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Purpose: This paper proposes a motion planning technique for precise path and trajectory tracking in an underactuated, non-minimum phase, spatial overhead crane. Besides having a number of independent actuators that is smaller than the number of degrees of freedom, tip control on this system presents unstable internal dynamics that leads to divergent solution of the inverse dynamic problem. Method: The paper exploits the representation of the controlled output as a separable function of the actuated (i.e., the platform translations) and unactuated (i.e., the swing angles) coordinates to easily formulate the internal dynamics, without any approximation, and to study its stability. Then, output redefinition is adopted within the internal dynamics to stabilize it, leading to stable and causal reference commands for the platform translations. Results: Besides proposing the theoretical formulation of this novel method, the paper includes the numerical validation and the experimental application on a laboratory setup. Comparison with the state-of-the-art input shaping is also proposed. Conclusion: The results, obtained through different reference trajectories, clearly show that almost exact tracking is obtained also in the experiments, by outperforming the benchmarks. [ABSTRACT FROM AUTHOR]

Soil moisture estimation from agriculture fields using SAR measurements is a challenging process owing to the presence of vegetation canopy. In this study, the soil moisture (SM) is retrieved from multi-polarization airborne L- and C-band E-SAR data of different agriculture fields by using the radar parameter, Radar Vegetation Index (RVI). The retrieval methodology employs the semi-empirical Water Cloud Model (WCM) for vegetation-soil system modeling, followed by an inversion algorithm based on a Look Up Table approach. The impact of using different vegetation descriptors, both from in situ measured (Leaf Area Index, Wet Biomass and Vegetation Water Content) and radar derived (L-band RVI and C-band RVI), on the WCM inversion for SM retrieval is examined. The use of the RVI as the vegetation descriptor, which is obtained from C-band data, improves soil moisture retrieval with an RMSE of 7–8% volumetric soil moisture at L-band. [ABSTRACT FROM AUTHOR]

We apply the concept of servo constraints to end-effector trajectory tracking control of parallel robots with structural link flexibilities. Such servo constraints deliver the inverse robot model where solution approaches via projections are proposed, which transform the resulting differential-algebraic equations to ordinary differential equations. The applicable solution process depends on the existence and stability of the internal dynamics. When using the exact end-effector of flexible link robots as output, this internal dynamics is usually unstable. Then a two-point boundary value problem is considered in the framework of stable inversion to obtain the noncausal solution offline. This solution is used as a feedforward control, which is initially combined only with actuator feedback control. To also account for errors within the link flexibility, the well-known linear–quadratic regulator is adapted to end-effector trajectory tracking based on differential-algebraic equations. Finally, we propose a systematic input–output feedback linearization approach, which uses servo constraints for flexible link parallel robots. Here a minimum phase system is obtained by tracking a redefined end-effector output, which is an approximation of the exact end-effector position. All control concepts are validated experimentally with a parallel robot having a highly flexible link. The results allow us to compare different control approaches and show the superior performance of controllers that rely on a flexible multibody model in contrast to classical rigid multibody modeling. [ABSTRACT FROM AUTHOR]

Model-based network discovery measures, such as the brain effective connectivity, require fitting of generative process models to measurements obtained from key areas across the network. For distributed dynamic phenomena, such as generalized seizures and slow-wave sleep, studying effective connectivity from real-time recordings is significantly complicated since (i) outputs from only a subnetwork can be practically measured, and (ii) exogenous subnetwork inputs are unobservable. Model fitting, therefore, constitutes a challenging blind module identification or model inversion problem for finding both the parameters and the many unknown inputs of the subnetwork. We herein propose a novel estimation framework for identifying nonlinear dynamic subnetworks in the case of slowly-varying, otherwise unknown local inputs. Starting with approximate predictions obtained using Cubature Kalman filtering, residuals of local output predictions are utilized to improve upon local input estimates. The algorithm performance is tested on both simulated and clinical EEG of induced seizures under electroconvulsive therapy (ECT). For the simulated network, the algorithm significantly boosted the estimation accuracy for inputs and connections from noisy EEG. For the clinical data, the algorithm predicted increased subnetwork inputs during the pre-stimulus anesthesia condition. Importantly, it predicted an increased frontocentral connectivity during the generalized seizure that is commensurate with electrode placement and that corroborates the clinical hypothesis of increased frontal focality of therapeutic ECT seizures. The proposed framework can be extended to account for several input configurations and can in principle be applied to study effective connectivity within brain subnetworks defined at the microscale (cortical lamina interaction) or at the macroscale (sensory integration). [ABSTRACT FROM AUTHOR]

