A Novel Decentralized Fixed-Time Tracking Control for Modular Robot Manipulators: Theoretical and Experimental Verification

This paper presents a novel decentralized fixed-time tracking control approach, which realizes the advantages of modular robot manipulators (MRMs) with fixed-time convergence, strong robustness, and high tracking performance. First, to estimate the total uncertainty of MRMs, the fixed-time observer based on the extended state is developed. Then, combined with the disturbance observer, a novel decentralized control method based on a fixed-time control strategy was devised to accomplish global fixed-time convergence of MRMs. And, stability analysis based on Lyapunov is utilized to obtain the fixed-time stability as well as convergence time of MRMs. Finally, numerical analysis and experiment respectively verify the excellent tracking ability of the presented decentralized fixed-time tracking control.

Neutron capture cross section of 169Tm measured at the CSNS Back-n facility in the energy region from 30 keV to 300 keV

The capture cross sections of the 169Tm(n, γ) reaction were measured at the back streaming white neutron beam line (Back-n) of the China Spallation Neutron Source (CSNS) using four C6D6 liquid scintillation detectors. To obtain accurate cross sections, the background subtraction, normalization, and correction were carefully taken into consideration in the data analysis. For the resonance at 3.9 eV, the R-matrix code SAMMY was used to determine the resonance parameters with internally normalization method. While the average capture cross sections of 169Tm in the energy range between 30 keV and 300 keV were extracted relative to the 197Au(n, γ) reaction. The measured cross sections of the 169Tm(n, γ) reaction were reported in logarithmically equidistant energy bins with 20 bins per energy decade with a total uncertainty of 5.4%-7.0% in this paper, and described in terms of average resonance parameters by means of a Hauser-Feshbach calculation with fluctuations. Both of the point-wise cross sections and the averge resonance parameters showed fair agreement with the evaluated values of ENDF/B-VIII.0 library in the energy region studied.

Balanced estimate and uncertainty assessment of European climate change using the large EURO-CORDEX regional climate model ensemble

Large multiscenario multimodel ensembles (MMEs) of regional climate model (RCM) experiments driven by global climate models (GCMs) are made available worldwide and aim at providing robust estimates of climate changes and associated uncertainties. Due to many missing combinations of emission scenarios and climate models leading to sparse scenario–GCM–RCM matrices, these large ensembles, however, are very unbalanced, which makes uncertainty analyses impossible with standard approaches. In this paper, the uncertainty assessment is carried out by applying an advanced statistical approach, called QUALYPSO, to a very large ensemble of 87 EURO-CORDEX climate projections, the largest MME based on regional climate models ever produced in Europe. This analysis provides a detailed description of this MME, including (i) balanced estimates of mean changes for near-surface temperature and precipitation in Europe, (ii) the total uncertainty of projections and its partition as a function of time, and (iii) the list of the most important contributors to the model uncertainty. For changes in total precipitation and mean temperature in winter (DJF) and summer (JJA), the uncertainty due to RCMs can be as large as the uncertainty due to GCMs at the end of the century (2071–2099). Both uncertainty sources are mainly due to a small number of individual models clearly identified. Due to the highly unbalanced character of the MME, mean estimated changes can drastically differ from standard average estimates based on the raw ensemble of opportunity. For the RCP4.5 emission scenario in central–eastern Europe for instance, the difference between balanced and direct estimates is up to 0.8 ∘C for summer temperature changes and up to 20 % for summer precipitation changes at the end of the century.

A novel Technique for Improving the Angular Accuracy of Doppler VOR Receivers

Doppler VOR (D-VOR) transmitters are used as navigation aids in aviation. They transmit an omnidirectional phase reference in an amplitude-modulated (AM) sideband and directional phase information on a frequency-modulated (FM) subcarrier. In an airborne D-VOR navigation receiver, a directional information (azimuth angle) related to the position of the aircraft and the location of the transmitter can be derived from the difference of these two phase signals. In this work, the accuracy of AM and FM phase signals is firstly investigated analytically and afterwards verified by measurements. It will be shown that in established procedures, phase inaccuracy is dominated by the AM signal, since the FM signal is about 21 dB less noisy. Subsequently, a novel method is presented that improves the accuracy of the azimuth angle by orders of magnitude in case of D-VOR transmitters. This new method inherently reduces noise of the AM phase and thus yields a significant increase in accuracy. As a result, the remaining FM phase uncertainty becomes dominant for the total uncertainty of the bearing indication. Finally, the application of the new method to real measured signals confirms the theoretical expectations.

Uncertainty Analysis of an Optoelectronic Strain Measurement System for Flywheel Rotors

The strain in a fast spinning carbon fiber flywheel rotor is of great interest for condition monitoring, as well as for studying long-term aging effects in the carbon fiber matrix. Optoelectronic strain measurement is a contactless measurement principle where a special reflective pattern is applied to the rotor which is scanned by a stationary optical setup. It does not require any active electronic components on the rotor and is suited for operation in a vacuum. In this paper, the influences of the key parts comprising the optoelectronic strain measurement are analyzed. The influence of each part on the measurement result including the uncertainty is modeled. The total uncertainty, as well as each part’s contribution is calculated. This provides a valuable assessment of requirements for component selection, as well as tolerances of mechanical parts and processes to reach a final target measurement uncertainty or to estimate the uncertainty of a given setup. We have shown that the edge quality of the special reflective pattern has the strongest influence, and how to improve it. Considering all influences, it is possible to measure strain with an uncertainty of less than 1% at a rotation speed of 500Hz.

