Researcher Database

（Last updated : 2023-03-30 11:01:43）

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NOMURA Yasutoshi

Department / Course College of Science and Engineering Department of Civil and Environmental Engineering

Title / Position Professor

Academic background
1.	1994/04/01～1998/03/31	Department of Informatics │ Faculty of Informatics │ Kansai University │ Graduated
2.	1998/04/01～2003/03/31	Doctoral Program, Informatics Major │ Graduate School of Informatics │ Kansai University │ Completed

Business career
1.	2003/06/01 ～ 2003/08/31	Postdoctoral Research Fellow │ Organization for Research and Development of Innovative Science and Technology │ Kansai University
2.	2003/09/01 ～ 2007/03/31	Research Associate │ Graduate School of Engineering │ Graduate school of Kobe University
3.	2007/04/01 ～ 2008/03/31	Visiting Scholar │ Aerospace Engineering Sciences │ University of Colorado Boulder
4.	2008/04/01 ～ 2008/08/31	Assistant professor │ Organization for Advanced and Integrated Research │ Kobe University
5.	2009/04/01 ～ 2014/03/31	Assistant professor │ Department of Mechanical Engineering, College of Science and Engineering │ Ritsumeikan University
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Subject of research
1.	Research on productivity improvement using AI/ICT/IoT in the construction sector
2.	Utilization of AI/ICT/IoT in disaster prevention

Research summary
We are researching systems to monitor the health and safety of civil engineering, architectural, and mechanical structures by making full use of AI (Artificial Intelligence) and IoT (Internet of Things). AI is expected in many fields in recent years because it can automatically and precisely analyze large amounts of data that are difficult for humans to analyze, save labor, and find new knowledge. We are working daily to understand and utilize AI/IoT to develop "technologies to save labor and reduce manpower in construction work" and "technologies to obtain new knowledge that cannot be obtained by conventional statistical analysis methods. Forecasting Weather Information for Disaster Prevention Gr. Disaster prevention weather information released by the Japan Meteorological Agency does not cover all regions in Japan. In this course, we are developing a technology to accurately predict road surface temperature in winter on national roads (in order to optimize the timing of spraying anti-freeze) by installing IoT sensors on national roads, which is not possible using only disaster prevention weather information. We have also developed a simple rain gauge that monitors rainfall to accurately predict rainfall outside the forecast area of disaster prevention weather information. Data Assimilation Gr. Data assimilation is an attempt to build reliable models that reproduce actual phenomena well by incorporating information from actual observation data into physical simulation models. We are working on "detection of structural damage and identification of the extent of damage based on seismic response evaluation". **Applied Deep Learning Gr. 　Deep learning is a model of the information processing mechanism of the human brain, and has recently brought about a once-in-a-decade breakthrough in the fields of artificial intelligence and machine learning. In this research group, we are working on "detection and quantification of surface damage (cracks and corrosion) on railroad bridges," "annual prediction of the number of times pavement damage occurs on urban expressways," and "damage detection of machinery and equipment in various plants.

Present specialized field
Social systems engineering/ Safety system, Structural engineering/ Earthquake engineering/ Maintenance management engineering, Sensitivity Informatics/Soft Computing (Keyword：Damage Detection, Crack Detection, PZT sensor, Baseline-free Structural damage detection，Applied Soft Computing, Pattern recognition, Neural Network, Chaos)

Books
1.	2020/11/20	Structural Monitoring Technology │ (Co-authored)

Papers
1.	2022/09	Evaluation of Crack Propagation in Concrete Bridges from Vehicle-mounted Camera Images Using Deep Learning and Image Processing │ Frontiers in Built Environment │ 8,pp.1-14 (Co-authored)
2.	2022/03	Anomaly Detection for Solar Panel Joint Clamps Using Deep Learning Based Detection and Recognition Techniques │ Journal of the Society of Materials Science, Japan │ 71 (3),pp.289-295 (Co-authored)
3.	2022/03	Active Control for a Seismic Isolated Bridge to Prevent Rubber Bearings from Hardening against Earthquake exceeding a Design Seismic Motion │ Journal of structural engineering. A │ 68A,pp.202-212 (Co-authored)
4.	2022/03	Unsupervised Anomaly Detection in Rotating Machinery Using Variational Autoencoder │ Journal of the Society of Materials Science, Japan │ 71 (3),pp.296-302 (Co-authored)
5.	2020/09	A Review on Anomaly Detection Techniques Using Deep Learning │ Journal of the Society of Materials Science, Japan │ 69 (9),pp.650-656 (Sole-authored)
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Academic conference presentation
1.	2022/11	Detection of Corrosion Progress using deep learning and image processing (Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent (SCIS & ISIS2022))
2.	2015/09	Structural damage Quantification Based on Image Correlation and PSO (The 4th International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering)
3.	2015/06	Structural damage quantification Based on Recurrence plots and relaxation method of particle filter (The 7th ECCOMAS Thermatic Conference on Smart Structures and Materials (Smart2015))
4.	2014/11/17	Structural damage quantification using recurrence plot and merging particle filter (The 4th International Symposium on Life-Cycle Civil Engineering (IALCCE2014))
5.	2014/07/15	Early-Stage Damage Identification subjected to Chaotic Excitation (The 6th World Conference on Structural Control and Monitoring)
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