Evaluating an Online Resourcefulness Training Intervention Pilot Test Using Six Critical Parameters.

PubMed

Musil, Carol M; Zauszniewski, Jaclene A; Burant, Christopher J; Toly, Valerie B; Warner, Camille B

2015-12-01

Few resources are available to help grandmother caregivers to grandchildren manage their complex family situations that may have immediate and long-term consequences for themselves and their families. Resourcefulness training is an intervention designed to help grandmothers improve their ability to deal with these problems. The purpose of this pilot study was to evaluate the necessity, feasibility, acceptability, fidelity, safety, and effectiveness (i.e., effect sizes) of an online, computer-based resourcefulness training intervention that was adapted from a face-to-face intervention. Twelve grandmothers raising or living with grandchildren participated in the pilot intervention that included (a) watching an instructional video on resourcefulness, (b) completing two online questionnaires over a 6-week time period, and (c) writing in an online journal every day for 4 weeks. Data are evaluated within the context of the six parameters important to intervention development. Qualitative and quantitative results provide initial support for all six parameters. Recommendations to improve aspects of the intervention are discussed. © The Author(s) 2015.

PRESTO: online calculation of carbon in harvested wood products

Treesearch

Coeli M. Hoover; Sarah J. Beukema; Donald C.E. Robinson; Katherine M. Kellock; Diana A. Abraham

2014-01-01

Carbon stored in harvested wood products is recognized under international carbon accounting protocols, and some crediting systems may permit the inclusion of harvested wood products when calculating carbon sequestration. For managers and landowners, however, estimating carbon stored in harvested wood products may be difficult. PRESTO (PRoduct EStimation Tool Online)...

78 FR 70283 - Pacific Fishery Management Council; Online Webinar

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-25

... analysis, data-poor overfishing limit (OFL) estimates for kelp greenling and the Washington stock of cabezon, and other business in preparation for the SSC's March 2014 meeting. The online SSC Groundfish... a final draft of the 2013 cowcod rebuilding analysis; (2) review new data-poor OFL estimates for...

Fast Quantitative Susceptibility Mapping with L1-Regularization and Automatic Parameter Selection

PubMed Central

Bilgic, Berkin; Fan, Audrey P.; Polimeni, Jonathan R.; Cauley, Stephen F.; Bianciardi, Marta; Adalsteinsson, Elfar; Wald, Lawrence L.; Setsompop, Kawin

2014-01-01

Purpose To enable fast reconstruction of quantitative susceptibility maps with Total Variation penalty and automatic regularization parameter selection. Methods ℓ1-regularized susceptibility mapping is accelerated by variable-splitting, which allows closed-form evaluation of each iteration of the algorithm by soft thresholding and FFTs. This fast algorithm also renders automatic regularization parameter estimation practical. A weighting mask derived from the magnitude signal can be incorporated to allow edge-aware regularization. Results Compared to the nonlinear Conjugate Gradient (CG) solver, the proposed method offers 20× speed-up in reconstruction time. A complete pipeline including Laplacian phase unwrapping, background phase removal with SHARP filtering and ℓ1-regularized dipole inversion at 0.6 mm isotropic resolution is completed in 1.2 minutes using Matlab on a standard workstation compared to 22 minutes using the Conjugate Gradient solver. This fast reconstruction allows estimation of regularization parameters with the L-curve method in 13 minutes, which would have taken 4 hours with the CG algorithm. Proposed method also permits magnitude-weighted regularization, which prevents smoothing across edges identified on the magnitude signal. This more complicated optimization problem is solved 5× faster than the nonlinear CG approach. Utility of the proposed method is also demonstrated in functional BOLD susceptibility mapping, where processing of the massive time-series dataset would otherwise be prohibitive with the CG solver. Conclusion Online reconstruction of regularized susceptibility maps may become feasible with the proposed dipole inversion. PMID:24259479

Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter

NASA Astrophysics Data System (ADS)

Dong, Guangzhong; Wei, Jingwen; Chen, Zonghai; Sun, Han; Yu, Xiaowei

2017-10-01

To overcome the range anxiety, one of the important strategies is to accurately predict the range or dischargeable time of the battery system. To accurately predict the remaining dischargeable time (RDT) of a battery, a RDT prediction framework based on accurate battery modeling and state estimation is presented in this paper. Firstly, a simplified linearized equivalent-circuit-model is developed to simulate the dynamic characteristics of a battery. Then, an online recursive least-square-algorithm method and unscented-Kalman-filter are employed to estimate the system matrices and SOC at every prediction point. Besides, a discrete wavelet transform technique is employed to capture the statistical information of past dynamics of input currents, which are utilized to predict the future battery currents. Finally, the RDT can be predicted based on the battery model, SOC estimation results and predicted future battery currents. The performance of the proposed methodology has been verified by a lithium-ion battery cell. Experimental results indicate that the proposed method can provide an accurate SOC and parameter estimation and the predicted RDT can solve the range anxiety issues.

astroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation

NASA Astrophysics Data System (ADS)

Jennings, E.; Madigan, M.

2017-04-01

Given the complexity of modern cosmological parameter inference where we are faced with non-Gaussian data and noise, correlated systematics and multi-probe correlated datasets,the Approximate Bayesian Computation (ABC) method is a promising alternative to traditional Markov Chain Monte Carlo approaches in the case where the Likelihood is intractable or unknown. The ABC method is called "Likelihood free" as it avoids explicit evaluation of the Likelihood by using a forward model simulation of the data which can include systematics. We introduce astroABC, an open source ABC Sequential Monte Carlo (SMC) sampler for parameter estimation. A key challenge in astrophysics is the efficient use of large multi-probe datasets to constrain high dimensional, possibly correlated parameter spaces. With this in mind astroABC allows for massive parallelization using MPI, a framework that handles spawning of processes across multiple nodes. A key new feature of astroABC is the ability to create MPI groups with different communicators, one for the sampler and several others for the forward model simulation, which speeds up sampling time considerably. For smaller jobs the Python multiprocessing option is also available. Other key features of this new sampler include: a Sequential Monte Carlo sampler; a method for iteratively adapting tolerance levels; local covariance estimate using scikit-learn's KDTree; modules for specifying optimal covariance matrix for a component-wise or multivariate normal perturbation kernel and a weighted covariance metric; restart files output frequently so an interrupted sampling run can be resumed at any iteration; output and restart files are backed up at every iteration; user defined distance metric and simulation methods; a module for specifying heterogeneous parameter priors including non-standard prior PDFs; a module for specifying a constant, linear, log or exponential tolerance level; well-documented examples and sample scripts. This code is hosted online at https://github.com/EliseJ/astroABC.

An offline approach for output-only Bayesian identification of stochastic nonlinear systems using unscented Kalman filtering

NASA Astrophysics Data System (ADS)

Erazo, Kalil; Nagarajaiah, Satish

2017-06-01

In this paper an offline approach for output-only Bayesian identification of stochastic nonlinear systems is presented. The approach is based on a re-parameterization of the joint posterior distribution of the parameters that define a postulated state-space stochastic model class. In the re-parameterization the state predictive distribution is included, marginalized, and estimated recursively in a state estimation step using an unscented Kalman filter, bypassing state augmentation as required by existing online methods. In applications expectations of functions of the parameters are of interest, which requires the evaluation of potentially high-dimensional integrals; Markov chain Monte Carlo is adopted to sample the posterior distribution and estimate the expectations. The proposed approach is suitable for nonlinear systems subjected to non-stationary inputs whose realization is unknown, and that are modeled as stochastic processes. Numerical verification and experimental validation examples illustrate the effectiveness and advantages of the approach, including: (i) an increased numerical stability with respect to augmented-state unscented Kalman filtering, avoiding divergence of the estimates when the forcing input is unmeasured; (ii) the ability to handle arbitrary prior and posterior distributions. The experimental validation of the approach is conducted using data from a large-scale structure tested on a shake table. It is shown that the approach is robust to inherent modeling errors in the description of the system and forcing input, providing accurate prediction of the dynamic response when the excitation history is unknown.

The evaluation of distributed damage in concrete based on sinusoidal modeling of the ultrasonic response.

PubMed

Sepehrinezhad, Alireza; Toufigh, Vahab

2018-05-25

Ultrasonic wave attenuation is an effective descriptor of distributed damage in inhomogeneous materials. Methods developed to measure wave attenuation have the potential to provide an in-site evaluation of existing concrete structures insofar as they are accurate and time-efficient. In this study, material classification and distributed damage evaluation were investigated based on the sinusoidal modeling of the response from the through-transmission ultrasonic tests on polymer concrete specimens. The response signal was modeled as single or the sum of damping sinusoids. Due to the inhomogeneous nature of concrete materials, model parameters may vary from one specimen to another. Therefore, these parameters are not known in advance and should be estimated while the response signal is being received. The modeling procedure used in this study involves a data-adaptive algorithm to estimate the parameters online. Data-adaptive algorithms are used due to a lack of knowledge of the model parameters. The damping factor was estimated as a descriptor of the distributed damage. The results were compared in two different cases as follows: (1) constant excitation frequency with varying concrete mixtures and (2) constant mixture with varying excitation frequencies. The specimens were also loaded up to their ultimate compressive strength to investigate the effect of distributed damage in the response signal. The results of the estimation indicated that the damping was highly sensitive to the change in material inhomogeneity, even in comparable mixtures. In addition to the proposed method, three methods were employed to compare the results based on their accuracy in the classification of materials and the evaluation of the distributed damage. It is shown that the estimated damping factor is not only sensitive to damage in the final stages of loading, but it is also applicable in evaluating micro damages in the earlier stages providing a reliable descriptor of damage. In addition, the modified amplitude ratio method is introduced as an improvement of the classical method. The proposed methods were validated to be effective descriptors of distributed damage. The presented models were also in good agreement with the experimental data. Copyright © 2018 Elsevier B.V. All rights reserved.

Innovative recruitment using online networks: lessons learned from an online study of alcohol and other drug use utilizing a web-based, respondent-driven sampling (webRDS) strategy.

PubMed

Bauermeister, José A; Zimmerman, Marc A; Johns, Michelle M; Glowacki, Pietreck; Stoddard, Sarah; Volz, Erik

2012-09-01

We used a web version of Respondent-Driven Sampling (webRDS) to recruit a sample of young adults (ages 18-24) and examined whether this strategy would result in alcohol and other drug (AOD) prevalence estimates comparable to national estimates (National Survey on Drug Use and Health [NSDUH]). We recruited 22 initial participants (seeds) via Facebook to complete a web survey examining AOD risk correlates. Sequential, incentivized recruitment continued until our desired sample size was achieved. After correcting for webRDS clustering effects, we contrasted our AOD prevalence estimates (past 30 days) to NSDUH estimates by comparing the 95% confidence intervals of prevalence estimates. We found comparable AOD prevalence estimates between our sample and NSDUH for the past 30 days for alcohol, marijuana, cocaine, Ecstasy (3,4-methylenedioxymethamphetamine, or MDMA), and hallucinogens. Cigarette use was lower than NSDUH estimates. WebRDS may be a suitable strategy to recruit young adults online. We discuss the unique strengths and challenges that may be encountered by public health researchers using webRDS methods.

Modeling, simulation and control for a cryogenic fluid management facility, preliminary report

NASA Technical Reports Server (NTRS)

Turner, Max A.; Vanbuskirk, P. D.

1986-01-01

The synthesis of a control system for a cryogenic fluid management facility was studied. The severe demand for reliability as well as instrumentation and control unique to the Space Station environment are prime considerations. Realizing that the effective control system depends heavily on quantitative description of the facility dynamics, a methodology for process identification and parameter estimation is postulated. A block diagram of the associated control system is also produced. Finally, an on-line adaptive control strategy is developed utilizing optimization of the velocity form control parameters (proportional gains, integration and derivative time constants) in appropriate difference equations for direct digital control. Of special concern are the communications, software and hardware supporting interaction between the ground and orbital systems. It is visualized that specialist in the OSI/ISO utilizing the Ada programming language will influence further development, testing and validation of the simplistic models presented here for adaptation to the actual flight environment.

Systematic study of magnetar outbursts

NASA Astrophysics Data System (ADS)

Coti Zelati, F.; Rea, N.; Pons, J. A.; Campana, S.; Esposito, P.

2017-12-01

We present the results of the systematic study of all magnetar outbursts observed to date through a reanalysis of data acquired in about 1100 X-ray observations. We track the temporal evolution of the luminosity for all these events, model empirically their decays, and estimate the characteristic decay time-scales and the energy involved. We study the link between different parameters (maximum luminosity increase, outburst peak luminosities, quiescent X-ray and bolometric luminosities, energetics, decay time-scales, magnetic field, spin-down luminosity and age), and reveal several correlations between different quantities. We discuss our results in the framework of the models proposed to explain the triggering mechanism and evolution of magnetar outbursts. The study is complemented by the Magnetar Outburst Online Catalog (http://www.magnetars.ice.csic.es), an interactive database where the user can plot any combination of the parameters derived in this work and download all reduced data.

VizieR Online Data Catalog: PTPS stars. III. The evolved stars sample (Niedzielski+, 2016)

NASA Astrophysics Data System (ADS)

Niedzielski, A.; Deka-Szymankiewicz, B.; Adamczyk, M.; Adamow, M.; Nowak, G.; Wolszczan, A.

2015-11-01

We present basic atmospheric parameters (Teff, logg, vt and [Fe/H]), rotation velocities and absolute radial velocities as well as luminosities, masses, ages and radii for 402 stars (including 11 single-lined spectroscopic binaries), mostly subgiants and giants. For 272 of them we present parameters for the first time. For another 53 stars we present estimates of Teff and log g based on photometric calibrations. We also present basic properties of the complete list of 744 stars that form the PTPS evolved stars sample. We examined stellar masses for 1255 stars in five other planet searches and found some of them likely to be significantly overestimated. Applying our uniformly determined stellar masses we confirm the apparent increase of companions masses for evolved stars, and we explain it, as well as lack of close-in planets with limited effective radial velocity precision for those stars due to activity. (5 data files).

Direct adaptive control of manipulators in Cartesian space

NASA Technical Reports Server (NTRS)

Seraji, H.

1987-01-01

A new adaptive-control scheme for direct control of manipulator end effector to achieve trajectory tracking in Cartesian space is developed in this article. The control structure is obtained from linear multivariable theory and is composed of simple feedforward and feedback controllers and an auxiliary input. The direct adaptation laws are derived from model reference adaptive control theory and are not based on parameter estimation of the robot model. The utilization of adaptive feedforward control and the inclusion of auxiliary input are novel features of the present scheme and result in improved dynamic performance over existing adaptive control schemes. The adaptive controller does not require the complex mathematical model of the robot dynamics or any knowledge of the robot parameters or the payload, and is computationally fast for on-line implementation with high sampling rates. The control scheme is applied to a two-link manipulator for illustration.

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.

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

NASA Technical Reports Server (NTRS)

Williams, Peggy S.

2004-01-01

The NASA F-15 Intelligent Flight Control System project team has developed a series of flight control concepts designed to demonstrate the benefits of a neural network-based adaptive controller. The objective of the team is to develop and flight-test control systems that use neural network technology to optimize the performance of the aircraft under nominal conditions as well as stabilize the aircraft under failure conditions. Failure conditions include locked or failed control surfaces as well as unforeseen damage that might occur to the aircraft in flight. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to the baseline aerodynamic derivatives in flight. This set of open-loop flight tests was performed in preparation for a future phase of flights in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed a pitch frequency sweep and an automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. An examination of flight data shows that addition of the flight-identified aerodynamic derivative increments into the simulation improved the pitch handling qualities of the aircraft.

Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning.

PubMed

Zhou, Tao; Liu, Fanghui; Bhaskar, Harish; Yang, Jie

2017-09-12

In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank. The proposed dictionary learning technique incorporates a reconstruction error that improves the reliability of classification. Also, a multiconstraint objective function is designed to enable active learning of a discriminative and robust dictionary. Further, an optimal solution is obtained by iteratively computing the dictionary, coefficients, and by simultaneously learning the classifier parameters. Finally, a simple yet effective likelihood function is implemented to estimate the optimal state of the target during tracking. Moreover, to make the dictionary adaptive to the variations of the target and background during tracking, an online update criterion is employed while learning the new dictionary. Experimental results on a publicly available benchmark dataset have demonstrated that the proposed tracking algorithm performs better than other state-of-the-art trackers.

Integration of On-Line and Off-Line Diagnostic Algorithms for Aircraft Engine Health Management

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2007-01-01

This paper investigates the integration of on-line and off-line diagnostic algorithms for aircraft gas turbine engines. The on-line diagnostic algorithm is designed for in-flight fault detection. It continuously monitors engine outputs for anomalous signatures induced by faults. The off-line diagnostic algorithm is designed to track engine health degradation over the lifetime of an engine. It estimates engine health degradation periodically over the course of the engine s life. The estimate generated by the off-line algorithm is used to update the on-line algorithm. Through this integration, the on-line algorithm becomes aware of engine health degradation, and its effectiveness to detect faults can be maintained while the engine continues to degrade. The benefit of this integration is investigated in a simulation environment using a nonlinear engine model.

Non-rigid CT/CBCT to CBCT registration for online external beam radiotherapy guidance

NASA Astrophysics Data System (ADS)

Zachiu, Cornel; de Senneville, Baudouin Denis; Tijssen, Rob H. N.; Kotte, Alexis N. T. J.; Houweling, Antonetta C.; Kerkmeijer, Linda G. W.; Lagendijk, Jan J. W.; Moonen, Chrit T. W.; Ries, Mario

2018-01-01

Image-guided external beam radiotherapy (EBRT) allows radiation dose deposition with a high degree of accuracy and precision. Guidance is usually achieved by estimating the displacements, via image registration, between cone beam computed tomography (CBCT) and computed tomography (CT) images acquired at different stages of the therapy. The resulting displacements are then used to reposition the patient such that the location of the tumor at the time of treatment matches its position during planning. Moreover, ongoing research aims to use CBCT-CT image registration for online plan adaptation. However, CBCT images are usually acquired using a small number of x-ray projections and/or low beam intensities. This often leads to the images being subject to low contrast, low signal-to-noise ratio and artifacts, which ends-up hampering the image registration process. Previous studies addressed this by integrating additional image processing steps into the registration procedure. However, these steps are usually designed for particular image acquisition schemes, therefore limiting their use on a case-by-case basis. In the current study we address CT to CBCT and CBCT to CBCT registration by the means of the recently proposed EVolution registration algorithm. Contrary to previous approaches, EVolution does not require the integration of additional image processing steps in the registration scheme. Moreover, the algorithm requires a low number of input parameters, is easily parallelizable and provides an elastic deformation on a point-by-point basis. Results have shown that relative to a pure CT-based registration, the intrinsic artifacts present in typical CBCT images only have a sub-millimeter impact on the accuracy and precision of the estimated deformation. In addition, the algorithm has low computational requirements, which are compatible with online image-based guidance of EBRT treatments.

Hybrid adaptive ascent flight control for a flexible launch vehicle

NASA Astrophysics Data System (ADS)

Lefevre, Brian D.

For the purpose of maintaining dynamic stability and improving guidance command tracking performance under off-nominal flight conditions, a hybrid adaptive control scheme is selected and modified for use as a launch vehicle flight controller. This architecture merges a model reference adaptive approach, which utilizes both direct and indirect adaptive elements, with a classical dynamic inversion controller. This structure is chosen for a number of reasons: the properties of the reference model can be easily adjusted to tune the desired handling qualities of the spacecraft, the indirect adaptive element (which consists of an online parameter identification algorithm) continually refines the estimates of the evolving characteristic parameters utilized in the dynamic inversion, and the direct adaptive element (which consists of a neural network) augments the linear feedback signal to compensate for any nonlinearities in the vehicle dynamics. The combination of these elements enables the control system to retain the nonlinear capabilities of an adaptive network while relying heavily on the linear portion of the feedback signal to dictate the dynamic response under most operating conditions. To begin the analysis, the ascent dynamics of a launch vehicle with a single 1st stage rocket motor (typical of the Ares 1 spacecraft) are characterized. The dynamics are then linearized with assumptions that are appropriate for a launch vehicle, so that the resulting equations may be inverted by the flight controller in order to compute the control signals necessary to generate the desired response from the vehicle. Next, the development of the hybrid adaptive launch vehicle ascent flight control architecture is discussed in detail. Alterations of the generic hybrid adaptive control architecture include the incorporation of a command conversion operation which transforms guidance input from quaternion form (as provided by NASA) to the body-fixed angular rate commands needed by the hybrid adaptive flight controller, development of a Newton's method based online parameter update that is modified to include a step size which regulates the rate of change in the parameter estimates, comparison of the modified Newton's method and recursive least squares online parameter update algorithms, modification of the neural network's input structure to accommodate for the nature of the nonlinearities present in a launch vehicle's ascent flight, examination of both tracking error based and modeling error based neural network weight update laws, and integration of feedback filters for the purpose of preventing harmful interaction between the flight control system and flexible structural modes. To validate the hybrid adaptive controller, a high-fidelity Ares I ascent flight simulator and a classical gain-scheduled proportional-integral-derivative (PID) ascent flight controller were obtained from the NASA Marshall Space Flight Center. The classical PID flight controller is used as a benchmark when analyzing the performance of the hybrid adaptive flight controller. Simulations are conducted which model both nominal and off-nominal flight conditions with structural flexibility of the vehicle either enabled or disabled. First, rigid body ascent simulations are performed with the hybrid adaptive controller under nominal flight conditions for the purpose of selecting the update laws which drive the indirect and direct adaptive components. With the neural network disabled, the results revealed that the recursive least squares online parameter update caused high frequency oscillations to appear in the engine gimbal commands. This is highly undesirable for long and slender launch vehicles, such as the Ares I, because such oscillation of the rocket nozzle could excite unstable structural flex modes. In contrast, the modified Newton's method online parameter update produced smooth control signals and was thus selected for use in the hybrid adaptive launch vehicle flight controller. In the simulations where the online parameter identification algorithm was disabled, the tracking error based neural network weight update law forced the network's output to diverge despite repeated reductions of the adaptive learning rate. As a result, the modeling error based neural network weight update law (which generated bounded signals) is utilized by the hybrid adaptive controller in all subsequent simulations. Comparing the PID and hybrid adaptive flight controllers under nominal flight conditions in rigid body ascent simulations showed that their tracking error magnitudes are similar for a period of time during the middle of the ascent phase. Though the PID controller performs better for a short interval around the 20 second mark, the hybrid adaptive controller performs far better from roughly 70 to 120 seconds. Elevating the aerodynamic loads by increasing the force and moment coefficients produced results very similar to the nominal case. However, applying a 5% or 10% thrust reduction to the first stage rocket motor causes the tracking error magnitude observed by the PID controller to be significantly elevated and diverge rapidly as the simulation concludes. In contrast, the hybrid adaptive controller steadily maintains smaller errors (often less than 50% of the corresponding PID value). Under the same sets of flight conditions with flexibility enabled, the results exhibit similar trends with the hybrid adaptive controller performing even better in each case. Again, the reduction of the first stage rocket motor's thrust clearly illustrated the superior robustness of the hybrid adaptive flight controller.

Integrated model reference adaptive control and time-varying angular rate estimation for micro-machined gyroscopes

NASA Astrophysics Data System (ADS)

Tsai, Nan-Chyuan; Sue, Chung-Yang

2010-02-01

Owing to the imposed but undesired accelerations such as quadrature error and cross-axis perturbation, the micro-machined gyroscope would not be unconditionally retained at resonant mode. Once the preset resonance is not sustained, the performance of the micro-gyroscope is accordingly degraded. In this article, a direct model reference adaptive control loop which is integrated with a modified disturbance estimating observer (MDEO) is proposed to guarantee the resonant oscillations at drive mode and counterbalance the undesired disturbance mainly caused by quadrature error and cross-axis perturbation. The parameters of controller are on-line innovated by the dynamic error between the MDEO output and expected response. In addition, Lyapunov stability theory is employed to examine the stability of the closed-loop control system. Finally, the efficacy of numerical evaluation on the exerted time-varying angular rate, which is to be detected and measured by the gyroscope, is verified by intensive simulations.

A square root ensemble Kalman filter application to a motor-imagery brain-computer interface

PubMed Central

Kamrunnahar, M.; Schiff, S. J.

2017-01-01

We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%–90% for the hand movements and 70%–90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models. PMID:22255799

Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction

PubMed Central

Li, Zhencai; Wang, Yang; Liu, Zhen

2016-01-01

The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model. PMID:27467703

Do adverts increase the probability of finding online cognitive behavioural therapy for depression? Cross-sectional study

PubMed Central

Goldsmith, Lesley; Hewson, Paul; Kamel Boulos, Maged N; Williams, Christopher J

2012-01-01

Objective To estimate the effect of online adverts on the probability of finding online cognitive behavioural therapy (CBT) for depression. Design Exploratory online cross-sectional study of search experience of people in the UK with depression in 2011. (1) The authors identified the search terms over 6 months entered by users who subsequently clicked on the advert for online help for depression. (2) A panel of volunteers across the UK recorded websites presented by normal Google search for the term ‘depression’. (iii) The authors examined these websites to estimate probabilities of knowledgeable and naive internet users finding online CBT and the improved probability by addition of a Google advert. Participants (1) 3868 internet users entering search terms related to depression into Google. (2) Panel, recruited online, of 12 UK participants with an interest in depression. Main outcome measures Probability of finding online CBT for depression with/without an advert. Results The 3868 users entered 1748 different search terms but the single keyword ‘depression’ resulted in two-thirds of the presentations of, and over half the ‘clicks’ on, the advert. In total, 14 different websites were presented to our panel in the first page of Google results for ‘depression’. Four of the 14 websites had links enabling access to online CBT in three clicks for knowledgeable users. Extending this approach to the 10 most frequent search terms, the authors estimated probabilities of finding online CBT as 0.29 for knowledgeable users and 0.006 for naive users, making it unlikely CBT would be found. Adding adverts that linked directly to online CBT increased the probabilities to 0.31 (knowledgeable) and 0.02 (naive). Conclusions In this case, online CBT was not easy to find and online adverts substantially increased the chance for naive users. Others could use this approach to explore additional impact before committing to long-term Google AdWords advertising budgets. Trial registration This exploratory case study was a substudy within a cluster randomised trial, registered on http://www.clinicaltrials.gov (reference: NCT01469689). (The trial will be reported subsequently). PMID:22508957

Financial Bottom Line: Estimating the Cost of Faculty/Adjunct Turnover and Attrition for Online Programs

ERIC Educational Resources Information Center

Betts, Kristen S.; Sikorski, Bernadine

2008-01-01

Turnover and attrition of online faculty and adjunct faculty is a reality. While there are no reported national statistics or data on annual turnover/attrition for online faculty/adjunct, the overall costs of recruiting, training, and replacing faculty/adjunct can be staggering. Moreover, the short and long term effects of online faculty/adjunct…

The Approximate Bayesian Computation methods in the localization of the atmospheric contamination source

NASA Astrophysics Data System (ADS)

Kopka, P.; Wawrzynczak, A.; Borysiewicz, M.

2015-09-01

In many areas of application, a central problem is a solution to the inverse problem, especially estimation of the unknown model parameters to model the underlying dynamics of a physical system precisely. In this situation, the Bayesian inference is a powerful tool to combine observed data with prior knowledge to gain the probability distribution of searched parameters. We have applied the modern methodology named Sequential Approximate Bayesian Computation (S-ABC) to the problem of tracing the atmospheric contaminant source. The ABC is technique commonly used in the Bayesian analysis of complex models and dynamic system. Sequential methods can significantly increase the efficiency of the ABC. In the presented algorithm, the input data are the on-line arriving concentrations of released substance registered by distributed sensor network from OVER-LAND ATMOSPHERIC DISPERSION (OLAD) experiment. The algorithm output are the probability distributions of a contamination source parameters i.e. its particular location, release rate, speed and direction of the movement, start time and duration. The stochastic approach presented in this paper is completely general and can be used in other fields where the parameters of the model bet fitted to the observable data should be found.

An online tool for tracking soil nitrogen

NASA Astrophysics Data System (ADS)

Wang, J.; Umar, M.; Banger, K.; Pittelkow, C. M.; Nafziger, E. D.

2016-12-01

Near real-time crop models can be useful tools for optimizing agricultural management practices. For example, model simulations can potentially provide current estimates of nitrogen availability in soil, helping growers decide whether more nitrogen needs to be applied in a given season. Traditionally, crop models have been used at point locations (i.e. single fields) with homogenous soil, climate and initial conditions. However, nitrogen availability across fields with varied weather and soil conditions at a regional or national level is necessary to guide better management decisions. This study presents the development of a publicly available, online tool that automates the integration of high-spatial-resolution forecast and past weather and soil data in DSSAT to estimate nitrogen availability for individual fields in Illinois. The model has been calibrated with field experiments from past year at six research corn fields across Illinois. These sites were treated with applications of different N fertilizer timings and amounts. The tool requires minimal management information from growers and yet has the capability to simulate nitrogen-water-crop interactions with calibrated parameters that are more appropriate for Illinois. The results from the tool will be combined with incoming field experiment data from 2016 for model validation and further improvement of model's predictive accuracy. The tool has the potential to help guide better nitrogen management practices to maximize economic and environmental benefits.

Analyzing the Social Networks of High- and Low-Performing Students in Online Discussion Forums

ERIC Educational Resources Information Center

Ghadirian, Hajar; Salehi, Keyvan; Ayub, Ahmad Fauzi Mohd

2018-01-01

An ego network is an individual's social network relationships with core members. In this study, the ego network parameters in online discussion spaces of high- and low-performing students were compared. The extent to which students' ego networks changed over the course were also analyzed. Participation in 7 weeks of online discussions were…

VizieR Online Data Catalog: Kepler Mission. VII. Eclipsing binaries in DR3 (Kirk+, 2016)

NASA Astrophysics Data System (ADS)

Kirk, B.; Conroy, K.; Prsa, A.; Abdul-Masih, M.; Kochoska, A.; Matijevic, G.; Hambleton, K.; Barclay, T.; Bloemen, S.; Boyajian, T.; Doyle, L. R.; Fulton, B. J.; Hoekstra, A. J.; Jek, K.; Kane, S. R.; Kostov, V.; Latham, D.; Mazeh, T.; Orosz, J. A.; Pepper, J.; Quarles, B.; Ragozzine, D.; Shporer, A.; Southworth, J.; Stassun, K.; Thompson, S. E.; Welsh, W. F.; Agol, E.; Derekas, A.; Devor, J.; Fischer, D.; Green, G.; Gropp, J.; Jacobs, T.; Johnston, C.; Lacourse, D. M.; Saetre, K.; Schwengeler, H.; Toczyski, J.; Werner, G.; Garrett, M.; Gore, J.; Martinez, A. O.; Spitzer, I.; Stevick, J.; Thomadis, P. C.; Vrijmoet, E. H.; Yenawine, M.; Batalha, N.; Borucki, W.

2016-07-01

The Kepler Eclipsing Binary Catalog lists the stellar parameters from the Kepler Input Catalog (KIC) augmented by: primary and secondary eclipse depth, eclipse width, separation of eclipse, ephemeris, morphological classification parameter, and principal parameters determined by geometric analysis of the phased light curve. The previous release of the Catalog (Paper II; Slawson et al. 2011, cat. J/AJ/142/160) contained 2165 objects, through the second Kepler data release (Q0-Q2). In this release, 2878 objects are identified and analyzed from the entire data set of the primary Kepler mission (Q0-Q17). The online version of the Catalog is currently maintained at http://keplerEBs.villanova.edu/. A static version of the online Catalog associated with this paper is maintained at MAST https://archive.stsci.edu/kepler/eclipsing_binaries.html. (10 data files).

Innovative Recruitment Using Online Networks: Lessons Learned From an Online Study of Alcohol and Other Drug Use Utilizing a Web-Based, Respondent-Driven Sampling (webRDS) Strategy

PubMed Central

Bauermeister, José A.; Zimmerman, Marc A.; Johns, Michelle M.; Glowacki, Pietreck; Stoddard, Sarah; Volz, Erik

2012-01-01

Objective: We used a web version of Respondent-Driven Sampling (webRDS) to recruit a sample of young adults (ages 18–24) and examined whether this strategy would result in alcohol and other drug (AOD) prevalence estimates comparable to national estimates (National Survey on Drug Use and Health [NSDUH]). Method: We recruited 22 initial participants (seeds) via Facebook to complete a web survey examining AOD risk correlates. Sequential, incentivized recruitment continued until our desired sample size was achieved. After correcting for webRDS clustering effects, we contrasted our AOD prevalence estimates (past 30 days) to NSDUH estimates by comparing the 95% confidence intervals of prevalence estimates. Results: We found comparable AOD prevalence estimates between our sample and NSDUH for the past 30 days for alcohol, marijuana, cocaine, Ecstasy (3,4-methylenedioxymethamphetamine, or MDMA), and hallucinogens. Cigarette use was lower than NSDUH estimates. Conclusions: WebRDS may be a suitable strategy to recruit young adults online. We discuss the unique strengths and challenges that may be encountered by public health researchers using webRDS methods. PMID:22846248

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

PubMed Central

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

2007-01-01

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

Disparity between online and offline tests in accelerated aging tests of LED lamps under electric stress.

PubMed

Wang, Yao; Jing, Lei; Ke, Hong-Liang; Hao, Jian; Gao, Qun; Wang, Xiao-Xun; Sun, Qiang; Xu, Zhi-Jun

2016-09-20

The accelerated aging tests under electric stress for one type of LED lamp are conducted, and the differences between online and offline tests of the degradation of luminous flux are studied in this paper. The transformation of the two test modes is achieved with an adjustable AC voltage stabilized power source. Experimental results show that the exponential fitting of the luminous flux degradation in online tests possesses a higher fitting degree for most lamps, and the degradation rate of the luminous flux by online tests is always lower than that by offline tests. Bayes estimation and Weibull distribution are used to calculate the failure probabilities under the accelerated voltages, and then the reliability of the lamps under rated voltage of 220 V is estimated by use of the inverse power law model. Results show that the relative error of the lifetime estimation by offline tests increases as the failure probability decreases, and it cannot be neglected when the failure probability is less than 1%. The relative errors of lifetime estimation are 7.9%, 5.8%, 4.2%, and 3.5%, at the failure probabilities of 0.1%, 1%, 5%, and 10%, respectively.

Identification and control of plasma vertical position using neural network in Damavand tokamak.

