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The flight area of drones and other unmanned aerial vehicles (UAVs) had been highly restricted but has been relaxing, including flights beyond the scope of sight. Deregulation without aircraft-reliability improvement increases the risk of accidents. However, demanding high reliability for all aircraft leads to an increase in the price of the aircraft. Therefore, if airspace restrictions are relaxed for more reliable aircraft, the cost of higher reliability and its benefits can be balanced. This will improve efficiency and optimize cost-effectiveness. The purpose of this proposal is to balance the cost of aircraft-reliability improvement (which allows flight to continue in the event of a failure) and its advantages. Specifically, the author proposes rules that apply more relaxed airspace restrictions to UAVs with higher FCLs (Flight Continuity Possibility Levels) and stricter airspace restrictions to those with lower FCLs.

Suspension is an important chassis part which is vital to ride comfort [1]. However, it is difficult to achieve our targeted comfortability level in a short time. Therefore, improving efficiency of damper development is our primary challenge. We have launched a project which aims to reduce the workload on developing dampers by introducing analytical approaches to the improvement of ride comfort. To be more specific, we have been putting effort into developing the damping force prediction, the vehicle dynamics prediction and subjective rating prediction. This paper describes subjective rating prediction method which output a subjective rating corresponding to the physical value of the vehicle dynamics with deep learning. As a result of verification using objective data which was not used for learning process, DNN (Deep Neural Network) prediction method could fairly precisely predict subjective rating of the expert driver.

To cope with the increasing number of advanced features (e.g., smart-phone integration and side-blind zone alert.) being deployed in vehicles, automotive manufacturers are designing flexible hardware architectures which can accommodate increasing feature content with as fewer as possible hardware changes so as to keep future costs down. In this paper, we propose a formal and quantitative definition of flexibility, a related methodology and a tool flow aimed at maximizing the flexibility of an automotive hardware architecture with respect to the features that are of greater importance to the designer. We define flexibility as the ability of an architecture to accommodate future changes in features with no changes in hardware (no addition/replacement of processors, buses, or memories). We utilize an optimization framework based on mixed integer linear programming (MILP) which computes the flexibility of the architecture while guaranteeing performance and safety requirements.

The closed ecology experiment facility (CEEF) has a small positive pressure control system consisting of rubber buffers and a mechanical subsystem. In the present study, effects of small temperature disturbances caused by changes of surrounding conditions on the pressure control system are investigated experimentally and in numerical simulations. Though solar radiation causes a pressure disturbance in the facility, choosing the proper diameter of ducts which connect the rubber buffers to the modules, the rubber buffers can follow fluctuations of low frequency, like daily atmospheric fluctuations and pressure changes caused by temperature control of the facility’s air conditioners, and can cut off those of high frequency due to changes of environmental conditions.

It is necessary to develop a small positive pressure control system for the closed ecology experiment facility (CEEF) to protect against over-differential pressure loading. In the present study, a numerical method was developed to calculate the quantity of state of the closed module, which is fitted with rubber buffers, for the small positive pressure control system. Experiments to examine the pressure change of the closed module were carried out at CEEF. Comparison of calculated and experimental results showed that the present dynamic simulation is suited to estimating the quantity of state of the closed module.