A new method for production forecasting and uncertainty quantification, applicable for realistic naturally fractured reservoirs (NFRs) represented as general discrete-fracture-matrix (DFM) models, is developed and applied. The forecasting procedure extends a recently developed data-space inversion (DSI) technique that generates production predictions using only prior-model simulation results and observed data. The method does not provide posterior (history-matched) geological models. Rather, the DSI method treats production data as random variables. The prior distribution is estimated from the flow simulations performed on prior geological models, and the posterior data-variable distribution is sampled using a data-space randomized maximum likelihood method. The DSI treatment requires the parameterization of data variables to render them approximately multivariate Gaussian. The complex production data considered here (resulting from frequent well shut-ins) is treated using a new reparameterization that involves principal component analysis combined with histogram transformation. The DSI method is first applied for two-dimensional DFM systems involving multiple fracture scenarios. In this case, comparison with a rejection sampling procedure is possible, and we show that the DSI results for P10, P50, and P90 statistics are consistent with rejection sampling results. The DSI method is then applied to a realistic NFR that has undergone 15 years of primary production and is under consideration for waterflooding. To construct the DSI representation, around 400 prior DFM models, which correspond to different geologic concepts and properties, are simulated. Two different reference 'true' models, along with different data-assimilation durations, are considered to evaluate the performance of the DSI procedure. In all cases, the DSI predictions are shown to be consistent with the forecasts from the 'true' model and to provide reasonable quantification of forecast uncertainty. [ABSTRACT FROM AUTHOR]

Background:: Many mechanical experiments in plasticity-induced fatigue are prepared by the recourse to finite element simulations. Usual simulation outputs, like local stress estimations or lifetime predictions, are useful to choose boundary conditions and the shape of a specimen. In practice, many other numerical data are also generated by these simulations. But unfortunately, these data are ignored, although they can facilitate the calibration procedure. The focus of this paper is to illustrate a new simulation protocol for finite-element model calibration. By the recourse to hyper-reduction of mechanical models, more data science is involved in the proposed protocol, in order to solve less nonlinear mechanical equations during the calibration of mechanical parameters. Usually, the location of the crack initiation is very sensitive to the heterogeneities in the material. The proposed protocol is versatile enough in order to focus the hyper-reduced predictions where the first crack is initiated during the fatigue test. Methods:: In this paper, we restrict our attention to elastoplasticity or elastoviscoplasticity without damage nor crack propagation. We propose to take advantage of the duration of both the experiment design and the experimental protocol, to collect numerical data aiming to reduce the computational complexity of the calibration procedure. Until experimental data are available, we have time to prepare the calibration by substituting numerical data to nonlinear equations. This substitution is performed by the recourse to the hyper-reduction method (Ryckelynck in J Comput Phys 202(1):346-366, , Int J Numer Method Eng 77(1):75-89, ). An hyper-reduced order model involves a reduced basis for the displacement approximation, a reduced basis for stress predictions and a reduced integration domain for the setting of reduced governing equations. The reduced integration domain incorporates a zone of interest that covers the location of the crack initiation. This zone of interest is updated according to experimental observations performed during the fatigue test. Results:: Bending experiments have been performed to study the influence of a grain boundary on AM1 superalloy oligocyclic fatigue at high temperature. The proposed hyper-reduction framework is shown to be relevant for the modeling of these experiments. To account for the microstructure generated by a real industrial casting process, the specimen has been machined in a turbine blade. The model calibration aims to identify the loading condition applied on the specimen in order to estimate the stress at the point where the first crack is initiated, before the crack propagation. The model parameters are related to the load distribution on the specimen. The calibration speed-up obtained by hyper-reduction is almost 1000, including the update of the reduced integration domain focused on the experimental location of the crack initiation. The related electric-energy saving is 99.9 %. [ABSTRACT FROM AUTHOR]

