Onset and Offset Detection of Respiratory EMG Data Based on Energy Operator Signal

Abstract The high energy Operator Product Expansion for the product of two electromagnetic currents is extended to the sub-eikonal level in a rigorous way. I calculate the impact factors for polarized and unpolarized structure functions, define new distribution functions, and derive the evolution equations for unpolarized and polarized structure functions in the flavor singlet and non-singlet case.

Download Full-text

A Single-Terminal Fault Location Method for HVDC Transmission Lines Based on a Hybrid Deep Network

Electronics ◽

10.3390/electronics10030255 ◽

2021 ◽

Vol 10 (3) ◽

pp. 255

Author(s):

Lei Wang ◽

Yigang He ◽

Lie Li

Keyword(s):

Deep Learning ◽

Transmission Lines ◽

Fault Location ◽

Short Term Memory ◽

Engineering Application ◽

Energy Operator ◽

Sampling Frequency ◽

Learning Technology ◽

Long Distance ◽

Hvdc Transmission

High voltage direct current (HVDC) transmission systems play an increasingly important role in long-distance power transmission. Realizing accurate and timely fault location of transmission lines is extremely important for the safe operation of power systems. With the development of modern data acquisition and deep learning technology, deep learning methods have the feasibility of engineering application in fault location. The traditional single-terminal traveling wave method is used for fault location in HVDC systems. However, many challenges exist when a high impedance fault occurs including high sampling frequency dependence and difficulty to determine wave velocity and identify wave heads. In order to resolve these problems, this work proposed a deep hybrid convolutional neural network (CNN) and long short-term memory (LSTM) network model for single-terminal fault location of an HVDC system containing mixed cables and overhead line segments. Simultaneously, a variational mode decomposition–Teager energy operator is used in feature engineering to improve the effect of model training. 2D-CNN was employed as a classifier to identify fault segments, and LSTM as a regressor integrated the fault segment information of the classifier to achieve precise fault location. The experimental results demonstrate that the proposed method has high accuracy of fault location, with the effects of fault types, noise, sampling frequency, and different HVDC topologies in consideration.

Download Full-text

A hybrid hidden Markov model towards fault detection of rotating components

Journal of Vibration and Control ◽

10.1177/1077546315627934 ◽

2016 ◽

Vol 23 (19) ◽

pp. 3175-3195 ◽

Cited By ~ 8

Author(s):

Ayan Sadhu ◽

Guru Prakash ◽

Sriram Narasimhan

Keyword(s):

Fault Detection ◽

Markov Model ◽

Hidden Markov Model ◽

Time Scale ◽

Hidden Markov ◽

Energy Operator ◽

Fault Classification ◽

Multiple Faults ◽

Condition Indicators ◽

Model Based

A robust hybrid hidden Markov model-based fault detection method is proposed to perform multi-state fault classification of rotating components. The approach presented in this paper enhances the performance of the standard hidden Markov model (HMM) for fault detection by performing a series of pre-processing steps. First, the de-noised time-scale signatures are extracted using wavelet packet decomposition of the vibration data. Subsequently, the Teager Kaiser energy operator is employed to demodulate the time-scale components of the raw vibration signatures, following which the condition indicators are calculated. Out of several possible condition indicators, only relevant features are selected using a decision tree. This pre-processing improves the sensitivity of condition indicators under multiple faults. A Gaussian mixing model-based hidden Markov model (HMM) is then employed for fault detection. The proposed hybrid HMM is an improvement over traditional HMM in that it achieves better separation of the feature space leading to more robust state estimation under multiple fault states and measurement noise scenarios. A simulation employing modulated signals and two experimental validation studies are presented to demonstrate the performance of the proposed method.

Download Full-text

Perturbation-Theory Rules for Computing the Self-Energy Operator in Quantum Statistical Mechanics

Physical Review ◽

10.1103/physrev.131.2345 ◽

1963 ◽

Vol 131 (5) ◽

pp. 2345-2349 ◽

Cited By ~ 48

Author(s):

Gordon Baym ◽

Andrew M. Sessler

Keyword(s):

Perturbation Theory ◽

Statistical Mechanics ◽

Quantum Statistical Mechanics ◽

Energy Operator ◽

The Self ◽

Self Energy ◽

Quantum Statistical

Download Full-text

DESIGN AND IMPLEMENTATION OF WIRELESS MULTI-CHANNEL EEG RECORDING SYSTEM AND STUDY OF EEG CLUSTERING METHOD

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237206000427 ◽

2006 ◽

Vol 18 (06) ◽

pp. 276-283 ◽

Cited By ~ 17

Author(s):

ROBERT LIN ◽

REN-GUEY LEE ◽

CHWAN-LU TSENG ◽

YAN-FA WU ◽

JOE-AIR JIANG

Keyword(s):

Energy Operator ◽

Digital Processing ◽

Recording System ◽

Ar Model ◽

Characteristic Matrix ◽

Eeg Signal ◽

Eeg Signals ◽

Long Distance ◽

Communication Module ◽

Processing Circuit

A multi-channel wireless EEG (electroencephalogram) acquisition and recording system is developed in this work. The system includes an EEG sensing and transmission unit and a digital processing circuit. The former is composed of pre-amplifiers, filters, and gain amplifiers. The kernel of the later digital processing circuit is a micro-controller unit (MCU, TI-MSP430), which is utilized to convert the EEG signals into digital signals and fulfill the digital filtering. By means of Bluetooth communication module, the digitized signals are sent to the back-end such as PC or PDA. Thus, the patient's EEG signal can be observed and stored without any long cables such that the analogue distortion caused by long distance transmission can be reduced significantly. Furthermore, an integrated classification method, consisting of non-linear energy operator (NLEO), autoregressive (AR) model, and bisecting k-means algorithm, is also proposed to perform EEG off-line clustering at the back-end. First, the NLEO algorithm is utilized to divide the EEG signals into many small signal segments according to the features of the amplitude and frequency of EEG signals. The AR model is then applied to extract two characteristic values, i.e., frequency and amplitude (peak to peak value), of each segment and to form characteristic matrix for each segment of EEG signal. Finally, the improved modified k-means algorithm is utilized to assort similar EEG segments into better data classification, which allows accessing the long-term EEG signals more quickly.

Download Full-text