KEIO RESEARCHERS INFORMATION SYSTEM

Career 【 Display / hide 】

Career 【 Display / hide 】

• 1994.04

  -

  1996.03

  日本鋼管株式会社情報システム部情報技術室

• 1996.04

  -

  1998.03

  慶應義塾大学メディアネット本部ネットワークテクノロジーセンター（仮称）

• 1998.04

  -

  2001.03

  慶應義塾大学理工学部 ,助手

• 2000.04

  -

  2001.03

  慶應義塾大学理工学部1年 ,クラス担任

• 2000.04

  -

  2003.03

  慶應義塾大学理工学部システムデザイン工学科 ,執行部補助

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Academic Background 【 Display / hide 】

Academic Background 【 Display / hide 】

• 1989.03

  Keio University, Faculty of Engineering, 計測工学科

  University, Graduated

• 1991.03

  Keio University, Graduate School, Division of Science and Engineering, Computer Science

  Graduate School, Completed, Master's course

• 1994.03

  Keio University, Graduate School, Division of Science and Engineering, Computer Science

  Graduate School, Withdrawal after completion of doctoral course requirements, Doctoral course

Academic Degrees 【 Display / hide 】

Academic Degrees 【 Display / hide 】

• 工学, Keio University, 1995.03

Licenses and Qualifications 【 Display / hide 】

Licenses and Qualifications 【 Display / hide 】

• 第二種電気工事士免状, 2020.02

Research Areas 【 Display / hide 】

Research Areas 【 Display / hide 】

• Informatics / Mechanics and mechatronics (Intelligent Mechanics and Machine System)

• Informatics / Robotics and intelligent system (Intelligent Mechanics and Machine System)

• Manufacturing Technology (Mechanical Engineering, Electrical and Electronic Engineering, Chemical Engineering) / Communication and network engineering (Information and Communication Engineering)

• Manufacturing Technology (Mechanical Engineering, Electrical and Electronic Engineering, Chemical Engineering) / Control and system engineering (System Engineering)

• Informatics / Theory of informatics (Computer Science)

Books 【 Display / hide 】

Books 【 Display / hide 】

• Mn'M Workbook 3: Future Urban Intensities

  YAKOH TAKAHIRO, flick studio, 2014.03

  Scope: 42-47

• HUMAN – COMPUTER SYSTEMS INTERACTION: BACKGROUNDS AND APPLICATIONS 2 -Advances in Intelligent and Soft Computing-

  Sato Tomoya, Sakaino Syo, YAKOH TAKAHIRO, Springer Berlin / Heidelberg, 2012

  Scope: 91-107

• Remote and Telerobotics

  Shinichi Hamasaki, YAKOH TAKAHIRO, InTech, 2010.05

  Scope: 17-32

  　View Summary

  遠隔操作の操作性が通信遅延により低下してしまう問題点を、クロスモーダルな装飾により解消できる可能性があることを提案し、実験を通して検証している。

• Engineering & Neuro-Psychoanalysis Forum Book

  Charlotte Roesener, Tobias Deutsch, Roland Lang Brit Muller, and YAKOH TAKAHIRO, Springer-Verlag, 2008.10

  Scope: 324-338

  　View Summary

  工学と脳神経科学の境界領域の開拓を目指したフォーラムが査読論文を編纂したものであり、我々は脳の認知行動のモデル化や、シミュレータ構築、挙動に関する考察を記している。

• 数理工学基礎シリーズ「コンピュータの数理」

  矢向高弘、村上俊之、大西公平, 朝倉書店, 2000.10

  　View Summary

  コンピュータのハードウェアの基礎から、2進数による数値計算法、C言語によるプログラミング、計算誤差にまで言及した工学者向けの教科書である。

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Papers 【 Display / hide 】

Papers 【 Display / hide 】

• Real-Time Survey of Vaccine Safety of the mRNA-1273 SARS-CoV-2 Vaccine in Workplace Vaccination at Keio University

  Okumura K., Hara A., Inada I., Sugiyama D., Hoshino T., Yakoh T., Yokoyama H., Urushihara H.

