Comparison of Four RTK Receivers Operating in the Static and Dynamic Modes Using Measurement Robotic Arm

While the existing research provides a wealth of information about the static properties of RTK receivers, less is known about their dynamic properties, although it is clear that the vast majority of field operations take place when the machine is moving. A new method using a MRA for the evaluation of RTK receivers in movement with a precise circular reference trajectory (r = 3 m) was proposed. This reference method was developed with the greatest possible emphasis on the positional, time and repeatable accuracy of ground truth. Four phases of the measurement scenario (static, acceleration, uniform movement and deceleration) were used in order to compare four different types of RTK receiver horizontal operation accuracy over three measurement days. The worst result of one of the receivers was measured at SSR = 13.767% in dynamic movement. Since the same “low-cost” receiver without an INS unit had SSR = 98.14% in previous static measurements, so it can be assumed that the motion had a very significant effect on the dynamic properties of this receiver. On the other hand, the best “high-end” receiver with an INS unit had SSR = 96.938% during the dynamic testing scenarios. The median values of the deviations were always better during uniform movements than during acceleration or braking. In general, the positioning accuracy was worse in the dynamic mode than in the static one for all the receivers. Error indicators (RMSerr and Me) were found several times higher in the dynamic mode than in the static one. These facts should be considered in the future development of modern agricultural machinery and technology.

Adaptive discrete thin plate spline smoother

The discrete thin plate spline smoother fits smooth surfaces to large data sets efficiently. It combines the favourable properties of the finite element surface fitting and thin plate splines. The efficiency of its finite element grid is improved by adaptive refinement, which adapts the precision of the solution. It reduces computational costs by refining only in sensitive regions, which are identified using error indicators. While many error indicators have been developed for the finite element method, they may not work for the discrete smoother. In this article we show three error indicators adapted from the finite element method for the discrete smoother. A numerical experiment is provided to evaluate their performance in producing efficient finite element grids. References F. L. Bookstein. Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Trans. Pat. Anal. Mach. Int. 11.6 (1989), pp. 567–585. doi: 10.1109/34.24792. C. Chen and Y. Li. A robust method of thin plate spline and its application to DEM construction. Comput. Geosci. 48 (2012), pp. 9–16. doi: 10.1016/j.cageo.2012.05.018. L. Fang. Error estimation and adaptive refinement of finite element thin plate spline. PhD thesis. The Australian National University. http://hdl.handle.net/1885/237742. L. Fang. Error indicators and adaptive refinement of the discrete thin plate spline smoother. ANZIAM J. 60 (2018), pp. 33–51. doi: 10.21914/anziamj.v60i0.14061. M. F. Hutchinson. A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines. Commun. Stat. Simul. Comput. 19.2 (1990), pp. 433–450. doi: 10.1080/0361091900881286. W. F. Mitchell. A comparison of adaptive refinement techniques for elliptic problems. ACM Trans. Math. Soft. 15.4 (1989), pp. 326–347. doi: 10.1145/76909.76912. R. F. Reiniger and C. K. Ross. A method of interpolation with application to oceanographic data. Deep Sea Res. Oceanographic Abs. 15.2 (1968), pp. 185–193. doi: 10.1016/0011-7471(68)90040-5. S. Roberts, M. Hegland, and I. Altas. Approximation of a thin plate spline smoother using continuous piecewise polynomial functions. SIAM J. Numer. Anal. 41.1 (2003), pp. 208–234. doi: 10.1137/S0036142901383296. D. Ruprecht and H. Muller. Image warping with scattered data interpolation. IEEE Comput. Graphics Appl. 15.2 (1995), pp. 37–43. doi: 10.1109/38.365004. E. G. Sewell. Analysis of a finite element method. Springer, 2012. doi: 10.1007/978-1-4684-6331-6. L. Stals. Efficient solution techniques for a finite element thin plate spline formulation. J. Sci. Comput. 63.2 (2015), pp. 374–409. doi: 10.1007/s10915-014-9898-x. O. C. Zienkiewicz and J. Z. Zhu. A simple error estimator and adaptive procedure for practical engineerng analysis. Int. J. Numer. Meth. Eng. 24.2 (1987), pp. 337–357. doi: 10.1002/nme.1620240206.

