Time-Varying Pilot’s Intention Identification Based on ESAX-CSA-ELM Classification Method in Complex Environment
Abstract
:1. Introduction
2. Materials
2.1. Experimental Design
2.2. Experimental Design
2.3. Experimental Participants
2.4. Experimental Data
3. Methods
3.1. Data Processing
3.2. Extension of Symbolic Aggregate Approximation
3.3. Extreme Learning Machine Optimized by the Crow Search Algorithm
- (1)
- Extreme learning machine
- (2)
- Crow search algorithm
- (3)
- ESAX-CAS-ELM model
4. Results
4.1. Feature Extraction of Intention
4.2. Classification of Intention Generation
- (1)
- Determination of network structure
- (2)
- Determination of parameters
- (3)
- CAS-ELM performance evaluation
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Step NO. | Pilot’s Control Process |
---|---|
1 | Take-off run. |
2 | The speed is 55 knots and the front wheels are lifted. |
3 | Climbing airspeed (70~80 KIAS). |
4 | The height is 300 ft, the throttle is 2000~2100 RPM. |
5 | Crosswind turn at 500 ft height. |
6 | Downwind turn at 900 ft height. |
7 | The altitude is changed to 1000 ft and the throttle is 1800 RPM on the downwind leg to enter the cruise. The cruising airspeed is 90 KIAS. |
8 | When the aircraft is tangent to the head of the runway, the throttle is closed to 1500 RPM; it descends to 800 ft and prepares for the third turn with an airspeed of 80 KIAS. |
9 | The head of the runway is 45~50 degrees from the right-wing of the aircraft, enter the third turn. |
10 | The aircraft recovered from the third turn, throttled back to 1300~1400 RPM, extended the flaps at 10 degrees, and the airspeed is 70 KIAS. |
11 | Enter 500~550 ft in the fourth turn and recover from 400~350 ft in the fourth turn. |
12 | The throttle is 1200~1400 RPM, the flaps at 20 degrees. |
13 | Approach airspeed (65 KIAS). |
14 | After closing the throttle, the aircraft maintains a certain attitude, angle of pitch, and speed to taxi a certain distance, and then flattens (push the nose down and the aircraft flattens). |
Type of Signal | Method | Notes |
---|---|---|
EEG | 0.5 Hz high-pass filter | Frequencies less than 0.5 Hz are filtered. |
100 Hz low-pass filter magnetic induction | Frequencies greater than 100 Hz are filtered. | |
EDA | 0.3 Hz high-pass filter | Frequencies less than 0.3 Hz are filtered. |
RESP | 100 Hz low-pass filter | Frequencies greater than 100 Hz are filtered |
3 | 4 | 5 | ||
---|---|---|---|---|
−0.43 | −0.67 | −0.84 | ||
0.43 | 0 | −0.25 | ||
0.67 | 0.25 | |||
0.84 |
Performance Metrics | Formula |
---|---|
Precision rate | |
Recall rate | |
Accuracy rate |
Performance Metrics | Training Set | Testing Set |
---|---|---|
Precision rate | 0.8256 | 0.8226 |
Recall rate | 0.8341 | 0.8422 |
Accuracy rate | 0.8673 | 0.8452 |
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Wang, H.; Pan, T.; Si, H.; Zhang, H.; Shang, L.; Liu, H. Time-Varying Pilot’s Intention Identification Based on ESAX-CSA-ELM Classification Method in Complex Environment. Appl. Sci. 2022, 12, 4858. https://doi.org/10.3390/app12104858
Wang H, Pan T, Si H, Zhang H, Shang L, Liu H. Time-Varying Pilot’s Intention Identification Based on ESAX-CSA-ELM Classification Method in Complex Environment. Applied Sciences. 2022; 12(10):4858. https://doi.org/10.3390/app12104858
Chicago/Turabian StyleWang, Haibo, Ting Pan, Haiqing Si, Hongjia Zhang, Lei Shang, and Haibo Liu. 2022. "Time-Varying Pilot’s Intention Identification Based on ESAX-CSA-ELM Classification Method in Complex Environment" Applied Sciences 12, no. 10: 4858. https://doi.org/10.3390/app12104858
APA StyleWang, H., Pan, T., Si, H., Zhang, H., Shang, L., & Liu, H. (2022). Time-Varying Pilot’s Intention Identification Based on ESAX-CSA-ELM Classification Method in Complex Environment. Applied Sciences, 12(10), 4858. https://doi.org/10.3390/app12104858