  Vaccines （Vaccines)  10 （ 9 ）  2022.09

  ISSN  2076-393X

  　View Summary

  The mRNA-1273 Moderna COVID-19 vaccine was introduced to combat the COVID-19 global pandemic in 2020. Although the safety of the vaccine has been investigated worldwide, real-world safety data is scarce in Japan. An online, real-time survey of adverse events following immunization (AEFIs) with mRNA-1273 was conducted in the setting of a workplace vaccination program at the School of Pharmacy, Keio University from 26 June 2021, to 11 June 2022. Participants were requested to take four surveys during a seven-day follow-up period after each of the first, second, and third booster doses. The maximum number of responses, from 301 respondents, was obtained on day 0 (vaccination date) for the first dose. 98% of respondents reported local and systemic AEFIs for the second dose on day 1. No noticeable difference in local reactions was seen among the three doses. Females reported more AEFIs than males, and the young group (18–29 years) reported a higher rate than the middle age group (≥30 years) after the first dose. Age and gender differences in rates decreased at the second and third doses. This survey confirmed that the safety profile of mRNA-1273 in a real-world setting was similar to that derived from the clinical trials, and that the agent was well-tolerated.

  • Access to Document (DOI)

• Re-shooting Resistant Blind Watermarking Framework Based on Feature Separation With Gaussian Mixture Model

  Yakoh T., Oi M.

  IEEJ Transactions on Electrical and Electronic Engineering （IEEJ Transactions on Electrical and Electronic Engineering)  17 （ 4 ） 556 - 565 2022.04

  ISSN  19314973

  　View Summary

  Watermarking is a technology to embed digital data into a digital image. In particular, blind watermarking is expected to ensure the authenticity and/or accountability of digital images without compromising the visual impression of viewing. A re-shooting resistant blind watermarking method is proposed in this study. This method uses the features of a given image to select regions, which are modified to embed digital data. As the proposed embedding process preserves these image features, no markers are required to extract the embedded data from the watermarked image. Furthermore, based on the estimation of the watermarked image region, the method crops the re-shot image before the extraction. Therefore, the proposed method works, even if the re-shot image includes the outer area surrounding the watermarked image region. Moreover, the proposed method compensates for the geometric transformation of the re-shot image based on the definition of the coordinate system, which is based on the distribution of the image feature points. Therefore, the proposed method works when the re-shot image is taken diagonally in front of the watermarked image. Experimental results validated that the proposed method can extract the embedded digital data from re-shot images of a watermarked image under considerable conditions of re-shooting. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

  • Access to Document (DOI)

• An NLP-Inspired Data Augmentation Method for Adverse Event Prediction Using an Imbalanced Healthcare Dataset

  Ishikawa T., Yakoh T., Urushihara H.

  IEEE Access （IEEE Access)  10   81166 - 81176 2022

  　View Summary

  This paper proposes a data augmentation method for imbalanced healthcare datasets. This method was inspired by a data augmentation method in natural language processing (NLP) that generates synthetic sentences for training by replacing some words with similar words. The proposed method generates synthetic patient records by replacing patient backgrounds with similar backgrounds. In this paper, the cosine similarity of the distributed representations was used as the similarity metric between patient backgrounds. The distributed representations of the patient backgrounds were generated by the skip-gram model. To confirm the performance improvement with the proposed data augmentation method, the prediction performance of adverse events (AEs) caused by drug administration was experimentally evaluated on a real-world medical dataset with 1,510,137 records. The combination of the proposed data augmentation method and a conventional undersampling method resulted in an 80.0% improvement in accuracy and a 40.0% improvement in the precision and F1-score. The multifaceted evaluation demonstrated that the proposed method is effective, especially for predicting AEs with positive ratios ranging from 1.0% to 2.1%, which are difficult to predict with conventional machine learning methods but should be predictable in the medical field.

  • Access to Document (DOI)

• Saliency Prediction based on Object Recognition and Gaze Analysis

  Ishikawa T., Yakoh T.

