Evaluation of three tractor-guidance methods for parallel swathing at two field speeds

This study compared the accuracy (mean error and rms error) and precision (standard deviation of error) of three tractor-guidance methods (foam-marker, light-bar, and assisted-steering systems) at two field speeds (5.6 – and 11.5 km/h) for parallel swathing operations. Eighty-four replications of each combination of guidance method and field speed were conducted between 15 October and 22 December 2006 (504 total field passes). The foam-marker system was found to be significantly less accurate [larger mean error (p \u3c .0001) and had a larger rms error (p \u3c .0001)] than either the light-bar or the assisted-steering system. There was no significant difference in mean error (p = .6718) or rms error (p = .8841) by field speed. There was a significant interaction between guidance method and field speed for both mean error (p = .0009) and rms error (p = .003). Mean and rms errors for the foam-marker and the assisted-steering systems increased at higher field speed, while the mean and rms errors for the light-bar system decreased at higher speed. The assisted-steering system had a significantly lower (p = .0164) standard deviation of error (higher precision) than the foam-marker or the light-bar systems. There was no significant difference in the standard deviation of error by field speed (p = .6258) or by the interaction of guidance method and field speed (p = .2748)

Statistical measurements of the zero-crossing time of a noisy sinewave

Standard deviation of difference in zero crossing times of two identical sinewaves distorted by narrowband noise behaves similarly to average value of error, but deviation is approximately one order of magnitude larger

Note on the mean error and standard deviation in the output of a least square quadratic filter - case 20061-1

Equations for mean error and standard deviation in output of least squares quadratic filter - rada

Technology research for strapdown inertial experiment and digital flight control and guidance

A helicopter flight-test program to evaluate the performance of Honeywell's Tetrad - a strapdown, laser gyro, inertial navitation system is discussed. The results of 34 flights showed a mean final navigational velocity error of 5.06 knots, with a standard deviation of 3.84 knots; a corresponding mean final position error of 2.66 n.mi., with a standard deviation of 1.48 n.m.; and a modeled mean-position-error growth rate for the 34 tests of 1.96 knots, with a standard deviation of 1.09 knots. Tetrad's four-ring laser gyros provided reliable and accurate angular rate sensing during the test program and on sensor failures were detected during the evaluation. Criteria suitable for investigating cockpit systems in rotorcraft were developed. This criteria led to the development of two basic simulators. The first was a standard simulator which could be used to obtain baseline information for studying pilot workload and interactions. The second was an advanced simulator which integrated the RODAAS developed by Honeywell into this simulator. The second area also included surveying the aerospace industry to determine the level of use and impact of microcomputers and related components on avionics systems

Object Distance Measurement System Using Monocular Camera on Vehicle

To support autonomous vehicles that are currently often studied by various parties, the authors propose to make a system of predicting the distance of objects using monocular cameras on vehicles. Distance prediction uses four methods and the input parameter was obtained from images processed with MobileNets SSD. Calculations using linear regression are the simplest calculations among the four methods but have an error of 1% with a standard deviation of 1.65 meters. While using the first method, the average error value is 9% with a standard deviation of 0.43 meters. By using the second calculation, the average error resulted in 6% with a standard deviation of 0.35 meters. The experimental method had an average error of 1% with a standard deviation of 0.26 meters, so the experimental method was used

COMPARISON OF LOW COST PHOTOGRAMMETRIC SURVEY WITH TLS AND LEICA PEGASUS BACKPACK 3D MODELS

This paper considers Leica backpack and photogrammetric surveys of a mediaeval bastion in Padua, Italy. Furhtermore, terrestrial laser scanning (TLS) survey is considered in order to provide a state of the art reconstruction of the bastion. Despite control points are typically used to avoid deformations in photogrammetric surveys and ensure correct scaling of the reconstruction, in this paper a different approach is considered: this work is part of a project aiming at the development of a system exploiting ultra-wide band (UWB) devices to provide correct scaling of the reconstruction. In particular, low cost Pozyx UWB devices are used to estimate camera positions during image acquisitions. Then, in order to obtain a metric reconstruction, scale factor in the photogrammetric survey is estimated by comparing camera positions obtained from UWB measurements with those obtained from photogrammetric reconstruction. Compared with the TLS survey, the considered photogrammetric model of the bastion results in a RMSE of 21.9cm, average error 13.4cm, and standard deviation 13.5cm. Excluding the final part of the bastion left wing, where the presence of several poles make reconstruction more difficult, (RMSE) fitting error is 17.3cm, average error 11.5cm, and standard deviation 9.5cm. Instead, comparison of Leica backpack and TLS surveys leads to an average error of 4.7cm and standard deviation 0.6cm (4.2 cm and 0.3 cm, respectively, by excluding the final part of the left wing)

Construction of symmetry triangular fuzzy number procedure (STFNP) using statistical information for autoregressive forecasting

Single-point data are used for data collection. However, data collected by various data collection methods are often exposed to uncertainties that may affect the information presented by the quantitative results. This also causes the forecast model developed to be less precise because of the uncertainties contained in the input data. It is essential to describe the uncertainty in data to obtain a realistic result from data analysis. However, most studies focus on model uncertainty regardless of data uncertainty. The data processing carried out may not always take care of uncertainty. When uncertainties in the raw data are not sufficiently handled, this creates more errors that are included in the predicted model. Standard procedures are also very limited to be followed in order to transform a single-point value into Triangular Fuzzy Number (TFN), which addresses the uncertainty. Thus, the data preparation procedure of Symmetry Triangular Fuzzy Number (STFN) is presented in this study to build an improved autoregressive model for time series forecasting. This study presents the proposed Symmetry Triangular Fuzzy Number Procedure (STFNP) using percentage error method and standard deviation method for first-order autoregressive forecasting. Percentage error rate method involves three different percentage rates, while the second method uses the standard deviation of the data. Simulations and verification procedures are presented and are accompanied with numerical examples using actual datasets of Air Pollutant Index and stock markets of selected ASEAN countries. This study reveals that the percentage error and standard deviation methods, which were used to construct the TFN, can achieve the same or better accuracy as compared to a single-point procedure. The results of the simulations and experiments show that the standard deviation method produces better results compared to the other proposed approaches and the conventional approach. Besides, the systematic procedure to construct the TFN does not deviate from single-point procedures. Importantly, uncertain data being treated avoids more uncertainties that would have been brought to the outcome of the forecast model and consequently improves prediction accuracy