Kyushu University Academic Staff Educational and Research Activities Database
Researcher information (To researchers) Need Help? How to update
Hirofumi Amano Last modified date:2019.12.20



Graduate School
Undergraduate School


E-Mail
Phone
092-802-2646
Fax
092-802-2642
Academic Degree
Doctor of Engineering
Field of Specialization
Parallel Processing, Distributed Processing
Outline Activities
Amano's research interests include:
- single-level storage subsystems utilizing next-generation NVRAM
- safe off-site backup methods utilizing storage virtualization techniques;
- integration of modern electronic authentication techniques and conventional computer center service work flows.

With the help of the above research experiences, he gives a graduate-level course, "Advanced Program Design" featuring object-oriented software development and UML. In the undergraduate program, he gives a class of "Computer Systems II", discussing parallel and distributed processing.
Research
Research Interests
  • Studies on Augmented Fairness Scheduling for General-Purpose Computing Environments
    keyword : Threads, Scheduling, Mult9-Core CPUs, Virtual Machines
    2016.04.
  • Studies on Parallel Stream Data Distribution Mechanisms for Coupling Multiple Parallel Programs on Overlay Clouds
    keyword : Overlay Cloud, Inter-Cloud
    2015.10.
  • Studies on Single-Level Storage Subsystem Adaptable to Next-Generation NVRAM
    keyword : Single-Level Storage
    2015.09.
  • integration of modern electronic authentication techniques and conventional comuter center service tasks
    keyword : electronic authentication, computer center service tasks
    2011.04~2018.03.
  • Studies on Safe Off-Site Backup Methods Utilizing Storage Virtualization Techniques
    keyword : storage virtualization, off-site backup
    2010.04.
  • Job Submission Front-End for Grid Computing
    keyword : parallel processing, grid computing
    2000.04~2010.03job submission front-end for grid computing.
  • Remote Parallel Pipe Mechanism for Distributed Parallel Computing
    keyword : parallel processing, grid computing
    1998.04~2010.03remote parallel pipe mechanism for distributed parallel computing.
  • job scheduling for parallel supercomputers
    keyword : parallel processing, job scheduling
    1997.04~2002.03job scheduling for parallel supercomputers.
  • parallel input/output system for utilizing multiple disk drives
    keyword : parallel processing, parallel file system
    1994.04~1996.03parallel input/output system for utilizing multiple disk drives.
  • parallel database programming language for database applications
    keyword : parallel processing, parallel programming language, database, database programming language
    1991.04~2003.03parallel database programming language for database applications.
  • Pseudonatural Language Interface for Relational Databases
    keyword : Relational Database, User Interface
    1986.04~1993.03.
Academic Activities
Papers
1. Samih M. Mostafa, Hirofumi Amano, Effect of Clustering Data in Improving Machine Learning Model Accuracy, Journal of Theoretical and Applied Information Technology, Vol. 97, No. 21, 2973-2981, 2019.11, Supervised machine learning algorithms consider the relationship between dependent and independent variables rather than the relationship between the instances. Machine learning algorithms try to learn the relationship between the input and output from the historical data in order to attain precise predictions about unseen future. Conventional foretelling algorithms are usually based on a model learned and trained from historical data. The instances in the historical data may vary in its characteristics. The variation may be a result of difference in case's pertinence degree to some cases compared to others. However, the problem with such machine learning algorithms is their dealing with the whole data without considering this variation. This paper presents a novel technique to the trained model to improve the prediction accuracy. The proposed method clusters the data using K-means clustering algorithm, and then applies the prediction algorithm to every cluster. The value of K which gives the highest accuracy is selected. The authors performed comparative study of the proposed technique and popular prediction methods namely Linear Regression, Ridge, Lasso, and Elastic. On analysing on five datasets with different sizes and different number of clusters, it was observed that the accuracy of the proposed technique is better from the point of view of Root Mean Square Error (RMSE), and coefficient of determination (R2)..
