined from the derivatives of the parameter estimates using a simple moving average window to filter out noise. The system can be tuned to match the desired performance goals by making adjustments to parameters such as the sample size, convergence criteria, resample criteria, initial sampling method, resampling method, confidence in prior sample covariances, sample delay, and others.

About an adaptively weighted Kaplan-Meier estimate.

PubMed

Plante, Jean-François

2009-09-01

The minimum averaged mean squared error nonparametric adaptive weights use data from m possibly different populations to infer about one population of interest. The definition of these weights is based on the properties of the empirical distribution function. We use the Kaplan-Meier estimate to let the weights accommodate right-censored data and use them to define the weighted Kaplan-Meier estimate. The proposed estimate is smoother than the usual Kaplan-Meier estimate and converges uniformly in probability to the target distribution. Simulations show that the performances of the weighted Kaplan-Meier estimate on finite samples exceed that of the usual Kaplan-Meier estimate. A case study is also presented.

A Minimum Fuel Based Estimator for Maneuver and Natrual Dynamics Reconstruction

NASA Astrophysics Data System (ADS)

Lubey, D.; Scheeres, D.

2013-09-01

The vast and growing population of objects in Earth orbit (active and defunct spacecraft, orbital debris, etc.) offers many unique challenges when it comes to tracking these objects and associating the resulting observations. Complicating these challenges are the inaccurate natural dynamical models of these objects, the active maneuvers of spacecraft that deviate them from their ballistic trajectories, and the fact that spacecraft are tracked and operated by separate agencies. Maneuver detection and reconstruction algorithms can help with each of these issues by estimating mismodeled and unmodeled dynamics through indirect observation of spacecraft. It also helps to verify the associations made by an object correlation algorithm or aid in making those associations, which is essential when tracking objects in orbit. The algorithm developed in this study applies an Optimal Control Problem (OCP) Distance Metric approach to the problems of Maneuver Reconstruction and Dynamics Estimation. This was first developed by Holzinger, Scheeres, and Alfriend (2011), with a subsequent study by Singh, Horwood, and Poore (2012). This method estimates the minimum fuel control policy rather than the state as a typical Kalman Filter would. This difference ensures that the states are connected through a given dynamical model and allows for automatic covariance manipulation, which can help to prevent filter saturation. Using a string of measurements (either verified or hypothesized to correlate with one another), the algorithm outputs a corresponding string of adjoint and state estimates with associated noise. Post-processing techniques are implemented, which when applied to the adjoint estimates can remove noise and expose unmodeled maneuvers and mismodeled natural dynamics. Specifically, the estimated controls are used to determine spacecraft dependent accelerations (atmospheric drag and solar radiation pressure) using an adapted form of the Optimal Control based natural dynamics estimation scheme developed by Lubey and Scheeres (2012). In order to allow for direct comparison, the estimator developed here was modeled after a typical Kalman Filter. The estimator forces the terminal state to lie on a manifold that satisfies the least squares with a priori information cost function, thus establishing a link with a typical Kalman filter. Terms are collected into a pseudo-Kalman Gain, which creates an equivalent form in the state estimates and covariances between the two estimators. While the two estimators share common roots, the inclusion of control in the Minimum Fuel Estimator gives it special properties. For instance, the inclusion of adjoint noise can help to automatically prevent filter saturation in a manner similar to a State Noise Compensation Algorithm. This property is quite important when considering dynamics mismodeling as filter saturation will cause estimate divergence for mismodeled systems. Additional properties and alternative forms of the estimator are also explored in this study. Several implementations of this estimator are given in this paper. It is applied to LEO, GEO, and GTO orbits with drag and SRP mismodeling. The inclusion of unmodeled maneuvers is also considered. These numerical simulations verify the mathematical properties of this estimator, and demonstrate the advantages that this estimator has over typical Kalman Filters.

A control-theory model for human decision-making

NASA Technical Reports Server (NTRS)

Levison, W. H.; Tanner, R. B.

1971-01-01

A model for human decision making is an adaptation of an optimal control model for pilot/vehicle systems. The models for decision and control both contain concepts of time delay, observation noise, optimal prediction, and optimal estimation. The decision making model was intended for situations in which the human bases his decision on his estimate of the state of a linear plant. Experiments are described for the following task situations: (a) single decision tasks, (b) two-decision tasks, and (c) simultaneous manual control and decision making. Using fixed values for model parameters, single-task and two-task decision performance can be predicted to within an accuracy of 10 percent. Agreement is less good for the simultaneous decision and control situation.

