Driver's Mental Stress Visualization by Discrete Wavelet Multiresolution Analysis.

2000 ◽  
Vol 20 (79) ◽  
pp. 356-364
Author(s):  
Taro SEKINE ◽  
Masahiro TAKEI ◽  
Michiharu OKANO ◽  
Hiroyasu NAGAE ◽  
Yoshifuru SAITO ◽  
...  
Keyword(s):  
Multiresolution Analysis ◽  
Mental Stress ◽  
Discrete Wavelet ◽  
Wavelet Multiresolution Analysis

scholarly journals Wavelet Transform Based Fault Identification and Reconfiguration for a Reduced Switch Multilevel Inverter Fed Induction Motor Drive

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1023
Author(s):  
Arigela Satya Veerendra ◽  
Akeel A. Shah ◽  
Mohd Rusllim Mohamed ◽  
Chavali Punya Sekhar ◽  
Puiki Leung
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multiresolution Analysis ◽  
Short Circuit ◽  
Open Circuit ◽  
Multilevel Inverter ◽  
Fault Identification ◽  
Discrete Wavelet ◽  
The Novel ◽  
Inverter Topology

The multilevel inverter-based drive system is greatly affected by several faults occurring on switching elements. A faulty switch in the inverter can potentially lead to more losses, extensive downtime and reduced reliability. In this paper, a novel fault identification and reconfiguration process is proposed by using discrete wavelet transform and auxiliary switching cells. Here, the discrete wavelet transform exploits a multiresolution analysis with a feature extraction methodology for fault identification and subsequently for reconfiguration. For increasing the reliability, auxiliary switching cells are integrated to replace faulty cells in a proposed reduced-switch 5-level multilevel inverter topology. The novel reconfiguration scheme compensates open circuit and short circuit faults. The complexity of the proposed system is lower relative to existing methods. This proposed technique effectively identifies and classifies faults using the multiresolution analysis. Furthermore, the measured current and voltage values during fault reconfiguration are close to those under healthy conditions. The performance is verified using the MATLAB/Simulink platform and a hardware model.

scholarly journals Multiresolution analysis of the spatiotemporal variability in global radiation observed by a dense network of 99 pyranometers during the HOPE campaign

2016 ◽  
Author(s):  
Bomidi Lakshmi Madhavan ◽  
Hartwig Deneke ◽  
Jonas Witthuhn ◽  
Andreas Macke
Keyword(s):  
Autocorrelation Function ◽  
Multiresolution Analysis ◽  
Global Radiation ◽  
Power Spectra ◽  
Spatiotemporal Variability ◽  
Discrete Wavelet ◽  
Dense Network ◽  
Point Measurement ◽  
Single Station ◽  
Sky Conditions

Abstract. The time series of global radiation observed by a dense network of 99 autonomous pyranometers are investigated with a multiresolution analysis based on the maximum overlap discrete wavelet transform and the Haar wavelet. For different sky conditions, typical wavelet power spectra are calculated to quantify the timescale dependence of variability in global transmittance. The power spectra of global transmittance are found to be dominated by the direct irradiance component under all sky conditions. Distinctly higher variability is observed at all frequencies in the power spectra of global transmittance under broken cloud conditions compared to clear, cirrus or overcast skies. The spatial autocorrelation function including its frequency-dependence is determined to quantify the degree of similarity of two measurements as a function of their spatial separation. Distances ranging from 100 m to 10 km are considered, and a rapid decrease of the autocorrelation function is found with increasing frequency and distance. For frequencies below 1.0 min−1, variations in transmittance become completely uncorrelated already after several hundred meters. A method is introduced to estimate the deviation between a point measurement and a spatially averaged value for a surrounding domain, which takes into account domain size and averaging period, and is used to explore the representativeness of a single pyranometer observation for its surrounding region. Two distinct mechanisms are identified, which limit the representativeness: on the one hand, spatial averaging reduces variability and thus modifies the shape of the power spectrum. On the other hand, the correlation of variations of the spatially averaged field and a point measurement decreases rapidly with increasing temporal frequency. For a grid-box of 10 x 10 km2 and averaging periods of 1.5–3 h, the deviation of global transmittance between a point measurement and an area-averaged value depends on the prevailing sky conditions: 2.8 % (clear), 1.8 % (cirrus), 1.5 % (overcast) and 4.2 % (broken clouds). The global radiation observed at a single station is found to deviate from the spatial average by as much as 14–23 Wm−2 (clear), 8–26 Wm−2 (cirrus), 4–23 Wm−2 (overcast), and 31–79 Wm−2 (broken clouds) from domain averages ranging from 1 x 1 km2 to 10 x 10 km2 in area.

