Recently, traffic congestion on highways has become a greater problem due to heavy snow and other factors. To help vehicles avoid heavy traffic jams, sharing information on road and traffic conditions is increasingly important. However, during times of natural disaster, this problem becomes even more serious because mobile communication infrastructures, such as cellular systems, are disabled. In this paper, we describe methods of avoiding traffic congestion using information floating, which adopts direct wireless communication and does not require fixed communication infrastructure. In our proposed method, we supplement an established method by giving drivers a probabilistic choice of routes. We evaluate these methods by computer simulation and discuss their effectiveness on controlled-access highways, which have a unique characteristic in that vehicles can only enter or exit them via interchanges.
We construct the Landau-Maier-Saupe (LMS) model by applying a Landau expansion to the isotropic part of the free energy of the previously proposed Flory-HugginsMaier-Saupe (FHMS) model, in order to relax its numerical restriction. The FHMS model exhibits a rapid droplet formation due to molecular orientational effects, whose dynamics is governed by the Cahn-Hilliard-Cook equation. The numerical restriction inherent in the FHMS free energy limits simulations to a very early stage of the rapid droplet formation dynamics. The LMS model reproduces the rapid droplet formation of the FHMS model and enables phase separation simulations over longer times. Furthermore, long-time simulations clarify the range of applicability of the LMS model.
The violin is the musical instrument represented by the masterpieces of Antonio Stradivari (1644-1737), and numerous studies have been conducted on the physical properties of the violin. In recent years, the development of noncontact measurement techniques and the increasing use of numerical simulation have expanded the possibilities for research on violins. However, there are limited studies that have used finite element analysis to investigate acoustic effects reflecting string tension preload. Therefore, this study constructed an FE model of Titian Stradivari from Strad 3D computed tomography scan data, and numerical simulations were performed to reflect the string tension preload. The numerical simulation results reflecting the string tension preload were compared with the actual measured data from the laser doppler velocimeter and showed good agreement. Therefore, a 3D visualization of acoustic radiation was performed for each mode of the numerical simulation results. As a result, the acoustic directivity may become stronger as the frequency increases, and the string tension preload may affect the acoustic directivity.
This paper presents a particle-based virtual reality (VR) in-situ steering framework, called VR IS-PBVR, for simulations executed in high-performance computing (HPC) environments in nuclear engineering and computational fluid dynamics. The framework leverages particle-based volume rendering to convert simulation results into compact particle representations, enabling efficient remote visualization by transferring compressed data to a client-side VR environment. High frame rate rendering on head-mounted displays is achieved through an OpenXR-based VR backend. The main contributions of this work are threefold. First, we design and implement a VR-based in-situ steering framework tightly coupled with simulations running in HPC environments. Second, we realize immersive human-in-the-loop steering by combining fly-through navigation with direct spatial specification of observation and analysis regions within the VR space. Third, we ensure compatibility with commercially available head-mounted displays through OpenXR and demonstrate applicability to a CityLBM simulation with more than 10(8) cells.
In this study, we have analyzed the prediction rationale of a deep learning model for sex classification from 3D brain MRI using Approximate Inverse Model Explanations (AIME). A 3D DenseNet121 classifier has been trained on 566 T1-weighted IXI scans. The model has achieved 98.2% accuracy on a 114-case validation set. Global importance has shown a sign-reversal pattern between classes: peripheral regions contribute to Male prediction, whereas central regions contribute to Female prediction. Local importance has been consistent with this pattern and has highlighted strong peripheral reliance in misclassified cases. Controlled experiments (skull-stripped retraining, masking sensitivity, and agematched analysis) have indicated substantial dependence on extra-brain information. Crossvalidation and leave-one-site-out evaluation have supported the robustness of these findings.
Tritium retention in plasma-facing components is a critical issue for fuel-cycle management in fusion reactors, and accurate evaluation of triton impact behavior is essential. To improve the fidelity of triton-wall interaction analysis in the Large Helical Device, we developed a high-accuracy computational and visualization framework that combines STL-based 3D models with VR-enabled representation of triton impact points and impact velocity vectors. D-D fusion born triton production was modeled using FIT3D-DD, and particle orbits were computed with LORBIT. Collision detection with plasma-facing structures was performed with high-resolution triangular mesh models exported from CAD designs. The STL-based wall model eliminated non-physical impact artifacts that appeared in the conventional cross-section-rotation geometry and provided physically consistent distributions of impact points. The visualization of impact velocity vectors within the VR environment enabled detailed qualitative assessment of incident-angle distributions, revealing geometric features-such as directional asymmetry between clockwise and counterclockwise toroidal magnetic field configurations-that cannot be captured by point-based visualization alone. This integrated approach demonstrates the effectiveness of combining precise geometric modeling with immersive VR visualization for interpreting energetic-particle behavior in fusion devices. The framework offers a valuable tool for correlating impact characteristics with material analyses and supports future optimization of plasma-facing component design.
