Updated on 2024/06/12

 
TOKUNAGA Terumasa
 
Scopus Paper Info  
Total Paper Count: 0  Total Citation Count: 0  h-index: 5

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

Affiliation
Faculty of Computer Science and Systems Engineering Department of Artificial Intelligence
Job
Associate Professor
External link

Research Interests

  • neuroscience

  • Statistical Machine Learning

  • Image Analysis

  • Bayesian Inference

  • Anomaly detection

Research Areas

  • Informatics / Perceptual information processing

Undergraduate Education

  • 2006.03   Kyushu University   Faculty of Science   Graduated   Japan

Post Graduate Education

  • 2011.03   Kyushu University   Doctoral Program   Completed   Japan

  • 2008.03   Kyushu University   Master's Course   Completed   Japan

Degree

  • Kyushu University  -  Doctor of Science   2011.03

Biography in Kyutech

  • 2019.04
     

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

  • 2015.04
    -
    2019.03
     

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

Biography before Kyutech

  • 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 - 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   Meiji University   Meiji Institute for Advanced Study of Mathematical Science   visiting researcher   Japan

  • 2012.04 - 2013.04   Research Organization for information Science and Technology   Other Staff   Japan

  • 2011.04 - 2012.03   Meiji University   Meiji Institute for Advanced Study of Mathematical Science   Postdoctoral Researcher   Japan

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Academic Society Memberships

  • 2013.04   情報処理学会MPS研究会   Japan

  • 2020.10   日本分子生物学会   Japan

  • 2022.04   Japanese Society for Mathematical Biology   Japan

  • 2016.10   The Seismologocal Society of Japan   Japan

  • 2014.07 - 2018.03   The International Society for Computational Biology   United States

  • 2013.08   日本統計学会   Japan

  • 2012.04   日本測地学会   Japan

  • 2006.04   地球電磁気・地球惑星圏学会   Japan

  • 2006.04   Japan Geoscience Union   Japan

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Papers

  • Layer-wise External Attention by well-localized attention map for efficient deep anomaly detection Invited Reviewed International journal

    Keiichi Nakanishi, Ryo Shiroma, Tokihisa Hayakawa, Ryoya Katafuchi, Terumasa Tokunaga

    SN Computer Science ( Springer Nature )   592 ( 5 )   2024.05

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    Authorship:Last author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)

    The external attention mechanism offers a promising approach to enhance image anomaly detection (Hayakawa et al., in: IMPROVE, pp. 100-–110, 2023). Nevertheless, the effectiveness of this method is contingent upon the judicious selection of an intermediate layer with external attention. In this study, we performed a comprehensive series of experiments to clarify the mechanisms through which external attention improves detection performance. We assessed the performance of the LEA-Net (Hayakawa et al., in: IMPROVE, pp. 100–110, 2023), which implements layer-wise external attention, using MVTec AD and Plant Village datasets. The detection performances of the LEA-Net were compared with that of the baseline model under different anomaly maps generated by three unsupervised approaches. In addition, we investigated the relationship between the detection performance of LEA-Net and the selection of an attention point, which means an intermediate layer where external attention is applied. The findings reveal that the synergy between the dataset and the generated anomaly map influenced the effectiveness of the LEA-Net. For poorly localized anomaly maps, the selection of the attention point becomes a pivotal factor in determining detection efficiency. At shallow attention points, a well-localized attention map successfully notably improves the detection performance. For deeper attention points, the overall intensity of the attention map is essential; this intensity can be substantially amplified by layer-wise external attention, even for a low-intensity anomaly map. Overall, the results suggest that for layer-wise external attention, the positional attributes of anomalies hold greater significance than the overall intensity or visual appearance of the anomaly map.

    DOI: 10.1007/s42979-024-02912-3

    DOI: 10.1007/s42979-024-02912-3

    Other Link: https://link.springer.com/article/10.1007/s42979-024-02912-3#citeas

  • Layer-wise External Attention for Efficient Deep Anomaly Detection Reviewed

    Tokihisa Hayakawa, Keiichi Nakanishi, Ryoya Katafuchi, Terumasa Tokunaga

    Proceedings of the 3rd International Conference on Image Processing and Vision Engineering   100 - 110   2023.01

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    チェコ   プラハ  

    DOI: 10.5220/0011856800003497

    DOI: 10.5220/0011856800003497

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    Other Link: https://www.scitepress.org/Link.aspx?doi=10.5220/0011856800003497

  • Behavioral forgetting of olfactory learning is mediated by interneuron-regulated network plasticity in Caenorhabditis elegans Reviewed International journal

    Jamine Teo, Itsuki Kurokawa, Yuuki Onishi, Noriko Sato, Tomohiro Kitazono, Terumasa Tokunaga, Manabi Fujiwara, Takeshi Ishihara

    eNeuro ( the Society for Neuroscience )   9 ( 4 )   2022.08

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    Forgetting is important for animals to manage acquired memories to enable adaptation to changing environments; however, the neural network in mechanisms of forgetting is not fully understood. To understand the mechanisms underlying forgetting, we examined olfactory adaptation, a form of associative learning, in Caenorhabditis elegans (C. elegans). The forgetting of diacetyl olfactory adaptation in C. elegans is regulated by secreted signals from AWC sensory neurons via the TIR-1/JNK-1 pathway. These signals cause a decline of the sensory memory trace in AWA neurons where diacetyl is mainly sensed. To further understand the neural network that regulates this forgetting, we investigated the function of interneurons downstream of AWA and AWC neurons. We found that a pair of interneurons, AIA, is indispensable for the proper regulation of behavioral forgetting of diacetyl olfactory adaptation. Loss of or inactivation of AIA caused the impairment of the chemotaxis recovery after adaptation without causing severe chemotaxis defects in naïve animal. AWA Ca2+ imaging analyses suggested that loss or inactivation of AIA interneurons did not affect the decline of the sensory memory trace after the recovery. Furthermore, AIA responses to diacetyl were observed in naïve and after the recovery, but not just after the conditioning, suggesting that AIA responses after the recovery are required for the chemotaxis to diacetyl. We propose that the functional neuronal circuit for attractive chemotaxis to diacetyl is changed temporally at the recovery phase so that AIA interneurons are required for chemotaxis, although AIAs are dispensable for attractive chemotaxis to diacetyl in naïve animals.

    DOI: https://doi.org/10.1523/ENEURO.0084-22.2022

    DOI: https://doi.org/10.1523/ENEURO.0084-22.2022

    Other Link: https://www.eneuro.org/content/early/2022/08/17/ENEURO.0084-22.2022

  • 半教師あり二値分類のためのクラス事前確率を用いたコスト関数の提案 Reviewed

    中西 慶一, 徳永 旭将

    第25回画像の認識・理解シンポジウム ( 情報処理学会コンピュータビジョンとイメージメディア(CVIM)研究会 )   2022.07

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    日本   姫路   2022.07.25  -  2022.07.28

    本稿は半教師あり二値分類のための新しいコスト関数を 提案する. 提案するコスト関数は, Focal Loss を半教師あり分類のために拡張した損失関数とクラス事前確率罰則項 から構成される. クラス事前確率罰則項を導入することで, ラベルなしデータにおける正例の割合がわかる場合, その 事前情報を直接的に推論に活用することが可能である. 提 案するコスト関数の有用性を評価するため, CIFAR-10 を 用いた画像二値分類試験を行った. その結果, 少量のラベ ルありデータセットや不均衡なデータセットにおいて, 提 案するコスト関数は既存のコスト関数と比べて優れた性能を示すことを確認した.

    Other Link: https://sites.google.com/view/miru2022/program#h.44rbhnwbmvlj

  • Behavioral Forgetting of Olfactory Learning Is Mediated by Interneuron-Regulated Network Plasticity in Caenorhabditis elegans Reviewed

    Teo J.H.M., Kurokawa I., Onishi Y., Sato N., Kitazono T., Tokunaga T., Fujiwara M., Ishihara T.

    eNeuro   9 ( 4 )   2022.07

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    DOI: 10.1523/ENEURO.0084-22.2022

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

    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

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    We propose an application of the independent component analysis (ICA) to separate satellite-induced time-varying stray fields from magnetic field data obtained using onboard multiple magnetometers. The ICA is a method for estimating source signals at multiple sites so that the estimated source signals can become statistically independent of each other. Since stray field variations are statistically independent of external natural field variations, the ICA method is expected to separate the natural variations from stray fields. Thus, we applied the ICA to magnetic field data from the first Quasi-Zenith Satellite, which has two triaxial fluxgate magnetometers, without using an extendable boom. First, we removed the long-period trend from the original data to create detrended data. Then, we applied the FastICA algorithm to the detrended data and obtained six independent components (ICs). The stray fields were successfully separated into three ICs (noise ICs), and the natural signals were represented by the other three ICs (signal ICs). Finally, we restored the observed signals from the signal ICs, and confirmed that the natural phenomena variations were not altered by the processing step. We also proposed a selection method of the noise ICs using the C coefficient, which is the coefficient of the variance of the mixing vectors. There was a large difference in C between the ICs whose C coefficients are the largest 3rd and 4th ones. Overall, these results demonstrate the possibility that the ICA method can support for boom-less magnetic observations in future satellite missions.

