College of Industrial Technology

About: College of Industrial Technology is a education organization based out in Amagasaki, Japan. It is known for research contribution in the topics: Gravitational microlensing & Ion. The organization has 1475 authors who have published 2308 publications receiving 24987 citations. The organization is also known as: Sangyo Gijutsu Tanki Daigaku.

Optimized geometries and electronic structures of graphyne and its family

Abstract: The optimized geometries of carbon allotropes related to graphite, called graphyne, graphdiyne, graphyne-3, and graphyne-4, as well as their electronic band structures were calculated using a full-potential linear combination of atomic orbitals method in the local-density approximation. These carbon allotropes consist of hexagons connected by linear carbon chains. The bond length of a hexagon is a little longer than that of the bond that links a hexagon to the outside carbon. Furthermore, part of the linear carbon chain is composed of acetylenic linkages (---C\ensuremath{\equiv}C---) rather than cumulative linkages (=C=C=). The binding energies are 7.95 eV/atom for graphyne and 7.78 eV/atom for graphdiyne, and the optimized lattice lengths are 6.86 \AA{} for graphyne and 9.44 \AA{} for graphdiyne. These materials are semiconductors with moderate band gaps. The band gap occurs at the M point or \ensuremath{\Gamma} point depending on the number of acetylenic linkages that are contained between the nearest-neighboring hexagons. The effective masses are very small for both conduction and valence bands.

Frequency of solar-like systems and of ice and gas giants beyond the snow line from high-magnification microlensing events in 2005-2008

Abstract: We present the first measurement of the planet frequency beyond the "snow line," for the planet-to-star mass-ratio interval –4.5 200) microlensing events during 2005-2008. The sampled host stars have a typical mass M_(host) ~ 0.5 M_⊙, and detection is sensitive to planets over a range of planet-star-projected separations (s ^(–1)_(max)R_E, s_(max)R_E), where R_E ~ 3.5 AU(M_(host)/M_⊙)^(1/2) is the Einstein radius and s_(max) ~ (q/10^(–4.3))^(1/3). This corresponds to deprojected separations roughly three times the "snow line." We show that the observations of these events have the properties of a "controlled experiment," which is what permits measurement of absolute planet frequency. High-magnification events are rare, but the survey-plus-follow-up high-magnification channel is very efficient: half of all high-mag events were successfully monitored and half of these yielded planet detections. The extremely high sensitivity of high-mag events leads to a policy of monitoring them as intensively as possible, independent of whether they show evidence of planets. This is what allows us to construct an unbiased sample. The planet frequency derived from microlensing is a factor 8 larger than the one derived from Doppler studies at factor ~25 smaller star-planet separations (i.e., periods 2-2000 days). However, this difference is basically consistent with the gradient derived from Doppler studies (when extrapolated well beyond the separations from which it is measured). This suggests a universal separation distribution across 2 dex in planet-star separation, 2 dex in mass ratio, and 0.3 dex in host mass. Finally, if all planetary systems were "analogs" of the solar system, our sample would have yielded 18.2 planets (11.4 "Jupiters," 6.4 "Saturns," 0.3 "Uranuses," 0.2 "Neptunes") including 6.1 systems with two or more planet detections. This compares to six planets including one two-planet system in the actual sample, implying a first estimate of 1/6 for the frequency of solar-like systems.

A spiral antenna backed by a conducting plane reflector

Abstract: An Archimedean planar spiral antenna is numerically analyzed in the presence of a conducting plane reflector. The analysis shows that the spiral antenna backed by the plane reflector has two distinct regions in the current distribution, which explain the radiation of a circularly polarized wave for the outer circumference C ranging over about 1.3 \lambda and C > 2.9 \lambda , where \lambda is a free-space wavelength. Further consideration is given to a truncated spiral antenna whose outer circumference is on the order of 1.4 \lambda . The truncated spiral antenna maintains a decaying current distribution and radiates a circularly polarized wave over a 1:1.2 frequency bandwidth. It is also demonstrated that a power gain on the order of 8.5 dB is realized over the same frequency range.

Self-healing in nanocomposite hydrogels.

Abstract: Polymer hydrogels with characteristics distinct from those of solid materials are one of the most promising candidates for smart materials. Here, we report that a nanocomposite hydrogel (NC gel) consisting of a unique polymer/clay network structure, can exhibit complete self-healing through autonomic reconstruction of crosslinks across a damaged interface. Mechanical damage in NC gels can be repaired without the use of a healing agent, and even sections of NC gels separated by cutting, from whichever the same or different kinds of NC gel, perfectly (re-)combine by just contacting the cut surfaces together at mildly elevated temperatures. In NC gels, the autonomic fusion of cut surfaces as well as the self-healing could be achieved not only immediately after being cut but also after a long waiting time.

Analysis of function of rectified linear unit used in deep learning

Abstract: Deep Learning is attracting much attention in object recognition and speech processing. A benefit of using the deep learning is that it provides automatic pre-training. Several proposed methods that include auto-encoder are being successfully used in various applications. Moreover, deep learning uses a multilayer network that consists of many layers, a huge number of units, and huge amount of data. Thus, executing deep learning requires heavy computation, so deep learning is usually utilized with parallel computation with many cores or many machines. Deep learning employs the gradient algorithm, however this traps the learning into the saddle point or local minima. To avoid this difficulty, a rectified linear unit (ReLU) is proposed to speed up the learning convergence. However, the reasons the convergence is speeded up are not well understood. In this paper, we analyze the ReLU by a using simpler network called the soft-committee machine and clarify the reason for the speedup. We also train the network in an on-line manner. The soft-committee machine provides a good test bed to analyze deep learning. The results provide some reasons for the speedup of the convergence of the deep learning.