The development of sensors that provide geospatial information on crop and soil conditions has been a primary success for precision agriculture. However, further developments are needed to integrate geospatial data into computer algorithms that spatially optimize crop production while considering potential environmental impacts and resource limitations. The objective of this research was to combine several information technologies, including remote sensing, a cropping system model, and a geographic information system (GIS), to synthesize and interpret geospatial data collected during two irrigation scheduling experiments conducted in 2009 and 2011 in a 5-ha cotton field in central Arizona. The Geospatial Simulation (GeoSim) plug-in for Quantum GIS was used to manage geospatial data and conduct site-specific simulations with the CSM-CROPGRO-Cotton model. Simulated annealing optimization was used to adjust five model parameters to simulate site-specific conditions in 320 zones across the field. Using input parameters for GeoSim, a multiple criteria objective function was developed to incorporate measured and simulated leaf area index (LAI), crop canopy height, seed cotton yield, and evapotranspiration (ET) for site-specific optimization of CSM-CROPGRO-Cotton. Parameter identifiability and equifinality issues associated with model inversion were investigated. The optimized model was used for post hoc analysis of irrigation rates that maximized site-specific irrigation water use efficiency. With spatial optimization, the model was able to simulate LAI with root mean squared errors (RMSE) of 15 and 8 % in the 2009 and 2011 experiments, respectively. The RMSEs between measured and simulated canopy height, seed cotton yield, and ET were 5 % or less in both seasons. Some parameters were more identifiable than others during model inversions. Multiple temporal estimates of LAI were effective for constraining the model's specific leaf area parameter (SLAVR, cm g), but lack of information on root growth reduce identifiability of a parameter related to that process (SRGF0). Post-hoc simulation analysis of irrigation management options showed that irrigation schedules based on remotely sensed vegetation indices increased irrigation water use efficiency as compared to traditional scheduling methods, particularly in the 2009 growing season. In 2011, the analysis showed that all scheduling methods resulted in excess irrigation application, and higher deep seepage rates were simulated in that season. Taken together, the results demonstrate that well-designed software tools and algorithms for data processing and interpretation can be potentially transformative for integrating multiple geospatial data sets to compute optimum scenarios for precision irrigation management. [ABSTRACT FROM AUTHOR]

The aim of this paper is to evaluate the effectiveness of using video data for voice source parametrization in the representation of voice production through physical modeling. Laryngeal imaging techniques can be effectively used to obtain vocal fold video sequences and to derive time patterns of relevant glottal cues, such as folds edge position or glottal area. In many physically based numerical models of the vocal folds, these parameters are estimated from the inverse filtered glottal flow waveform, obtained from audio recordings of the sound pressure at lips. However, this model inversion process is often problematic and affected by accuracy and robustness issues. It is here discussed how video analysis of the fold vibration might be effectively coupled to the parametric estimation algorithms based on voice recordings, to improve accuracy and robustness of model inversion. [ABSTRACT FROM AUTHOR]

This study was conducted to model the fraction of intercepted photosynthetically active radiation (fIPAR) in heterogeneous row-structured orchards, and to develop methodologies for accurate mapping of the instantaneous fIPAR at field scale using remote sensing imagery. The generation of high-resolution maps delineating the spatial variation of the radiation interception is critical for precision agriculture purposes such as adjusting management actions and harvesting in homogeneous within-field areas. Scaling- up and model inversion methods were investigated to estimate fIPAR using the 3D radiative transfer model, Forest Light Interaction Model (FLIGHT). The model was tested against airborne and field measurements of canopy reflectance and fIPAR acquired on two commercial peach and citrus orchards, where study plots showing a gradient in the canopy structure were selected. High-resolution airborne multi-spectral imagery was acquired at 10 nm bandwidth and 150 mm spatial resolution using a miniaturized multi-spectral camera on board an unmanned aerial vehicle (UAV). In addition, simulations of the land surface bidirectional reflectance were conducted to understand the relationships between canopy architecture and fIPAR. Input parameters used for the canopy model, such as the leaf and soil optical properties, canopy architecture, and sun geometry were studied in order to assess the effect of these inputs on canopy reflectance, vegetation indices and fIPAR. The 3D canopy model approach used to simulate the discontinuous row-tree canopies yielded spectral RMSE values below 0.03 (visible region) and below 0.05 (near-infrared) when compared against airborne canopy reflectance imagery acquired over the sites under study. The FLIGHT model assessment conducted for fIPAR estimation against field measurements yielded RMSE values below 0.08. The simulations conducted suggested the usefulness of these modeling methods in heterogeneous row-structured orchards, and the high sensitivity of the normalized difference vegetation index and fIPAR to background, row orientation, percentage cover and sun geometry. Mapping fIPAR from high-resolution airborne imagery through scaling- up and model inversion methods conducted with the 3D model yielded RMSE error values below 0.09 for the scaling- up approach , and below 0.10 for the model inversion conducted with a look-up table. The generation of intercepted radiation maps in row-structured tree orchards is demonstrated to be feasible using a miniaturized multi-spectral camera on board UAV platforms for precision agriculture purposes. [ABSTRACT FROM AUTHOR]

