Staff Introduction

: 宮島洋文 Hirofumi MIYAJIMA

- Email
miyajimanagasaki-u.ac.jp

- Position / Degree Institute of Integrated Science and Technology, Associate Professor
School of Information and Data Sciences, Associate Professor
Doctor (Engineering)

- Specialized Field Machine Learning, Fuzzy Inference Method, Simple Secret Calculation, Parallel Processing

- External Links researchmap

CV

Mar.2010	Hokkaido University, School of Engineering, Graduated
Mar.2012	Osaka University, Graduate School of Information Science and Technology, Master Course, Completed
Sep.2013	Osaka University, Graduate School of Information Science and Technology, Doctor Course, Left School
Aug.2015	Nagasaki University, Graduate School of Biomedical Sciences, Researcher
Mar.2016	Kagoshima University, Graduate School of Science and Engineering, Doctor Course (Early Completion)
Jul.2016	Nagasaki University, Graduate School of Biomedical Sciences, Assistant Professor
Apr.2017	Okayama University of Science, Faculty of Informatics, Lecturer

Research Activities

A study on fast and secure concealment computation for edge computing

Society 5.0 aims to integrate cyber space with real space. In these days, with the huge amount of information, the real space is also shifting from the usual cloud system to the edge computing system for the Internet of Things (IoT). Then, what is machine learning suitable for edge computing? In this study, purpose of our research is to propose a system that executes learning while dividing data obtained from “things” into multiple servers. In other words, we propose and implement a system based on the idea of “division (distribution) of learning data + parallel computation = fast and secure learning method (suitable for edge computing)”.

Study on machine learning of fuzzy system

Much research has been done on machine learning that supports artificial intelligence (AI). In particular, neural networks (NN) such as CNN have received much attention from theory to application. NN can easily answer “what to do” to many problems, but it is difficult to answer “why do that”. This is the so-called “black box problem” of AI. The fuzzy system is one of the systems that realizes machine learning and answers such problems. We are studying new learning methods for this system. For example, consider the following problem in which a moving object arrives at a destination avoiding obstacles. The usual machine learning method gives a route for the answer, but it is difficult to answer “why do that”. The fuzzy system gives the following qualitative rules as a result of learning.

Educational Activities

Class

School of Information and Data Sciences：First-year Seminar, CalculusⅢ, Graph Theory and Optimization, Cognitive System A/B, Network Security, Research Project

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