logo
言語
ブックマーク
ログアウト

ジャーナル

論文検索サイト

アカウント

© 2022 Steel Science Portal

ヘルプ

詳細検索

論文検索サイト

site
site
site
site

システム/制御/情報 Vol. 35 (2022), No. 5

ISIJ International
belloff
オンライン版ISSN: 2185-811X
冊子版ISSN: 1342-5668
発行機関: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

Backnumber

  1. Vol. 37 (2024)

    1. No.2
    2. 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

キーワードランキング

16 May. (Last 30 Days)

システム/制御/情報 Vol. 35 (2022), No. 5

動的スケジューリング問題のための長期メモリを用いた並列分散遺伝的プログラミング

林田 智弘, 広谷 大助, 西崎 一郎, 関崎 真也, 前田 伊吹

pp. 93-100

DOI:

10.5687/iscie.35.93

抄録

Genetic Programming (GP) is an evolutionary computation method that optimizes the rules defining the relationship between environmental states and system output. GP is an effective method for dynamic environments in which the information repeatedly changes multiple times. On the other hand, in GP, the rules are evaluved as the environmental state changes, so the rules acquired in the distant past disappear in time, and re-learning is required in a dynamic environment. This paper proposes an optimization method for the dynamic scheduling problem where new jobs arrive intermittently. Specifically, a method to improve the learning efficiency of GP in such a periodic dynamic environment by dividing the population into several subpopulations and recording the environmental states or their characteristics. This paper conducts some numerical experiments on the dynamic scheduling problems in which new jobs arrive irregularly to verify the usefulness of the proposed method.

他の人はこちらも検索

mendeley

ブックマーク

詳細

PDFダウンロード

SNSによる共有

論文タイトル

動的スケジューリング問題のための長期メモリを用いた並列分散遺伝的プログラミング

twitter
facebook
mendeley

Bibtex

Ris

敵対的学習によるクラス推定を利用した部分ドメイン適応

甲野 晴太, 植田 考哉, 高野 諒, 西川 郁子

pp. 101-108

DOI:

10.5687/iscie.35.101

抄録

Domain adaptation is an approach to transfer knowledge from a certain source domain to another target domain. Several studies are recently reported on partial domain adaptation (PDA), where the class set of the source domain is larger than that of the target domain as a more realistic setting, but then the source domain specific classes make the adaptation more difficult. Most existing methods for PDA give small weights to the source domain specific classes to prevent the target data from being matched. The present paper proposes a PDA method which introduces a novel mechanism that gives additional weights to an individual target data by estimating the probability that the data belongs to each source class. The estimation is given by multiple discriminators that measure the distance between the data distribution of each source class and the entire target data distribution through adversarial training against a data encoder. Computer experiments using two handwritten digit datasets as two domains show that the proposed method achieves more stable and accurate domain adaptation compared with state-of-the-art existing methods for PDA.

mendeley

ブックマーク

詳細

PDFダウンロード

SNSによる共有

論文タイトル

敵対的学習によるクラス推定を利用した部分ドメイン適応

twitter
facebook
mendeley

Bibtex

Ris

ドローン空撮画像を用いた地表のセグメンテーションと密なオプティカルフローに基づく着陸可能領域の抽出

菊本 智寛, 張本 暘, 吉田 武史, 浦久保 孝光

pp. 109-117

DOI:

10.5687/iscie.35.109

抄録

In order to realize autonomous drones that collect information and transport supplies by air, it is required to automatically detect a safe landing site in an unknown environment. In this paper, we propose a method to find a candidate landing site using ground images captured by a monocular camera from a drone in flight. The proposed method evaluates the safety of the ground surface by combining the surface classification through Semantic Segmentation and the flatness estimation from dense optical flow. The evaluation is performed for each pixel of the captured images, and a detailed shape of the possible landing area can be obtained. We applied the method to actual images taken by a drone and verified that the landable area was extracted from an altitude of about 100 meters.

mendeley

ブックマーク

詳細

PDFダウンロード

SNSによる共有

論文タイトル

ドローン空撮画像を用いた地表のセグメンテーションと密なオプティカルフローに基づく着陸可能領域の抽出

twitter
facebook
mendeley

Bibtex

Ris

ロボットセルシステムのための階層的最適化

倉谷 僚一, 小島 岳史, 藤井 春香, 的場 俊亮, 齊藤 ゆみ, 高西 完治

pp. 118-125

DOI:

10.5687/iscie.35.118

抄録

Optimization in layouts and robot motions is an essential yet challenging task for robotic cell systems in crowded spaces. In the conventional research, since the robot posture is not explicitly considered, there may be no solution or the operation time may be long if the motion is optimized for the layout obtained by the arrangement optimization. In this work, focusing on the importance of robot posture, we propose a hierarchical optimization framework that newly adds posture optimization between placement optimization and motion optimization. Furthermore, when solving the placement optimization with a genetic algorithm, we devised an objective function in order to consider the design constraints. We confirm the proposed method can solve the optimization problem quickly by experiments.

mendeley

ブックマーク

詳細

PDFダウンロード

SNSによる共有

論文タイトル

ロボットセルシステムのための階層的最適化

twitter
facebook
mendeley

Bibtex

Ris

二重倒立振子制御問題へのTWEANNアプローチ

平賀 元彰, 渡辺 優, 大倉 和博

pp. 126-132

DOI:

10.5687/iscie.35.126

抄録

We discuss the effect of mutation probabilities on the performance of Mutation-Based Evolving Artificial Neural Networks (MBEANN), which is one of the methods of Topology and Weight Evolving Artificial Neural Networks (TWEANN). TWEANN is an approach for evolving both structures and weights of artificial neural networks. TWEANN is expected to perform well than an approach using a fixed-topology neural network with only evolving weight values. The phenotype of MBEANN consists of sub-networks, and the topology of the neural network grows independently within them. Moreover, the structural mutations of MBEANN are designed to reduce the influence on the fitness value. In this study, we focus on the effect of structural mutation probabilities on performance by using a double pole balancing problem without velocity inputs. The performance of MBEANN is compared with NeuroEvolution of Augumenting Topologies (NEAT), which is a typical method of TWEANN. The results show that MBEANN has a higher task achievement rate regardless of the mutation probabilities and task difficulty. From the comparison with NEAT, MBEANN shows a higher performance even with a larger network structure due to the phenotype that consists of sub-networks.

mendeley

ブックマーク

詳細

PDFダウンロード

SNSによる共有

論文タイトル

二重倒立振子制御問題へのTWEANNアプローチ

twitter
facebook
mendeley

Bibtex

Ris

論文アクセスランキング

16 May. (Last 30 Days)

この機能はログイン後に利用できます。
下のボタンをクリックしてください。

詳細検索

論文タイトル

著者

抄録

ジャーナル名

年

出版日を西暦で入力してください（4桁の数字）。

検索したいキーワードを入力して下さい