PubMed

Rasouli, H; Rasouli, C; Koohi, A

2013-02-01

In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

Angular on-line tube current modulation in multidetector CT examinations of children and adults: The influence of different scanning parameters on dose reduction

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

Papadakis, Antonios E.; Perisinakis, Kostas; Damilakis, John

2007-07-15

The purpose of this study was to assess the potential of angular on-line tube current modulation on dose reduction in pediatric and adult patients undergoing multidetector computed tomography (MDCT) examinations. Five physical anthropomorphic phantoms that simulate the average individual as neonate, 1-year-old, 5-year-old, 10-year-old, and adult were employed in the current study. Phantoms were scanned with the use of on-line tube current modulation (TCM). Percent dose reduction (%DR) factors achieved by applying TCM, were determined for standard protocols used for head and neck, shoulder, thorax, thorax and abdomen, abdomen, abdomen and pelvis, pelvis, and whole body examinations. A preliminary studymore » on the application of TCM in MDCT examinations of adult patients was performed to validate the results obtained in anthropomorphic phantoms. Dose reduction was estimated as the percentage difference of the modulated milliamperes for each scan and the preset milliamperes prescribed by the scan protocol. The dose reduction in children was found to be much lower than the corresponding reduction achieved for adults. For helical scans the %DR factors, ranged between 1.6% and 7.4% for the neonate, 2.9% and 8.7% for the 1-year old, 2% and 6% for the 5-year-old, 5% and 10.9% for the 10-year-old, and 10.4% and 20.7% for the adult individual. For sequential scans the corresponding %DR factors ranged between 1.3% and 6.7%, 4.5% and 11%, 4.2% and 6.6%, 6.4% and 12.3%, and 8.9% and 23.3%, respectively. Broader beam collimations are associated with decreased %DR factors, when other scanning parameters are held constant. TCM did not impair image noise. In adult patients, the %DR values were found to be in good agreement with the corresponding results obtained in the anthropomorphic adult phantom. In conclusion, on-line TCM may be considered as a valuable tool for reducing dose in routine CT examinations of pediatric and adult patients. However, the dose reduction achieved with TCM in neonates and young children was found to be lower than that obtained for adults. Therefore, on-line TCM should work as an additional means to reduce dose and should not replace other conventional means of reducing dose, especially in neonates and young children.« less

Developing and applying modern methods of leakage monitoring and state estimation of fuel at the Novovoronezh nuclear power plant

NASA Astrophysics Data System (ADS)

Povarov, V. P.; Tereshchenko, A. B.; Kravchenko, Yu. N.; Pozychanyuk, I. V.; Gorobtsov, L. I.; Golubev, E. I.; Bykov, V. I.; Likhanskii, V. V.; Evdokimov, I. A.; Zborovskii, V. G.; Sorokin, A. A.; Kanyukova, V. D.; Aliev, T. N.

2014-02-01

The results of developing and implementing the modernized fuel leakage monitoring methods at the shut-down and running reactor of the Novovoronezh nuclear power plant (NPP) are presented. An automated computerized expert system integrated with an in-core monitoring system (ICMS) and installed at the Novovoronezh NPP unit no. 5 is described. If leaky fuel elements appear in the core, the system allows one to perform on-line assessment of the parameters of leaky fuel assemblies (FAs). The computer expert system units designed for optimizing the operating regimes and enhancing the fuel usage efficiency at the Novovoronezh NPP unit no. 5 are now being developed.

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.

Joint amalgamation of most parsimonious reconciled gene trees

PubMed Central

Scornavacca, Celine; Jacox, Edwin; Szöllősi, Gergely J.

2015-01-01

Motivation: Traditionally, gene phylogenies have been reconstructed solely on the basis of molecular sequences; this, however, often does not provide enough information to distinguish between statistically equivalent relationships. To address this problem, several recent methods have incorporated information on the species phylogeny in gene tree reconstruction, leading to dramatic improvements in accuracy. Although probabilistic methods are able to estimate all model parameters but are computationally expensive, parsimony methods—generally computationally more efficient—require a prior estimate of parameters and of the statistical support. Results: Here, we present the Tree Estimation using Reconciliation (TERA) algorithm, a parsimony based, species tree aware method for gene tree reconstruction based on a scoring scheme combining duplication, transfer and loss costs with an estimate of the sequence likelihood. TERA explores all reconciled gene trees that can be amalgamated from a sample of gene trees. Using a large scale simulated dataset, we demonstrate that TERA achieves the same accuracy as the corresponding probabilistic method while being faster, and outperforms other parsimony-based methods in both accuracy and speed. Running TERA on a set of 1099 homologous gene families from complete cyanobacterial genomes, we find that incorporating knowledge of the species tree results in a two thirds reduction in the number of apparent transfer events. Availability and implementation: The algorithm is implemented in our program TERA, which is freely available from http://mbb.univ-montp2.fr/MBB/download_sources/16__TERA. Contact: celine.scornavacca@univ-montp2.fr, ssolo@angel.elte.hu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25380957

Effects of gender and game type on autonomic nervous system physiological parameters in long-hour online game players.

PubMed

Lin, Tung-Cheng

2013-11-01

Online game playing may induce physiological effects. However, the physical mechanisms that cause these effects remain unclear. The purpose of this study was to examine the physiological effects of long-hour online gaming from an autonomic nervous system (ANS) perspective. Heart rate variability (HRV), a valid and noninvasive electrocardiographic method widely used to investigate ANS balance, was used to measure physiological effect parameters. This study used a five-time, repeated measures, mixed factorial design. Results found that playing violent games causes significantly higher sympathetic activity and diastolic blood pressure than playing nonviolent games. Long-hour online game playing resulted in the gradual dominance of the parasympathetic nervous system due to physical exhaustion. Gaming workload was found to modulate the gender effects, with males registering significantly higher sympathetic activity and females significantly higher parasympathetic activity in the higher gaming workload group.

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

NASA Technical Reports Server (NTRS)

Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan

2003-01-01

An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

Reduced order modeling, statistical analysis and system identification for a bladed rotor with geometric mistuning

NASA Astrophysics Data System (ADS)

Vishwakarma, Vinod

Modified Modal Domain Analysis (MMDA) is a novel method for the development of a reduced-order model (ROM) of a bladed rotor. This method utilizes proper orthogonal decomposition (POD) of Coordinate Measurement Machine (CMM) data of blades' geometries and sector analyses using ANSYS. For the first time ROM of a geometrically mistuned industrial scale rotor (Transonic rotor) with large size of Finite Element (FE) model is generated using MMDA. Two methods for estimating mass and stiffness mistuning matrices are used a) exact computation from sector FE analysis, b) estimates based on POD mistuning parameters. Modal characteristics such as mistuned natural frequencies, mode shapes and forced harmonic response are obtained from ROM for various cases, and results are compared with full rotor ANSYS analysis and other ROM methods such as Subset of Nominal Modes (SNM) and Fundamental Model of Mistuning (FMM). Accuracy of MMDA ROM is demonstrated with variations in number of POD features and geometric mistuning parameters. It is shown for the aforementioned case b) that the high accuracy of ROM studied in previous work with Academic rotor does not directly translate to the Transonic rotor. Reasons for such mismatch in results are investigated and attributed to higher mistuning in Transonic rotor. Alternate solutions such as estimation of sensitivities via least squares, and interpolation of mass and stiffness matrices on manifolds are developed, and their results are discussed. Statistics such as mean and standard deviations of forced harmonic response peak amplitude are obtained from random permutations, and are shown to have similar results as those of Monte Carlo simulations. These statistics are obtained and compared for 3 degree of freedom (DOF) lumped parameter model (LPM) of rotor, Academic rotor and Transonic rotor. A state -- estimator based on MMDA ROM and Kalman filter is also developed for offline or online estimation of harmonic forcing function from measurements of forced response. Forcing function is estimated for synchronous excitation of 3DOF rotor model, Academic rotor and Transonic rotor from measurement of response at few nodes. For asynchronous excitation forcing function is estimated only for 3DOF rotor model and Academic rotor from measurement of response. The impact of number of measurement locations and accuracy of ROM on the estimation of forcing function is discussed. iv.

Fast online generalized multiscale finite element method using constraint energy minimization

NASA Astrophysics Data System (ADS)

Chung, Eric T.; Efendiev, Yalchin; Leung, Wing Tat

2018-02-01

Local multiscale methods often construct multiscale basis functions in the offline stage without taking into account input parameters, such as source terms, boundary conditions, and so on. These basis functions are then used in the online stage with a specific input parameter to solve the global problem at a reduced computational cost. Recently, online approaches have been introduced, where multiscale basis functions are adaptively constructed in some regions to reduce the error significantly. In multiscale methods, it is desired to have only 1-2 iterations to reduce the error to a desired threshold. Using Generalized Multiscale Finite Element Framework [10], it was shown that by choosing sufficient number of offline basis functions, the error reduction can be made independent of physical parameters, such as scales and contrast. In this paper, our goal is to improve this. Using our recently proposed approach [4] and special online basis construction in oversampled regions, we show that the error reduction can be made sufficiently large by appropriately selecting oversampling regions. Our numerical results show that one can achieve a three order of magnitude error reduction, which is better than our previous methods. We also develop an adaptive algorithm and enrich in selected regions with large residuals. In our adaptive method, we show that the convergence rate can be determined by a user-defined parameter and we confirm this by numerical simulations. The analysis of the method is presented.

SiGN-SSM: open source parallel software for estimating gene networks with state space models.

PubMed

Tamada, Yoshinori; Yamaguchi, Rui; Imoto, Seiya; Hirose, Osamu; Yoshida, Ryo; Nagasaki, Masao; Miyano, Satoru

2011-04-15

SiGN-SSM is an open-source gene network estimation software able to run in parallel on PCs and massively parallel supercomputers. The software estimates a state space model (SSM), that is a statistical dynamic model suitable for analyzing short time and/or replicated time series gene expression profiles. SiGN-SSM implements a novel parameter constraint effective to stabilize the estimated models. Also, by using a supercomputer, it is able to determine the gene network structure by a statistical permutation test in a practical time. SiGN-SSM is applicable not only to analyzing temporal regulatory dependencies between genes, but also to extracting the differentially regulated genes from time series expression profiles. SiGN-SSM is distributed under GNU Affero General Public Licence (GNU AGPL) version 3 and can be downloaded at http://sign.hgc.jp/signssm/. The pre-compiled binaries for some architectures are available in addition to the source code. The pre-installed binaries are also available on the Human Genome Center supercomputer system. The online manual and the supplementary information of SiGN-SSM is available on our web site. tamada@ims.u-tokyo.ac.jp.

Non-Contact Surface Roughness Measurement by Implementation of a Spatial Light Modulator

PubMed Central

Aulbach, Laura; Salazar Bloise, Félix; Lu, Min; Koch, Alexander W.

2017-01-01

The surface structure, especially the roughness, has a significant influence on numerous parameters, such as friction and wear, and therefore estimates the quality of technical systems. In the last decades, a broad variety of surface roughness measurement methods were developed. A destructive measurement procedure or the lack of feasibility of online monitoring are the crucial drawbacks of most of these methods. This article proposes a new non-contact method for measuring the surface roughness that is straightforward to implement and easy to extend to online monitoring processes. The key element is a liquid-crystal-based spatial light modulator, integrated in an interferometric setup. By varying the imprinted phase of the modulator, a correlation between the imprinted phase and the fringe visibility of an interferogram is measured, and the surface roughness can be derived. This paper presents the theoretical approach of the method and first simulation and experimental results for a set of surface roughnesses. The experimental results are compared with values obtained by an atomic force microscope and a stylus profiler. PMID:28294990

A novel health indicator for on-line lithium-ion batteries remaining useful life prediction

NASA Astrophysics Data System (ADS)

Zhou, Yapeng; Huang, Miaohua; Chen, Yupu; Tao, Ye

2016-07-01

Prediction of lithium-ion batteries remaining useful life (RUL) plays an important role in an intelligent battery management system. The capacity and internal resistance are often used as the batteries health indicator (HI) for quantifying degradation and predicting RUL. However, on-line measurement of capacity and internal resistance are hardly realizable due to the not fully charged and discharged condition and the extremely expensive cost, respectively. Therefore, there is a great need to find an optional way to deal with this plight. In this work, a novel HI is extracted from the operating parameters of lithium-ion batteries for degradation modeling and RUL prediction. Moreover, Box-Cox transformation is employed to improve HI performance. Then Pearson and Spearman correlation analyses are utilized to evaluate the similarity between real capacity and the estimated capacity derived from the HI. Next, both simple statistical regression technique and optimized relevance vector machine are employed to predict the RUL based on the presented HI. The correlation analyses and prediction results show the efficiency and effectiveness of the proposed HI for battery degradation modeling and RUL prediction.

Convergence of the standard RLS method and UDUT factorisation of covariance matrix for solving the algebraic Riccati equation of the DLQR via heuristic approximate dynamic programming

NASA Astrophysics Data System (ADS)

Moraes Rêgo, Patrícia Helena; Viana da Fonseca Neto, João; Ferreira, Ernesto M.

2015-08-01

The main focus of this article is to present a proposal to solve, via UDUT factorisation, the convergence and numerical stability problems that are related to the covariance matrix ill-conditioning of the recursive least squares (RLS) approach for online approximations of the algebraic Riccati equation (ARE) solution associated with the discrete linear quadratic regulator (DLQR) problem formulated in the actor-critic reinforcement learning and approximate dynamic programming context. The parameterisations of the Bellman equation, utility function and dynamic system as well as the algebra of Kronecker product assemble a framework for the solution of the DLQR problem. The condition number and the positivity parameter of the covariance matrix are associated with statistical metrics for evaluating the approximation performance of the ARE solution via RLS-based estimators. The performance of RLS approximators is also evaluated in terms of consistence and polarisation when associated with reinforcement learning methods. The used methodology contemplates realisations of online designs for DLQR controllers that is evaluated in a multivariable dynamic system model.

Evaluation of software sensors for on-line estimation of culture conditions in an Escherichia coli cultivation expressing a recombinant protein.

PubMed

Warth, Benedikt; Rajkai, György; Mandenius, Carl-Fredrik

2010-05-03

Software sensors for monitoring and on-line estimation of critical bioprocess variables have mainly been used with standard bioreactor sensors, such as electrodes and gas analyzers, where algorithms in the software model have generated the desired state variables. In this article we propose that other on-line instruments, such as NIR probes and on-line HPLC, should be used to make more reliable and flexible software sensors. Five software sensor architectures were compared and evaluated: (1) biomass concentration from an on-line NIR probe, (2) biomass concentration from titrant addition, (3) specific growth rate from titrant addition, (4) specific growth rate from the NIR probe, and (5) specific substrate uptake rate and by-product rate from on-line HPLC and NIR probe signals. The software sensors were demonstrated on an Escherichia coli cultivation expressing a recombinant protein, green fluorescent protein (GFP), but the results could be extrapolated to other production organisms and product proteins. We conclude that well-maintained on-line instrumentation (hardware sensors) can increase the potential of software sensors. This would also strongly support the intentions with process analytical technology and quality-by-design concepts. 2010 Elsevier B.V. All rights reserved.

Truncated RAP-MUSIC (TRAP-MUSIC) for MEG and EEG source localization.

PubMed

Mäkelä, Niko; Stenroos, Matti; Sarvas, Jukka; Ilmoniemi, Risto J

2018-02-15

Electrically active brain regions can be located applying MUltiple SIgnal Classification (MUSIC) on magneto- or electroencephalographic (MEG; EEG) data. We introduce a new MUSIC method, called truncated recursively-applied-and-projected MUSIC (TRAP-MUSIC). It corrects a hidden deficiency of the conventional RAP-MUSIC algorithm, which prevents estimation of the true number of brain-signal sources accurately. The correction is done by applying a sequential dimension reduction to the signal-subspace projection. We show that TRAP-MUSIC significantly improves the performance of MUSIC-type localization; in particular, it successfully and robustly locates active brain regions and estimates their number. We compare TRAP-MUSIC and RAP-MUSIC in simulations with varying key parameters, e.g., signal-to-noise ratio, correlation between source time-courses, and initial estimate for the dimension of the signal space. In addition, we validate TRAP-MUSIC with measured MEG data. We suggest that with the proposed TRAP-MUSIC method, MUSIC-type localization could become more reliable and suitable for various online and offline MEG and EEG applications. Copyright © 2017 Elsevier Inc. All rights reserved.

Vehicle dynamics control of four in-wheel motor drive electric vehicle using gain scheduling based on tyre cornering stiffness estimation

NASA Astrophysics Data System (ADS)

Xiong, Lu; Yu, Zhuoping; Wang, Yang; Yang, Chen; Meng, Yufeng

2012-06-01

This paper focuses on the vehicle dynamic control system for a four in-wheel motor drive electric vehicle, aiming at improving vehicle stability under critical driving conditions. The vehicle dynamics controller is composed of three modules, i.e. motion following control, control allocation and vehicle state estimation. Considering the strong nonlinearity of the tyres under critical driving conditions, the yaw motion of the vehicle is regulated by gain scheduling control based on the linear quadratic regulator theory. The feed-forward and feedback gains of the controller are updated in real-time by online estimation of the tyre cornering stiffness, so as to ensure the control robustness against environmental disturbances as well as parameter uncertainty. The control allocation module allocates the calculated generalised force requirements to each in-wheel motor based on quadratic programming theory while taking the tyre longitudinal/lateral force coupling characteristic into consideration. Simulations under a variety of driving conditions are carried out to verify the control algorithm. Simulation results indicate that the proposed vehicle stability controller can effectively stabilise the vehicle motion under critical driving conditions.

Intra-operative feedback and dynamic compensation for image-guided robotic focal ultrasound surgery.

PubMed

Chauhan, S; Amir, H; Chen, G; Hacker, A; Michel, M S; Koehrmann, K U

2008-11-01

This paper describes a non-invasive remote temperature measurement technique integrated with a biomechatronic surgery system devised in our laboratory and named FUSBOT (Focal Ultrasound Surgery RoBOT). FUSBOTs use High-Intensity Focused Ultrasound (HIFU) for ablation of cancers/tumors and targets accessible through various soft-tissue acoustic windows in the human body. The focused ultrasound beam parameters are chosen so that biologically significant temperature rises are achieved only within the focal volume. In this paper, FUSBOT(BS), a customized system for breast surgery, is taken as a representative example to demonstrate the implementation and the results of non-invasive feedback during ablation. An 8-axis PC-based controller controls various sub-sections of the system within a safe constrained work envelope. Temperature is a prime target parameter in ablative procedures, and it is of paramount importance that means should be devised for its measurement and control in order to design optimal dose protocols and judge the efficacy of FUS systems. A customized sensory interface is devised and integrated with FUSBOT(BS), and dedicated software algorithms are embedded for surgical planning based on real-time guidance and feedback. Variations in the physical parameters of the tissue interacting with the incident modality are used as surgical feedback. The use of real-time ultrasound imaging and data processed from various sensors to deduce lesion position and thermal feedback during surgery, as integrated with the robotic system for online surgical planning, is described. Dynamic registration algorithms are developed for compensation and re-registration of the robotic end-effector with respect to the target, and representative empirical outcomes for lesion tracking and online temperature estimation in various biological tissues are presented.

A National Study of Online Learning Leaders in US Higher Education

ERIC Educational Resources Information Center

Fredericksen, Eric E.

2017-01-01

Online learning in US higher education continues to grow dramatically. The most recent estimates indicate that about 30% of all students enroll in at least one online course (Allen & Seamen, 2016). As this important type of academic offering has become increasingly important to institutions of higher education, Presidents and Provosts have…

Online Course-Taking and Student Outcomes in California Community Colleges

ERIC Educational Resources Information Center

Hart, Cassandra M. D.; Friedmann, Elizabeth; Hill, Michael

2018-01-01

This paper uses fixed effects analyses to estimate differences in student performance under online versus face-to-face course delivery formats in the California Community College system. On average, students have poorer outcomes in online courses in terms of the likelihood of course completion, course completion with a passing grade, and receiving…

Online Estimation of Allan Variance Coefficients Based on a Neural-Extended Kalman Filter

PubMed Central

Miao, Zhiyong; Shen, Feng; Xu, Dingjie; He, Kunpeng; Tian, Chunmiao

2015-01-01

As a noise analysis method for inertial sensors, the traditional Allan variance method requires the storage of a large amount of data and manual analysis for an Allan variance graph. Although the existing online estimation methods avoid the storage of data and the painful procedure of drawing slope lines for estimation, they require complex transformations and even cause errors during the modeling of dynamic Allan variance. To solve these problems, first, a new state-space model that directly models the stochastic errors to obtain a nonlinear state-space model was established for inertial sensors. Then, a neural-extended Kalman filter algorithm was used to estimate the Allan variance coefficients. The real noises of an ADIS16405 IMU and fiber optic gyro-sensors were analyzed by the proposed method and traditional methods. The experimental results show that the proposed method is more suitable to estimate the Allan variance coefficients than the traditional methods. Moreover, the proposed method effectively avoids the storage of data and can be easily implemented using an online processor. PMID:25625903

On-line estimation and compensation of measurement delay in GPS/SINS integration

NASA Astrophysics Data System (ADS)

Yang, Tao; Wang, Wei

2008-10-01

The chief aim of this paper is to propose a simple on-line estimation and compensation method of GPS/SINS measurement delay. The causes of time delay for GPS/SINS integration are analyzed in this paper. New Kalman filter state equations augmented by measurement delay and modified measurement equations are derived. Based on an open-loop Kalman filter, several simulations are run, results of which show that by the proposed method, the estimation and compensation error of measurement delay is below 0.1s.

Online Kinematic and Dynamic-State Estimation for Constrained Multibody Systems Based on IMUs

PubMed Central

Torres-Moreno, José Luis; Blanco-Claraco, José Luis; Giménez-Fernández, Antonio; Sanjurjo, Emilio; Naya, Miguel Ángel

2016-01-01

This article addresses the problems of online estimations of kinematic and dynamic states of a mechanism from a sequence of noisy measurements. In particular, we focus on a planar four-bar linkage equipped with inertial measurement units (IMUs). Firstly, we describe how the position, velocity, and acceleration of all parts of the mechanism can be derived from IMU signals by means of multibody kinematics. Next, we propose the novel idea of integrating the generic multibody dynamic equations into two variants of Kalman filtering, i.e., the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), in a way that enables us to handle closed-loop, constrained mechanisms, whose state space variables are not independent and would normally prevent the direct use of such estimators. The proposal in this work is to apply those estimators over the manifolds of allowed positions and velocities, by means of estimating a subset of independent coordinates only. The proposed techniques are experimentally validated on a testbed equipped with encoders as a means of establishing the ground-truth. Estimators are run online in real-time, a feature not matched by any previous procedure of those reported in the literature on multibody dynamics. PMID:26959027

Side-information-dependent correlation channel estimation in hash-based distributed video coding.

PubMed

Deligiannis, Nikos; Barbarien, Joeri; Jacobs, Marc; Munteanu, Adrian; Skodras, Athanassios; Schelkens, Peter

2012-04-01

In the context of low-cost video encoding, distributed video coding (DVC) has recently emerged as a potential candidate for uplink-oriented applications. This paper builds on a concept of correlation channel (CC) modeling, which expresses the correlation noise as being statistically dependent on the side information (SI). Compared with classical side-information-independent (SII) noise modeling adopted in current DVC solutions, it is theoretically proven that side-information-dependent (SID) modeling improves the Wyner-Ziv coding performance. Anchored in this finding, this paper proposes a novel algorithm for online estimation of the SID CC parameters based on already decoded information. The proposed algorithm enables bit-plane-by-bit-plane successive refinement of the channel estimation leading to progressively improved accuracy. Additionally, the proposed algorithm is included in a novel DVC architecture that employs a competitive hash-based motion estimation technique to generate high-quality SI at the decoder. Experimental results corroborate our theoretical gains and validate the accuracy of the channel estimation algorithm. The performance assessment of the proposed architecture shows remarkable and consistent coding gains over a germane group of state-of-the-art distributed and standard video codecs, even under strenuous conditions, i.e., large groups of pictures and highly irregular motion content.

Online multispectral fluorescence lifetime values estimation and overlay onto tissue white-light video frames

NASA Astrophysics Data System (ADS)

Gorpas, Dimitris; Ma, Dinglong; Bec, Julien; Yankelevich, Diego R.; Marcu, Laura

2016-03-01

Fluorescence lifetime imaging has been shown to be a robust technique for biochemical and functional characterization of tissues and to present great potential for intraoperative tissue diagnosis and guidance of surgical procedures. We report a technique for real-time mapping of fluorescence parameters (i.e. lifetime values) onto the location from where the fluorescence measurements were taken. This is achieved by merging a 450 nm aiming beam generated by a diode laser with the excitation light in a single delivery/collection fiber and by continuously imaging the region of interest with a color CMOS camera. The interrogated locations are then extracted from the acquired frames via color-based segmentation of the aiming beam. Assuming a Gaussian profile of the imaged aiming beam, the segmentation results are fitted to ellipses that are dynamically scaled at the full width of three automatically estimated thresholds (50%, 75%, 90%) of the Gaussian distribution's maximum value. This enables the dynamic augmentation of the white-light video frames with the corresponding fluorescence decay parameters. A fluorescence phantom and fresh tissue samples were used to evaluate this method with motorized and hand-held scanning measurements. At 640x512 pixels resolution the area of interest augmented with fluorescence decay parameters can be imaged at an average 34 frames per second. The developed method has the potential to become a valuable tool for real-time display of optical spectroscopy data during continuous scanning applications that subsequently can be used for tissue characterization and diagnosis.

On-Line Model-Based System For Nuclear Plant Monitoring

NASA Astrophysics Data System (ADS)

Tsoukalas, Lefteri H.; Lee, G. W.; Ragheb, Magdi; McDonough, T.; Niziolek, F.; Parker, M.

1989-03-01

A prototypical on-line model-based system, LASALLE1, developed at the University of Illinois in collaboration with the Illinois Department of Nuclear Safety (IDNS) is described. Its main purpose is to interpret about 300 signals, updated every two minutes at IDNS from the LaSalle Nuclear Power Plant, and to diagnose possible abnormal conditions. It is written in VAX/VMS OPS5 and operates on both the on-line and testing modes. In its knowledge base, operator and plant actions pertaining to the Emergency Operating Procedure(EOP) A-01, are encoded. This is a procedure driven by a reactor's coolant level and pressure signals; with the purpose of shutting down the reactor, maintaining adequate core cooling and reducing the reactor pressure and temperature to cold shutdown conditions ( about 90 to 200 °F). The monitoring of the procedure is performed from the perspective of Emergency Preparedness. Two major issues are addressed in this system. First, the management of the short-term or working memory of the system. LASALLE1 must reach its inferences, display its conclusion and update a message file every two minutes before a new set of data arrives from the plant. This was achieved by superimposing additional layers of control over the inferencing strategies inherent in OPS5, and developing special rules for the management of the used or outdated information. The second issue is the representation of information and its uncertainty. The concepts of information granularity and performance-level, which are based on a coupling of Probability Theory and the theory of Fuzzy Sets, are used for this purpose. The estimation of the performance-level incorporates a mathematical methodology which accounts for two types of uncertainty encountered in monitoring physical systems: Random uncertainty, in the form of of probability density functions generated by observations, measurements and sensors data and fuzzy uncertainty represented by membership functions based on symbolic , stochastic or numerical models estimating the "plausible", "possible" or "expected" values of the system parameters. Examples from both the on-line mode and the testing mode of the system will be discussed to illustrate the present methodology.

Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks.

PubMed

Savran, Aydogan; Tasaltin, Ramazan; Becerikli, Yasar

2006-04-01

This paper describes the development of a neural network (NN) based adaptive flight control system for a high performance aircraft. The main contribution of this work is that the proposed control system is able to compensate the system uncertainties, adapt to the changes in flight conditions, and accommodate the system failures. The underlying study can be considered in two phases. The objective of the first phase is to model the dynamic behavior of a nonlinear F-16 model using NNs. Therefore a NN-based adaptive identification model is developed for three angular rates of the aircraft. An on-line training procedure is developed to adapt the changes in the system dynamics and improve the identification accuracy. In this procedure, a first-in first-out stack is used to store a certain history of the input-output data. The training is performed over the whole data in the stack at every stage. To speed up the convergence rate and enhance the accuracy for achieving the on-line learning, the Levenberg-Marquardt optimization method with a trust region approach is adapted to train the NNs. The objective of the second phase is to develop intelligent flight controllers. A NN-based adaptive PID control scheme that is composed of an emulator NN, an estimator NN, and a discrete time PID controller is developed. The emulator NN is used to calculate the system Jacobian required to train the estimator NN. The estimator NN, which is trained on-line by propagating the output error through the emulator, is used to adjust the PID gains. The NN-based adaptive PID control system is applied to control three angular rates of the nonlinear F-16 model. The body-axis pitch, roll, and yaw rates are fed back via the PID controllers to the elevator, aileron, and rudder actuators, respectively. The resulting control system has learning, adaptation, and fault-tolerant abilities. It avoids the storage and interpolation requirements for the too many controller parameters of a typical flight control system. Performance of the control system is successfully tested by performing several six-degrees-of-freedom nonlinear simulations.

Estimating User Influence in Online Social Networks Subject to Information Overload

NASA Astrophysics Data System (ADS)

Li, Pei; Sun, Yunchuan; Chen, Yingwen; Tian, Zhi

2014-11-01

Online social networks have attracted remarkable attention since they provide various approaches for hundreds of millions of people to stay connected with their friends. Due to the existence of information overload, the research on diffusion dynamics in epidemiology cannot be adopted directly to that in online social networks. In this paper, we consider diffusion dynamics in online social networks subject to information overload, and model the information-processing process of a user by a queue with a batch arrival and a finite buffer. We use the average number of times a message is processed after it is generated by a given user to characterize the user influence, which is then estimated through theoretical analysis for a given network. We validate the accuracy of our estimation by simulations, and apply the results to study the impacts of different factors on the user influence. Among the observations, we find that the impact of network size on the user influence is marginal while the user influence decreases with assortativity due to information overload, which is particularly interesting.

Meiotic gene-conversion rate and tract length variation in the human genome.

PubMed

Padhukasahasram, Badri; Rannala, Bruce

2013-02-27

Meiotic recombination occurs in the form of two different mechanisms called crossing-over and gene-conversion and both processes have an important role in shaping genetic variation in populations. Although variation in crossing-over rates has been studied extensively using sperm-typing experiments, pedigree studies and population genetic approaches, our knowledge of variation in gene-conversion parameters (ie, rates and mean tract lengths) remains far from complete. To explore variability in population gene-conversion rates and its relationship to crossing-over rate variation patterns, we have developed and validated using coalescent simulations a comprehensive Bayesian full-likelihood method that can jointly infer crossing-over and gene-conversion rates as well as tract lengths from population genomic data under general variable rate models with recombination hotspots. Here, we apply this new method to SNP data from multiple human populations and attempt to characterize for the first time the fine-scale variation in gene-conversion parameters along the human genome. We find that the estimated ratio of gene-conversion to crossing-over rates varies considerably across genomic regions as well as between populations. However, there is a great degree of uncertainty associated with such estimates. We also find substantial evidence for variation in the mean conversion tract length. The estimated tract lengths did not show any negative relationship with the local heterozygosity levels in our analysis.European Journal of Human Genetics advance online publication, 27 February 2013; doi:10.1038/ejhg.2013.30.

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.

Machine Learning Based Diagnosis of Lithium Batteries

NASA Astrophysics Data System (ADS)

Ibe-Ekeocha, Chinemerem Christopher

The depletion of the world's current petroleum reserve, coupled with the negative effects of carbon monoxide and other harmful petrochemical by-products on the environment, is the driving force behind the movement towards renewable and sustainable energy sources. Furthermore, the growing transportation sector consumes a significant portion of the total energy used in the United States. A complete electrification of this sector would require a significant development in electric vehicles (EVs) and hybrid electric vehicles (HEVs), thus translating to a reduction in the carbon footprint. As the market for EVs and HEVs grows, their battery management systems (BMS) need to be improved accordingly. The BMS is not only responsible for optimally charging and discharging the battery, but also monitoring battery's state of charge (SOC) and state of health (SOH). SOC, similar to an energy gauge, is a representation of a battery's remaining charge level as a percentage of its total possible charge at full capacity. Similarly, SOH is a measure of deterioration of a battery; thus it is a representation of the battery's age. Both SOC and SOH are not measurable, so it is important that these quantities are estimated accurately. An inaccurate estimation could not only be inconvenient for EV consumers, but also potentially detrimental to battery's performance and life. Such estimations could be implemented either online, while battery is in use, or offline when battery is at rest. This thesis presents intelligent online SOC and SOH estimation methods using machine learning tools such as artificial neural network (ANN). ANNs are a powerful generalization tool if programmed and trained effectively. Unlike other estimation strategies, the techniques used require no battery modeling or knowledge of battery internal parameters but rather uses battery's voltage, charge/discharge current, and ambient temperature measurements to accurately estimate battery's SOC and SOH. The developed algorithms are evaluated experimentally using two different batteries namely lithium iron phosphate (LiFePO 4) and lithium titanate (LTO), both subjected to constant and dynamic current profiles. Results highlight the robustness of these algorithms to battery's nonlinear dynamic nature, hysteresis, aging, dynamic current profile, and parametric uncertainties. Consequently, these methods are susceptible and effective if incorporated with the BMS of EVs', HEVs', and other battery powered devices.

The development of indonesian online game addiction questionnaire.

PubMed

Jap, Tjibeng; Tiatri, Sri; Jaya, Edo Sebastian; Suteja, Mekar Sari

2013-01-01

Online game is an increasingly popular source of entertainment for all ages, with relatively prevalent negative consequences. Addiction is a problem that has received much attention. This research aims to develop a measure of online game addiction for Indonesian children and adolescents. The Indonesian Online Game Addiction Questionnaire draws from earlier theories and research on the internet and game addiction. Its construction is further enriched by including findings from qualitative interviews and field observation to ensure appropriate expression of the items. The measure consists of 7 items with a 5-point Likert Scale. It is validated by testing 1,477 Indonesian junior and senior high school students from several schools in Manado, Medan, Pontianak, and Yogyakarta. The validation evidence is shown by item-total correlation and criterion validity. The Indonesian Online Game Addiction Questionnaire has good item-total correlation (ranging from 0.29 to 0.55) and acceptable reliability (α = 0.73). It is also moderately correlated with the participant's longest time record to play online games (r = 0.39; p<0.01), average days per week in playing online games (ρ = 0.43; p<0.01), average hours per days in playing online games (ρ = 0.41; p<0.01), and monthly expenditure for online games (ρ = 0.30; p<0.01). Furthermore, we created a clinical cut-off estimate by combining criteria and population norm. The clinical cut-off estimate showed that the score of 14 to 21 may indicate mild online game addiction, and the score of 22 and above may indicate online game addiction. Overall, the result shows that Indonesian Online Game Addiction Questionnaire has sufficient psychometric property for research use, as well as limited clinical application.

