Imprecise Measurements Research Articles

Finite-time synchronization control for semi-Markov jump neural networks with mode-dependent stochastic parametric uncertainties

This paper addresses the problem of synchronization control for semi-Markov jump neural networks with mode-dependent stochastic parametric uncertainties covering a finite-time period. A more general semi-Markov jump neural network is developed by the mode-dependent stochastic parametric uncertainties technique, where both upper and lower bounds of parametric uncertainties are taken into consideration in determining the imprecise measurements. The time-varying semi-Markov chain information subject to uncertainty is established, and sufficient conditions are achieved combine with some integral method. Finally, two numerical examples are exhibited to verify the effectiveness of the produced scheme.

Moderate deviation for random elliptic PDE with small noise

Partial differential equations with random inputs have become popular models to characterize physical systems with uncertainty coming from imprecise measurement and intrinsic randomness. In this paper, we perform asymptotic rare-event analysis for such elliptic PDEs with random inputs. In particular, we consider the asymptotic regime that the noise level converges to zero suggesting that the system uncertainty is low, but does exist. We develop sharp approximations of the probability of a large class of rare events.

A quadratic boundedness approach to a neural network-based simultaneous estimation of actuator and sensor faults

The paper is devoted to the problem of a neural network-based robust simultaneous actuator and sensor faults estimator design for the purpose of the fault diagnosis of nonlinear systems. In particular, the methodology of designing a neural network-based fault estimator is developed. The main novelty of the approach is associated with possibly simultaneous sensor and actuator faults under imprecise measurements. For this purpose, a linear parameter-varying description of a recurrent neural network is exploited. The proposed approach guaranties a predefined disturbance attenuation level and convergence of the estimator. In particular, it uses the quadratic boundedness approach to provide uncertainty intervals of the achieved estimates. The final part of the paper presents an illustrative example concerning the application of the proposed approach to the multitank system fault diagnosis.

3D spheroid cultures from human astrocyte- and neuronal- like cells: New in vitro models to assess magnetite nanoparticle-induced adverse effects on CNS

Global Navigation Satellite Systems (GNSS) positioning in constrained environments suffers from the presence of Non Line Of Sight and multipath receptions so that, in addition to contain imprecise measurements (e.g. due to atmospheric effects), the set of GNSS pseudo-range observations includes some outliers. In this study, we evaluate the interest of the belief function framework as an alternative to the Interval Analysis approach classically used in localization to deal with imprecise data and possibly outliers. Following the basic idea of the RANSAC (RANdom SAmpling Consensus) algorithm, we propose a new detection of the outliers based on an evidential measure of the consistency of the solution. Each pseudo-range (PR) observation generates a 2D basic belief assignment (bba) that quantifies, for any 2D set, the possibility (according to the observed PR) that it includes the GNSS receiver. Outlier detection is then performed by evaluating directly the consistency of subsets of bbas and the inlier PR information is aggregated through the combination of corresponding bbas. In the case of a dynamic receiver, filtering is performed by combining the bbas derived from the new observations to a bba predicted from estimation at previous time step. Proposed approach was evaluated on two actual datasets acquired in urban environment. Results are evaluated both in terms of precision of the localization and in terms of guarantee of the solution. They compared with former approaches either in belief function framework or using interval analysis, stating the interest of the proposed algorithm.

Using coherence to improve calculation of active acoustic intensity

The Phase and Amplitude Gradient Estimator (PAGE) method was developed as an alternative to the traditional method for calculating energy-based acoustic measures such as active acoustic intensity [E. B. Whiting et al. J. Acoust. Soc. Am. 142, 2208–2218 (2017)]. While this method shows many marked improvements over the traditional method, such as a higher valid frequency bandwidth for broadband sources, contaminating noise can lead to inaccurate results. Both the traditional method and the PAGE method can perform poorly when microphone pairs exhibit low coherence, whether caused by noise or by imprecise measurements. By using a coherence weighting in the least-squares pressure gradient calculations, where the weighting can vary across frequency, better estimates of the pressure gradient can be obtained, which in turn yield more accurate results for intensity. Additionally, a coherence-based approach may be used to mitigate the negative impact of contaminating noise, most especially for uncorrelated contaminating noise. These improvements, though requiring a greater amount of computation time, can be integrated into the PAGE method processing procedures to create a more stable method for calculating acoustic intensity. [Work supported by NSF.]