    DOI: 10.1029/2020JA028790

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    Other Link: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020JA028790

  • Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection based on Reconstructability of Colors Reviewed International journal

    Katafuchi, R. and Tokunaga, T.

    In Proceedings of the International Conference on Image Processing and Vision Engineering - IMPROVE ( SciTePress )   112 - 120   2021.04

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    Virtual   Virtual   2021.04.28  -  2021.04.30

    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 u tilizes 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: 10.5220/0010463201120120

    DOI: 10.5220/0010463201120120

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  • Cohesive and anisotropic vascular endothelial cell motility driving angiogenic morphogenesis Reviewed

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

    Scientific Reports   9 ( 1 )   2019.12

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    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: 10.1038/s41598-019-45666-2

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  • SPF-CellTracker: Tracking Multiple Cells with Strongly-Correlated Moves Using a Spatial Particle Filter Reviewed International journal

    Hirose O., Kawaguchi S., Tokunaga T., Toyoshima Y., Teramoto T., Kuge S., Ishihara T., Iino Y., Yoshida R.

    IEEE/ACM Transactions on Computational Biology and Bioinformatics ( Institute of Electrical and Electronics Engineers Inc. )   15 ( 6 )   1822 - 1831   2018.11

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  • Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space Reviewed International journal

    Toyoshima Y., Tokunaga T., Hirose O., Kanamori M., Teramoto T., Jang M.S., Kuge S., Ishihara T., Yoshida R., Iino Y.

    PLoS Computational Biology ( Public Library of Science )   12 ( 6 )   2016.06

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  • Automated detection and tracking of many cells by using 4D live-cell imaging data Reviewed

    Tokunaga T., Hirose O., Kawaguchi S., Toyoshima Y., Teramoto T., Ikebata H., Kuge S., Ishihara T., Iino Y., Yoshida R.

    Bioinformatics ( Oxford University Press )   30 ( 12 )   2014.06

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1093/bioinformatics/btu271

    DOI: 10.1093/bioinformatics/btu271

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    PubMed

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  • 3次元動画像内の非常に多数の細胞領域を自動追跡するための 粒子フィルタ手法の開発

    広瀬 修, 川口 翔太郎, 徳永 旭将, 豊島 有, 寺本 孝行, 佐藤 賢二, 池端 久貴, 佐藤 博文, 久下 小百合, 石原 健, 飯野 雄一, 吉田 亮

    人工知能学会全国大会論文集 ( 一般社団法人 人工知能学会 )   JSAI2014 ( 0 )   2C32in - 2C32in   2014.01

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    <p>細胞動画像の典型的な特徴として多数の追跡対象物が密に存在し視覚的に類似していることが挙げられる.このような動画像に対し標準的な粒子フィルタを利用した場合,本来の追跡対象を別の対象物と誤認し追跡に容易に失敗する.この問題に対し多数の細胞の共変動性に着目し追跡のための補助情報としてこれを利用することで追跡精度の向上を目指す.</p>

    DOI: 10.11517/pjsai.jsai2014.0_2c32in

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  • Extraction of groove feelings from drum data using non-negative matrix factorization Reviewed

    Ohya Y., Nakamura K., Tokunaga T.

    6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012   125 - 130   2012.12

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    In this paper, we propose the algorithm to extract the groove feeling from drum data. In the previous researches, extraction of the groove feeling requires pre-separated acoustic sources. We employed non-negative matrix factorization (NMF) to make separated information on hitting time from monaural wave data in which multiple acoustic sources are mixed. We applied our algorithm to a drum data and obtained a difference of time fluctuations among instruments, which relates to groove feeling and impression. The result implies that the proposed algorithm can extract some sensitive differences of nuance of drumming. © 2012 IEEE.

    DOI: 10.1109/SCIS-ISIS.2012.6505312

    DOI: 10.1109/SCIS-ISIS.2012.6505312

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  • Separation of stationary and non-stationary sources with a generalized eigenvalue problem Reviewed

    Hara S., Kawahara Y., Washio T., von Bünau P., Tokunaga T., Yumoto K.

    Neural Networks   33   7 - 20   2012.09

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    Non-stationary effects are ubiquitous in real world data. In many settings, the observed signals are a mixture of underlying station ary and non-stationary sources that cannot be measured directly. For example, in EEG analysis, electrodes on the scalp record the activity from several sources located inside the brain, which one could only measure invasively. Discerning stationary and non-stationary contributions is an important step towards uncovering the mechanisms of the data generating system. To that end, in Stationary Subspace Analysis (SSA), the observed signal is modeled as a linear superposition of stationary and non-stationary sources, where the aim is to separate the two groups in the mixture. In this paper, we propose the first SSA algorithm that has a closed form solution. The novel method, Analytic SSA (ASSA), is more than 100 times faster than the state-of-the-art, numerically stable, and guaranteed to be optimal when the covariance between stationary and non-stationary sources is time-constant. In numerical simulations on wide range of settings, we show that our method yields superior results, even for signals with time-varying group-wise covariance. In an application to geophysical data analysis, ASSA extracts meaningful components that shed new light on the Pi 2 pulsations of the geomagnetic field. © 2012 Elsevier Ltd.

    DOI: 10.1016/j.neunet.2012.04.001

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  • Onset time determination of precursory events in time series data by an extension of Singular Spectrum Transformation Invited Reviewed International journal

    International Journal of Circuits, Systems and Signal Processing   5 ( 1 )   46 - 60   2011.10

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    To predict an occurrence of extraordinary phenomena, such as earthquakes, failures of engineering systems and financial market crushes, it is important to identify precursory events in time series. However, existing methods are limited in their applicability for real world precursor detections. Recently, Ide and Inoue [1] have developed an SSA-based change-point detection method, called singular spectrum transformation (SST). SST is suitable for detecting various types of change-points, but real world precursor detections can be far more difficult than expected. In general, precursory events are observed as minute and less-visible fluctuations preceding an onset of massive fluctuations of extraordinary phenomena and therefore they are easily over-looked. To overcome this point, we extend the conventional SST to the multivariable SST. The originality of our strategy is in focusing on synchronism detections of precursory events in multiple sequences of univariate time series. We performed some experiments by using artificial data and showed the superiority of multivariable SST in detecting onset of precursory events. Furthermore, the superiority is also shown statistically in determining the onset of precursory events by using real world time series.

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  • Identification of full-substorm onset from ground-magnetometer data by singular value transformation Reviewed

    Tokunaga Terumasa, Yumoto Kiyohumi, Uozumi Teiji, CPMN Group

    Memoirs of the Faculty of Science, Kyushu University. Series D, Earth and planetary sciences ( Faculty of Science, Kyushu University )   32 ( 3 )   63 - 73   2011.03

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (bulletin of university, research institution)

    Pi 2 magnetic pulsations observed on the ground are a good indicator of the auroral breakup. However, they have not only corresponding full-substorm onsets but also most pseudobreakups. Another well-known substorm related phenomenon observed on the ground is positive bays. In order to identify full-substorm onsets from ground-magnetometer data, we developed a new algorithm based on "Singular Spectrum Analysis(SSA)". The algorithm enables us to screen Pi 2 pulsations accompanied by the magnetic positive bay. We applied proposed algorithm to ground-magnetometer data and compared to the obtained results with Polar/UVI data. As a result, we succeeded in identifying 62% of the full-substorm onsets from ground-magnetometer data obtained in the nighttime sector between 21 and 03LT.

    DOI: 10.5109/19197

    CiNii Article

    CiNii Research

  • AKR modulation and global Pi2 oscillation Reviewed International journal

    Uozumi T., Yumoto K., Tokunaga T., Solovyev S.I., Shevtsov B.M., Marshall R., Liou K., Ohtani S., Abe S., Ikeda A., Kitamura K., Yoshikawa A., Kawano H., Itonaga M.

    Journal of Geophysical Research: Space Physics   116 ( 6 )   2011.01

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    DOI: 10.1029/2010JA016042

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  • 変化点検出を応用した時系列データからの突発現象の前兆検出アルゴリズム Reviewed

    徳永 旭将, 池田 大輔, 中村 和幸, 樋口 知之, 吉川 顕正, 魚住 禎司, 藤本 晶子, 森岡 昭, 湯元清文, CpmnGroup

    研究報告バイオ情報学(BIO)   2010 ( 14 )   1 - 6   2010.12

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    Authorship:Lead author, Corresponding author   Language:Japanese   Publishing type:Research paper (international conference proceedings)

    一般に,前兆現象は突発現象にそのものに比べて非常に目立ちにくく,その開始時刻は曖昧である.従来よく用いられてきた変化点検出法を適用した場合,このような微小で緩慢な変化は見逃されやすい.Tokunaga et al.1) では,Ide and Inoue2) の提案した特異スペクトル分析を応用した変化点検出法 (SST) を,多次元データを用いたアルゴリズム (MSST) へと拡張することで,鋭敏に前兆現象の開始時刻を推定出来ることを示した.MSST は,緩慢な変化も検出できる鋭敏な手法であるが,実データへの適用では誤検出が問題になる.本稿では,突発現象の大まかな開始時刻を予め検出し,さらに検出された時刻の前後で前兆現象の開始時刻と終了時刻を個別に探索することで,誤検出を劇的に減少させることができることを示す.

