Publication

Christopher Aaron O’Hara and Takehisa Yairi, “Graph-based meta-learning for context-aware sensor management in nonlinear safety-critical environments”,Advanced Robotics, doi:10.1080/01691864.2024.2327083

M. Natsumeda and T. Yairi, “Consistent Pretext and Auxiliary Tasks With Relative Remaining Useful Life Estimation,” in IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2024.3353923.

W. Liu and T. Yairi, “A unifying view of multivariate state space models for soft sensors in industrial processes,” in IEEE Access, doi: 10.1109/ACCESS.2023.3344932.

二木 浩司, 矢入 健久,  「深層オートエンコーダと拡張カルマンフィルタの併用による物体画像列からの3次元回転運動推定」, システム制御情報学会論文誌,  特集「データと学習による制御」(採録済み, 掲載予定 ), 2023.

M. Natsumeda and T. Yairi, “Feature Selection with Partial Autoencoding for Zero-Sample Fault Diagnosis,” in IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2023.3286882.

X. Phong Nguyen, T. H. Tran, N. B. Pham, D. N. Do and T. Yairi, “Human Language Explanation for a Decision Making Agent via Automated Rationale Generation,” in IEEE Access, vol. 10, pp. 110727-110741, 2022, doi: 10.1109/ACCESS.2022.3214323.

Phong X. NGUYEN, Hung Q. CAO, Khang V. T. NGUYEN, Hung NGUYEN, Takehisa YAIRI, SeCAM: Tightly Accelerate the Image Explanation via Region-Based Segmentation, IEICE Transactions on Information and Systems, 2022, E105.D 巻, 8 号, p. 1401-1417

Ryosuke Matsuo, Shinya Yasuda, Taichi Kumagai, Natsuhiko Sato, Hiroshi Yoshida and Takehisa Yairi, Residual Reinforcement Learning for Logistics Cart Transportation,  Advanced Robotics, vol.36, no.8, pp404-421, 2022, doi:10.1080/01691864.2022.2046504 

K. Minoda, F. Schilling, V. Wüest, D. Floreano and T. Yairi, “VIODE: A Simulated Dataset to Address the Challenges of Visual-Inertial Odometry in Dynamic Environments,” in IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1343-1350, April 2021, doi: 10.1109/LRA.2021.3058073.

Hidekazu Karino; Takehisa Yairi; Tetsujiro Ninomiya; Koichi Hori, “Estimating Aerodynamic Coefficients from Uncertain Data of D-SEND Aircraft with Gaussian Process Regression”,   Transactions of the JSASS, Vol. 63, No. 6, November 2020 

Yoshiyuki Anzai, Takehisa Yairi, Naoya Takeishi, Yuichi Tsuda, Naoko Ogawa,  “Visual localization for asteroid touchdown operation based on local image features”, Astrodynamics 4, 149–161, 2020 

Takaaki Tagawa, Yukihiro Tadokoro and Takehisa Yairi, “Scalable Change Analysis and Representation Using Characteristic Function” , International Journal of Prognostics and Health Management:Vol 11 (1) 002,  pages: 13, 2020 

D. M. DeLatte, S. T. Crites, N. Guttenberg, E. J. Tasker and T. Yairi, “Segmentation Convolutional Neural Networks for Automatic Crater Detection on Mars,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. doi: 10.1109/JSTARS.2019.2918302 

D.M. DeLatte, S.T. Crites, N. Guttenberg, T. Yairi, Automated crater detection algorithms from a machine learning perspective in the convolutional neural network era, Advances in Space Research, Volume 64, Issue 8, 2019, Pages 1615-1628,  ISSN 0273-1177,  https://doi.org/10.1016/j.asr.2019.07.017. 

Samir Khan, Chun Fui Liew, Takehisa Yairi, Richard McWilliam, Unsupervised anomaly detection in unmanned aerial vehicles, Applied Soft Computing,  Volume 83, 2019,105650, ISSN 1568-4946,  https://doi.org/10.1016/j.asoc.2019.105650. 

矢入 健久, “機械学習とシステム同定：動的システム学習研究の動向, 計測と制御”, 2019, 58 巻, 3 号, p. 176-181, 

矢入健久, “典型例で眺める機械学習の様々なタスク”,  ガスタービン学会誌, 2019年9月, Vol.47, No.5, 特集：エネルギー産業への機械学習の応用, p.282-287, 2019 

Koji Minoda, Takehisa Yairi, and Koichi Hori, “Data-driven health monitoring of high dimensional time-varying systems by tracking dynamic modes,” Asia Pacific Conference of the Prognostics and Health Management Society (PHMAP), Beijing, China,  July 2019.

