scholarly journals Weighted Polynomial Approximation for Automated Detection of Inspiratory Flow Limitation

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Sheng-Cheng Huang ◽  
Hao-Yu Jan ◽  
Tieh-Cheng Fu ◽  
Wen-Chen Lin ◽  
Geng-Hong Lin ◽  
...  
Keyword(s):  
Polynomial Approximation ◽  
Feature Detection ◽  
Absolute Error ◽  
Time Algorithm ◽  
Inspiratory Flow ◽  
Detection Methods ◽  
Flow Limitation ◽  
Third Order ◽  
Time Curve ◽  
Order Polynomial

Inspiratory flow limitation (IFL) is a critical symptom of sleep breathing disorders. A characteristic flattened flow-time curve indicates the presence of highest resistance flow limitation. This study involved investigating a real-time algorithm for detecting IFL during sleep. Three categories of inspiratory flow shape were collected from previous studies for use as a development set. Of these, 16 cases were labeled as non-IFL and 78 as IFL which were further categorized into minor level (20 cases) and severe level (58 cases) of obstruction. In this study, algorithms using polynomial functions were proposed for extracting the features of IFL. Methods using first- to third-order polynomial approximations were applied to calculate the fitting curve to obtain the mean absolute error. The proposed algorithm is described by the weighted third-order (w.3rd-order) polynomial function. For validation, a total of 1,093 inspiratory breaths were acquired as a test set. The accuracy levels of the classifications produced by the presented feature detection methods were analyzed, and the performance levels were compared using a misclassification cobweb. According to the results, the algorithm using the w.3rd-order polynomial approximation achieved an accuracy of 94.14% for IFL classification. We concluded that this algorithm achieved effective automatic IFL detection during sleep.

scholarly journals Visual Characteristics of Afterimage under Dark Surround Conditions

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1404
Author(s):  
Hung-Chung Li ◽  
Pei-Li Sun
Keyword(s):  
Polynomial Regression ◽  
Model Performance ◽  
Absolute Error ◽  
Color Difference ◽  
Luminance Contrast ◽  
White Background ◽  
Strongly Correlated ◽  
Third Order ◽  
Order Polynomial ◽  
Visual Characteristics

Three psycho-visual experiments were carried out to investigate visual afterimage characteristics of high luminance LEDs under dark surround conditions. The results show that the luminance of illumination, exposure time and background luminance are the primary factors influencing the afterimage’s duration, the color difference between stimulus and background, and visibility. Besides, visual afterimage characteristics are strongly correlated with the luminance contrast between the stimulus and the background, but not for the color difference with a white background. A third-order polynomial regression model was proposed to accurately estimate the afterimage duration, the color difference between stimulus and background, and visibility. The model performance showed high R-squared, low root-mean-square error (RMSE) and mean absolute error (MAE) values between the predicted and visual characteristics.

scholarly journals Augmented Reality and Machine Learning Incorporation Using YOLOv3 and ARKit

2021 ◽  
Vol 11 (13) ◽  
pp. 6006
Author(s):  
Huy Le ◽  
Minh Nguyen ◽  
Wei Qi Yan ◽  
Hoa Nguyen
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Object Detection ◽  
Feature Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Annotation ◽  
Machine Learning Model ◽  
Potential Benefits ◽  
Feature Detection And Tracking

Augmented reality is one of the fastest growing fields, receiving increased funding for the last few years as people realise the potential benefits of rendering virtual information in the real world. Most of today’s augmented reality marker-based applications use local feature detection and tracking techniques. The disadvantage of applying these techniques is that the markers must be modified to match the unique classified algorithms or they suffer from low detection accuracy. Machine learning is an ideal solution to overcome the current drawbacks of image processing in augmented reality applications. However, traditional data annotation requires extensive time and labour, as it is usually done manually. This study incorporates machine learning to detect and track augmented reality marker targets in an application using deep neural networks. We firstly implement the auto-generated dataset tool, which is used for the machine learning dataset preparation. The final iOS prototype application incorporates object detection, object tracking and augmented reality. The machine learning model is trained to recognise the differences between targets using one of YOLO’s most well-known object detection methods. The final product makes use of a valuable toolkit for developing augmented reality applications called ARKit.