    CiNii Article

    CiNii Research

  • Detecting precursory events in time series data by an extension of singular spectrum transformation Reviewed International journal

    366 - 374   2010.12

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    Scopus

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  • Detecting Precursory Events in Time Series Data by an Extension of Singular Spectrum Transformation Reviewed International journal

    徳永 旭将

    Proceedings of the 10th WSEAS International Conference on Applied Computer Science   366 - 374   2010.01

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  • Propagation characteristics of Pi 2 pulsations observed at high- And low-latitude MAGDAS/CPMN stations: A statistical study Reviewed

    Uozumi T., Abe S., Kitamura K., Tokunaga T., Yoshikawa A., Kawano H., Marshall R., Morris R.J., Shevtsov B.M., Solovyev S.I., McNamara D.J., Liou K., Ohtani S., Itonaga M., Yumoto K.

    Journal of Geophysical Research: Space Physics   114 ( 11 )   2009.11

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    The objective of this study is to understand better the propagation of Pi 2 waves in the nighttime region. We examined Pi 2 oscillations that showed high correlation between high- and low-latitude Magnetic Data Acquisition System/Circum Pan-Pacific Magnetometer Network stations (correlation coefficient: |γ| ≥ 0.75). For each horizontal component (H and D) we examined the magnetic local time (MLT) dependence of the delay time of high-latitude Pi 2 oscillations that corresponds to the highest correlation with the low-latitude Pi 2 oscillation. We found the delay time of the high-latitude H showed remarkable MLT dependence, especially in the premidnight sector: we found that in the premidnight sector the high-latitude H oscillation tends to delay from the low-latitude oscillation (<100 s). On the other hand, the delay time of the high-latitude D oscillation was not significant (∼±10 s) in the entire nighttime sector. We propose a Pi 2 propagation model to explain the observed delay time of high-correlation highlatitude H. The model quantitatively explains the trend of the event distribution. We also examined the spatial distribution of high-correlation Pi 2 events relative to the center of auroral breakups. It was found that the high-correlation Pi 2 events tend to occur away from the center of auroral breakups by more than 1.5 MLT. The present result suggests that the high-correlation H component Pi 2 oscillations at high latitude are a manifestation of forced Alfvén waves excited by fast magnetosonic waves. Copyright 2009 by the American Geophysical Union.

    DOI: 10.1029/2009JA014163

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  • A new index to monitor temporal and long-term variations of the equatorial electrojet by MAGDAS/CPMN real-time data: EE-index Reviewed

    Uozumi T., Yumoto K., Kitamura K., Abe S., Kakinami Y., Shinohara M., Yoshikawa A., Kawano H., Ueno T., Tokunaga T., McNamara D., Ishituka J.K., Dutra S.L.G., Damtie B., Doumbia V., Obrou O., Rabiu A.B., Adimula I.A., Othman M., Fairos M., Otadoy R.E.S.

    Earth, Planets and Space   60 ( 7 )   785 - 790   2008.01

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1186/BF03352828

    Scopus

    Other Link: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=52149085015&origin=inward

  • Global features of Pi 2 pulsations obtained by independent component analysis Reviewed International journal

    Tokunaga T., Kohta H., Yoshikawa A., Uozumi T., Yumoto K.

    Geophysical Research Letters ( American Geophysical Union )   34 ( 14 )   2007.07

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1029/2007GL030174

    Scopus

    Other Link: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=34548580614&origin=inward

  • High-top clouds play an efficient part in moisture transport to the Antarctic

    Kazue Suzuki, Terumasa Tokunaga, Takashi Yamanouchi, Hideaki Motoyama

    ESS Open Archive ( Wiley and Authorea )   2022.08

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    Language:English   Publishing type:Article, review, commentary, editorial, etc. (other)

    DOI: 10.1002/essoar.10512276.1

    DOI: 10.1002/essoar.10512276.1

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

    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

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    Japan   オンライン  

    This study evaluated the impact of Atmospheric River (AR) clouds on snowfall amounts based on limited observation data to estimate the surface mass balance (SMB) of Antarctica. To accomplish this, we attempted to identify the snowfall cloud at Syowa Station, Antarctica. We constructed a new convolutional neural network (CNN) architecture with multinomial and binary classifications for National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images over five years. The CNN was based on VGG16, and concatenate layers were added as the inception module. We replaced all the convolution layers with global average pooling to reduce the number of parameters. Based on the positive CNN sample re- sult, the multinomial classification emphasized the entire cloud structure, while the binary classification focused on cloud continuity. The results indicated accuracies of 71.00% and 65.37% for binary and multinomial classifications, respectively.

    DOI: 10.1007/978-3-030-73113-7_7

    Other Link: https://link.springer.com/chapter/10.1007/978-3-030-73113-7_7

  • 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

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    <p>This study evaluated snowfall values based on limited observation data to estimate the surface mass balance (SMB) of Antarctica. To accomplish this, we attempted to identify the snowfall cloud at Syowa Station, Antarctica. We constructed a new convolutional neural network (CNN) architecture with multinomial and binary classifications and added National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR) images over five years. The CNN was based on VGG16, and concatenate layers were added as the inception module. We replaced all the convolution layers with global average pooling to reduce the number of parameters. Based on the positive CNN sample result, the multinomial classification emphasized the entire cloud structure, while the binary classification focused on cloud continuity. The results indicated accuracies of 71.00% for binary and 65.37% for multinomial classifications.</p>

    DOI: 10.11517/pjsai.JSAI2020.0_3F1ES205

    CiNii Article

    CiNii Research

    Other Link: https://ci.nii.ac.jp/naid/130007857079

  • An Inspirational Collaboration between Measurements, Mathematical Modeling and Data Science for Image Analysis Invited Reviewed

    TOKUNAGA Terumasa

    Journal of The Society of Instrument and Control Engineers ( The Society of Instrument and Control Engineers )   58 ( 3 )   166 - 170   2019.03

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    Authorship:Lead author, Last author, Corresponding author   Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (scientific journal)

    DOI: 10.11499/sicejl.58.166

    CiNii Article

    CiNii Research

    Other Link: https://ndlsearch.ndl.go.jp/books/R000000004-I029600573

  • The cloud patterns in the snowfall conditions around Syowa Station, Antarctica detected by Convolutional Neural Network

    Suzuki Kazue, Tokunaga Terumasa, Fukuchi Misaki, Hirasawa Naohiko, Yamanouchi Takashi

    Summaries of JSSI and JSSE Joint Conference on Snow and Ice Research ( The Japanese Society of Snow and Ice / Japan Society for Snow Engineering )   2019 ( 0 )   119   2019.01

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    Language:English   Publishing type:Research paper (conference, symposium, etc.)

    DOI: 10.14851/jcsir.2019.0_119

    CiNii Article

    CiNii Research

    Other Link: https://ci.nii.ac.jp/naid/130007743208

  • Causality analysis of the whole-brain activity data in C. elegans II

    Iwasaki Y., Sato H., Oe S., Kuge S., Teramoto T., Tokunaga T., Hirose O., Wu S., Toyoshima Y., Jang M. S., Yoshida R., Iino Y., Ishihara T.

    Meeting Abstracts of the Physical Society of Japan ( The Physical Society of Japan )   73.2 ( 0 )   2315 - 2315   2018.01

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    DOI: 10.11316/jpsgaiyo.73.2.0_2315

    CiNii Article

    CiNii Research

  • Causality analysis of the whole-brain activity data in C. elegans

    Iwasaki Y., Sato H., Oe S., Teramoto T., Tokunaga T., Hirose O., Wu S., Toyoshima Y., Jang M. S., Yoshida R., Iino Y., Ishihara T.

    Meeting Abstracts of the Physical Society of Japan ( The Physical Society of Japan )   73.1 ( 0 )   2853 - 2853   2018.01

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    Language:Japanese   Publishing type:Research paper (international conference proceedings)

    DOI: 10.11316/jpsgaiyo.73.1.0_2853

    CiNii Article

    CiNii Research

  • 線虫の全脳活動データに対する位相解析

    岩崎 唯史, 寺本 孝行, 大江 紗, 徳永 旭将, 広瀬 修, S. Wu, 豊島 有, ジャン ムンソン, 吉田 亮, 飯野 雄一, 石原 健

    2017年度日本物理学会第72回年次大会講演集 ( 一般社団法人日本物理学会 )   2017.03

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    日本   大阪   2017.03.17  -  2017.03.20

    DOI: https://doi.org/10.11316/jpsgaiyo.72.1.0_2817

  • 空間粒子フィルタによる多数の細胞の同時追跡

    広瀬 修, 川口 翔太朗, 徳永 旭将

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 ( 東京 : 電子情報通信学会 )   115 ( 112 )   137 - 141   2015.06

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    Language:Japanese   Publishing type:Research paper (bulletin of university, research institution)

    CiNii Article

    CiNii Research

    Other Link: http://id.ndl.go.jp/bib/026589082

  • Data Assimilation for Reconstructing a Whole Neuronal System of C. Elegans : The Current State and Issue Invited Reviewed

    Terumasa Tokunaga, Ryo Yoshida, Yuishi Iwasaki

    Journal of The Japan Society for Simulation Technology ( Japan Society for Simulation Technology )   32 ( 4 )   287 - 294   2013.12

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    Authorship:Corresponding author   Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (other)

    J-GLOBAL

  • Annual and semi-annual Sq variations at 96° MM MAGDAS I and II stations in Africa Reviewed International journal

    El Hawary R., Yumoto K., Yamazaki Y., Mahrous A., Ghamry E., Meloni A., Badi K., Kianji G., Uiso C.B.S., Mwiinga N., Joao L., Affluo T., Sutcliffe P.R., Mengistu G., Baki P., Abe S., Ikeda A., Fujimoto A., Tokunaga T.

