Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380
[TOP] | [2014] | [2015] | [2016] | [2017] | [2018] | [2019] | [2020] | [Japanese] / [English]
R2017-14
A note on a detection method of cascading failure-occurrences in n-component parallel systems by using likelihood-ratio test
Shuhei Ota, Mitsuhiro Kimura (Hosei Univ.)
pp. 1 - 6
R2017-15
Reliability Analysis for CANs based-on Discrete Time Markov Chain
Ryohei Satoh, Satoshi Fukumoto, Mamoru Ohara (Tokyo Metropolitan Univ.)
pp. 7 - 12
R2017-16
A Practical Use of GNSS for a Train Protection System and its Reliability
Akira Asano (Kyosan), Hiroshi Mochizuki, Hideo Nakamura (Nihon Univ.)
pp. 13 - 18
R2017-17
Computing System Signatures of Connected-(r,s)-out-of-(m,n):F Lattice Systems
Taishin Nakamura, Hisashi Yamamoto (Tokyo Metropolitan Univ.), Takashi Shinzato (Tamagawa Univ.), Xiao Xiao (Tokyo Metropolitan Univ.), Tomoaki Akiba (Chiba Inst. of Tech.)
pp. 19 - 24
R2017-18
Simulation-based interval estimation of software reliability measures
Xiao Xiao (Tokyo Metropolitan Univ.), Tadashi Dohi (Hiroshima Univ.)
pp. 25 - 30
R2017-19
The Software Failure and Reliability Assessment Tool (SFRAT)
-- A Platform to Foster Collaboration --
Vidhyashree Nagaraju, Venkateswaran Shekar (Univ. of Massachusetts Dartmouth), Thierry Wandji (NAVAIR), Lance Fiondella (Univ. of Massachusetts Dartmouth)
pp. 31 - 36
R2017-20
Sequential prediction of software bug via nonparametric maximum likelihood estimator
Yasuhiro Saito (JCGA), Tadashi Dohi (Hiroshima Univ.)
pp. 37 - 42
R2017-21
Downward Convex of Upper Limit Estimated using Gompertz Curve Model with Data Described using Logistic Curve Model
Daisuke Satoh (NTT)
pp. 43 - 48
R2017-22
Reliability and Maintainability Assessment Tool for Open Source Software Based on Deep Learning
Yoshinobu Tamura (Tokyo City Univ.), Shigeru Yamada (Tottori Univ.)
pp. 49 - 54
R2017-23
Software Reliability Assessment Based on a Discretized Model by Bayes' Theory
Shinji Inoue (Kansai Univ.), Shigeru Yamada (Tottori Univ.)
pp. 55 - 60
Note: Each article is a technical report without peer review, and its polished version will be published elsewhere.