Jan Peter Hessling

University of Borås

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Yu. M. Galperin

University of Oslo

Clemens Elster

Physikalisch-Technische Bundesanstalt

Pingyu Zhu

Guangzhou University

V. S. Shumeǐko

Chalmers University of Technology

Yusaku Fujii

Kiryu University

T J Esward

National Physical Laboratory

Göran Wendin

Chalmers University of Technology

Dawei Liu

Lanzhou University of Technology

M. Jonson

University of Gothenburg

Anders Mannikoff

University of Borås

Cooperative Institutions

Chalmers University of Technology

Norwegian University of Science and Technology

Physico-Technical Institute

Hunan University of Science and Technology

University of Gothenburg

Institute for Basic Science

Swedish University of Agricultural Sciences

Springer Nature (Germany)

University of Minnesota

University of Borås

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A novel method of estimating dynamic measurement errors

Measurement Science and Technology (2006)

Jan Peter Hessling

A general method for estimating an upper bound of the dynamic measurement error in the time domain is derived, assuming that the complex-valued frequency response function of the system is known. This dynamic error bound related to the dynamic response of the system can be included in the measurement uncertainty as an uncorrected systematic error. The only restriction in the analysis is on linearity of the system, which can include electrical filters, mechanical sensors, amplifiers and similar subsystems. Calculations are verified by simulations for two example systems. The results for one of the most commonly used models of force and pressure transducers and accelerometers are presented emphasizing the main differences to a common estimate. For signals with well-confined spectra the linear dynamic error arises for two reasons: varying amplification and delay with frequency. The latter is analogous to the well-known bandwidth limiting dispersion of signals. For extended spectrum signals/simple pulses the asymptotic tails of the spectra may generate the major part of the error. No widespread robust, general and systematic method of quantifying the measurement error caused by these effects exists, despite that they may generate signal distortion far beyond the common prediction.

Distortion (music)

SIGNAL (programming language)

10.1088/0957-0233/17/10/028

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Citations (52)

Current noise in biased superconducting weak links

EPL (Europhysics Letters) (1996)

Jan Peter Hessling V. S. Shumeǐko Yu. M. Galperin Göran Wendin

Non-equilibrium current noise of a short quasi–one-dimensional constriction between two superconductors is considered. We derive a general expression for the frequency-dependent current-current correlation function valid for arbitrary temperatures, transparencies, and bias voltages. This formalism is then applied to a single current-carrying quantum mode with perfect transparency, and at zero frequency and temperature. Contrary to a transparent channel separating two normal conductors, a weak link between two superconductors exhibits a finite level of noise. The source of noise is the fractional Andreev scattering of quasi-particles with energies |E| greater than the half-width Δ of the superconducting gap. For high bias voltages, V >> Δ/e, the zero-frequency limit of the noise spectrum, S(0), as well as the excess current Iexc, are twice as large than for a normal-superconductor junction, S(0) = (2/5)|e|Iexc.

Andreev reflection

Quantum noise

10.1209/epl/i1996-00414-0

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Citations (22)

A novel method of evaluating dynamic measurement uncertainty utilizing digital filters

Measurement Science and Technology (2009)

Jan Peter Hessling

For every measurement, a measurement uncertainty should be associated with the estimate of the measurand. In particular, this applies to all the common non-stationary transient dynamic measurements made with linear time-invariant measurement systems, despite the present standard treatment providing little guidance for this case. To complete recent studies on the estimation and correction of the systematic dynamic error, the remaining evaluation of measurement uncertainty is addressed here. A method based on digital filtering will be utilized to find the generically time-dependent dynamic contribution to the uncertainty. It will be shown that the widely accepted approach to calculate the stationary uncertainty can be extended to transient measurements, provided some complementary techniques are used and the sensitivity is generalized to be a time-dependent signal instead of a constant. For illustration, the method is applied to the same measurement system analysed in the related published studies.

Transient (computer programming)

LTI system theory

Propagation of uncertainty

10.1088/0957-0233/20/5/055106

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Citations (27)

Uncertainty Quantification and Model Calibration

InTech eBooks (2017)

Jan Peter Hessling

Uncertainty quantification may appear daunting for practitioners due to its inherent complexity but can be intriguing and rewarding for anyone with mathematical ambitions and genuine concern for modeling quality. Uncertainty quantification is what remains to be done when too much credibility has been invested in deterministic analyses and unwarranted assumptions. Model calibration describes the inverse operation targeting optimal prediction and refers to inference of best uncertain model estimates from experimental calibration data. The limited applicability of most state-of-the-art approaches to many of the large and complex calculations made today makes uncertainty quantification and model calibration major topics open for debate, with rapidly growing interest from both science and technology, addressing subtle questions such as credible predictions of climate heating.

