Selecting creep models using Bayesian methods

The development of creep prediction models has been a field of extensive research and many different models have already been proposed. This paper presents an evaluation method of the prediction quality of creep models for specific experimental data. Within the scope of this paper, the model according to Bockhold and the model according to Heidolf are examined. First, the parameters of the models are identified with respect to existing experimental data. This is done using a sampling based approach of Bayesian updating developed by Ba?ant and Chern. In extension to the method by Ba?ant and Chern, the uncertainty coming from inaccurate measurement data is taken into account in the definition of the likelihood function within the updating algorithm. The more inaccurate the measurements are, the more uncertain the estimated model parameters and model prognoses become. The identification is performed for different short- and long-term creep tests. The intension is not to validate these models intensively, but to evaluate their prognoses for the individually tested creep behavior. The results show that the identifiability of the models?? parameters is different for both models and consequently the models prognoses differ in their uncertainties. Second, the models are evaluated using two different strategies: the stochastic model selection according to MacKay, Beck and Yuen based on the Ockham factor, and a comparison of the uncertainties taking into account parameter and model uncertainties. The results of the evaluation of the creep models differ for various experimental tests. Model Heidolf is more flexible and gives a better fit to the data, however, it fails to predict reliable long-term creep deformations using only short-term measurements compared to model Bockhold. Comparing the evaluation methods, the analysis of uncertainties of the creep prognosis proofs to be more stable than the evaluation using the stochastic model selection.     

Consumer perception of boar meat as affected by labelling information,malodorous compounds and sensitivity to androstenone

This study aimed to assess the influence of two label conditions on the acceptance of boar meat. A central location test was conducted with 145 consumers each assessing 4 pieces of pork loin.     

Effect of Thermomechanical Fatigue on Precipitation Microstructure in Two Precipitation-Hardened Cast Aluminum Alloys

Thermal loading induces modifications of the precipitation microstructure of Al–Si–Cu–Mg alloys. This study focuses on the effect of deformation on precipitation microstructure during thermomechanical loadings. Several specimens were thermomechanically cycled while others were exposed to the same thermal cycles without any mechanical loading. The nature and morphological characteristics of the precipitation microstructure of the thermomechanically cycled specimens are compared to those of the thermally aged ones, using transmission electron microscopy (TEM), in order to assess the effect of deformation on the precipitation microstructure and especially on the kinetics of precipitate growth. The absence of any significant effect of superimposed straining during thermal cycling is discussed. Implications for the prevision of yield strength degradation during service operation are briefly presented.

     

Introduction to the special issue on IEEE NEWCAS 2017

A contactless detector is presented for evaluating hand tremors caused by exercise-induced fatigue and early Parkinson’s disease. The device consists of a spiral coil, a microcontroller, and an inductive sensor circuitry. Theory shows that the resonant frequency of the circuitry increases when the distance between the hand and the spiral coil decreases, thus small variations of distance from tremor can be detected from the changes of resonant frequencies. A mechanical hand was built for experiments to simulate human hand tremors with repeatability at a fixed frequency. The magnitudes and frequencies of the tremors in the mechanical hand were quantitatively identified using the inductive sensor. Hence, feasibility and accuracy of the contactless hand tremor detector were determined. A triaxial accelerometer was used for comparison. By comparing spectral distributions and magnitudes of the tremors, the inductive sensor performed better than the accelerometer. The detector was applied to evaluate actual hand tremors of three subjects who had undergone exercise to induce tremors. The tremor waveform amplitudes of the subjects were quantitatively analyzed by the standard deviations method. The increased signal energies of exercise-induced tremor within 8–12 Hz were confirmed. Then, a subject with early Parkinson’s disease was evaluated by the proposed hand tremor detector. The tremor magnitudes and frequencies of the patient hand were quantitatively identified within in 4–7 Hz. Therefore, the new contactless hand tremor detector can be developed as a clinical instrument for monitoring the fatigue symptoms of post-exercise and diagnosing the early Parkinson’s disease.     

Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks

In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for image classification. We pose the age estimation problem as a deep classification problem followed by a softmax expected value refinement. The key factors of our solution are: deep learned models from large data, robust face alignment, and expected value formulation for age regression. We validate our methods on standard benchmarks and achieve state-of-the-art results for both real and apparent age estimation.     

The Effect of Implanted Al and/or Y on the Scale Formation on Low-Al Fe20Cr2Al Alloy

The very early stages of the oxidation of an Fe20Cr2Al alloy, unmodified and ion-implanted by aluminium, yttrium and a combination of both elements, Al and Y, were studied at 1100 °C in oxygen using two-stage-oxidation exposures with ¹⁸O₂ as a tracer and subsequent characterisation of the scales using SIMS analyses of distribution of oxygen isotopes and oxide-related negative ion clusters, SEM observations of the surface morphology and photoluminescence spectroscopy analysis of the phase composition. The scales formed in all cases, except for the Al-implanted alloy, exhibited layered structures, with the outer part comprising Fe- and Cr-rich oxide, and the inner part being Al₂O₃, which grew due to a mixed outward–inward mechanism . The alumina sub-layers contained the transient oxides and α-Al₂O₃. Implanted Al significantly affected the mechanism of the scale growth, providing that the scale consisted essentially of α-Al₂O₃, and grew via a mixed inward-outward mechanism typical for scales on alumina formers.     

Towards impedance-based temperature estimation for Li-ion battery packs

In order to meet the required power and energy demand of battery-powered applications, battery packs are constructed from a multitude of battery cells. For safety and control purposes, an accurate estimate of the temperature of each battery cell is of vital importance. Using electrochemical impedance spectroscopy (EIS), the battery temperature can be inferred from the impedance. However, performing EIS measurements simultaneously at the same frequency on each cell in a battery pack introduces crosstalk interference in surrounding cells, which may cause EIS measurements in battery packs to be inaccurate. Also, currents flowing through the pack interfere with impedance measurements on the cell level. In this paper, we propose, analyse, and validate a method for estimating the battery temperature in a battery pack in the presence of these disturbances. First, we extend an existing and effective estimation framework for impedance-based temperature estimation towards estimating the temperature of each cell in a pack in the presence of crosstalk and (dis)charge currents. Second, the proposed method is analysed and validated on a two-cell battery pack, which is the first step towards development of this method for a full-size battery pack. Monte Carlo simulations are used to find suitable measurement settings that yield small estimation errors and it is demonstrated experimentally that, over a range of temperatures, the method yields an accuracy of ±1°C in terms of bias, in the presence of both disturbances.