activity
20142023
most citedClassification of volcanic ash particles using a convolutional neural network and probability

83 citations · 129 across the 12 of their papers we have counts for

collaborators

12 papers

cs.CV2023

Classification of the lunar surface pattern by AI architectures: Does AI see a rabbit in the Moon?

Daigo Shoji

In Asian countries, there is a tradition that a rabbit, known as the Moon rabbit, lives on the Moon. Typically, two reasons are mentioned for the origin of this tradition. The firs…

cs.CV2022

Rabbit, toad, and the Moon: Can machine categorize them into one class?

Daigo Shoji

Recent machine learning algorithms such as neural networks can classify objects and actions in video frames with high accuracy. Here, I discuss a classification of objects based on…

astro-ph.EP2022★ 9 cited

Monte Carlo Simulation of Sugar Synthesis on Icy Dust Particles Intermittently Irradiated by UV in a Protoplanetary Disk

Hitoshi Takehara, Daigo Shoji, Shigeru Ida

Context. While synthesis of organic molecules in molecular clouds or protoplanetary disks is complex, observations of interstellar grains, analyses of carbonaceous chondrites, and…

astro-ph.EP2021

Tidal evolution of the eccentric moon around dwarf planet (225088) Gonggong

Sota Arakawa, Ryuki Hyodo, Daigo Shoji +1

Recent astronomical observations revealed that (225088) Gonggong, a 1000-km-sized trans-Neptunian dwarf planet, hosts an eccentric satellite, Xiangliu, with an eccentricity of appr…

astro-ph.EP2020

Enceladus Farm: Can plants grow with Enceladus' water? (Preliminary Report)

Daigo Shoji

Enceladus is a saturnian satellite that should have liquid water inside of it (subsurface ocean). Measurements and experiments on water plume from Enceladus have revealed that Ence…

astro-ph.EP2019★ 7 cited

Angle of repose of Martian wet sand using discrete element method: Implication for the seasonal cycle of recurring slope lineae(RSL) by relative humidity

Daigo Shoji, Shoko Imamura, Maya Nakamura +1

Seasonal cycle of Martian relative humidity shows that, in the southern and the northern RSL sites, humidity decreases when RSL increment while humidity is high on the fading seaso…

astro-ph.EP2018

Condition for the deflection of vertical cracks at dissimilar ice interfaces on Europa

Daigo Shoji

The surface of Europa contains many quasi-circular morphologies called lenticulae. Although the formation mechanism of lenticulae is not understood, sill intrusion from the subsurf…

physics.geo-ph2018★ 83 cited

Classification of volcanic ash particles using a convolutional neural network and probability

Daigo Shoji, Rina Noguchi, Shizuka Otsuki +1

Analyses of volcanic ash are typically performed either by qualitatively classifying ash particles by eye or by quantitatively parameterizing its shape and texture. While complex s…

cs.CV2017

What does a convolutional neural network recognize in the moon?

Daigo Shoji

Many people see a human face or animals in the pattern of the maria on the moon. Although the pattern corresponds to the actual variation in composition of the lunar surface, the c…

physics.geo-ph2017

Shape recognition of volcanic ash by simple convolutional neural network

Daigo Shoji, Rina Noguchi

Shape analyses of tephra grains result in understanding eruption mechanism of volcanoes. However, we have to define and select parameter set such as convexity for the precise discr…

astro-ph.EP2016★ 12 cited

Frequency-dependent tidal dissipation in a viscoelastic Saturnian core and expansion of Mimas' semi-major axis

Daigo Shoji, Hauke Hussmann

Regarding tidal dissipation in Saturn, usually parameterized by Saturn's quality factor Q, there remains a discrepancy between conventional estimates and the latest determination t…

astro-ph.EP2014★ 18 cited

Thermal-orbital coupled tidal heating and habitability of Martian-sized extrasolar planets around M stars

Daigo Shoji, Kei Kurita

M type stars are good targets in the search for habitable extrasolar planets. Because of their low effective temperatures, the habitable zone of M stars is very close to the star i…