Adaptive space-time finite element methods for parabolic optimal control problems

We present, analyze, and test locally stabilized space-time finite element methods on fully unstructured simplicial space-time meshes for the numerical solution of space-time tracking parabolic optimal control problems with the standard L 2-regularization. We derive a priori discretization error estimates in terms of the local mesh-sizes for shape-regular meshes. The adaptive version is driven by local residual error indicators, or, alternatively, by local error indicators derived from a new functional a posteriori error estimator. The latter provides a guaranteed upper bound of the error, but is more costly than the residual error indicators. We perform numerical tests for benchmark examples having different features. In particular, we consider a discontinuous target in form of a first expanding and then contracting ball in 3d that is fixed in the 4d space-time cylinder.

Assessment of a Gauge-Radar-Satellite Merged Hourly Precipitation Product for Accurately Monitoring the Characteristics of the Super-Strong Meiyu Precipitation over the Yangtze River Basin in 2020

The recently developed gauge-radar-satellite merged hourly precipitation dataset (CMPAS-NRT) offers broad applications in scientific research and operations, such as intelligent grid forecasting, meteorological disaster monitoring and warning, and numerical model testing and evaluation. In this paper, we take a super-long Meiyu precipitation process experienced in the Yangtze River basin in the summer of 2020 as the research object, and evaluate the monitoring capability of the CMPAS-NRT for the process from multiple perspectives, such as error indicators, precipitation characteristics, and daily variability in different rainfall areas, using dense surface rain-gauge observation data as a reference. The results show that the error indicators for CMPAS-NRT are in good agreement with the gauge observations. The CMPAS-NRT can accurately reflect the evolution of precipitation during the whole rainy season, and can accurately capture the spatial distribution of rainbands, but there is an underestimation of extreme precipitation. At the same time, the CMPAS-NRT product features the phenomenon of overestimation of precipitation at the level of light rain. In terms of daily variation of precipitation, the precipitation amount, frequency, and intensity are basically consistent with the observations, except that there is a lag in the peak frequency of precipitation, and the frequency of precipitation at night is less than observed, and the intensity of precipitation is higher than observed. Overall, the CMPAS-NRT product can successfully reflect the precipitation characteristics of this super-heavy Meiyu precipitation event, and has a high potential hydrological utilization value. However, further improvement of the precipitation algorithm is needed to solve the problems of overestimation of light rainfall and underestimation of extreme precipitation in order to provide more accurate hourly precipitation monitoring dataset.

Path Loss Characterization Using Machine Learning Models for GS-to-UAV-Enabled Communication in Smart Farming Scenarios

The purpose of this paper was to predict the path loss characterization of the ground-to-air (G2A) communication channel between the ground sensor (GS) and unmanned aerial vehicle (UAV) using machine learning (ML) models in smart farming (SF) scenarios. Two ML algorithms such as support vector regression (SVR) and artificial neural network (ANN) were studied to analyze the measured data in different scenarios with Napier and Ruzi grass farms as the measurement locations. The proposed empirical GS-to-UAV two-ray (GUT-R) model and the ML models were compared to characterize path loss prediction models. The performances of the path loss prediction models were evaluated using the statistical error indicators in different measurement locations and UAV trajectories. To obtain the statistical error indicators, the accuracy path loss results of UAV trajectory at 2 m altitudes showed the SVR model (MAE = 1.252 dB, RMSE = 3.067 dB, and R2 = 0.972) and the ANN model (MAE = 1.150 dB, RMSE = 2.502 dB, and R2 = 0.981) for the Napier scenario. In the Ruzi scenario, the SVR model (MAE = 1.202 dB, RMSE = 2.962 dB, and R2 = 0.965) and the ANN model (MAE = 1.146 dB, RMSE = 2.507 dB, and R2 = 0.983) were presented. For UAV trajectory at 5 m altitudes, the SVR model (MAE = 2.125 dB, RMSE = 4.782 dB, and R2 = 0.933) and the ANN model (MAE = 2.025 dB, RMSE = 4.439 dB, and R2 = 0.950) were resulted in the Napier scenario. In the Ruzi scenario, the SVR model (MAE = 2.112 dB, RMSE = 4.682 dB, and R2 = 0.935) and the ANN model (MAE = 2.016 dB, RMSE = 4.407 dB, and R2 = 0.954) were displayed. The proposed ML models using SVR and ANN can optimally predict the path loss characterization in SF scenarios, where the accuracy was 95% for the SVR and 97% for the ANN.