  IEEJ Transactions on Electronics, Information and Systems （IEEJ Transactions on Electronics, Information and Systems)  141 （ 1 ） 76 - 84 2021.01

  ISSN  03854221

  　View Summary

  Predicting the human visual attention in an image is called saliency prediction, and is an active research area in the field of neuroscience and computer vision. Early works on saliency prediction was performed by using low-level features. In recent years, convolutional neural networks (CNN) have been adapted for saliency prediction and achieved the state-of-the-art performance. However, the eye-gaze depends on the personality of each viewer and conventional methods did not take into account such individual properties of the viewer. Therefore, this paper proposes a novel saliency prediction method considering the influence of eye-gaze. Assuming that personality can be expressed as the degree of attention to an object, our proposed method considers the personality by learning which objects are likely to be perceived by each viewer, and weighting the universal saliency map with the generated mask based on the object detection results. The experimental results show that the proposed universal saliency map achieves higher accuracy than conventional methods on the public dataset, and the proposed weighted saliency map can reflect the variation of the eye-gaze influences among viewers. (1)

  • Access to Document (DOI)

• Saliency prediction based on object recognition and gaze analysis

  Ishikawa T., Yakoh T.

  Electronics and Communications in Japan （Electronics and Communications in Japan)   2021

  ISSN  19429533

  　View Summary

  Predicting the human visual attention in an image is called saliency prediction and is an active research area in the field of neuroscience and computer vision. Early works on saliency prediction was performed by using low-level features. In recent years, convolutional neural networks have been adapted for saliency prediction and achieved the state-of-the-art performance. However, the eye-gaze depends on the personality of each viewer and conventional methods did not take into account such individual properties of the viewer. Therefore, this paper proposes a novel saliency prediction method considering the influence of eye-gaze. Assuming that personality can be expressed as the degree of attention to an object, our proposed method considers the personality by learning which objects are likely to be perceived by each viewer and weighting the universal saliency map with the generated mask based on the object detection results. The experimental results show that the proposed universal saliency map achieves higher accuracy than conventional methods on the public dataset, and the proposed weighted saliency map can reflect the variation of the eye-gaze influences among viewers.

  • Access to Document (DOI)

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Papers, etc., Registered in KOARA 【 Display / hide 】

Papers, etc., Registered in KOARA 【 Display / hide 】

• AICカンファレンス2023実行委員長挨拶

  Yakō, Takahiro

  AICカンファレンス予稿集 （慶應義塾大学AI・高度プログラミングコンソーシアム)     1 - 1 2023

• Three dimensional visual-auditory-tactile feedback for teleoperation

  Yako, Takahiro

  科学研究費補助金研究成果報告書 2015

• Performance evaluation of decorators for a teleoperation with visual-auditory-tactile feedbacks

  Yako, Takahiro

  科学研究費補助金研究成果報告書 2010

Reviews, Commentaries, etc. 【 Display / hide 】

Reviews, Commentaries, etc. 【 Display / hide 】

• 学界情報　国際会議レポート

  YAKOH TAKAHIRO

  電気学会論文誌D 132 ( 7 ) PNL7-4 2012.07

  Article, review, commentary, editorial, etc. (scientific journal), Single Work

• 分散リアルタイムネットワーク

  YAKOH TAKAHIRO, HIROAKI NISHI

  日本機械学会誌 106 ( 1021 ) 36 - 37 2003.12

  Article, review, commentary, editorial, etc. (scientific journal), Joint Work

Presentations 【 Display / hide 】

Presentations 【 Display / hide 】

• Uncertainty Principle in Real-Time Communication - Battle of Quickness against Correctness in NBCS -

  YAKOH TAKAHIRO

  12th International Workshop on Advanced Motion Control (Sarajevo, Bosnia and Herzegovina) , 

  2012.03

  , 

  Oral presentation (invited, special)