    Earth, Planets and Space   64 ( 6 )   425 - 432   2012.01

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.5047/eps.2011.10.013

    Scopus

    Other Link: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84876370337&origin=inward

  • Pi 2 waves simultaneously observed by Cluster and CPMN ground-based magnetometers near the plasmapause Reviewed International journal

    Kawano H., Ohtani S., Uozumi T., Tokunaga T., Yoshikawa A., Yumoto K., Lucek E.A., André M.

    Annales Geophysicae   29 ( 9 )   1663 - 1672   2011.10

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.5194/angeo-29-1663-2011

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    Other Link: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=80053422182&origin=inward

  • Pi 2 waves simultaneously observed by Cluster and CPMN ground-based magnetometers near the plasmapause Reviewed International journal

    Hideaki Kawano, Shin-ichi Ohtani, Teiji Uozumi, Terumasa Tokunaga, Akimasa Yoshikawa, Kiyohum Yumoto, E. A. Lucek, M. Andre, the CPMN group

    Annales Geophysicae ( European Geophysical Union )   29 ( 9 )   1663 - 1672   2011.09

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    DOI: https://doi.org/10.5194/angeo-29-1663-2011

  • STPデータからの異常検出法の開発: Substorm precursor検出への応用

    徳永 旭将, 中村 和幸, 樋口 知之, 吉川 顕正, 魚住 禎司, 池田 大輔, 藤本 晶子, 森岡 昭, 湯元 清文, Tokunaga Terumasa, Nakamura Kazuyuki, Higuchi Tomoyuki, Yoshikawa Akimasa, Uozumi Teiji, Ikeda Daisuke, Fujimoto Akiko, Morioka Akira, Yumoto Kiyofumi

    宇宙航空研究開発機構特別資料: 第6回「宇宙環境シンポジウム」講演論文集 = JAXA Special Publication: Proceedings of the 6th Spacecraft Enivironment Symposium ( 宇宙航空研究開発機構 )   JAXA-SP-09-006   260 - 265   2010.02

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    第6回宇宙環境シンポジウム (2009年2月29日-30日. 北九州国際会議場)

    CiNii Article

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    Other Link: http://id.nii.ac.jp/1696/00005141/

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Conference Prsentations (Oral, Poster)

  • Cost-effective Deep Image Segmentation with Partial Patch Annotations based on Semi-supervised Learning

    Terumasa Tokunaga, Keiichi Nakanishi, Ryoya Katafuchi, Kohki Miyama, Shou Watanabe

    The 4th International Symposium on Neuromorphic AI Hardware  2022.12  Research Center for Neuromorphic AI Hardware

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    Event date: 2022.12.13 - 2022.12.14   Language:English   Country:Japan  

    Image segmentation plays a central role in various real-world image processing applications, including object detection, object tracking, medical image diagnosis, scene understanding, and video surveillance. Over the last decade, supervised approaches based on deep learning (DL) have achieved outstanding performance in various segmentation tasks. However, most DL-based approaches require numerous pixel-wise annotations for training. This requirement often creates serious bottlenecks for research projects. Additionally, pixel-wise annotations of objects with indistinct boundaries can sometimes be arbitrary. In this study, we propose a novel semi-supervised image segmentation method called Cost-effective image Segmentation with Partial Annotations (CoSPA). Our main contributions are as follows: (1) Our method considerably reduces annotation costs of DL-based image segmentation maintaining practical performance, (2) We propose a novel cost function designed for CoSPA, (3) We experimentally demonstrate the effectiveness of CoSPA under extremely low annotation cost using real-world image datasets.

  • Layer-wise External Attention for Efficient Deep Anomaly Detection

    Tokihisa Hayakawa, Keiichi Nakanishi, Ryoya Katafuchi, Terumasa Tokunaga

    3rd International Conference on Image Processing and Vision Engineering  2023.04 

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    Event date: 2023.04.21 - 2023.04.23   Language:English   Country:Czech Republic  

    Other Link: https://dblp.org/db/conf/improve/improve2023.html

  • 外部視覚注視機構による深層学習の異常検出における注視領域生成過程の解析

    城間 亮、中西 慶一、片渕 凌也、徳永 旭将

    2024年度 人工知能学会全国大会(第38回)  2024.05  人工知能学会

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    Event date: 2024.05.28 - 2024.05.31   Language:Japanese   Country:Japan  

  • Bidirectional 2D Reservoir Network for Image Anomaly Detection without any Training

    Keiichi Nakanishi, Terumasa Tokunaga

    The 5th International Symposium on Neuromorphic AI Hardware  the Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology

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    Event date: 2024.02.29 - 2024.03.02   Language:English   Country:Japan  

    Other Link: https://www.brain.kyutech.ac.jp/~neuro/2023/11/30/5th-sympo/?lang=en

  • Heavy Snow Cloud Detection in Satellite Images Based on Semi-Supervised Image Segmentation

    Lin Magari, Terumasa Tokunaga, Kazue Suzuki

    The 14th Symposium on Polar Science  2023.11  National Institute of Polar Research

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    Event date: 2023.11.14 - 2023.11.15   Language:English   Country:Japan  

    Other Link: https://www.nipr.ac.jp/symposium2023/

  • The Reproduction of Calcium Ion Response in C. elegans Olfactory Neurons using Echo State Networks

    IIBMP2023  2023.09 

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    Event date: 2023.09.07 - 2023.09.08   Language:Japanese   Country:Japan  

    Other Link: https://smartconf.jp/content/iibmp2023/posterpresentation

  • The application of Echo State Network in reproducing the membrane potential response of olfactory neurons in C. elegans

    IIBMP2023  2023.09 

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    Event date: 2023.09.07 - 2023.09.08   Language:Japanese   Country:Japan  

    Other Link: https://smartconf.jp/content/iibmp2023/posterpresentation

  • 外部視覚注視機構による深層学習の異常検出能力と注視領域生成方法の関係について

    城間亮, 早川季寿, 中西慶一, 徳永旭将

    第26回画像の認識・理解シンポジウム(MIRU2023)  2023.07  電子情報通信学会PRMU研究会

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    Event date: 2023.07.26 - 2023.07.29   Language:Japanese   Country:Japan  

  • パッチ画像を用いた半教師あり学習に基づくセグメンテーションフレームワーク

    中⻄ 慶一, 徳永 旭将

    第26回画像の認識・理解シンポジウム (MIRU2023)  2023.07  電子情報通信学会PRMU研究会

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    Event date: 2023.07.26 - 2023.07.29   Language:Japanese   Country:Japan  

  • Heavy Snow Cloud Detection in Satellite Images Based on Semi-Supervised Image Segmentation

    Lin Magari, Terumasa Tokunaga, Kazue Suzuki

    2023.05 

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    Event date: 2023.05.21 - 2023.05.26   Language:English   Country:Japan  

  • Prediction of off-angle from GaN surface morphology images using deep learning

    The 9th Meeting on Advanced Power Semiconductors Division  2022.12 

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    Event date: 2022.12.19 - 2022.12.21   Language:Japanese   Country:Japan  

  • A rule-based anomaly detection method using binary segmentation as preprocessing

    Kazumichi Tanaka, Sansei Hori, Keiichi Nakanishi, Terumasa Tokunaga

    The 4th International Symposium on Neuromorphic AI Hardware  2022.12  Research Center for Neuromorphic AI Hardware

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    Event date: 2022.12.13 - 2022.12.14   Language:English   Country:Japan  

    Semiconductor visual inspection systems are required to further improve productivity and quality control in proportion to the increasing demand for semiconductor products. In machine vision systems, the combination of conventional rule-based anomaly detection methods, which are characterized by their high explanatory power for the basis of judgment, and deep learning methods, which are characterized by their high extraction and classification accuracy for features such as complex shapes, is an effective means from the perspective of productivity and quality assurance.
    We propose a rule-based anomaly detection method that uses binary segmentation [1] as a preprocessing step. Figure 1 shows the inspection flow model of the proposed method. Binary segmentation is applied to the input image for regions that are difficult to distinguish from defect regions by clear human definition and that are a cause of over-detection with characteristics such as shape and pixel value. The extracted regions are then combined with the input image as a mask. Then, a rule-based abnormality detection inspection is applied to the synthesized image.

