Predictive Control-Based Completeness Analysis and Global Calibration of Robot Vision Features

This paper provides an in-depth study and analysis of robot vision features for predictive control and a global calibration of their feature completeness. The acquisition and use of the complete macrofeature set are studied in the context of a robot task by defining the complete macrofeature set at the level of the overall purpose and constraints of the robot vision servo task. The visual feature set that can fully characterize the macropurpose and constraints of a vision servo task is defined as the complete macrofeature set. Due to the complexity of the task, a part of the features of the complete macrofeature set is obtained directly from the image, and another part of the features is obtained from the image by inference. The task is guaranteed to be completely based on a robust calibration-free visual serving strategy based on interference observer that is proposed to complete the visual serving task with high performance. To address the problems of singular values, local minima, and insufficient robustness in the traditional scale-free vision servo algorithm, a new scale-free vision servo method is proposed to construct a dual closed-loop vision servo structure based on interference observer, which ensures the closed-loop stability of the system through the Q-filter-based interference observer, while estimating and eliminating the interference consisting of hand-eye mapping model uncertainty and controlled robot input interference. The equivalent interference consisting of hand-eye mapping model uncertainty, controlled robot input interference, and detection noise is estimated and eliminated to obtain an inner-loop structure that presents a nominal model externally, and then an outer-loop controller is designed according to the nominal model to achieve the best performance of the system dynamic performance and robustness to optimally perform the vision servo task.

Global Evaluation of the Ecosystem Demography Model (ED v3.0)

Terrestrial ecosystems play a critical role in the global carbon cycle but have highly uncertain future dynamics. Ecosystem modelling that includes the scaling-up of underlying mechanistic ecological processes has the potential to improve the accuracy of future projections, while retaining key process-level detail. Over the past two decades, multiple modelling advances have been made to meet this challenge, including the Ecosystem Demography (ED) model and its derivatives including ED2 and FATES. Here, we present the global evaluation of the Ecosystem Demography model (ED v3.0), which likes its predecessors features the formal scaling of physiological processes of individual-based vegetation dynamics to ecosystem scales, together with integrated submodules of soil biogeochemistry and soil hydrology, while retaining explicit tracking of vegetation 3-D structure. This new version builds on previous versions and provides the first global calibration and evaluation, global tracking of the effects of climate and land-use change on vegetation 3-D structure, new spin-up process and input datasets, as well as numerous other advances. Model evaluation was performed with respect to a set of important benchmarking datasets, and model estimates were within observational constraints for multiple key variables including: (i) global patterns of dominant plant functional types (broadleaf vs evergreen); (ii) spatial distribution, seasonal cycle, and interannual trends of global Gross Primary Production (GPP); (iii) global interannual variability of Net Biome Production (NBP); and (iv) global patterns of vertical structure including leaf area and canopy height. With this global model version, it is now possible to simulate vegetation dynamics from local to global scales and from seconds to centuries, with a consistent mechanistic modelling framework amendable to data from multiple traditional and new remote sensing sources, including lidar.

On the implementation of post-processing of runoff forecast ensembles

Different post-processing techniques are frequently employed to improve the outcome of ensemble forecasting models. The main reason is to compensate for biases caused by errors in model structure or initial conditions, and as a correction for under- or overdispersed ensembles. Here we use the Ensemble Model Output Statistics method to post-process the ensemble output from a continental scale hydrological model, LISFLOOD, as used in the European Flood Awareness System (EFAS). We develop a method for local calibration and interpolation of the post-processing parameters and compare it with a more traditional global calibration approach for 678 stations in Europe based on long term observations of runoff and meteorological variables. For the global calibration we also test a reduced model with only a variance inflation factor. Whereas the post-processing improved the results for the first 1-2 days lead time, the improvement was less for increasing lead times of the verification period. This was the case both for the local and global calibration methods. As the post-processing is based on assumptions about the distribution of forecast errors, we also present an analysis of the ensemble output that provides some indications of what to expect from the post-processing.

Introducing Metamodel-Based Global Calibration of Material-Specific Simulation Parameters for Discrete Element Method

An important prerequisite for the generation of realistic material behavior with the Discrete Element Method (DEM) is the correct determination of the material-specific simulation parameters. Usually, this is done in a process called calibration. One main disadvantage of classical calibration is the fact that it is a non-learning approach. This means the knowledge about the functional relationship between parameters and simulation responses does not evolve over time, and the number of necessary simulations per calibration sequence respectively per investigated material stays the same. To overcome these shortcomings, a new method called Metamodel-based Global Calibration (MBGC) is introduced. Instead of performing expensive simulation runs taking several minutes to hours of time, MBGC uses a metamodel which can be computed in fractions of a second to search for an optimal parameter set. The metamodel was trained with data from several hundred simulation runs and is able to predict simulation responses in dependence of a given parameter set with very high accuracy. To ensure usability for the calibration of a wide variety of bulk materials, the variance of particle size distributions (PSD) is included in the metamodel via parametric PSD-functions, whose parameters serve as additional input values for the metamodel.

On-Site Global Calibration of Mobile Vision Measurement System Based on Virtual Omnidirectional Camera Model

The mobile vision measurement system (MVMS) is widely used for location and attitude measurement in aircraft takeoff and landing, and its on-site global calibration is crucial to obtaining high-accuracy measurement aimed at obtaining the transformation relationship between the MVMS coordinate system and the local-tangent-plane coordinate system. In this paper, several new ideas are proposed to realize the global calibration of the MVMS effectively. First, the MVMS is regarded as azimuth and pitch measurement equipment with a virtual single image plane at focal length 1. Second, a new virtual omnidirectional camera model constructed by three mutual orthogonal image planes is put forward, which effectively resolves the problem of global calibration error magnification when the angle between the virtual single image plane and view axis of the system becomes small. Meanwhile, an expanded factorial linear method is proposed to solve the global calibration equations, which effectively restrains the influence of calibration data error. Experimental results with synthetic data verify the validity of the proposed method.

Toward a global calibration for quantifying past oxygenation in oxygen minimum zones using benthic Foraminifera

Oxygen minimum zones (OMZs) are oceanic areas largely depleted in dissolved oxygen, nowadays considered in expansion in the face of global warming. To investigate the relationship between OMZ expansion and global climate changes during the late Quaternary, quantitative oxygen reconstructions are needed but are still in their early development. Here, past bottom water oxygenation (BWO) was quantitatively assessed through a new, fast, semi-automated, and taxon-independent morphometric analysis of benthic foraminiferal tests, developed and calibrated using WNP (western North Pacific, including its marginal seas), ENP (eastern North Pacific), and ESP (eastern South Pacific) OMZ samples. This new approach is based on an average size and shape index for each sample. This method, as well as two already published micropalaeontological techniques based on benthic foraminiferal assemblages' variability and porosity investigation of a single species, was calibrated here based on availability of new data from 45 core tops recovered along an oxygen gradient (from 0.03 to 2.88 mL L−1) from the WNP, ENP, EEP (eastern Equatorial Pacific), ESP, SWACM (southwest African continental margin), and AS (Arabian Sea) OMZs. Global calibrated transfer functions are herein proposed for these methods. These micropalaeontological reconstruction approaches were then applied to a palaeorecord from the ENP OMZ to examine the consistency and limits of these methods, as well as the relative influence of bottom and pore waters on these micropalaeontological tools. Both the assemblage and morphometric approaches (which are also ultimately based on the ecological response of the complete assemblage and faunal succession according to BWO) gave similar and consistent past BWO reconstructions, while the porosity approach (based on a single species and its unique response to a mixed signal of bottom and pore waters) showed ambiguous estimations.