A Framework for Jointly Training GAN with Person Re-Identification Model

2021 ◽  
pp. 36-51
Author(s):  
Zhongwei Zhao ◽  
Ran Song ◽  
Qian Zhang ◽  
Peng Duan ◽  
Youmei Zhang
Keyword(s):  
Identification Model

What Does an End-to-End Dialect Identification Model Learn About Non-Dialectal Information?

2020 ◽  
Author(s):  
Shammur A. Chowdhury ◽  
Ahmed Ali ◽  
Suwon Shon ◽  
James Glass
Keyword(s):  
End To End ◽  
Identification Model

A Statistical Identification Model (SIM) For Textural Images

2014 ◽  
Vol 17 (2) ◽  
pp. 227-234
Author(s):  
Ali Abdulmunim Ibrahim ◽  
Keyword(s):  
Statistical Identification ◽  
Identification Model

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.

Development of a Systematic Procedure to Establish Customized Shading Behavior Identification Model

2021 ◽  
pp. 110793
Author(s):  
Zhengrong li ◽  
Han Zhu ◽  
Bin Dong ◽  
Xiaofeng Xu
Keyword(s):  
Systematic Procedure ◽  
Identification Model
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