Faculty Profiles - TOKUNAGA Terumasa

Papers - TOKUNAGA Terumasa

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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

Authorship：Last author Language：English Publishing type：Research paper (international conference proceedings)

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

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84877794120&origin=inward

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

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

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

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84863869958&origin=inward

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

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

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.

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=80053970902&origin=inward

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

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

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 International journal

徳永旭将, 池田大輔, 中村和幸

情報処理学会研究報告 ( 東京 : 情報処理学会 ) 2010年度 ( 5 ) 1 - 6 2011.02

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

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

徳永旭将

Memoirs of the Faculty of Sciences, Kyushu University, Series D, Earth and Planetary Sciences 32 63 - 73 2011.01

AKR modulation and Global Pi 2 oscillation : Jan.24, 1997 event Reviewed International journal

徳永旭将

Journal of Geophysical Research 印刷中 2011.01

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

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

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

Onset Time Determination of Precursory Events of Singular Spectrum Transformation Invited Reviewed International journal

徳永旭将

International Journal of Circuits, Systems and Signal processing 5 46 - 60 2010.01

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

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

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

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=72149099064&origin=inward

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

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

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

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

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

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

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

<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

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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Papers - TOKUNAGA Terumasa