The Development of Indonesian Online Game Addiction Questionnaire

PubMed Central

Jap, Tjibeng; Tiatri, Sri; Jaya, Edo Sebastian; Suteja, Mekar Sari

2013-01-01

Online game is an increasingly popular source of entertainment for all ages, with relatively prevalent negative consequences. Addiction is a problem that has received much attention. This research aims to develop a measure of online game addiction for Indonesian children and adolescents. The Indonesian Online Game Addiction Questionnaire draws from earlier theories and research on the internet and game addiction. Its construction is further enriched by including findings from qualitative interviews and field observation to ensure appropriate expression of the items. The measure consists of 7 items with a 5-point Likert Scale. It is validated by testing 1,477 Indonesian junior and senior high school students from several schools in Manado, Medan, Pontianak, and Yogyakarta. The validation evidence is shown by item-total correlation and criterion validity. The Indonesian Online Game Addiction Questionnaire has good item-total correlation (ranging from 0.29 to 0.55) and acceptable reliability (α = 0.73). It is also moderately correlated with the participant's longest time record to play online games (r = 0.39; p<0.01), average days per week in playing online games (ρ = 0.43; p<0.01), average hours per days in playing online games (ρ = 0.41; p<0.01), and monthly expenditure for online games (ρ = 0.30; p<0.01). Furthermore, we created a clinical cut-off estimate by combining criteria and population norm. The clinical cut-off estimate showed that the score of 14 to 21 may indicate mild online game addiction, and the score of 22 and above may indicate online game addiction. Overall, the result shows that Indonesian Online Game Addiction Questionnaire has sufficient psychometric property for research use, as well as limited clinical application. PMID:23560113

A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems.

PubMed

Kazemi, Mahdi; Arefi, Mohammad Mehdi

2017-03-01

In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Reducing the Anaerobic Digestion Model No. 1 for its application to an industrial wastewater treatment plant treating winery effluent wastewater.

PubMed

García-Diéguez, Carlos; Bernard, Olivier; Roca, Enrique

2013-03-01

The Anaerobic Digestion Model No. 1 (ADM1) is a complex model which is widely accepted as a common platform for anaerobic process modeling and simulation. However, it has a large number of parameters and states that hinder its calibration and use in control applications. A principal component analysis (PCA) technique was extended and applied to simplify the ADM1 using data of an industrial wastewater treatment plant processing winery effluent. The method shows that the main model features could be obtained with a minimum of two reactions. A reduced stoichiometric matrix was identified and the kinetic parameters were estimated on the basis of representative known biochemical kinetics (Monod and Haldane). The obtained reduced model takes into account the measured states in the anaerobic wastewater treatment (AWT) plant and reproduces the dynamics of the process fairly accurately. The reduced model can support on-line control, optimization and supervision strategies for AWT plants. Copyright © 2013 Elsevier Ltd. All rights reserved.

Fast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models

NASA Astrophysics Data System (ADS)

Field, Scott E.; Galley, Chad R.; Hesthaven, Jan S.; Kaye, Jason; Tiglio, Manuel

2014-07-01

We propose a solution to the problem of quickly and accurately predicting gravitational waveforms within any given physical model. The method is relevant for both real-time applications and more traditional scenarios where the generation of waveforms using standard methods can be prohibitively expensive. Our approach is based on three offline steps resulting in an accurate reduced order model in both parameter and physical dimensions that can be used as a surrogate for the true or fiducial waveform family. First, a set of m parameter values is determined using a greedy algorithm from which a reduced basis representation is constructed. Second, these m parameters induce the selection of m time values for interpolating a waveform time series using an empirical interpolant that is built for the fiducial waveform family. Third, a fit in the parameter dimension is performed for the waveform's value at each of these m times. The cost of predicting L waveform time samples for a generic parameter choice is of order O(mL+mcfit) online operations, where cfit denotes the fitting function operation count and, typically, m ≪L. The result is a compact, computationally efficient, and accurate surrogate model that retains the original physics of the fiducial waveform family while also being fast to evaluate. We generate accurate surrogate models for effective-one-body waveforms of nonspinning binary black hole coalescences with durations as long as 105M, mass ratios from 1 to 10, and for multiple spherical harmonic modes. We find that these surrogates are more than 3 orders of magnitude faster to evaluate as compared to the cost of generating effective-one-body waveforms in standard ways. Surrogate model building for other waveform families and models follows the same steps and has the same low computational online scaling cost. For expensive numerical simulations of binary black hole coalescences, we thus anticipate extremely large speedups in generating new waveforms with a surrogate. As waveform generation is one of the dominant costs in parameter estimation algorithms and parameter space exploration, surrogate models offer a new and practical way to dramatically accelerate such studies without impacting accuracy. Surrogates built in this paper, as well as others, are available from GWSurrogate, a publicly available python package.

Cardiometabolic prevention consultation in the Netherlands: screening uptake and detection of cardiometabolic risk factors and diseases--a pilot study.

PubMed

Van der Meer, Victor; Nielen, Markus M J; Drenthen, Anton J M; Van Vliet, Mieke; Assendelft, Willem J J; Schellevis, Francois G

2013-02-26

Until now, cardiometabolic risk assessment in Dutch primary health care was directed at case-finding, and structured, programmatic prevention is lacking. Therefore, the Prevention Consultation cardiometabolic risk (PC CMR), a stepwise approach to identify and manage patients with cardiometabolic risk factors, was developed. The aim of this study was 1) to evaluate uptake rates of the two steps of the PC CMR, 2) to assess the rates of newly diagnosed hypertension, hypercholesterolemia, diabetes mellitus and chronic kidney disease and 3) to explore reasons for non-participation. Sixteen general practices throughout the Netherlands were recruited to implement the PC CMR during 6 months. In eight practices eligible patients aged between 45 and 70 years without a cardiometabolic disease were actively invited by a personal letter ('active approach') and in eight other practices eligible patients were informed about the PC CMR only by posters and leaflets in the practice ('passive approach'). Participating patients completed an online risk estimation (first step). Patients estimated as having a high risk according to the online risk estimation were advised to visit their general practice to complete the risk profile with blood pressure measurements and blood tests for cholesterol and glucose and to receive recommendations about risk lowering interventions (second step). The online risk estimation was completed by 521 (33%) and 96 (1%) of patients in the practices with an active and passive approach, respectively. Of these patients 392 (64%) were estimated to have a high risk and were referred to the practice; 142 of 392 (36%) consulted the GP. A total of 31 (22%) newly diagnosed patients were identified. Hypertension, hypercholesterolemia, diabetes and chronic kidney disease were diagnosed in 13%, 11%, 1% and 0%, respectively. Privacy risks were the most frequently mentioned reason not to participate. One third of the patients responded to an active invitation to complete an online risk estimation. A passive invitation resulted in only a small number of participating patients. Two third of the participants of the online risk estimation had a high risk, but only one third of them attended the GP office. One in five visiting patients had a diagnosed cardiometabolic risk factor or disease.

Regional Earthquake Shaking and Loss Estimation

NASA Astrophysics Data System (ADS)

Sesetyan, K.; Demircioglu, M. B.; Zulfikar, C.; Durukal, E.; Erdik, M.

2009-04-01

This study, conducted under the JRA-3 component of the EU NERIES Project, develops a methodology and software (ELER) for the rapid estimation of earthquake shaking and losses in the Euro-Mediterranean region. This multi-level methodology developed together with researchers from Imperial College, NORSAR and ETH-Zurich is capable of incorporating regional variability and sources of uncertainty stemming from ground motion predictions, fault finiteness, site modifications, inventory of physical and social elements subjected to earthquake hazard and the associated vulnerability relationships. GRM Risk Management, Inc. of Istanbul serves as sub-contractor tor the coding of the ELER software. The methodology encompasses the following general steps: 1. Finding of the most likely location of the source of the earthquake using regional seismotectonic data base and basic source parameters, and if and when possible, by the estimation of fault rupture parameters from rapid inversion of data from on-line stations. 2. Estimation of the spatial distribution of selected ground motion parameters through region specific ground motion attenuation relationships and using shear wave velocity distributions.(Shake Mapping) 4. Incorporation of strong ground motion and other empirical macroseismic data for the improvement of Shake Map 5. Estimation of the losses (damage, casualty and economic) at different levels of sophistication (0, 1 and 2) that commensurate with the availability of inventory of human built environment (Loss Mapping) Both Level 0 (similar to PAGER system of USGS) and Level 1 analyses of the ELER routine are based on obtaining intensity distributions analytically and estimating total number of casualties and their geographic distribution either using regionally adjusted intensity-casualty or magnitude-casualty correlations (Level 0) of using regional building inventory data bases (Level 1). Level 0 analysis is similar to the PAGER system being developed by USGS. For given basis source parameters the intensity distributions can be computed using: a)Regional intensity attenuation relationships, b)Intensity correlations with attenuation relationship based PGV, PGA, and Spectral Amplitudes and, c)Intensity correlations with synthetic Fourier Amplitude Spectrum. In Level 1 analysis EMS98 based building vulnerability relationships are used for regional estimates of building damage and the casualty distributions. Results obtained from pilot applications of the Level 0 and Level 1 analysis modes of the ELER software to the 1999 M 7.4 Kocaeli, 1995 M 6.1 Dinar, and 2007 M 5.4 Bingol earthquakes in terms of ground shaking and losses are presented and comparisons with the observed losses are made. The regional earthquake shaking and loss information is intented for dissemination in a timely manner to related agencies for the planning and coordination of the post-earthquake emergency response. However the same software can also be used for scenario earthquake loss estimation and related Monte-Carlo type simulations.

Near infrared spectroscopy (NIRS) for on-line determination of quality parameters in intact olives.

PubMed

Salguero-Chaparro, Lourdes; Baeten, Vincent; Fernández-Pierna, Juan A; Peña-Rodríguez, Francisco

2013-08-15

The acidity, moisture and fat content in intact olive fruits were determined on-line using a NIR diode array instrument, operating on a conveyor belt. Four sets of calibrations models were obtained by means of different combinations from samples collected during 2009-2010 and 2010-2011, using full-cross and external validation. Several preprocessing treatments such as derivatives and scatter correction were investigated by using the root mean square error of cross-validation (RMSECV) and prediction (RMSEP), as control parameters. The results obtained showed RMSECV values of 2.54-3.26 for moisture, 2.35-2.71 for fat content and 2.50-3.26 for acidity parameters, depending on the calibration model developed. Calibrations for moisture, fat content and acidity gave residual predictive deviation (RPD) values of 2.76, 2.37 and 1.60, respectively. Although, it is concluded that the on-line NIRS prediction results were acceptable for the three parameters measured in intact olive samples in movement, the models developed must be improved in order to increase their accuracy before final NIRS implementation at mills. Copyright © 2013 Elsevier Ltd. All rights reserved.

Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

1997-01-01

A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

Online monitoring of thermo-cycles and its correlation with microstructure in laser cladding of nickel based super alloy

NASA Astrophysics Data System (ADS)

Muvvala, Gopinath; Patra Karmakar, Debapriya; Nath, Ashish Kumar

2017-01-01

Laser cladding, basically a weld deposition technique, is finding applications in many areas including surface coatings, refurbishment of worn out components and generation of functionally graded components owing to its various advantages over conventional methods like TIG, PTA etc. One of the essential requirements to adopt this technique in industrial manufacturing is to fulfil the increasing demand on product quality which could be controlled through online process monitoring and correlating the signals with the mechanical and metallurgical properties. Rapid thermo-cycle i.e. the fast heating and cooling rates involved in this process affect above properties of the deposited layer to a great extent. Therefore, the current study aims to monitor the thermo-cycles online, understand its variation with process parameters and its effect on different quality aspects of the clad layer, like microstructure, elemental segregations and mechanical properties. The effect of process parameters on clad track geometry is also studied which helps in their judicious selection to deposit a predefined thickness of coating. In this study Inconel 718, a nickel based super alloy is used as a clad material and AISI 304 austenitic steel as a substrate material. The thermo-cycles during the cladding process were recorded using a single spot monochromatic pyrometer. The heating and cooling rates were estimated from the recorded thermo-cycles and its effects on microstructures were characterised using SEM and XRD analyses. Slow thermo-cycles resulted in severe elemental segregations favouring Laves phase formation and increased γ matrix size which is found to be detrimental to the mechanical properties. Slow cooling also resulted in termination of epitaxial growth, forming equiaxed grains near the surface, which is not preferred for single crystal growth. Heat treatment is carried out and the effect of slow cooling and the increased γ matrix size on dissolution of segregated elements in metal matrix is studied.

Single-camera visual odometry to track a surgical X-ray C-arm base.

PubMed

Esfandiari, Hooman; Lichti, Derek; Anglin, Carolyn

2017-12-01

This study provides a framework for a single-camera odometry system for localizing a surgical C-arm base. An application-specific monocular visual odometry system (a downward-looking consumer-grade camera rigidly attached to the C-arm base) is proposed in this research. The cumulative dead-reckoning estimation of the base is extracted based on frame-to-frame homography estimation. Optical-flow results are utilized to feed the odometry. Online positional and orientation parameters are then reported. Positional accuracy of better than 2% (of the total traveled distance) for most of the cases and 4% for all the cases studied and angular accuracy of better than 2% (of absolute cumulative changes in orientation) were achieved with this method. This study provides a robust and accurate tracking framework that not only can be integrated with the current C-arm joint-tracking system (i.e. TC-arm) but also is capable of being employed for similar applications in other fields (e.g. robotics).

Thermal conductivity and emissivity measurements of uranium carbides

NASA Astrophysics Data System (ADS)

Corradetti, S.; Manzolaro, M.; Andrighetto, A.; Zanonato, P.; Tusseau-Nenez, S.

2015-10-01

Thermal conductivity and emissivity measurements on different types of uranium carbide are presented, in the context of the ActiLab Work Package in ENSAR, a project within the 7th Framework Program of the European Commission. Two specific techniques were used to carry out the measurements, both taking place in a laboratory dedicated to the research and development of materials for the SPES (Selective Production of Exotic Species) target. In the case of thermal conductivity, estimation of the dependence of this property on temperature was obtained using the inverse parameter estimation method, taking as a reference temperature and emissivity measurements. Emissivity at different temperatures was obtained for several types of uranium carbide using a dual frequency infrared pyrometer. Differences between the analyzed materials are discussed according to their compositional and microstructural properties. The obtainment of this type of information can help to carefully design materials to be capable of working under extreme conditions in next-generation ISOL (Isotope Separation On-Line) facilities for the generation of radioactive ion beams.

Quality assessment for recycling aggregates from construction and demolition waste: An image-based approach for particle size estimation.

PubMed

Di Maria, Francesco; Bianconi, Francesco; Micale, Caterina; Baglioni, Stefano; Marionni, Moreno

2016-02-01

The size distribution of aggregates has direct and important effects on fundamental properties of construction materials such as workability, strength and durability. The size distribution of aggregates from construction and demolition waste (C&D) is one of the parameters which determine the degree of recyclability and therefore the quality of such materials. Unfortunately, standard methods like sieving or laser diffraction can be either very time consuming (sieving) or possible only in laboratory conditions (laser diffraction). As an alternative we propose and evaluate the use of image analysis to estimate the size distribution of aggregates from C&D in a fast yet accurate manner. The effectiveness of the procedure was tested on aggregates generated by an existing C&D mechanical treatment plant. Experimental comparison with manual sieving showed agreement in the range 81-85%. The proposed technique demonstrated potential for being used on on-line systems within mechanical treatment plants of C&D. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evaluation of Online Log Variables That Estimate Learners' Time Management in a Korean Online Learning Context

ERIC Educational Resources Information Center

Jo, Il-Hyun; Park, Yeonjeong; Yoon, Meehyun; Sung, Hanall

2016-01-01

The purpose of this study was to identify the relationship between the psychological variables and online behavioral patterns of students, collected through a learning management system (LMS). As the psychological variable, time and study environment management (TSEM), one of the sub-constructs of MSLQ, was chosen to verify a set of time-related…

Online machining error estimation method of numerical control gear grinding machine tool based on data analysis of internal sensors

NASA Astrophysics Data System (ADS)

Zhao, Fei; Zhang, Chi; Yang, Guilin; Chen, Chinyin

2016-12-01

This paper presents an online estimation method of cutting error by analyzing of internal sensor readings. The internal sensors of numerical control (NC) machine tool are selected to avoid installation problem. The estimation mathematic model of cutting error was proposed to compute the relative position of cutting point and tool center point (TCP) from internal sensor readings based on cutting theory of gear. In order to verify the effectiveness of the proposed model, it was simulated and experimented in gear generating grinding process. The cutting error of gear was estimated and the factors which induce cutting error were analyzed. The simulation and experiments verify that the proposed approach is an efficient way to estimate the cutting error of work-piece during machining process.

Online Activity Levels Are Related to Caffeine Dependency.

PubMed

Phillips, James G; Landhuis, C Erik; Shepherd, Daniel; Ogeil, Rowan P

2016-05-01

Online activity could serve in the future as behavioral markers of emotional states for computer systems (i.e., affective computing). Hence, this study considered relationships between self-reported stimulant use and online study patterns. Sixty-two undergraduate psychology students estimated their daily caffeine use, and this was related to study patterns as tracked by their use of a Learning Management System (Blackboard). Caffeine dependency was associated with less time spent online, lower rates of file access, and fewer online activities completed. Reduced breadth or depth of processing during work/study could be used as a behavioral marker of stimulant use.

Utility Estimation for Pediatric Vesicoureteral Reflux: Methodological Considerations Using an Online Survey Platform.

PubMed

Tejwani, Rohit; Wang, Hsin-Hsiao S; Lloyd, Jessica C; Kokorowski, Paul J; Nelson, Caleb P; Routh, Jonathan C

2017-03-01

The advent of online task distribution has opened a new avenue for efficiently gathering community perspectives needed for utility estimation. Methodological consensus for estimating pediatric utilities is lacking, with disagreement over whom to sample, what perspective to use (patient vs parent) and whether instrument induced anchoring bias is significant. We evaluated what methodological factors potentially impact utility estimates for vesicoureteral reflux. Cross-sectional surveys using a time trade-off instrument were conducted via the Amazon Mechanical Turk® (https://www.mturk.com) online interface. Respondents were randomized to answer questions from child, parent or dyad perspectives on the utility of a vesicoureteral reflux health state and 1 of 3 "warm-up" scenarios (paralysis, common cold, none) before a vesicoureteral reflux scenario. Utility estimates and potential predictors were fitted to a generalized linear model to determine what factors most impacted utilities. A total of 1,627 responses were obtained. Mean respondent age was 34.9 years. Of the respondents 48% were female, 38% were married and 44% had children. Utility values were uninfluenced by child/personal vesicoureteral reflux/urinary tract infection history, income or race. Utilities were affected by perspective and were higher in the child group (34% lower in parent vs child, p <0.001, and 13% lower in dyad vs child, p <0.001). Vesicoureteral reflux utility was not significantly affected by the presence or type of time trade-off warm-up scenario (p = 0.17). Time trade-off perspective affects utilities when estimated via an online interface. However, utilities are unaffected by the presence, type or absence of warm-up scenarios. These findings could have significant methodological implications for future utility elicitations regarding other pediatric conditions. Copyright © 2017 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Evolution of Internet addiction in Greek adolescent students over a two-year period: the impact of parental bonding.

PubMed

Siomos, Konstantinos; Floros, Georgios; Fisoun, Virginia; Evaggelia, Dafouli; Farkonas, Nikiforos; Sergentani, Elena; Lamprou, Maria; Geroukalis, Dimitrios

2012-04-01

We present results from a cross-sectional study of the entire adolescent student population aged 12-18 of the island of Kos and their parents, on Internet abuse, parental bonding and parental online security practices. We also compared the level of over involvement with personal computers of the adolescents to the respective estimates of their parents. Our results indicate that Internet addiction is increased in this population where no preventive attempts were made to combat the phenomenon from the initial survey, 2 years ago. This increase is parallel to an increase in Internet availability. The best predictor variables for Internet and computer addiction were parental bonding variables and not parental security practices. Parents tend to underestimate the level of computer involvement when compared to their own children estimates. Parental safety measures on Internet browsing have only a small preventive role and cannot protect adolescents from Internet addiction. The three online activities most associated with Internet addiction were watching online pornography, online gambling and online gaming. © Springer-Verlag 2012

Parameter estimation for lithium ion batteries

NASA Astrophysics Data System (ADS)

Santhanagopalan, Shriram

With an increase in the demand for lithium based batteries at the rate of about 7% per year, the amount of effort put into improving the performance of these batteries from both experimental and theoretical perspectives is increasing. There exist a number of mathematical models ranging from simple empirical models to complicated physics-based models to describe the processes leading to failure of these cells. The literature is also rife with experimental studies that characterize the various properties of the system in an attempt to improve the performance of lithium ion cells. However, very little has been done to quantify the experimental observations and relate these results to the existing mathematical models. In fact, the best of the physics based models in the literature show as much as 20% discrepancy when compared to experimental data. The reasons for such a big difference include, but are not limited to, numerical complexities involved in extracting parameters from experimental data and inconsistencies in interpreting directly measured values for the parameters. In this work, an attempt has been made to implement simplified models to extract parameter values that accurately characterize the performance of lithium ion cells. The validity of these models under a variety of experimental conditions is verified using a model discrimination procedure. Transport and kinetic properties are estimated using a non-linear estimation procedure. The initial state of charge inside each electrode is also maintained as an unknown parameter, since this value plays a significant role in accurately matching experimental charge/discharge curves with model predictions and is not readily known from experimental data. The second part of the dissertation focuses on parameters that change rapidly with time. For example, in the case of lithium ion batteries used in Hybrid Electric Vehicle (HEV) applications, the prediction of the State of Charge (SOC) of the cell under a variety of road conditions is important. An algorithm to predict the SOC in time intervals as small as 5 ms is of critical demand. In such cases, the conventional non-linear estimation procedure is not time-effective. There exist methodologies in the literature, such as those based on fuzzy logic; however, these techniques require a lot of computational storage space. Consequently, it is not possible to implement such techniques on a micro-chip for integration as a part of a real-time device. The Extended Kalman Filter (EKF) based approach presented in this work is a first step towards developing an efficient method to predict online, the State of Charge of a lithium ion cell based on an electrochemical model. The final part of the dissertation focuses on incorporating uncertainty in parameter values into electrochemical models using the polynomial chaos theory (PCT).

The Correlation between Attendance and Participation with Respect to Student Achievement in an Online Learning Environment

ERIC Educational Resources Information Center

Rapposelli, Joseph Anthony

2014-01-01

The recent and rapid growth of technology during the last several years has dramatically increased the number of new online degree programs and courses in the United States. As a result, enrollment into these online programs and courses has also increased. The United States Distance Learning Association (USDLA) estimated there was a total of 12.2…

Online Sensor Fault Detection Based on an Improved Strong Tracking Filter

PubMed Central

Wang, Lijuan; Wu, Lifeng; Guan, Yong; Wang, Guohui

2015-01-01

We propose a method for online sensor fault detection that is based on the evolving Strong Tracking Filter (STCKF). The cubature rule is used to estimate states to improve the accuracy of making estimates in a nonlinear case. A residual is the difference in value between an estimated value and the true value. A residual will be regarded as a signal that includes fault information. The threshold is set at a reasonable level, and will be compared with residuals to determine whether or not the sensor is faulty. The proposed method requires only a nominal plant model and uses STCKF to estimate the original state vector. The effectiveness of the algorithm is verified by simulation on a drum-boiler model. PMID:25690553

Knock probability estimation through an in-cylinder temperature model with exogenous noise

NASA Astrophysics Data System (ADS)

Bares, P.; Selmanaj, D.; Guardiola, C.; Onder, C.

2018-01-01

This paper presents a new knock model which combines a deterministic knock model based on the in-cylinder temperature and an exogenous noise disturbing this temperature. The autoignition of the end-gas is modelled by an Arrhenius-like function and the knock probability is estimated by propagating a virtual error probability distribution. Results show that the random nature of knock can be explained by uncertainties at the in-cylinder temperature estimation. The model only has one parameter for calibration and thus can be easily adapted online. In order to reduce the measurement uncertainties associated with the air mass flow sensor, the trapped mass is derived from the in-cylinder pressure resonance, which improves the knock probability estimation and reduces the number of sensors needed for the model. A four stroke SI engine was used for model validation. By varying the intake temperature, the engine speed, the injected fuel mass, and the spark advance, specific tests were conducted, which furnished data with various knock intensities and probabilities. The new model is able to predict the knock probability within a sufficient range at various operating conditions. The trapped mass obtained by the acoustical model was compared in steady conditions by using a fuel balance and a lambda sensor and differences below 1 % were found.

The utility of online panel surveys versus computer-assisted interviews in obtaining substance-use prevalence estimates in the Netherlands.

PubMed

Spijkerman, Renske; Knibbe, Ronald; Knoops, Kim; Van De Mheen, Dike; Van Den Eijnden, Regina

2009-10-01

Rather than using the traditional, costly method of personal interviews in a general population sample, substance-use prevalence rates can be derived more conveniently from data collected among members of an online access panel. To examine the utility of this method, we compared the outcomes of an online survey with those obtained with the computer-assisted personal interviews (CAPI) method. Data were gathered from a large sample of online panellists and in a two-stage stratified sample of the Dutch population using the CAPI method. The Netherlands. Participants  The online sample comprised 57 125 Dutch online panellists (15-64 years) of Survey Sampling International LLC (SSI), and the CAPI cohort 7204 respondents (15-64 years). All participants answered identical questions about their use of alcohol, cannabis, ecstasy, cocaine and performance-enhancing drugs. The CAPI respondents were asked additionally about internet access and online panel membership. Both data sets were weighted statistically according to the distribution of demographic characteristics of the general Dutch population. Response rates were 35.5% (n = 20 282) for the online panel cohort and 62.7% (n = 4516) for the CAPI cohort. The data showed almost consistently lower substance-use prevalence rates for the CAPI respondents. Although the observed differences could be due to bias in both data sets, coverage and non-response bias were higher in the online panel survey. Despite its economic advantage, the online panel survey showed stronger non-response and coverage bias than the CAPI survey, leading to less reliable estimates of substance use in the general population. © 2009 The Authors. Journal compilation © 2009 Society for the Study of Addiction.

Ensemble-Based Parameter Estimation in a Coupled General Circulation Model

DOE PAGES

Liu, Y.; Liu, Z.; Zhang, S.; ...

2014-09-10

Parameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean–atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parametermore » estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Altogether, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.« less

The economic case for digital interventions for eating disorders among United States college students.

PubMed

Kass, Andrea E; Balantekin, Katherine N; Fitzsimmons-Craft, Ellen E; Jacobi, Corinna; Wilfley, Denise E; Taylor, C Barr

2017-03-01

Eating disorders (EDs) are serious health problems affecting college students. This article aimed to estimate the costs, in United States (US) dollars, of a stepped care model for online prevention and treatment among US college students to inform meaningful decisions regarding resource allocation and adoption of efficient care delivery models for EDs on college campuses. Using a payer perspective, we estimated the costs of (1) delivering an online guided self-help (GSH) intervention to individuals with EDs, including the costs of "stepping up" the proportion expected to "fail"; (2) delivering an online preventive intervention compared to a "wait and treat" approach to individuals at ED risk; and (3) applying the stepped care model across a population of 1,000 students, compared to standard care. Combining results for online GSH and preventive interventions, we estimated a stepped care model would cost less and result in fewer individuals needing in-person psychotherapy (after receiving less-intensive intervention) compared to standard care, assuming everyone in need received intervention. A stepped care model was estimated to achieve modest cost savings compared to standard care, but these estimates need to be tested with sensitivity analyses. Model assumptions highlight the complexities of cost calculations to inform resource allocation, and considerations for a disseminable delivery model are presented. Efforts are needed to systematically measure the costs and benefits of a stepped care model for EDs on college campuses, improve the precision and efficacy of ED interventions, and apply these calculations to non-US care systems with different cost structures. © 2017 Wiley Periodicals, Inc.

7 CFR 42.132 - Determining cumulative sum values.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 2 2011-01-01 2011-01-01 false Determining cumulative sum values. 42.132 Section 42... REGULATIONS STANDARDS FOR CONDITION OF FOOD CONTAINERS On-Line Sampling and Inspection Procedures § 42.132 Determining cumulative sum values. (a) The parameters for the on-line cumulative sum sampling plans for AQL's...

Remote Monitoring, Inorganic Monitoring

EPA Science Inventory

This chapter provides an overview of applicability, amenability, and operating parameter ranges for various inorganic parameters:this chapter will also provide a compilation of existing and new online technologies for determining inorganic compounds in water samples. A wide vari...

Aerial robot intelligent control method based on back-stepping

NASA Astrophysics Data System (ADS)

Zhou, Jian; Xue, Qian

2018-05-01

The aerial robot is characterized as strong nonlinearity, high coupling and parameter uncertainty, a self-adaptive back-stepping control method based on neural network is proposed in this paper. The uncertain part of the aerial robot model is compensated online by the neural network of Cerebellum Model Articulation Controller and robust control items are designed to overcome the uncertainty error of the system during online learning. At the same time, particle swarm algorithm is used to optimize and fix parameters so as to improve the dynamic performance, and control law is obtained by the recursion of back-stepping regression. Simulation results show that the designed control law has desired attitude tracking performance and good robustness in case of uncertainties and large errors in the model parameters.

Engagement with Online Tobacco Marketing Among Adolescents in the US: 2013-2014 to 2014-2015.

PubMed

Soneji, Samir; Yang, JaeWon; Moran, Meghan Bridgid; Tan, Andy S L; Sargent, James; Knutzen, Kristin E; Choi, Kelvin

2018-05-05

To assess changes in engagement with online tobacco and electronic cigarette marketing ('online tobacco marketing') among adolescents in the US between 2013 and 2015. We assessed the prevalence of 6 forms of engagement with online tobacco marketing, both overall and by brand, among adolescents sampled in Wave 1 (2013-2014; N=13,651) and Wave 2 (2014-2015; N=12,172) of the nationally representative Population Assessment for Tobacco and Health Study. Engagement was analyzed by tobacco use status: non-susceptible never tobacco users; susceptible never tobacco users; ever tobacco users, but not within the past year; and past-year tobacco users. Among all adolescents, the estimated prevalence of engagement with at least one form of online tobacco marketing increased from 8.7% in 2013-2014 to 20.9% in 2014-2015. The estimated prevalence of engagement also increased over time across all tobacco use statuses (e.g., from 10.5% to 26.6% among susceptible adolescents). Brand-specific engagement increased over time for cigarette, cigar, and e-cigarette brands. Engagement with online tobacco marketing, both for tobacco and e-cigarettes, increased almost two-fold over time. This increase emphasizes the dynamic nature of online tobacco marketing and its ability to reach youth. The Food and Drug Administration, in cooperation with social networking sites, should consider new approaches to regulate this novel form of marketing.

Three-dimensional online surface reconstruction of augmented fluorescence lifetime maps using photometric stereo (Conference Presentation)

NASA Astrophysics Data System (ADS)

Unger, Jakob; Lagarto, Joao; Phipps, Jennifer; Ma, Dinglong; Bec, Julien; Sorger, Jonathan; Farwell, Gregory; Bold, Richard; Marcu, Laura

2017-02-01

Multi-Spectral Time-Resolved Fluorescence Spectroscopy (ms-TRFS) can provide label-free real-time feedback on tissue composition and pathology during surgical procedures by resolving the fluorescence decay dynamics of the tissue. Recently, an ms-TRFS system has been developed in our group, allowing for either point-spectroscopy fluorescence lifetime measurements or dynamic raster tissue scanning by merging a 450 nm aiming beam with the pulsed fluorescence excitation light in a single fiber collection. In order to facilitate an augmented real-time display of fluorescence decay parameters, the lifetime values are back projected to the white light video. The goal of this study is to develop a 3D real-time surface reconstruction aiming for a comprehensive visualization of the decay parameters and providing an enhanced navigation for the surgeon. Using a stereo camera setup, we use a combination of image feature matching and aiming beam stereo segmentation to establish a 3D surface model of the decay parameters. After camera calibration, texture-related features are extracted for both camera images and matched providing a rough estimation of the surface. During the raster scanning, the rough estimation is successively refined in real-time by tracking the aiming beam positions using an advanced segmentation algorithm. The method is evaluated for excised breast tissue specimens showing a high precision and running in real-time with approximately 20 frames per second. The proposed method shows promising potential for intraoperative navigation, i.e. tumor margin assessment. Furthermore, it provides the basis for registering the fluorescence lifetime maps to the tissue surface adapting it to possible tissue deformations.

A Comprehensive Software and Database Management System for Glomerular Filtration Rate Estimation by Radionuclide Plasma Sampling and Serum Creatinine Methods.

PubMed

Jha, Ashish Kumar

2015-01-01

Glomerular filtration rate (GFR) estimation by plasma sampling method is considered as the gold standard. However, this method is not widely used because the complex technique and cumbersome calculations coupled with the lack of availability of user-friendly software. The routinely used Serum Creatinine method (SrCrM) of GFR estimation also requires the use of online calculators which cannot be used without internet access. We have developed user-friendly software "GFR estimation software" which gives the options to estimate GFR by plasma sampling method as well as SrCrM. We have used Microsoft Windows(®) as operating system and Visual Basic 6.0 as the front end and Microsoft Access(®) as database tool to develop this software. We have used Russell's formula for GFR calculation by plasma sampling method. GFR calculations using serum creatinine have been done using MIRD, Cockcroft-Gault method, Schwartz method, and Counahan-Barratt methods. The developed software is performing mathematical calculations correctly and is user-friendly. This software also enables storage and easy retrieval of the raw data, patient's information and calculated GFR for further processing and comparison. This is user-friendly software to calculate the GFR by various plasma sampling method and blood parameter. This software is also a good system for storing the raw and processed data for future analysis.

An Adaptive Nonlinear Aircraft Maneuvering Envelope Estimation Approach for Online Applications

NASA Technical Reports Server (NTRS)

Schuet, Stefan R.; Lombaerts, Thomas Jan; Acosta, Diana; Wheeler, Kevin; Kaneshige, John

2014-01-01

A nonlinear aircraft model is presented and used to develop an overall unified robust and adaptive approach to passive trim and maneuverability envelope estimation with uncertainty quantification. The concept of time scale separation makes this method suitable for the online characterization of altered safe maneuvering limitations after impairment. The results can be used to provide pilot feedback and/or be combined with flight planning, trajectory generation, and guidance algorithms to help maintain safe aircraft operations in both nominal and off-nominal scenarios.

Long-term simulation of the activated sludge process at the Hanover-Gümmerwald pilot WWTP.