A Cartesian grid method is developed for air-oil two-phase flows driven by rotating objects by combining the immersed boundary method of the body-force type (IB-BF) and the volume of fluid (VOF) type method. This study focuses on the numerical penetration of fluid phases into solid objects and proposes a simple numerical treatment to reduce this nonphysical behavior. The developed method is applied to air-oil two-phase flows driven by a rotating rotor with teeth. The computational results with multiple grid size and time increment conditions were employed to investigate the effects of the proposed numerical treatment on the numerical penetration of the oil into the rotor, the behavior of the oil around the rotor, and the torque acting on the rotor.
This study investigates the prediction accuracy of the Smagorinsky model in low Reynolds number periodic unsteady anisotropic turbulence. This model is required to decrease the value of the model constant with decreasing Reynolds number under low Reynolds number conditions, as seen in wall turbulence. The turbulent kinetic energy results predicted using the Smagorinsky model obtained in this study are compared with those obtained using the Vreman and coherent structure models. A large-eddy simulation based on the fourth-order central difference method is used in this study. Here, the model constants of each model are calibrated using turbulent fields under a high Reynolds number condition. Values of turbulent kinetic energy are presented by using not only time series results but also periodic averaged results. For the turbulence fields analysed in this study, the results obtained from the Smagorinsky model predictions agree with those obtained from the Vreman and coherent structure models under the low Reynolds number conditions.
To excite optical vortices using high-power millimeter waves, we use a miter bend with a spiral phase mirror. Through numerical simulations, we demonstrate that vortex beams can be successfully excited by employing a spiral phase mirror that appropriately accounts for the phase difference between the input and output modes. The simulations also reveal the generation of higher-order modes caused by diffraction inherent to the miter bend structure and unintended reflections arising from the singularity at the optical axis of the spiral phase mirror. Additionally, we propose a method to estimate the topological charge, which corresponds to the vorticity, from real-valued data. The simulation results confirm that vortex beams are successfully excited as the dominant mode.
This study investigates the application of inchworm robots for mobile additive manufacturing (AM). A method for controlling the center of gravity (COG) during locomotion and designing unit segments for efficient fabrication is proposed. The relationship between link lengths and fabrication efficiency was analyzed, and unit segments were designed to balance stability and production efficiency. Simulations were conducted to evaluate the stability of the robot's locomotion, confirming that the proposed design allows the COG to remain within the supporting leg area. These findings contribute to understanding the feasibility of inchworm robots as platforms for mobile AM systems.
Base station antennas are an indispensable component for mobile communication. For low wind load, circular cylinder structure with small diameter is usually employed for base station antenna. However, omnidirectional pattern with horizontal polarization and wide operating bandwidth is difficult to excite by such a cylinder structure. This paper presents the simulation and design method of Halo antenna with parasitic element for ease of fabrication and bandwidth enhancement. The proposed structure is Halo antenna with inner parasitic element. For ease of fabrication, several dimensions are fixed for the ease of fabrication and maintaining the inner capacity. The proposed antenna in this paper achieves operating bandwidth from 876 MHz to 975 MHz (10.8 %) with |S11| under -10 dB. In addition, the omnidirectional pattern with horizontal polarization are confirmed in operating frequency band.
A high-frequency electromagnetic field analysis based on the finite element method is known for poor convergence of iterative methods. Furthermore, false convergence may occur in which a physically correct solution cannot be obtained even though the convergence criteria are met. In this paper, we show that false convergence occurs in a high-frequency electromagnetic field analysis based on the finite element method. We propose and compare two methods to avoid false convergence that are currently executable: lowering the convergence judgment value of the residual norm of the conjugate orthogonal conjugate gradient method and increasing the wavelength resolution.
