TOKUNAGA Terumasa

写真a

Title

Associate Professor

Laboratory

680-4 Kawazu, Iizuka-shi, Fukuoka

Scopus Paper Info  
Total Paper Count: 0  Total Citation Count: 0  h-index: 6

Citation count denotes the number of citations in papers published for a particular year.

Undergraduate Education 【 display / non-display

  • 2006.03   Kyushu University   Faculty of Science   Graduated   JAPAN

Post Graduate Education 【 display / non-display

  • 2011.03  Kyushu University    Doctoral Program  Completed  JAPAN

  • 2008.03  Kyushu University    Master's Course  Completed  JAPAN

Degree 【 display / non-display

  • Kyushu University -  Doctor of Science  2011.03

Biography in Kyutech 【 display / non-display

  • 2019.04
    -
    Now

    Kyushu Institute of TechnologyFaculty of Computer Science and Systems Engineering   Department of Artificial Intelligence   Associate Professor  

  • 2015.04
    -
    2019.03

    Kyushu Institute of TechnologyFaculty of Computer Science and Systems Engineering   Department of Systems Design and Informatics   Associate Professor  

Biography before Kyutech 【 display / non-display

  • 2018.10
    -
    2022.03

    Japan Science and Technology Agency   JAPAN

  • 2018.08
    -
    2019.03

    Kyushu University   International Center for Space Weather Science and Education   Visiting Associate Professor   JAPAN

  • 2018.04
    -
    2019.03

    Research Organization of Information and Systems, The Institute of Statistical Mathmatics   Data Science Center for Creative Design and Manufacturing   Visiting Associate Professor   JAPAN

  • 2013.05
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    2015.03

    Research Organization of Information and Systems, The Institute of Statistical Mathmatics   Research and Development Center for Data Assimilation   Specially Appointed Assistant Professor   JAPAN

  • 2012.04
    -
    Now

    Meiji University   Meiji Institute for Advanced Study of Mathematical Science   visiting researcher   JAPAN

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Academic Society Memberships 【 display / non-display

  • 2016.10
    -
    Now
     

    The Seismologocal Society of Japan  JAPAN

  • 2014.07
    -
    2018.03
     

    The International Society for Computational Biology  UNITED STATES

  • 2006.04
    -
    Now
     

    Japan Geoscience Union  JAPAN

Specialized Field (scientific research fund) 【 display / non-display

  • Perceptual information processing

 

Publications (Article) 【 display / non-display

  • Identifying Snowfall Clouds at Syowa Station, Antarctica via a Convolutional Neural Network

    Kazue Suzuki, Masaki Shimomura, Kazuyuki Nakamura, Naohiko Hirasawa, Hironori Yabuki, Takashi Yamanouchi, Terumasa Tokunaga

    Advances in Artificial Intelligence - Selected Papers from the Annual Conference of Japanese Society of Artificial Intelligence (JSAI 2020)  ( Springer International Publishing )  1357 ( 1 ) 78 - 83   2021.07  [Refereed]

    Japan  オンライン 

    DOI

  • Signal and Noise Separation from Satellite Magnetic Field Data through Independent Component Analysis: Prospect of Magnetic Measurements without Boom and Noise Source Information

    Shun Imajo, Masahito Nosé, Mari Aida, Haruhisa Matsumoto, Nana Higashio, Terumasa Tokunaga , Ayako Matsuoka

    Journal of Geophysical Research: Space Physics  ( American Geophysical Union )  126 ( 5 )   2021.04  [Refereed]

    DOI Scopus

  • Image-Based Plant Disease Diagnosis with Unsupervised Anomaly Detection Based on Reconstructability of Colors

    Ryoya Katafuchi, Terumasa Tokunaga

    Proceedings of International Conference on Image Processing and Vision Engineering  ( SciTePress )  1   112 - 120   2021.04  [Refereed]

    オンライン  オンライン  2021.04  -  2021.04

     View Summary

    This paper proposes an unsupervised anomaly detection technique for image-based plant disease diagnosis. The construction of large and publicly available datasets containing labeled images of healthy and diseased crop plants led to growing interest in computer vision techniques for automatic plant disease diagnosis. Although supervised image classifiers based on deep learning can be a powerful tool for plant disease diagnosis, they require a huge amount of labeled data. The data mining technique of anomaly detection includes unsupervised approaches that do not require rare samples for training classifiers. We propose an unsupervised anomaly detection technique for image-based plant disease diagnosis that is based on the reconstructability of colors; a deep encoder-decoder network trained to reconstruct the colors of healthy plant images should fail to reconstruct colors of symptomatic regions. Our proposed method includes a new image-based framework for plant disease detection that utilizes a conditional adversarial network called pix2pix and a new anomaly score based on CIEDE2000 color difference. Experiments with PlantVillage dataset demonstrated the superiority of our proposed method compared to an existing anomaly detector at identifying diseased crop images in terms of accuracy, interpretability and computational efficiency.

