Modeling, Guidance and Control of “esso Osaka” Model

Modeling and assessment of student effective supervision drivers

Drivers are motivating and provocative factors of the supervision process in complex, uncertain and fluctuating conditions to achieve student’s empowerment. The first purpose of this study is to evaluate the fit of the model of effective driving instruction for graduate and doctoral students and the second one is the pathology of the guidance process of this model. The research is done in ways ...

Evaluation of D-isomer of 18F-FBPA for oncology PET focusing on the differentiation of glioma and inflammation

Objective(s): L-4-borono-2-18F-fluoro-phenylalanine (L-[18F]FBPA), a substrate of L-type amino acid transporter 1 (LAT1), is a tumor-specific probe used in positron emission tomography (PET). On the other hand, it has not been examined whether another isomer D-[18F]FBPA accumulates specifically in the tumor. He...

Incremental Predictive Command of Velocity to Be Gained Guidance Method

In this paper, a new incremental predictive guidance method based on implicit form of velocity to be gained algorithm is proposed. In this approach, the generalized incremental predictive control (GIPC) approach is applied to the linearized model for compensating the guidance error. Instead of using the present state in popular model based predictive controller (MPC), in the new method both pre...

Abstracts, The 13th Annual General Meeting of ARCCNM, Osaka, Japan

Consistently Trained Artificial Neural Network for Automatic Ship Berthing Control

In this paper, consistently trained Artificial Neural Network controller for automatic ship berthing is discussed. Minimum time course changing manoeuvre is utilised to ensure such consistency and a new concept named ‘virtual window’ is introduced. Such consistent teaching data are then used to train two separate multi‐layered feed forward neural networks for com...