飛行ロボットにおける人間・ロボットインタラクションの実現に向けて : ユーザー同伴モデルとセンシングインターフェース

学位の種別: 課程博士審査委員会委員 : （主査）東京大学准教授 矢入 健久, 東京大学教授 堀 浩一, 東京大学教授 岩崎 晃, 東京大学教授 土屋 武司, 東京理科大学教授 溝口 博University of Tokyo(東京大学

Micro-controller controlled Z-source inverter for grid connected solar energy system

This project report covers basic knowledge of micro-controller, Z-source inverter and solar energy system in the first part and explains the design process of a micro-controller controlled Z-source inverter in second part. In last part, simulation will be done on the Z-source inverter circuit and hardware will be constructed to verify the theoretical result.Bachelor of Engineerin

Unsupervised anomaly detection in unmanned aerial vehicles.

Having a real-time anomaly detection solution indicates a continuous stream of operational and labelled data that must satisfy a number of resource and latency requirements. Traditional solutions to the problem rely heavily on well-defined features and prior supervised knowledge, where most techniques refer to hand-crafted rules derived from known conditions. While successful in controlled situations, these rules assume that good data is available for them to detect anomalies; indicating that these rules will fail to generalise beyond known scenarios. To investigate these issues, current literature is examined for solutions that can be used to detect known and unknown anomalous instances whilst functioning as an out-of-the-box approach for efficient decision-making. The applicability of the isolation forest is discussed for engineering applications using the Aero-Propulsion System Simulation dataset as a benchmark where it is shown to outperform other unsupervised distance-based approaches. In addition, the authors have carried out real-time experiments on an unmanned aerial vehicle to highlight further applications of the method. Finally, some conclusions are drawn with respect to its simplicity and robustness in handling diagnostic problems

Unsupervised anomaly detection in unmanned aerial vehicles

Having a real-time anomaly detection solution indicates a continuous stream of operational and labelled data that must satisfy a number of resource and latency requirements. Traditional solutions to the problem rely heavily on well-defined features and prior supervised knowledge, where most techniques refer to hand-crafted rules derived from known conditions. While successful in controlled situations, these rules assume that good data is available for them to detect anomalies; indicating that these rules will fail to generalise beyond known scenarios. To investigate these issues, current literature is examined for solutions that can be used to detect known and unknown anomalous instances whilst functioning as an out-of-the-box approach for efficient decision-making. The applicability of the isolation forest is discussed for engineering applications using the Aero-Propulsion System Simulation dataset as a benchmark where it is shown to outperform other unsupervised distance-based approaches. In addition, the authors have carried out real-time experiments on an unmanned aerial vehicle to highlight further applications of the method. Finally, some conclusions are drawn with respect to its simplicity and robustness in handling diagnostic problems