  • GL-CANomaly: Global and Local adversarial image completion networks for ANomaly detection

    Takara Ishimoto, Keiichi Nakanishi, Sansei Hori, Terumasa Tokunaga

    The 4th International Symposium on Neuromorphic AI Hardware  2022.12  Research Center for Neuromorphic AI Hardware

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    Event date: 2022.12.13 - 2022.12.14   Language:English   Country:Japan  

  • Resorvoir-based neuron model to emulate cellular neural activity responding to odor in C. elegans

    Ryosuke Ishibashi, Takumi Nakamura, Noriko Sato, Takeshi Ishihara, Yuichiro Tanaka, Hakaru Tamukoh, Terumasa Tokunaga

    The 4th International Symposium on Neuromorphic AI Hardware  2022.12  Research Center for Neuromorphic AI Hardware

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    Event date: 2022.12.13 - 2022.12.14   Language:English   Country:Japan  

    The nervous system of Caenorhabditis elegans (C. elegans) realizes sensory-motor transformation for many odorants despite it consists of only 302 neurons. Recently, we have developed a novel live-cell imaging technique based on genetically encoded voltage and calcium indicators. Our imaging system enables simultaneous measurements of voltage and Ca2+ responses to specific sequential odor stimulation at a single cellular level. The series of experiments interestingly suggest that an individual neuron can sensitively and quantitatively express time-varying environmental information quite unlike the formal neuron. In this study, we attempted to model a single-neuron of C. elegans in a data-driven manner. As a pilot study, we validated whether voltage responses in AWA, which is one of the olfactory sensory neurons of C. elegans, can be reproduced by a simple echo state network (ESN). For input data, we used synthetic square waves that imitate sequential odor stimulation in in-vivo experiments. For target signals, actual measurements of voltage responses in AWA were used. The prediction test by the trained ESN showed that the transient variations of membrane potentials can be successfully reproduced while we failed to reproduce quasi-stationary depolarizations during odorant stimulation (Figure 1). The result warrants further research on more realistic ESN models including multiple voltage dependent ion channels.

  • Distribution-Free Semi-Supervised Cost Function with a Class-Prior Probability

    Keiichi Nakanishi, Terumasa Tokunaga

    The 4th International Symposium on Neuromorphic AI Hardware  2022.12  Research Center for Neuromorphic AI Hardware

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    Event date: 2022.12.13 - 2022.12.14   Language:English   Country:Japan  

    Supervised learning requires a large amount of labeled data to obtain high generalization performance. The labeled data is created by annotation. However, the annotation has a lot of problems, such as high cost. In this study, we propose a novel semi-supervised cost function for using unlabeled data effectively, which consists of two important terms. There is a class-prior probability penalty and an extended Focal loss function. A class-prior probability prevents overfitting to labeled data by utilizing prior knowledge for a percentage of positive samples over unlabeled data. We performed binary image segmentation to evaluate the proposed cost function's effectiveness. As a result, the proposed cost function performs flexible segmentation of objects with ambiguous shapes, because the output values of the boundary regions transition smoothly compared with Binary Crossentropy (BCE) and PNU Loss.

  • GL-CANomaly: Global and Local adversarial image Completion networks for ANomaly detection

    2022.11 

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    Event date: 2022.11.20 - 2022.11.23   Language:Japanese   Country:Japan  

  • クラス事前確率を用いた分布を仮定しない半教師ありコスト関数とその応用

    中西慶一, 片渕 凌也, 堀 三晟, 徳永旭将

    第25回情報論的学習理論ワークショップ (IBIS2022)  2022.11  電子情報通信学会 情報論的学習理論と機械学習研究会

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    Event date: 2022.11.20 - 2022.11.23   Language:Japanese   Country:Japan  

  • 半教師あり2クラス分類に基づく尤度を利用した 粒子フィルタによる線虫の神経細胞追跡手法の提案

    坂田 大地, 中西 慶一, 佐藤 則子, 石原 健, 徳永 旭将

    第11回生命医薬情報学連合大会  2022.09  日本バイオインフォマティクス学会, 日本オミックス医学会

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    Event date: 2022.09.13 - 2022.09.15   Language:English   Country:Japan  

  • 膜電位イメージングデータの画像解析パイプラインの開発

    Terumasa Tokunaga, Noriko Sato, Takeshi Ishihara, Keiichi Nakanishi, Daichi Sakata, takumi Namamura

    線虫研究の未来を創る会  2022.08 

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    Event date: 2022.08.29 - 2022.08.30   Language:Japanese  

  • A Class-prior probability regularization with an extended Focal Loss for efficient Semi-supervised classification

    A Class-prior probability regularization with an extended Focal Loss for efficient Semi-supervised classification

    The 3rd International Sympojium on Neuromorphic AI Hardware 

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    Event date: 2022.03.18 - 2022.03.19   Language:English   Country:Japan  

  • 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  日本気象学会

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    Event date: 2021.12.02 - 2021.12.08   Language:Japanese  

    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.

  • LEA-Net: Layer-wise External Attention Network for Efficient Color Anomaly Detection

    The 24th Information-Based Induction Sciences Workshop 

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    Event date: 2021.11.10 - 2021.11.13   Language:Japanese  

  • CNNを用いた顔認識に対するマスク着用の影響と改善方法の検討

    溝田 十悟, 徳永 旭将

    第24回情報論的学習理論ワークショップ (IBIS2021)  電子情報通信学会 情報論的学習理論と機械学習研究会

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    Event date: 2021.11.10 - 2021.11.13   Language:Japanese  

  • 複数の脈波抽出領域と独立成分分析を用いたサーマルカメラによる非接触バイタルセンシング手法の提案

    野見山 陸, 徳永 旭将

    第24回情報論的学習理論ワークショップ (IBIS2021)  電子情報通信学会 情報論的学習理論と機械学習研究会

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    Event date: 2021.11.10 - 2021.11.13   Language:Japanese  

  • 言語の違いに頑健なText-to-Imageモデルの構築に向けた展望

    仲地 早司, 徳永 旭将

    第24回情報論的学習理論ワークショップ (IBIS2021)  電子情報通信学会 情報論的学習理論と機械学習研究会

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    Event date: 2021.11.10 - 2021.11.13   Language:Japanese  

  • Aurora Image Segmentation with Deep PNU Learning

    The 150 th SGEPSS General Assembly 

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    Event date: 2021.10.31 - 2021.11.04   Language:Japanese  

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

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    Event date: 2021.09.21 - 2021.09.23   Language:English  

    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  Genetics Society of America

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    Event date: 2021.06.21 - 2021.06.24   Language:English  

    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  Japan Geoscience Union

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    Event date: 2021.05.30 - 2021.06.06   Language:Japanese  

  • 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 Invited

    徳永 旭将, 石原 健, 佐藤 則子, 岩崎 唯史

    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  学術変革領域(A)「分子サイバネティクス」

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    Event date: 2021.05.10   Language:Japanese  

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

    Ryoya Katafuchi, Terumasa Tokunaga

    International Conference on Image Processing and Vision Engineering  Institute for Systems and Technologies of Information, Control and Communication

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    Event date: 2021.04.28 - 2021.04.30   Language:English  

    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. Al- though 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 un- supervised 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.

  • 機械学習・数値シミュレーション・観測融合による宇宙プラズマ現象予測モデル開発に向けた学習データの整備 Invited

    深沢 圭一郎, 木村 智樹, 徳永 旭将, 中野 慎也

    2020年度ISEE研究集会「太陽地球圏環境予測のためのモデル研究の展望」  名古屋大学宇宙地球環境研究所

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    Event date: 2021.03.25   Language:Japanese  

  • Overlapping Cluster Analysis of Whole-Brain Imaging Data of C.elegans: Detection of Functional Hub Neuron

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    Event date: 2021.03.12 - 2021.03.15   Language:Japanese  

  • 独立成分分析を用いた人工衛星干渉磁場の分離: 伸展物と事前情報を用いない磁場観測

    今城 峻, 能勢 正仁, 相田 真里, 松本 晴久, 東尾 奈々, 徳永 旭将, 松岡 彩子

    統計数理研究所共同研究集会 「宇宙地球環境の理解に向けての統計数理的アプローチ」  統計数理研究所, 名古屋大学宇宙地球環境研究所,名古屋大学数理データ科学教育研究センター

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    Event date: 2020.12.08   Language:Japanese  

  • An image processing pipeline for quantifying spatiotemporal evolution of voltage responses inside a single cell of C. elegans

    Terumasa Tokunaga, Noriko Sato, Yuishi Iwasaki, Takeshi Ishihara

    The 43rd Annual Meeting of the Molecular Biology Society of Japan  The Molecular Biology Society of Japan

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    Event date: 2020.12.02 - 2020.12.04  

  • 非対称的な相互作用を持つマルコフ確率場を変形モデルとした非剛体イメージレジストレーション技術の開発

    長村徹, 徳永旭将

    第23回情報論的学習理論ワークショップ  電子情報通信学会 情報論的学習理論と機械学習研究会

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    Event date: 2020.11.23 - 2020.11.26   Language:English  

  • GLCICによる欠損補間に基づく教師なし画像異常検知法の提案

    深町 悠貴, 徳永 旭将

    第23回情報論的学習理論ワークショップ  電子情報通信学会 情報論的学習理論と機械学習研究会

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    Event date: 2020.11.23 - 2020.11.26   Language:Japanese  