Spectrum of Slip Processes on the Subduction Interface in a Continuum Framework Resolved by Rate-and State Dependent Friction and Adaptive Time Stepping

NASA Astrophysics Data System (ADS)

Herrendoerfer, R.; van Dinther, Y.; Gerya, T.

2015-12-01

To explore the relationships between subduction dynamics and the megathrust earthquake potential, we have recently developed a numerical model that bridges the gap between processes on geodynamic and earthquake cycle time scales. In a self-consistent, continuum-based framework including a visco-elasto-plastic constitutive relationship, cycles of megathrust earthquake-like ruptures were simulated through a purely slip rate-dependent friction, albeit with very low slip rates (van Dinther et al., JGR, 2013). In addition to much faster earthquakes, a range of aseismic slip processes operate at different time scales in nature. These aseismic processes likely accommodate a considerable amount of the plate convergence and are thus relevant in order to estimate the long-term seismic coupling and related hazard in subduction zones. To simulate and resolve this wide spectrum of slip processes, we innovatively implemented rate-and state dependent friction (RSF) and an adaptive time-stepping into our continuum framework. The RSF formulation, in contrast to our previous friction formulation, takes the dependency of frictional strength on a state variable into account. It thereby allows for continuous plastic yielding inside rate-weakening regions, which leads to aseismic slip. In contrast to the conventional RSF formulation, we relate slip velocities to strain rates and use an invariant formulation. Thus we do not require the a priori definition of infinitely thin, planar faults in a homogeneous elastic medium. With this new implementation of RSF, we succeed to produce consistent cycles of frictional instabilities. By changing the frictional parameter a, b, and the characteristic slip distance, we observe a transition from stable sliding to stick-slip behaviour. This transition is in general agreement with predictions from theoretical estimates of the nucleation size, thereby to first order validating our implementation. By incorporating adaptive time-stepping based on a fraction of characteristic slip distance over maximum slip velocity, we are able to resolve stick-slip events and increase computational speed. In this better resolved framework, we examine the role of aseismic slip on the megathrust cycle and its dependence on subduction velocity.

Reduced rank regression via adaptive nuclear norm penalization

PubMed Central

Chen, Kun; Dong, Hongbo; Chan, Kung-Sik

2014-01-01

Summary We propose an adaptive nuclear norm penalization approach for low-rank matrix approximation, and use it to develop a new reduced rank estimation method for high-dimensional multivariate regression. The adaptive nuclear norm is defined as the weighted sum of the singular values of the matrix, and it is generally non-convex under the natural restriction that the weight decreases with the singular value. However, we show that the proposed non-convex penalized regression method has a global optimal solution obtained from an adaptively soft-thresholded singular value decomposition. The method is computationally efficient, and the resulting solution path is continuous. The rank consistency of and prediction/estimation performance bounds for the estimator are established for a high-dimensional asymptotic regime. Simulation studies and an application in genetics demonstrate its efficacy. PMID:25045172

On Using Exponential Parameter Estimators with an Adaptive Controller

NASA Technical Reports Server (NTRS)

Patre, Parag; Joshi, Suresh M.

2011-01-01

Typical adaptive controllers are restricted to using a specific update law to generate parameter estimates. This paper investigates the possibility of using any exponential parameter estimator with an adaptive controller such that the system tracks a desired trajectory. The goal is to provide flexibility in choosing any update law suitable for a given application. The development relies on a previously developed concept of controller/update law modularity in the adaptive control literature, and the use of a converse Lyapunov-like theorem. Stability analysis is presented to derive gain conditions under which this is possible, and inferences are made about the tracking error performance. The development is based on a class of Euler-Lagrange systems that are used to model various engineering systems including space robots and manipulators.

A bottom-up, vulnerability-based framework for identifying the adaptive capacity of water resources systems in a changing climate

NASA Astrophysics Data System (ADS)

Culley, Sam; Noble, Stephanie; Timbs, Michael; Yates, Adam; Giuliani, Matteo; Castelletti, Andrea; Maier, Holger; Westra, Seth

