IEICE ESS Fundamentals Review
Online ISSN : 1882-0875
ISSN-L : 1882-0875
Volume 16, Issue 2
Displaying 1-24 of 24 articles from this issue
Table of Contents
Origins of Technology
Proposed by Editorial Committee
Review Papers
Proposed by NLP (Nonlinear Problems)
  • Keiji KONISHI, Yoshiki SUGITANI
    2022 Volume 16 Issue 2 Pages 76-82
    Published: October 01, 2022
    Released on J-STAGE: October 01, 2022

    Amplitude death has been intensively investigated in the field of nonlinear science for almost 40 years. In the present article, we review the use of analytical tools for robust stability/control, which have been provided in the field of system control, to obtain the conditions under which amplitude death occurs when the number of oscillators and the network topologies are unknown. Specifically, we explain our results for two representative couplings for inducing amplitude death, dynamical coupling and delayed coupling.

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Proposed by SIS (Smart Info-Media Systems)
  • Yoshitaka KAMEYA
    2022 Volume 16 Issue 2 Pages 83-92
    Published: October 01, 2022
    Released on J-STAGE: October 01, 2022

    Machine learning models of high predictive performance, such as deep neural networks and ensemble models, now play a central role in the current artificial intelligence technologies and have started to be applied to the problems related to our health or properties. However, one of the primary obstacles here is the opacity of such high-performance models. So far, dozens of techniques for reducing the opacity have been explored, and form a research field called “explainable aritificial intelligence (XAI).” In this paper, I review the past literature on XAI, organize key concepts and techniques in the current XAI research, and discuss the future direction of XAI.

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Proposed by HWS (Hardware Security)
Proposed by SIP (Signal Processing)
  • Takayuki NAKACHI, Yukihiro BANDOH
    2022 Volume 16 Issue 2 Pages 100-114
    Published: October 01, 2022
    Released on J-STAGE: October 01, 2022

    With the arrival of the big data era, the amount of digital content on the network has rapidly increased. The use of edge/cloud computing has become widespread in various fields such as big data analysis. Examples include data compression to reduce the amount of data, data mining to extract significant information from data, and machine learning to automatically learn models for the classification and prediction of data. However, the use of edge/cloud computing is premised on the reliability of service providers, and there is a concern that privacy invasion problems may occur as a result of the lack of reliability and the unauthorized use or loss of data owing to accidents. To solve the problems, privacy-preserving sparse modeling based on the random unitary transform has been proposed. The features of privacy-preserving sparse modeling are that 1) it can process encrypted data without changing the algorithm of sparse modeling and 2) it guarantees no degradation of sparse modeling performance, without a significant increase in the amount of calculation. In this article, we overview the basic mechanism of the privacy-preserving sparse modeling and introduce application examples to edge AI.

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