研究者詳細 - 工藤　博章

論文 - 工藤　博章

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Multicrystalline Informatics Applied to Multicrystalline Silicon for Unraveling the Microscopic Root Cause of Dislocation Generation 査読有り

Yamakoshi, K; Ohno, Y; Kutsukake, K; Kojima, T; Yokoi, T; Yoshida, H; Tanaka, H; Liu, X; Kudo, H; Usami, N

ADVANCED MATERIALS 36 巻 ( 8 ) 頁： e2308599 2023年12月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Advanced Materials

A comprehensive analysis of optical and photoluminescence images obtained from practical multicrystalline silicon wafers is conducted, utilizing various machine learning models for dislocation cluster region extraction, grain segmentation, and crystal orientation prediction. As a result, a realistic 3D model that includes the generation point of dislocation clusters is built. Finite element stress analysis on the 3D model coupled with crystal growth simulation reveals inhomogeneous and complex stress distribution and that dislocation clusters are frequently formed along the slip plane with the highest shear stress among twelve equivalents, concentrated along bending grain boundaries (GBs). Multiscale analysis of the extracted GBs near the generation point of dislocation clusters combined with ab initio calculations has shown that the dislocation generation due to the concentration of shear stress is caused by the nanofacet formation associated with GB bending. This mechanism cannot be captured by the Haasen-Alexander-Sumino model. Thus, this research method reveals the existence of a dislocation generation mechanism unique to the multicrystalline structure. Multicrystalline informatics linking experimental, theoretical, computational, and data science on multicrystalline materials at multiple scales is expected to contribute to the advancement of materials science by unraveling complex phenomena in various multicrystalline materials.

DOI： 10.1002/adma.202308599

Web of Science

Scopus

PubMed

3D CNN and grad-CAM based visualization for predicting generation of dislocation clusters in multicrystalline silicon 査読有り

Kyoka Hara, Takuto Kojima, Kentaro Kutsukake, Hiroaki Kudo, Noritaka Usami

APL machine learning 2023年9月

A machine learning-based prediction of crystal orientations for multicrystalline materials 査読有り

Kyoka Hara, Takuto Kojima, Kentaro Kutsukake, Hiroaki Kudo, Noritaka Usami

APL machine learning 2023年6月

Estimation of Crystal Orientation of Grains on Polycrystalline Silicon Substrate by Recurrent Neural Network 査読有り

Kato Hikaru, Kamibeppu Soichiro, Kojima Takuto, Matsumoto Tetsuya, Kudo Hiroaki, Takeuchi Yoshinori, Kutsukake Kentaro, Usami Noritaka

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 17 巻 ( 11 ) 頁： 1685 - 1687 2022年11月

記述言語：日本語掲載種別：研究論文（学術雑誌）出版者・発行元：IEEJ Transactions on Electrical and Electronic Engineering

To analyze crystal defects in polycrystalline silicon substrates, it is necessary to measure the crystal orientation at high speed. In this paper, we propose a method for simultaneous and fast estimation of the crystal orientation of an entire substrate by measuring the reflected light from a single rotation of a light source. The effectiveness of the proposed method is verified by the orientation estimation experiments. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

DOI： 10.1002/tee.23676

Web of Science

Scopus

その他リンク： https://onlinelibrary.wiley.com/doi/full-xml/10.1002/tee.23676

Improved Speech Separation Performance from Monaural Mixed Speech Based on Deep Embedding Network 査読有り

Dang S., Matsumoto T., Kudo H., Takeuchi Y.

電気学会論文誌Ｃ（電子・情報・システム部門誌） 142 巻 ( 6 ) 頁： 643 - 649 2022年6月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：一般社団法人電気学会

Speech separation refers to the separation of mixed utterances in which multiple people are speaking simultaneously. This topic was created to address the cocktail party problem, and it has also evolved into a front-end procedure for speech-related research recently. There are two types of models for addressing it: classification methods and regression methods. Classification methods avoid considering the permutation problem, and regression methods are more precise. Deep clustering (DC) is a common deep learning classification technique that uses a deep embedding network to embed signals in the underlying manifold and allows signals with similar properties to cluster together in the embedded space. The ideal binary mask (IBM) supervises the model. Even if it is entirely isolated via supervised binary masking, this setting is susceptible to mistake. To remedy this flaw, this paper proposes a network with two modules based on deep embedding. More precise masks, such as the ideal ratio mask (IRM), can now be used instead of IBM. Furthermore, the cascade structure may preserve DC’s high performance. On average, the suggested model beats the original DC model by 1.55dB in SNR, 4.45dB in SDR, 4.41dB in SDRi, 0.16 in STOI, and 0.3 in PESQ on a mixture of Japanese Newspaper Article Sentences (JNAS). Finally, a comparison is made with ChimeraNet which has a similar structure, the results prove the proposed model’s superiority over previous DC-related studies.

