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PubMed2002

Synchronizing chaotic dynamics with uncertainties based on a sliding mode control design.

The synchronization of two chaotic systems with uncertainties is studied in this paper. A feedback controller is provided based on a sliding mode control design. A kind of extended state observer is used to compensate for the systems' uncertainties, such as the structure difference or parameter mismatching, using only the available synchronizing error. Then the feedback controller becomes physically realizable based on the states of the observer, and can be used to synchronize two continuous chaotic systems. Illustrative examples of the synchronization of Duffing and Van der Pol oscillators as well as two Lorenz systems with parameter mismatching are proposed to show the effectiveness of this method.

Journal Article↗
PubMed2006

Robust control for convection suppression in a fluid layer: The effects of boundary properties, actuator lag, and major parameter uncertainties.

The robustness of control is a requirement to maintain a fluid layer at conductive equilibrium heated to a highly supercritical condition. Robustness determines how much uncertainties, or design parameter mismatches, can be tolerated. Both linear stability analysis and three-dimensional fully nonlinear simulations are used for the study of the linear quadratic Gaussian (LQG) controller. The parameter mismatches from the nominal conditions are introduced into the plant model, while the LQG compensator assumes nominal conditions. The mismatches arise from boundary properties, actuator lag, sensor level uncertainty, and wall thickness, as well as from the major parameters such as Prandtl number, Rayleigh number, wave number, and truncation number in the reduced-order model. The results suggest that the LQG compensator action can preserve closed-loop stability at over ten times the critical Rayleigh number, provided that the mismatches in the sensor level and wall thickness are small. Mismatches in the Prandtl number and wall material properties have little impact. Mismatches in Rayleigh number and wave number are relatively benign compared with the sensor and thickness parameters. Techniques for measuring the plant output temperature at multiple levels with sufficient accuracy may be an implementation challenge.

Journal Article↗
PubMed2000

"Weighing" a closed system and the time-energy uncertainty principle

A gedanken experiment is proposed for "weighing" the total mass of a closed system from within the system. We prove that for an internal observer the time tau, required to measure the total energy with accuracy DeltaE, is bounded according to tauDeltaE>Planck's over 2pi. This time-energy uncertainty principle for a closed system follows from the measurement backreaction on the system. We generally examine what other conserved observables are in principle measurable within a closed system and what are the corresponding uncertainty relations.

Journal Article↗
PubMed2006

Multimode uncertainty relations and separability of continuous variable states.

A multimode uncertainty relation (generalizing the Robertson-Schrödinger relation) is derived as a necessary constraint on the second moments of n pairs of canonical operators. In turn, necessary conditions for the separability of multimode continuous variable states under (m+n)-mode bipartitions are derived from the uncertainty relation. These conditions are proven to be necessary and sufficient for (1+n)-mode Gaussian states and for (m+n)-mode bisymmetric Gaussian states.

Journal Article↗
PubMed2004

Determination of standard uncertainties in fits to pair distribution functions.

Methods are developed to establish the limits of statistical uncertainty for values computed from a pair distribution function (PDF) or models fit to a PDF. This is done by computation of a variance-covariance matrix for the PDF from the uncertainties in the diffraction intensities. The application of this variance-covariance matrix also offers optimal weighting for least-squares refinement.

Journal Article↗
PubMed1994

Local identifiability of a receptor-binding radiopharmacokinetic system having measured parameters of known uncertainty.