Effects of rotation errors on goniometric measurements

Goniometric measurements are essential for the determination of many optical quantities, and quantifying the effects of errors in the rotation axes on these quantities is a complex task. In this paper, we show how a measurement model for a four-axis goniometric system can be developed to allow the effects of alignment and rotation errors to be included in the uncertainty of the measurement. We use three different computational methods to propagate the uncertainties due to several error sources through the model to the rotation angles and then to the measurement of bidirectional reflectance and integrated diffuse reflectance, a task that would otherwise be intractable. While all three methods give the same result, the GTC Python package is the simplest and intrinsically provides a full uncertainty budget, including all correlations between measurement parameters. We then demonstrate how the development of a measurement model and the use of GTC has improved our understanding of the system. As a consequence, taking advantage of negative correlations between measurements in different geometries allows us to minimise the total uncertainty in integrated diffuse reflectance, lowering the standard uncertainty from 0.0029 to 0.0015.

Estimating global landslide susceptibility and its uncertainty through ensemble modelling

This study assesses global landslide susceptibility (LSS) at the coarse 36-km spatial resolution of global satellite soil moisture observations, to prepare for a subsequent combination of a global LSS map with dynamic soil moisture estimates for landslide modelling. Global LSS estimation intrinsically contains uncertainty, arising from errors in the underlying data, the spatial mismatch between landslide events and predictor information, and large-scale model generalizations. For a reliable uncertainty assessment, this study combines methods from the landslide community with common practices in meteorological modelling to create an ensemble of global LSS maps. The predictive LSS models are obtained from a mixed effects logistic regression, associating hydrologically triggered landslide data from the Global Landslide Catalog (GLC) with predictor variables from the Catchment land surface modeling system (incl. input parameters of soil (hydrological) properties and resulting climatological statistics of water budget estimates), geomorphological and lithological data. Road network density is introduced as a random effect to mitigate potential landslide inventory bias. We use a blocked random cross validation to assess the model uncertainty that propagates into the LSS maps. To account for other uncertainty sources, such as input uncertainty, we also perturb the predictor variables and obtain an ensemble of LSS maps. The perturbations are optimized so that the total predicted uncertainty fits the observed discrepancy between the ensemble average LSS and the landslide presence or absence from the GLC. We find that the most reliable total uncertainty estimates are obtained through the inclusion of a topography-dependent perturbation between 15 % and 20 % to the predictor variables. The areas with the largest LSS uncertainty coincide with moderate ensemble average LSS. The spatial patterns of the average LSS agree well with previous global studies and yield areas under the Receiver Operation Characteristic between 0.63 and 0.9 for independent regional to continental landslide inventories.

Textronics spacer knitted material tests as a key element of the diagnostic system that monitors above the proper level of seat vibration

This research is focused on the construction and examination of a prototype of a spacer knitted material with integrated sensors. The combination of textiles with elements of electronics, computer science, and a knowledge of automation is called textronics. This type of material has been proposed as a component of diagnostic systems to monitor the extension level of vibration in employee seats at selected workstations or in children’s chairs. The purpose of the diagnostic system is to improve personal protective equipment (PPE) and increase employee safety. The spacer knitted material was tested with vibration frequencies in the range of 0–40 Hz to develop metrological properties under reproducible and repeatable conditions. The tested spacer knitted material meets the requirements of sensory properties such as vibration. The tested material is characterized by the following metrological parameters: total uncertainty U = 4.5%, sensitivity Sa = 0.64 [V/s2/m] and excitability threshold of 5 Pa with simultaneous high coefficient of low-frequency vibration damping of effective amplitude transmissibility (SEAT) = 2.3. Spacer knitted materials are modern constructs that enable the creation of new hybrid structures that have other properties, e.g., sensory suppression, in addition to spatial form.

Iterative Forecasting Improves Near-Term Predictions of Methane Ebullition Rates

Near-term, ecological forecasting with iterative model refitting and uncertainty partitioning has great promise for improving our understanding of ecological processes and the predictive skill of ecological models, but to date has been infrequently applied to predict biogeochemical fluxes. Bubble fluxes of methane (CH4) from aquatic sediments to the atmosphere (ebullition) dominate freshwater greenhouse gas emissions, but it remains unknown how best to make robust near-term CH4 ebullition predictions using models. Near-term forecasting workflows have the potential to address several current challenges in predicting CH4 ebullition rates, including: development of models that can be applied across time horizons and ecosystems, identification of the timescales for which predictions can provide useful information, and quantification of uncertainty in predictions. To assess the capacity of near-term, iterative forecasting workflows to improve ebullition rate predictions, we developed and tested a near-term, iterative forecasting workflow of CH4 ebullition rates in a small eutrophic reservoir throughout one open-water period. The workflow included the repeated updating of a CH4 ebullition forecast model over time with newly-collected data via iterative model refitting. We compared the CH4 forecasts from our workflow to both alternative forecasts generated without iterative model refitting and a persistence null model. Our forecasts with iterative model refitting estimated CH4 ebullition rates up to 2 weeks into the future [RMSE at 1-week ahead = 0.53 and 0.48 loge(mg CH4 m−2 d−1) at 2-week ahead horizons]. Forecasts with iterative model refitting outperformed forecasts without refitting and the persistence null model at both 1- and 2-week forecast horizons. Driver uncertainty and model process uncertainty contributed the most to total forecast uncertainty, suggesting that future workflow improvements should focus on improved mechanistic understanding of CH4 models and drivers. Altogether, our study suggests that iterative forecasting improves week-to-week CH4 ebullition predictions, provides insight into predictability of ebullition rates into the future, and identifies which sources of uncertainty are the most important contributors to the total uncertainty in CH4 ebullition predictions.