Measurement Error Uncertainty Research Articles

Power requirements for bat-inspired flapping flight with heavy, highly articulated and cambered wings.

Bats fly with highly articulated and heavy wings. To understand their power requirements, we develop a three-dimensional reduced-order model, and apply it to flights of Cynopterus brachyotis, the lesser dog-faced fruit bat. Using previously measured wing kinematics, the model computes aerodynamic forces using blade element momentum theory, and incorporates inertial forces of the flapping wing using the measured mass distribution of the membrane wing and body. The two are combined into a Lagrangian equation of motion, and we performed Monte Carlo simulations to address uncertainties in measurement errors and modelling assumptions. We find that the camber of the armwing decreases with flight speed whereas the handwing camber is more independent of speed. Wing camber disproportionately impacts energetics, mainly during the downstroke, and increases the power requirement from 8% to 22% over flight speed U = 3.2-7.4 m s-1. We separate total power into aerodynamic and inertial components, and aerodynamic power into parasitic, profile and induced power, and find strong agreement with previous theoretical and experimental studies. We find that inertia of wings help to balance aerodynamic forces, alleviating the muscle power required for weight support and thrust generation. Furthermore, the model suggests aerodynamic forces assist in lifting the heavy wing during upstroke.

Uncertainty Propagation and Assessment of Five-Axis On-Machine Measurement Error

Abstract As an important technology of adaptive machining, on-machine measurement (OMM) is always used to measure the shape of the to-be-cut surface. Due to the multi-source errors in an OMM system, the confidence of the measurement accuracy becomes particularly important before planning the next cutting procedure. OMM is implemented by multi-components involving machine tools, fixture, and on-machine probe. Each component introduces uncertainty of error into the measurement result. The positioning error, alignment error, and eccentricity error are investigated in terms of the expectation, uncertainty, and probability density function. By the kinematics of the components participated in the measurement, these errors are accumulated to affect the measurement results. Thus, the uncertainty of the final measurement result is determined by the accumulation of uncertainty of error sources. In order to evaluate the uncertainty of measurement error, an uncertainty propagation model involving positioning error of five-axis machine tools, alignment error of workpiece, and eccentricity error of the stylus tip is established in terms of the kinematic chain of OMM. Then, the uncertainty of the measurement result in vector form is obtained. Finally, the simulation and the experiment about the impeller are employed to validate the effectiveness and repeatability of the uncertainty propagation model for five-axis OMM.

On-machine measurement and temperature compensation method of NURBS surface interpolation for semicircular narrow neck thickness based on ultra-precision machine tool

Flexible joint is a typical low-rigidity micro part, which has four narrow neck structures evenly distributed around its central axis. In the ultra-precision machining process, it is crucial to control the dimensional accuracy of the narrow necks to improve stiffness consistency. However, the limited space of the neck structures and the high accuracy requirements bring extremely difficulties to the measuring process. In this paper, a novel on-machine measurement method for the semicircular narrow necks was presented and a measuring device was developed based on the combination of high linearity incremental measurement method and on-machine measurement technology. Moreover, the uncertainty of measurement error was analyzed in detail. In view of the measurement error caused by temperature drift, a compensation method based on non-uniform rational B-spline surface interpolation was used to enhance the measurement accuracy. The experimental results showed that the measurement accuracy enhanced over 30% after compensation, which met the measuring requirement of flexible joint.

On-machine measurement and temperature compensation method of NURBS surface interpolation for semicircular narrow neck thickness based on ultra-precision machine tool

Abstract Flexible joint is a typical low-rigidity micro part, which has four narrow neck structures evenly distributed around its central axis. In the ultra-precision machining process, It is crucial to control the dimensional accuracy of the narrow necks to improve stiffness consistency. However, the limited space of the neck structures and the high accuracy requirements bring extremely difficulties to the measuring process. In this paper, a novel on-machine measurement method for the semicircular narrow necks was presented and a measuring device was developed based on the combination of high linearity incremental measurement method and on-machine measurement technology. Moreover, the uncertainty of measurement error was analyzed in detail. In view of the measurement error caused by temperature drift, a compensation method based on non-uniform rational B-spline (NURBS) surface interpolation was used to enhance the measurement accuracy. The experimental results showed that the measurement accuracy enhanced over 30% after compensation, which met the measuring requirement of flexible joint.

