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Abstract. The purpose of this study was to develop a new educational system with an easy-to-use interface in order to support comprehension of the biological effects of radiation on the human body within a short period of time. A paint spray-gun was used as a gamma rays source mock-up for the system. The application screen shows the figure of a human body for radiation deposition using the γ-Sprayer, a virtual radiation source, as well as equivalent dosage and a panel for setting the irradiation conditions. While the learner stands in front of the PC monitor, the virtual radiation source is used to deposit radiation on the graphic of the human body that is displayed. Tissue damage is calculated using an interpolation method from the data calculated by the PHITS simulation code in advance while the learner is pulling the trigger with respect to the irradiation time, incident position, and distance from the screen. It was confirmed that the damage was well represented by the interpolation method. The augmented γ-Spray system was assessed by questionnaire. Pre-post questionnaire was taken for our 41 students in National Institute of Technology, Kagawa College. It was also confirmed that the system has a capability of teaching the basic radiation protection concept, quantitative feeling of the radiation dose, and the biological effects

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When a robot considers an action-decision based on a future prediction, it is necessary to know the property of disturbance signals from the outside environment. On the other hand, the properties of disturbance signals cannot be described simply, such as non-periodic function, nonlinear time-varying function nor almost-periodic function. In case of a robot control, sampling rate for control will be affected description of disturbance signals such as frequency or amplitude. If the sampling rate for acquiring a disturbance signal is not correct, the action will be taken far from its actual property. In general, future prediction using machine learning is based on the tendency obtained through past training or learning. In this case, an optimal action will be determined uniquely based on a property of disturbance. However, in this type of situation, the learning time increases in proportional to the amount of training data, either, the tendency may not be found using prediction, in the worst case. In this paper, we focus on prediction for almost-periodic disturbance. In particular, we consider the situation where almost-periodic disturbance signals occur. From this perspective, we propose a method that identifies the frequency of an almost- periodic function based on the frequency of the disturbance using Fourier transform, nearest-neighbor one-step-ahead forecasts and Nyquist-Shannon sampling theorem.