江上周作，西村悟史，福田賢一郎: 3次元仮想空間を用いた日常生活行動のナレッジグラフ構築，第53回人工知能学会セマンティックウェブとオントロジー研究会, SIG-SWO-053-04, pp.1-10 (2021)
塚越雄登，江上周作，清雄一，田原康之，大須賀昭彦: 学内情報のナレッジグラフの洗練による⽋損推定の考察，電子情報通信学会人工知能と知識処理研究会，信学技法, Vol.120, No.362, AI2020-38, pp.85-90 (2021)
Toru Kouyama, Eri Tatsumi, Yasuhiro Yokota, Koki Yumoto, Manabu Yamada, Rie Honda, Shingo Kameda, Hidehiko Suzuki, Naoya Sakatani, Masahiko Hayakawa,Tomokatsu Morota, Moe Matsuoka, Yuichiro Cho, Chikatoshi Honda, Hirotaka Sawada, Kazuo Yoshioka and Seiji Sugita
Accurate measurements of the surface brightness and its spectrophotometric properties are essential for obtaining reliable observations of the physical and material properties of planetary bodies. To measure the surface brightness of Ryugu accurately, we calibrated the optical navigation cameras (ONCs) of Hayabusa2 using both standard stars and Ryugu itself during the rendezvous phase including two touchdown operations for sampling. These calibration results showed that the nadir-viewing telescopic camera (ONC-T) and nadir-viewing wide-angle camera (ONC-W1) experienced substantial variation in sensitivity. In particular, ONC-W1 showed significant sensitivity degradation (~60%) after the first touchdown operation. We estimated the degradations to be caused by front lens contamination by fine-grain materials lifted from the Ryugu surface due to thruster gas for ascent back maneuver and sampler projectile impact upon touchdown. While ONC-T is located very close to W1 on the spacecraft, its degradation in sensitivity was only ~15% over the entire rendezvous phase. If in fact dust is really the main cause for the degradation, this lighter damage likely resulted from dust protection by the long hood attached to ONC-T. However, because large variations in the absolute sensitivity occurred after the touchdown events, which should be due to dust effect, uncertainty for the absolute sensitivity was rather large (3-4%). On the other hand, the change in relative spectral responsivity (i.e., 0.55-μm-band normalized responsivity) of ONC-T was small (1%). The variation in relative responsivity during the proximity phase has been well calibrated to have only a small uncertainty (< 1%). Furthermore, the degradation (i.e., increase) in the full width at half maximum of the point spread function of ONC-T and W1 was almost negligible, although the blurring effect due to dust scattering was confirmed in W1. These optical degradations due to the touchdown events were carefully monitored as a function of time along with other time-related deteriorations, such as the dark current level and hot pixels. We also conducted a new calibration of the flat-field change as a function of the detector temperature by observing the onboard flat-field lamp and validating with Ryugu's disk images. The results of these calibrations showed that ONC-T and W1 maintained their scientific performance by updating the calibration parameters.
Mitochondrial sorting and assembly machinery operates by β-barrel switching
Hironori Takeda, Akihisa Tsutsumi, Tomohiro Nishizawa, Caroline Lindau, Jon V. Busto, Lena-Sophie Wenz, Lars Ellenrieder, Kenichiro Imai, Sebastian P. Straub, Waltraut Mossmann, Jian Qiu, Yu Yamamori, Kentaro Tomii, Junko Suzuki, Takeshi Murata, Satoshi Ogasawara, Osamu Nureki, Thomas Becker, Nikolaus Pfanner, Nils Wiedemann, Masahide Kikkawa, Toshiya Endo
Nature 06 January 2021
呂暁東，江上周作: SWIMオントロジーの構築と応⽤に関する研究，航空無線，Vol.106，pp.43-47 (2020)
Rebuilding Ring-Type Assembly of Peroxiredoxin by Chemical Modification
Tomoki Himiyama, Yuko Tsuchiya, Yasushige Yonezawa, Tsutomu Nakamura
Bioconjug Chem. 17 Dec. 2020
Tsukagoshi, Y., Egami, S., Sei, Y., Tahara, Y., Ohsuga, A.: Ontology-Based Correlation Detection among Heterogeneous Data Sets: A Case Study of University Campus Issues, The 3rd IEEE International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), pp.25-32 (2020)
江上周作, 大向一輝, 山本泰智, 伊藤真和吏, 坂根昌一, 網淳子, 奥村貴史: SARS-CoV-2感染リスクオントロジーの提案, 第52回人工知能学会セマンティックウェブとオントロジー研究会, SIG-SWO-052-02, pp.1-10 (2020)
Taehoon Kim, Wijae Cho, Akiyoshi Matono, Kyoung-Sook Kim:
PinSout: Automatic 3D Indoor Space Construction from Point Clouds with Deep Learning, ACM SIGSPATIAL International Conferences on Advances in Geographic Information Systems 2020, Pages 211-214, 3 - 6 Nov. 2020.