PubMed

Makinia, Jacek; Rosenwinkel, Karl-Heinz; Spering, Volker

2005-04-01

The aim of this study was to obtain a validated model, consisting of the Activated Sludge Model No. 3 (ASM3) and the EAWAG bio-P module, which could be used as a decision tool for estimating the maximum allowable peak flow to wastewater treatment plants during stormwater conditions. The databases used for simulations originated from the Hanover-Gummerwald pilot plant subjected to a series of controlled, short-term hydraulic shock loading experiments. The continuous influent wastewater composition was generated using on-line measurements of only three parameters (COD, N-NH4+, P-PO4 3-). Model predictions were compared with on-line data from different locations in the activated sludge system including the aerobic zone (concentrations of N-NH4+, N-NO3-) and secondary effluent (concentrations of P-PO4 3-). The simulations confirmed experimental results concerning the capabilities of the system for handling increased flows during stormwater events. No (or minor) peaks of N-NH4+ were predicted for the line with the double dry weather flowrate, whereas peaks of N-NH4+ at the line with the quadruple dry weather flowrate were normally exceeding 8 g Nm(-3) (similar to the observations).

VizieR Online Data Catalog: Catalog of Earth-Like Exoplanet Survey Targets (Chandler+, 2016)

NASA Astrophysics Data System (ADS)

Chandler, C. O.; McDonald, I.; Kane, S. R.

2016-07-01

We present the Catalog of Earth-Like Exoplanet Survey Targets (CELESTA), a database of habitable zones around 37000 nearby stars. The first step in creating CELESTA was assembling the input data. The Revised Hipparcos Catalog (van Leeuwen 2007, Cat. I/311) is a stellar catalog based on the original Hipparcos mission (Perryman et al. 1997, Cat. I/239) data set. Hipparcos, launched in 1989, recorded with great precision the parallax of nearby stars, ultimately leading to a database of 118218 stars. McDonald et al. 2012 (cat. J/MNRAS/427/343) calculated effective temperatures and luminosities for the Hipparcos stars. The next step was selecting appropriate stars for the construction of CELESTA. The Stellar Parameter Catalog of 103663 stars included many stars that were not suitable for our purposes, especially stars off the Main-Sequence (MS) branch, e.g., giants. Please refer to Section 3.2 in the paper for additional details about the star selection. The final CELESTA catalog contains 37354 stars (see Table2), each with a set of associated attributes, e.g., estimated mass, measured distance. The complete database can also be found online at a dedicated host (http://www.celesta.info/). (2 data files).

On-line diagnosis of inter-turn short circuit fault for DC brushed motor.

PubMed

Zhang, Jiayuan; Zhan, Wei; Ehsani, Mehrdad

2018-06-01

Extensive research effort has been made in fault diagnosis of motors and related components such as winding and ball bearing. In this paper, a new concept of inter-turn short circuit fault for DC brushed motors is proposed to include the short circuit ratio and short circuit resistance. A first-principle model is derived for motors with inter-turn short circuit fault. A statistical model based on Hidden Markov Model is developed for fault diagnosis purpose. This new method not only allows detection of motor winding short circuit fault, it can also provide estimation of the fault severity, as indicated by estimation of the short circuit ratio and the short circuit resistance. The estimated fault severity can be used for making appropriate decisions in response to the fault condition. The feasibility of the proposed methodology is studied for inter-turn short circuit of DC brushed motors using simulation in MATLAB/Simulink environment. In addition, it is shown that the proposed methodology is reliable with the presence of small random noise in the system parameters and measurement. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

VizieR Online Data Catalog: Catalogue of features in the S4G (Herrera-Endoqui+, 2015)

NASA Astrophysics Data System (ADS)

Herrera-Endoqui, M.; Diaz-Garcia, S.; Laurikainen, E.; Salo, H.

2015-08-01

Table 2 contains the properties of bars, ring- and lens-structures in the S4G. Data for bars contains the visual estimated barlength, the maximum ellipticity in the bar region, the visual estimated position angle, and the barlength obtained from the ellipticity maximum. They are given in both the sky plane and the disk plane, the conversion is made using P4 orientation parameters (Salo et al., 2015ApJS..219....4S; Table 1). For bars the disk plane values are given only when a reliable ellipticity maximum was found and the galaxy inclination i<65 deg. For other features the parameters are obtained from fitting ellipses to points tracing the structure. A quality flag for our measurement is also given: 1 indicates a good fit and unambiguously identified feature, 2 indicates a hard to trace feature, 3 indicates an uncertain feature identification (due to high inclination of host galaxy or incomplete feature). Table 3 contains the properties of spiral arms in the S4G. Type of spiral arms, the pitch angle, the inner and the outer radius are given for every spiral segment (see the catalogue web page). The type of spiral arms are taken from Buta et al. (2015ApJS..217...32B, Cat. J/ApJS/217/32): G for grand design, M for multiple, and F for flocculent spiral arms. Our estimation of the quality of the fit is also given (1.0 = good; 2.0 = acceptable). (2 data files).

Monitoring and control of the biogas process based on propionate concentration using online VFA measurement.

PubMed

Boe, Kanokwan; Steyer, Jean-Philippe; Angelidaki, Irini

2008-01-01

Simple logic control algorithms were tested for automatic control of a lab-scale CSTR manure digester. Using an online VFA monitoring system, propionate concentration in the reactor was used as parameter for control of the biogas process. The propionate concentration was kept below a threshold of 10 mM by manipulating the feed flow. Other online parameters such as pH, biogas production, total VFA, and other individual VFA were also measured to examine process performance. The experimental results showed that a simple logic control can successfully prevent the reactor from overload, but with fluctuations of the propionate level due to the nature of control approach. The fluctuation of propionate concentration could be reduced, by adding a lower feed flow limit into the control algorithm to prevent undershooting of propionate response. It was found that use of the biogas production as a main control parameter, rather than propionate can give a more stable process, since propionate was very persistent and only responded very slowly to the decrease of the feed flow which lead to high fluctuation of biogas production. Propionate, however, was still an excellent parameter to indicate process stress under gradual overload and thus recommended as an alarm in the control algorithm. Copyright IWA Publishing 2008.

Influence of local calibration on the quality of online wet weather discharge monitoring: feedback from five international case studies.

PubMed

Caradot, Nicolas; Sonnenberg, Hauke; Rouault, Pascale; Gruber, Günter; Hofer, Thomas; Torres, Andres; Pesci, Maria; Bertrand-Krajewski, Jean-Luc

2015-01-01

This paper reports about experiences gathered from five online monitoring campaigns in the sewer systems of Berlin (Germany), Graz (Austria), Lyon (France) and Bogota (Colombia) using ultraviolet-visible (UV-VIS) spectrometers and turbidimeters. Online probes are useful for the measurement of highly dynamic processes, e.g. combined sewer overflows (CSO), storm events, and river impacts. The influence of local calibration on the quality of online chemical oxygen demand (COD) measurements of wet weather discharges has been assessed. Results underline the need to establish local calibration functions for both UV-VIS spectrometers and turbidimeters. It is suggested that practitioners calibrate locally their probes using at least 15-20 samples. However, these samples should be collected over several events and cover most of the natural variability of the measured concentration. For this reason, the use of automatic peristaltic samplers in parallel to online monitoring is recommended with short representative sampling campaigns during wet weather discharges. Using reliable calibration functions, COD loads of CSO and storm events can be estimated with a relative uncertainty of approximately 20%. If no local calibration is established, concentrations and loads are estimated with a high error rate, questioning the reliability and meaning of the online measurement. Similar results have been obtained for total suspended solids measurements.

Online absolute pose compensation and steering control of industrial robot based on six degrees of freedom laser measurement

NASA Astrophysics Data System (ADS)

Yang, Juqing; Wang, Dayong; Fan, Baixing; Dong, Dengfeng; Zhou, Weihu

2017-03-01

In-situ intelligent manufacturing for large-volume equipment requires industrial robots with absolute high-accuracy positioning and orientation steering control. Conventional robots mainly employ an offline calibration technology to identify and compensate key robotic parameters. However, the dynamic and static parameters of a robot change nonlinearly. It is not possible to acquire a robot's actual parameters and control the absolute pose of the robot with a high accuracy within a large workspace by offline calibration in real-time. This study proposes a real-time online absolute pose steering control method for an industrial robot based on six degrees of freedom laser tracking measurement, which adopts comprehensive compensation and correction of differential movement variables. First, the pose steering control system and robot kinematics error model are constructed, and then the pose error compensation mechanism and algorithm are introduced in detail. By accurately achieving the position and orientation of the robot end-tool, mapping the computed Jacobian matrix of the joint variable and correcting the joint variable, the real-time online absolute pose compensation for an industrial robot is accurately implemented in simulations and experimental tests. The average positioning error is 0.048 mm and orientation accuracy is better than 0.01 deg. The results demonstrate that the proposed method is feasible, and the online absolute accuracy of a robot is sufficiently enhanced.

On-line vs off-line electrical conductivity characterization. Polycarbonate composites developed with multiwalled carbon nanotubes by compounding technology

NASA Astrophysics Data System (ADS)

Llorens-Chiralt, R.; Weiss, P.; Mikonsaari, I.

2014-05-01

Material characterization is one of the key steps when conductive polymers are developed. The dispersion of carbon nanotubes (CNTs) in a polymeric matrix using melt mixing influence final composite properties. The compounding becomes trial and error using a huge amount of materials, spending time and money to obtain competitive composites. Traditional methods to carry out electrical conductivity characterization include compression and injection molding. Both methods need extra equipments and moulds to obtain standard bars. This study aims to investigate the accuracy of the data obtained from absolute resistance recorded during the melt compounding, using an on-line setup developed by our group, and to correlate these values with off-line characterization and processing parameters (screw/barrel configuration, throughput, screw speed, temperature profile and CNTs percentage). Compounds developed with different percentages of multi walled carbon nanotubes (MWCNTs) and polycarbonate has been characterized during and after extrusion. Measurements, on-line resistance and off-line resistivity, showed parallel response and reproducibility, confirming method validity. The significance of the results obtained stems from the fact that we are able to measure on-line resistance and to change compounding parameters during production to achieve reference values reducing production/testing cost and ensuring material quality. Also, this method removes errors which can be found in test bars development, showing better correlation with compounding parameters.

Use of an online smoking cessation community promotes abstinence: Results of propensity score weighting.

PubMed

Graham, Amanda L; Papandonatos, George D; Erar, Bahar; Stanton, Cassandra A

2015-12-01

We estimated the causal effects of use of an online smoking cessation community on 30-day point prevalence abstinence at 3 months. Participants (N = 492) were adult current smokers in the enhanced Internet arm of The iQUITT Study, a randomized trial of Internet and telephone treatment for smoking cessation. All participants accessed a Web-based smoking-cessation program that included a large, established online community. Automated tracking metrics of passive (e.g., reading forum posts, viewing member profiles) and active (e.g., writing forum posts, sending private messages) community use were extracted from the site at 3 months. Self-selected community use defines the groups of interest: "None," "Passive," and "Both" (passive + active). Inverse probability of treatment weighting corrected for baseline imbalances on demographic, smoking, psychosocial, and medical history variables. Propensity weights estimated via generalized boosted models were used to calculate Average Treatment Effects (ATE) and Average Treatment effects on the Treated (ATT). Patterns of community use were: None = 198 (40.2%), Passive = 110 (22.4%), and Both = 184 (37.4%). ATE-weighted abstinence rates were: None = 4.2% (95% CI = 1.5-6.9); Passive = 15.1% (95% CI = 8.4-21.9); Both = 20.4% (95% CI = 13.9-26.8). ATT-weighted abstinence rates indicated even greater benefits of community use. Community users were more likely to quit smoking at 3 months than nonusers. The estimated benefit from use of online community resources was even larger among subjects with high propensity to use them. No differences in abstinence emerged between passive and passive/active users. Results suggest that lurking in online communities confers specific abstinence benefits. Implications of these findings for online cessation communities are discussed. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Risk assessment of the entry of canine-rabies into Papua New Guinea via sea and land routes.

PubMed

Brookes, Victoria J; Keponge-Yombo, Andy; Thomson, David; Ward, Michael P

2017-09-15

Canine-rabies is endemic in parts of Indonesia and continues to spread eastwards through the Indonesian archipelago. Papua New Guinea (PNG) has a land border with Papua Province, Indonesia, as well as logging and fishing industry connections throughout Asia. PNG has a Human Development Index of 0.505; therefore, an incursion of canine-rabies could have devastating impacts on human (7.5 million) and animal populations. Given the known difficulties of rabies elimination in resource-scarce environments, an incursion of rabies into PNG would also likely compromise the campaign for global elimination of rabies. A previous qualitative study to determine routes for detailed risk assessment identified logging, fishing and three land-routes (unregulated crossers ["shopper-crossers"], traditional border crossers and illegal hunters) as potential high risk routes for entry of rabies-infected dogs into PNG. The objective of the current study was to quantify and compare the probability of entry of a rabies-infected dog via these routes into PNG and to identify the highest risk provinces and border districts to target rabies prevention and control activities. Online questionnaires were used to elicit expert-opinion about quantitative model parameter values. A quantitative, stochastic model was then used to assess risk, and parameters with the greatest influence on the estimated mean number of rabies-infected dogs introduced/year were identified via global sensitivity analysis (Sobol method). Eight questionnaires - including 7 online - were implemented and >220 empirical distributions were parameterised using >2900 expert-opinions. The highest risk provinces for combined sea routes were West Sepik, Madang and Western Province, driven by the number of vessels and the probability of bringing dogs. The highest risk border districts for combined land routes were Vanimo-Green River and South Fly, driven by the number of people crossing the border and the number of dogs (with hunters). Overall, the risk posed by land routes was much higher than the risk of rabies introduction by sea routes. This study provides a foundation to develop targeted border control measures, surveillance and response strategies for canine-rabies for the highest risk routes and regions in PNG. Sensitivity analysis using the Sobol method played a key role in this study and directed further data collection to refine risk estimates. The ease of expert-elicitation using online methods demonstrates the feasibility of using such methods for animal and human disease surveillance in PNG. Copyright © 2017 Elsevier B.V. All rights reserved.

VizieR Online Data Catalog: 3.6um S4G Galactic bars characterization (Diaz-Garcia+, 2016)

NASA Astrophysics Data System (ADS)

Diaz-Garcia, S.; Salo, H.; Laurikainen, E.; Herrera-Endoqui, M.

2015-11-01

Here, we provide the bar strength measurements of a sample of ~600 barred galaxies drawn from the Spitzer Survey of Stellar Structure in Galaxies (Sheth et al., 2010, Cat. J/PASP/122/1397). Bars were identified based on the morphological classifications by Buta et al. (2015, Cat. J/ApJS/217/32). Besides, we provide a parameterization of the stellar contribution to the rotation curve and an estimate to the stellar-to-halo mass ratio within the optical radius for a sample of 1345 non-highly inclined galaxies (i<65°). The radial force profiles and rotation curve decomposition models of each of these galaxies are also given. Table A1 contains fundamental parameters of the galaxies such as the total stellar mass and distances (values for all the S4G sample are calculated in Munoz-Mateos et al., 2015ApJS..219....3M). Besides, we provide an estimate of the scale-heights and optical radii. We also list the inclination-corrected HI maximum velocities, the parameters of the stellar and halo components of the rotation curves, and the estimates of the halo-to-stellar mass ratios within the optical disk. In Table A2 it is given the gravitational torque parameters and radii, with and without spiral arms and halo correction. In Table A3 it is provided the maximum normalized Fourier amplitudes and radii (for the m = 2, 4, 6 and 8 components) and the bar ellipticities (from Herrera-Endoqui et al., 2015A&A...582A..86H) deprojected to the disk plane using the orientation parameters from S4G Pipeline 4 (Salo et al., 2015, Cat. J/ApJS/219/4). The evaluation of the gravitational torques and m=2 Fourier amplitude at the bar radius is also listed in both tables. In the directory "rfp" we provide the gravitational torque radial profiles, with and without spiral arms and halo correction, even Fourier amplitudes and m=2 phase of 1345 non-highly inclined disk S4G galaxies ("radialforce_profiles.dat"). Likewise, for the same sample, in the directory "rcdm" we tabulate the rotation curve decomposition model ("rotationcurve_decomposition.dat"), with the stellar component inferred from the 3.6~μm imaging and the halo component estimated using the universal rotation curve models). (5 data files).

Characterisation and qualification of natural organic matter with a new online fluorescene sensor

NASA Astrophysics Data System (ADS)

Wagner, Martin; Dahlaus, Anna; Moldaenke, Christian; Schmidt, Wido

2016-04-01

Natural organic water compounds are determined usually with the bulk parameter DOC (dissolved organic carbon). The DOC is a heterogeneous parameter which consists of various organic fractions and shows often spatially as well as temporally a high dynamic range. The fluorescence spectroscopy is a tool for measuring individual DOC groups in a quick and easy way. A fluorescence sensor was developed within the framework of a research project that provides online detection of humic substances and organic polymers. Humic substances can be differentiated fulvic and humic acids, bio-polymers in proteins and algal chlorophyll-a. The chlorophyll fluorescence can be additionally assigned to green algae and diatoms as well as in cyanobacteria. The sensor has been tested during several measurement programs and was used in various waterworks for monitoring of raw water and treated water. The sensor is based on LED technology, works long term stable and is of low maintenance due to an autonomous cleaning unit. Using the sensor qualitative and quantitative changes of the raw water during drinking water treatment could be estimated efficiently. The processing stage of flocculation/filtration showed a significant reduction in the humic substances concentration, where macromolecular humic acids were removed with higher efficiency than low molecular weighted fulvic acids. Dynamical, seasonal-related processes in the water body of a drinking water reservoir could also be traced. Seasonal and single-event-related changes in temperature, turbidity and the composition of humic substances and algae were collected with high sensitivity for example during the autumn circulation in the water body.

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

Liu, Y.; Liu, Z.; Zhang, S.

Parameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean–atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parametermore » estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Altogether, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.« less

Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data.

PubMed

Holsclaw, Tracy; Hallgren, Kevin A; Steyvers, Mark; Smyth, Padhraic; Atkins, David C

2015-12-01

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased Type I and Type II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in online supplemental materials. (c) 2016 APA, all rights reserved).

Confidence assignment for mass spectrometry based peptide identifications via the extreme value distribution.

PubMed

Alves, Gelio; Yu, Yi-Kuo

2016-09-01

There is a growing trend for biomedical researchers to extract evidence and draw conclusions from mass spectrometry based proteomics experiments, the cornerstone of which is peptide identification. Inaccurate assignments of peptide identification confidence thus may have far-reaching and adverse consequences. Although some peptide identification methods report accurate statistics, they have been limited to certain types of scoring function. The extreme value statistics based method, while more general in the scoring functions it allows, demands accurate parameter estimates and requires, at least in its original design, excessive computational resources. Improving the parameter estimate accuracy and reducing the computational cost for this method has two advantages: it provides another feasible route to accurate significance assessment, and it could provide reliable statistics for scoring functions yet to be developed. We have formulated and implemented an efficient algorithm for calculating the extreme value statistics for peptide identification applicable to various scoring functions, bypassing the need for searching large random databases. The source code, implemented in C ++ on a linux system, is available for download at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbp/qmbp_ms/RAId/RAId_Linux_64Bit yyu@ncbi.nlm.nih.gov Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.

Near Real-Time Earthquake Exposure and Damage Assessment: An Example from Turkey

NASA Astrophysics Data System (ADS)

Kamer, Yavor; Çomoǧlu, Mustafa; Erdik, Mustafa

2014-05-01

Confined by infamous strike-slip North Anatolian Fault from the north and by the Hellenic subduction trench from the south Turkey is one of the most seismically active countries in Europe. Due this increased exposure and the fragility of the building stock Turkey is among the top countries exposed to earthquake hazard in terms of mortality and economic losses. In this study we focus recent and ongoing efforts to mitigate the earthquake risk in near real-time. We present actual results of recent earthquakes, such as the M6 event off-shore Antalya which occurred on 28 December 2013. Starting at the moment of detection, we obtain a preliminary ground motion intensity distribution based on epicenter and magnitude. Our real-time application is further enhanced by the integration of the SeisComp3 ground motion parameter estimation tool with the Earthquake Loss Estimation Routine (ELER). SeisComp3 provides the online station parameters which are then automatically incorporated into the ShakeMaps produced by ELER. The resulting ground motion distributions are used together with the building inventory to calculate expected number of buildings in various damage states. All these analysis are conducted in an automated fashion and are communicated within a few minutes of a triggering event. In our efforts to disseminate earthquake information to the general public we make extensive use of social networks such as Tweeter and collaborate with mobile phone operators.

VizieR Online Data Catalog: CCD photometry of Pal 1 (Borissova+ 1995)

NASA Astrophysics Data System (ADS)

Borissova, J.; Spassova, N.

1997-06-01

A CCD photometry of the halo cluster Palomar 1 is presented in the Thuan-Gunn photometric system. The principal sequences of the color-magnitude diagrams are delineated in different spectral bands. The color- magnitude diagrams of the cluster show a well defined red horizontal branch, a subgiant branch and a main-sequence down to about two magnitudes below the main sequence turnoff. The giant branch is absent and the brightest stars are the horizontal branch stars. The age of the cluster determined by comparison with the isochrones of Bell & VandenBerg (1987ApJS...63..335B) is consistent with an age in the interval 12-14Gyr. A distance modulus of (m-M)g0=15.38+/-0.15 magnitude and E(g-r)=0.16 has been derived. An estimate of the cluster structural parameters such as core radius and concentration parameter gives rc=1.5pc and c=1.46. A mass estimate of 1.1x103M⊙ and a mass-to-light ratio of 1.79 have been obtained using King's (1966AJ.....71...64K) method. The morphology of color-magnitude diagrams allows Pal 1 to be interpreted as probably a globular cluster rather than an old open one. For a description of the uvgr photometric system, see e.g. (1 data file).

Online Psychology: Trial and Error in Course Development

ERIC Educational Resources Information Center

Harman, Marsha J.

2009-01-01

Online courses appear to be the future if colleges and universities choose to increase enrollments with students who need more flexibility in scheduling. The challenge has been to create a course that is rigorous with the limitations to physical presence of the instructor and the parameters inherent in technological delivery. This article relates…

Online boosting for vehicle detection.

PubMed

Chang, Wen-Chung; Cho, Chih-Wei

2010-06-01

This paper presents a real-time vision-based vehicle detection system employing an online boosting algorithm. It is an online AdaBoost approach for a cascade of strong classifiers instead of a single strong classifier. Most existing cascades of classifiers must be trained offline and cannot effectively be updated when online tuning is required. The idea is to develop a cascade of strong classifiers for vehicle detection that is capable of being online trained in response to changing traffic environments. To make the online algorithm tractable, the proposed system must efficiently tune parameters based on incoming images and up-to-date performance of each weak classifier. The proposed online boosting method can improve system adaptability and accuracy to deal with novel types of vehicles and unfamiliar environments, whereas existing offline methods rely much more on extensive training processes to reach comparable results and cannot further be updated online. Our approach has been successfully validated in real traffic environments by performing experiments with an onboard charge-coupled-device camera in a roadway vehicle.

Generalisability of an online randomised controlled trial: an empirical analysis.

PubMed

Wang, Cheng; Mollan, Katie R; Hudgens, Michael G; Tucker, Joseph D; Zheng, Heping; Tang, Weiming; Ling, Li

2018-02-01

Investigators increasingly use online methods to recruit participants for randomised controlled trials (RCTs). However, the extent to which participants recruited online represent populations of interest is unknown. We evaluated how generalisable an online RCT sample is to men who have sex with men in China. Inverse probability of sampling weights (IPSW) and the G-formula were used to examine the generalisability of an online RCT using model-based approaches. Online RCT data and national cross-sectional study data from China were analysed to illustrate the process of quantitatively assessing generalisability. The RCT (identifier NCT02248558) randomly assigned participants to a crowdsourced or health marketing video for promotion of HIV testing. The primary outcome was self-reported HIV testing within 4 weeks, with a non-inferiority margin of -3%. In the original online RCT analysis, the estimated difference in proportions of HIV tested between the two arms (crowdsourcing and health marketing) was 2.1% (95% CI, -5.4% to 9.7%). The hypothesis that the crowdsourced video was not inferior to the health marketing video to promote HIV testing was not demonstrated. The IPSW and G-formula estimated differences were -2.6% (95% CI, -14.2 to 8.9) and 2.7% (95% CI, -10.7 to 16.2), with both approaches also not establishing non-inferiority. Conducting generalisability analysis of an online RCT is feasible. Examining the generalisability of online RCTs is an important step before an intervention is scaled up. NCT02248558. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

A Web-Based System for Bayesian Benchmark Dose Estimation.

PubMed

Shao, Kan; Shapiro, Andrew J

2018-01-11

Benchmark dose (BMD) modeling is an important step in human health risk assessment and is used as the default approach to identify the point of departure for risk assessment. A probabilistic framework for dose-response assessment has been proposed and advocated by various institutions and organizations; therefore, a reliable tool is needed to provide distributional estimates for BMD and other important quantities in dose-response assessment. We developed an online system for Bayesian BMD (BBMD) estimation and compared results from this software with U.S. Environmental Protection Agency's (EPA's) Benchmark Dose Software (BMDS). The system is built on a Bayesian framework featuring the application of Markov chain Monte Carlo (MCMC) sampling for model parameter estimation and BMD calculation, which makes the BBMD system fundamentally different from the currently prevailing BMD software packages. In addition to estimating the traditional BMDs for dichotomous and continuous data, the developed system is also capable of computing model-averaged BMD estimates. A total of 518 dichotomous and 108 continuous data sets extracted from the U.S. EPA's Integrated Risk Information System (IRIS) database (and similar databases) were used as testing data to compare the estimates from the BBMD and BMDS programs. The results suggest that the BBMD system may outperform the BMDS program in a number of aspects, including fewer failed BMD and BMDL calculations and estimates. The BBMD system is a useful alternative tool for estimating BMD with additional functionalities for BMD analysis based on most recent research. Most importantly, the BBMD has the potential to incorporate prior information to make dose-response modeling more reliable and can provide distributional estimates for important quantities in dose-response assessment, which greatly facilitates the current trend for probabilistic risk assessment. https://doi.org/10.1289/EHP1289.

VizieR Online Data Catalog: XCS-DR1 Cluster Catalogue (Mehrtens+, 2012)

NASA Astrophysics Data System (ADS)

Mehrtens, N.; Romer, A. K.; Hilton, M.; Lloyd-Davies, E. J.; Miller, C. J.; Stanford, S. A.; Hosmer, M.; Hoyle, B.; Collins, C. A.; Liddle, A. R.; Viana, P. T. P.; Nichol, R. C.; Stott, J. P.; Dubois, E. N.; Kay, S. T.; Sahlen, M.; Young, O.; Short, C. J.; Christodoulou, L.; Watson, W. A.; Davidson, M.; Harrison, C. D.; Baruah, L.; Smith, M.; Burke, C.; Mayers, J. A.; Deadman, P.-J.; Rooney, P. J.; Edmondson, E. M.; West, M.; Campbell, H. C.; Edge, A. C.; Mann, R. G.; Sabirli, K.; Wake, D.; Benoist, C.; da Costa, L.; Maia, M. A. G.; Ogando, R.

2013-04-01

The XMM Cluster Survey (XCS) is a serendipitous search for galaxy clusters using all publicly available data in the XMM-Newton Science Archive. Its main aims are to measure cosmological parameters and trace the evolution of X-ray scaling relations. In this paper we present the first data release from the XMM Cluster Survey (XCS-DR1). This consists of 503 optically confirmed, serendipitously detected, X-ray clusters. Of these clusters, 256 are new to the literature and 357 are new X-ray discoveries. We present 463 clusters with a redshift estimate (0.06

Modeling and Compensation of the Internal Friction Torque of a Travelling Wave Ultrasonic Motor.

PubMed

Giraud, F; Sandulescu, P; Amberg, M; Lemaire-Semail, B; Ionescu, F

2011-01-01

This paper deals with the control and experimentation of a one-degree-of-freedom haptic stick, actuated by a travelling wave ultrasonic motor. This type of actuator has many interesting properties such as low-speed operation capabilities and a high torque-to-weight ratio, making it appropriate for haptic applications. However, the motor used in this application displays nonlinear behavior due to the necessary contact between its rotor and stator. Moreover, due to its energy conversion process, the torque applied to the end-effector is not a straightforward function of the supply current or voltage. This is why a force-feedback control strategy is presented, which includes an online parameter estimator. Experimental runs are then presented to examine the fidelity of the interface.

A real-time optical tracking and measurement processing system for flying targets.

PubMed

Guo, Pengyu; Ding, Shaowen; Zhang, Hongliang; Zhang, Xiaohu

2014-01-01

Optical tracking and measurement for flying targets is unlike the close range photography under a controllable observation environment, which brings extreme conditions like diverse target changes as a result of high maneuver ability and long cruising range. This paper first designed and realized a distributed image interpretation and measurement processing system to achieve resource centralized management, multisite simultaneous interpretation and adaptive estimation algorithm selection; then proposed a real-time interpretation method which contains automatic foreground detection, online target tracking, multiple features location, and human guidance. An experiment is carried out at performance and efficiency evaluation of the method by semisynthetic video. The system can be used in the field of aerospace tests like target analysis including dynamic parameter, transient states, and optical physics characteristics, with security control.

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.

Dynamic modelling and estimation of the error due to asynchronism in a redundant asynchronous multiprocessor system

NASA Technical Reports Server (NTRS)

Huynh, Loc C.; Duval, R. W.

1986-01-01

The use of Redundant Asynchronous Multiprocessor System to achieve ultrareliable Fault Tolerant Control Systems shows great promise. The development has been hampered by the inability to determine whether differences in the outputs of redundant CPU's are due to failures or to accrued error built up by slight differences in CPU clock intervals. This study derives an analytical dynamic model of the difference between redundant CPU's due to differences in their clock intervals and uses this model with on-line parameter identification to idenitify the differences in the clock intervals. The ability of this methodology to accurately track errors due to asynchronisity generate an error signal with the effect of asynchronisity removed and this signal may be used to detect and isolate actual system failures.

Robot trajectory tracking with self-tuning predicted control

NASA Technical Reports Server (NTRS)

Cui, Xianzhong; Shin, Kang G.

1988-01-01

A controller that combines self-tuning prediction and control is proposed for robot trajectory tracking. The controller has two feedback loops: one is used to minimize the prediction error, and the other is designed to make the system output track the set point input. Because the velocity and position along the desired trajectory are given and the future output of the system is predictable, a feedforward loop can be designed for robot trajectory tracking with self-tuning predicted control (STPC). Parameters are estimated online to account for the model uncertainty and the time-varying property of the system. The authors describe the principle of STPC, analyze the system performance, and discuss the simplification of the robot dynamic equations. To demonstrate its utility and power, the controller is simulated for a Stanford arm.

A Real-Time Optical Tracking and Measurement Processing System for Flying Targets

PubMed Central

Guo, Pengyu; Ding, Shaowen; Zhang, Hongliang; Zhang, Xiaohu

2014-01-01

Optical tracking and measurement for flying targets is unlike the close range photography under a controllable observation environment, which brings extreme conditions like diverse target changes as a result of high maneuver ability and long cruising range. This paper first designed and realized a distributed image interpretation and measurement processing system to achieve resource centralized management, multisite simultaneous interpretation and adaptive estimation algorithm selection; then proposed a real-time interpretation method which contains automatic foreground detection, online target tracking, multiple features location, and human guidance. An experiment is carried out at performance and efficiency evaluation of the method by semisynthetic video. The system can be used in the field of aerospace tests like target analysis including dynamic parameter, transient states, and optical physics characteristics, with security control. PMID:24987748

Change Semantic Constrained Online Data Cleaning Method for Real-Time Observational Data Stream

NASA Astrophysics Data System (ADS)

Ding, Yulin; Lin, Hui; Li, Rongrong

2016-06-01

Recent breakthroughs in sensor networks have made it possible to collect and assemble increasing amounts of real-time observational data by observing dynamic phenomena at previously impossible time and space scales. Real-time observational data streams present potentially profound opportunities for real-time applications in disaster mitigation and emergency response, by providing accurate and timeliness estimates of environment's status. However, the data are always subject to inevitable anomalies (including errors and anomalous changes/events) caused by various effects produced by the environment they are monitoring. The "big but dirty" real-time observational data streams can rarely achieve their full potential in the following real-time models or applications due to the low data quality. Therefore, timely and meaningful online data cleaning is a necessary pre-requisite step to ensure the quality, reliability, and timeliness of the real-time observational data. In general, a straightforward streaming data cleaning approach, is to define various types of models/classifiers representing normal behavior of sensor data streams and then declare any deviation from this model as normal or erroneous data. The effectiveness of these models is affected by dynamic changes of deployed environments. Due to the changing nature of the complicated process being observed, real-time observational data is characterized by diversity and dynamic, showing a typical Big (Geo) Data characters. Dynamics and diversity is not only reflected in the data values, but also reflected in the complicated changing patterns of the data distributions. This means the pattern of the real-time observational data distribution is not stationary or static but changing and dynamic. After the data pattern changed, it is necessary to adapt the model over time to cope with the changing patterns of real-time data streams. Otherwise, the model will not fit the following observational data streams, which may led to large estimation error. In order to achieve the best generalization error, it is an important challenge for the data cleaning methodology to be able to characterize the behavior of data stream distributions and adaptively update a model to include new information and remove old information. However, the complicated data changing property invalidates traditional data cleaning methods, which rely on the assumption of a stationary data distribution, and drives the need for more dynamic and adaptive online data cleaning methods. To overcome these shortcomings, this paper presents a change semantics constrained online filtering method for real-time observational data. Based on the principle that the filter parameter should vary in accordance to the data change patterns, this paper embeds semantic description, which quantitatively depicts the change patterns in the data distribution to self-adapt the filter parameter automatically. Real-time observational water level data streams of different precipitation scenarios are selected for testing. Experimental results prove that by means of this method, more accurate and reliable water level information can be available, which is prior to scientific and prompt flood assessment and decision-making.

A numerical scheme to calculate temperature and salinity dependent air-water transfer velocities for any gas

NASA Astrophysics Data System (ADS)

Johnson, M. T.

2010-10-01

The ocean-atmosphere flux of a gas can be calculated from its measured or estimated concentration gradient across the air-sea interface and the transfer velocity (a term representing the conductivity of the layers either side of the interface with respect to the gas of interest). Traditionally the transfer velocity has been estimated from empirical relationships with wind speed, and then scaled by the Schmidt number of the gas being transferred. Complex, physically based models of transfer velocity (based on more physical forcings than wind speed alone), such as the NOAA COARE algorithm, have more recently been applied to well-studied gases such as carbon dioxide and DMS (although many studies still use the simpler approach for these gases), but there is a lack of validation of such schemes for other, more poorly studied gases. The aim of this paper is to provide a flexible numerical scheme which will allow the estimation of transfer velocity for any gas as a function of wind speed, temperature and salinity, given data on the solubility and liquid molar volume of the particular gas. New and existing parameterizations (including a novel empirical parameterization of the salinity-dependence of Henry's law solubility) are brought together into a scheme implemented as a modular, extensible program in the R computing environment which is available in the supplementary online material accompanying this paper; along with input files containing solubility and structural data for ~90 gases of general interest, enabling the calculation of their total transfer velocities and component parameters. Comparison of the scheme presented here with alternative schemes and methods for calculating air-sea flux parameters shows good agreement in general. It is intended that the various components of this numerical scheme should be applied only in the absence of experimental data providing robust values for parameters for a particular gas of interest.

