logo
Language
Bookmarks
Log Out

Journals

Search Sites

Account

© 2022 Steel Science Portal

How to use

Advanced Search

Search Sites

site
site
site
site

Transactions of the Institute of Systems, Control and Information Engineers Vol. 31 (2018), No. 12

ISIJ International
belloff
ONLINE ISSN: 2185-811X
PRINT ISSN: 1342-5668
Publisher: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

Backnumber

  1. Vol. 37 (2024)

    1. No.8
    2. No.7
    3. No.6
    4. No.5
    5. No.4
    6. No.3
    7. No.2
    8. No.1

  2. Vol. 36 (2023)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  3. Vol. 35 (2022)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  4. Vol. 34 (2021)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  5. Vol. 33 (2020)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  6. Vol. 32 (2019)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  7. Vol. 31 (2018)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  8. Vol. 30 (2017)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  9. Vol. 29 (2016)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  10. Vol. 28 (2015)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  11. Vol. 27 (2014)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  12. Vol. 26 (2013)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  13. Vol. 25 (2012)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  14. Vol. 24 (2011)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  15. Vol. 23 (2010)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  16. Vol. 22 (2009)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  17. Vol. 21 (2008)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  18. Vol. 20 (2007)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  19. Vol. 19 (2006)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  20. Vol. 18 (2005)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  21. Vol. 17 (2004)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  22. Vol. 16 (2003)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  23. Vol. 15 (2002)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  24. Vol. 14 (2001)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  25. Vol. 13 (2000)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  26. Vol. 12 (1999)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  27. Vol. 11 (1998)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  28. Vol. 10 (1997)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  29. Vol. 9 (1996)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  30. Vol. 8 (1995)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  31. Vol. 7 (1994)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  32. Vol. 6 (1993)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  33. Vol. 5 (1992)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  34. Vol. 4 (1991)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  35. Vol. 3 (1990)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  36. Vol. 2 (1989)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  37. Vol. 1 (1988)

    1. No.7
    2. No.6
    3. No.5
    4. No.4
    5. No.3
    6. No.2
    7. No.1

Keyword Ranking

21 Nov. (Last 30 Days)

Transactions of the Institute of Systems, Control and Information Engineers Vol. 31 (2018), No. 12

Short-time Estimation of R-R Interval from Facial Video Image with a Multiple-Measurement-Points-Voting-Method

Kazutoshi Ukai, Rashedur Rahman, Syoji Kobashi

pp. 403-411

DOI:

10.5687/iscie.31.403

Abstract

Modern society is called a stressful society due to long working hours with electronic equipment and/or human relationships in workplaces, and hence periodic stress checking is required. It is known that there is a relationship between heart rate variability and mental stress. Detection of R-R Interval (RRI) from facial video images is expected to enable noncontact and unconsciously monitoring of mental stress. This study proposes a high-accuracy and short-time RRI estimation method in facial video images, which is called multiple-measurement-points-voting-method, MMVM in short. MMVM can detect RRI of individual facial pixels with time-course color signal change,and estimate RRI from the detected RRIs of multiple points. MMVM was applied to 252 trials of 32 subjects, and was compared with ground truth RRI measured using plethysmographic sensor. The mean absolute error of MMVM was 17.7 ms. And, the minimum measurement duration to reach target accuracy of MMVM was 2.8 sec, and that of ICA method was 5.3 sec. MMVM shortens the measurement time by about 47%.

mendeley

Bookmark

Detail

PDF Download

Share it with SNS

Article Title

Short-time Estimation of R-R Interval from Facial Video Image with a Multiple-Measurement-Points-Voting-Method

twitter
facebook
mendeley

Bibtex

Ris

Measurement of Diurnal Variation in Tear Volume using Smartphone

Yoshiro Okazaki, Mamoru Iwabuchi, Tatsuki Takenaga, Taku Miyake, Toshiyuki Okubo, Norihiko Yokoi

pp. 412-418

DOI:

10.5687/iscie.31.412

Abstract

Evaluation of tear volume is essential for diagnosing dry eye disease. At the clinical site, dedicated devices such as slit lamp microscopy or Schirmer’s strip have been used to quantify tear volume. However, these devices have access only in medical office and therefore have limited availability for the public. Tear volume changes with environmental, physical or psychological situation such as room temperature, mental stresses or biorhythm. For that reason, measurement of tear volume in the daily life can be beneficial for efficient treatment and prevention of dry eye. In this study, a tear volume measurement system based on the principle of meniscometry was developed and implemented on the smartphone. Then, diurnal tear volume variation during the daily life was measured by this system. The result showed that tear volume significantly decreased from morning to evening as in the previous study (p<.05). This suggests that the smartphone-based measurement system can be useful for measuring tear volume in the daily life.