Local identifiability was determined for a receptor-binding radiopharmacokinetic system that included measured parameters of known uncertainty. Healthy subjects and patients with severe liver disease were studied with [99mTc] galactosylneoglycoalbumin (TcNGA). Measurements during the 30-min dynamic imaging study included the count rate over liver and heart, the quantity of TcNGA injected Lo, and the fraction-of-injected dose per liter of sampled plasma f. Typical relative standard deviations for these measurements were 1, 0.2, and 5 percent, respectively. A four-state nonlinear model describing the hepatic and plasma time-activity data was then used to calculate the standard error se(pj) for model parameters representing receptor concentration [R]o, the TcNGA-receptor forward binding rate constant kb, extrahepatic plasma volume Ve, hepatic plasma volume Vh, and hepatic plasma flow F. Accounting for the measurement uncertainties of Lo and f did not significantly increase the standard errors for parameters [R]o, kb, Ve, Vh and F. When the relative errors of Lo and f were increased to 40%, the change in se(pj) ranged from 10 to 100%, with parameter Vh being the most sensitive. The exception was se(kb), the increase of which was less than 1%. Imaging studies with reduced [R]o, typically associated with patients with liver disease, resulted in greater increases in all estimated parameter errors except se(kb) which had a lower increase. Lastly, the error propagation introduced by direct measurement of the liver observational coefficients sigma 12 and sigma 13 was investigated by simulating changes in the relative standard deviation in parameters sigma 12 and sigma 13 from 0 to 40%. Imaging studies from healthy subjects showed no increase in se(pj).(ABSTRACT TRUNCATED AT 250 WORDS)

Albumins↗
PubMed2006

Training reformulated radial basis function neural networks capable of identifying uncertainty in data classification.

This paper introduces a learning algorithm that can be used for training reformulated radial basis function neural networks (RBFNNs) capable of identifying uncertainty in data classification. This learning algorithm trains a special class of reformulated RBFNNs, known as cosine RBFNNs, by updating selected adjustable parameters to minimize the class-conditional variances at the outputs of their radial basis functions (RBFs). The experiments verify that quantum neural networks (QNNs) and cosine RBFNNs trained by the proposed learning algorithm are capable of identifying uncertainty in data classification, a property that is not shared by cosine RBFNNs trained by the original learning algorithm and conventional feed-forward neural networks (FFNNs). Finally, this study leads to a simple classification strategy that can be used to improve the classification accuracy of QNNs and cosine RBFNNs by rejecting ambiguous feature vectors based on their responses.

Algorithms↗
PubMed2004

Design of sliding mode controllers for bilinear systems with time varying uncertainties.

Sliding mode controllers for the bilinear systems with time varying uncertainties are developed in this paper. The bilinear coefficient matching condition which is similar to the traditional matching condition for linear system is defined for the homogeneous bilinear systems. It can be seen that the bilinear coefficient matching condition is very limited and is not generally applicable to the nonhomogeneous bilinear system. Thus, the sliding coefficient matching condition is also considered for the bilinear systems with time varying uncertainties. Then, the sufficient conditions are provided for the reaching mode of the time varying uncertain bilinear systems to be guaranteed by the designed sliding mode controllers. Moreover, the stability of the uncertain bilinear systems with the sliding mode controller is discussed. Simulation results are included to illustrate the effectiveness of the proposed sliding mode controllers.

Journal Article↗
PubMed2004

Adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties.

A new design approach of an adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties is presented in this paper. A fuzzy terminal sliding mode controller is designed to retain the advantages of the terminal sliding mode controller and to reduce the chattering occurred with the terminal sliding mode controller. The sufficient condition is provided for the uncertain system to be invariant on the sliding surface. The parameters of the output fuzzy sets in the fuzzy mechanism are adapted on-line to improve the performance of the fuzzy sliding mode control system. The bounds of the uncertainties are not required to be known in advance for the presented adaptive fuzzy sliding mode controller. The stability of the fuzzy control system is also guaranteed. Moreover, the chattering around the sliding surface in the sliding mode control can be reduced by the proposed design approach. Simulation results are included to illustrate the effectiveness of the proposed adaptive fuzzy terminal sliding mode controller.

Journal Article↗
PubMed2002

Illustration of sampling-based methods for uncertainty and sensitivity analysis.

