The purpose of this study is to determine the relationship between the accuracy of the estimation of the horizontal stiffness of the seismic isolation layer using the estimation method and the input ground motion. This study focuses on an experiment conducted in 2013 at E-Defense, entitled “Vibration test for developing countermeasures to reduce damage caused by collisions in full-scale seismically isolated buildings”. Two models, a multi-mass system model and a 3D model, were created with reference to the test specimens from this experiment, taking into account nonlinear characteristics. In numerical experiment example 1, a time history response analysis was conducted for each model with only uniaxial ground motion as input. In numerical experiment example 2, a time history response analysis was performed for the 3D model with triaxial ground motion as input. By comparing the results of each analysis with the experimental results of E-Defense, the relationship between the accuracy of the estimation of the horizontal stiffness of the seismic isolation layer by the estimation method and the input ground motion was examined. The results of this study indicate that time response analysis with triaxial ground motion as input is necessary for the practical application of the estimation method of the horizontal stiffness of the seismic isolation layer.

Micromagnetic simulations using the Landau-Lifshitz-Gilbert (LLG) equation were performed to clarify the effect of voids caused by lattice defects in iron-based structural materials on their magnetization processes. We analyzed the magnetization process of magnetic clusters with voids by applying an external magnetic field. Compared to void-free cases, notable differences were observed in the magnetic hysteresis curves, domain wall displacement (DWD), and variations in exchange and magnetic dipole energies. In particular, the pinning phenomenon, in which the domain wall stretches slightly and DWD is prevented around the voids, was observed, and this is thought to be due to a decrease in the exchange energy as the magnetic wall passes through the voids. Furthermore, as a result of changing the position and size of the voids, it was found the timing and amount of the energy change varied.

In recent years, data-driven services have been realized in various fields, and to achieve congestion avoidance, security of confidential information, and real-time response, edge devices that collect and process data close to the field environment are advancing the application of AI based on recurrent neural networks (RNNs). However, the constraints on the available energy and computational resources hinder the application due to the large processing load. In this study, we focused on reservoir computing, a concept derived from RNNs, which has relatively low computational complexity, and attempted to apply it to robot control. We constructed an evaluation environment using a physical simulator, with the ceiling-walking control of a drone-type quadruped robot as a target task. We also introduced a power consumption of the movement as a learning criterion, and examined a method that can optimize the power consumption during movement, and verified its effectiveness by numerical simulations.

In this study, computational fluid dynamics (CFD) analysis was used to calculate the aerodynamic coefficients (drag, lift, Magnus, and fluid torque) of a spinning table tennis ball. The simulation conditions included a translational velocity of 2.5–20 m/s and rotational velocity of 15–90 rps (Reynolds number: 6.6 × 10³–5.3 × 10⁴ and spin parameter: 0.094–4.524), and CFD analysis was performed to simulate the wind tunnel experiments. The Smagorinsky–Lilly LES model was used as the turbulence model. The calculated coefficients were in qualitative agreement with the experimental results from wind tunnel and flight experiments reported in various studies.