2. Samih M. Mostafa, Hirofumi Amano, An Adjustable Round Robin Scheduling Algorithm in Interactive Systems, Information Engineering Express, Vol. 5, No. 1, 11-18, 2019.05, CPU scheduling is considered as the basic job within the operating system. Scheduling criteria including waiting time, context switches and others have been suggested for comparing CPU scheduling algorithms. In this paper, a modified version of Round Robin algorithm is introduced as an attempt to combine the advantageous of low scheduling overhead of Round Robin and favor short process to minimize the average waiting time and number of context switches of running processes in interactive (time-shared) systems. A threshold is considered to determine whether the running process will be interrupted because of the expiration of its time slice specified by the Round Robin policy or will continue execution until termination. Derived results show that the suggested modification minimizes the average waiting time and number of context switches compared to Round Robin algorithm..
3. Samih M. Mostafa, Hirofumi Amano, Shigeru Kusakabe, Fairness and High Performance for Tasks in General Purpose Multicore Systems, International Journal of Research and Reviews in Applied Sciences, Volume 29, Issue 3, 74-86, 2016.12.
4. Samih Mohammed Mostafa, Shigeru Kusakabe, Hirofumi Amano, Fairness Scheduler for Multithreaded Programs in Virtual Machine Environment, The Fourth International Japan-Egypt Conference on Electronics, Communications and Computers, 2016.05.
5. Hirofumi Amano, Yuki Dohi, Hiromune Ikeda, A Safe and Versatile Storage Server for Off-Site Backup, International Journal of Computer & Information Science (IJCIS), Volume 16, No. 1, 1-11, 2015.03, [URL], This paper makes a proposal for a safe and versatile storage server for off-site backup based on secret sharing scheme and storage virtualization. This approach can save important data even after a disaster which destroys the primary data and some of its backup copies. It also prevents a secret from leaking to others. The design of the prototype utilizing the (3, 5)-threshold secret sharing scheme and iSCSI is described, and preliminary experiment is reported. Future enhancement plans are also discussed..
6. Hirofumi Amano, Yuki Dohi, Hiromune Ikeda, An Approach to Safe Off-Site Backup Utilizing Secret Sharing Scheme and Storage Virtualization, Proceedings of the IIAI International Conference on Advanced Information Technologies 2013 (IIAI AIT 2013), 2013.11, This paper makes a proposal for a safe and versatile off-site backup system based on secret sharing scheme and storage virtualization. This approach can save important data even after a disaster which destroys the primary data and some of its backup data. It also prevents a secret from leaking to others. The design of the prototype based on the (3, 5)-threshold secret sharing scheme and iSCSI is described, and preliminary experiment is reported..
7. Eisaku Sakane, Kento Aida, Manabu Higashida, Taizo Kobayashi, Hirofumi Amano, Mutsumi Aoyagi, Grid Operational Supports for Middleware Deployment and User Administration, The International Symposium on Grids and Clouds and the Open Grid Forum - ISGC2011, 2011.03, [URL].
8. An Operation of the Inter-University Grid Infrastructure.
9. A. Makinouchi, H. Amano, T. Tsuji, K. Kaneko, Issues on Parallel Processing of Object Databases, Nontraditional Database Systems, pp. 231-248, published by Taylor & Francis, Inc., New York, 2002.08.
Presentations
1. Hirofumi Amano, Yuki Dohi, Hiromune Ikeda, An Approach to Safe Off-Site Backup Utilizing Secret Sharing Scheme and Storage Virtualization, IIAI International Conference on Advanced Information Technologies 2013 (IIAI AIT 2013), 2013.11, This paper makes a proposal for a safe and versatile off-site backup system based on secret sharing scheme and storage virtualization. This approach can save important data even after a disaster which destroys the primary data and some of its backup data. It also prevents a secret from leaking to others. The design of the prototype based on the (3, 5)-threshold secret sharing scheme and iSCSI is described, and preliminary experiment is reported..
Membership in Academic Society
  • IEEE (The Institute of Electrical and Electronics Engineers, Inc.)
Educational
Educational Activities
Amano teaches "Advanced Program Design" for graduate students in the Department of Advanced Information Technology.
He also teaches "Introductory Seminar on Electrical Engineering and Computer Science I" for freshmen (1st year) and "Computer Systems II A" and "Computer Systems II B"for juniors (3rd year) in the Department of Electrical Engineering and Computer Science.