DOI： 10.1541/ieejeiss.142.643

Scopus

CiNii Research

Occurrence Prediction of Dislocation Regions in Photoluminescence Image of Multicrystalline Silicon Wafers Using Transfer Learning of Convolutional Neural Network 査読有り

Hiroaki KUDO , Tetsuya MATSUMOTO , Kentaro KUTSUKAKE , Noritaka USAMI

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E104.A 巻 ( 6 ) 頁： 857 - 865 2021年6月

Occlusion Avoidance Behavior During Gazing at a Rim Drawn by Blue-Yellow Opposite Colors 査読有り

Miho SHINOHARA, Yukina TAMURA, Shinya MOCHIDUKI, Hiroaki KUDO, Mitsuho YAMADA

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E104.A 巻 ( 6 ) 頁： 897 - 901 2021年6月

Application of artificial neural network to optimize sensor positions for accurate monitoring: an example with thermocouples in a crystal growth furnace 査読有り

Abderahmane Boucetta,Kentaro Kutsukake,Takuto Kojima,Hiroaki Kudo,Tetsuya Matsumoto,Noritaka Usami

Applied Physics Express 12 巻 ( 12 ) 頁： 125503 2019年11月

Eye Movement Measurement of Gazing at the Rim of a Column in Stereo Images with Yellow-Blue Equiluminance Random Dots 査読有り

Shinya MOCHIDUKI, Ayaka NUNOMURA, Hiroaki KUDO, Mitsuho YAMADA

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences E102-A 巻 ( 9 ) 頁： 1196-1204 2019年9月

3D visualization and analysis of dislocation clusters in multicrystalline silicon ingot by approach of data science 査読有り

Yusuke Hayama, Tetsuya Matsumoto, Tetsuro Muramatsu, Kentaro Kutsukake, Hiroaki Kudo, Noritaka Usami

Solar Energy Materials and Solar Cells 189 巻頁： 239-244 2019年1月

Application of weighted Voronoi diagrams to analyze nucleation sites of multicrystalline silicon ingots 査読有り

Tetsuro Muramatsu, Yusuke Hayama, Kentaro Kutsukake, Kensaku Maeda, Tetsuya Matsumoto, Hiroaki Kudo, Kozo Fujiwara, Noritaka Usami

Journal of Crystal Growth 499 巻頁： 62-66 2018年10月

Study on Incongruence between Binocular Images when Gazing at the Rim of a Column with Equiluminance Random Dots 査読有り

Shinya MOCHIDUKI, Reina WATANABE, Miyuki SUGANUMA, Hiroaki KUDO, Noboru OHNISHI, Mitsuho YAMADA

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences E101-A 巻 ( 6 ) 頁： 884-891 2018年6月

リアルタイム字幕作成におけるスライド中の発話された数式抽出査読有り

川口弘哲，竹内義則，松本哲也，工藤博章，大西　昇

電子情報通信学会論文誌 D J97-D 巻 ( 5 ) 頁： 1035-1043 2014年5月

A Novel Method for Exploring Patch-level Context to Improve Image Categorization Performance 査読有り

Shuang BAI, Tetsuya MATSUMOTO, Yoshinori TAKEUCHI, Hiroaki KUDO, and Noboru OHNISHI

IEEJ Transactions on Electronics, Information and Systems C 133 巻 ( 12 ) 頁： 2264-2274 2013年12月

Informative patches sampling for image classification by utilizing bottom-up and top-down information 査読有り

Shuang BAI, Tetsuya MATSUMOTO, Yoshinori TAKEUCHI, Hiroaki KUDO, and Noboru OHNISHI

Machine Vision and Applications 24 巻頁： 959-970 2013年7月

Incorporating Contextual Information into Bag-of-Visual-Words Framework for Effective Object Categorization 査読有り

Shuang BAI, Tetsuya MATSUMOTO, Yoshinori TAKEUCHI, Hiroaki KUDO, and Noboru OHNISHI

IEICE Transactions on Information and Systems E95-D 巻 ( 12 ) 頁： 3060-3068 2012年12月

Incorporating Top-Down Guidance for Extracting Informative Patches for Image Classification 査読有り

Shuang BAI, Tetsuya MATSUMOTO, Yoshinori TAKEUCHI, Hiroaki KUDO, and Noboru OHNISHI

IEICE Transactions on Information and Systems E95-D 巻 ( 3 ) 頁： 880-883 2012年3月

同一画像内における部分複写の検出—回転角と反転の推定精度の向上— 査読有り

富澤　圭，松本哲也，工藤博章，竹内義則，大西　昇

電気学会論文誌Ｃ　電子・情報・システム部門誌 131 巻 ( 12 ) 頁： 2121-2122 2011年12月

色覚異常のための代表色の分布に基づいた画像変換査読有り

大畑深志，工藤博章，松本哲也，竹内義則，大西　昇

電気学会論文誌Ｃ　電子・情報・システム部門誌 130 巻 ( 12 ) 頁： 2176-2177 2010年12月

スティックスリップ音の不快要因―ピーク周波数と包絡線― 査読有り

川口弘哲，松本哲也，竹内義則，工藤博章，大西　昇

電気学会論文誌Ｃ　電子・情報・システム部門誌 130 巻 ( 12 ) 頁： 2180-2181 2010年12月

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