A novel method for improving optical component smoothing quality in robotic smoothing systems by compensating path errors.

Force and vibration generated in apical direction by three endodontic files of different kinematics during simulated canal preparation: An in vitro analytical study.

Three-Dimensional Quantitative Comparative Analysis of Trapezial-Metacarpal Joint Surface Curvatures in Human Populations

As one of the coherent diffractive imaging (CDI) techniques, coherent modulation imaging (CMI) is a lensless phase imaging technology with diffraction limited resolution in theory. Unlike multiple measurement phase retrieval algorithms, the CMI can achieve fast convergence speed with single-shot measurement by introducing a pre-characterized random phase modulator. Besides, it has simple structure without reference wave based on iterative engine. Despite the fact that the matured phase imaging can be used to implement the on-line wave diagnostics of laser pulse, in this work we accurately measure the face-type of optical component with peak-to-valley value below 0.5<i>λ</i> (<i>λ</i> = 632.8 nm) by using the CMI for the first time. In order to verify its measurement capability, 10 quartz windows with a diameter of 80 mm and PV value between 0.1<i>λ</i> and 0.5<i>λ</i> are repeatedly measured. Compared with the results of commercial interferometer, the root mean square error (Root MSE) of the peak-to-valley (PV) ratio of the results of the CMI is 0.0305<i>λ</i>, and the Root MSE of the root mean square (RMS) is 0.00522<i>λ.</i> The measurement accuracy of PV ratio and RMS can reach 0.1<i>λ</i> and 0.01<i>λ</i> respectively. In addition, the parallel flat with PV ratio = <i>λ</i>/20 is measured and analyzed with CMI, and its noise level is also analyzed. Considering that the potential improvement of CMI is available in the future, the CMI is expected to become a new technique for optical metrology with high precision, which is different from interferometry.

The aim of the present research is to calculate the Relative Accuracy (R.A) and Root Mean Square Error (RMSE) for the location of Technical Institute, Mosul in Nineveh Governorate, Iraq. at the northern part of Mosul city by using surveying Instruments (GPS 1230 , Total Station 06 & Level NAK2), to calculate the coordinates and elevations of (16) Ground Control Points which covered the study area. The field data, remote sensing techniques and GIS software (Arc Map GIS10.3) are used to check the digital map of study area that helped the administrator to take the correct decision, determining positions of the future expansionist for buildings, roads, measuring lengths, areas and there coordinates, directions as well as the total costs estimation needed for any development from the final map. The gradient percentage between the highest and lowest points is calculated to be (9.954%). Relative accuracy for the based measured points (R.A) = 1:60000), and Root Mean Square error in Easting coordinates (RMSE) = ± 0.102m, in Northing coordinates (RMSN) = ± 0.096m, the Root Mean Square for the resultant (RMSR) = ± 0.099m, and Root Mean Square for Elevations ( RMSRL)= ± 0.005m. We Matched and correlated (10) measured points from study area with their graphic data positions on the aerial photo, determining the offsets for position and elevations as follows: (RMSE = ± 0.076m), (RMSN = ± 0.125m), (RMSR = ± 0.088m), and (Root Mean Square for Elevations = RMSRL= ± 0.045m). This research enable us after checking the results to produce new maps from Aerial photograph or Digital Image to use it for land uses and future extensions in whole study area.

Radar antennas in the open air are affected by dynamic wind loading during azimuthal scanning. In this state, the acquisition and prediction of the aerodynamic load is the focus of the structure design for the antenna. We performed wind tunnel force tests on flat plate antennas with different aspect ratios. The changes in mean, maximum, and root mean square values of the aerodynamic coefficients relative to the antenna aspect ratio and reduced frequency are given for the first time. The effects of the antenna aspect ratio and reduced frequency on aerodynamic characteristics are analyzed. The experimental results indicate that the magnitude of the azimuthal moment is related to the antenna aspect ratio and the reduced frequency. It is worth noting that the antenna aspect ratio has a significant impact on the mean, maximum, and root mean square values of dynamic aerodynamic coefficients. The mean, maximum, and root mean square values of the rolling moment coefficient, azimuth moment coefficient, and pitching moment coefficient monotonously decrease with the increase in aspect ratio. In addition, the influence of reduced frequency on the dynamic aerodynamic coefficient is closely related to the antenna aspect ratio. Under the same antenna aspect ratio, increasing the reduced frequency will increase the mean, maximum, and root mean square values of the aerodynamic coefficient. For flat plate antennas with different aspect ratios, at the condition of increasing the aspect ratios, the mean, maximum, and root mean square values of aerodynamic coefficients do not change significantly with the change of reduced frequency.