Quantifying and interpreting network effects: ‘Own-network’ is not the same as ‘firm-level’

This note revisits existing research on network effects in the mobile telephony industry. It highlights that – defined correctly – own-network and firm-level network externalities are two different concepts, and that mixing them up leads to inaccurate interpretations. In particular, I demonstrate that the ratio of cross- to own-network effects is an imprecise measure of compatibility because it does not accurately isolate network effects that convey market power from effects that do not. As an alternative, I propose the ratio of industry- to firm-level network effects.

Global finite‐time output stabilization of nonlinear systems with unknown measurement sensitivity

SummaryThis paper focuses on the output stabilization problem for a class of strict‐feedback systems with unknown nonlinear dynamics and unknown measurement sensitivity. The imprecise sensor measurements render the existing robust control approaches such as neuro or fuzzy control infeasible. A solution is provided in this paper by a feat of the concept of tuning functions and barrier Lyapunov functions (BLFs). Utilizing the modification capability of tuning functions, global stability is preserved. Exploiting the potential robustness of BLFs, the effect of unmodeled dynamics is counteracted under sensitivity errors. The combination of tuning functions with BLFs further leads to a distinct property that the real system output converges to an adjustable region in finite time. Compared with the existing results, the need for prior knowledge of system nonlinearities and measurement sensitivity is removed in this paper. Finally, simulation results illustrate the established theoretical findings.

Integration of Z-numbers and Bayesian decision theory: A hybrid approach to decision making under uncertainty and imprecision

For over four decades decision support systems have been emerging to facilitate decision making under uncertain conditions and in a rapidly changing unstructured environment. Most decision support approaches, such as Bayesian decision theory and computing with words (CW), compare and analyze the consequences of different decision alternatives. Bayesian decision methods use probability theory to handle uncertainty and have been widely used in different areas for estimation and prediction as well as offering decision support. On the other hand, computing with words and approximate reasoning apply fuzzy set theory to deal with imprecise measurements and inexact information and are most concerned with propositions stated in natural language. The concept of a Z-number [1] has been recently introduced to represent propositions and their reliability in natural language. This work proposes a methodology that integrates Z-numbers and Bayesian decision theory to provide decision support when precise measurements and exact values of parameters and probabilities are not available. The relationships and computing methods required for such integration are derived and mathematically proved. The proposed hybrid methodology benefits from both approaches and provides a decision support system based on imprecise and uncertain information drawn from natural language. In order to demonstrate the proof of concept, the proposed methodology has been applied to a realistic case study on breast cancer diagnosis.

Artificial nighttime lights and the “real” well-being of nations: ‘Measuring economic growth from outer space’ and welfare from right here on Earth

Abstract. GDP remains too much of an imprecise measure of the standard of living. There is a need for either substitutes or complements. Nighttime lights are a reasonable indicator of the extent, scale, and intensity of socio-economic activities, but a poor measure of national welfare. However, if nighttime lights are understood to constitute externalities, then their effects can be used to adjust measured growth for welfare. From that angle, nighttime lights appear to exert sub-optimal positive externalities in developing countries, and supra-optimal negative externality in developed countries. This means that even if we assume equal growth rates in developing and developed countries, welfare is enhanced by increasing nighttime lights in developing countries and reduced by increasing nighttime lights in developed countries. Keywords. Artificial lights and economic growth, Nighttime lights and growth, Growth and welfare, Nighttime lights and real well-being (welfare). JEL. H23, O15, D62, I12, O47, I31, R13, Q52.

Correlations in local measurements and entanglement in many-body systems

While entanglement plays an important role in characterizing quantum many-body systems, it is hardly possible to directly access many-body entanglement in real experiments. In this paper, we study how bipartite entanglement of many-body states is manifested in the correlation of local measurement outcomes. In particular, we consider a measure of correlation defined as the statistical distance between the joint probability distribution of local measurement outcomes and the product of its marginal distributions. Various bounds of this measure are obtained and several examples of many-body states are considered as a testbed for the measure. We also generalize the framework to the case of imprecise measurement and argue that the considered measure is related to the concept of quantum macroscopicity.