2015-04-01

Water resource system infrastructure and operating policies are commonly designed on the assumption that the statistics of future rainfall, temperature and other hydrometeorological variables are equal to those of the historical record. There is now substantial evidence demonstrating that this assumption is no longer valid, and that climate change will significantly impact water resources systems worldwide. Under different climatic inputs, the performance of these systems may degrade to a point where they become unable to meet the primary objectives for which they were built. In such a changing context, using existing infrastructure more efficiently - rather than planning additional infrastructure - becomes key to restore the system's performance at acceptable levels and minimize financial investments and associated risk. The traditional top-down approach for assessing climate change impacts relies on the use of a cascade of models from the global to the local scale. However, it is often difficult to utilize this top-down approach in a decision-making procedure, as there is disparity amongst various climate projections, arising from incomplete scientific understanding of the complicated processes and feedbacks within the climate system, and model limitations in reproducing those relationships. In contrast with this top-down approach, this study contributes a framework to identify the adaptive capacity of water resource systems under changing climatic conditions adopting a bottom-up, vulnerability-based approach. The performance of the current system management is first assessed for a comprehensive range of climatic conditions, which are independent of climate model forecasts. The adaptive capacity of the system is then estimated by re-evaluating the performance of a set of adaptive operating policies, which are optimized for each climatic condition under which the system is simulated. The proposed framework reverses the perspective by identifying water system vulnerability drivers and by enhancing the adaptive capacity of the system to respond to unforeseen events, in order to design robust and resilient adaptation measures. The approach is demonstrated on the multipurpose operation of the Lake Como system, located in Northern Italy, accounting for flood protection and irrigation supply. Numerical results show that our framework successfully identified the failure boundary based on current system management policies, which is demonstrated as being particularly sensitive to decreases in both precipitation and temperature. To estimate the likelihood of the climate being in states causing system failures and to provide a time frame for reaching such states, we consider 22 climate model projections; these projections suggest that the current management policies will lead to a high chance of failure over the next 40 years. The adaptive capacity of the re-optimized operating policies exhibits the potential for partially mitigating adverse climate change impacts and for extending the life of the system.

Real-time moving horizon estimation for a vibrating active cantilever

NASA Astrophysics Data System (ADS)

Abdollahpouri, Mohammad; Takács, Gergely; Rohaľ-Ilkiv, Boris

2017-03-01

Vibrating structures may be subject to changes throughout their operating lifetime due to a range of environmental and technical factors. These variations can be considered as parameter changes in the dynamic model of the structure, while their online estimates can be utilized in adaptive control strategies, or in structural health monitoring. This paper implements the moving horizon estimation (MHE) algorithm on a low-cost embedded computing device that is jointly observing the dynamic states and parameter variations of an active cantilever beam in real time. The practical behavior of this algorithm has been investigated in various experimental scenarios. It has been found, that for the given field of application, moving horizon estimation converges faster than the extended Kalman filter; moreover, it handles atypical measurement noise, sensor errors or other extreme changes, reliably. Despite its improved performance, the experiments demonstrate that the disadvantage of solving the nonlinear optimization problem in MHE is that it naturally leads to an increase in computational effort.

Significant improvements of electrical discharge machining performance by step-by-step updated adaptive control laws

NASA Astrophysics Data System (ADS)

Zhou, Ming; Wu, Jianyang; Xu, Xiaoyi; Mu, Xin; Dou, Yunping

2018-02-01

In order to obtain improved electrical discharge machining (EDM) performance, we have dedicated more than a decade to correcting one essential EDM defect, the weak stability of the machining, by developing adaptive control systems. The instabilities of machining are mainly caused by complicated disturbances in discharging. To counteract the effects from the disturbances on machining, we theoretically developed three control laws from minimum variance (MV) control law to minimum variance and pole placements coupled (MVPPC) control law and then to a two-step-ahead prediction (TP) control law. Based on real-time estimation of EDM process model parameters and measured ratio of arcing pulses which is also called gap state, electrode discharging cycle was directly and adaptively tuned so that a stable machining could be achieved. To this end, we not only theoretically provide three proved control laws for a developed EDM adaptive control system, but also practically proved the TP control law to be the best in dealing with machining instability and machining efficiency though the MVPPC control law provided much better EDM performance than the MV control law. It was also shown that the TP control law also provided a burn free machining.

Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications

PubMed Central

Huang, Jian; Zhang, Cun-Hui

2013-01-01

The ℓ1-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weighted ℓ1-penalized estimators for convex loss functions of a general form, including the generalized linear models. We study the estimation, prediction, selection and sparsity properties of the weighted ℓ1-penalized estimator in sparse, high-dimensional settings where the number of predictors p can be much larger than the sample size n. Adaptive Lasso is considered as a special case. A multistage method is developed to approximate concave regularized estimation by applying an adaptive Lasso recursively. We provide prediction and estimation oracle inequalities for single- and multi-stage estimators, a general selection consistency theorem, and an upper bound for the dimension of the Lasso estimator. Important models including the linear regression, logistic regression and log-linear models are used throughout to illustrate the applications of the general results. PMID:24348100