Uncertainty Quantification

10.5772/65579

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Citations (37)

ROBUSTNESS OF WILKS' CONSERVATIVE ESTIMATE OF CONFIDENCE INTERVALS

International Journal for Uncertainty Quantification (2015)

Jan Peter Hessling Jeffrey Uhlmann

The striking generality and simplicity of Wilks' method has made it popular for quantifying modeling uncertainty. A conservative estimate of the confidence interval is obtained from a very limited set of randomly drawn model sample values, with probability set by the assigned so-called stability. In contrast, the reproducibility of the estimated limits, or robustness, is beyond our control as it is strongly dependent on the probability distribution of model results. The inherent combination of random sampling and faithful estimation in Wilks' approach is here shown to often result in poor robustness. The estimated confidence interval is consequently not a well-defined measure of modeling uncertainty. To remedy this deficiency, adjustments of Wilks' approach as well as alternative novel, effective but less known approaches based on deterministic sampling are suggested. For illustration, the robustness of Wilks' estimate for uniform and normal model distributions are compared.

Robustness

Generality

10.1615/int.j.uncertaintyquantification.2015012623

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Citations (3)

DYNAMIC MEASUREMENT UNCERTAINTY OF HV VOLTAGE DIVIDERS

Jan Peter Hessling Anders Mannikoff

Recently we proposed an approach based on digital filtering for evaluating dynamic non-stationary contributions to the measurement uncertainty. A dynamic simulator instead of a digital filter bank is here utilized for deriving the dynamic measurement uncertainty of mixed capacitive voltage dividers. These are used for lightning impulse measurements, during a calibration measurement of a standard lightning impulse.

Impulse generator

Source

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Optimal road hump for comfortable speed reduction

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2008)

Pingyu Zhu Jan Peter Hessling Dawei Liu

We propose a practical tool for exploring the dynamic relation between road profiles and vehicle motion in the time domain. It is symmetric in the sense that the direct (vehicle response to a given road profile) and the inverse (ideal road profile for a given response) problems are both solved by means of digital filtering. The dynamics of each vehicle is described by linear time-invariant differential equations found from a frequency domain vibration analysis. These are then synthesized in a time-domain digital 'vehicle filter'. The approach is here applied to ideal road hump construction for single axle vehicle models. The responses at various speeds to the derived optimal hump as well as some existing profiles are compared. These results are condensed into simple guidelines for optimal road hump design.

10.1117/12.819725

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Citations (5)

Flicker noise induced by dynamic impurities in a quantum point contact

Physical review. B, Condensed matter (1995)

Jan Peter Hessling Yu. M. Galperin

We calculate low-frequency noise (LFN) in a quantum point contact (QPC) which is electrostatically defined in a 2D electron gas of a GaAs-AlGaAs heterostructure. The conventional source of LFN in such systems are scattering potentials fluctuating in time acting upon injected electrons. One can discriminate between potentials of different origin -- noise may be caused by the externally applied gate- and source-drain voltages, the motion of defects with internal degrees of freedom close to the channel, electrons hopping between localized states in the doped region, etc. In the present study we propose a model of LFN based upon the assumption that there are many dynamic defects in the surrounding of a QPC. A general expression for the time-dependent current-current correlation function is derived and applied to a QPC with quantized conductance. It is shown that the level of LFN is significantly different at and between the steps in a plot of the conductance vs. gate voltage. On the plateaus, the level of noise is found to be low and strongly model-dependent. At the steps, LFN is much larger and only weakly model-dependent. As long as the system is biased to be at a fixed position relative the conductance step,

Flicker noise

10.1103/physrevb.52.5082

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Citations (13)

Propagation of dynamic measurement uncertainty

Measurement Science and Technology (2011)

Jan Peter Hessling

The time-dependent measurement uncertainty has been evaluated in a number of recent publications, starting from a known uncertain dynamic model. This could be defined as the 'downward' propagation of uncertainty from the model to the targeted measurement. The propagation of uncertainty 'upward' from the calibration experiment to a dynamic model traditionally belongs to system identification. The use of different representations (time, frequency, etc) is ubiquitous in dynamic measurement analyses. An expression of uncertainty in dynamic measurements is formulated for the first time in this paper independent of representation, joining upward as well as downward propagation. For applications in metrology, the high quality of the characterization may be prohibitive for any reasonably large and robust model to pass the whiteness test. This test is therefore relaxed by not directly requiring small systematic model errors in comparison to the randomness of the characterization. Instead, the systematic error of the dynamic model is propagated to the uncertainty of the measurand, analogously but differently to how stochastic contributions are propagated. The pass criterion of the model is thereby transferred from the identification to acceptance of the total accumulated uncertainty of the measurand. This increases the relevance of the test of the model as it relates to its final use rather than the quality of the calibration. The propagation of uncertainty hence includes the propagation of systematic model errors. For illustration, the 'upward' propagation of uncertainty is applied to determine if an appliance box is damaged in an earthquake experiment. In this case, relaxation of the whiteness test was required to reach a conclusive result.

Propagation of uncertainty

Representation

Characterization

Identification

Uncertainty Quantification

10.1088/0957-0233/22/10/105105

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Citations (12)

Dynamic calibration of uni-axial material testing machines

Mechanical Systems and Signal Processing (2007)

Jan Peter Hessling

Base (topology)

Machine tool

10.1016/j.ymssp.2007.07.010

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Citations (19)