Thermal Error Modeling of Precision Positioning Stage

2013 ◽  
Vol 694-697 ◽  
pp. 767-770
Author(s):  
Jing Shu Wang ◽  
Ming Chi Feng
Keyword(s):  
Thermal Deformation ◽  
Thermal Error ◽  
Error Modeling ◽  
Precision Positioning ◽  
Third Order ◽  
Element Analysis ◽  
The Finite Element Analysis ◽  
Positioning Stage ◽  
Order Polynomial ◽  
The Relationship

As the thermal deformation significantly impacts the accuracy of precision positioning stage, it is necessary to realize the thermal error. The thermal deformation of the positioning stage is simulated by the finite element analysis. The relationship between the temperature variation and thermal error is fitted third-order polynomial function whose parameters are determined by genetic algorithm neural network (GANN). The operators of the GANN are optimized through a parametric study. The results show that the model can describe the relationship between the temperature and thermal deformation well.

scholarly journals Direct Calculation of Thermodynamic Wet-Bulb Temperature as a Function of Pressure and Elevation

2013 ◽  
Vol 30 (8) ◽  
pp. 1757-1765 ◽  
Author(s):  
Sayed-Hossein Sadeghi ◽  
Troy R. Peters ◽  
Douglas R. Cobos ◽  
Henry W. Loescher ◽  
Colin S. Campbell
Keyword(s):  
Air Temperature ◽  
Mean Absolute Error ◽  
Direct Calculation ◽  
Ambient Air ◽  
Absolute Error ◽  
Mean Square ◽  
Air Temperatures ◽  
Order Polynomial ◽  
The Mean ◽  
Very High

Abstract A simple analytical method was developed for directly calculating the thermodynamic wet-bulb temperature from air temperature and the vapor pressure (or relative humidity) at elevations up to 4500 m above MSL was developed. This methodology was based on the fact that the wet-bulb temperature can be closely approximated by a second-order polynomial in both the positive and negative ranges in ambient air temperature. The method in this study builds upon this understanding and provides results for the negative range of air temperatures (−17° to 0°C), so that the maximum observed error in this area is equal to or smaller than −0.17°C. For temperatures ≥0°C, wet-bulb temperature accuracy was ±0.65°C, and larger errors corresponded to very high temperatures (Ta ≥ 39°C) and/or very high or low relative humidities (5% < RH < 10% or RH > 98%). The mean absolute error and the root-mean-square error were 0.15° and 0.2°C, respectively.

scholarly journals 0471 INSPIRATORY FLOW LIMITATION IN A LARGE AMBULATORY COHORT OF SUSPECTED SLEEP APNOEA PATIENTS

SLEEP ◽  
2017 ◽  
Vol 40 (suppl_1) ◽  
pp. A176-A176
Author(s):  
NM Skjodt ◽  
S Sarraf ◽  
RS Platt
Keyword(s):  
Sleep Apnoea ◽  
Inspiratory Flow ◽  
Flow Limitation

Calibration of pneumotachographs using a calibrated syringe

2003 ◽  
Vol 95 (2) ◽  
pp. 571-576 ◽  
Author(s):  
Yongquan Tang ◽  
Martin J. Turner ◽  
Johnny S. Yem ◽  
A. Barry Baker
Keyword(s):  
Calibration Method ◽  
Linear Range ◽  
Data Sets ◽  
Constant Flow ◽  
Calibration Constant ◽  
Third Order ◽  
Polynomial Coefficients ◽  
Calibration Curves ◽  
Order Polynomial ◽  
Better Than

Pneumotachograph require frequent calibration. Constant-flow methods allow polynomial calibration curves to be derived but are time consuming. The iterative syringe stroke technique is moderately efficient but results in discontinuous conductance arrays. This study investigated the derivation of first-, second-, and third-order polynomial calibration curves from 6 to 50 strokes of a calibration syringe. We used multiple linear regression to derive first-, second-, and third-order polynomial coefficients from two sets of 6–50 syringe strokes. In part A, peak flows did not exceed the specified linear range of the pneumotachograph, whereas flows in part B peaked at 160% of the maximum linear range. Conductance arrays were derived from the same data sets by using a published algorithm. Volume errors of the calibration strokes and of separate sets of 70 validation strokes ( part A) and 140 validation strokes ( part B) were calculated by using the polynomials and conductance arrays. Second- and third-order polynomials derived from 10 calibration strokes achieved volume variability equal to or better than conductance arrays derived from 50 strokes. We found that evaluation of conductance arrays using the calibration syringe strokes yields falsely low volume variances. We conclude that accurate polynomial curves can be derived from as few as 10 syringe strokes, and the new polynomial calibration method is substantially more time efficient than previously published conductance methods.

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