Soil Roughness Measuring System Combined With Image Processing

Identification and Kinematic Calculation of Laser Tracker Errors

Refractive and Aberrometric Outcomes of Intracorneal Ring Segments for Keratoconus: Mechanical versus Femtosecond-assisted Procedures

Benchmarking Functional Status in Older Adults

Identification of position-dependent geometric errors with non-integer exponents for Linear axis using double ball bar

The article is devoted to the process of adaptation of the control system of a mobile robot for its movement along a line. To this end, the article proposes a fuzzy logic and structural model for robot control, combining two methods of adaptation. The first method consists in variation of the parameters of the triangular membership functions and structure of the fuzzy rules, which are used to describe the input and output variables of a fuzzy robot control system. In order to improve the reliability of the solutions proposed in the structure of the fuzzy inference, hard and soft arithmetic operations are used. Evaluation of the system is based on calculation of a standard deviation (RMSE - root mean square error). The best solution for the fuzzy system is the option, in which the RMSE is minimal. The second method envisages a change in the structure of the control code of a mobile robot. The results of the experimental studies presented in the article show that without the use of the methods of the structural and parametric adaptation the accuracy of the soft fuzzy model is 2,7 times higher than the accuracy of a rigid fuzzy model, which has a dead zone, reducing its stability. However, a combination of the above methods makes the accuracy of the rigid model higher than that of the soft model. At that, a deadband is ruled out and computational complexity is reduced. In order to explain the proposed methods of adaptation the article presents the results of the experimental studies evaluating the movement of a mobile robot along a line on an oval track.

Fossil fuels have traditionally played a crucial role in global energy production, but they cause significant environmental challenges. In response, the scientific community has shifted its focus toward renewable alternative fuels. In this context, biodiesel and alcohols have emerged as promising options for diesel engines. This study is centered on predicting the cylinder pressure of a single-cylinder four-stroke diesel engine fuelled with a diesel fuel-biodiesel (methyl ester)-isopropanol ternary blend using three machine learning algorithms: Gaussian Process Regression (GPR), Artificial Neural Networks (ANN), and Ensembles of Trees (ET). The cylinder pressure data is collected under the full throttle condition and different engine speeds. GPR, ANN, and ET algorithms are trained and compared using root mean square error and regression analysis. GPR exhibits outstanding prediction performance during the validation, with a lower root mean square error of 0.12686, and r2 of 1.00. ANN also exhibits strong prediction performance, with a validation of root mean square error of 0.47081, and a r2 of 1.00. ET, while showing a slightly higher validation root mean square error of 1.73370, maintains strong predictive capability with an r2 of 0.99. However, a comparison between the measured cylinder pressure data and the predicted values reveals a qualitatively and quantitatively closer agreement, particularly for ANN. These findings can suggest the practicality and reliability of these algorithms for predicting cylinder pressure in internal combustion engine studies. In conclusion, this study can contribute to the expanding body of research on alternative fuels and machine learning applications in internal combustion engines.

The purpose of this investigation was to cross-validate the ithlete™ heart rate variability smart phone application with an electrocardiograph for determining ultra-short-term root mean square of successive R-R intervals. The root mean square of successive R-R intervals was simultaneously determined via electrocardiograph and ithlete™ at rest in twenty five healthy participants. There were no significant differences between the electrocardiograph and ithlete™ derived root mean square of successive R-R interval values (p > 0.05) and the correlation was near perfect (r = 0.99, p < 0.001). In addition, the ithlete™ revealed a Standard Error of the Estimate of 1.47 and Bland Altman plot showed that the limits of agreement ranged from 2.57 below to 2.63 above the constant error of −0.03. In conclusion, the ithlete™ appeared to provide a suitably accurate measure of root mean square of successive R-R intervals when compared to the electrocardiograph measures obtained in the laboratory within the current sample of healthy adult participants. The current study lays groundwork for future research determining the efficacy of ithlete™ for reflecting athletic training status over a chronic conditioning period.

Large eddy simulation of supersonic mixing layers using a compressible filtered mass density function method

In this study, the prediction of the concentration of gaseous pollutants around Ughelli West gas flow station in Delta State of Nigeria was carried out using Geostatistical technique in GIS environment. Since air pollutants negatively affect quality of air, lives and the environment, there is therefore the need to frequently monitor air quality, have thorough understanding of the pollutants’ concentration and their spatial distribution in an environment. The gaseous pollutants data of volatile organic compounds (VOCs), methane (CH4), nitrogen dioxide (NO2), sulphur dioxide (SO2) and ozone (O3), were obtained using Multi-parameter gas monitor while that of fine particulate matter (PM2.5) was obtained with SPM meter for a period of three months. Thermo Anemometer was used to obtain the values of wind speed, ambient temperature, atmospheric pressure and relative humidity. Artificial Neural Network designer software (Pythia) was used to validate the acquired field data; predict the concentration of the gaseous pollutants at selected distances from the flow station. The geospatial coordinates of the flow station were obtained using Global Navigation Satellite System (GNSS) receivers; the geospatial modelling and analysis were performed with ArcGIS software and ordinary kriging method of Geostatistical techniques. The results of the maximum concentration for the gaseous pollutants in the study area were 28.17 µg/m3, 19.44 µg/m3, 0.37 µg/m3, 49.81 µg/m3, 0.061 µg/m3 and 0.047µg/m3 for VOCs, CH4, NO2, PM2.5, O3 and SO2 respectively. The root mean square error for the concentration of the gaseous pollutants, ozone and sulphur (IV) oxide in the study area were 0.01618 and 0.008417 indicating a good interpolation model, while their root mean square standard errors, which show the reliability of the predicted values, were 0.70513551 and 0.8459251 respectively. These results conform with the report of other researchers that a better kriging method yields a smaller root mean square and a standard root mean square closer to one. The developed prediction maps for the gaseous pollutants in this study revealed that the study area will experience lower concentration of gaseous pollutants at a distance of 400 m and above.