Baseline estimation in flame's spectra by using neural networks and robust statistics

NASA Astrophysics Data System (ADS)

Garces, Hugo; Arias, Luis; Rojas, Alejandro

2014-09-01

This work presents a baseline estimation method in flame spectra based on artificial intelligence structure as a neural network, combining robust statistics with multivariate analysis to automatically discriminate measured wavelengths belonging to continuous feature for model adaptation, surpassing restriction of measuring target baseline for training. The main contributions of this paper are: to analyze a flame spectra database computing Jolliffe statistics from Principal Components Analysis detecting wavelengths not correlated with most of the measured data corresponding to baseline; to systematically determine the optimal number of neurons in hidden layers based on Akaike's Final Prediction Error; to estimate baseline in full wavelength range sampling measured spectra; and to train an artificial intelligence structure as a Neural Network which allows to generalize the relation between measured and baseline spectra. The main application of our research is to compute total radiation with baseline information, allowing to diagnose combustion process state for optimization in early stages.

Resource-Efficient Measurement-Device-Independent Entanglement Witness

DOE PAGES

Verbanis, E.; Martin, A.; Rosset, D.; ...

2016-05-09

Imperfections in experimental measurement schemes can lead to falsely identifying, or over estimating, entanglement in a quantum system. A recent solution to this is to define schemes that are robust to measurement imperfections—measurement-device-independent entanglement witness (MDI-EW). This approach can be adapted to witness all entangled qubit states for a wide range of physical systems and does not depend on detection efficiencies or classical communication between devices. In this paper, we extend the theory to remove the necessity of prior knowledge about the two-qubit states to be witnessed. Moreover, we tested this model via a novel experimental implementation for MDI-EW thatmore » significantly reduces the experimental complexity. Finally, by applying it to a bipartite Werner state, we demonstrate the robustness of this approach against noise by witnessing entanglement down to an entangled state fraction close to 0.4.« less

Accurate Biomass Estimation via Bayesian Adaptive Sampling

NASA Technical Reports Server (NTRS)

Wheeler, Kevin R.; Knuth, Kevin H.; Castle, Joseph P.; Lvov, Nikolay

2005-01-01

The following concepts were introduced: a) Bayesian adaptive sampling for solving biomass estimation; b) Characterization of MISR Rahman model parameters conditioned upon MODIS landcover. c) Rigorous non-parametric Bayesian approach to analytic mixture model determination. d) Unique U.S. asset for science product validation and verification.

An adaptive technique for estimating the atmospheric density profile during the AE mission

NASA Technical Reports Server (NTRS)

Argentiero, P.

1973-01-01

A technique is presented for processing accelerometer data obtained during the AE missions in order to estimate the atmospheric density profile. A minimum variance, adaptive filter is utilized. The trajectory of the probe and probe parameters are in a consider mode where their estimates are unimproved but their associated uncertainties are permitted an impact on filter behavior. Simulations indicate that the technique is effective in estimating a density profile to within a few percentage points.

A Bayesian hierarchical model for discrete choice data in health care.

PubMed

Antonio, Anna Liza M; Weiss, Robert E; Saigal, Christopher S; Dahan, Ely; Crespi, Catherine M

2017-01-01

In discrete choice experiments, patients are presented with sets of health states described by various attributes and asked to make choices from among them. Discrete choice experiments allow health care researchers to study the preferences of individual patients by eliciting trade-offs between different aspects of health-related quality of life. However, many discrete choice experiments yield data with incomplete ranking information and sparsity due to the limited number of choice sets presented to each patient, making it challenging to estimate patient preferences. Moreover, methods to identify outliers in discrete choice data are lacking. We develop a Bayesian hierarchical random effects rank-ordered multinomial logit model for discrete choice data. Missing ranks are accounted for by marginalizing over all possible permutations of unranked alternatives to estimate individual patient preferences, which are modeled as a function of patient covariates. We provide a Bayesian version of relative attribute importance, and adapt the use of the conditional predictive ordinate to identify outlying choice sets and outlying individuals with unusual preferences compared to the population. The model is applied to data from a study using a discrete choice experiment to estimate individual patient preferences for health states related to prostate cancer treatment.

Late twentieth century land-cover change in the basin and range ecoregions of the United States

USGS Publications Warehouse

Soulard, Christopher E.; Sleeter, Benjamin M.