Genetic algorithm and graph theory based matrix factorization method for online friend recommendation.

PubMed

Li, Qu; Yao, Min; Yang, Jianhua; Xu, Ning

2014-01-01

Online friend recommendation is a fast developing topic in web mining. In this paper, we used SVD matrix factorization to model user and item feature vector and used stochastic gradient descent to amend parameter and improve accuracy. To tackle cold start problem and data sparsity, we used KNN model to influence user feature vector. At the same time, we used graph theory to partition communities with fairly low time and space complexity. What is more, matrix factorization can combine online and offline recommendation. Experiments showed that the hybrid recommendation algorithm is able to recommend online friends with good accuracy.

On-Line Safe Flight Envelope Determination for Impaired Aircraft

NASA Technical Reports Server (NTRS)

Lombaerts, Thomas; Schuet, Stefan; Acosta, Diana; Kaneshige, John

2015-01-01

The design and simulation of an on-line algorithm which estimates the safe maneuvering envelope of aircraft is discussed in this paper. The trim envelope is estimated using probabilistic methods and efficient high-fidelity model based computations of attainable equilibrium sets. From this trim envelope, a robust reachability analysis provides the maneuverability limitations of the aircraft through an optimal control formulation. Both envelope limits are presented to the flight crew on the primary flight display. In the results section, scenarios are considered where this adaptive algorithm is capable of computing online changes to the maneuvering envelope due to impairment. Furthermore, corresponding updates to display features on the primary flight display are provided to potentially inform the flight crew of safety critical envelope alterations caused by the impairment.

Metamodeling-based approach for risk assessment and cost estimation: Application to geological carbon sequestration planning

NASA Astrophysics Data System (ADS)

Sun, Alexander Y.; Jeong, Hoonyoung; González-Nicolás, Ana; Templeton, Thomas C.

2018-04-01

Carbon capture and storage (CCS) is being evaluated globally as a geoengineering measure for significantly reducing greenhouse emission. However, long-term liability associated with potential leakage from these geologic repositories is perceived as a main barrier of entry to site operators. Risk quantification and impact assessment help CCS operators to screen candidate sites for suitability of CO2 storage. Leakage risks are highly site dependent, and a quantitative understanding and categorization of these risks can only be made possible through broad participation and deliberation of stakeholders, with the use of site-specific, process-based models as the decision basis. Online decision making, however, requires that scenarios be run in real time. In this work, a Python based, Leakage Assessment and Cost Estimation (PyLACE) web application was developed for quantifying financial risks associated with potential leakage from geologic carbon sequestration sites. PyLACE aims to assist a collaborative, analytic-deliberative decision making processes by automating metamodel creation, knowledge sharing, and online collaboration. In PyLACE, metamodeling, which is a process of developing faster-to-run surrogates of process-level models, is enabled using a special stochastic response surface method and the Gaussian process regression. Both methods allow consideration of model parameter uncertainties and the use of that information to generate confidence intervals on model outputs. Training of the metamodels is delegated to a high performance computing cluster and is orchestrated by a set of asynchronous job scheduling tools for job submission and result retrieval. As a case study, workflow and main features of PyLACE are demonstrated using a multilayer, carbon storage model.

IN-SYNC I: Homogeneous stellar parameters from high-resolution apogee spectra for thousands of pre-main sequence stars

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

Cottaar, Michiel; Meyer, Michael R.; Covey, Kevin R.

2014-10-20

Over two years, 8859 high-resolution H-band spectra of 3493 young (1-10 Myr) stars were gathered by the multi-object spectrograph of the APOGEE project as part of the IN-SYNC ancillary program of the SDSS-III survey. Here we present the forward modeling approach used to derive effective temperatures, surface gravities, radial velocities, rotational velocities, and H-band veiling from these near-infrared spectra. We discuss in detail the statistical and systematic uncertainties in these stellar parameters. In addition, we present accurate extinctions by measuring the E(J – H) of these young stars with respect to the single-star photometric locus in the Pleiades. Finally, wemore » identify an intrinsic stellar radius spread of about 25% for late-type stars in IC 348 using three (nearly) independent measures of stellar radius, namely, the extinction-corrected J-band magnitude, the surface gravity, and the Rsin i from the rotational velocities and literature rotation periods. We exclude that this spread is caused by uncertainties in the stellar parameters by showing that the three estimators of stellar radius are correlated, so that brighter stars tend to have lower surface gravities and larger Rsin i than fainter stars at the same effective temperature. Tables providing the spectral and photometric parameters for the Pleiades and IC 348 have been provided online.« less

Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach

PubMed Central

Henriques, David; Rocha, Miguel; Saez-Rodriguez, Julio; Banga, Julio R.

2015-01-01

Motivation: Systems biology models can be used to test new hypotheses formulated on the basis of previous knowledge or new experimental data, contradictory with a previously existing model. New hypotheses often come in the shape of a set of possible regulatory mechanisms. This search is usually not limited to finding a single regulation link, but rather a combination of links subject to great uncertainty or no information about the kinetic parameters. Results: In this work, we combine a logic-based formalism, to describe all the possible regulatory structures for a given dynamic model of a pathway, with mixed-integer dynamic optimization (MIDO). This framework aims to simultaneously identify the regulatory structure (represented by binary parameters) and the real-valued parameters that are consistent with the available experimental data, resulting in a logic-based differential equation model. The alternative to this would be to perform real-valued parameter estimation for each possible model structure, which is not tractable for models of the size presented in this work. The performance of the method presented here is illustrated with several case studies: a synthetic pathway problem of signaling regulation, a two-component signal transduction pathway in bacterial homeostasis, and a signaling network in liver cancer cells. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: julio@iim.csic.es or saezrodriguez@ebi.ac.uk PMID:26002881

Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach.

PubMed

Henriques, David; Rocha, Miguel; Saez-Rodriguez, Julio; Banga, Julio R

2015-09-15

Systems biology models can be used to test new hypotheses formulated on the basis of previous knowledge or new experimental data, contradictory with a previously existing model. New hypotheses often come in the shape of a set of possible regulatory mechanisms. This search is usually not limited to finding a single regulation link, but rather a combination of links subject to great uncertainty or no information about the kinetic parameters. In this work, we combine a logic-based formalism, to describe all the possible regulatory structures for a given dynamic model of a pathway, with mixed-integer dynamic optimization (MIDO). This framework aims to simultaneously identify the regulatory structure (represented by binary parameters) and the real-valued parameters that are consistent with the available experimental data, resulting in a logic-based differential equation model. The alternative to this would be to perform real-valued parameter estimation for each possible model structure, which is not tractable for models of the size presented in this work. The performance of the method presented here is illustrated with several case studies: a synthetic pathway problem of signaling regulation, a two-component signal transduction pathway in bacterial homeostasis, and a signaling network in liver cancer cells. Supplementary data are available at Bioinformatics online. julio@iim.csic.es or saezrodriguez@ebi.ac.uk. © The Author 2015. Published by Oxford University Press.

IN-SYNC I: Homogeneous Stellar Parameters from High-resolution APOGEE Spectra for Thousands of Pre-main Sequence Stars

NASA Astrophysics Data System (ADS)

Cottaar, Michiel; Covey, Kevin R.; Meyer, Michael R.; Nidever, David L.; Stassun, Keivan G.; Foster, Jonathan B.; Tan, Jonathan C.; Chojnowski, S. Drew; da Rio, Nicola; Flaherty, Kevin M.; Frinchaboy, Peter M.; Skrutskie, Michael; Majewski, Steven R.; Wilson, John C.; Zasowski, Gail

2014-10-01

Over two years, 8859 high-resolution H-band spectra of 3493 young (1-10 Myr) stars were gathered by the multi-object spectrograph of the APOGEE project as part of the IN-SYNC ancillary program of the SDSS-III survey. Here we present the forward modeling approach used to derive effective temperatures, surface gravities, radial velocities, rotational velocities, and H-band veiling from these near-infrared spectra. We discuss in detail the statistical and systematic uncertainties in these stellar parameters. In addition, we present accurate extinctions by measuring the E(J - H) of these young stars with respect to the single-star photometric locus in the Pleiades. Finally, we identify an intrinsic stellar radius spread of about 25% for late-type stars in IC 348 using three (nearly) independent measures of stellar radius, namely, the extinction-corrected J-band magnitude, the surface gravity, and the Rsin i from the rotational velocities and literature rotation periods. We exclude that this spread is caused by uncertainties in the stellar parameters by showing that the three estimators of stellar radius are correlated, so that brighter stars tend to have lower surface gravities and larger Rsin i than fainter stars at the same effective temperature. Tables providing the spectral and photometric parameters for the Pleiades and IC 348 have been provided online.

Identification of damage in composite structures using Gaussian mixture model-processed Lamb waves

NASA Astrophysics Data System (ADS)

Wang, Qiang; Ma, Shuxian; Yue, Dong

2018-04-01

Composite materials have comprehensively better properties than traditional materials, and therefore have been more and more widely used, especially because of its higher strength-weight ratio. However, the damage of composite structures is usually varied and complicated. In order to ensure the security of these structures, it is necessary to monitor and distinguish the structural damage in a timely manner. Lamb wave-based structural health monitoring (SHM) has been proved to be effective in online structural damage detection and evaluation; furthermore, the characteristic parameters of the multi-mode Lamb wave varies in response to different types of damage in the composite material. This paper studies the damage identification approach for composite structures using the Lamb wave and the Gaussian mixture model (GMM). The algorithm and principle of the GMM, and the parameter estimation, is introduced. Multi-statistical characteristic parameters of the excited Lamb waves are extracted, and the parameter space with reduced dimensions is adopted by principal component analysis (PCA). The damage identification system using the GMM is then established through training. Experiments on a glass fiber-reinforced epoxy composite laminate plate are conducted to verify the feasibility of the proposed approach in terms of damage classification. The experimental results show that different types of damage can be identified according to the value of the likelihood function of the GMM.

Student Online Plagiarism: How Do We Respond?

ERIC Educational Resources Information Center

Scanlon, Patrick M.

2003-01-01

The perception that Internet plagiarism by university students is on the rise has alarmed college teachers, leading to the adoption of electronic plagiarism checkers, among other responses. Although some recent studies suggest that estimates of online plagiarism may be exaggerated, cause for concern remains. This article reviews quantitative…

Pricing Secrets Revealed: An Insider's Perspective on How Custom Courses Are Priced.

ERIC Educational Resources Information Center

Hartnett, John

2002-01-01

Describes one vendor's methods for pricing the development of online learning courses. Highlights include estimating the time needed; estimating the size of the course by counting the number of potential screens; estimating time spent by learners; comparing similar projects; and estimating time needed by each member of the project team. (LRW)

Simultaneous Estimation of Microphysical Parameters and Atmospheric State Variables With Radar Data and Ensemble Square-root Kalman Filter

NASA Astrophysics Data System (ADS)

Tong, M.; Xue, M.

2006-12-01

An important source of model error for convective-scale data assimilation and prediction is microphysical parameterization. This study investigates the possibility of estimating up to five fundamental microphysical parameters, which are closely involved in the definition of drop size distribution of microphysical species in a commonly used single-moment ice microphysics scheme, using radar observations and the ensemble Kalman filter method. The five parameters include the intercept parameters for rain, snow and hail/graupel, and the bulk densities of hail/graupel and snow. Parameter sensitivity and identifiability are first examined. The ensemble square-root Kalman filter (EnSRF) is employed for simultaneous state and parameter estimation. OSS experiments are performed for a model-simulated supercell storm, in which the five microphysical parameters are estimated individually or in different combinations starting from different initial guesses. When error exists in only one of the microphysical parameters, the parameter can be successfully estimated without exception. The estimation of multiple parameters is found to be less robust, with end results of estimation being sensitive to the realization of the initial parameter perturbation. This is believed to be because of the reduced parameter identifiability and the existence of non-unique solutions. The results of state estimation are, however, always improved when simultaneous parameter estimation is performed, even when the estimated parameters values are not accurate.

Dynamic electrical impedance imaging with the interacting multiple model scheme.

PubMed

Kim, Kyung Youn; Kim, Bong Seok; Kim, Min Chan; Kim, Sin; Isaacson, David; Newell, Jonathan C

2005-04-01

In this paper, an effective dynamical EIT imaging scheme is presented for on-line monitoring of the abruptly changing resistivity distribution inside the object, based on the interacting multiple model (IMM) algorithm. The inverse problem is treated as a stochastic nonlinear state estimation problem with the time-varying resistivity (state) being estimated on-line with the aid of the IMM algorithm. In the design of the IMM algorithm multiple models with different process noise covariance are incorporated to reduce the modeling uncertainty. Simulations and phantom experiments are provided to illustrate the proposed algorithm.

Demonstrating the conservation of angular momentum using spherical magnets

NASA Astrophysics Data System (ADS)

Lindén, Johan; Slotte, Joakim; Källman, Kjell-Mikael

2018-01-01

An experimental setup for demonstrating the conservation of angular momentum of rotating spherical magnets is described. Two spherical Nd-Fe-B magnets are placed on a double inclined plane and projected towards each other with pre-selected impact parameters ranging from zero to a few tens of millimeters. After impact, the two magnets either revolve vigorously around the common center of mass or stop immediately, depending on the value of the impact parameter. Using a pick-up coil connected to an oscilloscope, the angular frequency for the rotating magnets was measured, and an estimate for the angular momentum was obtained. A high-speed video camera captured the impact and was used for measuring linear and angular velocities of the magnets. A very good agreement between the initial angular momentum before the impact and the final angular momentum of the revolving dumbbell is observed. The two rotating magnets, and the rotating electromagnetic field emanating from them, can also be viewed as a toy model for the newly discovered gravitational waves, where two black holes collide after revolving around each other. (Enhanced online)

An adaptive technique for a redundant-sensor navigation system.

NASA Technical Reports Server (NTRS)

Chien, T.-T.

1972-01-01

An on-line adaptive technique is developed to provide a self-contained redundant-sensor navigation system with a capability to utilize its full potentiality in reliability and performance. This adaptive system is structured as a multistage stochastic process of detection, identification, and compensation. It is shown that the detection system can be effectively constructed on the basis of a design value, specified by mission requirements, of the unknown parameter in the actual system, and of a degradation mode in the form of a constant bias jump. A suboptimal detection system on the basis of Wald's sequential analysis is developed using the concept of information value and information feedback. The developed system is easily implemented, and demonstrates a performance remarkably close to that of the optimal nonlinear detection system. An invariant transformation is derived to eliminate the effect of nuisance parameters such that the ambiguous identification system can be reduced to a set of disjoint simple hypotheses tests. By application of a technique of decoupled bias estimation in the compensation system the adaptive system can be operated without any complicated reorganization.

Systematic study of magnetar outbursts

NASA Astrophysics Data System (ADS)

Coti Zelati, Francesco; Rea, Nanda; Pons, José A.; Campana, Sergio; Esposito, Paolo

2018-02-01

We present the results of the systematic study of all magnetar outbursts observed to date, through a reanalysis of data acquired in about 1100 X-ray observations. We track the temporal evolution of the outbursts' soft X-ray spectral properties and the luminosities of the single spectral components as well as of the total emission. We model empirically all outburst light curves, and estimate the characteristic decay time-scales as well as the energetics involved. We investigate the link between different parameters (e.g. the luminosity at the peak of the outburst and in quiescence, the maximum luminosity increase, the decay time-scale and energy of the outburst, the neutron star surface dipolar magnetic field and characteristic age, etc.), and unveil several correlations among these quantities. We discuss our results in the context of the internal crustal heating and twisted bundle models for magnetar outbursts. This study is complemented by the Magnetar Outburst Online Catalogue (http://magnetars.ice.csic.es), an interactive data base where the user can plot any combination of the parameters derived in this work, and download all data.

A Method of Predicting Queuing at Library Online PCs

ERIC Educational Resources Information Center

Beranek, Lea G.

2006-01-01

On-campus networked personal computer (PC) usage at La Trobe University Library was surveyed during September 2005. The survey's objectives were to confirm peak usage times, to measure some of the relevant parameters of online PC usage, and to determine the effect that 24 new networked PCs had on service quality. The survey found that clients…

Measuring Housework Participation: The Gap between "Stylised" Questionnaire Estimates and Diary-Based Estimates

ERIC Educational Resources Information Center

Kan, Man Yee

2008-01-01

This article compares stylised (questionnaire-based) estimates and diary-based estimates of housework time collected from the same respondents. Data come from the Home On-line Study (1999-2001), a British national household survey that contains both types of estimates (sample size = 632 men and 666 women). It shows that the gap between the two…

78 FR 73233 - 60-Day Notice of Proposed Information Collection: Online Application for Nonimmigrant Visa

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-05

... Application for Nonimmigrant Visa AGENCY: Department of State. ACTION: Notice of request for public comment...: Title of Information Collection: Online Application for Nonimmigrant Visa OMB Control Number: 1405-0182...: DS-160 Respondents: All Nonimmigrant Visa Applicants Estimated Number of Respondents: 11,100,276...

Informational Environments: Organizational Contexts of Online Information Use.

ERIC Educational Resources Information Center

Lamb, Roberta; King, John Leslie; Kling, Rob

2003-01-01

Examines sustained use and non-use of online services within organizations using an open-systems view that overcomes limitations of traditional approaches that led to over-estimates of use. Focuses on the informational environments of firms in three industries: law, real estate, and biotech/pharmaceuticals; and discusses insights from an intranets…

A Study of Teaching in Isolation and the Effects on Self-Efficacy for First and Second Grade Teachers in the Online School Setting

ERIC Educational Resources Information Center

Zagorski, Kathleen Honoria

2011-01-01

Online learning in the elementary school grades is popular in the United States. A recent study by Allen and Seaman (2009) estimated that the number of U.S. students enrolled in at least one online or blended course was over 1 million. Minimal research has been done on how working in isolation, when a teacher is removed from the daily contact with…

Attitude determination and parameter estimation using vector observations - Theory

NASA Technical Reports Server (NTRS)

Markley, F. Landis

1989-01-01

Procedures for attitude determination based on Wahba's loss function are generalized to include the estimation of parameters other than the attitude, such as sensor biases. Optimization with respect to the attitude is carried out using the q-method, which does not require an a priori estimate of the attitude. Optimization with respect to the other parameters employs an iterative approach, which does require an a priori estimate of these parameters. Conventional state estimation methods require a priori estimates of both the parameters and the attitude, while the algorithm presented in this paper always computes the exact optimal attitude for given values of the parameters. Expressions for the covariance of the attitude and parameter estimates are derived.

Adaptive Heat Engine.

PubMed

Allahverdyan, A E; Babajanyan, S G; Martirosyan, N H; Melkikh, A V

2016-07-15

A major limitation of many heat engines is that their functioning demands on-line control and/or an external fitting between the environmental parameters (e.g., temperatures of thermal baths) and internal parameters of the engine. We study a model for an adaptive heat engine, where-due to feedback from the functional part-the engine's structure adapts to given thermal baths. Hence, no on-line control and no external fitting are needed. The engine can employ unknown resources; it can also adapt to results of its own functioning that make the bath temperatures closer. We determine resources of adaptation and relate them to the prior information available about the environment.

Online Tools for Uncovering Data Quality (DQ) Issues in Satellite-Based Global Precipitation Products

NASA Technical Reports Server (NTRS)

Liu, Zhong; Heo, Gil

2015-01-01

Data quality (DQ) has many attributes or facets (i.e., errors, biases, systematic differences, uncertainties, benchmark, false trends, false alarm ratio, etc.)Sources can be complicated (measurements, environmental conditions, surface types, algorithms, etc.) and difficult to be identified especially for multi-sensor and multi-satellite products with bias correction (TMPA, IMERG, etc.) How to obtain DQ info fast and easily, especially quantified info in ROI Existing parameters (random error), literature, DIY, etc.How to apply the knowledge in research and applications.Here, we focus on online systems for integration of products and parameters, visualization and analysis as well as investigation and extraction of DQ information.

Research on On-Line Modeling of Fed-Batch Fermentation Process Based on v-SVR

NASA Astrophysics Data System (ADS)

Ma, Yongjun

The fermentation process is very complex and non-linear, many parameters are not easy to measure directly on line, soft sensor modeling is a good solution. This paper introduces v-support vector regression (v-SVR) for soft sensor modeling of fed-batch fermentation process. v-SVR is a novel type of learning machine. It can control the accuracy of fitness and prediction error by adjusting the parameter v. An on-line training algorithm is discussed in detail to reduce the training complexity of v-SVR. The experimental results show that v-SVR has low error rate and better generalization with appropriate v.

Bootstrap percolation on spatial networks

NASA Astrophysics Data System (ADS)

Gao, Jian; Zhou, Tao; Hu, Yanqing

2015-10-01

Bootstrap percolation is a general representation of some networked activation process, which has found applications in explaining many important social phenomena, such as the propagation of information. Inspired by some recent findings on spatial structure of online social networks, here we study bootstrap percolation on undirected spatial networks, with the probability density function of long-range links’ lengths being a power law with tunable exponent. Setting the size of the giant active component as the order parameter, we find a parameter-dependent critical value for the power-law exponent, above which there is a double phase transition, mixed of a second-order phase transition and a hybrid phase transition with two varying critical points, otherwise there is only a second-order phase transition. We further find a parameter-independent critical value around -1, about which the two critical points for the double phase transition are almost constant. To our surprise, this critical value -1 is just equal or very close to the values of many real online social networks, including LiveJournal, HP Labs email network, Belgian mobile phone network, etc. This work helps us in better understanding the self-organization of spatial structure of online social networks, in terms of the effective function for information spreading.

On-Line Use of Three-Dimensional Marker Trajectory Estimation From Cone-Beam Computed Tomography Projections for Precise Setup in Radiotherapy for Targets With Respiratory Motion

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

Worm, Esben S., E-mail: esbeworm@rm.dk; Department of Medical Physics, Aarhus University Hospital, Aarhus; Hoyer, Morten

2012-05-01

Purpose: To develop and evaluate accurate and objective on-line patient setup based on a novel semiautomatic technique in which three-dimensional marker trajectories were estimated from two-dimensional cone-beam computed tomography (CBCT) projections. Methods and Materials: Seven treatment courses of stereotactic body radiotherapy for liver tumors were delivered in 21 fractions in total to 6 patients by a linear accelerator. Each patient had two to three gold markers implanted close to the tumors. Before treatment, a CBCT scan with approximately 675 two-dimensional projections was acquired during a full gantry rotation. The marker positions were segmented in each projection. From this, the three-dimensionalmore » marker trajectories were estimated using a probability based method. The required couch shifts for patient setup were calculated from the mean marker positions along the trajectories. A motion phantom moving with known tumor trajectories was used to examine the accuracy of the method. Trajectory-based setup was retrospectively used off-line for the first five treatment courses (15 fractions) and on-line for the last two treatment courses (6 fractions). Automatic marker segmentation was compared with manual segmentation. The trajectory-based setup was compared with setup based on conventional CBCT guidance on the markers (first 15 fractions). Results: Phantom measurements showed that trajectory-based estimation of the mean marker position was accurate within 0.3 mm. The on-line trajectory-based patient setup was performed within approximately 5 minutes. The automatic marker segmentation agreed with manual segmentation within 0.36 {+-} 0.50 pixels (mean {+-} SD; pixel size, 0.26 mm in isocenter). The accuracy of conventional volumetric CBCT guidance was compromised by motion smearing ({<=}21 mm) that induced an absolute three-dimensional setup error of 1.6 {+-} 0.9 mm (maximum, 3.2) relative to trajectory-based setup. Conclusions: The first on-line clinical use of trajectory estimation from CBCT projections for precise setup in stereotactic body radiotherapy was demonstrated. Uncertainty in the conventional CBCT-based setup procedure was eliminated with the new method.« less

A self-paced brain-computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training.

PubMed

Tsui, Chun Sing Louis; Gan, John Q; Roberts, Stephen J

2009-03-01

Due to the non-stationarity of EEG signals, online training and adaptation are essential to EEG based brain-computer interface (BCI) systems. Self-paced BCIs offer more natural human-machine interaction than synchronous BCIs, but it is a great challenge to train and adapt a self-paced BCI online because the user's control intention and timing are usually unknown. This paper proposes a novel motor imagery based self-paced BCI paradigm for controlling a simulated robot in a specifically designed environment which is able to provide user's control intention and timing during online experiments, so that online training and adaptation of the motor imagery based self-paced BCI can be effectively investigated. We demonstrate the usefulness of the proposed paradigm with an extended Kalman filter based method to adapt the BCI classifier parameters, with experimental results of online self-paced BCI training with four subjects.

How Affiliation Disclosure and Control Over User-Generated Comments Affects Consumer Health Knowledge and Behavior: A Randomized Controlled Experiment of Pharmaceutical Direct-to-Consumer Advertising on Social Media.

PubMed

DeAndrea, David Christopher; Vendemia, Megan Ashley

2016-07-19

More people are seeking health information online than ever before and pharmaceutical companies are increasingly marketing their drugs through social media. The aim was to examine two major concerns related to online direct-to-consumer pharmaceutical advertising: (1) how disclosing an affiliation with a pharmaceutical company affects how people respond to drug information produced by both health organizations and online commenters, and (2) how knowledge that health organizations control the display of user-generated comments affects consumer health knowledge and behavior. We conducted a 2×2×2 between-subjects experiment (N=674). All participants viewed an infographic posted to Facebook by a health organization about a prescription allergy drug. Across conditions, the infographic varied in the degree to which the health organization and commenters appeared to be affiliated with a drug manufacturer, and the display of user-generated comments appeared to be controlled. Affiliation disclosure statements on a health organization's Facebook post increased perceptions of an organization-drug manufacturer connection, which reduced trust in the organization (point estimate -0.45, 95% CI -0.69 to -0.24) and other users who posted comments about the drug (point estimate -0.44, 95% CI -0.68 to -0.22). Furthermore, increased perceptions of an organization-manufacturer connection reduced the likelihood that people would recommend the drug to important others (point estimate -0.35, 95% CI -0.59 to -0.15), and share the drug post with others on Facebook (point estimate -0.37, 95% CI -0.64 to -0.16). An affiliation cue next to the commenters' names increased perceptions that the commenters were affiliated with the drug manufacturer, which reduced trust in the comments (point estimate -0.81, 95% CI -1.04 to -0.59), the organization that made the post (point estimate -0.68, 95% CI -0.90 to -0.49), the likelihood of participants recommending the drug (point estimate -0.61, 95% CI -0.82 to -0.43), and sharing the post with others on Facebook (point estimate -0.63, 95% CI -0.87 to -0.43). Cues indicating that a health organization removed user-generated comments from a post increased perceptions that the drug manufacturer influenced the display of the comments, which negatively affected trust in the comments (point estimate -0.35, 95% CI -0.53 to -0.20), the organization (point estimate -0.31, 95% CI -0.47 to -0.17), the likelihood of recommending the drug (point estimate -0.26, 95% CI -0.41 to -0.14), and the likelihood of sharing the post with others on Facebook (point estimate -0.28, 95% CI -0.45 to -0.15). (All estimates are unstandardized indirect effects and 95% bias-corrected bootstrap confidence intervals.) Concern over pharmaceutical companies hiding their affiliations and strategically controlling user-generated comments is well founded; these practices can greatly affect not only how viewers evaluate drug information online, but also how likely they are to propagate the information throughout their online and offline social networks.

Linear and non-linear systems identification for adaptive control in mechanical applications vibration suppression

NASA Astrophysics Data System (ADS)

Cazzulani, Gabriele; Resta, Ferruccio; Ripamonti, Francesco

2012-04-01

During the last years, more and more mechanical applications saw the introduction of active control strategies. In particular, the need of improving the performances and/or the system health is very often associated to vibration suppression. This goal can be achieved considering both passive and active solutions. In this sense, many active control strategies have been developed, such as the Independent Modal Space Control (IMSC) or the resonant controllers (PPF, IRC, . . .). In all these cases, in order to tune and optimize the control strategy, the knowledge of the system dynamic behaviour is very important and it can be achieved both considering a numerical model of the system or through an experimental identification process. Anyway, dealing with non-linear or time-varying systems, a tool able to online identify the system parameters becomes a key-point for the control logic synthesis. The aim of the present work is the definition of a real-time technique, based on ARMAX models, that estimates the system parameters starting from the measurements of piezoelectric sensors. These parameters are returned to the control logic, that automatically adapts itself to the system dynamics. The problem is numerically investigated considering a carbon-fiber plate model forced through a piezoelectric patch.

Optimal stimulus scheduling for active estimation of evoked brain networks.

PubMed

Kafashan, MohammadMehdi; Ching, ShiNung

2015-12-01

We consider the problem of optimal probing to learn connections in an evoked dynamic network. Such a network, in which each edge measures an input-output relationship between sites in sensor/actuator-space, is relevant to emerging applications in neural mapping and neural connectivity estimation. We show that the problem of scheduling nodes to a probe (i.e., stimulate) amounts to a problem of optimal sensor scheduling. By formulating the evoked network in state-space, we show that the solution to the greedy probing strategy has a convenient form and, under certain conditions, is optimal over a finite horizon. We adopt an expectation maximization technique to update the state-space parameters in an online fashion and demonstrate the efficacy of the overall approach in a series of detailed numerical examples. The proposed method provides a principled means to actively probe time-varying connections in neuronal networks. The overall method can be implemented in real time and is particularly well-suited to applications in stimulation-based cortical mapping in which the underlying network dynamics are changing over time.

Optimal stimulus scheduling for active estimation of evoked brain networks

NASA Astrophysics Data System (ADS)

Kafashan, MohammadMehdi; Ching, ShiNung

2015-12-01

Objective. We consider the problem of optimal probing to learn connections in an evoked dynamic network. Such a network, in which each edge measures an input-output relationship between sites in sensor/actuator-space, is relevant to emerging applications in neural mapping and neural connectivity estimation. Approach. We show that the problem of scheduling nodes to a probe (i.e., stimulate) amounts to a problem of optimal sensor scheduling. Main results. By formulating the evoked network in state-space, we show that the solution to the greedy probing strategy has a convenient form and, under certain conditions, is optimal over a finite horizon. We adopt an expectation maximization technique to update the state-space parameters in an online fashion and demonstrate the efficacy of the overall approach in a series of detailed numerical examples. Significance. The proposed method provides a principled means to actively probe time-varying connections in neuronal networks. The overall method can be implemented in real time and is particularly well-suited to applications in stimulation-based cortical mapping in which the underlying network dynamics are changing over time.

Online selective kernel-based temporal difference learning.

PubMed

Chen, Xingguo; Gao, Yang; Wang, Ruili

2013-12-01

In this paper, an online selective kernel-based temporal difference (OSKTD) learning algorithm is proposed to deal with large scale and/or continuous reinforcement learning problems. OSKTD includes two online procedures: online sparsification and parameter updating for the selective kernel-based value function. A new sparsification method (i.e., a kernel distance-based online sparsification method) is proposed based on selective ensemble learning, which is computationally less complex compared with other sparsification methods. With the proposed sparsification method, the sparsified dictionary of samples is constructed online by checking if a sample needs to be added to the sparsified dictionary. In addition, based on local validity, a selective kernel-based value function is proposed to select the best samples from the sample dictionary for the selective kernel-based value function approximator. The parameters of the selective kernel-based value function are iteratively updated by using the temporal difference (TD) learning algorithm combined with the gradient descent technique. The complexity of the online sparsification procedure in the OSKTD algorithm is O(n). In addition, two typical experiments (Maze and Mountain Car) are used to compare with both traditional and up-to-date O(n) algorithms (GTD, GTD2, and TDC using the kernel-based value function), and the results demonstrate the effectiveness of our proposed algorithm. In the Maze problem, OSKTD converges to an optimal policy and converges faster than both traditional and up-to-date algorithms. In the Mountain Car problem, OSKTD converges, requires less computation time compared with other sparsification methods, gets a better local optima than the traditional algorithms, and converges much faster than the up-to-date algorithms. In addition, OSKTD can reach a competitive ultimate optima compared with the up-to-date algorithms.

Visual object tracking by correlation filters and online learning

NASA Astrophysics Data System (ADS)

Zhang, Xin; Xia, Gui-Song; Lu, Qikai; Shen, Weiming; Zhang, Liangpei

2018-06-01

Due to the complexity of background scenarios and the variation of target appearance, it is difficult to achieve high accuracy and fast speed for object tracking. Currently, correlation filters based trackers (CFTs) show promising performance in object tracking. The CFTs estimate the target's position by correlation filters with different kinds of features. However, most of CFTs can hardly re-detect the target in the case of long-term tracking drifts. In this paper, a feature integration object tracker named correlation filters and online learning (CFOL) is proposed. CFOL estimates the target's position and its corresponding correlation score using the same discriminative correlation filter with multi-features. To reduce tracking drifts, a new sampling and updating strategy for online learning is proposed. Experiments conducted on 51 image sequences demonstrate that the proposed algorithm is superior to the state-of-the-art approaches.

On-line depth measurement for laser-drilled holes based on the intensity of plasma emission

NASA Astrophysics Data System (ADS)

Ho, Chao-Ching; Chiu, Chih-Mu; Chang, Yuan-Jen; Hsu, Jin-Chen; Kuo, Chia-Lung

2014-09-01

The direct time-resolved depth measurement of blind holes is extremely difficult due to the short time interval and the limited space inside the hole. This work presents a method that involves on-line plasma emission acquisition and analysis to obtain correlations between the machining processes and the optical signal output. Given that the depths of laser-machined holes can be estimated on-line using a coaxial photodiode, this was employed in our inspection system. Our experiments were conducted in air under normal atmospheric conditions without gas assist. The intensity of radiation emitted from the vaporized material was found to correlate with the depth of the hole. The results indicate that the estimated depths of the laser-drilled holes were inversely proportional to the maximum plasma light emission measured for a given laser pulse number.

Application of nonlinear least-squares regression to ground-water flow modeling, west-central Florida

USGS Publications Warehouse

Yobbi, D.K.

2000-01-01

A nonlinear least-squares regression technique for estimation of ground-water flow model parameters was applied to an existing model of the regional aquifer system underlying west-central Florida. The regression technique minimizes the differences between measured and simulated water levels. Regression statistics, including parameter sensitivities and correlations, were calculated for reported parameter values in the existing model. Optimal parameter values for selected hydrologic variables of interest are estimated by nonlinear regression. Optimal estimates of parameter values are about 140 times greater than and about 0.01 times less than reported values. Independently estimating all parameters by nonlinear regression was impossible, given the existing zonation structure and number of observations, because of parameter insensitivity and correlation. Although the model yields parameter values similar to those estimated by other methods and reproduces the measured water levels reasonably accurately, a simpler parameter structure should be considered. Some possible ways of improving model calibration are to: (1) modify the defined parameter-zonation structure by omitting and/or combining parameters to be estimated; (2) carefully eliminate observation data based on evidence that they are likely to be biased; (3) collect additional water-level data; (4) assign values to insensitive parameters, and (5) estimate the most sensitive parameters first, then, using the optimized values for these parameters, estimate the entire data set.

