Changing amount of radiation dose has been a great concern by citizens near the difficult-to-return area after the Fukushima Daiichi nuclear disaster. Rigorous assessment of public safety including inaccessible forest areas at a meter level is one of the keys in the disaster recovery plan. Each measurement point near the ground surface in UAV-based sensing is exposed from the effects of radiation emission from various angles in the fields including forest and plain, and thus acquisition of the attenuation coefficients on site is not realistic by simply using the spatial integration of point-kernels or exponential decay functions. The proposed method in this paper utilizes the vertical and horizontal distance correction. The field test at a residence in the forests near the difficult-to-return area was performed, and in this area we found the improvement of 42% in RMSE (root mean squared error) from the results of a base model. Importantly, the experimental result suggested that the proposed method has the potential to efficiently accept using the different measurement data, both measured points near the ground surface and the flight heights during the UAV sensing.

In recent years, personal mobility (PM), electric vehicles that are smaller and slower than conventional automobiles, have been attracting attention, and are expected to be used on sidewalks and in shared spaces where vehicles and pedestrians can coexist. PM is also expected to be used as a means of transportation for the physically challenged and the elderly who have difficulty getting around by themselves, and research is being conducted to automate PM operation. In this situation, our group proposed a PM motion control method based on a spatio-temporal potential that takes into account the motion characteristics of obstacles. However, this method is not sufficient to avoid obstacles with large angular velocities, such as pedestrians. It is also not a practical method because of the time required for computation. Therefore, this paper proposes a new practical PM motion control method that improves these points. By defining a new potential field that takes into account the angular velocity of the obstacle, the proposed method improves the performance against obstacles whose avoidance performance was insufficient in the previous method. At the same time, to increase practicality, a reduction algorithm with variable resolution of the potential field is incorporated as a method to reduce computational complexity while maintaining avoidance performance as much as possible. Finally, the effectiveness of the proposed method is verified through numerical simulations assuming a shared space.

In this paper, we consider an identification problem of linear systems in the presence of not only stochastic disturbances such as white noises but also persistent disturbances such as constant and sinusoidal disturbances with unknown frequencies. The object is to identify the plants in the presence of persistent disturbances and estimate the frequencies of the sinusoidal disturbances. The purpose of this paper is to select a suitable identification model and analyze its identifiability condition. First, we select the identification model whose one-step-ahead predictor corresponds to the steady-state Kalman predictor, which is the optimal one-step-ahead model. Second, we show the condition for the existence of the steady-state Kalman predictor and analyze the identifiability condition of the identification model. Finally, through a numerical example, we show the fact we can identify the system in the presence of persistent disturbances using the selected identification model if the identifiability condition is satisfied.

This paper is concerned with secure communication of RGB images using lumped parameter/distributed parameter chaotic synchronization. As a lumped parameter system generating chaos, we use a discrete-time system describing by a generalized Henon map and construct its synchronization system. Since the discrete-time chaotic system has three-dimensional state variables, it is convenient when designing a secure communication system for RGB images. However, the generalized Henon system and its synchronization system have only a few common encryption keys. To increase the common encryption key, we introduce another chaotic system described by a hyperbolic partial differential equation with logistic boundary condition. Several snapshots of the solution of the hyperbolic system are used to generate a long-length common encryption key of the secure communication system proposed in this paper.

We develop a system that supports self-training in the shoot form of basketball to reduce teaching costs of club activities based on pose estimation. Advice for improving students' shoot form is generated based on time-series machine learning using LSTM.