A Bias Compensated Cross-Relation approach to Thermocouple Characterisation

The measurement of fast changing temperature fluctuations is a challenging problem due to the inherent limited bandwidth of temperature sensors. This results in a measured signal that is a lagged and attenuated version of the input. Compensation can be performed provided an accurate, parameterised sensor model is available. However, to account for the in influence of the measurement environment and changing conditions such as gas velocity, the model must be estimated in-situ. The cross-relation method of blind deconvolution is one approach for in-situ characterisation of sensors. However, a drawback with the method is that it becomes positively biased and unstable at high noise levels. In this paper, the cross-relation method is cast in the discrete-time domain and a bias compensation approach is developed. It is shown that the proposed compensation scheme is robust and yields unbiased estimates with lower estimation variance than the uncompensated version. All results are verified using Monte-Carlo simulations.

Noise resilient discrete-time cross-relation sensor characterisation

In-situ characterisation of thermocouple sensors is a challenging problem. Recently the authors presented a blind characterisation technique based on the cross-relation method of blind identification. The method allows in-situ identification of two thermocouple probes, each with a different dynamic response, using only sampled sensor measurement data. While the technique offers certain advantages over alternative methods, including low estimation variance and the ability to compensate for noise induced bias, the robustness of the method is limited by the multimodal nature of the cost function. In this paper, a normalisation term is proposed which improves the convexity of
the cost function. Further, a normalisation and bias compensation hybrid approach is presented that exploits the advantages of both normalisation and bias compensation. It is found that the optimum of the hybrid cost function is less biased and more stable than when only normalisation is applied. All results were verified by simulation.

A Bias Compensated Cross-Relation approach to Thermocouple Characterisation

The measurement of fast changing temperature fluctuations is a challenging problem due to the inherent limited bandwidth of temperature sensors. This results in a measured signal that is a lagged and attenuated version of the input. Compensation can be performed provided an accurate, parameterised sensor model is available. However, to account for the influence of the measurement environment and changing conditions such as gas velocity, the model must be estimated in-situ. The cross-relation method of blind deconvolution is one approach for in-situ characterisation of sensors. However, a drawback with the method is that it becomes positively biased and unstable at high noise levels. In this paper, the cross-relation method is cast in the discrete-time domain and a bias compensation approach is developed. It is shown that the proposed compensation scheme is robust and yields unbiased estimates with lower estimation variance than the uncompensated version. All results are verified using Monte-Carlo simulations.

LSQ13fn: A type II-Plateau supernova with a possibly low metallicity progenitor that breaks the standardised candle relation∗

We present optical imaging and spectroscopy of supernova (SN) LSQ13fn, a type II supernova with several hitherto-unseen properties. Although it initially showed strong symmetric spectral emission features attributable to He ii, N iii, and C iii, reminiscent of some interacting SNe, it transitioned into an object that would fall more naturally under a type II-Plateau (IIP) classification. However, its spectral evolution revealed several unusual properties: metal lines appeared later than expected, were weak, and some species were conspicuous by their absence. Furthermore, the line velocities were found to be lower than expected given the plateau brightness, breaking the SN IIP standardised candle method for distance estimates. We found that, in combination with a short phase of early-time ejecta-circumstellar material interaction, metal-poor ejecta, and a large progenitor radius could reasonably account for the observed behaviour. Comparisons with synthetic model spectra of SNe IIP of a given progenitor mass would imply a progenitor star metallicity as low as 0.1 Z_⊙. LSQ13fn highlights the diversity of SNe II and the many competing physical effects that come into play towards the final stages of massive star evolution immediately preceding core-collapse.

Using Vibrational Infrared Biomolecular Spectroscopy to Detect Heat-Induced Changes of Molecular Structure in Relation to Nutrient Availability of Prairie Whole Oat Grains on a Molecular Basis

Background To our knowledge, there is little study on the interaction between nutrient availability and molecular structure changes induced by different processing methods in dairy cattle. The objective of this study was to investigate the effect of heat processing methods on interaction between nutrient availability and molecular structure in terms of functional groups that are related to protein and starch inherent structure of oat grains with two continued years and three replication of each year. Method The oat grains were kept as raw (control) or heated in an air-draft oven (dry roasting: DO) at 120 °C for 60 min and under microwave irradiation (MIO) for 6 min. The molecular structure features were revealed by vibrational infrared molecular spectroscopy. Results The results showed that rumen degradability of dry matter, protein and starch was significantly lower (P <0.05) for MIO compared to control and DO treatments. A higher protein α-helix to β-sheet and a lower amide I to starch area ratio were observed for MIO compared to DO and/or raw treatment. A negative correlation (−0.99, P < 0.01) was observed between α-helix or amide I to starch area ratio and dry matter. A positive correlation (0.99, P < 0.01) was found between protein β-sheet and crude protein. Conclusion The results reveal that oat grains are more sensitive to microwave irradiation than dry heating in terms of protein and starch molecular profile and nutrient availability in ruminants.