研究業績（論文、学会発表リスト）

【掲載論文(査読有)】

1. S. Yonezawa, K. Tajiri, S. Nakata, Y. Nagai, Z. Wang, K. Segawa, Y. Ando and Y. Maeno “Thermodynamic evidence for nematic superconductivity in CuxBi2Se3” Nature Phys. 13, 123-126 (2017).
2. K. Tajiri, R. Kawahara, and Y. Matsuo “Optimizing Edge-Cloud Cooperation for Machine Learning Accuracy Considering Transmission Latency and Bandwidth Congestion”, IEICE Transactions on Communications, Vol.E106-B, No.9, pp.827-836.
3. K. Tajiri, R. Kawahara “Optimization of Data and Model Transfer for Federated Learning to Manage Large-Scale Network”, IEEE Transactions on Network and Service Management (Early Access).

【学術論文、国際会議】

1. Kengo Tajiri, Yasuhiro Ikeda, Yuusuke Nakano, Keishiro Watanabe“Dividing Deep Learning Model for Continuous Anomaly Detection of Inconsistent ICT Systems” IEEE/IFIP NOMS 2020: 1-5
2. Kengo Tajiri, Tomoharu Iwata, Yoichi Matsuo, Keishiro Watanabe “Fault Detection of ICT systems with Deep Learning Model for Missing Data” IEEE/IFIP IM 2021: 445-451
3. Kengo Tajiri, Ryoichi Kawahara, Yoichi Matsuo“Optimizing Edge-Cloud Cooperation for Machine Learning Accuracy Considering Transmission Latency and Bandwidth Congestion” NOMS 2022: 1-9
4. Kengo Tajiri and Ryoichi Kawahara“Optimizing Data Distribution for Federated Learning under Bandwidth Constraint” IEEE ICC 2023: 3732–3737
5. Kengo Tajiri and Ryoichi Kawahara“Data Transfer for Balancing Model Convergence and Training Time in Federated Learning” IEEE GLOBECOM 2023
6. Kengo Tajiri and Ryoichi Kawahara“Optimizing IoT Data Collection for Federated Learning under Constraint of Wireless Bandwidth” IEEE ICC 2024
7. Daisuke Kikuta, Hiroki Ikeuchi, Kengo Tajiri, and Yuusuke Nakano, “RouteExplainer: An Explanation Framework for Vehicle Routing Problem” PAKDD 2024: 30-42
8. Daisuke Kikuta, Hiroki Ikeuchi, Kengo Tajiri, Yuta Toyama, Masaki Nakamura, and Yuusuke Nakano “Electric Vehicle Routing for Emergency Power Supply with Deep Reinforcement Learning” AAMAS 2024: 2336-2338
9. Kengo Tajiri and Takuya Iwamoto “Collision Detection for Robot Arm Assisted with Digital Twin System” IEEE/ASME AIM 2024: 524 – 529
10. Yoichi Matsuo and Kengo Tajiri “Adversarial Deep Domain Adaptation Method for Unsupervised Anomaly Detection” 6GNet 2024: 84 - 92
11. Yoshie Morita, Kengo Tajiri, and Yoichi Matsuo “Vehicle Traffic Density Estimation with Deep Learning for Predicting Communication Traffic Volume by Vehicle Communication Services” IEEE VTC2024-Fall: 1 – 6

【受賞歴】

1. 田尻兼悟, 米澤進吾, Zhiwei Wang, 瀬川耕治, 安藤陽一, 前野悦輝　「ドープされたトポロジカル絶縁体CuxBi2Se3 の超伝導状態における比熱」 第1回TMS領域研究会 (京都, 2015年12/11-13)　ポスター賞
2. 第27回情報ネットワーク研究会研究賞 (2020)