Simulation of Random Events for Air Traffic Applications

Resilience to uncertainties must be ensured in air traffic management. Unexpected events can either be disruptive, like thunderstorms or the famous volcano ash cloud resulting from the Eyjafjallajökull eruption in Iceland, or simply due to imprecise measurements or incomplete knowledge of the environment. While human operators are able to cope with such situations, it is generally not the case for automated decision support tools. Important examples originate from the numerous attempts made to design algorithms able to solve conflicts between aircraft occurring during flights. The STARGATE (STochastic AppRoach for naviGATion functions in uncertain Environment) project was initiated in order to study the feasibility of inherently robust automated planning algorithms that will not fail when submitted to random perturbations. A mandatory first step is the ability to simulate the usual stochastic phenomenons impairing the system: delays due to airport platforms or air traffic control (ATC) and uncertainties on the wind velocity. The work presented here will detail algorithms suitable for the simulation task.

Mechanisms of implementing public health interventions: a pooled causal mediation analysis of randomised trials

BackgroundThe World Health Organization recommends that nations implement evidence-based nutritional guidelines and policies in settings such as schools and childcare services to improve public health nutrition. Understanding the causal mechanism by which implementation strategies exert their effects could enhance guideline implementation. The aim of this study was to assess the mechanisms by which implementation strategies improved schools and childcare services’ adherence to nutrition guidelines.MethodsWe conducted a mechanism evaluation of an aggregated dataset generated from three randomised controlled trials conducted in schools and childcare services in New South Wales, Australia. Each trial examined the impact of implementation strategies that targeted Theoretical Domains Framework constructs including knowledge, skills, professional role and identity, environmental context and resources. We pooled aggregated organisation level data from each trial, including quantitative assessments of the Theoretical Domains Framework constructs, as well as measures of school or childcare nutrition guideline compliance, the primary implementation outcome. We used causal mediation analysis to estimate the average indirect and direct effects of the implementation strategies and assessed the robustness of our findings to varying levels of unmeasured and unknown confounding.ResultsWe included 121 schools or childcare services in the pooled analysis: 79 allocated to receive guideline and policy implementation strategies and 42 to usual practice. Overall, the interventions improved compliance (odds ratio = 6.64; 95% CI [2.58 to 19.09]); however, the intervention effect was not mediated by any of the four targeted Theoretical Domains Framework constructs (average causal mediation effects through knowledge = − 0.00 [− 0.05 to 0.04], skills = 0.01 [− 0.02 to 0.07], professional role and identity = 0.00 [− 0.03 to 0.03] and environmental context and resources = 0.00 [− 0.02 to 0.06]). The intervention had no significant effect on the four targeted Theoretical Domains Framework constructs, and the constructs were not associated with school or childcare nutrition guideline compliance. Potentially, this lack of effect could be explained by imprecise measurement of the mediators. Alternatively, it is likely that that the interventions were operating via alternative mechanisms that were not captured by the four Theoretical Domains Framework constructs we explored.ConclusionsEven though public health implementation strategies led to meaningful improvements in school or childcare nutrition guideline compliance, these effects were not mediated by key targeted constructs of the Theoretical Domains Framework. Future research should explore the mechanistic role of other Theoretical Domains Framework constructs and evaluate system-level mechanisms informed by an ecological framework.Trial registrationAll trials were prospectively registered with the Australian New Zealand Clinical Trials Registry (ACTRN12613000543785. Registered 15/05/2013; ACTRN12614001148662. Registered 30/10/2014; ACTRN12615001032549. Registered 1/10/2015).

An interval-based approach to model input uncertainty in M/M/1 simulation

This paper presents an interval-based discrete-event simulation (IBS) mechanism to help improve the robustness of simulations that incorporate input uncertainty. This proposed simulation mechanism is based on imprecise probability and models the parameter and model-form uncertainty without the need of sensitivity analysis in traditional simulation practice. The imprecise probabilistic measure given in an interval form is used as the input of simulation in our proposed IBS mechanism, where the statistical distribution parameters have interval values. A parameterization technique for interval probability distributions is proposed in this paper. An interval random variate generation method is used to run the simulation. Uncertainty propagation is achieved by applying Kaucher interval arithmetic. The proposed mechanism is illustrated with an M/M/1 queueing system.

