Faculty Profiles - TOKUNAGA Terumasa

Conference Prsentations (Oral, Poster) -

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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

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.

外観検査AIによる製造業のアップデート Invited

花手洋樹; 徳永旭将

エッセンスフォーラム2024 2024.08 株式会社エッセンス

画素単位のマスクを用いない大域・局所的推論の一致性に基づく領域分割

中西慶一, 徳永旭将

第27回画像の認識・理解シンポジウム MIRU2024 2024.08 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

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

https://www.brain.kyutech.ac.jp/~neuro/2024/12/17/the-6th-international-symposium-on-neuromorphic-ai-hardware/?lang=en

Ren Takeyama, Keiichi Nakanishi, Terumasa Tokunaga

The 6th International Symposium on Neuromorphic AI Hardware 2025.03 the Society for Neuromorphic AI Hardware

Towards Resource-Efficient Machine Learning: Investigating Double Descent in Deep Mutual Learning

Fumika Komori, Keiichi Nakanishi, Terumasa Tokunaga

The 6th International Symposium on Neuromorphic AI Hardware 2025.03 the Society for Neuromorphic AI Hardware

Modeling Olfactory Adaptation in C. elegans Using Echo State Networks with Optimized Leak Rates

Kosuke Takashima, Ryosuke Ishibashi, Keiichi Nakanishi, Hakaru Tamukoh, Yuichiro Tanaka, Takeshi Ishihara, Noriko Sato, Terumasa Tokunaga

The 6th International Symposium on Neuromorphic AI Hardware 2025.03 the Society for Neuromorphic AI Hardware

Pooling-Enhanced Bidirectional 2D Reservoir Computing for Image Anomaly Detection

Keiichi Nakanishi, Ryosuke Ishibashi, Ren Takeyama, Terumasa Tokunaga

The 6th International Symposium on Neuromorphic AI Hardware 2025.03 the Society for Neuromorphic AI Hardware

Modeling Neural Responses in C. elegans Olfactory Neurons Using Deep Echo State Networks

Ryosuke Ishibashi, Keiichi Nakanishi, Yuichiro Tanaka, Hakaru Tamukoh, Takeshi Ishihara, Noriko Sato, Terumasa Tokunaga

The 6th International Symposium on Neuromorphic AI Hardware 2025.03 the Society for Neuromorphic AI Hardware

クラス事前確率を用いた半教師あり多クラス分類におけるコスト関数の提案

三山晃季, 中西慶一, 徳永旭将

第27回画像の認識・理解シンポジウム MIRU2024 2024.08 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

欠損補完に基づく画像異常検知に対してマスクサイズが与える影響の評価

古里和暉 , 中西慶一 , 徳永旭将

第27回画像の認識・理解シンポジウム MIRU2024 2024.08 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

外部視覚注視機構による異常検出性能の高い注視領域生成方法の解析

城間亮, 中西慶一, 片渕凌也, 徳永旭将

第27回画像の認識・理解シンポジウム MIRU2024 2024.08 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

EVSによる共振撮影を利用した透明物体のインスタンスセグメンテーションの検討

芦原新太郎, 徳永旭将

第27回画像の認識・理解シンポジウム MIRU2024 2024.08 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

欠損補完に基づく画像異常検知に対してマスクの色が与える影響の評価

持田海音, 中西慶一, 徳永旭将

第27回画像の認識・理解シンポジウム MIRU2024 2024.08 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

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

Evaluation of feature extractor trained by grayscale images for industrial anomaly detection

Sansei Hori, Kazumichi Tanaka, Keiichi Nakanishi, Terumasa Tokunaga

The 5th International Symposium on Neuromorphic AI Hardware 2024.03 the Society for Neuromorphic AI Hardware

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

Keiichi Nakanishi, Terumasa Tokunaga

The 5th International Symposium on Neuromorphic AI Hardware 2024.03 the Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology

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

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

IIBMP2023 2023.09

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

IIBMP2023 2023.09

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

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

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

Lin Magari, Terumasa Tokunaga, Kazue Suzuki

2023.05

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

The 9th Meeting on Advanced Power Semiconductors Division 2022.12

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

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

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

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

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

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

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

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

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

The 3rd International Sympojium on Neuromorphic AI Hardware

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

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

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

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

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

Aurora Image Segmentation with Deep PNU Learning

The 150 th SGEPSS General Assembly

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

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

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

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」

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

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

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

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

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

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

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

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

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

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

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

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

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

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

JpGU - AGU Joint Meeting 2020 Japan Geoscience Union

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

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

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

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

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

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

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

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

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

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)

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

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

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

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

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

Analyzing whole neural activities to elucidate the mechanisms underlying sensory integration

Asia Pacific Worm Meeting 2018

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

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

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

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

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

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

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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