scholarly journals An Ultrasound Study of Coarticulatory Resistance and Coarticulatory Aggression

2016 ◽  
Vol 4 (2) ◽  
pp. 314
Author(s):  
M Irfana ◽  
N Sreedevi
Keyword(s):  
Root Mean Square ◽  
Ultrasound Imaging ◽  
Imaging Technique ◽  
Mean Square ◽  
Front Vowel ◽  
Contextual Variation ◽  
Ultrasound Study ◽  
Better Than

<em><span lang="EN-IN">The term 'coarticulatory resistance' refers to the degree to which a given segment, a consonant or a vowel, resists potential interference of neighbouring segments. The phoneme has coarticulatory resistance exert stronger influence on neighbouring phoneme and exhibit less contextual variation, this characteristic termed as 'coarticulatory aggression'. The present study aims to analyse the coarticulatory resistance and coarticulatory aggression based on ultrasound imaging technique. Thirty adult Malayalam speakers participated as subjects. The stimuli consisted of VCV sequences, with C corresponding to voiced/voiceless counterparts of dental stops (/t̪/, /d̪/) or retroflex stops (/ʈ/, /ɖ/) or velar stops (/k/, /g/), in the context of vowels /a, i, u/. Measurements of coarticulation resistance of consonants, preceding vowels and following vowels were carried out based on Root Mean Square (RMS) distance between the tongue contours of vowels and consonants. Results showed that coarticulatory resistance of consonants were decreased in the order from retroflex followed by velars and dentals. High front vowel /i/ resisted coarticulation of preceding consonant better than other vowels considered. It highlights the trend of </span><span lang="EN-IN">Degree of Articulatory Constraint (DAC) </span><span lang="EN-IN">model for both consonant and vowel system.</span></em>

scholarly journals Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262009
Author(s):  
Rui Zhang ◽  
Hejia Song ◽  
Qiulan Chen ◽  
Yu Wang ◽  
Songwang Wang ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Time Scales ◽  
Hemorrhagic Fever ◽  
Early Warning Systems ◽  
Stochastic Gradient Descent ◽  
Mean Square ◽  
Forecasting Models ◽  
Best Fitting ◽  
Different Time Scales ◽  
Better Than

Objectives This study intends to build and compare two kinds of forecasting models at different time scales for hemorrhagic fever incidence in China. Methods Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) were adopted to fit monthly, weekly and daily incidence of hemorrhagic fever in China from 2013 to 2018. The two models, combined and uncombined with rolling forecasts, were used to predict the incidence in 2019 to examine their stability and applicability. Results ARIMA (2, 1, 1) (0, 1, 1)12, ARIMA (1, 1, 3) (1, 1, 1)52 and ARIMA (5, 0, 1) were selected as the best fitting ARIMA model for monthly, weekly and daily incidence series, respectively. The LSTM model with 64 neurons and Stochastic Gradient Descent (SGDM) for monthly incidence, 8 neurons and Adaptive Moment Estimation (Adam) for weekly incidence, and 64 neurons and Root Mean Square Prop (RMSprop) for daily incidence were selected as the best fitting LSTM models. The values of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the models combined with rolling forecasts in 2019 were lower than those of the direct forecasting models for both ARIMA and LSTM. It was shown from the forecasting performance in 2019 that ARIMA was better than LSTM for monthly and weekly forecasting while the LSTM was better than ARIMA for daily forecasting in rolling forecasting models. Conclusions Both ARIMA and LSTM could be used to build a prediction model for the incidence of hemorrhagic fever. Different models might be more suitable for the incidence prediction at different time scales. The findings can provide a good reference for future selection of prediction models and establishments of early warning systems for hemorrhagic fever.

Ultrasonic Measurement of Differential Displacement and Strain in a Vascular Model

1997 ◽  
Vol 19 (1) ◽  
pp. 19-38 ◽  
Author(s):  
L.K. Ryan ◽  
F.S. Foster
Keyword(s):  
Root Mean Square ◽  
Intravascular Ultrasound ◽  
Ultrasound Imaging ◽  
Axial Direction ◽  
Root Mean Square Difference ◽  
Mean Square ◽  
Tracking Accuracy ◽  
Lateral Direction ◽  
Intravascular Ultrasound Imaging ◽  
Mean Square Difference

The potential of intravascular ultrasound imaging for characterizing regional arterial elasticity was examined in an experimental tissue-equivalent vessel model. Differential intrawall displacement measurement, the first step in regional elasticity determination, was investigated using a crosscorrelation tracking algorithm. Calibration studies showed that tracking accuracy varied significantly with tracking direction (axial versus lateral) and position in the field of the transducer. Midfield geometric error in the axial direction for a nominal displacement of 100 μm was 5.5 μm whereas the corresponding error in the lateral direction was 31.7 μm. Displacement was tracked in serial intravascular images of vessel phantoms acquired during stepwise pressurization experiments from 0–250 mmHg. Two-dimensional grey scale maps of axial, lateral and net intrawall displacement components over the full pressurization range were generated. Displacement profiles demonstrated successful detection of differential radial displacement and good correlation with theoretical profiles (root mean square difference 3%). The corresponding experimental strain profiles were significantly noisier (root mean square difference 76%) due to small fluctuations in the displacement data. This work demonstrates that, with further refinement, regional strain mapping in vessel walls with intravascular ultrasound imaging is feasible. Mechanical characterization of arteries may provide a new tool to aid in assessing and treating atherosclerotic lesions.

scholarly journals Road classification based on SVM and vehicle body vibration

2021 ◽  
Vol 268 ◽  
pp. 01074
Author(s):  
Xiaoyi Fu ◽  
Yuzhuang Zhao
Keyword(s):  
Root Mean Square ◽  
Recognition Rate ◽  
Mean Value ◽  
Driving Safety ◽  
Vehicle Body ◽  
Mean Square ◽  
Surface Information ◽  
The Mean ◽  
Reduce Energy Consumption ◽  
Better Than

Obtaining road surface information to make the vehicle run in the best condition can not only reduce energy consumption and vehicle loss, but also improve driving safety. In this paper, specific car body information was preprocessed as root mean square value, and SVM offline training was used. The recognition rate of off-road and highway can reach 98%. Compared with traditional threshold recognition, SVM has better adaptability and robustness. On the premise of keeping easy to obtain, the discrimination accuracy of the root mean square value is obviously better than the original value and the mean value.

Comparison of Flying Capacitor Fifteen Level Inverter and Thirty Three Level Inverter PWM Control Strategies

2017 ◽  
Vol 6 (2) ◽  
pp. 368
Author(s):  
Farouk Hadj Benali ◽  
Fouad Azzouz
Keyword(s):  
Root Mean Square ◽  
Output Voltage ◽  
Control Strategies ◽  
Harmonic Distortion ◽  
Total Harmonic Distortion ◽  
Mean Square ◽  
Pwm Control ◽  
Simulation Results ◽  
Multicarrier Pwm ◽  
Better Than

<p>In the paper two PWM control strategies of multilevel flying capacitor inverter is proposed. The study starts with a presentation of the Flying capacitor inverter and the two PWM control strategies (SPWM and Suboptimal PWM). Than a section which presents a brief recall of the triangular multicarrier PWM and the sinusoidal multicarrier PWM. A comparison between the two PWM control strategies based on the simulation results is made. The subjects of comparison are the root mean square rms of the output voltage and the total harmonic distortion THD. The obtained results have proved that the Suboptimal PWM is better than the SPWM. Simulations are carried out by PSIM program.</p>

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.

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