2012-01-01

As part of the US Geological Survey's Land Cover Trends project, land-use/land-cover change estimates between 1973 and 2000 are presented for the basin and range ecoregions, including Northern, Central, Mojave, and Sonoran. Landsat data were employed to estimate and characterize land-cover change from 1973, 1980, 1986, 1992, and 2000 using a post-classification comparison. Overall, spatial change was 2.5% (17,830 km2). Change increased steadily between 1973 and 1986 but decreased slightly between 1992 and 2000. The grassland/shrubland class, frequently used for livestock grazing, constituted the majority of the study area and had a net decrease from an estimated 83.8% (587,024 km2) in 1973 to 82.6% (578,242 km2) in 2000. The most common land-use/land-cover conversions across the basin and range ecoregions were indicative of the changes associated with natural, nonmechanical disturbances (i.e., fire), and grassland/shrubland loss to development, agriculture, and mining. This comprehensive look at contemporary land-use/land-cover change provides critical insight into how the deserts of the United States have changed and can be used to inform adaptive management practices of public lands.

An evaluation of inferential procedures for adaptive clinical trial designs with pre-specified rules for modifying the sample size.

PubMed

Levin, Gregory P; Emerson, Sarah C; Emerson, Scott S

2014-09-01

Many papers have introduced adaptive clinical trial methods that allow modifications to the sample size based on interim estimates of treatment effect. There has been extensive commentary on type I error control and efficiency considerations, but little research on estimation after an adaptive hypothesis test. We evaluate the reliability and precision of different inferential procedures in the presence of an adaptive design with pre-specified rules for modifying the sampling plan. We extend group sequential orderings of the outcome space based on the stage at stopping, likelihood ratio statistic, and sample mean to the adaptive setting in order to compute median-unbiased point estimates, exact confidence intervals, and P-values uniformly distributed under the null hypothesis. The likelihood ratio ordering is found to average shorter confidence intervals and produce higher probabilities of P-values below important thresholds than alternative approaches. The bias adjusted mean demonstrates the lowest mean squared error among candidate point estimates. A conditional error-based approach in the literature has the benefit of being the only method that accommodates unplanned adaptations. We compare the performance of this and other methods in order to quantify the cost of failing to plan ahead in settings where adaptations could realistically be pre-specified at the design stage. We find the cost to be meaningful for all designs and treatment effects considered, and to be substantial for designs frequently proposed in the literature. © 2014, The International Biometric Society.

RLS Channel Estimation with Adaptive Forgetting Factor for DS-CDMA Frequency-Domain Equalization

NASA Astrophysics Data System (ADS)

Kojima, Yohei; Tomeba, Hiromichi; Takeda, Kazuaki; Adachi, Fumiyuki

Frequency-domain equalization (FDE) based on the minimum mean square error (MMSE) criterion can increase the downlink bit error rate (BER) performance of DS-CDMA beyond that possible with conventional rake combining in a frequency-selective fading channel. FDE requires accurate channel estimation. Recently, we proposed a pilot-assisted channel estimation (CE) based on the MMSE criterion. Using MMSE-CE, the channel estimation accuracy is almost insensitive to the pilot chip sequence, and a good BER performance is achieved. In this paper, we propose a channel estimation scheme using one-tap recursive least square (RLS) algorithm, where the forgetting factor is adapted to the changing channel condition by the least mean square (LMS)algorithm, for DS-CDMA with FDE. We evaluate the BER performance using RLS-CE with adaptive forgetting factor in a frequency-selective fast Rayleigh fading channel by computer simulation.

Hybrid Adaptive Flight Control with Model Inversion Adaptation

NASA Technical Reports Server (NTRS)

Nguyen, Nhan

2011-01-01

This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.

Respiratory syncytial virus tracking using internet search engine data.

PubMed

Oren, Eyal; Frere, Justin; Yom-Tov, Eran; Yom-Tov, Elad

2018-04-03

Respiratory Syncytial Virus (RSV) is the leading cause of hospitalization in children less than 1 year of age in the United States. Internet search engine queries may provide high resolution temporal and spatial data to estimate and predict disease activity. After filtering an initial list of 613 symptoms using high-resolution Bing search logs, we used Google Trends data between 2004 and 2016 for a smaller list of 50 terms to build predictive models of RSV incidence for five states where long-term surveillance data was available. We then used domain adaptation to model RSV incidence for the 45 remaining US states. Surveillance data sources (hospitalization and laboratory reports) were highly correlated, as were laboratory reports with search engine data. The four terms which were most often statistically significantly correlated as time series with the surveillance data in the five state models were RSV, flu, pneumonia, and bronchiolitis. Using our models, we tracked the spread of RSV by observing the time of peak use of the search term in different states. In general, the RSV peak moved from south-east (Florida) to the north-west US. Our study represents the first time that RSV has been tracked using Internet data results and highlights successful use of search filters and domain adaptation techniques, using data at multiple resolutions. Our approach may assist in identifying spread of both local and more widespread RSV transmission and may be applicable to other seasonal conditions where comprehensive epidemiological data is difficult to collect or obtain.