Riku Sasaki, Naoya Takeishi, Takehisa Yairi, and Koichi Hori,  “Neural Gray-Box Identification of Nonlinear Partial Differential Equations”, 16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, August 26-30, 2019 

Takehisa Yairi, Yusuke Fukushima, Chun Fui Liew, Yuki Sakai, Yukihito Yamaguchi, “A Data-Driven Approach to Anomaly Detection and Health Monitoring for Artificial Satellites”, Proceedings of the Second World Congress on Condition Monitoring (WCCM), 02-05 December 2019 – Singapore, ISBN: 978-981-11-0744-3

Samir Khan and Takehisa Yairi, “A review on the application of deep learning in system health management”, Mechanical Systems and Signal Processing , Vol. 107, pp.241-265, 2018 

Ryo Sakagami,  Naoya Takeishi, Takehisa Yairi and Koichi Hori, “Visualization Methods for Spacecraft Telemetry Data Using Change-point Detection and Clustering”,  Aerospace Technology Japan, Vol. 17, No. 2 , p. 244-252, 2019 

D. M. DeLatte, S. T. Crites, N. Guttenberg , E. J. Tasker, T. Yairi, “Experiments in Segmenting Mars Craters using Convolutional Neural Networks”, International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS 2018), Madrid, June 4-6 2018 

Kentaro Abe, Samir Khan, Takehisa Yairi and Chun Fui Liew, “Towards Anomaly detection using Variational Long Short-term Memory Autoencoders for System Health Monitoring Control”,Joint Workshop on Deep (or Machine) Learning for Safety-Critical Applications in Engineering, Stockholm, July, 2018. 

Rem Hida, Naoya Takeishi, Takehisa Yairi, Koichi Hori, “Dynamic and Static Topic Model for Analyzing Time-Series Document Collections”, 56th Annual Meeting of the Association for Computational Linguistics (ACL), 15-20 July 2018   Melbourne 

Naoya Takeishi, Takehisa Yairi, Yoshinobu Kawahara, “Factorially Switching Dynamic Mode Decomposition for Koopman Analysis of Time-Variant Systems”, Conference: 2018 IEEE Conference on Decision and Control (CDC), Miami Beach, FL, USA, Dec 17-19, 2018 

H. Karino, T. Yairi, T. Ninomiya and K. Hori, “Estimating Aerodynamic Characteristics of D-SEND Aircraft with Gaussian Process Regression”,  SICE Annual Conference, Nara, Japan, Sept. 2018 

Takehisa Yairi, Naoya Takeishi, Tetsuo Oda, Yuta Nakajima, Naoki Nishimura, Noboru Takata , “A Data-driven Health Monitoring Method for Satellite Housekeeping Data based on Probabilistic Clustering and Dimensionality Reduction”, IEEE Transactions on Aerospace and Electronic Systems, Vol.53 (3), pp.1-18, June 2017. 

Naoya Takeishi, Yoshinobu Kawahara, and Takehisa Yairi,  “Subspace dynamic mode decomposition for stochastic Koopman analysis”,  Physical Review E, vol. 96, pp. 033310, 18 September 2017, DOI: 10.1103/PhysRevE.96.033310 

Naoya Takeishi and Takehisa Yairi, “Visual Monocular Localization, Mapping, and Motion Estimation of a Rotating Small Celestial Body,” Journal of Robotics and Mechatronics, vol. 29, no. 5, pp. 856-863, 2017 

Takehisa Yairi,”Data-driven Health Monitoring for Artificial Satellites based on Clustering and Dimensionality Reduction”,Korea-China-Japan Joint Workshop on Prognostics and Health Management, 24 February, 2017 Jeju National University Ara Convention Hall 

Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi, “Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition”, Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017

Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi,  “Sparse Nonnegative Dynamic Mode Decomposition”, 2017 IEEE International Conference on Image Processing, Beijing, Sept. 2017.

Naoya Takeishi, Yoshinobu Kawahara, Yasuo Tabei, Takehisa Yairi, “Bayesian Dynamic Mode Decomposition”,  the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne, August 2017. 