    DOI arXiv

  • Identifying the Snowfall Cloud at Syowa Station, Antarctica via a Convolutional Neural Network

    SUZUKI Kazue, SHIMOMURA Masaki, NAKAMURA Kazuyuki, HIRASAWA Naohiko, YABUKI Hironori, YAMANOUCHI Takashi, TOKUNAGA Terumasa

    Proceedings of the Annual Conference of JSAI  ( The Japanese Society for Artificial Intelligence )  2020 ( 0 ) 3F1ES205 - 3F1ES205   2020.01

     View Summary

    <p>近年の温暖化環境における南極氷床の涵養量の変動のふるまいは、地球全体の水収支に大きな影響を及ぼすことから関心が高まっているが,その厳しい環境や降雪量の観測自体が難しいという現状である。限定された観測データを組み合わせ降雪量推定モデルの開発を行ってきた。 今回は南極・昭和基地において観測された降雪時の雲画像に対して、CNNを適用し,二値および三値の自動識別を試みた。「Atmospheric River」と呼ばれる高高度の連なる雲が降雪に寄与しているとし,その雲構造をもつ降雪時の画像を正例,その雲構造がない,もしくは画像視野が十分でない場合準正例とした。 ネットワーク構造としてはVGG16にInception構造を加え,全結合層をGlobal Average Poolingに置き換えてパラメタ数を削減した。 学習に対し正例138, 準正例477, 負例511のサンプルを用いた。 二値問題には正例と準正例を正例として扱った。 識別精度は,二値(三値)分類は71.00%(65.37%)であった。 Grad-CAMによる可視化結果は三値分類時に雲構造を捉えられている様子を示していた。</p>

    DOI CiNii

  • Cohesive and anisotropic vascular endothelial cell motility driving angiogenic morphogenesis

    Takubo N., Yura F., Naemura K., Yoshida R., Tokunaga T., Tokihiro T., Kurihara H.

    Scientific Reports    9 ( 1 )   2019.12  [Refereed]

     View Summary

    Vascular endothelial cells (ECs) in angiogenesis exhibit inhomogeneous collective migration called “cell mixing”, in which cells change their relative positions by overtaking each other. However, how such complex EC dynamics lead to the formation of highly ordered branching structures remains largely unknown. To uncover hidden laws of integration driving angiogenic morphogenesis, we analyzed EC behaviors in an in vitro angiogenic sprouting assay using mouse aortic explants in combination with mathematical modeling. Time-lapse imaging of sprouts extended from EC sheets around tissue explants showed directional cohesive EC movements with frequent U-turns, which often coupled with tip cell overtaking. Imaging of isolated branches deprived of basal cell sheets revealed a requirement of a constant supply of immigrating cells for ECs to branch forward. Anisotropic attractive forces between neighboring cells passing each other were likely to underlie these EC motility patterns, as evidenced by an experimentally validated mathematical model. These results suggest that cohesive movements with anisotropic cell-to-cell interactions characterize the EC motility, which may drive branch elongation depending on a constant cell supply. The present findings provide novel insights into a cell motility-based understanding of angiogenic morphogenesis.

    DOI Scopus

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Conference Prsentations (Oral, Poster) 【 display / non-display

  • Atmospheric Riverによる南極域へのエアロゾル輸送(1)

    鈴木 香寿恵, 原圭 一郎, 徳永 旭将, 後藤 大輔, 平沢 尚彦, 山内 恭

    気象学会2021年度秋季大会  (オンライン)  2021.12  -  2021.12  日本気象学会

     View Summary

    近年,水蒸気輸送と豪雨(雪)には対流性の雲が連なって形成されるAtmospheric River(AR)が関連していると考えられるようになり,南極域においても極方向の水蒸気フラックス強化となる背景場とARの観測事例が報告されている[1].そこで,昭和基地の降雪時にARと判別できる雲画像を用いたCNNによる自動識別に取り組んできた[2].また,客観解析データを用いた全球規模のARとエアロゾル輸送の関連について報告がされており,Aerosol Atmospheric River (AAR)となって高濃度エアロゾルが輸送されることが示されている.本研究では,これまで行ってきたARによる水蒸気輸送だけではなく,陸起源と考えられる大気中微量物質の輸送も同時に捉え,大気による物質輸送過程を機械学習による予測モデルを構築することを目指す.まずは,ARとエアロゾル輸送の関連について2009年のブリザードイベントについて調べた.

