| Project/Area Number |
10450370
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| Research Category |
Grant-in-Aid for Scientific Research (B).
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Aerospace engineering
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| Research Institution | The University of Tokyo, Graduate School of Engineering |
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Principal Investigator |
SUZUKI Shinji The University of Tokyo, Graduate School of Engineering, Professor, 大学院・工学系研究科, 教授 (30196828)
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| Co-Investigator(Kenkyū-buntansha) |
KARASAWA Kenji The University of Tokyo, Graduate School of Engineering, Assistant, 大学院・工学系研究科, 助手 (60134491)
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| Project Period (FY) |
1998 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| Budget Amount *help |
¥11,100,000 (Direct Cost: ¥11,100,000)
Fiscal Year 2000: ¥1,600,000 (Direct Cost: ¥1,600,000)
Fiscal Year 1999: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1998: ¥8,600,000 (Direct Cost: ¥8,600,000)
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| Keywords | Flight Safety / Pilot Model / Kalman Filter / Neural Network / Optimization / Simulator / ニューラルネットワーク / 飛行力学 / 最適推定 / 航空事故 / シミュレータ / 人間機械系 |
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| Research Abstract |
This research is aiming for improving flight safety through the analysis of human pilot behavior at landing operation. In the fiscal year of 1998, we measured the viewpoint of a pilot operating a flight simulator and analyzed where the pilot put attention through a landing flight. This measurement revealed that the pilot cannot monitor the instrument panel under about 100 ft height. In the fiscal year of 1999, we made a numerical model of the human pilot. This model contains the Kalman Filter which can estimate the state variables from the input data and the output data to minimize the estimation error. In this analysis, the distribution ratio of a pilot attentiveness are dealt with unknown parameters and are optimized to minimize the estimation error index in the Kalman Filter. It is found that the visual cue of the runway side edges is more important than the others in the state estimation in the final approach phase, and that attention should be distributed to several visual cues to minimize the total sum of the estimation errors. These results are confirmed from experimental data obtained by using a flight simulator. In the final year of this research, the pilot model is automatically constructed using neural network from the results of the simulator tests. The main inputs to this model are observation cues such as the horizon and the runway, and the output is the elevator angle. The data on two cases (with wind, or with no wind) are used for learning. Next, the constructed pilot models are simulated in various cases and we check their validity and generalization. Finally, the models are evaluated using factor analysis.
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