IEICE ESS Fundamentals Review
Online ISSN : 1882-0875
ISSN-L : 1882-0875
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Volume 11, Issue 2
Displaying 1-26 of 26 articles from this issue
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Cover
Table of Contents
Preface
Special Contribution
Review Papers
Proposed by SSS
  • Approaches to Improve Safety of Air Transportation System
    Transition from Safety Specifications towards Safety Management
    Sakae NAGAOKA
    2017 Volume 11 Issue 2 Pages 100-107
    Published: October 01, 2017
    Released on J-STAGE: October 01, 2017
    DOIhttps://doi.org/10.1587/essfr.11.2_100
    JOURNAL FREE ACCESS
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    Safety is a paramount requirement and risk-based safety management has been carried out in civil air transportation. One approach to improve safety is to provide regulations and standards for subsystems such as aircraft systems. For their implementation, safety assessments are required to determine whether the estimated risk is acceptable or not. Many trials of risk assessment such as collision risk evaluation have been carried out. In addition to such standards for subsystems, as a proactive strategy, safety management is now required for the states and service providers associated with the safe operation of aircraft. This paper overviews the transition of approaches for improving aviation safety and briefly describes the methods of risk assessment and the concept of safety management.
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Proposed by BioX
Proposed by CCS
  • Combinatorial Optimization by Quantum Neural Networks and Its Applications
    Mikio HASEGAWA
    2017 Volume 11 Issue 2 Pages 113-117
    Published: October 01, 2017
    Released on J-STAGE: October 01, 2017
    DOIhttps://doi.org/10.1587/essfr.11.2_113
    JOURNAL FREE ACCESS
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    An optimization method based on energy minimization of the Ising Hamiltonian has been proposed in recent research (S. Utsunomiya et al., Optics Express 19, 2011). Large-scale implementation of such a high-speed optimization method has also been proposed. (T. Inagaki et al., Science, 234, 2016). This paper introduces the quantum neural networks, that can be realized by high-speed devices and applies them to combinatorial ptimization problems. In such schemes using quantum neural networks, an objective function for target optimization problems has to be implemented on mutual connections. In this paper, a mutually connected neural network has been implemented on quantum neural networks to solve combinatorial optimization problems. The simulation results show that quantum neural networks can solve combinatorial optimization problems.
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