Enabling Robust State Estimation Through Measurement Error Covariance Adaptation

Accurate platform localization is an integral component of most robotic systems. As these robotic systems become more ubiquitous, it is necessary to develop robust state estimation algorithms that are able to withstand novel and non-cooperative environments. When dealing with novel and non-cooperative environments, little is known a priori about the measurement error uncertainty, thus, there is a requirement that the uncertainty models of the localization algorithm be adaptive. Within this paper, we propose the batch covariance estimation technique, which enables robust state estimation through the iterative adaptation of the measurement uncertainty model. The adaptation of the measurement uncertainty model is granted through non-parametric clustering of the residuals, which enables the characterization of the measurement uncertainty via a Gaussian mixture model. The provided Gaussian mixture model can be utilized within any non-linear least squares optimization algorithm by approximately characterizing each observation with the sufficient statistics of the assigned cluster (i.e., each observation's uncertainty model is updated based upon the assignment provided by the non-parametric clustering algorithm). The proposed algorithm is verified on several GNSS collected data sets, where it is shown that the proposed technique exhibits some advantages when compared to other robust estimation techniques when confronted with degraded data quality.

Considering the uncertainties in empirical correlations for vertical countercurrent flow limitation (CCFL) with TRACE

Abstract TRACE is used to calculate the thermal-hydraulic sequence in nuclear power plants for accident analysis. In some postulated accidents, countercurrent flow limitation (CCFL) phenomena can occur. This phenomenon is calculated by means of empirical relationships in TRACE. Usually, not all empirical relationships used in TRACE are accessible to the user who develops the computational model for accident analysis, but in the case of CCFL, the user must specify the parameters of the empirical relationship. Additional preliminary work is needed for fitting the parameters so that experimental data matches the computational calculations and the accident analysis can be performed. Furthermore, uncertainties in measurement errors from experimental data lead to user-based variations in the parameters of the empirical relationship for CCFL. Therefore, an alternative procedure in which accident analysis is performed with consideration of the uncertainties in the empirical relationships is presented. The uncertainty was quantified by means of a stochastic linear regression model for the Wallis correlation and the Bankoff correlation. An uncertainty analysis was subsequently performed to demonstrate the influence of uncertainty in certain cases of CCFL.

An Improved Bayesian Structural Identification Using the First Two Derivatives of Log-Likelihood Measure

The posterior density of structural parameters conditioned by the measurement is obtained by a differential evolution adaptive Metropolis algorithm (DREAM). The surface of the formal log-likelihood measure is studied considering the uncertainty of measurement error to illustrate the problem of equifinality. To overcome the problem of equifinality, the first two derivatives of the log-likelihood measure are proposed to formulate a new informal likelihood measure for the sake of improving the accuracy of the estimator. Moreover, the proposed measure also reduces the standard deviation (uncertain range) of the posterior samples. The benefit of the proposed approach is demonstrated by simulations on identifying the structural parameters with limit output data and noise polluted measurements.

Development of a composite variability metric for clinical application

Methods: Our methodology involves taking advantage of an existing body of knowledge in the form of ODEs and using this to reason more effectively with the real-time data available at the bedside. A standard first-order Euler approximation of the system of ODEs is mapped directly to a DBN. Delta nodes capture the changes in the model variables in each time step. Model parameters are modeled as continuous gaussian nodes and allowed to vary over time to allow them adjust to the individual patient. The framework allows a clear distinction to be made between true values and measured values, thus accounting for data uncertainty. Results: We applied our methodology to track and predict glycemia levels in critically ill patients in receipt of intravenous glucose and insulin. By using the DBN framework, we account for uncertainty in the reactions of unstable patients and uncertainty in measurement errors. We evaluated our approach on real data for 12 critically ill patients. We compared our results to an existing approach whereby model parameters were reestimated using a nonlinear optimization algorithm. In 10 of 12 cases, the DBN approach outperformed the existing approach, very substantially so in some cases. Conclusions: Much knowledge of human physiology is formalized as systems of differential equations. We have devised a methodology for incorporating this knowledge in a DBN framework and demonstrated its effectiveness on a nonlinear system where evidence is sparse and infrequent. The methodology is used to predict a critically ill patient's plasma glucose levels in response to insulin and glucose infusions. With the data available, which are sporadic and may be inaccurate and incomplete, the DBN approach outperforms a previous approach demonstrating that the DBN method is effective at reestimating model parameters and reasoning with sparse and potentially unreliable data.