  • 宇宙プラズマ現象予測モデル開発に向けた機械学習・数値シミュレーション・観測による学習データの整備

    深沢 圭一郎, 木村 智樹, 徳永 旭将, 中野 慎也

    第148回 地球電磁気・地球惑星圏学会総会及び講演会  地球電磁気・地球惑星圏学会

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    Event date: 2020.11.01 - 2020.11.04   Language:English  

    The machine learning has become a powerful tool to find the relation between variables thanks to the deep learning technique. This performs greatly in the classification, regression and recently generative modeling in the engineering and commercial areas. However, due to the satisfaction of physical laws in the scientific research area, the application of machine learning has some difficulties. In particular, the generative modeling is very sensitive to scientific data since the generated data is not guaranteed by the physical laws.
    To overcome these problems, we have tried to apply machine learning to space plasma physics. In the observation there are many lacks data in space and time. Using the technique of GAN (Generative Adversarial Networks), we have challenged to represent the lack data of aurora image by ASI (All-Sky Imager) of THEMIS. Now we use the natural training data not only the observation data and we have obtained the smooth represented data, however these data cannot satisfy the physical laws. Then we prepare the training data of only observation.
    From this thought the preparing the training data is the most important for machine learning. Then we have prepared the global simulation data of magnetosphere using real solar wind data for the generation and forecast the configuration of the magnetosphere. These data are the very large size and time elapsed data so that the data set cannot be stored in often case and usual machine learning cannot treat these data set. However recently there are 3D CNN (convolutional neural network) and RNN (recurrent neural network) which can be trained by 3D data set and these data set may become very important. In this study, we show the database of this data set data, representation of the auroral image and their status.

  • Development of Deformable Image Registration Technique using MRFs Based Deformation Model with Asymmetric Interaction

    IIBMP2020 

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    Event date: 2020.09.01 - 2020.09.03   Language:Japanese  

  • GANに基づくCIEDE2000異常度スコアを用いた色異常検知方法の提案

    片渕 凌也, 徳永 旭将

    第23回画像の認識・理解シンポジウム  電子情報通信学会パターン認識・メディア理解(PRMU)研究専門委員会

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    Event date: 2020.08.02 - 2020.08.05   Language:Japanese  

    本論文では, 色に現れる異常の検出を目的とした, 教師なし異常検出方法を提案する. 提案手法では, 敵対的生成ネットワークに基づく教師なし学習に基づき, 色の再構成可能性を評価することで, 正常データでは見られなかった色の異常の検知を行う. また, 色の再構成可能性の評価のため, CIEDE2000 色差に基づく異常度スコアを提案する. 実験では, PlantVillage データセットを用いた病気の植物とその病変領域の検出に対して提案手法の性能評価を行い,ベースラインである AnoGAN との比較を行う. 実験の結果, 提案手法が色の異常検知問題に対して検出性能, 解釈可能性および計算効率性の観点から AnoGAN より優れた性能を発揮することを示す.

  • All-Sky Imagerデータの複数の脈動パッチを包括的に追跡するためのパイプライン

    野見山 陸, 三好 由純, 遠山 航平, 小川 泰信, 細川 敬祐, 徳永 旭将

    第23回 画像の認識・理解シンポジウム  電子情報通信学会パターン認識・メディア理解(PRMU)研究専門委員会

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    Event date: 2020.08.02 - 2020.08.05   Language:Japanese  

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

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

    JpGU-AGU Joint Meeting 2020  Japan Geoscience Union Meeting

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    Event date: 2020.07.12 - 2020.07.16   Language:English  

    We propose an application of the independent component analysis (ICA) to separate satellite- induced time-varying noises from magnetic data obtained by onboard multiple magnetometers. The ICA is a method of estimating source signals observed at multiple sites so that estimated source signals are statistically independent of each other. Since satellite noises are clearly independent of external natural variations, the ICA is expected to separate the satellite noises. We applied the ICA to the magnetic data measured by the first Quasi-Zenith Satellite (QZS-1) that has two triaxial fluxgate magnetometers without an extendable mast. First, we removed the long-period trend from the original data. Then, we applied the FastICA to the detrended data and obtained six independent components (ICs). Satellite-induced noises were successfully classified into three ICs (noise ICs). Natural signals are represented by the rest three ICs (signal ICs). Finally, we restored the observed signals from the signal ICs. We confirmed that amplitude and waveform of natural phenomena were not altered by the processing. We also proposed the method of automatic determination of noise ICs using the D score, which is similar to the normalized coefficient of variance of the mixing vectors. We confirmed that the three largest D scores, which give the noise ICs, are much larger than the three smallest D, which give the signal ICs. The automatic determination of noise ICs by this method was 95% identical to that by visual inspection. These results demonstrated that the ICA method can provide for mast-less magnetic observations in future satellite missions.

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

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

    The 34th Annual Conference of the Japanese Society for Artificial Intelligence  The Japanese Society for Artificial Intelligence

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    Event date: 2020.06.09 - 2020.06.12   Language:English  

  • A Pipeline for Comprehensive Tracking of Pulsating Patches in All-Sky Imager Data

    JpGU - AGU Joint Meeting 2020  Japan Geoscience Union

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    Event date: 2020.05.24 - 2020.05.28   Language:English  

  • 映像IoT技術による赤ちゃん見守りシステム

    村田 健史, 深沢 圭一郎, 徳永 旭将, 水原 隆道, 野見山 陸, Somnuk Phon-Amnuaisuk

    第151回情報システムと社会環境研究発表会 

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    Event date: 2020.02.28   Language:Japanese  

  • Whole-brain calcium imaging analyses of dynamics of neural network in C. elegans

    Yuko Murakami, Suzu Oe, Motonari Ichinose, Takayuki Teramoto, Yu Toyoshima, Terumasa Tokunaga, Osamu Hirose, Stephan Wu, Jang Moon-Song, Hirofumi Sato, Sayuri Kuge, Yuishi Iwasaki, Ryo Yoshida, Yuichi Iino, Takeshi Ishihara

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    Event date: 2019.12.18 - 2019.12.20   Language:Japanese  

  • Whole neuronal analyses of the behavioral switching depending on associative learning in C. elegans

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    Event date: 2019.12.18 - 2019.12.20   Language:Japanese  

  • Application of Machine Learning to magnetospheric physics and preparation of training data for global magnetospheric configuration and physics

    Keiichiro Fukazawa, Tomoki Kimura, Terumasa Tokunaga, Shinya Nakano

    AGU Fall Meeting 2019  America Geophysics Union

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    Event date: 2019.12.09 - 2019.12.13   Language:English  

  • 全脳カルシウムイメージングによる線虫の神経動態解析

    村上 悠子, 大江 紗, 寺本 孝行, 豊島 有, 徳永 旭将, Stephan Wu, 広瀬 修, Jang Moon-Sun, 佐藤 博文, 金森 真奈美, 久下 小百合, 岩崎 唯史, 吉田 亮, 飯野 雄一, 石原 健

    第42回日本分子生物学会年会  日本分子生物学会

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    Event date: 2019.12.03 - 2019.12.06   Language:Japanese  

  • 線虫の連合学習の記憶に基づく行動スイッチング:中枢神経回路活動可視化による解析

    大江 紗, 村上 悠子, 寺本 孝行, 豊島 有, 徳永 旭将, Stephan Wu, 広瀬 修, Moon-Sun Jang, 佐藤 博文, 金森 真奈美, 久下 小百合, 岩崎 唯史, 吉田 亮, 飯野 雄一, 石原 健

    第42回日本分子生物学会年会  日本分子生物学会

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    Event date: 2019.12.03 - 2019.12.06   Language:Japanese  

  • Analyzing whole-brain dynamics of C. elegans with statistical approach

    Yuko Murakami, Suzu Oe, Takayuki Teramoto, Yu Toyoshima, Terumasa Tokunaga, Osamu Hirose, Stephen Wu, Moon-Sun Jang, Hirofumi Sato, Manami Kanamori, Sayuri Kuge, Yuishi Iwasaki, Ryo Yoshida, Yuichi Iino, Takeshi Ishihara

    The 20th International Conference on Systems Biology 

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    Event date: 2019.11.01 - 2019.11.05   Language:English  

  • CNNを用いた南極域における降雪時の雲パターン検出

    鈴木 香寿恵, 徳永 旭将, 福地 岬稀,平沢 尚彦,矢吹 裕伯,山内 恭

    日本気象学会2019年度秋季大会  日本気象学会

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    Event date: 2019.10.28 - 2019.10.31   Language:Japanese  

  • ディープラーニングによる南極昭和基地周辺における降雪をもたらす雲の検出

    鈴木 香寿恵, 徳永 旭将, 福地 岬稀,平沢 尚彦,矢吹 裕伯,山内 恭

    氷雪研究大会2019  日本氷雪学会

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    Event date: 2019.09.08 - 2019.09.11   Language:Japanese  

  • LSTMを用いた分類問題における判断根拠可視化の検討

    齊藤 剛史, 徳永 旭将

    第22回画像の認識・理解シンポジウム (MIRU2019) 

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    Event date: 2019.07.29 - 2019.08.01   Language:Japanese  

  • Exploring the information processing of neural network through whole-brain activity-imaging of C. elegans