Shinya Fujita, Takehisa Yairiy, Koichi Hori, Shuhei Komatsu, “An Active Beacon-Based Target Localization Method for Unmanned Aerial Vehicles with Particle Filter”, 2017 20th International Conference on Information Fusion (Fusion), Xi’an, China, July 2017 

Samir KHAN and Takeshisa YAIRI , “Perspectives on Using Deep Learning for System Health Management”, Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP-2017), Jeju, Korea, July 2017 

Samir KHAN and Takeshisa YAIRI , “Towards a Cloud-based Machine Learning for Health Monitoring and Fault Diagnosis”, Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP-2017), Jeju, Korea, July 2017 

Riku SASAKI, Naoya TAKEISHI, Takehisa YAIRI, Koichi HORI, Kazunari IDE and Hiroyoshi KUBO, “A health monitoring method for wind power generators with hidden Markov and probabilistic principal components analysis models”, Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP-2017), Jeju, Korea, July 2017 

Taichi KIATAMURA, Naoya TAKEISHI, Takehisa YAIRI and Koichi HORI , “Abnormal sound detection for rotary parts in noisy environment by one-class SVM and non-negative matrix factorization”, Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP-2017), Jeju, Korea, July 2017 

Chun Fui Liew and Takehisa Yairi, “Robust Face Alignment with Random Forest: Analysis of Initialization, Landmarks Regression, and Shape Regularization Methods”,  IEICE Transaction on Information and Systems,  Volume and Number: Vol.E99-D,No.2,pp. 496-504, Feb. 2016 

Naoya Takeishi, Takehisa Yairi, “Dynamic Grouped Mixture Models for Intermittent Multivariate Sensor Data”,  The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016.  Apr.19-22, Auckland, New Zealand. 

Kosuke Akimoto , Naoya Takeishi , Takehisa Yairi, Koichi Hori, Naoki Nishimura, Noboru Takata,  “Tree-based Nonparametric Prediction of Normal Sensor Measurement Range Using Temporal Information”, S-9b-2, International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS 2016), Beijing, June 20-22 2016 

Naoya Takeishi and Takehisa Yairi, “Dynamic Visual Simultaneous Localization and Mapping for Asteroid Exploration”, International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS 2016), Beijing, June 20-22 2016 

Takehide HIRATA, Yoshinobu KAWAHARA, Takehisa YAIRI, Kazuya ASANO, Ichiro MAEDA, Toshihiro SASAKI, Kazuo MACHIDA , “New monitoring technique for detecting buckling in the continuous annealing line using canonical correlation analysis”,  SICE Journal of Control, Measurement, and System Integration,  Vol. 8, No. 3, p.214-220, (2015)  

桑原絢一、酒匂信匡、矢入健久、”次元削減を用いた超小型衛星の画像劣化発生条件推定” ,日本航空宇宙学会論文集, Vol. 63 (2015) No. 4 p. 119-128 , DOI: 10.2322/jjsass.63.119 

Liew Chun Fui, Takehisa Yairi, “Facial Expression Recognition and Analysis: A Comparison Study of Feature Descriptors”,  IPSJ Transactions on Computer Vision and Applications, Vol. 7 (2015) pp. 104-120 

Naoya Takeishi,Akira Tanimoto,Takehisa Yairi,Yuichi Tsuda,Fuyuto Terui,Naoko Ogawa,Yuya Mimasu,”Evaluation of Interest-Region Detectors and Descriptors for Automatic Landmark Tracking on Asteroids”, TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 58(1), 45-53, 2015  

矢入健久, “衛星の状態監視システムのつくりかた －過去のデータに基づく異常検知－”, 情報処理学会誌,Vol.56, No.8, pp.777-780, 2015年8月 

Naoya Takeishi; Takehisa Yairi; Yuichi Tsuda; Fuyuto Terui; Naoko Ogawa; Yuya Mimasu, “Simultaneous estimation of shape and motion of an asteroid for automatic navigation”, Proceedings – IEEE International Conference on Robotics and Automation. 2015;2015-June(June):2861-2866. 

C. F. Liew and T. Yairi, “Designing a compact hexacopter with gimballed lidar and powerful onboard Linux computer,” IEEE International Conference on Information and Automation (ICIA),   pp.2523-2528, 8-10 Aug. 2015 

Sakurada, Mayu; Yairi, Takehisa; Nakajima, Yuta; Nishimura, Naoki; Parikh, Devi, “Semantic classification of spacecraft’s status: integrating system intelligence and human knowledge,” Semantic Computing (ICSC), 2015 IEEE International Conference on , vol., no., pp.81-84, 7-9 Feb. 2015,  Anaheim, CA, USA