• コンピュータネットワークを用いた広域制御

  矢向高弘

  日本機械学会　運動と制御研究会（MOVIC）, 

  1999.11

  　View Summary

  コンピュータネットワークによる実時間通信の可能性を示すとともに、広域制御の具体的な応用例を紹介した。

Intellectual Property Rights, etc. 【 Display / hide 】

Intellectual Property Rights, etc. 【 Display / hide 】

• 情報処理システム，制御装置，検出装置，情報処理方法，制御方法，検出方法，及びプログラム

  Date applied： 特許出願2016-230388  2016.11 

  Patent, Single

• マスタスレーブ装置，マスタ装置，スレーブ装置，制御方法及びコンピュータプログラム

  Date applied： 特願 2006-302787  2006.11 

  Date announced： 特開2008-119757  2008.05 

  Patent, Joint

• マルチメディア通信装置

  Date applied： 特願2005-216804  2005.07 

  Date announced： 特開2007-036650  2007.02 

  Patent, Single

• 電気レオロジー素子およびこれを備えた電気レオロジーデバイス

  Date applied： 特願2002-127489  2002.04 

  Date announced： 特開2003-322196  2003.11 

  Patent, Joint

• フリクションフリードライブシステム

  Date applied： 特願2000-075609  2000.03 

  Date announced： 特開2001-263444  2001.09 

  Patent, Joint

Awards 【 Display / hide 】

Awards 【 Display / hide 】

• ファナックFAロボット財団論文賞

  Takahiro Yakoh, Masanao Ito, Kouhei Ohnishi, 2003.03, 財団法人ファナックFAロボット財団, 仮想力伝搬に基づく協調マニピュレータの分解制御

  Type of Award： Award from publisher, newspaper, foundation, etc.

Courses Taught 【 Display / hide 】

Courses Taught 【 Display / hide 】

• ADVANCED RESEARCH ON SYSTEM DESIGN AND MANAGEMENT

  2024

• DATA SCIENCE PROJECT

  2024

• TRUSTWORTHY INTELLIGENT SYSTEMS

  2024

• RESEARCH ON SYSTEM DESIGN AND MANAGEMENT

  2024

• RESEARCH ON PROJECT DESIGN AND MANAGEMENT

  2024

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Courses Previously Taught 【 Display / hide 】

Courses Previously Taught 【 Display / hide 】

• 情報学基礎

  Keio University

  2015.04

  -

  2016.03

  , 

  Spring Semester, Lecture, Within own ｆaculty, 1h, 250people

• 実時間システム設計論

  Keio University

  2015.04

  -

  2016.03

  , 

  Autumn Semester, Lecture, Within own ｆaculty, 1h, 12people

• システムデザイン工学演習

  Keio University

  2015.04

  -

  2016.03

  , 

  Autumn Semester, Seminar, Within own ｆaculty, 2h, 12people

• 工学材料

  Keio University

  2015.04

  -

  2016.03

  , 

  Autumn Semester, Lecture, Lecturer outside of Keio, 1h, 90people

• 分散処理システム

  Keio University

  2015.04

  -

  2016.03

  , 

  Autumn Semester, Lecture, Within own ｆaculty, 1h, 50people

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Memberships in Academic Societies 【 Display / hide 】

Memberships in Academic Societies 【 Display / hide 】

• 8th France-Japan Congress on Mechatronics 2010, 

  2009.08

  -

  2010.11

• IEEE International Workshop on Factory Communication Systems 2010, 

  2009.07

  -

  2010.05

• IEEE International Conference on Industrial Informatics 2010, 

  2008.12

  -

  2010.07

• IEEE International Workshop on Factory Communication Systems 2008, 

  2007.08

  -

  2008

• IEEE International Workshop on Factory Communication Systems 2006, 

  2005.12

  -

  2006

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Committee Experiences 【 Display / hide 】

Committee Experiences 【 Display / hide 】

• 2017.09

  -

  2018.06

  Technical Program Committee, IEEE International Workshop on Factory Communication Systems 2018

• 2016.10

  -

  2017.06

  Program Committee, IEEE International Workshop on Factory Communication Systems 2017

• 2015.10

  -

  2016.05

  Program Committee, IEEE International Workshop on Factory Communication Systems 2016

• 2014.11

  -

  2015.05

  Program Committee, IEEE International Workshop on Factory Communication Systems 2015

• 2013.07

  -

  2014.05

  Technical Program Committee, IEEE International Workshop on Factory Communication Systems 2014

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