    Yu Toyoshima,Hirofumi Sato,Manami Kanamori,Stephen Wu,Moon-Sun Jang,Yuko Murakami,Suzu Oe,Terumasa Tokunaga, Osamu Hirose, Sayuri Kuge,Takayuki Teramoto,Yuishi Iwasaki,Ryo Yoshida,Takeshi Ishihara,Yuichi Iino

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    Event date: 2019.07.25 - 2019.07.28   Language:English  

  • 線虫の全脳活動データ解析: 使っているシナプス結合と使っていないシナプス結合

    岩崎唯史, 佐藤博文, 豊島有, 大江紗, 村上悠子, 寺本孝行, Stephen Wu, 徳永旭将, ジャンムンソン, 吉田亮, 石原健, 飯野雄一

    日本物理学会第74回年次大会  日本物理学会

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    Event date: 2019.03.14 - 2019.03.17   Language:Japanese  

  • 線虫の連合学習の記憶に基づく行動スイッチング:中枢神経回路の活動可視化による解析

    大江紗

    第41回日本分子生物学会年会  日本分子生物学会年会

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    Event date: 2018.11.28 - 2018.11.30   Language:Japanese  

  • 再帰型ニューラルネットワークを用いた太陽風パラメータからのサブストーム規模の予測

    河村光次郎

    地球電磁気・地球惑星圏学会第144回総会及び講演会  地球電磁気・地球惑星圏学会

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    Event date: 2018.11.23 - 2018.11.27   Language:Japanese  

  • カーネル密度関数の局所変形に基づくトポロジー保存可能なイメージアライメント手法の開発に向けて

    綿島正剛

    第21回情報論的学習理論ワークショップ  電子情報通信学会 情報論的学習理論と機械学習 (IBISML) 研究会

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    Event date: 2018.11.04 - 2018.11.07   Language:Japanese  

  • Analyzing whole neural activities to elucidate the mechanisms underlying sensory integration

    Asia Pacific Worm Meeting 2018 

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    Event date: 2018.07.09 - 2018.07.12   Language:English  

  • The detection of cloud pattern in the Antarctic using Convolution Neural Network for estimation of the snowfall amount

    Kazue Suzuki

    15th Annual Meeting Asia Oceania Geosciences Society  Asia Oceania Geosciences Society

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    Event date: 2018.06.03 - 2018.06.08   Language:English  

  • ベイズ推定による楽曲間内挿に基づく和音モーフィング法の提案

    榎田皓太

    火の国情報シンポジウム2018 

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    Event date: 2018.03.01 - 2018.03.02   Language:Japanese  

  • カーネル 密度関数の局所変形に基づく線状構造物に対する非剛体イメージアライメント手 法の開発

    徳永旭将

    第20回情報論的学習理論ワークショップ  電子情報通信学会 情報論的学習理論と機械学習研究会

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    Event date: 2017.11.08 - 2017.11.10   Language:Japanese  

  • カーネル密度関数 の局所変形に基づく線状構造物に対する非剛体イメージアライメント手法の開発

    綿島正剛

    日本統計関連学会連合大会2017  日本統計関連学会連合

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    Event date: 2017.09.03 - 2017.09.06   Language:Japanese  

  • 線虫の全脳イメー ジングによる行動を制御する情報処理機構の解析

    大江 紗

    第40回日本神経科学大会  日本神経科学学会

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    Event date: 2017.07.20 - 2017.07.23   Language:English  

  • 線虫の全脳活動データに対する位相解析

    岩崎 唯史, 寺本 孝行, 大江 紗, 徳永 旭将, 広瀬 修, S. Wu, 豊島 有, ジャン ムンソン, 吉田 亮, 飯野 雄一, 石原 健

    2017年度日本物理学会第72回年次大会  日本物理学会

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    Event date: 2017.03.17 - 2017.03.20   Language:Japanese  

    線虫C. elegansの神経系は302個の神経細胞から構成され,そのうち約170個が頭部に集中している.頭部に存在するこれら神経細胞の同時イメージングデータに対して,同期/非同期オーダーパラメータを用いた位相解析,および類似度行列に基づいたクラスタ解析を行った.本発表ではこれらの結果について報告する.また,シナプス結合に異常がある変異体での結果と野生型での結果の違いについても言及する.

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Industrial Property

  • 画像処理装置、画像処理方法および画像処理プログラム

    徳永 旭将、中西 慶一

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    Application no:特願2024-062424  Date applied:2024.04.08

  • 画像処理装置、画像処理方法及び画像処理プログラム

    徳永 旭将、中西 慶一

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    Application no:特願2024-010605  Date applied:2024.01.26

  • 異常検出装置、異常検出方法及び異常検出プログラム

    徳永 旭将

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    Application no:特願2023-203749  Date applied:2023.12.01

  • 光退色補正装置及びこれを用いた膜電位変動の解析装置、並びに光退色補正方法、発表時間補正プログラム

    德永 旭将, 中村 匠

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    Application no:特願2023-057149  Date applied:2023.03.31

  • 画像処理・解析装置および画像処理・解析手法

    徳永 旭将

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    Application no:PCT/JP2023/010330  Date applied:2023.03.16

  • 外観検査システム、及び外部駆動型視注視機構

    徳永 旭将, 片渕 凌也

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    Application no:特願2022-115074  Date applied:2022.07.19

  • 画像処理・解析装置および画像処理・解析方法

    徳永 旭将, 片渕 凌也

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    Application no:2022-59045, PCT/JP2023/010330  Date applied:2022.03.31

  • 非接触脈拍推定装置

    徳永 旭将, 野見山 陸

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    Application no:2022-035911  Date applied:2022.03.09

    本発明の第1の観点は、サーマルカメラにより取得した映像データから脈拍を推定する非接触脈拍推定装置であって、前記映像データから抽出された複数の脈波信号に周波数解析を行う周波数解析部と、前記周波数解析部が出力した周波数成分のうち、脈拍の周波数である可能性がある周波数範囲である脈拍周波数範囲における最大の振幅を有する周波数成分を脈拍成分として決定する脈拍成分決定部とを備える、非接触脈拍推定装置である。

  • GLCICによる欠損補間に基づく教師なし画像異常検知システム

    徳永旭将

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    Application no:2021-189725  Date applied:2021.11.21

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Works

Lectures

  • データ同化の基盤となる逐次ベイズの考え方・アルゴリズムおよびデータ同化適用例

    (独)日本学術振興会R052 DXプラズマプロセス委員会 第2回研究会 『データ同化によるプラズマ解析の高精度化への道』  2023.08  DXプラズマプロセス委員会

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    Event date: 2023.08.08   Language:Japanese   Presentation type:Invited lecture   Venue:東京都   Country:Japan  

    Other Link: https://www.dxplasma.org/meetings/data/002.html

  • 高性能かつコストエフェクティブな外観検査AIに向けた 統計的機械学習の先進的応用

    EICE SIS研6月研究会  2022.06  PSJ-AVM

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    Event date: 2022.06.09 - 2022.06.10   Language:Japanese   Presentation type:Invited lecture   Venue:九州工業大学若松地区  

  • 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

    第2回分子サイバネティクス,第46回分子ロボティクス定例研究会  2021.05  分子ロボティクス研究会

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    Event date: 2021.05.10   Language:Japanese   Presentation type:Invited lecture   Venue:オンライン  

  • 少量のデータにより異常を含む画像を自動検知するAI技術

    エッセンスフォーラム2023  2023.08  株式会社エッセンス

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    Event date: 2023.08.22   Language:Japanese   Presentation type:Invited lecture   Venue:東京都  

    Other Link: https://esse-sense.com/forum2023?gclid=EAIaIQobChMIrZ7V3oevgQMVuGgPAh3mqw8pEAAYASAAEgL9__D_BwE

  • 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」  2017.12  名古屋大学

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    Presentation type:Invited lecture   Venue: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  2017.10  京都大学・学術情報メディアセンター

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    Presentation type:Invited lecture   Venue:京都大学吉田キャンパス  

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

    International Workshop on Quantitative Biology 2017 At Keio University  2017.04  Japanese society for quantitative biology

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    Presentation type:Invited lecture  

  • 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  2014.05  大阪大学免疫学フロンティア研究センター

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    Presentation type:Invited lecture   Venue: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  金沢大学がん進展制御研究所

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    Presentation type:Invited lecture  

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Press

  • 九州工業大学ニューロモルフィックAIハードウェア研究センターの紹介記事   Newspaper, magazine

    田中 啓文, 徳永 旭将, 古川 徹夫

    産経新聞西部本部  産経新聞九州山口版  2023.08.04

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    Author:Other  

    九州工業大学ニューロモルフィックAIハードウェア研究センター, PARKS, 大学発スタートアップエコシステム

Grants-in-Aid for Scientific Research

  • 外観検査AIを迅速に構築する外部駆動型視覚注視機構の確立

    Grant number:22K12169  2022.04 - 2025.03   基盤研究(C)

  • カーネル密度関数の局所変形による汎用的イメージアライメント法の開発

    Grant number:15K16087  2015.04 - 2017.03   若手研究(B)