Adaptive exponential integrate-and-fire model as an effective description of neuronal activity.

PubMed

Brette, Romain; Gerstner, Wulfram

2005-11-01

We introduce a two-dimensional integrate-and-fire model that combines an exponential spike mechanism with an adaptation equation, based on recent theoretical findings. We describe a systematic method to estimate its parameters with simple electrophysiological protocols (current-clamp injection of pulses and ramps) and apply it to a detailed conductance-based model of a regular spiking neuron. Our simple model predicts correctly the timing of 96% of the spikes (+/-2 ms) of the detailed model in response to injection of noisy synaptic conductances. The model is especially reliable in high-conductance states, typical of cortical activity in vivo, in which intrinsic conductances were found to have a reduced role in shaping spike trains. These results are promising because this simple model has enough expressive power to reproduce qualitatively several electrophysiological classes described in vitro.

A hybrid robust fault tolerant control based on adaptive joint unscented Kalman filter.

PubMed

Shabbouei Hagh, Yashar; Mohammadi Asl, Reza; Cocquempot, Vincent

2017-01-01

In this paper, a new hybrid robust fault tolerant control scheme is proposed. A robust H ∞ control law is used in non-faulty situation, while a Non-Singular Terminal Sliding Mode (NTSM) controller is activated as soon as an actuator fault is detected. Since a linear robust controller is designed, the system is first linearized through the feedback linearization method. To switch from one controller to the other, a fuzzy based switching system is used. An Adaptive Joint Unscented Kalman Filter (AJUKF) is used for fault detection and diagnosis. The proposed method is based on the simultaneous estimation of the system states and parameters. In order to show the efficiency of the proposed scheme, a simulated 3-DOF robotic manipulator is used. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Responding to climate change in New York State: the ClimAID integrated assessment for effective climate change adaptation in New York State. Final report.

PubMed

2011-12-01

Climate change is already beginning to affect New York State, and these impacts are projected to grow. At the same time, the state has the ability to develop adaptation strategies to prepare for and respond to climate risks now and in the future. The ClimAID assessment provides information on climate change impacts and adaptation for eight sectors in New York State: water resources, coastal zones, ecosystems, agriculture, energy, transportation,telecommunications, and public health. Observed climate trends and future climate projections were developed for seven regions across the state. Within each of the sectors, climate risks, vulnerabilities, and adaptation strategies are identified. Integrating themes across all of the sectors are equity and environmental justice and economics.Case studies are used to examine specific vulnerabilities and potential adaptation strategies in each of the eight sectors. These case studies also illustrate the linkages among climate vulnerabilities, risks, and adaptation, and demonstrate specific monitoring needs. Stakeholder participation was critical to the ClimAID assessment process to ensure relevance to decision makers across the state.

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

PubMed Central

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2016-01-01

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. PMID:27999361

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation.

PubMed

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2016-12-19

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms.

Adaptive Local Realignment of Protein Sequences.

PubMed

DeBlasio, Dan; Kececioglu, John

2018-06-11

While mutation rates can vary markedly over the residues of a protein, multiple sequence alignment tools typically use the same values for their scoring-function parameters across a protein's entire length. We present a new approach, called adaptive local realignment, that in contrast automatically adapts to the diversity of mutation rates along protein sequences. This builds upon a recent technique known as parameter advising, which finds global parameter settings for an aligner, to now adaptively find local settings. Our approach in essence identifies local regions with low estimated accuracy, constructs a set of candidate realignments using a carefully-chosen collection of parameter settings, and replaces the region if a realignment has higher estimated accuracy. This new method of local parameter advising, when combined with prior methods for global advising, boosts alignment accuracy as much as 26% over the best default setting on hard-to-align protein benchmarks, and by 6.4% over global advising alone. Adaptive local realignment has been implemented within the Opal aligner using the Facet accuracy estimator.

The Novel Nonlinear Adaptive Doppler Shift Estimation Technique and the Coherent Doppler Lidar System Validation Lidar

NASA Technical Reports Server (NTRS)

Beyon, Jeffrey Y.; Koch, Grady J.

