scholarly journals State Identification of Underdetermined Grids

10.5772/8881 ◽  
2010 ◽  
Author(s):  
Martin Wolter
Keyword(s):  
State Identification

scholarly journals Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3003
Author(s):  
Ting Pan ◽  
Haibo Wang ◽  
Haiqing Si ◽  
Yao Li ◽  
Lei Shang
Keyword(s):  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Practical Significance ◽  
Support Vector ◽  
State Identification ◽  
Feature Parameter ◽  
The Time Domain ◽  
Pilot Fatigue ◽  
Identification Model

Fatigue is an important factor affecting modern flight safety. It can easily lead to a decline in pilots’ operational ability, misjudgments, and flight illusions. Moreover, it can even trigger serious flight accidents. In this paper, a wearable wireless physiological device was used to obtain pilots’ electrocardiogram (ECG) data in a simulated flight experiment, and 1440 effective samples were determined. The Friedman test was adopted to select the characteristic indexes that reflect the fatigue state of the pilot from the time domain, frequency domain, and non-linear characteristics of the effective samples. Furthermore, the variation rules of the characteristic indexes were analyzed. Principal component analysis (PCA) was utilized to extract the features of the selected feature indexes, and the feature parameter set representing the fatigue state of the pilot was established. For the study on pilots’ fatigue state identification, the feature parameter set was used as the input of the learning vector quantization (LVQ) algorithm to train the pilots’ fatigue state identification model. Results show that the recognition accuracy of the LVQ model reached 81.94%, which is 12.84% and 9.02% higher than that of traditional back propagation neural network (BPNN) and support vector machine (SVM) model, respectively. The identification model based on the LVQ established in this paper is suitable for identifying pilots’ fatigue states. This is of great practical significance to reduce flight accidents caused by pilot fatigue, thus providing a theoretical foundation for pilot fatigue risk management and the development of intelligent aircraft autopilot systems.

scholarly journals State Identification of Transformer Under DC Bias Based on Wavelet Singular Entropy

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 58944-58952
Author(s):  
Jun Liu ◽  
Huarong Zeng ◽  
Wei Niu ◽  
Peilong Chen ◽  
Kui Xu ◽  
...  
Keyword(s):  
State Identification ◽  
Dc Bias

Lower-Limb Rehabilitation at Home

2021 ◽  
Vol 13 (2) ◽  
pp. 12-23
Author(s):  
Seanglidet Yean ◽  
Bu-Sung Lee ◽  
Chai Kiat Yeo
Keyword(s):  
Lower Limbs ◽  
Ageing Population ◽  
Joint Angles ◽  
Functional Activities ◽  
State Identification ◽  
Motion State ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation ◽  
Rehabilitation Exercise ◽  
At Home

Ageing causes loss of muscle strength, especially on the lower limbs, resulting in higher risk to injuries during functional activities. The path to recovery is through physiotherapy and adopt customized rehabilitation exercise to assist the patients. Hence, lowering the risk of incorrect exercise at home involves the use of biofeedback for physical rehabilitation patients and quantitative reports for clinical physiotherapy. This research topic has garnered much attention in recent years owing to the fast ageing population and the limited number of clinical experts. In this paper, the authors survey the existing works in exercise assessment and state identification. The evaluation results in the accuracy of 95.83% average classifying exercise motion state using the proposed raw signal. It confirmed that raw signals have more impact than using sensor-fused Euler and joint angles in the state identification prediction model.

scholarly journals A method for identifying emergency processes in energy conversion pulse systems

2015 ◽  
Vol 11 (2) ◽  
pp. 74-79
Author(s):  
Dmitry O Tey ◽  
Artem V Gusakov ◽  
Nizam D Keramov
Keyword(s):  
Fourier Transform ◽  
Energy Conversion ◽  
Real Time ◽  
Pulse Energy ◽  
Sampling Rate ◽  
The State ◽  
Main Process ◽  
State Identification ◽  
Conversion System ◽  
Energy Conversion System

The article discusses the problem of identification of the state of the pulse energy conversion system in real time. Investigated a method of reducing the size and the sampling rate of data describing the state of the system wavelet transform, for applying a Fourier transform. Proposed and experimentally tested the algorithm state identification pulse energy conversion system that allows you to determine in real time during the main process of energy conversion

Sign in / Sign up

Export Citation Format

Share Document