Portfel wieloskładnikowy z nieprecyzyjną wartością bieżącą daną trapezoidalną liczby rozmytej

The article includes an analysis of a multiple asset portfolio, paying special attention to an imprecision risk, burdening the component instruments. The imprecision of decision premises is modeled in the imprecisely stated present value of portfolio assets, given subjectively by the investor in the form of trapezoidal fuzzy numbers. Next, for each asset and consisting portfolio we define imprecision measures appointed based on a fuzzy discounting factor. Analyzed theoretical model takes into account not only rational premises of a decision, but also allows for an inclusion of behavioral, technical and technological factors. During the performed research, relations between imprecision risk measures of assets and portfolio were found. Imprecision risk assessments are computed based on energy and entropy measures. Also, a case study is given, presenting mechanics of the model and methods of calculating risk measures. Performed analysis led to formulating some conclusions about the form and behavior of imprecision risk burdening a portfolio.

Flood modelling improvement using automatic calibration of two dimensional river software SRH-2D

River model calibration is essential for reliable model prediction. The manual calibration method is laborious and time-consuming and requires expert knowledge. River engineering software is now equipped with more complex tools that require a high number of parameters as input, rendering the task of model calibration even more difficult. This paper presents the calibration tool O.P.P.S. (Optimisation Program for PEST and SRH-2D) and then uses it in multiple calibration scenarios. O.P.P.S. combines PEST, a calibration software and SRH-2D, a bi-dimensional hydraulic and sediment model for river systems, into an easy-to-use set of forms. O.P.P.S is designed to minimise the user’s interaction with the involved program to carry out rapid and functional calibration processes. PEST uses the Gauss–Marquardt–Levenberg algorithm to adjust the model’s parameters by minimising an objective function containing the differences between field observation and model-generated values. The tool is used to conduct multiple calibration series of the modelled Ha! Ha! river in Quebec, with varying information content in the observation fields. A sensitivity study is also conducted to assess the behaviour of the calibration process in the presence of erroneous or imprecise measurements.

PID Control and Stability Analysis of an ith leg of a Six Degree of Freedom Machining Bed

Process of material removal from work piece to get desired shape is usually termed as machining. Operations such as milling, drilling and turning are categorized as machining. Current spindle techniques allow quick material removal. Performance envelope is limited in such case for higher spindles. The high speed tools are also vulnerable to external and internal vibrations. Cuts made and parts created in existence of these vibrations will result in dimensional inaccuracy and imprecise measurements. A solution of these problems is to restrict the tool motion in a single dimension and instead give Six-DOF to the machine bed. A six degree of freedom robotic mechanism is proposed to tackle this problem to be used as a bed for machining. Proposed machining bed is a six degree of freedom parallel manipulator known as Stewart Platform, in which the tool is fixed and work piece is mounted on a movable top plate which gives controlled motion in six degrees of freedom. Overview of design for a six degree of freedom machining bed is discussed in this paper. The analysis of machining bed is done on MATLAB®. Close loop response, stability through Bode Plot, Nyquist Plot and Root Loci and selection of a controller when working in open loop conditions is discussed in detail in this paper. The results verify that a six degree of freedom bed can be used for the purpose of machining.

The impact of calibration on medical devices performance and patient safety

Hospitals and health care providers are striving to reduce health care cost without compromising the quality of health care and appropriate medical diagnosis. They are becoming more aware of the importance of medical devices calibration in their health care facilities, and the impact it has on both the quality of health services provided to the patients, and patient’s safety. Healthcare providers are beginning to realize that the maintenance of medical devices alone, without assuring proper calibration, may not be sufficient enough to ensure proper function, adequate and reliable measurements. Accurate and reliable measurements are crucial for appropriate medical decisions. On the contrary, un-calibrated medical devices may lead to imprecise measurements. These measurements will have a significant negative impact on the quality of the healthcare provided to patients and might increase the healthcare cost by subjecting patients to excessive medical treatment. The study was conducted on a representative sample of 20.5% from total asset (1034) of high risk medical devices representing 6 devices categories. The objective of this study is to examine the impact of calibration of medical devices on performance and patient’s safety by investigating devices performance against international standard reference or manufacturer recommendations. 34% of the sample failed the visual test, 5% failed the safety test, and 58% failed the performance test. However, it is important to note that there was no death or serious incidents associated with these devices. In conclusion, these devices do not meet the international standard and continued use of such devices can compromise patient’s safety.

Inferring genetic interactions from comparative fitness data.