2006-01-01

The signal processing aspect of a 2-m wavelength coherent Doppler lidar system under development at NASA Langley Research Center in Virginia is investigated in this paper. The lidar system is named VALIDAR (validation lidar) and its signal processing program estimates and displays various wind parameters in real-time as data acquisition occurs. The goal is to improve the quality of the current estimates such as power, Doppler shift, wind speed, and wind direction, especially in low signal-to-noise-ratio (SNR) regime. A novel Nonlinear Adaptive Doppler Shift Estimation Technique (NADSET) is developed on such behalf and its performance is analyzed using the wind data acquired over a long period of time by VALIDAR. The quality of Doppler shift and power estimations by conventional Fourier-transform-based spectrum estimation methods deteriorates rapidly as SNR decreases. NADSET compensates such deterioration in the quality of wind parameter estimates by adaptively utilizing the statistics of Doppler shift estimate in a strong SNR range and identifying sporadic range bins where good Doppler shift estimates are found. The authenticity of NADSET is established by comparing the trend of wind parameters with and without NADSET applied to the long-period lidar return data.

Adaptive clutter rejection filters for airborne Doppler weather radar applied to the detection of low altitude windshear

NASA Technical Reports Server (NTRS)

Keel, Byron M.

1989-01-01

An optimum adaptive clutter rejection filter for use with airborne Doppler weather radar is presented. The radar system is being designed to operate at low-altitudes for the detection of windshear in an airport terminal area where ground clutter returns may mask the weather return. The coefficients of the adaptive clutter rejection filter are obtained using a complex form of a square root normalized recursive least squares lattice estimation algorithm which models the clutter return data as an autoregressive process. The normalized lattice structure implementation of the adaptive modeling process for determining the filter coefficients assures that the resulting coefficients will yield a stable filter and offers possible fixed point implementation. A 10th order FIR clutter rejection filter indexed by geographical location is designed through autoregressive modeling of simulated clutter data. Filtered data, containing simulated dry microburst and clutter return, are analyzed using pulse-pair estimation techniques. To measure the ability of the clutter rejection filters to remove the clutter, results are compared to pulse-pair estimates of windspeed within a simulated dry microburst without clutter. In the filter evaluation process, post-filtered pulse-pair width estimates and power levels are also used to measure the effectiveness of the filters. The results support the use of an adaptive clutter rejection filter for reducing the clutter induced bias in pulse-pair estimates of windspeed.

Two-stage sequential sampling: A neighborhood-free adaptive sampling procedure

USGS Publications Warehouse

Salehi, M.; Smith, D.R.

2005-01-01

Designing an efficient sampling scheme for a rare and clustered population is a challenging area of research. Adaptive cluster sampling, which has been shown to be viable for such a population, is based on sampling a neighborhood of units around a unit that meets a specified condition. However, the edge units produced by sampling neighborhoods have proven to limit the efficiency and applicability of adaptive cluster sampling. We propose a sampling design that is adaptive in the sense that the final sample depends on observed values, but it avoids the use of neighborhoods and the sampling of edge units. Unbiased estimators of population total and its variance are derived using Murthy's estimator. The modified two-stage sampling design is easy to implement and can be applied to a wider range of populations than adaptive cluster sampling. We evaluate the proposed sampling design by simulating sampling of two real biological populations and an artificial population for which the variable of interest took the value either 0 or 1 (e.g., indicating presence and absence of a rare event). We show that the proposed sampling design is more efficient than conventional sampling in nearly all cases. The approach used to derive estimators (Murthy's estimator) opens the door for unbiased estimators to be found for similar sequential sampling designs. ?? 2005 American Statistical Association and the International Biometric Society.

Rotation otolith tilt-translation reinterpretation (ROTTR) hypothesis: a new hypothesis to explain neurovestibular spaceflight adaptation.

PubMed

Merfeld, Daniel M

2003-01-01

Normally, the nervous system must process ambiguous graviceptor (e.g., otolith) cues to estimate tilt and translation. The neural processes that help perform these estimation processes must adapt upon exposure to weightlessness and readapt upon return to Earth. In this paper we present a review of evidence supporting a new hypothesis that explains some aspects of these adaptive processes. This hypothesis, which we label the rotation otolith tilt-translation reinterpretation (ROTTR) hypothesis, suggests that the neural processes resulting in spaceflight adaptation include deterioration in the ability of the nervous system to use rotational cues to help accurately estimate the relative orientation of gravity ("tilt"). Changes in the ability to estimate gravity then also influence the ability of the nervous system to estimate linear acceleration ("translation"). We explicitly hypothesize that such changes in the ability to estimate "tilt" and "translation" will be measurable upon return to Earth and will, at least partially, explain the disorientation experienced when astronauts return to Earth. In this paper, we present the details and implications of ROTTR, review data related to ROTTR, and discuss the relationship of ROTTR to the influential otolith tilt-translation reinterpretation (OTTR) hypothesis as well as discuss the distinct differences between ROTTR and OTTR.