A sequence of linear, monotonic, and nonmonotonic test problems is used to illustrate sampling-based uncertainty and sensitivity analysis procedures. Uncertainty results obtained with replicated random and Latin hypercube samples are compared, with the Latin hypercube samples tending to produce more stable results than the random samples. Sensitivity results obtained with the following procedures and/or measures are illustrated and compared: correlation coefficients (CCs), rank correlation coefficients (RCCs), common means (CMNs), common locations (CLs), common medians (CMDs), statistical independence (SI), standardized regression coefficients (SRCs), partial correlation coefficients (PCCs), standardized rank regression coefficients (SRRCs), partial rank correlation coefficients (PRCCs), stepwise regression analysis with raw and rank-transformed data, and examination of scatter plots. The effectiveness of a given procedure and/or measure depends on the characteristics of the individual test problems, with (1) linear measures (i.e., CCs, PCCs, SRCs) performing well on the linear test problems, (2) measures based on rank transforms (i.e., RCCs, PRCCs, SRRCs) performing well on the monotonic test problems, and (3) measures predicated on searches for nonrandom patterns (i.e., CMNs, CLs, CMDs, SI) performing well on the nonmonotonic test problems.

Journal Article↗
PubMed2001

Sensitivity analysis, Monte Carlo risk analysis, and Bayesian uncertainty assessment.

Standard statistical methods understate the uncertainty one should attach to effect estimates obtained from observational data. Among the methods used to address this problem are sensitivity analysis, Monte Carlo risk analysis (MCRA), and Bayesian uncertainty assessment. Estimates from MCRAs have been presented as if they were valid frequentist or Bayesian results, but examples show that they need not be either in actual applications. It is concluded that both sensitivity analyses and MCRA should begin with the same type of prior specification effort as Bayesian analysis.

Bayes Theorem↗
PubMed1997

Scenarios, uncertainty and conditional forecasts of the world population.

This study is concerned with the methods available for the forecasting of future trends in the world's population. Particular attention is given to the problem of the uncertainties that these forecasts include. "The purpose of this paper is to show how subjective and data-based probabilistic assessments of error can be combined, to give a user a realistic assessment of the uncertainty of demographic forecasts, and to apply these concepts to forecasts of the world population. Moreover, we shall show how conditional forecasts can provide a simple conceptual framework in which to view scenarios. They can be particularly useful in the evaluation of proposed policies. Indeed, the so-called environmental impact assessments...that are now mandatory in many countries for major construction projects typically contain elements of conditional forecasting." The concepts discussed are illustrated by comparing a scenario of future global population growth prepared at the Institute of Applied Systems Analysis with a UN population projection.

Forecasting↗
PubMed2003

Estimating the predicted environmental concentration of the residues of veterinary medicines: should uncertainty and variability be ignored?

European directives require that all veterinary medicines be assessed to determine the harmful effects that their use may have on the environment. Fundamental to this assessment is the calculation of the predicted environmental concentration (PEC), which is dependent on the type of drug, its associated treatment characteristics, and the route by which residues enter the environment. Deterministic models for the calculation of the PEC have previously been presented. In this article, the inclusion of variability and uncertainty within such models is introduced. In particular, models for the calculation of the PEC for residues excreted directly onto pasture by grazing animals are considered and comparison of deterministic and stochastic results suggest that uncertainty and variability cannot be ignored.

Animals↗
PubMed1994

Professional uncertainty: nurses in the technologically intense arena.

The implementation of medical technology in health care continues to escalate. Using a grounded theory methodological approach, nurses were interviewed regarding their feelings about medical technology and its impact on their practice. Through the medium of participant observation nurses were observed as they worked in a technologically intense general hospital in the United States. Nurses were ambivalent regarding the benefits of medical technology to nursing practice and patient care. In particular, they questioned whether the use of techniques facilitated relationships with patients or detracted from them. Findings showed that, in the practice setting, it appeared that most of the nurses' time was spent in co-ordinating, assessing and manipulating equipment. Technology structured nurses' time and gave their role some parameters. Technology also shaped the nurse-patient relationship and limited interactions to educational instruction, physical assessments and strategies to support the technology. Nurses' ambivalence regarding the benefits of technology appeared to be symptomatic of professional uncertainty. The hospital focus on technology is contrary to the considerable appreciation of the psychosocial nurse-patient relationship with which the neophyte nurse has been imbued. This practice-educational dichotomy causes role uncertainty for the registered nurse.