Toward Accurate Reservoir Simulations on Unstructured Grids: Design of Simple Error Estimators and Critical Benchmarking of Consistent Discretization Methods for Practical Implementation

Summary In this paper, we aim to identify discretization errors caused by non-K-orthogonal grids upfront through simple preprocessing tools and perform a comparative study of a set of representative, state-of-the-art, consistent discretizations [multipoint flux approximation (MPFA-O), mimetic finite difference (MFD), nonlinear two-pointflux approximation (NTPFA, TPFA), and average multipoint flux approximation (AvgMPFA)] to select the method most suited for inclusion in a commercial reservoir simulator. To predict the potential impact of discretization errors, we propose two types of error indicators. Static indicators measure the degree of nonconsistency of the two-point method at a cell level, and dynamic indicators measure how local discretization errors affect flow paths. The latter are computed using a series of idealized tracer simulations. By changing monitoring and injection points, one can mimic the reservoir-development strategy and thus focus on the errors introduced on quantities of real interest. To assess the practical usability of various consistent methods and validate our new error indicators, we use a set of representative grid models generated by contemporary commercial tools, for which we discuss static error indicators and compare tracer responses for the various discretization methods. We also compare degrees of freedom, sparsity, and the condition number of the alternative methods and discuss challenges related to their practical implementation. Our results indicate that tracer simulations constitute an efficient tool to identify and classify discretization errors and quantify their potential impact. We observe distinctively different behavior with the inconsistent two-point method and the consistent methods, which agree closely in terms of accuracy of the response. We also note a deficiency in the commercial realization of so-called Depogrids, which can result in unnecessarily complicated polytopal cells with hundreds of faces. Our overall conclusion is that NTPFA and AvgMPFA are the most viable solutions for integration into a commercial simulator, with the linear AvgMPFA method being the least invasive.

Fidelity Assessment of Real-Time Hybrid Substructure Testing: a Review and the Application of Artificial Neural Networks

Real-Time Hybrid Substructure (RTHS) testing is a commonly used method to investigate the dynamical influence of a component on a mechanical system. In RTHS, a part of the dynamical system is tested experimentally, while the remaining structure is simulated numerically in a co-simulation. There are several error sources in the RTHS loop that distort the test outcome. To investigate the reliability of the test, the fidelity of the test must be quantified. In many engineering applications, however, there is no reference solution available to which the test outcome can be validated against. This work reviews currently existing accuracy measures used in RTHS. Furthermore, using Artificial Neural Networks (ANN) to predict the fidelity of the RTHS test outcome when no reference solution is available is proposed. Appropriate input features for the network, such as dynamic properties of the system and existing error indicators, are discussed. ANN training was performed on a data set from a virtual RTHS (vRTHS) simulation of a dynamical system with contact. The training process was successful, meaning that the correlation between the ANN prediction and the true fidelity value was > 99 %. Then, the network was applied to data of experimental RTHS tests of the same dynamical system and achieved a correlation of 98 %, which proves that the relation found by the ANN captured the relation between the chosen input features and the error measure. The application of the trained ANN to data from a linear vRTHS test revealed that further improvement of the network and the choice of input features is necessary. This work suggests that ANNs could be a meaningful tool to predict the fidelity of the RTHS test outcome in the absence of a reference solution, especially if more data from different RTHS tests were aggregated to train them.

ANALISIS KESALAHAN PESERTA DIDIK DALAM MENYELESAIKAN SOAL CERITA BERDASARKAN PROSEDUR NEWMAN

The purpose of this study is to explore information about the challenges, difficulties, and mistakes that experienced by students in solving passage questions with the material of the Linear System with Two-Variable based on the Newman procedure. This research is qualitative research with a descriptive approach. The subjects of the research consisted of 6 students from 31 students eight grade of MTs. Miftahul Ulum Bululawang with 2 upper groups, 2 intermediate groups, and 2 lower groups. The data are collected using exercises (question and answer) and interviews. The data validity test was performed by using triangulation techniques. Data analysis is carried out based on Newman's error indicators, those are reading, understanding the problem (Comprehension), transforming the problem (Transformation), process skills, and writing the final answer (Encoding). The results of this study indicate that the mistakes made by the upper group are the type of misreading (Reading), understanding the problem (Comprehension), transformation problem (Transformation), processing skills problem (Process Skills), and problem in writing the final answer (Encoding). The types of mistakes that the intermediate group made are reading, process skills, and final answer writing (Encoding). The type of mistakes made by the lower group is reading (Reading). Students do not write the variables used for example.