Integrated direct/indirect adaptive robust motion trajectory tracking control of pneumatic cylinders

NASA Astrophysics Data System (ADS)

Meng, Deyuan; Tao, Guoliang; Zhu, Xiaocong

2013-09-01

This paper studies the precision motion trajectory tracking control of a pneumatic cylinder driven by a proportional-directional control valve. An integrated direct/indirect adaptive robust controller is proposed. The controller employs a physical model based indirect-type parameter estimation to obtain reliable estimates of unknown model parameters, and utilises a robust control method with dynamic compensation type fast adaptation to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. Due to the use of projection mapping, the robust control law and the parameter adaption algorithm can be designed separately. Since the system model uncertainties are unmatched, the recursive backstepping technology is adopted to design the robust control law. Extensive comparative experimental results are presented to illustrate the effectiveness of the proposed controller and its performance robustness to parameter variations and sudden disturbances.

Evaluating sampling designs by computer simulation: A case study with the Missouri bladderpod

USGS Publications Warehouse

Morrison, L.W.; Smith, D.R.; Young, C.; Nichols, D.W.

2008-01-01

To effectively manage rare populations, accurate monitoring data are critical. Yet many monitoring programs are initiated without careful consideration of whether chosen sampling designs will provide accurate estimates of population parameters. Obtaining accurate estimates is especially difficult when natural variability is high, or limited budgets determine that only a small fraction of the population can be sampled. The Missouri bladderpod, Lesquerella filiformis Rollins, is a federally threatened winter annual that has an aggregated distribution pattern and exhibits dramatic interannual population fluctuations. Using the simulation program SAMPLE, we evaluated five candidate sampling designs appropriate for rare populations, based on 4 years of field data: (1) simple random sampling, (2) adaptive simple random sampling, (3) grid-based systematic sampling, (4) adaptive grid-based systematic sampling, and (5) GIS-based adaptive sampling. We compared the designs based on the precision of density estimates for fixed sample size, cost, and distance traveled. Sampling fraction and cost were the most important factors determining precision of density estimates, and relative design performance changed across the range of sampling fractions. Adaptive designs did not provide uniformly more precise estimates than conventional designs, in part because the spatial distribution of L. filiformis was relatively widespread within the study site. Adaptive designs tended to perform better as sampling fraction increased and when sampling costs, particularly distance traveled, were taken into account. The rate that units occupied by L. filiformis were encountered was higher for adaptive than for conventional designs. Overall, grid-based systematic designs were more efficient and practically implemented than the others. ?? 2008 The Society of Population Ecology and Springer.

Variable Cadence Walking and Ground Adaptive Standing with a Powered Ankle Prosthesis

PubMed Central

Shultz, Amanda H.; Lawson, Brian E.; Goldfarb, Michael

2015-01-01

Abstract This paper describes a control approach that provides walking and standing functionality for a powered ankle prosthesis, and demonstrates the efficacy of the approach in experiments in which a unilateral transtibial amputee subject walks with the prosthesis at variable cadences, and stands on various slopes. Both controllers incorporate a finite-state structure that emulates healthy ankle joint behavior via a series of piecewise passive impedance functions. The walking controller incorporates an algorithm to modify impedance parameters based on estimated cadence, while the standing controller incorporates an algorithm to modulate the ankle equilibrium angle in order to adapt to the ground slope and user posture, and the supervisory controller selects between the walking and standing controllers. The system is shown to reproduce several essential biomechanical features of the healthy joint during walking, particularly relative to a passive prosthesis, and is shown to adapt to variable cadences. The system is also shown to adapt to slopes over a range of ± 15 deg and to provide support to the user in a manner that is biomimetic, as validated by quasi-static stiffness measurements recorded by the prosthesis. Data from standing trials indicate that the user places more weight on the powered prosthesis than on his passive prosthesis when standing on sloped surfaces, particularly at angles of 10 deg or greater. The authors also demonstrated that the prosthesis typically began providing support within 1 s of initial contact with the ground. Further, the supervisory controller was shown to be effective in switching between walking and standing, as well as in determining ground slope just prior to the transition from the standing controller to the walking controller, where the estimated ground slope was within 1.25 deg of the actual ground slope for all trials. PMID:25955789