In the 5th generation mobile communication system (5G), there is a strong demand for higher speed and higher capacity. The beamforming technique using phased-array antennas is effective for long-distance radio propagation and for reducing interference signals in the high SHF (super high frequency) band because the phase shift of the antenna array elements can be controlled to produce a narrow-band beam and to control the directivity. We have developed the 28 GHz array antenna for small cells and evaluated the beamforming characteristics using an RF analog phase shifter control scheme. In this paper, we verify the computer simulation and evaluation results of the beamforming characteristics of a newly developed 4×4 phased array antenna.

This study proposes a numerical method for searching for vectors whose sum of all elements is maximum. The vector search can be applied to many scientific problems. If we use numerical computations without considering rounding errors, in a worst-case scenario, we might find incorrect vectors as a result. We propose herein an adaptive method designed to work accurately, regardless of the rounding error problems, based on an error-free transformation of a floating-point vector.

In 2021, fuel debris samplings are planned to start as part of a step-by-step process at the Fukushima Daiichi nuclear power station. The dose rate of the fuel debris for safety treatments of the fuel debris should be predicted. However, various elements are mixed in the fuel debris, and thus predicting the dose rate will be challenging. Therefore, we conducted a large number of Monte Carlo radiation transport simulations for cases where parameters such as fuel debris size, composition, and density were significantly changed. Consequently, we obtained a simple and analytical formula that can predict the dose rate using a minimum number of parameters.

We derive analytic expressions for the vector potential of electromagnetic wave radiated from a relativistic electron moving on a spiral orbit under an approximation that the velocity along the spiral orbit is much larger than that perpendicular to the axis. Based on this expression, we show that the electromagnetic wave has a spiral wave front and carries angular momentum.

This paper focuses on saddle point problems with a 2-by-2 block coefficient matrix. When the number of columns in the upper-right block and the number of rows in the lower-left block of the coefficient matrix is large, the convergence behavior of Krylov subspace methods for the saddle point problems tends to be poor even if the upper-left block is a well-conditioned matrix. In this paper, an efficient approach for solving the saddle point problems using block structure of the problems is proposed. The most time-consuming part of our proposed approach is the solution of a linear system with multiple right-hand sides. To solve the linear system with multiple right-hand sides efficiently, we propose to apply Block Krylov subspace methods to this linear system. Numerical experiments show that the proposed approach with Block Krylov subspace methods can solve the saddle point problems more efficiently than the conventional approach in terms of the number of iterations and the computation time.

A flow simulation was developed to understand the flow surrounding a train that was passing through a tornado and the resulting aerodynamic forces acting on the vehicle. Unsteady Reynolds averaged Navier–Stokes equations were solved to reproduce a previously-conducted laboratory experiment, in which a model train runs through a stationary tornado-like swirling flow. The simulation reproduced the unsteady aerodynamic forces acting on the train reported in the experiment. Furthermore, the computation successfully revealed how the flow field changes as a train passes through a tornado-like swirling flow.

A relation of preconditioners of domain decomposition method is shown for numerical analysis of 3-Dimensional (3D) magnetostatic problems taking the magnetic vector potential as an unknown function. The iterative domain decomposition method is combined with the Preconditioned Conjugate Gradient (PCG) procedure and the Hierarchical Domain Decomposition Method (HDDM) which is adopted in parallel computing. Our previously employed preconditioner was the Neumann-Neumann (NN) preconditioner. Numerical results showed that the method was only effective for small number subdomain problems. In this paper, we consider its improvement making use of the Balancing Domain Decomposition DIAGonal scaling (BDD-DIAG) preconditioner and show the asymptotic equivalence between BDD-DIAG and the simplified diagonal scaling (diag) preconditioner, which is derived from the following numerical evidences. Finally, nonlinear processing is also tried for the first time.

Water level prediction is becoming increasingly important. However, physical models tend to become difficult to apply when it comes to some small rivers which have insufficient hydrological data. To address it, nowadays, deep learning methods are increasingly being applied to climate prediction analysis as an alternative to computationally expensive physical models for its features of flexible data-driven learning and universality. In our paper, we focus on the precipitation-only water level forecasting problem by using long-short-term memory (LSTM) based hybrid model, and try predicting the future water level of all the rivers in Japan by using simulated precipitation data from the database for Policy Decision making for Future climate change (d4PDF).

In this paper, a novel topology optimization method, in which gaussian filter is selected as spatial smoothing method, is presented. By changing standard deviation parameter, strength of filtering operation can be adjusted in the present method. Thanks to this, optimum solution, shape of which has multi-layer shield structure and multi-layer flux barrier, can be obtained. To validate the effectiveness, the present method is applied to shape optimization problem of magnetic shield and synchronous reluctance motor. From the results, it can be seen that the present method can get better solutions than that of the conventional method.

Three-dimensional data visualization employing the CT and MRI techniques is widely used to understand the complex internal structure of the human body. However, the position and depth information often become unclear when three-dimensional data are rendered transparently. In this study, we aimed to improve the accuracy of the perceived 3D structure by introducing multiple iso-surfaces as a visual guide. For the purpose, we conducted psychophysical experiments. The experimental results suggest that multiple iso-surfaces improve the accuracy of the perceived depth. It was also found that the accuracy of perceived depth changes with the distance between the iso-surfaces.

Recently, to generate metal foam models for either open or closed state, a sphere-function-based method and a radial-basis-function-based one have been proposed. In these methods, implicit functions are employed for representing shapes of metal foams. In this paper, these methods based on spheres and radial basis functions have similarly been extended by employing characteristics of implicit functions, so that open/closed states and their intermediates can be represented. It is an advantage of the extended methods that any states of metal foams can be represented by only one implicit function.

A fast linear solver for topology optimization using a deflation technique with Proper Orthogonal Decomposition (POD) is discussed. The topology optimization method based on evolutionary algorithms requires huge computational cost. In this reason, a deflated Preconditioned Conjugate Gradient (PCG) method is introduced so as to reduce the cost of finite element analysis. The deflation technique decomposes the solution into fast and slowly converging components. The slow components can be solved by direct methods with low computational cost due to small dimensions. Therefore, the deflated PCC method can improve the convergence of PCG. However, the deflated PCG requires to find the slow components. In this study, a POD method with snapshots is introduced. In the optimization process, solution vectors corresponding to parents are used for the snapshots. Orthogonal vectors for the deflation are constructed from the snapshots. Numerical results show that the present method can reduce the computational cost.