MULTI-OBJECTIVE ONLINE OPTIMIZATION OF BEAM LIFETIME AT APS

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

Sun, Yipeng

In this paper, online optimization of beam lifetime at the APS (Advanced Photon Source) storage ring is presented. A general genetic algorithm (GA) is developed and employed for some online optimizations in the APS storage ring. Sextupole magnets in 40 sectors of the APS storage ring are employed as variables for the online nonlinear beam dynamics optimization. The algorithm employs several optimization objectives and is designed to run with topup mode or beam current decay mode. Up to 50\\% improvement of beam lifetime is demonstrated, without affecting the transverse beam sizes and other relevant parameters. In some cases, the top-upmore » injection efficiency is also improved.« less

An improved method for nonlinear parameter estimation: a case study of the Rössler model

NASA Astrophysics Data System (ADS)

He, Wen-Ping; Wang, Liu; Jiang, Yun-Di; Wan, Shi-Quan

2016-08-01

Parameter estimation is an important research topic in nonlinear dynamics. Based on the evolutionary algorithm (EA), Wang et al. (2014) present a new scheme for nonlinear parameter estimation and numerical tests indicate that the estimation precision is satisfactory. However, the convergence rate of the EA is relatively slow when multiple unknown parameters in a multidimensional dynamical system are estimated simultaneously. To solve this problem, an improved method for parameter estimation of nonlinear dynamical equations is provided in the present paper. The main idea of the improved scheme is to use all of the known time series for all of the components in some dynamical equations to estimate the parameters in single component one by one, instead of estimating all of the parameters in all of the components simultaneously. Thus, we can estimate all of the parameters stage by stage. The performance of the improved method was tested using a classic chaotic system—Rössler model. The numerical tests show that the amended parameter estimation scheme can greatly improve the searching efficiency and that there is a significant increase in the convergence rate of the EA, particularly for multiparameter estimation in multidimensional dynamical equations. Moreover, the results indicate that the accuracy of parameter estimation and the CPU time consumed by the presented method have no obvious dependence on the sample size.

Online sequential Monte Carlo smoother for partially observed diffusion processes

NASA Astrophysics Data System (ADS)

Gloaguen, Pierre; Étienne, Marie-Pierre; Le Corff, Sylvain

2018-12-01

This paper introduces a new algorithm to approximate smoothed additive functionals of partially observed diffusion processes. This method relies on a new sequential Monte Carlo method which allows to compute such approximations online, i.e., as the observations are received, and with a computational complexity growing linearly with the number of Monte Carlo samples. The original algorithm cannot be used in the case of partially observed stochastic differential equations since the transition density of the latent data is usually unknown. We prove that it may be extended to partially observed continuous processes by replacing this unknown quantity by an unbiased estimator obtained for instance using general Poisson estimators. This estimator is proved to be consistent and its performance are illustrated using data from two models.

Estimation of Ecosystem Parameters of the Community Land Model with DREAM: Evaluation of the Potential for Upscaling Net Ecosystem Exchange

NASA Astrophysics Data System (ADS)

Hendricks Franssen, H. J.; Post, H.; Vrugt, J. A.; Fox, A. M.; Baatz, R.; Kumbhar, P.; Vereecken, H.

2015-12-01

Estimation of net ecosystem exchange (NEE) by land surface models is strongly affected by uncertain ecosystem parameters and initial conditions. A possible approach is the estimation of plant functional type (PFT) specific parameters for sites with measurement data like NEE and application of the parameters at other sites with the same PFT and no measurements. This upscaling strategy was evaluated in this work for sites in Germany and France. Ecosystem parameters and initial conditions were estimated with NEE-time series of one year length, or a time series of only one season. The DREAM(zs) algorithm was used for the estimation of parameters and initial conditions. DREAM(zs) is not limited to Gaussian distributions and can condition to large time series of measurement data simultaneously. DREAM(zs) was used in combination with the Community Land Model (CLM) v4.5. Parameter estimates were evaluated by model predictions at the same site for an independent verification period. In addition, the parameter estimates were evaluated at other, independent sites situated >500km away with the same PFT. The main conclusions are: i) simulations with estimated parameters reproduced better the NEE measurement data in the verification periods, including the annual NEE-sum (23% improvement), annual NEE-cycle and average diurnal NEE course (error reduction by factor 1,6); ii) estimated parameters based on seasonal NEE-data outperformed estimated parameters based on yearly data; iii) in addition, those seasonal parameters were often also significantly different from their yearly equivalents; iv) estimated parameters were significantly different if initial conditions were estimated together with the parameters. We conclude that estimated PFT-specific parameters improve land surface model predictions significantly at independent verification sites and for independent verification periods so that their potential for upscaling is demonstrated. However, simulation results also indicate that possibly the estimated parameters mask other model errors. This would imply that their application at climatic time scales would not improve model predictions. A central question is whether the integration of many different data streams (e.g., biomass, remotely sensed LAI) could solve the problems indicated here.

System identification for modeling for control of flexible structures

NASA Technical Reports Server (NTRS)

Mettler, Edward; Milman, Mark

1986-01-01

The major components of a design and operational flight strategy for flexible structure control systems are presented. In this strategy an initial distributed parameter control design is developed and implemented from available ground test data and on-orbit identification using sophisticated modeling and synthesis techniques. The reliability of this high performance controller is directly linked to the accuracy of the parameters on which the design is based. Because uncertainties inevitably grow without system monitoring, maintaining the control system requires an active on-line system identification function to supply parameter updates and covariance information. Control laws can then be modified to improve performance when the error envelopes are decreased. In terms of system safety and stability the covariance information is of equal importance as the parameter values themselves. If the on-line system ID function detects an increase in parameter error covariances, then corresponding adjustments must be made in the control laws to increase robustness. If the error covariances exceed some threshold, an autonomous calibration sequence could be initiated to restore the error enveloped to an acceptable level.

Interpolated memory tests reduce mind wandering and improve learning of online lectures.

PubMed

Szpunar, Karl K; Khan, Novall Y; Schacter, Daniel L

2013-04-16

The recent emergence and popularity of online educational resources brings with it challenges for educators to optimize the dissemination of online content. Here we provide evidence that points toward a solution for the difficulty that students frequently report in sustaining attention to online lectures over extended periods. In two experiments, we demonstrate that the simple act of interpolating online lectures with memory tests can help students sustain attention to lecture content in a manner that discourages task-irrelevant mind wandering activities, encourages task-relevant note-taking activities, and improves learning. Importantly, frequent testing was associated with reduced anxiety toward a final cumulative test and also with reductions in subjective estimates of cognitive demand. Our findings suggest a potentially key role for interpolated testing in the development and dissemination of online educational content.

Interpolated memory tests reduce mind wandering and improve learning of online lectures

PubMed Central

Szpunar, Karl K.; Khan, Novall Y.; Schacter, Daniel L.

2013-01-01

The recent emergence and popularity of online educational resources brings with it challenges for educators to optimize the dissemination of online content. Here we provide evidence that points toward a solution for the difficulty that students frequently report in sustaining attention to online lectures over extended periods. In two experiments, we demonstrate that the simple act of interpolating online lectures with memory tests can help students sustain attention to lecture content in a manner that discourages task-irrelevant mind wandering activities, encourages task-relevant note-taking activities, and improves learning. Importantly, frequent testing was associated with reduced anxiety toward a final cumulative test and also with reductions in subjective estimates of cognitive demand. Our findings suggest a potentially key role for interpolated testing in the development and dissemination of online educational content. PMID:23576743

On the robustness of EC-PC spike detection method for online neural recording.

PubMed

Zhou, Yin; Wu, Tong; Rastegarnia, Amir; Guan, Cuntai; Keefer, Edward; Yang, Zhi

2014-09-30

Online spike detection is an important step to compress neural data and perform real-time neural information decoding. An unsupervised, automatic, yet robust signal processing is strongly desired, thus it can support a wide range of applications. We have developed a novel spike detection algorithm called "exponential component-polynomial component" (EC-PC) spike detection. We firstly evaluate the robustness of the EC-PC spike detector under different firing rates and SNRs. Secondly, we show that the detection Precision can be quantitatively derived without requiring additional user input parameters. We have realized the algorithm (including training) into a 0.13 μm CMOS chip, where an unsupervised, nonparametric operation has been demonstrated. Both simulated data and real data are used to evaluate the method under different firing rates (FRs), SNRs. The results show that the EC-PC spike detector is the most robust in comparison with some popular detectors. Moreover, the EC-PC detector can track changes in the background noise due to the ability to re-estimate the neural data distribution. Both real and synthesized data have been used for testing the proposed algorithm in comparison with other methods, including the absolute thresholding detector (AT), median absolute deviation detector (MAD), nonlinear energy operator detector (NEO), and continuous wavelet detector (CWD). Comparative testing results reveals that the EP-PC detection algorithm performs better than the other algorithms regardless of recording conditions. The EC-PC spike detector can be considered as an unsupervised and robust online spike detection. It is also suitable for hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

Real-time auto-adaptive margin generation for MLC-tracked radiotherapy

NASA Astrophysics Data System (ADS)

Glitzner, M.; Fast, M. F.; de Senneville, B. Denis; Nill, S.; Oelfke, U.; Lagendijk, J. J. W.; Raaymakers, B. W.; Crijns, S. P. M.

2017-01-01

In radiotherapy, abdominal and thoracic sites are candidates for performing motion tracking. With real-time control it is possible to adjust the multileaf collimator (MLC) position to the target position. However, positions are not perfectly matched and position errors arise from system delays and complicated response of the electromechanic MLC system. Although, it is possible to compensate parts of these errors by using predictors, residual errors remain and need to be compensated to retain target coverage. This work presents a method to statistically describe tracking errors and to automatically derive a patient-specific, per-segment margin to compensate the arising underdosage on-line, i.e. during plan delivery. The statistics of the geometric error between intended and actual machine position are derived using kernel density estimators. Subsequently a margin is calculated on-line according to a selected coverage parameter, which determines the amount of accepted underdosage. The margin is then applied onto the actual segment to accommodate the positioning errors in the enlarged segment. The proof-of-concept was tested in an on-line tracking experiment and showed the ability to recover underdosages for two test cases, increasing {{V}90 %} in the underdosed area about 47 % and 41 % , respectively. The used dose model was able to predict the loss of dose due to tracking errors and could be used to infer the necessary margins. The implementation had a running time of 23 ms which is compatible with real-time requirements of MLC tracking systems. The auto-adaptivity to machine and patient characteristics makes the technique a generic yet intuitive candidate to avoid underdosages due to MLC tracking errors.

Prostate tissue characterization/classification in 144 patient population using wavelet and higher order spectra features from transrectal ultrasound images.

PubMed

Pareek, Gyan; Acharya, U Rajendra; Sree, S Vinitha; Swapna, G; Yantri, Ratna; Martis, Roshan Joy; Saba, Luca; Krishnamurthi, Ganapathy; Mallarini, Giorgio; El-Baz, Ayman; Al Ekish, Shadi; Beland, Michael; Suri, Jasjit S

2013-12-01

In this work, we have proposed an on-line computer-aided diagnostic system called "UroImage" that classifies a Transrectal Ultrasound (TRUS) image into cancerous or non-cancerous with the help of non-linear Higher Order Spectra (HOS) features and Discrete Wavelet Transform (DWT) coefficients. The UroImage system consists of an on-line system where five significant features (one DWT-based feature and four HOS-based features) are extracted from the test image. These on-line features are transformed by the classifier parameters obtained using the training dataset to determine the class. We trained and tested six classifiers. The dataset used for evaluation had 144 TRUS images which were split into training and testing sets. Three-fold and ten-fold cross-validation protocols were adopted for training and estimating the accuracy of the classifiers. The ground truth used for training was obtained using the biopsy results. Among the six classifiers, using 10-fold cross-validation technique, Support Vector Machine and Fuzzy Sugeno classifiers presented the best classification accuracy of 97.9% with equally high values for sensitivity, specificity and positive predictive value. Our proposed automated system, which achieved more than 95% values for all the performance measures, can be an adjunct tool to provide an initial diagnosis for the identification of patients with prostate cancer. The technique, however, is limited by the limitations of 2D ultrasound guided biopsy, and we intend to improve our technique by using 3D TRUS images in the future.

Computer vision based method and system for online measurement of geometric parameters of train wheel sets.

PubMed

Zhang, Zhi-Feng; Gao, Zhan; Liu, Yuan-Yuan; Jiang, Feng-Chun; Yang, Yan-Li; Ren, Yu-Fen; Yang, Hong-Jun; Yang, Kun; Zhang, Xiao-Dong

2012-01-01

Train wheel sets must be periodically inspected for possible or actual premature failures and it is very significant to record the wear history for the full life of utilization of wheel sets. This means that an online measuring system could be of great benefit to overall process control. An online non-contact method for measuring a wheel set's geometric parameters based on the opto-electronic measuring technique is presented in this paper. A charge coupled device (CCD) camera with a selected optical lens and a frame grabber was used to capture the image of the light profile of the wheel set illuminated by a linear laser. The analogue signals of the image were transformed into corresponding digital grey level values. The 'mapping function method' is used to transform an image pixel coordinate to a space coordinate. The images of wheel sets were captured when the train passed through the measuring system. The rim inside thickness and flange thickness were measured and analyzed. The spatial resolution of the whole image capturing system is about 0.33 mm. Theoretic and experimental results show that the online measurement system based on computer vision can meet wheel set measurement requirements.

Simultaneous versus sequential optimal experiment design for the identification of multi-parameter microbial growth kinetics as a function of temperature.

PubMed

Van Derlinden, E; Bernaerts, K; Van Impe, J F

2010-05-21

Optimal experiment design for parameter estimation (OED/PE) has become a popular tool for efficient and accurate estimation of kinetic model parameters. When the kinetic model under study encloses multiple parameters, different optimization strategies can be constructed. The most straightforward approach is to estimate all parameters simultaneously from one optimal experiment (single OED/PE strategy). However, due to the complexity of the optimization problem or the stringent limitations on the system's dynamics, the experimental information can be limited and parameter estimation convergence problems can arise. As an alternative, we propose to reduce the optimization problem to a series of two-parameter estimation problems, i.e., an optimal experiment is designed for a combination of two parameters while presuming the other parameters known. Two different approaches can be followed: (i) all two-parameter optimal experiments are designed based on identical initial parameter estimates and parameters are estimated simultaneously from all resulting experimental data (global OED/PE strategy), and (ii) optimal experiments are calculated and implemented sequentially whereby the parameter values are updated intermediately (sequential OED/PE strategy). This work exploits OED/PE for the identification of the Cardinal Temperature Model with Inflection (CTMI) (Rosso et al., 1993). This kinetic model describes the effect of temperature on the microbial growth rate and encloses four parameters. The three OED/PE strategies are considered and the impact of the OED/PE design strategy on the accuracy of the CTMI parameter estimation is evaluated. Based on a simulation study, it is observed that the parameter values derived from the sequential approach deviate more from the true parameters than the single and global strategy estimates. The single and global OED/PE strategies are further compared based on experimental data obtained from design implementation in a bioreactor. Comparable estimates are obtained, but global OED/PE estimates are, in general, more accurate and reliable. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Use of Online Sources of Information by Dental Practitioners: Findings from The Dental Practice-Based Research Network

PubMed Central

Funkhouser, Ellen; Agee, Bonita S.; Gordan, Valeria V.; Rindal, D. Brad; Fellows, Jeffrey L.; Qvist, Vibeke; McClelland, Jocelyn; Gilbert, Gregg H.

2013-01-01

Objectives Estimate the proportion of dental practitioners who use online sources of information for practice guidance. Methods From a survey of 657 dental practitioners in The Dental Practice Based Research Network, four indicators of online use for practice guidance were calculated: read journals online, obtained continuing education (CDE) through online sources, rated an online source as most influential, and reported frequently using an online source for guidance. Demographics, journals read, and use of various sources of information for practice guidance in terms of frequency and influence were ascertained for each. Results Overall, 21% (n=138) were classified into one of the four indicators of online use: 14% (n=89) rated an online source as most influential and 13% (n=87) reported frequently using an online source for guidance; few practitioners (5%, n=34) read journals online, fewer (3%, n=17) obtained CDE through online sources. Use of online information sources varied considerably by region and practice characteristics. In general, the 4 indicators represented practitioners with as many differences as similarities to each other and to offline users. Conclusion A relatively small proportion of dental practitioners use information from online sources for practice guidance. Variation exists regarding practitioners’ use of online source resources and how they rate the value of offline information sources for practice guidance. PMID:22994848

The Challenges of Online Courses for the Instructor

ERIC Educational Resources Information Center

Jacobs, Pearl

2013-01-01

Universities across the country are steadily increasing their use of online courses. Society's demand for lifelong learning will encourage the advancement of distance learning. Research tells us that today the average person changes careers every ten years. In addition, the U.S. Department of Labor estimates that about 10% of workers change jobs…

Success in Online Credit Recovery: Factors Influencing Student Academic Performance

ERIC Educational Resources Information Center

Nourse, David

2017-01-01

Recent estimates show nearly 90% of school districts nationwide offer some form of online credit recovery. Additionally, credit recovery services have become one of the fastest growing areas of educational software. Despite the widespread adoption of these programs, there is a lack of scholarly research on the effectiveness, rigor, and suitability…

Exploring the Effect of Student Confusion in Massive Open Online Courses

ERIC Educational Resources Information Center

Yang, Diyi; Kraut, Robert E.; Rose, Carolyn P.

2016-01-01

Although thousands of students enroll in Massive Open Online Courses (MOOCs) for learning and self-improvement, many get confused, harming learning and increasing dropout rates. In this paper, we quantify these effects in two large MOOCs. We first describe how we automatically estimate students' confusion by looking at their clicking behavior on…

Accuracy of self-reported versus actual online gambling wins and losses.

PubMed

Braverman, Julia; Tom, Matthew A; Shaffer, Howard J

2014-09-01

This study is the first to compare the accuracy of self-reported with actual monetary outcomes of online fixed odds sports betting, live action sports betting, and online casino gambling at the individual level of analysis. Subscribers to bwin.party digital entertainment's online gambling service volunteered to respond to the Brief Bio-Social Gambling Screen and questions about their estimated gambling results on specific games for the last 3 or 12 months. We compared the estimated results of each subscriber with his or her actual betting results data. On average, between 34% and 40% of the participants expressed a favorable distortion of their gambling outcomes (i.e., they underestimated losses or overestimated gains) depending on the time period and game. The size of the discrepancy between actual and self-reported results was consistently associated with the self-reported presence of gambling-related problems. However, the specific direction of the reported discrepancy (i.e., favorable vs. unfavorable bias) was not associated with gambling-related problems. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Online tools for uncovering data quality issues in satellite-based global precipitation products

NASA Astrophysics Data System (ADS)

Liu, Z.; Heo, G.

2015-12-01

Accurate and timely available global precipitation products are important to many applications such as flood forecasting, hydrological modeling, vector-borne disease research, crop yield estimates, etc. However, data quality issues such as biases and uncertainties are common in satellite-based precipitation products and it is important to understand these issues in applications. In recent years, algorithms using multi-satellites and multi-sensors for satellite-based precipitation estimates have become popular, such as the TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Analysis (TMPA) and the latest Integrated Multi-satellitE Retrievals for GPM (IMERG). Studies show that data quality issues for multi-satellite and multi-sensor products can vary with space and time and can be difficult to summarize. Online tools can provide customized results for a given area of interest, allowing customized investigation or comparison on several precipitation products. Because downloading data and software is not required, online tools can facilitate precipitation product evaluation and comparison. In this presentation, we will present online tools to uncover data quality issues in satellite-based global precipitation products. Examples will be presented as well.

A well-designed online transfusion reaction reporting system improves the estimation of transfusion reaction incidence and quality of care in transfusion practice.

PubMed

Yeh, Su-Peng; Chang, Ci-Wen; Chen, Ju-Chuan; Yeh, Wan-Chen; Chen, Pei-Chi; Chuang, Su-Jung; Lin, Chiou-Ping; Hsu, Ling-Nu; Chen, Han-Mih; Lu, Jang-Jih; Peng, Ching-Tien

2011-12-01

Recognizing and reporting a transfusion reaction is important in transfusion practice. However, the actual incidence of transfusion reactions is frequently underestimated. We designed an online transfusion reaction reporting system for nurses who take care of transfusion recipients. The common management before and after transfusion and the 18 most common transfusion reactions were itemized as tick boxes. We found the overall documented incidence of transfusion reaction increased dramatically, from 0.21% to 0.61% per unit of blood, after we started using an online reporting system. Overall, 94% (30/32) of nurses took only 1 week to become familiar with the new system, and 88% (28/32) considered the new system helpful in improving the quality of clinical transfusion care. By using an intranet connection, blood bank physicians can also identify patients who are having a reaction and provide appropriate recommendations immediately. A well-designed online reporting system may improve the ability to estimate the incidence of transfusion reactions and the quality of transfusion care.

Determining the accuracy of maximum likelihood parameter estimates with colored residuals

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; Klein, Vladislav

1994-01-01

An important part of building high fidelity mathematical models based on measured data is calculating the accuracy associated with statistical estimates of the model parameters. Indeed, without some idea of the accuracy of parameter estimates, the estimates themselves have limited value. In this work, an expression based on theoretical analysis was developed to properly compute parameter accuracy measures for maximum likelihood estimates with colored residuals. This result is important because experience from the analysis of measured data reveals that the residuals from maximum likelihood estimation are almost always colored. The calculations involved can be appended to conventional maximum likelihood estimation algorithms. Simulated data runs were used to show that the parameter accuracy measures computed with this technique accurately reflect the quality of the parameter estimates from maximum likelihood estimation without the need for analysis of the output residuals in the frequency domain or heuristically determined multiplication factors. The result is general, although the application studied here is maximum likelihood estimation of aerodynamic model parameters from flight test data.

The online romance scam: a serious cybercrime.

PubMed

Whitty, Monica T; Buchanan, Tom

2012-03-01

The Online Romance Scam is a relatively new form of fraud that became apparent in about 2008. In this crime, criminals pretend to initiate a relationship through online dating sites then defraud their victims of large sums of money. This paper presents some descriptive statistics about knowledge and victimization of the online dating romance scam in Great Britain. Our study found that despite its newness, an estimated 230,000 British citizens may have fallen victim to this crime. We conclude that there needs to be some rethinking about providing avenues for victims to report the crime or at least making them more comfortable when doing so.

Self-Exciting Point Process Modeling of Conversation Event Sequences

NASA Astrophysics Data System (ADS)

Masuda, Naoki; Takaguchi, Taro; Sato, Nobuo; Yano, Kazuo

Self-exciting processes of Hawkes type have been used to model various phenomena including earthquakes, neural activities, and views of online videos. Studies of temporal networks have revealed that sequences of social interevent times for individuals are highly bursty. We examine some basic properties of event sequences generated by the Hawkes self-exciting process to show that it generates bursty interevent times for a wide parameter range. Then, we fit the model to the data of conversation sequences recorded in company offices in Japan. In this way, we can estimate relative magnitudes of the self excitement, its temporal decay, and the base event rate independent of the self excitation. These variables highly depend on individuals. We also point out that the Hawkes model has an important limitation that the correlation in the interevent times and the burstiness cannot be independently modulated.

Real-time diagnostics of the reusable rocket engine using on-line system identification

NASA Technical Reports Server (NTRS)

Guo, T.-H.; Merrill, W.; Duyar, A.

1990-01-01

A model-based failure diagnosis system has been proposed for real-time diagnosis of SSME failures. Actuation, sensor, and system degradation failure modes are all considered by the proposed system. In the case of SSME actuation failures, it was shown that real-time identification can effectively be used for failure diagnosis purposes. It is a direct approach since it reduces the detection, isolation, and the estimation of the extent of the failures to the comparison of parameter values before and after the failure. As with any model-based failure detection system, the proposed approach requires a fault model that embodies the essential characteristics of the failure process. The proposed diagnosis approach has the added advantage that it can be used as part of an intelligent control system for failure accommodation purposes.

Wavelet phase extracting demodulation algorithm based on scale factor for optical fiber Fabry-Perot sensing.

PubMed

Zhang, Baolin; Tong, Xinglin; Hu, Pan; Guo, Qian; Zheng, Zhiyuan; Zhou, Chaoran

2016-12-26

Optical fiber Fabry-Perot (F-P) sensors have been used in various on-line monitoring of physical parameters such as acoustics, temperature and pressure. In this paper, a wavelet phase extracting demodulation algorithm for optical fiber F-P sensing is first proposed. In application of this demodulation algorithm, search range of scale factor is determined by estimated cavity length which is obtained by fast Fourier transform (FFT) algorithm. Phase information of each point on the optical interference spectrum can be directly extracted through the continuous complex wavelet transform without de-noising. And the cavity length of the optical fiber F-P sensor is calculated by the slope of fitting curve of the phase. Theorical analysis and experiment results show that this algorithm can greatly reduce the amount of computation and improve demodulation speed and accuracy.

A model for the massive binary V340 Muscae

NASA Astrophysics Data System (ADS)

Hauck, Norbert

2016-02-01

A synthetic light curve has been fitted to photometric data from the ASAS-3 database. The parameters of the best solution are well consistent with those derived from stellar models for both components for an initial metallicity Z=0.020 and a common age of 5 Myr. Therefore, we can reliably estimate the absolute dimensions of this close eclipsing binary system. Apparently, the O-type primary star has a mass of about 22.65 Msun and a radius of 10.35 Rsun. For the secondary star, likely a late B-type dwarf, we obtain about 3.1 Msun and 2.1 Rsun. Their mass ratio of about 0.138 might be the lowest found so far in O-type binaries. [English and German online-version of this paper available under www.bav-astro.eu/rb/rb2016-2/1.html].

A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

NASA Astrophysics Data System (ADS)

Khawaja, Taimoor Saleem

A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior and any abnormal or novel data during real-time operation. The results of the scheme are interpreted as a posterior probability of health (1 - probability of fault). As shown through two case studies in Chapter 3, the scheme is well suited for diagnosing imminent faults in dynamical non-linear systems. Finally, the failure prognosis scheme is based on an incremental weighted Bayesian LS-SVR machine. It is particularly suited for online deployment given the incremental nature of the algorithm and the quick optimization problem solved in the LS-SVR algorithm. By way of kernelization and a Gaussian Mixture Modeling (GMM) scheme, the algorithm can estimate "possibly" non-Gaussian posterior distributions for complex non-linear systems. An efficient regression scheme associated with the more rigorous core algorithm allows for long-term predictions, fault growth estimation with confidence bounds and remaining useful life (RUL) estimation after a fault is detected. The leading contributions of this thesis are (a) the development of a novel Bayesian Anomaly Detector for efficient and reliable Fault Detection and Identification (FDI) based on Least Squares Support Vector Machines, (b) the development of a data-driven real-time architecture for long-term Failure Prognosis using Least Squares Support Vector Machines, (c) Uncertainty representation and management using Bayesian Inference for posterior distribution estimation and hyper-parameter tuning, and finally (d) the statistical characterization of the performance of diagnosis and prognosis algorithms in order to relate the efficiency and reliability of the proposed schemes.

Algorithm for improving psychophysical threshold estimates by detecting sustained inattention in experiments using PEST.

PubMed

Rinderknecht, Mike D; Ranzani, Raffaele; Popp, Werner L; Lambercy, Olivier; Gassert, Roger

2018-05-10

Psychophysical procedures are applied in various fields to assess sensory thresholds. During experiments, sampled psychometric functions are usually assumed to be stationary. However, perception can be altered, for example by loss of attention to the presentation of stimuli, leading to biased data, which results in poor threshold estimates. The few existing approaches attempting to identify non-stationarities either detect only whether there was a change in perception, or are not suitable for experiments with a relatively small number of trials (e.g., [Formula: see text] 300). We present a method to detect inattention periods on a trial-by-trial basis with the aim of improving threshold estimates in psychophysical experiments using the adaptive sampling procedure Parameter Estimation by Sequential Testing (PEST). The performance of the algorithm was evaluated in computer simulations modeling inattention, and tested in a behavioral experiment on proprioceptive difference threshold assessment in 20 stroke patients, a population where attention deficits are likely to be present. Simulations showed that estimation errors could be reduced by up to 77% for inattentive subjects, even in sequences with less than 100 trials. In the behavioral data, inattention was detected in 14% of assessments, and applying the proposed algorithm resulted in reduced test-retest variability in 73% of these corrected assessments pairs. The novel algorithm complements existing approaches and, besides being applicable post hoc, could also be used online to prevent collection of biased data. This could have important implications in assessment practice by shortening experiments and improving estimates, especially for clinical settings.

Estimation of tool wear during CNC milling using neural network-based sensor fusion

NASA Astrophysics Data System (ADS)

Ghosh, N.; Ravi, Y. B.; Patra, A.; Mukhopadhyay, S.; Paul, S.; Mohanty, A. R.; Chattopadhyay, A. B.

2007-01-01

Cutting tool wear degrades the product quality in manufacturing processes. Monitoring tool wear value online is therefore needed to prevent degradation in machining quality. Unfortunately there is no direct way of measuring the tool wear online. Therefore one has to adopt an indirect method wherein the tool wear is estimated from several sensors measuring related process variables. In this work, a neural network-based sensor fusion model has been developed for tool condition monitoring (TCM). Features extracted from a number of machining zone signals, namely cutting forces, spindle vibration, spindle current, and sound pressure level have been fused to estimate the average flank wear of the main cutting edge. Novel strategies such as, signal level segmentation for temporal registration, feature space filtering, outlier removal, and estimation space filtering have been proposed. The proposed approach has been validated by both laboratory and industrial implementations.

Bayesian Parameter Estimation for Heavy-Duty Vehicles

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

Miller, Eric; Konan, Arnaud; Duran, Adam

2017-03-28

Accurate vehicle parameters are valuable for design, modeling, and reporting. Estimating vehicle parameters can be a very time-consuming process requiring tightly-controlled experimentation. This work describes a method to estimate vehicle parameters such as mass, coefficient of drag/frontal area, and rolling resistance using data logged during standard vehicle operation. The method uses Monte Carlo to generate parameter sets which is fed to a variant of the road load equation. Modeled road load is then compared to measured load to evaluate the probability of the parameter set. Acceptance of a proposed parameter set is determined using the probability ratio to the currentmore » state, so that the chain history will give a distribution of parameter sets. Compared to a single value, a distribution of possible values provides information on the quality of estimates and the range of possible parameter values. The method is demonstrated by estimating dynamometer parameters. Results confirm the method's ability to estimate reasonable parameter sets, and indicates an opportunity to increase the certainty of estimates through careful selection or generation of the test drive cycle.« less

Impact of concentration of ellagitannins in oak wood on their levels and organoleptic influence in red wine.

PubMed

Michel, Julien; Jourdes, Michael; Silva, Maria A; Giordanengo, Thomas; Mourey, Nicolas; Teissedre, Pierre-Louis

2011-05-25

Some wood substances such as ellagitannins can be extracted during wine aging in oak barrels. The level of these hydrolyzable tannins in wine depends of some parameters of oak wood. Their impact on the organoleptic perception of red wine is poorly known. In our research, oak staves were classified in three different groups according to their level of ellagitannins estimated by NIRS (near infrared spectroscopy) online procedure (Oakscan). First, the ellagitannin level and composition were determine for each classified stave and an excellent correlation between the NIRS classification (low, medium and high potential level of ellagitannin) and the ellagitannin content estimated by HPLC-UV was found. Each different group of NIRS classified staves was then added to red wine during its aging in a stainless tank, and the extraction and evolution of the ellagitannins were monitored. A good correlation between the NIRS classification and the concentration of ellagitannins in red wine aging in contact with the classified staves was observed. The influence of levels of ellagitannins on the resulting wine perception was estimated by a trained judge's panel, and it reveals that the level of ellagitannins in wine has an impact on the roundness and amplitude of the red wine.

New battery model considering thermal transport and partial charge stationary effects in photovoltaic off-grid applications

NASA Astrophysics Data System (ADS)

Sanz-Gorrachategui, Iván; Bernal, Carlos; Oyarbide, Estanis; Garayalde, Erik; Aizpuru, Iosu; Canales, Jose María; Bono-Nuez, Antonio

2018-02-01

The optimization of the battery pack in an off-grid Photovoltaic application must consider the minimum sizing that assures the availability of the system under the worst environmental conditions. Thus, it is necessary to predict the evolution of the state of charge of the battery under incomplete daily charging and discharging processes and fluctuating temperatures over day-night cycles. Much of previous development work has been carried out in order to model the short term evolution of battery variables. Many works focus on the on-line parameter estimation of available charge, using standard or advanced estimators, but they are not focused on the development of a model with predictive capabilities. Moreover, normally stable environmental conditions and standard charge-discharge patterns are considered. As the actual cycle-patterns differ from the manufacturer's tests, batteries fail to perform as expected. This paper proposes a novel methodology to model these issues, with predictive capabilities to estimate the remaining charge in a battery after several solar cycles. A new non-linear state space model is proposed as a basis, and the methodology to feed and train the model is introduced. The new methodology is validated using experimental data, providing only 5% of error at higher temperatures than the nominal one.

Effects of LiDAR point density, sampling size and height threshold on estimation accuracy of crop biophysical parameters.

PubMed

Luo, Shezhou; Chen, Jing M; Wang, Cheng; Xi, Xiaohuan; Zeng, Hongcheng; Peng, Dailiang; Li, Dong

2016-05-30

Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R² = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m²), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data.

On-line hemodiafiltration. Gold standard or top therapy?

PubMed

Passlick-Deetjen, Jutta; Pohlmeier, Robert

2002-01-01

In summary, on-line HDF is an extracorporeal blood purification therapy with increased convective removal of uremic toxins as compared to the most frequently used low- or high-flux HD therapy. The clinical advantages of on-line HDF have shown to be dose dependent, which makes on-line HDF superior to other therapies with less convective solute removal. Among the therapies with high convective solute removal, i.e. on-line HDF, on-line HF and double high-flux dialysis, it is difficult to finally decide on the best therapy, as direct comparisons of these therapies are not performed. Theoretical considerations like the relative to on-line HDF lower achievable Kt/Vurea with on-line HF, allow to state that on-line HDF is the top therapy now available for patients with ESRD. A gold standard may be defined as something with which everything else is compared if one tries to establish it in the respective field. In order to declare on-line HDF as the gold standard in renal replacement therapy, we need more direct comparisons of on-line HDF with other therapies, including mortality as an outcome parameter. However, based on our current knowledge, it does not seem to be too speculative that high-quality clinical studies will establish on-line HDF in the next years as the new gold standard in renal replacement therapy.

