Calculation of tonal rail circuits with consideration of a complicated waveform

2021 ◽  
pp. 104-112
Author(s):  
M. I. Shestakov ◽  
Keyword(s):  
Root Mean Square ◽  
Classical Method ◽  
Wide Spectrum ◽  
Discrete Fourier Transformation ◽  
Single Frequency ◽  
Signal Spectrum ◽  
Complex Signal ◽  
Mean Square ◽  
Operating Modes ◽  
Track Circuits

The article deals with the problem of calculating modern audio frequency track circuits, the working signal of which is a frequency-manipulated signal transmitting Bauer codes. Such signals have a wide spectrum, which leads to the error in the calculations performed according to the classical method for a single frequency. Increasing the code transfer rate in order to reduce the response time of track circuits further expands the signal spectrum, which negatively affects the safety of such rail circuits. To determine the width and composition of the signal spectrum, a discrete Fourier transformation is used. The new method of calculating rail circuits proposed in this article allows us to determine the root-mean-square value of the signal at the output of the rail circuit with a known amplitude-frequency characteristic of the rail circuit and the form of a complex signal at the input. The obtained ratio of the root-mean-square signal levels at the input and output of the rail circuit allows us to solve the problem of calculating the operating modes of the rail circuit taking into account its nonlinear amplitude-frequency characteristics and taking into account the complex signal form, including consideration of the specific transmitted Bauer code. This solution allows you to increase the reliability of the calculation of the adjustment tables of the tonal track circuits and, as a result, increase the reliability and safety of their operation.

A mechanical-reflex oscillator hypothesis for parkinsonian hand tremor

1976 ◽  
Vol 40 (6) ◽  
pp. 990-998 ◽  
Author(s):  
R. N. Stiles ◽  
R. S. Pozos
Keyword(s):  
Spectral Analysis ◽  
Steady State ◽  
Root Mean Square ◽  
Fold Increase ◽  
Normal Subjects ◽  
Single Frequency ◽  
Mean Square ◽  
Hand Tremor ◽  
S Period ◽  
Rms Amplitude

Spectral analysis was performed on postural hand tremor records obtained from 22 parkinsonian subjects. Of these 22 subjects, 18 had postural hand tremor that occurred primarily at a single frequency during any one 16-s period. In general, this tremor occurred at different steady-state frequencies (each calculated over 16 s) between about 4 Hz and 8–9 Hz. This frequency decreased approximately 1 Hz for each 10-fold increase in displacement amplitude (root-mean-square, rms, amplitude determined at 16 cm from the wrist), decreasing from 8–9 Hz at about 30 mum to 3.75–4.0 Hz at about 30,000 mum. The major finding was that the frequency of parkinsonian hand tremor was nearly the same as that for hand tremor from normal subjects when these frequenceis were compared at similar rms displacement levels. This comparison, plus a comparison between other aspects of these two kinds of tremor, indicate that the mechanism for parkinsonian hand tremor is similar to that for large-displacement (greater than 100 mum) hand tremor of normal subjects, i.e., a mechanical-reflex oscillator mechanism.

The Relationship of Root Mean Square Value of Electromyography and Isometric Torque of Quadriceps in Normal Subjects

2016 ◽  
Vol 26 (1) ◽  
pp. 58
Author(s):  
Qiurong XIE ◽  
Zheng JIANG ◽  
Qinglu LUO ◽  
Jie LIANG ◽  
Xiaoling WANG ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Normal Subjects ◽  
Mean Square ◽  
Isometric Torque ◽  
Relationship Of ◽  
The Relationship

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

Intersession reliability of GPS-based and accelerometer-based physical variables in small-sided games with and without the offside rule

2021 ◽  
pp. 175433712098764
Author(s):  
Igor Junio de Oliveira Custódio ◽  
Gibson Moreira Praça ◽  
Leandro Vinhas de Paula ◽  
Sarah da Glória Teles Bredt ◽  
Fabio Yuzo Nakamura ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Global Positioning System ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Mean Square ◽  
Physical Demands ◽  
Intraclass Correlation Coefficients ◽  
Total Distance ◽  
Global Positioning ◽  
High Level

This study aimed to analyze the intersession reliability of global positioning system (GPS-based) distances and accelerometer-based (acceleration) variables in small-sided soccer games (SSG) with and without the offside rule, as well as compare variables between the tasks. Twenty-four high-level U-17 soccer athletes played 3 versus 3 (plus goalkeepers) SSG in two formats (with and without the offside rule). SSG were performed on eight consecutive weeks (4 weeks for each group), twice a week. The physical demands were recorded using a GPS with an embedded triaxial accelerometer. GPS-based variables (total distance, average speed, and distances covered at different speeds) and accelerometer-based variables (Player Load™, root mean square of the acceleration recorded in each movement axis, and the root mean square of resultant acceleration) were calculated. Results showed that the inclusion of the offside rule reduced the total distance covered (large effect) and the distances covered at moderate speed zones (7–12.9 km/h – moderate effect; 13–17.9 km/h – large effect). In both SSG formats, GPS-based variables presented good to excellent reliability (intraclass correlation coefficients – ICC > 0.62) and accelerometer-based variables presented excellent reliability (ICC values > 0.89). Based on the results of this study, the offside rule decreases the physical demand of 3 versus 3 SSG and the physical demands required in these SSG present high intersession reliability.

scholarly journals Design of the Class-E Power Amplifier with Finite DC Feed Inductance under Maximum-Rating Constraints

2021 ◽  
Vol 11 (9) ◽  
pp. 3727
Author(s):  
Ingrid Casallas ◽  
Carlos-Ivan Paez-Rueda ◽  
Gabriel Perilla ◽  
Manuel Pérez ◽  
Arturo Fajardo
Keyword(s):  
Quality Factor ◽  
Power Amplifier ◽  
Wide Spectrum ◽  
Circuit Simulation ◽  
Percentage Error ◽  
Resonant Circuit ◽  
Mean Square ◽  
Peak Voltage ◽  
Supply Current ◽  
Class E

This paper proposes an analytical expression set to determine the maximum values of currents and voltages in the Class-E Power Amplifier (PA) with Finite DC-Feed Inductance (FDI) under the following assumptions—ideal components (e.g., inductors and capacitors with infinite quality factor), a switch with zero rise and fall commutation times, zero on-resistance, and infinite off-resistance, and an infinite loaded quality factor of the output resonant circuit. The developed expressions are the average supply current, the RMS (Root Mean Square) current through the DC-feed inductance, the peak voltage and current in the switch, the RMS current through the switch, the peak voltages of the output resonant circuit, and the peak voltage and current in the PA load. These equations were obtained from the circuit analysis of this ideal amplifier and curve-fitting tools. Furthermore, the proposed expressions are a useful tool to estimate the maximum ratings of the amplifier components. The accuracy of the expressions was analyzed by the circuit simulation of twelve ideal amplifiers, which were designed to meet a wide spectrum of application scenarios. The resulting Mean Absolute Percentage Error (MAPE) of the maximum-rating constraints estimation was 2.64%.

scholarly journals GNINA 1.0: molecular docking with deep learning

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrew T. McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

AbstractMolecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

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