Issues related to velocity structure estimation in small coastal sedimentary plains: case of Tottori plain facing the Sea of Japan

Issues of predominant period of ground motion and derived underground velocity structure model are investigated in the coastal plains affected by the shallow soft sedimentary layer after the last ice age. It is found that two predominant periods due to the shallow soft layer and deeper drastic sedimentary boundaries are close in a small plain such as the Tottori plain, Japan as an example. This study analyzes the underground velocity structure derived from EHVSR (H/V spectrum ratio of earthquake ground motions) with the diffuse field theory. It is considered that the interaction of close predominant periods due to the different layer boundaries with high contrast may amplify the seismic ground motion in the period range that affects building structures in small plains in coastal area

A Study to Optimize Heterogeneous Resources for Open IoT

Recently, IoT technologies have been progressed, and many sensors and actuators are connected to networks. Previously, IoT services were developed by vertical integration style. But now Open IoT concept has attracted attentions which achieves various IoT services by integrating horizontal separated devices and services. For Open IoT era, we have proposed the Tacit Computing technology to discover the devices with necessary data for users on demand and use them dynamically. We also implemented elemental technologies of Tacit Computing. In this paper, we propose three layers optimizations to reduce operation cost and improve performance of Tacit computing service, in order to make as a continuous service of discovered devices by Tacit Computing. In optimization process, appropriate function allocation or offloading specific functions are calculated on device, network and cloud layer before full-scale operation.Comment: 3 pages, 1 figure, 2017 Fifth International Symposium on Computing and Networking (CANDAR2017), Nov. 201

Yukawa hierarchy from extra dimensions and infrared fixed points

We discuss the existence of hierarchy of Yukawa couplings in the models with extra spatial dimensions. The hierarchical structure is induced by the power behavior of the cutoff dependence of the evolution equations which yield large suppressions of couplings at the compactification scale. The values of coupling constants at this scale can be made stable almost independently of the initial input parameters by utilizing the infrared fixed point. We find that the Yukawa couplings converge to the fixed points very quickly because of the enhanced energy dependence of the suppression factor from extra dimensions as well as in the case of large gauge couplings at high-energy scale.Comment: 13 pages, 3 eps figure

ビドウ オヨビ ジュウリョク イジョウ オ モチイタ トットリ ヘイヤ ノ ジバン コウゾウ スイテイ ニ カンスル ケンキュウ

鳥取大学　博士（工学）　2002年03月26日授与　甲第127

LAN with collision avoidance : switch implementation and simulation study

Packet collisions and their resolution create a performance bottleneck in random access LANs. A hardware solution to this problem is to use collision avoidance switches. These switches allow the implementation of random access protocols without the penalty of collisions among packets. The simplest network based on collision avoidance is the Broadcast Star network, where all the stations are connected to a central switch. A more sophisticated architecture based on collision avoidance is the CAMB (Collision Avoidance Multiple Broadcast) Tree network, where concurrent broadcast is possible. This paper presents a design of a collision avoidance switch for the CAMB Tree using TTL devices. Simulation study exploring the performance of the Broadcast Star network in both synchronous and asynchronous operations is also presented in this paper

Singular behavior of the macroscopic quantities in the free molecular gas

Steady behavior of the free molecular gas is studied with a special interest in the behavior around a convex body. Two types of singular behavior are shown to occur at the level of the macroscopic quantities. Their occurrence and the strength of singularity are discussed in detail both numerically and analytically. A universal law behind them is revealed by the consideration of the local geometry of the boundary

Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representations

We propose an unsupervised method for 3D geometry-aware representation learning of articulated objects. Though photorealistic images of articulated objects can be rendered with explicit pose control through existing 3D neural representations, these methods require ground truth 3D pose and foreground masks for training, which are expensive to obtain. We obviate this need by learning the representations with GAN training. From random poses and latent vectors, the generator is trained to produce realistic images of articulated objects by adversarial training. To avoid a large computational cost for GAN training, we propose an efficient neural representation for articulated objects based on tri-planes and then present a GAN-based framework for its unsupervised training. Experiments demonstrate the efficiency of our method and show that GAN-based training enables learning of controllable 3D representations without supervision.Comment: 19 pages, project page https://nogu-atsu.github.io/ENARF-GAN