Calibration of a flexible measurement system based on industrial articulated robot and structured light sensor

NASA Astrophysics Data System (ADS)

Mu, Nan; Wang, Kun; Xie, Zexiao; Ren, Ping

2017-05-01

To realize online rapid measurement for complex workpieces, a flexible measurement system based on an articulated industrial robot with a structured light sensor mounted on the end-effector is developed. A method for calibrating the system parameters is proposed in which the hand-eye transformation parameters and the robot kinematic parameters are synthesized in the calibration process. An initial hand-eye calibration is first performed using a standard sphere as the calibration target. By applying the modified complete and parametrically continuous method, we establish a synthesized kinematic model that combines the initial hand-eye transformation and distal link parameters as a whole with the sensor coordinate system as the tool frame. According to the synthesized kinematic model, an error model is constructed based on spheres' center-to-center distance errors. Consequently, the error model parameters can be identified in a calibration experiment using a three-standard-sphere target. Furthermore, the redundancy of error model parameters is eliminated to ensure the accuracy and robustness of the parameter identification. Calibration and measurement experiments are carried out based on an ER3A-C60 robot. The experimental results show that the proposed calibration method enjoys high measurement accuracy, and this efficient and flexible system is suitable for online measurement in industrial scenes.

Fast Estimation of Defect Profiles from the Magnetic Flux Leakage Signal Based on a Multi-Power Affine Projection Algorithm

PubMed Central

Han, Wenhua; Shen, Xiaohui; Xu, Jun; Wang, Ping; Tian, Guiyun; Wu, Zhengyang

2014-01-01

Magnetic flux leakage (MFL) inspection is one of the most important and sensitive nondestructive testing approaches. For online MFL inspection of a long-range railway track or oil pipeline, a fast and effective defect profile estimating method based on a multi-power affine projection algorithm (MAPA) is proposed, where the depth of a sampling point is related with not only the MFL signals before it, but also the ones after it, and all of the sampling points related to one point appear as serials or multi-power. Defect profile estimation has two steps: regulating a weight vector in an MAPA filter and estimating a defect profile with the MAPA filter. Both simulation and experimental data are used to test the performance of the proposed method. The results demonstrate that the proposed method exhibits high speed while maintaining the estimated profiles clearly close to the desired ones in a noisy environment, thereby meeting the demand of accurate online inspection. PMID:25192314

Fast estimation of defect profiles from the magnetic flux leakage signal based on a multi-power affine projection algorithm.

PubMed

Han, Wenhua; Shen, Xiaohui; Xu, Jun; Wang, Ping; Tian, Guiyun; Wu, Zhengyang

2014-09-04

Magnetic flux leakage (MFL) inspection is one of the most important and sensitive nondestructive testing approaches. For online MFL inspection of a long-range railway track or oil pipeline, a fast and effective defect profile estimating method based on a multi-power affine projection algorithm (MAPA) is proposed, where the depth of a sampling point is related with not only the MFL signals before it, but also the ones after it, and all of the sampling points related to one point appear as serials or multi-power. Defect profile estimation has two steps: regulating a weight vector in an MAPA filter and estimating a defect profile with the MAPA filter. Both simulation and experimental data are used to test the performance of the proposed method. The results demonstrate that the proposed method exhibits high speed while maintaining the estimated profiles clearly close to the desired ones in a noisy environment, thereby meeting the demand of accurate online inspection.

The study of marine corrosion of copper alloys in chlorinated condenser cooling circuits: the role of microbiological components.

PubMed

Carvalho, Maria L; Doma, Jemimah; Sztyler, Magdalena; Beech, Iwona; Cristiani, Pierangela

2014-06-01

The present paper reports the on-line monitoring of corrosion behavior of the CuNi 70:30 and Al brass alloys exposed to seawater and complementary offline microbiological analyses. An electrochemical equipment with sensors specifically set for industrial application and suitable to estimate the corrosion (by linear polarization resistance technique), the biofilm growth (by the BIOX electrochemical probe), the chlorination treatment and other physical-chemical parameters of the water has been used for the on-line monitoring. In order to identify and better characterize the bacteria community present on copper alloys, tube samples were collected after a long period (1year) and short period (2days) of exposition to treated natural seawater (TNSW) and natural seawater (NSW). From the collected samples, molecular techniques such as DNA extraction, polymerase chain reaction (PCR), denaturing gradient gel electrophoresis (DGGE) and identification by sequencing were performed to better characterize and identify the microbial biodiversity present in the samples. The monitoring data confirmed the significant role played by biofouling deposition against the passivity of these Cu alloys in seawater and the positive influence of antifouling treatments based on low level dosages. Molecular analysis indicated biodiversity with the presence of Marinobacter, Alteromonas and Pseudomonas species. Copyright © 2013 Elsevier B.V. All rights reserved.

Rapid prototyping of an EEG-based brain-computer interface (BCI).

PubMed

Guger, C; Schlögl, A; Neuper, C; Walterspacher, D; Strein, T; Pfurtscheller, G

2001-03-01

The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g., late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEG-based brain-computer interface (BCI). This paper describes a new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid prototyping is possible by using Matlab, Simulink, and the Real-Time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and offline analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional digital signal processor (DSP) board. The BCI can be controlled over the Internet, LAN or modem. This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70% and 95% could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive (AAR) model and linear discriminant analysis (LDA).

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.

Source parameters of the 2013 Lushan, Sichuan, Ms7.0 earthquake and estimation of the near-fault strong ground motion

NASA Astrophysics Data System (ADS)

Meng, L.; Zhou, L.; Liu, J.

2013-12-01

Abstract: The April 20, 2013 Ms 7.0 earthquake in Lushan city, Sichuan province of China occurred as the result of east-west oriented reverse-type motion on a north-south striking fault. The source location suggests the event occurred on the Southern part of Longmenshan fault at a depth of 13km. The Lushan earthquake caused a great of loss of property and 196 deaths. The maximum intensity is up to VIII to IX at Boxing and Lushan city, which are located in the meizoseismal area. In this study, we analyzed the dynamic source process and calculated source spectral parameters, estimated the strong ground motion in the near-fault field based on the Brune's circle model at first. A dynamical composite source model (DCSM) has been developed further to simulate the near-fault strong ground motion with associated fault rupture properties at Boxing and Lushan city, respectively. The results indicate that the frictional undershoot behavior in the dynamic source process of Lushan earthquake, which is actually different from the overshoot activity of the Wenchuan earthquake. Based on the simulated results of the near-fault strong ground motion, described the intensity distribution of the Lushan earthquake field. The simulated intensity indicated that, the maximum intensity value is IX, and region with and above VII almost 16,000km2, which is consistence with observation intensity published online by China Earthquake Administration (CEA) on April 25. Moreover, the numerical modeling developed in this study has great application in the strong ground motion prediction and intensity estimation for the earthquake rescue purpose. In fact, the estimation methods based on the empirical relationship and numerical modeling developed in this study has great application in the strong ground motion prediction for the earthquake source process understand purpose. Keywords: Lushan, Ms7.0 earthquake; near-fault strong ground motion; DCSM; simulated intensity

Rapid Detection of Small Movements with GNSS Doppler Observables

NASA Astrophysics Data System (ADS)

Hohensinn, Roland; Geiger, Alain

2017-04-01

High-alpine terrain reacts very sensitively to varying environmental conditions. As an example, increasing temperatures cause thawing of permafrost areas. This, in turn causes an increasing threat by natural hazards like debris flow (e.g. rock glaciers) or rockfalls. The Institute of Geodesy and Photogrammetry is contributing to alpine mass-movement monitoring systems in different project areas in the Swiss Alps. A main focus lies on providing geodetic mass-movement information derived from GNSS static solutions on a daily and a sub-daily basis, obtained with low-cost and autonomous GNSS stations. Another focus is set on rapidly providing reliable geodetic information in real-time i.e. for an integration in early warning systems. One way to achieve this is the estimation of accurate station velocities from observations of range rates, which can be obtained as Doppler observables from time derivatives of carrier phase measurements. The key for this method lies in a precise modeling of prominent effects contributing to the observed range rates, which are satellite velocity, atmospheric delay rates and relativistic effects. A suitable observation model is then devised, which accounts for these predictions. The observation model, combined with a simple kinematic movement model forms the basis for the parameter estimation. Based on the estimated station velocities, movements are then detected using a statistical test. To improve the reliablity of the estimated parameters, another spotlight is set on an on-line quality control procedure. We will present the basic algorithms as well as results from first tests which were carried out with a low-cost GPS L1 phase receiver. With a u-blox module and a sampling rate of 5 Hz, accuracies on the mm/s level can be obtained and velocities down to 1 cm/s can be detected. Reliable and accurate station velocities and movement information can be provided within seconds.

Wave Phase-Sensitive Transformation of 3d-Straining of Mechanical Fields

NASA Astrophysics Data System (ADS)

Smirnov, I. N.; Speranskiy, A. A.

2015-11-01

It is the area of research of oscillatory processes in elastic mechanical systems. Technical result of innovation is creation of spectral set of multidimensional images which reflect time-correlated three-dimensional vector parameters of metrological, and\\or estimated, and\\or design parameters of oscillations in mechanical systems. Reconstructed images of different dimensionality integrated in various combinations depending on their objective function can be used as homeostatic profile or cybernetic image of oscillatory processes in mechanical systems for an objective estimation of current operational conditions in real time. The innovation can be widely used to enhance the efficiency of monitoring and research of oscillation processes in mechanical systems (objects) in construction, mechanical engineering, acoustics, etc. Concept method of vector vibrometry based on application of vector 3D phase- sensitive vibro-transducers permits unique evaluation of real stressed-strained states of power aggregates and loaded constructions and opens fundamental innovation opportunities: conduct of continuous (on-line regime) reliable monitoring of turboagregates of electrical machines, compressor installations, bases, supports, pipe-lines and other objects subjected to damaging effect of vibrations; control of operational safety of technical systems at all the stages of life cycle including design, test production, tuning, testing, operational use, repairs and resource enlargement; creation of vibro-diagnostic systems of authentic non-destructive control of anisotropic characteristics of materials resistance of power aggregates and loaded constructions under outer effects and operational flaws. The described technology is revolutionary, universal and common for all branches of engineering industry and construction building objects.

Biological profiling and dose-response modeling tools ...

EPA Pesticide Factsheets

Through its ToxCast project, the U.S. EPA has developed a battery of in vitro high throughput screening (HTS) assays designed to assess the potential toxicity of environmental chemicals. At present, over 1800 chemicals have been tested in up to 600 assays, yielding a large number of concentration-response data sets. Standard processing of these data sets involves finding a best fitting mathematical model and set of model parameters that specify this model. The model parameters include quantities such as the half-maximal activity concentration (or “AC50”) that have biological significance and can be used to inform the efficacy or potency of a given chemical with respect to a given assay. All of this data is processed and stored in an online-accessible database and website: http://actor.epa.gov/dashboard2. Results from these in vitro assays are used in a multitude of ways. New pathways and targets can be identified and incorporated into new or existing adverse outcome pathways (AOPs). Pharmacokinetic models such as those implemented EPA’s HTTK R package can be used to translate an in vitro concentration into an in vivo dose; i.e., one can predict the oral equivalent dose that might be expected to activate a specific biological pathway. Such predicted values can then be compared with estimated actual human exposures prioritize chemicals for further testing.Any quantitative examination should be accompanied by estimation of uncertainty. We are developing met

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

PubMed

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

2015-01-01

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

Leveraging social system networks in ubiquitous high-data-rate health systems.

PubMed

Massey, Tammara; Marfia, Gustavo; Stoelting, Adam; Tomasi, Riccardo; Spirito, Maurizio A; Sarrafzadeh, Majid; Pau, Giovanni

2011-05-01

Social system networks with high data rates and limited storage will discard data if the system cannot connect and upload the data to a central server. We address the challenge of limited storage capacity in mobile health systems during network partitions with a heuristic that achieves efficiency in storage capacity by modifying the granularity of the medical data during long intercontact periods. Patterns in the connectivity, reception rate, distance, and location are extracted from the social system network and leveraged in the global algorithm and online heuristic. In the global algorithm, the stochastic nature of the data is modeled with maximum likelihood estimation based on the distribution of the reception rates. In the online heuristic, the correlation between system position and the reception rate is combined with patterns in human mobility to estimate the intracontact and intercontact time. The online heuristic performs well with a low data loss of 2.1%-6.1%.

Smart EV Energy Management System to Support Grid Services

NASA Astrophysics Data System (ADS)

Wang, Bin

Under smart grid scenarios, the advanced sensing and metering technologies have been applied to the legacy power grid to improve the system observability and the real-time situational awareness. Meanwhile, there is increasing amount of distributed energy resources (DERs), such as renewable generations, electric vehicles (EVs) and battery energy storage system (BESS), etc., being integrated into the power system. However, the integration of EVs, which can be modeled as controllable mobile energy devices, brings both challenges and opportunities to the grid planning and energy management, due to the intermittency of renewable generation, uncertainties of EV driver behaviors, etc. This dissertation aims to solve the real-time EV energy management problem in order to improve the overall grid efficiency, reliability and economics, using online and predictive optimization strategies. Most of the previous research on EV energy management strategies and algorithms are based on simplified models with unrealistic assumptions that the EV charging behaviors are perfectly known or following known distributions, such as the arriving time, leaving time and energy consumption values, etc. These approaches fail to obtain the optimal solutions in real-time because of the system uncertainties. Moreover, there is lack of data-driven strategy that performs online and predictive scheduling for EV charging behaviors under microgrid scenarios. Therefore, we develop an online predictive EV scheduling framework, considering uncertainties of renewable generation, building load and EV driver behaviors, etc., based on real-world data. A kernel-based estimator is developed to predict the charging session parameters in real-time with improved estimation accuracy. The efficacy of various optimization strategies that are supported by this framework, including valley-filling, cost reduction, event-based control, etc., has been demonstrated. In addition, the existing simulation-based approaches do not consider a variety of practical concerns of implementing such a smart EV energy management system, including the driver preferences, communication protocols, data models, and customized integration of existing standards to provide grid services. Therefore, this dissertation also solves these issues by designing and implementing a scalable system architecture to capture the user preferences, enable multi-layer communication and control, and finally improve the system reliability and interoperability.

Fast machine-learning online optimization of ultra-cold-atom experiments.

PubMed

Wigley, P B; Everitt, P J; van den Hengel, A; Bastian, J W; Sooriyabandara, M A; McDonald, G D; Hardman, K S; Quinlivan, C D; Manju, P; Kuhn, C C N; Petersen, I R; Luiten, A N; Hope, J J; Robins, N P; Hush, M R

2016-05-16

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our 'learner' discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system.

Fast machine-learning online optimization of ultra-cold-atom experiments

PubMed Central

Wigley, P. B.; Everitt, P. J.; van den Hengel, A.; Bastian, J. W.; Sooriyabandara, M. A.; McDonald, G. D.; Hardman, K. S.; Quinlivan, C. D.; Manju, P.; Kuhn, C. C. N.; Petersen, I. R.; Luiten, A. N.; Hope, J. J.; Robins, N. P.; Hush, M. R.

2016-01-01

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ‘learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system. PMID:27180805

Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

PubMed

Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

2017-03-01

A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

AU-FREDI - AUTONOMOUS FREQUENCY DOMAIN IDENTIFICATION

NASA Technical Reports Server (NTRS)

Yam, Y.

1994-01-01

The Autonomous Frequency Domain Identification program, AU-FREDI, is a system of methods, algorithms and software that was developed for the identification of structural dynamic parameters and system transfer function characterization for control of large space platforms and flexible spacecraft. It was validated in the CALTECH/Jet Propulsion Laboratory's Large Spacecraft Control Laboratory. Due to the unique characteristics of this laboratory environment, and the environment-specific nature of many of the software's routines, AU-FREDI should be considered to be a collection of routines which can be modified and reassembled to suit system identification and control experiments on large flexible structures. The AU-FREDI software was originally designed to command plant excitation and handle subsequent input/output data transfer, and to conduct system identification based on the I/O data. Key features of the AU-FREDI methodology are as follows: 1. AU-FREDI has on-line digital filter design to support on-orbit optimal input design and data composition. 2. Data composition of experimental data in overlapping frequency bands overcomes finite actuator power constraints. 3. Recursive least squares sine-dwell estimation accurately handles digitized sinusoids and low frequency modes. 4. The system also includes automated estimation of model order using a product moment matrix. 5. A sample-data transfer function parametrization supports digital control design. 6. Minimum variance estimation is assured with a curve fitting algorithm with iterative reweighting. 7. Robust root solvers accurately factorize high order polynomials to determine frequency and damping estimates. 8. Output error characterization of model additive uncertainty supports robustness analysis. The research objectives associated with AU-FREDI were particularly useful in focusing the identification methodology for realistic on-orbit testing conditions. Rather than estimating the entire structure, as is typically done in ground structural testing, AU-FREDI identifies only the key transfer function parameters and uncertainty bounds that are necessary for on-line design and tuning of robust controllers. AU-FREDI's system identification algorithms are independent of the JPL-LSCL environment, and can easily be extracted and modified for use with input/output data files. The basic approach of AU-FREDI's system identification algorithms is to non-parametrically identify the sampled data in the frequency domain using either stochastic or sine-dwell input, and then to obtain a parametric model of the transfer function by curve-fitting techniques. A cross-spectral analysis of the output error is used to determine the additive uncertainty in the estimated transfer function. The nominal transfer function estimate and the estimate of the associated additive uncertainty can be used for robust control analysis and design. AU-FREDI's I/O data transfer routines are tailored to the environment of the CALTECH/ JPL-LSCL which included a special operating system to interface with the testbed. Input commands for a particular experiment (wideband, narrowband, or sine-dwell) were computed on-line and then issued to respective actuators by the operating system. The operating system also took measurements through displacement sensors and passed them back to the software for storage and off-line processing. In order to make use of AU-FREDI's I/O data transfer routines, a user would need to provide an operating system capable of overseeing such functions between the software and the experimental setup at hand. The program documentation contains information designed to support users in either providing such an operating system or modifying the system identification algorithms for use with input/output data files. It provides a history of the theoretical, algorithmic and software development efforts including operating system requirements and listings of some of the various special purpose subroutines which were developed and optimized for Lahey FORTRAN compilers on IBM PC-AT computers before the subroutines were integrated into the system software. Potential purchasers are encouraged to purchase and review the documentation before purchasing the AU-FREDI software. AU-FREDI is distributed in DEC VAX BACKUP format on a 1600 BPI 9-track magnetic tape (standard media) or a TK50 tape cartridge. AU-FREDI was developed in 1989 and is a copyrighted work with all copyright vested in NASA.

FIESTA—An R estimation tool for FIA analysts

Treesearch

Tracey S. Frescino; Paul L. Patterson; Gretchen G. Moisen; Elizabeth A. Freeman

2015-01-01

FIESTA (Forest Inventory ESTimation for Analysis) is a user-friendly R package that was originally developed to support the production of estimates consistent with current tools available for the Forest Inventory and Analysis (FIA) National Program, such as FIDO (Forest Inventory Data Online) and EVALIDator. FIESTA provides an alternative data retrieval and reporting...

Self-tuning wireless power transmission scheme based on on-line scattering parameters measurement and two-side power matching.

PubMed

Luo, Yanting; Yang, Yongmin; Chen, Zhongsheng

2014-04-10

Sub-resonances often happen in wireless power transmission (WPT) systems using coupled magnetic resonances (CMR) due to environmental changes, coil movements or component degradations, which is a serious challenge for high efficiency power transmission. Thus self-tuning is very significant to keep WPT systems following strongly magnetic resonant conditions in practice. Traditional coupled-mode ways is difficult to solve this problem. In this paper a two-port power wave model is presented, where power matching and the overall systemic power transmission efficiency are firstly defined by scattering (S) parameters. Then we propose a novel self-tuning scheme based on on-line S parameters measurements and two-side power matching. Experimental results testify the feasibility of the proposed method. These findings suggest that the proposed method is much potential to develop strongly self-adaptive WPT systems with CMR.

Online determination of biophysical parameters of mucous membranes of a human body

NASA Astrophysics Data System (ADS)

Lisenko, S. A.; Kugeiko, M. M.

2013-07-01

We have developed a method for online determination of biophysical parameters of mucous membranes (MMs) of a human body (transport scattering coefficient, scattering anisotropy factor, haemoglobin concentration, degrees of blood oxygenation, average diameter of capillaries with blood) from measurements of spectral and spatial characteristics of diffuse reflection. The method is based on regression relationships between linearly independent components of the measured light signals and the unknown parameters of MMs, obtained by simulation of the radiation transfer in the MM under conditions of its general variability. We have proposed and justified the calibration-free fibre-optic method for determining the concentration of haemoglobin in MMs by measuring the light signals diffusely reflected by the tissue in four spectral regions at two different distances from the illumination spot. We have selected the optimal wavelengths of optical probing for the implementation of the method.

Computer aided design of digital controller for radial active magnetic bearings

NASA Technical Reports Server (NTRS)

Cai, Zhong; Shen, Zupei; Zhang, Zuming; Zhao, Hongbin

1992-01-01

A five degree of freedom Active Magnetic Bearing (AMB) system is developed which is controlled by digital controllers. The model of the radial AMB system is linearized and the state equation is derived. Based on the state variables feedback theory, digital controllers are designed. The performance of the controllers are evaluated according to experimental results. The Computer Aided Design (CAD) method is used to design controllers for magnetic bearings. The controllers are implemented with a digital signal processing (DSP) system. The control algorithms are realized with real-time programs. It is very easy to change the controller by changing or modifying the programs. In order to identify the dynamic parameters of the controlled magnetic system, a special experiment was carried out. Also, the online Recursive Least Squares (RLS) parameter identification method is studied. It can be realized with the digital controllers. Online parameter identification is essential for the realization of an adaptive controller.

The MAID Legacy and Future

NASA Astrophysics Data System (ADS)

Tiator, Lothar

2018-05-01

The MAID project is a collection of theoretical models for pseudoscalar meson photo- and electroproduction from nucleons. It is online available and produces results in real time calculations. In addition to kinematical variables also model parameters, especially for baryon resonances, can be online changed and investigated. Over 20 years MAID has become quite popular and the MAID web pages have been called more than 7.7 million times.

Public Claims about Automatic External Defibrillators: An Online Consumer Opinions Study

PubMed Central

2011-01-01

Background Patients are no longer passive recipients of health care, and increasingly engage in health communications outside of the traditional patient and health care professional relationship. As a result, patient opinions and health related judgements are now being informed by a wide range of social, media, and online information sources. Government initiatives recognise self-delivery of health care as a valuable means of responding to the anticipated increased global demand for health resources. Automated External Defibrillators (AEDs), designed for the treatment of Sudden Cardiac Arrest (SCA), have recently become available for 'over the counter' purchase with no need for a prescription. This paper explores the claims and argumentation of lay persons and health care practitioners and professionals relating to these, and how these may impact on the acceptance, adoption and use of these devices within the home context. Methods We carry out a thematic content analysis of a novel form of Internet-based data: online consumer opinions of AED devices posted on Amazon.com, the world's largest online retailer. A total of #83 online consumer reviews of home AEDs are analysed. The analysis is both inductive, identifying themes that emerged from the data, exploring the parameters of public debate relating to these devices, and also driven by theory, centring around the parameters that may impact upon the acceptance, adoption and use of these devices within the home as indicated by the Technology Acceptance Model (TAM). Results Five high-level themes around which arguments for and against the adoption of home AEDs are identified and considered in the context of TAM. These include opinions relating to device usability, usefulness, cost, emotional implications of device ownership, and individual patient risk status. Emotional implications associated with AED acceptance, adoption and use emerged as a notable factor that is not currently reflected within the existing TAM. Conclusions The value, credibility and implications of the findings of this study are considered within the context of existing AED research, and related to technology acceptance theory. From a methodological perspective, this study demonstrates the potential value of online consumer reviews as a novel data source for exploring the parameters of public debate relating to emerging health care technologies. PMID:21592349

Public claims about automatic external defibrillators: an online consumer opinions study.

PubMed

Money, Arthur G; Barnett, Julie; Kuljis, Jasna

2011-05-18

Patients are no longer passive recipients of health care, and increasingly engage in health communications outside of the traditional patient and health care professional relationship. As a result, patient opinions and health related judgements are now being informed by a wide range of social, media, and online information sources. Government initiatives recognise self-delivery of health care as a valuable means of responding to the anticipated increased global demand for health resources. Automated External Defibrillators (AEDs), designed for the treatment of Sudden Cardiac Arrest (SCA), have recently become available for 'over the counter' purchase with no need for a prescription. This paper explores the claims and argumentation of lay persons and health care practitioners and professionals relating to these, and how these may impact on the acceptance, adoption and use of these devices within the home context. We carry out a thematic content analysis of a novel form of Internet-based data: online consumer opinions of AED devices posted on Amazon.com, the world's largest online retailer. A total of #83 online consumer reviews of home AEDs are analysed. The analysis is both inductive, identifying themes that emerged from the data, exploring the parameters of public debate relating to these devices, and also driven by theory, centring around the parameters that may impact upon the acceptance, adoption and use of these devices within the home as indicated by the Technology Acceptance Model (TAM). Five high-level themes around which arguments for and against the adoption of home AEDs are identified and considered in the context of TAM. These include opinions relating to device usability, usefulness, cost, emotional implications of device ownership, and individual patient risk status. Emotional implications associated with AED acceptance, adoption and use emerged as a notable factor that is not currently reflected within the existing TAM. The value, credibility and implications of the findings of this study are considered within the context of existing AED research, and related to technology acceptance theory. From a methodological perspective, this study demonstrates the potential value of online consumer reviews as a novel data source for exploring the parameters of public debate relating to emerging health care technologies.

Lessons from a low-order coupled chemistry meteorology model and applications to a high-dimensional chemical transport model

NASA Astrophysics Data System (ADS)

Haussaire, Jean-Matthieu; Bocquet, Marc

2016-04-01

Atmospheric chemistry models are becoming increasingly complex, with multiphasic chemistry, size-resolved particulate matter, and possibly coupled to numerical weather prediction models. In the meantime, data assimilation methods have also become more sophisticated. Hence, it will become increasingly difficult to disentangle the merits of data assimilation schemes, of models, and of their numerical implementation in a successful high-dimensional data assimilation study. That is why we believe that the increasing variety of problems encountered in the field of atmospheric chemistry data assimilation puts forward the need for simple low-order models, albeit complex enough to capture the relevant dynamics, physics and chemistry that could impact the performance of data assimilation schemes. Following this analysis, we developped a low-order coupled chemistry meteorology model named L95-GRS [1]. The advective wind is simulated by the Lorenz-95 model, while the chemistry is made of 6 reactive species and simulates ozone concentrations. With this model, we carried out data assimilation experiments to estimate the state of the system as well as the forcing parameter of the wind and the emissions of chemical compounds. This model proved to be a powerful playground giving insights on the hardships of online and offline estimation of atmospheric pollution. Building on the results on this low-order model, we test advanced data assimilation methods on a state-of-the-art chemical transport model to check if the conclusions obtained with our low-order model still stand. References [1] Haussaire, J.-M. and Bocquet, M.: A low-order coupled chemistry meteorology model for testing online and offline data assimilation schemes, Geosci. Model Dev. Discuss., 8, 7347-7394, doi:10.5194/gmdd-8-7347-2015, 2015.

VizieR Online Data Catalog: Formation of MW halo and its dwarf satellites (Mashonkina+, 2017)

NASA Astrophysics Data System (ADS)

Mashonkina, L.; Jablonka, P.; Pakhomov, Yu; Sitnova, T.; North, P.

2017-04-01

Tables A.1 and A.2 from the article are presented. The first table contains atomic parameters of FeI/II and TiI/II lines. The second atmospheric parameters and FeI/II, TiI/II nLTE abundances. (2 data files).

76 FR 58268 - Agency Information Collection Activities; Submission to OMB for Review and Approval; Comment...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-20

... simplify some assumptions and to make estimation methods consistent; and characterization as Agency burden...-1007 to (1) EPA online using http://www.regulations.gov (our preferred method), by e-mail to oppt.ncic...-HQ-OPPT-2010-1007, which is available for online viewing at http://www.regulations.gov , or in person...

Factors Affecting Perceived Learning, Satisfaction, and Quality in the Online MBA: A Structural Equation Modeling Approach

ERIC Educational Resources Information Center

Sebastianelli, Rose; Swift, Caroline; Tamimi, Nabil

2015-01-01

The authors examined how six factors related to content and interaction affect students' perceptions of learning, satisfaction, and quality in online master of business administration (MBA) courses. They developed three scale items to measure each factor. Using survey data from MBA students at a private university, the authors estimated structural…

78 FR 3893 - Columbia Gas Transmission, LLC; Notice of Request Under Blanket Authorization

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-17

... any natural gas service; however, Columbia would terminate service to one free gas customer pursuant to the terms of the lease agreement between the customer and Columbia. Columbia estimates that it... contact FERC Online Support at FERC Online[email protected] or call toll-free at (866) 206-3676, or, for...

On-Line Analysis of Southern FIA Data

Treesearch

Michael P. Spinney; Paul C. Van Deusen; Francis A. Roesch

2006-01-01

The Southern On-Line Estimator (SOLE) is a web-based FIA database analysis tool designed with an emphasis on modularity. The Java-based user interface is simple and intuitive to use and the R-based analysis engine is fast and stable. Each component of the program (data retrieval, statistical analysis and output) can be individually modified to accommodate major...

77 FR 132 - Agency Information Collection Activities; Submission for OMB Review; Comment Request; Information...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-03

... for OMB Review; Comment Request; Information Collection Plan for GovBenefits Online ACTION: Notice... Collection Plan for GovBenefits Online,'' to the Office of Management and Budget (OMB) for review and... response, and estimated total burden may be obtained from the RegInfo.gov Web site, http://www.reginfo.gov...

Are Online Exams an Invitation to Cheat?

ERIC Educational Resources Information Center

Harmon, Oskar R.; Lambrinos, James; Kennedy, Peter, Ed.

2008-01-01

In this study, the authors use data from two online courses in principles of economics to estimate a model that predicts exam scores from independent variables of student characteristics. In one course, the final exam was proctored, and in the other course, the final exam was not proctored. In both courses, the first three exams were unproctored.…

Research on a Defects Detection Method in the Ferrite Phase Shifter Cementing Process Based on a Multi-Sensor Prognostic and Health Management (PHM) System.

PubMed

Wan, Bo; Fu, Guicui; Li, Yanruoyue; Zhao, Youhu

2016-08-10

The cementing manufacturing process of ferrite phase shifters has the defect that cementing strength is insufficient and fractures always appear. A detection method of these defects was studied utilizing the multi-sensors Prognostic and Health Management (PHM) theory. Aiming at these process defects, the reasons that lead to defects are analyzed in this paper. In the meanwhile, the key process parameters were determined and Differential Scanning Calorimetry (DSC) tests during the cure process of resin cementing were carried out. At the same time, in order to get data on changing cementing strength, multiple-group cementing process tests of different key process parameters were designed and conducted. A relational model of cementing strength and cure temperature, time and pressure was established, by combining data of DSC and process tests as well as based on the Avrami formula. Through sensitivity analysis for three process parameters, the on-line detection decision criterion and the process parameters which have obvious impact on cementing strength were determined. A PHM system with multiple temperature and pressure sensors was established on this basis, and then, on-line detection, diagnosis and control for ferrite phase shifter cementing process defects were realized. It was verified by subsequent process that the on-line detection system improved the reliability of the ferrite phase shifter cementing process and reduced the incidence of insufficient cementing strength defects.

A hybrid optimization approach to the estimation of distributed parameters in two-dimensional confined aquifers

USGS Publications Warehouse

Heidari, M.; Ranjithan, S.R.

1998-01-01

In using non-linear optimization techniques for estimation of parameters in a distributed ground water model, the initial values of the parameters and prior information about them play important roles. In this paper, the genetic algorithm (GA) is combined with the truncated-Newton search technique to estimate groundwater parameters for a confined steady-state ground water model. Use of prior information about the parameters is shown to be important in estimating correct or near-correct values of parameters on a regional scale. The amount of prior information needed for an accurate solution is estimated by evaluation of the sensitivity of the performance function to the parameters. For the example presented here, it is experimentally demonstrated that only one piece of prior information of the least sensitive parameter is sufficient to arrive at the global or near-global optimum solution. For hydraulic head data with measurement errors, the error in the estimation of parameters increases as the standard deviation of the errors increases. Results from our experiments show that, in general, the accuracy of the estimated parameters depends on the level of noise in the hydraulic head data and the initial values used in the truncated-Newton search technique.In using non-linear optimization techniques for estimation of parameters in a distributed ground water model, the initial values of the parameters and prior information about them play important roles. In this paper, the genetic algorithm (GA) is combined with the truncated-Newton search technique to estimate groundwater parameters for a confined steady-state ground water model. Use of prior information about the parameters is shown to be important in estimating correct or near-correct values of parameters on a regional scale. The amount of prior information needed for an accurate solution is estimated by evaluation of the sensitivity of the performance function to the parameters. For the example presented here, it is experimentally demonstrated that only one piece of prior information of the least sensitive parameter is sufficient to arrive at the global or near-global optimum solution. For hydraulic head data with measurement errors, the error in the estimation of parameters increases as the standard deviation of the errors increases. Results from our experiments show that, in general, the accuracy of the estimated parameters depends on the level of noise in the hydraulic head data and the initial values used in the truncated-Newton search technique.