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    研究課題番号:15K16087
    医療画像や生物画像を想定し、異なる測定環境で得られた画像同士を共通の座標系に変換する”イメージ·アライメント”の開発を行う。既存のイメージ·アライメント法では、複雑な形状の物体を計測した画像や、画像の一部に欠損や不明 瞭な領域を含む画像に対しては、適切なアライメントができないという問題があった。本研究計画では、カーネル密度関数の局所変形という新たな観点から、高精度かつ汎用性の高いイメージ· アライメント法を提案する。

Contracts

  • 学習型動態モーフィングによる神経間シグナル伝達特性の解明(JST戦略的創造研究推進事業「さきがけ」)

    2018.10 - 2022.03

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    Grant type:Consigned research

    高速共焦点顕微鏡により計測されたCaイオンイメージングデータから、本来の時空間解像度を超えて動態を推定する”学習型動態モーフィング技術”の研究を行う。提案技術は、ベイズ推論に基づき動きや変形場を推定する”非剛体イメージレジストレーション”、複数の時空間解像度で計測されたイメージングデータを機械学習により統合する”深さ補間”、”画像超解像”技術から成る。それにより、ギャップ結合と化学シナプス結合のいずれが用いられたかをCaイメージングデータから判別する技術を確立する。さらに、宇宙科学に関するサブテーマを設定し、汎用性の実証と領域内外への水平展開を狙う。

  • 熱交換器 AIによる外観検査技術開発

    2022.11 - 2023.10

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    Grant type:Joint research

  • 半導体検査装置に関する機械的、画像処理的性能向上に関する研究

    2022.04 - 2025.03

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    Grant type:Joint research

  • 観測・数値シミュレーション・機械学習の融合による宇宙プラズマ現象予測モデルの開発

    2018.06 - 2019.03

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    Grant type:Other joint research

    本研究では、飛翔体による宇宙プラズマ観測データと数値プラズマシミュレーションを、機械学習によって統合的に解析することで、「低空間次元・小観測数・単地点観測」という観測データを時空間に拡張し、そこで起きる現象の変動を抽出することを目的とする(研究代表:深沢圭一郎, 京都大学)。

  • Hisaki観測・数値シミュレーション・機械学習の融合による宇宙プラズマ現象理解につながる手法の研究開発

    2018.04 - 2019.03

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    Grant type:Other joint research

Other External Funds

  • 多様かつ直感的なアダプテーション機能を有する継続的に利用できる外観検査AI技術の開発

    2023.08 - 2024.03

    JST研究成果展開事業大学発新産業創出プログラムプロジェクト推進型ビジネスモデル検証支援  

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    JST大学発新産業創出プログラム<プロジェクト推進型ビジネスモデル検証支援>は, 優れた技術シーズを基にしたベンチャー企業の創出を目的とし、起業と事業の成長に必要な知識の学習、およびビジネスモデルの仮説立案・検証を行う産業創出支援プログラムである。2023年度は全国より8件の新規課題が採択された。本研究では、製造業などの検査業務を自動化する外観検査AIについて, PoCとビジネスモデル検証を行う。

  • 試行錯誤のプロセスを大幅に低減する外観検査AI技術

    2022.08 - 2023.03

    大学発新産業創出プログラム (START) 大学・エコシステム推進型 PARKS 起業活動支援プログラム GAP Next   GAP NEXT

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    本事業シーズ技術では、ディープラーニングに基づく統計的機械学習、特に敵対性生成ネットワーク(Generative Adversarial Network: GAN)に基づき、大量の検査画像から異常を含む画像を自動検知するAI技術を提供する。ディープラーニングに基づく外観検査AIには、AIモデルの訓練に大量の正常・異常サンプルが必要である。しかしながら、生産ラインにおいて大量の異常サンプルを収集することは現実的ではない。本事業シーズ技術では、GANの訓練は良品サンプルのみで行う。これは、教師なし学習の一種である。より具体的には、GANに基づくImage Completion(画像の欠損補間)により、良品画像に対し意図的に与えた画像欠損を復元するプロセスを学習させる。画像復元には、Encoder-Decoderと呼ばれるタイプの深層ニューラルネットワークが用いられる。検査時には、不良の検出に適した欠損(詳細は後述)を検査画像に与える。より具体的には、1枚の検査対象画像に対し、異なる場所に欠損を与えた複数枚の欠損画像を作成する。それらに対し、学習済みのEncoder-Decoderネットワークにより欠損部分を補間する。さらに、欠損部分を補間した複数枚の画像を1枚の画像に合成する。この処理により、不良部分の有無に関わらず検査画像は”良品風”の画像に変換される。そのため、元の検査対象画像と良品風に変換された合成画像の差分を計算することで、異常度マップを作成することができる。この異常度マップに基づき不良部分を検出し、その結果をGUIでユーザに提示する。

  • 半教師あり機械学習と高速マシンビジョンの融合による低コストかつ超高速な半導体外観検査AIプラットフォームの開発

    2022.03 - 2025.03

    NEDO 官民による若手研究者発掘支援事業 共同研究フェーズ  

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    米国や中国が5兆円を超える半導体向けの産業政策を展開する一方、我が国の半導体産業は1990年代以降その地位を低下させている。半導体・電子回路は、AI、5G、IoT、DX、ロボティクスなど21世紀の産業や安全保障の土台となる最重要基盤であることら、国内の半導体製品の生産強化、安定化は喫緊の課題である。本提案では、世界最高峰の検査速度を誇るダイソータテストハンドラを実用化できる高い技術力を持つ上野精機株式会社と、九州工業大学が保有する先端的な画像認識AI技術、DNNモデル圧縮技術、組込み実装技術を融合させることで、半導体・電子部品の良・欠陥を、柔軟・低アノテーションコストかつ超高速で実現する次世代型の半導体外観検査AIプラットフォームを確立する。

  • NOAA/AVHRR雲画像を用いた降雪をもたらす雲の検出法および降雪量の推定

    2019.07 - 2020.03

    情報システム研究機構: ROIS-DS Joint Research Program  

  • 観測・数値シミュレーション・機械学習の融合による宇宙プラズマ現象予測モデルの開発

    2019.07 - 2020.03

    情報システム研究機構: ROIS-DS Joint Research Program  

  • 女性とこどものこころとからだの健康サポート

    2019.01 - 2022.03

    革新的イノベーション創出プログラム(COI STREAM)  

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Other Research Activities

  • 論文査読

    2017.05
    -
    2017.06

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    画像電子学会誌VC特集号のショートペーパーの査読

Career of Research abroad

  • 宇宙プラズマ環境場データの機械学習に基づく地球磁気圏応答特性の解明

    ジョンズホプキンス大学応用物理学研究所  Project Year:  2019.11.14 - 2020.03.20

Charge of off-campus class subject

  • 2019.08   Institution:九州大学

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    Level:postgraduate_courses  Country:Japan

Award for Educational Activities

Activities of Academic societies and Committees

  • 情報処理学会MPS研究会   情報処理学会数理モデル化と問題解決検討会運営委員  

    2013.04 - 2017.03

Social activity outside the university

  • 九州工業大学 新技術説明会

    Role(s):Lecturer

    科学技術振興機構、九州工業大学  オンライン  2023.12.15

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    Audience: General, Company, Governmental agency

    Type:Lecture

  • 大学見本市2023~イノベーション・ジャパンへの出展

    Role(s):Demonstrator

    科学技術振興機構  大学見本市2023~イノベーション・ジャパン  東京ビッグサイト  2023.08.24 - 2023.08.25

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    Audience: General, Company, Governmental agency

    Type:Other

  • 招待講演(データ同化の基盤となる逐次ベイズの考え方・アルゴリズムおよびデータ同化適用例)

    Role(s):Lecturer

    DXプラズマプロセス委員会  第2回 JSPSDXプラズマプロセス委員会  ベルサーユ八重洲  2023.08.08

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    Audience: Researchesrs, Scientific, Company, Governmental agency

    Type:Lecture

    https://www.dxplasma.org/meetings/data/002.html

  • サーマルカメラからのバイタルモニタリング技術

    Role(s):Lecturer

    科学技術振興機構  新技術説明会  オンライン  2022.12.15

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    Audience: Researchesrs, General, Company

    Type:Other

  • ベイジアンモデル応用: トラッキング

    Role(s):Lecturer

    九州工業大学  社会人向けデータサイエンス7日間集中講義  オンライン  2021.03.18

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    Audience: Researchesrs, General, Company

    Type:Seminar, workshop

  • 先端的な科学計測とデータサイエンスの理想的な協働へ向けて

    Role(s):Lecturer

    公益社団法人日本技術士会九州本部北九州地区支部  公益社団法人日本技術士会九州本部北九州地区支部2021年3月度CPD  北九州環境ミュージアム  2021.03.13

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    Audience: Researchesrs, General, Company

    Type:Lecture

    北九州環境ミュージアムにおいて, 公益社団法人日本技術士会九州本部北九州地区支部2021年3月度CPDに講師として参加し, 「先端的な科学計測とデータサイエンスの理想的な協働へ向けて」という題目で講演を行った。

  • 九州大学理学府地球惑星科学専攻特別講義

    Role(s):Lecturer

    九州大学  九州大学  2019.08.28 - 2019.08.30

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    Audience: College students, Graduate students

    Type:Other

  • 2014年統計数理研究所公開講座

    2014.12.08 - 2014.12.09

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    Type:Seminar, workshop

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