Daido University

About: Daido University is a education organization based out in Nagoya, Japan. It is known for research contribution in the topics: Ultimate tensile strength & Proton exchange membrane fuel cell. The organization has 209 authors who have published 423 publications receiving 3223 citations.

Switching Surge Analysis of an EHV Air-Insulated Substation Using the 3-D FDTD Method

Abstract: Switching overvoltages on busbars occur during the operation of circuit breakers and disconnectors in power plants and substations. The transient electromagnetic interferences arising from the switching surges may cause disturbances in sensitive electronic devices in low-voltage control circuits and may induce voltages on other busbars grounded for maintenance. To suppress the effect of electromagnetic interferences properly, it is useful to predict surge phenomena and design effective protection methodologies. The finite-difference time-domain (FDTD) method, which is a full-wave numerical approach, has become an effective tool for analyzing surge phenomena in a 3-D arrangement. In this study, first, we propose techniques for applying the FDTD method to the switching surge analysis of an extremely high voltage air-insulated substation. Second, using the proposed techniques, we calculate voltages induced on a grounded busbar due to a restrike at a disconnector during its operation in a 500 kV air-insulated substation and compare the calculated results with measured results for validation.

Changes in States of Carbon and Mechanical Properties with Aging at 50°C after Quenching in Low Carbon Steel

Abstract: The changes in the states of carbon (C) together with hardness and the tensile properties of low C steel (0.045C­0.34Mn in mass%) quenched from 710°C and aged at 50°C were investigated as a function of aging time using TEM and atom probe tomography. Vickers hardness increases at about 1.1 © 104 s, exhibits significant increase at 5.8 © 104 s (16 h) and maintains peak hardness untill 8.6 © 105 s (10 d) followed by a decrease after further aging time. At the start of peak aging, C clusters form with an irregular shape that resembles a sphere about 10 nm in diameter. The number of C atoms is about 700, and the C content is in the range of 1­2 at% at 1.0 © 105 s (28 h), where no enrichment of elements except for C is observed. At the end of peak aging, the plate-shaped precipitates (about 1 nm wide and 12 nm long) having a C content greater than 10 at% are distributed with the {100} habit plane, thus confirming the transition from C clusters to fine carbides. Lower yield strength (LYS) is the lowest for the specimen with solute C, and significantly increases for the specimen with C clusters and fine carbides in this order. LYS is determined presumably by the cutting mechanism for the C cluster specimen and the Orowan mechanism for the fine carbide specimen. The work hardening for the solute C and C cluster specimens is high, while the carbide specimen shows less work hardening. The C cluster is assumed to be decomposed into solute C through shearing by dislocations, causing work hardening and relatively good uniform elongation. Post uniform elongation (l-El) was the lowest for the C cluster specimen followed by the fine carbide specimen with the same strength level. This is because dynamic strain aging caused by solute C promotes the strain localization leading to the deterioration in l-El. [doi:10.2320/matertrans.MT-M2019351]

Synthesis of [Mo3S4] Clusters from Half-Sandwich Molybdenum(V) Chlorides and Their Application as Platforms for [Mo3S4Fe] Cubes.

Abstract: Triangular [Mo3S4] clusters are known to serve as platforms to accommodate a metal atom M, furnishing cubic [Mo3S4M] clusters. In this study, three [Mo3S4] clusters supported by η5-cyclopentadienyl...

Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer

Abstract: Advanced driver assistance and automated driving systems rely on risk estimation modules to predict and avoid dangerous situations. Current methods use expensive sensor setups and complex processing pipelines, limiting their availability and robustness. To address these issues, we introduce a novel deep learning based driving risk assessment framework for classifying dangerous lane change behavior in short video clips captured by a monocular camera. First, semantic segmentation masks were generated from individual video frames with a pre-trained Mask R-CNN model. Then, frames overlayed with these masks were fed into a time distributed CNN-LSTM network with a final softmax classification layer. This network was trained on a semi-naturalistic lane change dataset with annotated risk labels. A comprehensive comparison of state-of-the-art pre-trained feature extractors was carried out to find the best network layout and training strategy. The best result, with a 0.937 AUC score, was obtained with the proposed framework. Our code and trained models are available open-source1.