Modeling fatigue small-crack growth with confidence – A multistage approach

Measurement of fatigue small-crack growth on the surface of smooth or notch specimens has evolved into a mature experimental technique. However, post data processing and model correlation for fatigue small-crack growth has not progressed in a parallel manner to the measurement technique. A new physics-based, mathematically precise fatigue small-crack growth modeling method is proposed for the specifics of surface small-crack growth in heterogeneous metals and rigorous statistical analysis. A multistage fatigue modeling philosophy was implemented and the fatigue damage incubation, small-crack growth, and long-crack growth regimes were identified. The uncertainty in measurement errors and the effects of random variation of microstructural features on the small-crack growth were explored using Monte Carlo (MC) simulations. The reliability of life prediction is directly correlated to the uncertainty-sources of measurements and the random nature of material microstructures through uncertainty propagation in the model correlation process. Fatigue damage incubation life is proven to be an essential quantity, both mathematically and physically, and it is introduced into the modeling process when correlating multiple experiments under the same loading condition. The proposed model is demonstrated using fatigue surface crack growth measurements of a dual-phase Ti-alloy.

Fuzzy neural network model for habitat prediction and HEP for habitat quality estimation focusing on Japanese medaka (Oryzias latipes) in agricultural canals

In the field of agriculture, development of evaluation techniques for environmental changes is urgently required for the purpose of finding a balance between growth in agricultural productivity and environmental considerations. The habitat evaluation procedures (HEP) constitute one technique for habitat assessment. While HEP is widely applied to estimate both habitat quality and quantity in an environment, it appears to be necessary to develop an accurate habitat prediction model in order to evaluate environments precisely. In fact, habitat selection by fish is affected by complicated interaction between multiple environmental factors, which makes it difficult to relate physical environments to habitat preference. In the present study, we utilize artificial neural networks (ANNs), which are commonly applied to model complex systems, to predict the habitat selection of Japanese medaka (Oryzias latipes) in agricultural canals. Considering the essential vagueness of fish behavior, fuzzy membership functions are introduced into the input layer, which advances ANN to fuzzy neural network (FNN). In addition, symmetric triangular fuzzy numbers are employed to account for uncertainty in measurement errors and dispersions of physical environment. The FNN model precisely predicts the habitat preference of Japanese medaka in an agricultural canal, and the results show a good agreement between the calculated and observed habitat suitability indices (HSI). Finally, the habitat quality of two different reaches at the same point in time is compared using HEP, with a view of suitable habitat for Japanese medaka.

Numerical quantification of the significance of aquatic vegetation affecting spatial distribution of Japanese medaka (Oryzias latipes) in an agricultural canal

Aquatic vegetation plays a very important role in providing food, shelter, and nursery habitat and is also regarded as hydraulic resistance in the stream environment. To achieve better ecological restoration, this trade-off should be solved both hydraulically and ecologically. This study quantifies the effect of aquatic vegetation on the spatial distribution of Japanese medaka (Oryzias latipes) to evaluate its importance to fish habitat preference. The preference for aquatic vegetation index is calculated using a fuzzy preference intensity model (FPIM) with interactions among water depth, current velocity and cover ratio in an agricultural canal. In this model, simplified fuzzy reasoning is introduced to explicitly take the essential vagueness of fish behavior into consideration, and a simple genetic algorithm is used to search for an optimum model representation. Uncertainties in measurement errors and dispersions of the physical environment are positively taken into the model using symmetric triangular fuzzy numbers. To overcome the difficulty in model construction with insufficient data observed in an agricultural canal, this model was conjugated with a model developed in a laboratory experiment. The model obtained was then assessed using the AIC (Akaike’s Information Criterion) to evaluate the significance of vegetation index with a statistical approach. The results suggest the significance of vegetation index to habitat selection by Japanese medaka and that utilization of the AIC enables us to grasp the validity of an additional factor contributing to habitat prediction with a view to a definite scale

Conceptual uncertainty of measurement errors

It is shown that measurement errors can be viewed in three different ensembles of random values with distinct distribution laws. The conceptual uncertainty of an error associated with this phenomenon is discussed. The problem of an extension of interpretation of measurement errors in metrological normative documents is addressed.

An automated calorimetric method of loss measurement in electrical machines

Traditionally the losses dissipated in an electrical machine have been determined from the difference between the measured input and output powers. However, the achievable accuracy is limited by the uncertainty of measurement errors, particularly if the machine is supplied with distorted voltage and current waveforms. Therefore, a more appropriate approach is to employ a calorimetric method in which the losses are determined by direct measurement of the dissipated heat. This paper describes the control and instrumentation of such an automated calorimetric system which has been commissioned to accomodate machines of up to 75 kW rating, and in which losses can be measured to an accuracy better than 10 W.