How Affiliation Disclosure and Control Over User-Generated Comments Affects Consumer Health Knowledge and Behavior: A Randomized Controlled Experiment of Pharmaceutical Direct-to-Consumer Advertising on Social Media

PubMed Central

Vendemia, Megan Ashley

2016-01-01

Background More people are seeking health information online than ever before and pharmaceutical companies are increasingly marketing their drugs through social media. Objective The aim was to examine two major concerns related to online direct-to-consumer pharmaceutical advertising: (1) how disclosing an affiliation with a pharmaceutical company affects how people respond to drug information produced by both health organizations and online commenters, and (2) how knowledge that health organizations control the display of user-generated comments affects consumer health knowledge and behavior. Methods We conducted a 2×2×2 between-subjects experiment (N=674). All participants viewed an infographic posted to Facebook by a health organization about a prescription allergy drug. Across conditions, the infographic varied in the degree to which the health organization and commenters appeared to be affiliated with a drug manufacturer, and the display of user-generated comments appeared to be controlled. Results Affiliation disclosure statements on a health organization’s Facebook post increased perceptions of an organization-drug manufacturer connection, which reduced trust in the organization (point estimate –0.45, 95% CI –0.69 to –0.24) and other users who posted comments about the drug (point estimate –0.44, 95% CI –0.68 to –0.22). Furthermore, increased perceptions of an organization-manufacturer connection reduced the likelihood that people would recommend the drug to important others (point estimate –0.35, 95% CI –0.59 to –0.15), and share the drug post with others on Facebook (point estimate –0.37, 95% CI –0.64 to –0.16). An affiliation cue next to the commenters' names increased perceptions that the commenters were affiliated with the drug manufacturer, which reduced trust in the comments (point estimate –0.81, 95% CI –1.04 to –0.59), the organization that made the post (point estimate –0.68, 95% CI –0.90 to –0.49), the likelihood of participants recommending the drug (point estimate –0.61, 95% CI –0.82 to –0.43), and sharing the post with others on Facebook (point estimate –0.63, 95% CI –0.87 to –0.43). Cues indicating that a health organization removed user-generated comments from a post increased perceptions that the drug manufacturer influenced the display of the comments, which negatively affected trust in the comments (point estimate –0.35, 95% CI –0.53 to –0.20), the organization (point estimate –0.31, 95% CI –0.47 to –0.17), the likelihood of recommending the drug (point estimate –0.26, 95% CI –0.41 to –0.14), and the likelihood of sharing the post with others on Facebook (point estimate –0.28, 95% CI –0.45 to –0.15). (All estimates are unstandardized indirect effects and 95% bias-corrected bootstrap confidence intervals.) Conclusions Concern over pharmaceutical companies hiding their affiliations and strategically controlling user-generated comments is well founded; these practices can greatly affect not only how viewers evaluate drug information online, but also how likely they are to propagate the information throughout their online and offline social networks. PMID:27435883

A Novel Image Steganography Technique for Secured Online Transaction Using DWT and Visual Cryptography

NASA Astrophysics Data System (ADS)

Anitha Devi, M. D.; ShivaKumar, K. B.

2017-08-01

Online payment eco system is the main target especially for cyber frauds. Therefore end to end encryption is very much needed in order to maintain the integrity of secret information related to transactions carried online. With access to payment related sensitive information, which enables lot of money transactions every day, the payment infrastructure is a major target for hackers. The proposed system highlights, an ideal approach for secure online transaction for fund transfer with a unique combination of visual cryptography and Haar based discrete wavelet transform steganography technique. This combination of data hiding technique reduces the amount of information shared between consumer and online merchant needed for successful online transaction along with providing enhanced security to customer’s account details and thereby increasing customer’s confidence preventing “Identity theft” and “Phishing”. To evaluate the effectiveness of proposed algorithm Root mean square error, Peak signal to noise ratio have been used as evaluation parameters

Consumer Perceived Risk, Attitude and Online Shopping Behaviour; Empirical Evidence from Malaysia

NASA Astrophysics Data System (ADS)

Ariff, Mohd Shoki Md; Sylvester, Michele; Zakuan, Norhayati; Ismail, Khalid; Mat Ali, Kamarudin

2014-06-01

The development of e-commerce has increased the popularity of online shopping worldwide. In Malaysia, it was reported that online shopping market size was RM1.8 billion in 2013 and it is estimated to reach RM5 billion by 2015. However, online shopping was rated 11th out of 15 purposes of using internet in 2012. Consumers' perceived risks of online shopping becomes a hot topic to research as it will directly influence users' attitude towards online purchasing, and their attitude will have significant impact to the online purchasing behaviour. The conceptualization of consumers' perceived risk, attitude and online shopping behaviour of this study provides empirical evidence in the study of consumer online behaviour. Four types of risks - product risk, financial, convenience and non-delivery risks - were examined in term of their effect on consumers' online attitude. A web-based survey was employed, and a total of 300 online shoppers of a Malaysia largest online marketplace participated in this study. The findings indicated that product risk, financial and non-delivery risks are hazardous and negatively affect the attitude of online shoppers. Convenience risk was found to have positive effect on consumers' attitude, denoting that online buyers of this site trusted the online seller and they encountered less troublesome with the site. It also implies that consumers did not really concern on non-convenience aspect of online shopping, such as handling of returned products and examine the quality of products featured in the online seller website. The online buyers' attitude was significantly and positively affects their online purchasing behaviour. The findings provide useful model for measuring and managing consumers' perceived risk in internet-based transaction to increase their involvement in online shopping and to reduce their cognitive dissonance in the e-commerce setting.

Optimal Tuner Selection for Kalman-Filter-Based Aircraft Engine Performance Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2011-01-01

An emerging approach in the field of aircraft engine controls and system health management is the inclusion of real-time, onboard models for the inflight estimation of engine performance variations. This technology, typically based on Kalman-filter concepts, enables the estimation of unmeasured engine performance parameters that can be directly utilized by controls, prognostics, and health-management applications. A challenge that complicates this practice is the fact that an aircraft engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. Through Kalman-filter-based estimation techniques, the level of engine performance degradation can be estimated, given that there are at least as many sensors as health parameters to be estimated. However, in an aircraft engine, the number of sensors available is typically less than the number of health parameters, presenting an under-determined estimation problem. A common approach to address this shortcoming is to estimate a subset of the health parameters, referred to as model tuning parameters. The problem/objective is to optimally select the model tuning parameters to minimize Kalman-filterbased estimation error. A tuner selection technique has been developed that specifically addresses the under-determined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine that seeks to minimize the theoretical mean-squared estimation error of the Kalman filter. This approach can significantly reduce the error in onboard aircraft engine parameter estimation applications such as model-based diagnostic, controls, and life usage calculations. The advantage of the innovation is the significant reduction in estimation errors that it can provide relative to the conventional approach of selecting a subset of health parameters to serve as the model tuning parameter vector. Because this technique needs only to be performed during the system design process, it places no additional computation burden on the onboard Kalman filter implementation. The technique has been developed for aircraft engine onboard estimation applications, as this application typically presents an under-determined estimation problem. However, this generic technique could be applied to other industries using gas turbine engine technology.

Investigating the Impact of Uncertainty about Item Parameters on Ability Estimation

ERIC Educational Resources Information Center

Zhang, Jinming; Xie, Minge; Song, Xiaolan; Lu, Ting

2011-01-01

Asymptotic expansions of the maximum likelihood estimator (MLE) and weighted likelihood estimator (WLE) of an examinee's ability are derived while item parameter estimators are treated as covariates measured with error. The asymptotic formulae present the amount of bias of the ability estimators due to the uncertainty of item parameter estimators.…

Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

PubMed

Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

2017-06-09

In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

Bibliography for aircraft parameter estimation

NASA Technical Reports Server (NTRS)

Iliff, Kenneth W.; Maine, Richard E.

1986-01-01

An extensive bibliography in the field of aircraft parameter estimation has been compiled. This list contains definitive works related to most aircraft parameter estimation approaches. Theoretical studies as well as practical applications are included. Many of these publications are pertinent to subjects peripherally related to parameter estimation, such as aircraft maneuver design or instrumentation considerations.

Two-dimensional advective transport in ground-water flow parameter estimation

USGS Publications Warehouse

Anderman, E.R.; Hill, M.C.; Poeter, E.P.

1996-01-01

Nonlinear regression is useful in ground-water flow parameter estimation, but problems of parameter insensitivity and correlation often exist given commonly available hydraulic-head and head-dependent flow (for example, stream and lake gain or loss) observations. To address this problem, advective-transport observations are added to the ground-water flow, parameter-estimation model MODFLOWP using particle-tracking methods. The resulting model is used to investigate the importance of advective-transport observations relative to head-dependent flow observations when either or both are used in conjunction with hydraulic-head observations in a simulation of the sewage-discharge plume at Otis Air Force Base, Cape Cod, Massachusetts, USA. The analysis procedure for evaluating the probable effect of new observations on the regression results consists of two steps: (1) parameter sensitivities and correlations calculated at initial parameter values are used to assess the model parameterization and expected relative contributions of different types of observations to the regression; and (2) optimal parameter values are estimated by nonlinear regression and evaluated. In the Cape Cod parameter-estimation model, advective-transport observations did not significantly increase the overall parameter sensitivity; however: (1) inclusion of advective-transport observations decreased parameter correlation enough for more unique parameter values to be estimated by the regression; (2) realistic uncertainties in advective-transport observations had a small effect on parameter estimates relative to the precision with which the parameters were estimated; and (3) the regression results and sensitivity analysis provided insight into the dynamics of the ground-water flow system, especially the importance of accurate boundary conditions. In this work, advective-transport observations improved the calibration of the model and the estimation of ground-water flow parameters, and use of regression and related techniques produced significant insight into the physical system.

Estimation of dietary nutritional content using an online system with ability to assess the dieticians' accuracy.

PubMed

Aoki, Takeshi; Nakai, Shigeru; Yamauchi, Kazunobu

2006-01-01

We developed an online system for estimating dietary nutritional content. It also had the function of assessing the accuracy of the participating dieticians and ranking their performance. People who wished to have their meal estimated (i.e. clients) submitted images of their meal taken by digital camera to the server via the Internet, and dieticians estimated the nutritional content (i.e. calorie and protein content). The system assessed the accuracy of the dieticians and if it was satisfactory, the results were sent to the client. Clients received details of the calorie and protein content of their meals within 24 h by email. A total of 93 dieticians (71 students and 22 licensed practitioners) used the system. A two-way analysis of variance showed that there was a significant variation (P=0.004) among dieticians in their ability to estimate both calorie and protein content. There was a significant difference in values of both calorie (P=0.02) and protein (P<0.001) estimation accuracy between student dieticians and licensed dieticians. The estimation accuracy of the licensed nutritionists was 85% (SD 10) for calorie content and 78% (SD 17) for protein content.

Advances in parameter estimation techniques applied to flexible structures

NASA Technical Reports Server (NTRS)

Maben, Egbert; Zimmerman, David C.

1994-01-01

In this work, various parameter estimation techniques are investigated in the context of structural system identification utilizing distributed parameter models and 'measured' time-domain data. Distributed parameter models are formulated using the PDEMOD software developed by Taylor. Enhancements made to PDEMOD for this work include the following: (1) a Wittrick-Williams based root solving algorithm; (2) a time simulation capability; and (3) various parameter estimation algorithms. The parameter estimations schemes will be contrasted using the NASA Mini-Mast as the focus structure.

Impact of the time scale of model sensitivity response on coupled model parameter estimation

NASA Astrophysics Data System (ADS)

Liu, Chang; Zhang, Shaoqing; Li, Shan; Liu, Zhengyu

2017-11-01

That a model has sensitivity responses to parameter uncertainties is a key concept in implementing model parameter estimation using filtering theory and methodology. Depending on the nature of associated physics and characteristic variability of the fluid in a coupled system, the response time scales of a model to parameters can be different, from hourly to decadal. Unlike state estimation, where the update frequency is usually linked with observational frequency, the update frequency for parameter estimation must be associated with the time scale of the model sensitivity response to the parameter being estimated. Here, with a simple coupled model, the impact of model sensitivity response time scales on coupled model parameter estimation is studied. The model includes characteristic synoptic to decadal scales by coupling a long-term varying deep ocean with a slow-varying upper ocean forced by a chaotic atmosphere. Results show that, using the update frequency determined by the model sensitivity response time scale, both the reliability and quality of parameter estimation can be improved significantly, and thus the estimated parameters make the model more consistent with the observation. These simple model results provide a guideline for when real observations are used to optimize the parameters in a coupled general circulation model for improving climate analysis and prediction initialization.

A Systematic Approach for Model-Based Aircraft Engine Performance Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2010-01-01

A requirement for effective aircraft engine performance estimation is the ability to account for engine degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. This paper presents a linear point design methodology for minimizing the degradation-induced error in model-based aircraft engine performance estimation applications. The technique specifically focuses on the underdetermined estimation problem, where there are more unknown health parameters than available sensor measurements. A condition for Kalman filter-based estimation is that the number of health parameters estimated cannot exceed the number of sensed measurements. In this paper, the estimated health parameter vector will be replaced by a reduced order tuner vector whose dimension is equivalent to the sensed measurement vector. The reduced order tuner vector is systematically selected to minimize the theoretical mean squared estimation error of a maximum a posteriori estimator formulation. This paper derives theoretical estimation errors at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the estimation accuracy achieved through conventional maximum a posteriori and Kalman filter estimation approaches. Maximum a posteriori estimation results demonstrate that reduced order tuning parameter vectors can be found that approximate the accuracy of estimating all health parameters directly. Kalman filter estimation results based on the same reduced order tuning parameter vectors demonstrate that significantly improved estimation accuracy can be achieved over the conventional approach of selecting a subset of health parameters to serve as the tuner vector. However, additional development is necessary to fully extend the methodology to Kalman filter-based estimation applications.

Segmentation and intensity estimation of microarray images using a gamma-t mixture model.

PubMed

Baek, Jangsun; Son, Young Sook; McLachlan, Geoffrey J

2007-02-15

We present a new approach to the analysis of images for complementary DNA microarray experiments. The image segmentation and intensity estimation are performed simultaneously by adopting a two-component mixture model. One component of this mixture corresponds to the distribution of the background intensity, while the other corresponds to the distribution of the foreground intensity. The intensity measurement is a bivariate vector consisting of red and green intensities. The background intensity component is modeled by the bivariate gamma distribution, whose marginal densities for the red and green intensities are independent three-parameter gamma distributions with different parameters. The foreground intensity component is taken to be the bivariate t distribution, with the constraint that the mean of the foreground is greater than that of the background for each of the two colors. The degrees of freedom of this t distribution are inferred from the data but they could be specified in advance to reduce the computation time. Also, the covariance matrix is not restricted to being diagonal and so it allows for nonzero correlation between R and G foreground intensities. This gamma-t mixture model is fitted by maximum likelihood via the EM algorithm. A final step is executed whereby nonparametric (kernel) smoothing is undertaken of the posterior probabilities of component membership. The main advantages of this approach are: (1) it enjoys the well-known strengths of a mixture model, namely flexibility and adaptability to the data; (2) it considers the segmentation and intensity simultaneously and not separately as in commonly used existing software, and it also works with the red and green intensities in a bivariate framework as opposed to their separate estimation via univariate methods; (3) the use of the three-parameter gamma distribution for the background red and green intensities provides a much better fit than the normal (log normal) or t distributions; (4) the use of the bivariate t distribution for the foreground intensity provides a model that is less sensitive to extreme observations; (5) as a consequence of the aforementioned properties, it allows segmentation to be undertaken for a wide range of spot shapes, including doughnut, sickle shape and artifacts. We apply our method for gridding, segmentation and estimation to cDNA microarray real images and artificial data. Our method provides better segmentation results in spot shapes as well as intensity estimation than Spot and spotSegmentation R language softwares. It detected blank spots as well as bright artifact for the real data, and estimated spot intensities with high-accuracy for the synthetic data. The algorithms were implemented in Matlab. The Matlab codes implementing both the gridding and segmentation/estimation are available upon request. Supplementary material is available at Bioinformatics online.

Estimation of geographic variation in human papillomavirus vaccine uptake in men and women: an online survey using facebook recruitment.

PubMed

Nelson, Erik J; Hughes, John; Oakes, J Michael; Pankow, James S; Kulasingam, Shalini L

2014-09-01

Federally funded surveys of human papillomavirus (HPV) vaccine uptake are important for pinpointing geographically based health disparities. Although national and state level data are available, local (ie, county and postal code level) data are not due to small sample sizes, confidentiality concerns, and cost. Local level HPV vaccine uptake data may be feasible to obtain by targeting specific geographic areas through social media advertising and recruitment strategies, in combination with online surveys. Our goal was to use Facebook-based recruitment and online surveys to estimate local variation in HPV vaccine uptake among young men and women in Minnesota. From November 2012 to January 2013, men and women were recruited via a targeted Facebook advertisement campaign to complete an online survey about HPV vaccination practices. The Facebook advertisements were targeted to recruit men and women by location (25 mile radius of Minneapolis, Minnesota, United States), age (18-30 years), and language (English). Of the 2079 men and women who responded to the Facebook advertisements and visited the study website, 1003 (48.2%) enrolled in the study and completed the survey. The average advertising cost per completed survey was US $1.36. Among those who reported their postal code, 90.6% (881/972) of the participants lived within the previously defined geographic study area. Receipt of 1 dose or more of HPV vaccine was reported by 65.6% women (351/535), and 13.0% (45/347) of men. These results differ from previously reported Minnesota state level estimates (53.8% for young women and 20.8% for young men) and from national estimates (34.5% for women and 2.3% for men). This study shows that recruiting a representative sample of young men and women based on county and postal code location to complete a survey on HPV vaccination uptake via the Internet is a cost-effective and feasible strategy. This study also highlights the need for local estimates to assess the variation in HPV vaccine uptake, as these estimates differ considerably from those obtained using survey data that are aggregated to the state or federal level.

Estimation of Geographic Variation in Human Papillomavirus Vaccine Uptake in Men and Women: An Online Survey Using Facebook Recruitment

PubMed Central

Hughes, John; Oakes, J Michael; Pankow, James S; Kulasingam, Shalini L

2014-01-01

Background Federally funded surveys of human papillomavirus (HPV) vaccine uptake are important for pinpointing geographically based health disparities. Although national and state level data are available, local (ie, county and postal code level) data are not due to small sample sizes, confidentiality concerns, and cost. Local level HPV vaccine uptake data may be feasible to obtain by targeting specific geographic areas through social media advertising and recruitment strategies, in combination with online surveys. Objective Our goal was to use Facebook-based recruitment and online surveys to estimate local variation in HPV vaccine uptake among young men and women in Minnesota. Methods From November 2012 to January 2013, men and women were recruited via a targeted Facebook advertisement campaign to complete an online survey about HPV vaccination practices. The Facebook advertisements were targeted to recruit men and women by location (25 mile radius of Minneapolis, Minnesota, United States), age (18-30 years), and language (English). Results Of the 2079 men and women who responded to the Facebook advertisements and visited the study website, 1003 (48.2%) enrolled in the study and completed the survey. The average advertising cost per completed survey was US $1.36. Among those who reported their postal code, 90.6% (881/972) of the participants lived within the previously defined geographic study area. Receipt of 1 dose or more of HPV vaccine was reported by 65.6% women (351/535), and 13.0% (45/347) of men. These results differ from previously reported Minnesota state level estimates (53.8% for young women and 20.8% for young men) and from national estimates (34.5% for women and 2.3% for men). Conclusions This study shows that recruiting a representative sample of young men and women based on county and postal code location to complete a survey on HPV vaccination uptake via the Internet is a cost-effective and feasible strategy. This study also highlights the need for local estimates to assess the variation in HPV vaccine uptake, as these estimates differ considerably from those obtained using survey data that are aggregated to the state or federal level. PMID:25231937

Improved Estimates of Thermodynamic Parameters

NASA Technical Reports Server (NTRS)

Lawson, D. D.

1982-01-01

Techniques refined for estimating heat of vaporization and other parameters from molecular structure. Using parabolic equation with three adjustable parameters, heat of vaporization can be used to estimate boiling point, and vice versa. Boiling points and vapor pressures for some nonpolar liquids were estimated by improved method and compared with previously reported values. Technique for estimating thermodynamic parameters should make it easier for engineers to choose among candidate heat-exchange fluids for thermochemical cycles.

Vehicle dynamic prediction systems with on-line identification of vehicle parameters and road conditions.

PubMed

Hsu, Ling-Yuan; Chen, Tsung-Lin

2012-11-13

This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event.

Vehicle Dynamic Prediction Systems with On-Line Identification of Vehicle Parameters and Road Conditions

PubMed Central

Hsu, Ling-Yuan; Chen, Tsung-Lin

2012-01-01

This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event. PMID:23202231

An Online Observer for Minimization of Pulsating Torque in SMPM Motors

PubMed Central

Roșca, Lucian

2016-01-01

A persistent problem of surface mounted permanent magnet (SMPM) motors is the non-uniformity of the developed torque. Either the motor design or the motor control needs to be improved in order to minimize the periodic disturbances. This paper proposes a new control technique for reducing periodic disturbances in permanent magnet (PM) electro-mechanical actuators, by advancing a new observer/estimator paradigm. A recursive estimation algorithm is implemented for online control. The compensating signal is identified and added as feedback to the control signal of the servo motor. Compensation is evaluated for different values of the input signal, to show robustness of the proposed method. PMID:27089182

Needle Steering in Biological Tissue using Ultrasound-based Online Curvature Estimation

PubMed Central

Moreira, Pedro; Patil, Sachin; Alterovitz, Ron; Misra, Sarthak

2014-01-01

Percutaneous needle insertions are commonly performed for diagnostic and therapeutic purposes. Accurate placement of the needle tip is important to the success of many needle procedures. The current needle steering systems depend on needle-tissue-specific data, such as maximum curvature, that is unavailable prior to an interventional procedure. In this paper, we present a novel three-dimensional adaptive steering method for flexible bevel-tipped needles that is capable of performing accurate tip placement without previous knowledge about needle curvature. The method steers the needle by integrating duty-cycled needle steering, online curvature estimation, ultrasound-based needle tracking, and sampling-based motion planning. The needle curvature estimation is performed online and used to adapt the path and duty cycling. We evaluated the method using experiments in a homogenous gelatin phantom, a two-layer gelatin phantom, and a biological tissue phantom composed of a gelatin layer and in vitro chicken tissue. In all experiments, virtual obstacles and targets move in order to represent the disturbances that might occur due to tissue deformation and physiological processes. The average targeting error using our new adaptive method is 40% lower than using the conventional non-adaptive duty-cycled needle steering method. PMID:26229729

Fused smart sensor network for multi-axis forward kinematics estimation in industrial robots.

PubMed

Rodriguez-Donate, Carlos; Osornio-Rios, Roque Alfredo; Rivera-Guillen, Jesus Rooney; Romero-Troncoso, Rene de Jesus

2011-01-01

Flexible manipulator robots have a wide industrial application. Robot performance requires sensing its position and orientation adequately, known as forward kinematics. Commercially available, motion controllers use high-resolution optical encoders to sense the position of each joint which cannot detect some mechanical deformations that decrease the accuracy of the robot position and orientation. To overcome those problems, several sensor fusion methods have been proposed but at expenses of high-computational load, which avoids the online measurement of the joint's angular position and the online forward kinematics estimation. The contribution of this work is to propose a fused smart sensor network to estimate the forward kinematics of an industrial robot. The developed smart processor uses Kalman filters to filter and to fuse the information of the sensor network. Two primary sensors are used: an optical encoder, and a 3-axis accelerometer. In order to obtain the position and orientation of each joint online a field-programmable gate array (FPGA) is used in the hardware implementation taking advantage of the parallel computation capabilities and reconfigurability of this device. With the aim of evaluating the smart sensor network performance, three real-operation-oriented paths are executed and monitored in a 6-degree of freedom robot.

Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation

NASA Astrophysics Data System (ADS)

Li, Shan; Zhang, Shaoqing; Liu, Zhengyu; Lu, Lv; Zhu, Jiang; Zhang, Xuefeng; Wu, Xinrong; Zhao, Ming; Vecchi, Gabriel A.; Zhang, Rong-Hua; Lin, Xiaopei

2018-04-01

Parametric uncertainty in convection parameterization is one major source of model errors that cause model climate drift. Convection parameter tuning has been widely studied in atmospheric models to help mitigate the problem. However, in a fully coupled general circulation model (CGCM), convection parameters which impact the ocean as well as the climate simulation may have different optimal values. This study explores the possibility of estimating convection parameters with an ensemble coupled data assimilation method in a CGCM. Impacts of the convection parameter estimation on climate analysis and forecast are analyzed. In a twin experiment framework, five convection parameters in the GFDL coupled model CM2.1 are estimated individually and simultaneously under both perfect and imperfect model regimes. Results show that the ensemble data assimilation method can help reduce the bias in convection parameters. With estimated convection parameters, the analyses and forecasts for both the atmosphere and the ocean are generally improved. It is also found that information in low latitudes is relatively more important for estimating convection parameters. This study further suggests that when important parameters in appropriate physical parameterizations are identified, incorporating their estimation into traditional ensemble data assimilation procedure could improve the final analysis and climate prediction.

VizieR Online Data Catalog: A catalog of exoplanet physical parameters (Foreman-Mackey+, 2014)

NASA Astrophysics Data System (ADS)

Foreman-Mackey, D.; Hogg, D. W.; Morton, T. D.

2017-05-01

The first ingredient for any probabilistic inference is a likelihood function, a description of the probability of observing a specific data set given a set of model parameters. In this particular project, the data set is a catalog of exoplanet measurements and the model parameters are the values that set the shape and normalization of the occurrence rate density. (2 data files).

Joint Multi-Fiber NODDI Parameter Estimation and Tractography Using the Unscented Information Filter

PubMed Central

Reddy, Chinthala P.; Rathi, Yogesh

2016-01-01

Tracing white matter fiber bundles is an integral part of analyzing brain connectivity. An accurate estimate of the underlying tissue parameters is also paramount in several neuroscience applications. In this work, we propose to use a joint fiber model estimation and tractography algorithm that uses the NODDI (neurite orientation dispersion diffusion imaging) model to estimate fiber orientation dispersion consistently and smoothly along the fiber tracts along with estimating the intracellular and extracellular volume fractions from the diffusion signal. While the NODDI model has been used in earlier works to estimate the microstructural parameters at each voxel independently, for the first time, we propose to integrate it into a tractography framework. We extend this framework to estimate the NODDI parameters for two crossing fibers, which is imperative to trace fiber bundles through crossings as well as to estimate the microstructural parameters for each fiber bundle separately. We propose to use the unscented information filter (UIF) to accurately estimate the model parameters and perform tractography. The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF). Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters), which can be quite useful to measure model complexity. Results from in-vivo human brain data sets demonstrate the ability of our algorithm to trace through crossing fiber regions, while estimating orientation dispersion and other biophysical model parameters in a consistent manner along the tracts. PMID:27147956

Joint Multi-Fiber NODDI Parameter Estimation and Tractography Using the Unscented Information Filter.

PubMed

Reddy, Chinthala P; Rathi, Yogesh

2016-01-01

Tracing white matter fiber bundles is an integral part of analyzing brain connectivity. An accurate estimate of the underlying tissue parameters is also paramount in several neuroscience applications. In this work, we propose to use a joint fiber model estimation and tractography algorithm that uses the NODDI (neurite orientation dispersion diffusion imaging) model to estimate fiber orientation dispersion consistently and smoothly along the fiber tracts along with estimating the intracellular and extracellular volume fractions from the diffusion signal. While the NODDI model has been used in earlier works to estimate the microstructural parameters at each voxel independently, for the first time, we propose to integrate it into a tractography framework. We extend this framework to estimate the NODDI parameters for two crossing fibers, which is imperative to trace fiber bundles through crossings as well as to estimate the microstructural parameters for each fiber bundle separately. We propose to use the unscented information filter (UIF) to accurately estimate the model parameters and perform tractography. The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF). Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters), which can be quite useful to measure model complexity. Results from in-vivo human brain data sets demonstrate the ability of our algorithm to trace through crossing fiber regions, while estimating orientation dispersion and other biophysical model parameters in a consistent manner along the tracts.

An online training-monitoring system to prevent nonfunctional overreaching.

PubMed

Piacentini, Maria Francesca; Meeusen, Romain

2015-05-01

This longitudinal case study evaluated the effectiveness of an online training-monitoring system to prevent nonfunctional overreaching (NFOR). A female master track and field athlete was followed by means of a daily online training diary (www.spartanova.com) and a weekly profile of mood state (POMS). The online diary consists of objective training data and subjective feelings reported on a 10-cm visual analog scale. Furthermore, parameters that quantify and summarize training and adaptation to training were calculated. The novelty consists in the inclusion of a specific measuring parameter tested to detect NFOR (OR score). During track-season preparation, the athlete was facing some major personal changes, and extra training stress factors increased. Despite the fact that training load (TL) did not increase, the OR score showed a 222% and then a 997% increase compared with baseline. POMS showed a 167% increase in fatigue, a 38% decrease in vigor, a 32% increase in depression scores, and a total mood increase of 22%, with a 1-wk shift compared with the OR score. A 41% decrease in TL restored the OR score and POMS to baseline values within 10 d. The results demonstrate that immediate feedback obtained by "warning signals" to both athletes and coaches, based on individual baseline data, seems an optimal predictor of FOR/NFOR.

Real-Time Parameter Estimation in the Frequency Domain

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2000-01-01

A method for real-time estimation of parameters in a linear dynamic state-space model was developed and studied. The application is aircraft dynamic model parameter estimation from measured data in flight. Equation error in the frequency domain was used with a recursive Fourier transform for the real-time data analysis. Linear and nonlinear simulation examples and flight test data from the F-18 High Alpha Research Vehicle were used to demonstrate that the technique produces accurate model parameter estimates with appropriate error bounds. Parameter estimates converged in less than one cycle of the dominant dynamic mode, using no a priori information, with control surface inputs measured in flight during ordinary piloted maneuvers. The real-time parameter estimation method has low computational requirements and could be implemented

Adaptive optimal input design and parametric estimation of nonlinear dynamical systems: application to neuronal modeling.

PubMed

Madi, Mahmoud K; Karameh, Fadi N

2018-05-11

Many physical models of biological processes including neural systems are characterized by parametric nonlinear dynamical relations between driving inputs, internal states, and measured outputs of the process. Fitting such models using experimental data (data assimilation) is a challenging task since the physical process often operates in a noisy, possibly non-stationary environment; moreover, conducting multiple experiments under controlled and repeatable conditions can be impractical, time consuming or costly. The accuracy of model identification, therefore, is dictated principally by the quality and dynamic richness of collected data over single or few experimental sessions. Accordingly, it is highly desirable to design efficient experiments that, by exciting the physical process with smart inputs, yields fast convergence and increased accuracy of the model. We herein introduce an adaptive framework in which optimal input design is integrated with Square root Cubature Kalman Filters (OID-SCKF) to develop an online estimation procedure that first, converges significantly quicker, thereby permitting model fitting over shorter time windows, and second, enhances model accuracy when only few process outputs are accessible. The methodology is demonstrated on common nonlinear models and on a four-area neural mass model with noisy and limited measurements. Estimation quality (speed and accuracy) is benchmarked against high-performance SCKF-based methods that commonly employ dynamically rich informed inputs for accurate model identification. For all the tested models, simulated single-trial and ensemble averages showed that OID-SCKF exhibited (i) faster convergence of parameter estimates and (ii) lower dependence on inter-trial noise variability with gains up to around 1000 msec in speed and 81% increase in variability for the neural mass models. In terms of accuracy, OID-SCKF estimation was superior, and exhibited considerably less variability across experiments, in identifying model parameters of (a) systems with challenging model inversion dynamics and (b) systems with fewer measurable outputs that directly relate to the underlying processes. Fast and accurate identification therefore carries particular promise for modeling of transient (short-lived) neuronal network dynamics using a spatially under-sampled set of noisy measurements, as is commonly encountered in neural engineering applications. © 2018 IOP Publishing Ltd.

Multi-objective optimization in quantum parameter estimation

NASA Astrophysics Data System (ADS)

Gong, BeiLi; Cui, Wei

2018-04-01

We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.

Incorporation of prior information on parameters into nonlinear regression groundwater flow models: 2. Applications

USGS Publications Warehouse

Cooley, Richard L.

1983-01-01

This paper investigates factors influencing the degree of improvement in estimates of parameters of a nonlinear regression groundwater flow model by incorporating prior information of unknown reliability. Consideration of expected behavior of the regression solutions and results of a hypothetical modeling problem lead to several general conclusions. First, if the parameters are properly scaled, linearized expressions for the mean square error (MSE) in parameter estimates of a nonlinear model will often behave very nearly as if the model were linear. Second, by using prior information, the MSE in properly scaled parameters can be reduced greatly over the MSE of ordinary least squares estimates of parameters. Third, plots of estimated MSE and the estimated standard deviation of MSE versus an auxiliary parameter (the ridge parameter) specifying the degree of influence of the prior information on regression results can help determine the potential for improvement of parameter estimates. Fourth, proposed criteria can be used to make appropriate choices for the ridge parameter and another parameter expressing degree of overall bias in the prior information. Results of a case study of Truckee Meadows, Reno-Sparks area, Washoe County, Nevada, conform closely to the results of the hypothetical problem. In the Truckee Meadows case, incorporation of prior information did not greatly change the parameter estimates from those obtained by ordinary least squares. However, the analysis showed that both sets of estimates are more reliable than suggested by the standard errors from ordinary least squares.

75 FR 36721 - Proposed Collection Renewal

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-28

... interested in promoting global education in the classroom. Estimated annual number of respondents: 300... record of attendance. 2. Title: Speakers Match: Online Request for a Speaker Form. OMB Control Number.... Respondents: Educators interested in promoting global education in the classroom. Estimated annual number of...

Quantifying Users' Interconnectedness in Online Social Networks - An Indispensible Step for Economic Valuation

NASA Astrophysics Data System (ADS)

Gneiser, Martin; Heidemann, Julia; Klier, Mathias; Landherr, Andrea; Probst, Florian

Online social networks have been gaining increasing economic importance in light of the rising number of their users. Numerous recent acquisitions priced at enormous amounts have illustrated this development and revealed the need for adequate business valuation models. The value of an online social network is largely determined by the value of its users, the relationships between these users, and the resulting network effects. Therefore, the interconnectedness of a user within the network has to be considered explicitly to get a reasonable estimate for the economic value. Established standard business valuation models, however, do not sufficiently take these aspects into account. Thus, we propose a measure based on the PageRank-algorithm to quantify users’ interconnectedness in an online social network. This is a first but indispensible step towards an adequate economic valuation of online social networks.

The Effectiveness of Distance Education across Virginia's Community Colleges: Evidence from Introductory College-Level Math and English Courses

ERIC Educational Resources Information Center

Xu, Di; Jaggars, Shanna Smith

2011-01-01

Although online learning is rapidly expanding in the community college setting, there is little evidence regarding its effectiveness among community college students. In the current study, the authors used a statewide administrative data set to estimate the effects of taking one's first college-level math or English course online rather than face…

Student Learning and Student Services: Policy Issues

ERIC Educational Resources Information Center

SchWeber, Claudine

2008-01-01

An increasing number of students in the United States are involved in online education, according to research by the Sloan Foundation. By fall 2004, approximately 2.6 million students were estimated to be enrolled in at least one online course, an average growth rate of 24.8% from 2003-04; this figure represents a 5% increase over the 2002-03…

The Costs of Online Learning. Creating Sound Policy for Digital Learning: A Working Paper Series from the Thomas B. Fordham Institute

ERIC Educational Resources Information Center

Battaglino, Tamara Butler; Haldeman, Matt; Laurans, Eleanor

2012-01-01

The latest installment of the Fordham Institute's "Creating Sound Policy for Digital Learning" series investigates one of the more controversial aspects of digital learning: How much does it cost? In this paper, the Parthenon Group uses interviews with more than fifty vendors and online-schooling experts to estimate today's average…