The magnetic fields confining plasma in fusion reactors are analyzed using Poincare plots, which show intersections of magnetic field lines on a poloidal crosssection. Traditional methods for designing vacuum vessels and related structures involve slicing the reactor vertically and analyzing these plots, but this approach is inefficient for understanding plasma shapes globally. Key challenges include the computational cost of magnetic field line tracing and the difficulty in constructing continuous surfaces for the divertor legs due to the limited number of magnetic field lines reaching this area. To address these issues, a new and automatic method has been proposed to increase the number of magnetic field lines constituting the divertor legs by predicting optimal starting points for tracing. The proposed method involves placing starting points on orthogonal lines through the magnetic axis, resulting in a better representation of divertor legs. This new algorithm enhances the efficiency of generating Poincare plots that depict divertor leg regions more clearly than previous methods. Neural Networks predict voxel data representing the shape of magnetic field lines. Considering the Larmor radius, we calculate an envelope surface that encompasses the region where plasma exists and create 3D modeling data.
The "up3bd" code, which is an efficient three-dimensional (3D) electrostatic particle-in-cell (PIC) simulation code for study of transport dynamics in fusion boundary layer plasmas or other plasmas in nature, has been tested on various high-performance computing (HPC) systems which consist of different types of processor. The results of benchmark tests indicate that the up3bd code works faster on processors in which cache memory is larger or memory bandwidth is broader. Also, the types of computations that each architecture excels at and struggles with has been revealed.
Using machine learning, a method was developed to estimate the distribution of radiation sources arranged on a two-dimensional plane with high accuracy from the measured gamma-ray energy spectra. A new machine learning method was used to construct a Spectrum Renormalization Filter (SRF) to convert the experimental data to be verified into a spectrum close to the shape of a simulation. The method using SRF produced more accurate estimation results than a method improving the accuracy of the simulation data used for training the estimation of the radiation sources distribution.
We propose a novel linear interpolation-based method to enhance the quality of widely-used JPEG images, which suffer from compression artifacts. Our approach focuses on restoring quantized DCT coefficients that have been rounded to zero during the JPEG quantization step. By employing a simple linear interpolation technique in the frequency domain, our method offers a computationally efficient solution with computational cost growing linearly with the number of restored coefficients. Experimental results on standard test images demonstrate that our method can improve the PSNR by 0.6 to 1.0 dB.
The power transfer efficiency of contactless power transfer devices that use vinyl-insulated wires was predicted by combining electromagnetic field analysis using the finite element method and equivalent circuit analysis using the Runge-Kutta method. We examined whether the measured power transfer efficiency is the median of the values obtained from calculations that considered vinyl-insulated wire to be a single solid wire and litz wire (vinyl-insulated wire is considered to be in an intermediate state between these two types of wire). It was found that the measured power transfer efficiency was not the median of the two calculated values in some cases. One of the calculated values was always slightly smaller than the measured value. The proposed method can thus be used to predict the lower limit of power transfer efficiency.
This paper presents a novel reinforcement learning approach to enhance image matching, whereby the corresponding point candidates are detected from the feature points extracted from each of the two images. In general, robust estimation methods such as random sample consensus (RANSAC) are used to select valid corresponding points from the candidates. Therefore, we addressed the limitations of RANSAC random selection in the image-matching process, evaluating various reinforcement learning strategies, including deterministic and probabilistic approaches, and different value update mechanisms. The findings indicated that a probabilistic approach with suitable value updates provides a more robust solution for space-based navigation systems.
We develop a deep learning model based on an extended pix2pix framework to predict 3D spatial distributions of X-ray dose to realize real-time monitoring of medical staff's radiation exposure. Utilizing conditional generative adversarial networks, the model processes 3D voxel grids to estimate X-ray dose distributions rapidly, addressing the limitations of Monte Carlo simulations in real-time applications. Training employed simulated datasets generated via a Monte Carlo code: PHITS. The trained model achieves an approximately 150,000-fold speedup compared to the Monte Carlo simulations. While the model predicts distributions with characteristics similar to the true values, errors increase in regions shielded by the objects.
Characteristics of plasma radiation structure in radiative collapse were visualized using a two-dimensional radiation measurement and AutoEncoder (AE) on the Large Helical Device of the National Institute for Fusion Science, Japan. The state without collapse was treated as normal, and the state in which collapse is evolving was treated as abnormal. Using the anomaly detection by the AE, the collapse could be detected similar to 0.36 s before the collapse as increase in abnormality. Moreover, the abnormal radiation structure could be visualized as the profile of the reconstruction error which appeared similar to 0.46 s before the collapse from inboard side of the torus plasma.