Real-time Mental Stress Detection Technique Using Neural Networks towards Wearable Health Monitor

2021 ◽  
Author(s):  
Nilava Mukherjee ◽  
Sumitra Mukhopadhyay ◽  
Rajarshi Gupta
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Mental Stress ◽  
Detection Technique ◽  
Physiological Signals ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Optimum Number ◽  
Stress Detection ◽  
Detection Techniques

Abstract Motivation: In recent times, mental stress detection using physiological signals have received widespread attention from the technology research community. Although many motivating research works have already been reported in this area, the evidence of hardware implementation is occasional. The main challenge in stress detection research is using optimum number of physiological signals, and real-time detection with low complexity algorithm. Objective: In this work, a real-time stress detection technique is presented which utilises only photoplethysmogram (PPG) signal to achieve improved accuracy over multi-signal-based mental stress detection techniques. Methodology: A short segment of 5s PPG signal was used for feature extraction using an autoencoder (AE), and features were minimized using recursive feature elimination (RFE) integrated with a multi-class support vector machine (SVM) classifier. Results: The proposed AE-RFE-SVM based mental stress detection technique was tested with WeSAD dataset to detect four-levels of mental state, viz., baseline, amusement, meditation and stress and to achieve an overall accuracy, F1 score and sensitivity of 99%, 0.99 and 98% respectively for 5s PPG data. The technique provided improved performance over discrete wavelet transformation (DWT) based feature extraction followed by classification with either of the five types of classifiers, viz., SVM, random forest (RF), k-nearest neighbour (k-NN), linear regression (LR) and decision tree (DT). The technique was translated into a quad-core-based standalone hardware (1.2 GHz, and 1 GB RAM). The resultant hardware prototype achieves a low latency (~0.4 s) and low memory requirement (~1.7 MB). Conclusion: The present technique can be extended to develop remote healthcare system using wearable sensors.

Monitoring HVDC Systems Using Wavelet Multiresolution Analysis

2001 ◽  
Vol 21 (11) ◽  
pp. 54-55 ◽  
Author(s):  
A. M. Gouda ◽  
E. F. El-Saadany ◽  
M. M. A. Salama ◽  
V. K. Sood ◽  
A. Y. Chikhani
Keyword(s):  
Multiresolution Analysis ◽  
Wavelet Multiresolution Analysis

Nonlinear Aerodynamics Reduced-Order Model Based on Multi-Input Volterra Series

2013 ◽  
Vol 748 ◽  
pp. 421-426 ◽  
Author(s):  
Yi Bin Dou ◽  
Min Xu
Keyword(s):  
Multiresolution Analysis ◽  
Volterra Series ◽  
Coupling Effect ◽  
Harmonic Oscillation ◽  
Reduced Order Model ◽  
Least Square ◽  
Recursive Least Square ◽  
Order Model ◽  
Reduced Order ◽  
Wavelet Multiresolution Analysis

This paper gives a low-order approximations of multi-input Volterra series as a nonlinear reduced-order model (ROM) based on wavelet multiresolution analysis. The band-limited pseudorandom multilevel sequence (PRMS) is used as the identification signal and the QR decomposition recursive least square (QRD-RLS) method is utilized as identification method. At last, the ROM is applied to model the nonlinear aerodynamic moment of an airfoil undergoing simultaneous forced pitch and plunge harmonic oscillation. The results show that including the second-order Volterra cross kernels in ROM can capture the coupling effect which significantly improves accuracy in predicting nonlinear aerodynamics under simultaneous motion. And the wavelet multiresolution analysis efficiently reduces the number of identification coefficients for Volterra kernels.

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