The current development of satellite technology particularly in the sensors like POLDER and MISR, has emphasized more on directional reflectance measurements (i.e. spectral reflectance of the target measured from different view zenith and azimuth angles) of the earth surface features mainly the vegetation for retrieval of biophysical parameters at regional scale using radiative transfer models. This approach being physical process based and uses directional reflectance measurement has been found to better and more reliable compared to the conventional statistical approach used till date and takes care of anisotropic nature (i.e. reflectance from the target is different if measured from different view angles) of the target. Keeping this in view a field experiment was conducted in mustard crop to evaluate the radiative transfer model for biophysical parameter retrieval through its inversion with the objectives set as (i) to relate canopy biophysical parameters and geometry to its bidirectional reflectance, (ii) to evaluate a canopy reflectance model to best represent the radiative transfer within the canopy for its inversion and (iii) to retrieve crop biophysical parameters through inversion of the model. Two varieties of the mustard crop (Brassica juncea L) were grown with two nitrogen treatments. The bidirectional reflectance data obtained at 5 nm interval for a range of 400-1100 nm were integrated to IRS LISS-II sensor's four band values using Newton Cotes Integration technique. Biophysical parameters like leaf area index, leaf chlorophyll content, leaf length, plant height and average leaf inclination angle, biomass etc were estimated synchronizing with the bi-directional reflectance measurements. Radiative transfer model PROSAIL model was validated and its inversion was done to retrieve LAI and ALA. Look Up Table (LUT) of Bidirectional reflectance distribution function (BRDF) was prepared simulating through PROSAIL model varying only LAI (0.2 interval from 1.2 to 5.4 ) and ALA (5° interval from 40° to 55°) parameters and inversion was done using a merit function and numerical optimization technique given by Press et al. (1986). The derived LAI and ALA values from inversion were well matched with observed one with RMSE 0.521 and 5.57, respectively. [ABSTRACT FROM AUTHOR]

An underactuated multibody system has less control inputs than degrees of freedom. For trajectory tracking, often a feedforward control is necessary. Two different approaches for feedforward control design are presented. The first approach is based on a coordinate transformation into the nonlinear input-output normal-form. The second approach uses servo-constraints and results in a set of differential algebraic equations. A comparison shows that both feedforward control designs have a similar structure. The analysis of the mechanical design of underactuated multibody systems might show that they are nonminimum phase, i.e., they have unstable internal dynamics. Then the feedforward control cannot be computed by time integration and output trajectory tracking becomes a very challenging task. Therefore, based on the two presented feedforward control design approaches, it is shown that through the use of an optimization procedure underactuated multibody systems can be designed in such a way that they are minimum phase. Thus, feedforward control design using the two approaches is significantly simplified. [ABSTRACT FROM AUTHOR]