  • Observation and simulation of C. elegans whole-brain neural activities

    2021.09  -  2021.09 

     View Summary

    C. elegans is a model organism in which the structure (connectome) of the whole nervous system composed of 302 neurons has been determined. We performed whole-brain imaging by spinning disk confocal microscope combined with piezo objective positioner to obtain calcium imaging data of the whole head neurons. Further, we modeled the dynamics of neuronal ensembles based on the observed activity data and connectome data. As a result, we could perform virtual ablation of neurons or particular connections to gain insights into the information flow through the neural circuits.

  • Simultaneous measurements of membrane voltage and intracellular Ca2+ of AWA neurons by a gene encoded voltage indicator and GCaMP

    Takeshi Ishihara, Noriko Sato, Terumasa Tokunaga

    23rd International C.elegans conference  (virtual)  2021.06  -  2021.06  Genetics Society of America

     View Summary

    Measurement of neuronal activities in non-invasive and unanesthetized condition is important for understanding neuronal function in intact animals. Ca2+ imaging by fluorescent gene encoded calcium indicators (GECI) are a powerful way to measure neuronal activities in C. elegans. Although Ca2+ imaging revealed important aspects in neuronal functions, the measurement of neuronal membrane voltage is important to understand the neuronal functions. Furthermore, the relations of change of membrane voltages and changes of Ca2+ has not been fully understood. Recently, several types of gene encoded voltage indicators (GEVI) that are derived from 7TM proteins used for optogenetics has been developed to measure changes of membrane voltage in living animals. Even though the fluorescence of these GEVIs is dim, they showed fast time constants and relatively high fluorescent change depend on voltages. Among those GEVIs, we use paQuasAr3 for the voltage measurement, because it shows relatively higher fluorescence with other superior characteristics.
    Since AWA, one of the olfactory sensory neurons, which is responsible for diacetyl sensation, was reported to show all-or-none action potentials (Liu et al. 2018), we firstly analyzed AWA voltage changes induced by diacetyl. We found that fluorescence of paQuasAr3 expressed in AWA cell body is changed in response to diacetyl stimulation with high reproducibility. At the beginning of the stimulation, the transient increase and decrease of fluorescence intensity was observed, whereas the relatively higher fluorescence intensity was sustained during the stimulation. To elucidate relations between the Ca2+ responses and the voltage responses, we made wild-type animals expressing paQuasAr3 and GCaMP6f in AWA neurons, and measured both fluorescence at a cell body simultaneously. We found that the changes of paQuasAr3 started faster than the changes of GCaMP. These analyses will give insights on the neuronal functions in informational processing.

  • Development of training data with collaboration of observation, numerical simulation and machine leaning for space plasma phenomena forecast model

    Keiichiro FUKAZAWA, Tomoki KIMURA Terumasa TOKUNAGA, Shinya NAKANO

    Japan Geoscience Union Meeting 2021  (Virtual)  2021.05  -  2021.06  Japan Geoscience Union

  • 線虫C.elegansの細胞レベルの 膜電位/カルシウム同時イメージング 確立に向けて

    徳永 旭将, 石原 健, 佐藤 則子, 岩崎 唯史  [Invited]

    第2回分子サイバネティクス研究会,第46回分子ロボティクス定例研究会  (オンライン)  2021.05  -  2021.05  学術変革領域(A)「分子サイバネティクス」

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Lectures 【 display / non-display

  • バイオイメージ解析におけるベイズ統計と機械学習の応用

    名古屋大学宇宙地球環境研究所研究集会 「宇宙環境の理解に向けての統計数理的アプローチ」 ( 名古屋大学 )  2017.12.22  名古屋大学

  • 時空間パターン理解のためのベイズ統計・スパース推定の応用

    京都大学・学術情報メディアセンターセミナー ( 京都大学吉田キャンパス )  2017.10.17  京都大学・学術情報メディアセンター

  • Whole neural network analysis of C. elegans using an automated image processing pipeline

    International Workshop on Quantitative Biology 2017 At Keio University   2017.04.15  Japanese society for quantitative biology

  • バイオイメージ解析におけるベイズ統計の応用

    生命機能数理モデル検討会 ( 大阪大学免疫学フロンティア研究センター )  2014.05.28  大阪大学免疫学フロンティア研究センター

  • 4次元動態計測データからの神経細胞活動度の自動定量化

    第一回腫瘍分子生物学•生命情報共同セミナー   2014.03.19  金沢大学がん進展制御研究所