Darwinian fitness is a central concept in evolutionary biology. In practice, however, it is hardly possible to measure fitness for all genotypes in a natural population. Here, we present quantitative tools to make inferences about epistatic gene interactions when the fitness landscape is only incompletely determined due to imprecise measurements or missing observations. We demonstrate that genetic interactions can often be inferred from fitness rank orders, where all genotypes are ordered according to fitness, and even from partial fitness orders. We provide a complete characterization of rank orders that imply higher order epistasis. Our theory applies to all common types of gene interactions and facilitates comprehensive investigations of diverse genetic interactions. We analyzed various genetic systems comprising HIV-1, the malaria-causing parasite Plasmodium vivax, the fungus Aspergillus niger, and the TEM-family of β-lactamase associated with antibiotic resistance. For all systems, our approach revealed higher order interactions among mutations.

Parametric uncertainty analysis of solute transport process using fuzzy lattice Boltzmann scheme

Uncertainty analysis of groundwater flow and solute transport model is important from the point of safety measures in the field of nuclear science and technology. Researchers have already carried out this uncertainty analysis using traditional Monte Carlo simulations. However, in practice, Monte Carlo simulation may not be possible because of lack of data obtained from the field experiments. Therefore, demand is to investigate the uncertainty by using imprecise based method. In order to fulfill this demand, we have carried out the parametric uncertainty analysis of solute transport model using a fuzzy set method. This paper, proposes an innovative methodology for solving advection diffusion equation describing tracer transport through geological media in presence of imprecise measurements of model parameters such as tracer dispersion coefficient and groundwater flow velocity. Measurement around most likely value provides a spread causing an imprecision of the model parameters. Imprecision is addressed as a fuzzy variable and the membership function of each such fuzzy variable is expressed in the form of a triangular fuzzy number. The governing advection dispersion equation is numerically solved using lattice Boltzmann scheme along with fuzzy parameters of the representative model and due to this the present numerical approach is named as fuzzy lattice Boltzmann. Uncertainty quantification of the solute concentration as solution of the governing fuzzy differential equation is carried out and by using this uncertainty modeling, advantage of the fuzzy lattice Boltzmann approach for obtaining the numerical solution of a fuzzy partial differential equation is shown.

A hybrid approach for bioprocess state estimation using NIR spectroscopy and a sigma-point Kalman filter

Typically available online measurements in cultivations do not yield enough information to build up an optimization-based control for which model-based estimation techniques are necessary. More specifically, real-time information about the concentrations of substrates and biomass is the key to controlling and optimizing cultivations in a bioreactor. In addition to established techniques such as the measurement of dissolved oxygen, off-gas composition, and the amount of spent correction fluids for pH control, inline near-infrared spectroscopy (NIR) offers the possibility of gathering information online to estimate those concentrations, without the need for manual sampling. The NIR data can be transformed via the widely applied partial least squares (PLS) regression to estimate substrate and biomass concentrations. However, the spectra are corrupted by disturbances, such as gas bubbles from sparging, that can result in noisy and defective spectra and thus may lead to highly imprecise measurements. For feedback control, such imprecisions can lead to wrong decisions with respect to feeding rates, which may be hard to compensate for at a later stage. Moreover, information about operating conditions and physical constraints, such as feeding profiles and kinetic relations between substrates that could be used to rectify erroneous predictions, are disregarded in the PLS model. In this paper, we therefore present a hybrid approach of NIR spectroscopy and nonlinear, model-based state estimation to enable improved quality in the online estimation of substrates and biomass in aerated yeast cultivations. The feeding rates, off-gas concentrations, NIR spectra, and knowledge about the dynamics of the process are integrated seamlessly in a sigma-point Kalman filter (SPKF). The latter is formulated such that it is able to handle physical boundaries, which is especially important for fed-batch cultivations. In order to evaluate the hybrid approach, it is compared to different scenarios typically applied in fermentations. One uses only NIR data with a PLS regression model. The other works with an SPKF, but without NIR information. It is shown that the presented hybrid method outperforms the methods applied separately for the estimation of biomass, glucose, ethanol, ammonium, and phosphate in cultivations of a new, genetically modified strain of Saccharomyces cerevisiae. As only a few cultivations are needed for calibration, a reliable online state estimation is available in the early stage of development of this process.