n approach is presented for identifying statistical characteristics of stratigraphies from borehole and hydraulic data. The approach employs a Markov-chain based geostatistical framework in a stochastic inversion. Borehole data provide information on the stratigraphy while pressure and flux data provide information on the hydraulic performance of the medium. The use of Markov-chain geostatistics as opposed to covariance-based geostatistics can provide a more easily interpreted model geologically and geometrically. The approach hinges on the use of mean facies lengths (negative inverse auto-transition rates) and mean transition lengths (inverse cross-transition rates) as adjustable parameters in the stochastic inversion. Along with an unconstrained Markov-chain model, simplifying constraints to the Markov-chain model, including (1) proportionally-random and (2) symmetric spatial correlations, are evaluated in the stochastic inversion. Sensitivity analyses indicate that the simplifying constraints can facilitate the inversion at the cost of spatial correlation model generality. Inverse analyses demonstrate the feasibility of this approach, indicating that despite some low parameter sensitivities, all adjustable parameters do converge for a sufficient number of ensemble realizations towards their 'true' values. This paper extends the approach presented in Harp et al. (doi:, ) to (1) statistically characterize the hydraulic response of a geostatistical model, thereby incorporating an uncertainty analysis directly in the inverse method, (2) demonstrate that a gradient-based optimization strategy is sufficient, thereby providing relative computational efficiency compared to global optimization strategies, (3) demonstrate that the approach can be extended to a 3-D analysis, and (4) introduce the use of mean facies lengths and mean transition lengths as adjustable parameters in a geostatistical inversion, thereby allowing the approach to be extended to greater than two category Markov-chain models. [ABSTRACT FROM AUTHOR]

Climate reconstructions from data sensitive to past climates provide estimates of what these climates were like. Comparing these reconstructions with simulations from climate models allows to validate the models used for future climate prediction. It has been shown that for fossil pollen data, gaining estimates by inverting a vegetation model allows inclusion of past changes in carbon dioxide values. As a new generation of dynamic vegetation model is available we have developed an inversion method for one model, LPJ-GUESS. When this novel method is used with high-resolution sediment it allows us to bypass the classic assumptions of (1) climate and pollen independence between samples and (2) equilibrium between the vegetation, represented as pollen, and climate. Our dynamic inversion method is based on a statistical model to describe the links among climate, simulated vegetation and pollen samples. The inversion is realised thanks to a particle filter algorithm. We perform a validation on 30 modern European sites and then apply the method to the sediment core of Meerfelder Maar (Germany), which covers the Holocene at a temporal resolution of approximately one sample per 30 years. We demonstrate that reconstructed temperatures are constrained. The reconstructed precipitation is less well constrained, due to the dimension considered (one precipitation by season), and the low sensitivity of LPJ-GUESS to precipitation changes. [ABSTRACT FROM AUTHOR]

In multiangular remote sensing observations, the variable range of the solar zenith angle (SZN) is narrow. We takes the linear kernel-driven model as an example to analyze the parameter error propagation in inversion by using the observations at single sun position and to show that in such case it is unreliable to invert the BRDF tendency with SZN. To improve the algorithm, we suggest adding certain constraint for the changing trend of albedo with SZN in the model inversion. [ABSTRACT FROM AUTHOR]

When the remote sensing pixel is composed of multiple components and a non-isothermal surface, its directional signature of thermal-infrared radiation is mainly determined by the 3D structure of the pixel. In this paper, we present our simple directional thermal radiation model to describe the relation between the pixel thermal emission and the pixel’s component parameters, and invert the model to get the component temperatures. For the inversion algorithm, we focus on how to use the information of given observations in a more effective way. The information content in inversion procedure is studied. Our forward model and inversion method are validated using indoor directional measurement data. [ABSTRACT FROM AUTHOR]

Satellite remote sensing deals with a complex system coupling atmosphere and surface. Any physical model with reasonable precision needs several to tens of parameters. Without a priori knowledge of these parameters, Proposition 3 of Verstraete et al. requires the number of independent observations to be greater than the number of unknown parameters. This requirement can hardly be satisfied even in the coming EOS era. As Tarantola pointed out, the inversion problems in geoscience are always underdetermined in some sense. In order to make good use of every kind of a priori knowledge for effectively extracting information from remote sensing observations, the right question to set is as follows:Given an imperfect model and a certain amount of a priori information on model parameters, in which sense should one modify the a priori information, given the actual observation with noise? A priori knowledge of physical parameters can be presented in different ways such as physical limits, global statistical means and variance fora certain landcover type, or previous statistics and temporal variation of a specific target. When such a priori knowledge can be expressed as joint probability density. Bayessian theorem can be used in the inversion to obtain posterior probability densities of parameters using newly acquired observations. There is no prerequirement on how many independent observations must be made, and the knowledge gained merely depends on the information content of the new observations. Some specific problems about knowledge accumulation and renewal are also discussed. [ABSTRACT FROM AUTHOR]