Attitude of Health Personnel↗
PubMed1994

Uncertainties and ambiguities: measuring how medical students cope.

To develop psychometric measures specific to the ambiguities encountered in medicine and determine their value in predicting medical students' attitudes towards patients and their choice of residency, we administered to senior and first-year medical students a 25-item Likert-type questionnaire to assess their intolerance of ambiguity (ITA). Factor analysis yielded two dimensions that were converted to scales: 'Aversion to uncertainties in clinical medicine' (ITA1) and 'Preference for highly structured training environs' (ITA2). First-year students scored higher on ITA1 and lower on ITA2 than seniors. An excessive reliance on high-technology medicine, a negative orientation toward psychological problems, and Machiavellianism predicted ITA1. ITA1 was the best predictor of senior medical students' negative attributional style toward hypochondriac, geriatric and chronic pain patients. The following rank order of seniors' career choice was predicted by ITA1 scores: internal medicine, psychiatry and family medicine (lowest); radiology, surgery and anaesthesiology (highest). And by ITA2 scores: surgery, obstetrics and gynaecology, and surgical subspecialties (lowest); radiology, psychiatry and anaesthesiology (highest). We concluded that personality traits and role characteristics which predict 'Aversion to uncertainties in clinical medicine' are maladaptive to managing many primary care patients, and this mismatch is reflected in seniors' residency choice.

Adaptation, Psychological↗
PubMed1994

A general equilibrium analysis of rural-urban migration under uncertainty.

"This paper analyzes the implications of an exogenous shift in relative prices for an economy that suffers from urban unemployment, as well as uncertainty in the agricultural sector. Among other things, we show that with agricultural uncertainty, an exogenous shift in relative prices will lower agricultural profit. This result is in sharp contrast with the conventional case of risk-neutrality or certainty where agricultural profit is unaffected by changes in the terms of trade." The consequences for rural-urban migration in developing countries are implied.

Agriculture↗
PubMed1987

Integrating uncertainty and interindividual variability in environmental risk assessment.

An integrated, quantitative approach to incorporating both uncertainty and interindividual variability into risk prediction models is described. Individual risk R is treated as a variable distributed in both an uncertainty dimension and a variability dimension, whereas population risk I (the number of additional cases caused by R) is purely uncertain. I is shown to follow a compound Poisson-binomial distribution, which in low-level risk contexts can often be approximated well by a corresponding compound Poisson distribution. The proposed analytic framework is illustrated with an application to cancer risk assessment for a California population exposed to 1,2-dibromo-3-chloropropane from ground water.

California↗
PubMed1997

Skin cancer and inorganic arsenic: uncertainty-status of risk.

The current U.S. EPA standard for inorganic arsenic in drinking water is 50 ppb (microgram/L), dating to the National Interim Primary Drinking Water Regulation of 1976. The current EPA risk analysis predicts an increased lifetime skin cancer risk on the order of 3 or 4 per 1000 from chronic exposure at that concentration. Revision of the standard to only a few ppb, perhaps even less than 1 ppb, may be indicated by the EPA analysis to reduce the lifetime risk to an acceptable level. The cost to water utilities, and ultimately to their consumers, to conform to such a large reduction in the standard could easily reach several billion dollars, so it is particularly important to assess accurately the current risk and the risk reduction that would be achieved by a lower standard. This article addresses the major sources of uncertainty in the EPA analysis with respect to this objective. Specifically, it focuses on uncertainty and variability in the exposure estimates for the landmark study of Tseng and colleagues in Taiwan, analyzed using a reconstruction of the their exposure data. It is concluded that while the available dataset is suitable to establish the hazard of skin cancer, it is too highly summarized for reliable dose-response assessment. A new epidemiologic study is needed, designed for the requirements of dose-response assessment.

Arsenic↗