mendeley

Bookmark

Detail

PDF Download

Share it with SNS

Article Title

Measurement of Diurnal Variation in Tear Volume using Smartphone

twitter
facebook
mendeley

Bibtex

Ris

Fluorescent Staining for Detecting Larvae of the Japanese Scallops Mizuhopecten Yessoensis

Koichiro Enomoto, Masashi Toda, Yohei Shimizu, Yasuhiro Kuwahara

pp. 419-427

DOI:

10.5687/iscie.31.419

Abstract

In farming Japanese scallops Mizuhopecten yessoensis, larvae investigation and efficient seed collection are very important processes. During yearly larvae investigation from May to June, fishery managers count the number of larvae and measure the shell size. Identification technology has been developed using immunostaining to mark scallops. The resulting fluorescence images contain both fluorescence-stained and autofluorescent larvae. However, there is currently no technology for evaluating these images automatically, so the experts have to perform these tasks manually.
Our overall aim is to develop a system for automatically measuring the number and size of scallops. In this paper, we propose a method for detecting fluorescence-stained larva and measuring the items in the fluorescence images. We will present the experimental results and discuss the effectiveness of the proposed method. We will also introduce an application that facilitates measurement of scallops.

mendeley

Bookmark

Detail

PDF Download

Share it with SNS

Article Title

Fluorescent Staining for Detecting Larvae of the Japanese Scallops Mizuhopecten Yessoensis

twitter
facebook
mendeley

Bibtex

Ris

Dynamical Analysis of Pace and Trot Gait Using a 3D Quadrupedal Model

Mau Adachi, Tomoya Kamimura, Fumitoshi Matsuno

pp. 428-436

DOI:

10.5687/iscie.31.428

Abstract

Quadrupeds selectively use pace gait or trot gait in the middle of their locomotion speed range. However, it remains unclear why they select pace or trot gait depending on their species or conditions. In this paper, we use a simple 3D quadrupedal model to find periodic solutions corresponding to the motions of pace and trot gait. Using physical parameters of dogs, we compared obtained solutions of pace and trot using obtained touchdown angles of legs and three criteria: stability, energy efficiency, and maximum ground reaction force(GRF). From the results, we found that solutions of pace and trot had different touchdown angles of legs. In addition, we also found that regardless of locomotion speed, the solutions of pace were more stable and had smaller GRF than those of trot. On the other hand, the solutions of trot were more efficient motions than those of pace. These results can partially explain the reasons of selecting pace and trot gait by animals.

mendeley

Bookmark

Detail

PDF Download

Share it with SNS

Article Title

Dynamical Analysis of Pace and Trot Gait Using a 3D Quadrupedal Model

twitter
facebook
mendeley

Bibtex

Ris

Hybrid Prediction of the Appearance of Monkeys using Markov Chain Model and Support Vector Machine

Kazufumi Nakai, Nobuo Ezaki, Akihiko Sugiura

pp. 437-445

DOI:

10.5687/iscie.31.437

Abstract

Recently, agricultural damage caused by harmful animals, especially monkeys, is a critical problem in Japan. This paper proposes a system which predicts dates that monkeys approach farmland. In order to make predictions, monkeys’ activities were collected around a mountain for two years. As a result of the investigation, the monkeys were appeared on several points, and it was expected that they were moving according to specific pattern. Therefore, in this study, the Markov chain model that can handle state transitions stochastically was adopted as the method for monkeys’ behavior prediction. As a result of calculating the Markov chain on the order from 1st to 5th, 2nd order was optimal for this study. It could be considered that the monkeys were moving a specific pattern at a cycle of a few days. In the case of a two-class problem, i.e. monkeys appear or not, 57.5 % accuracy was obtained. In the multi-class problem, which place monkeys appear, the accuracy was 31.5 %. Additionally, the hybrid of Markov chain and Support Vector Machine brings more efficiency of the monkeys’ appearance prediction.

mendeley

Bookmark

Detail

PDF Download

Share it with SNS

Article Title

Hybrid Prediction of the Appearance of Monkeys using Markov Chain Model and Support Vector Machine

twitter
facebook
mendeley

Bibtex

Ris

Article Access Ranking

21 Nov. (Last 30 Days)

You can use this feature after you logged into the site.
Please click the button below.

Advanced Search

Article Title

Author

Abstract

Journal Title

Year

Please enter the publication date
with Christian era
(4 digits).

Please enter your search criteria.