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Transactions of the Institute of Systems, Control and Information Engineers Vol. 21 (2008), No. 6

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

Backnumber

  1. Vol. 35 (2022)

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  2. Vol. 34 (2021)

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  3. Vol. 33 (2020)

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  4. Vol. 32 (2019)

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  5. Vol. 31 (2018)

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  6. Vol. 30 (2017)

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  7. Vol. 29 (2016)

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  8. Vol. 28 (2015)

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  9. Vol. 27 (2014)

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  10. Vol. 26 (2013)

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  12. Vol. 24 (2011)

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  13. Vol. 23 (2010)

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  14. Vol. 22 (2009)

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  15. Vol. 21 (2008)

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  17. Vol. 19 (2006)

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  18. Vol. 18 (2005)

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  19. Vol. 17 (2004)

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  20. Vol. 16 (2003)

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  22. Vol. 14 (2001)

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  23. Vol. 13 (2000)

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  24. Vol. 12 (1999)

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  25. Vol. 11 (1998)

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  26. Vol. 10 (1997)

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  27. Vol. 9 (1996)

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  28. Vol. 8 (1995)

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  29. Vol. 7 (1994)

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  30. Vol. 6 (1993)

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  31. Vol. 5 (1992)

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  32. Vol. 4 (1991)

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  33. Vol. 3 (1990)

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  34. Vol. 2 (1989)

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  35. Vol. 1 (1988)

    1. No.7
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    3. No.5
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Transactions of the Institute of Systems, Control and Information Engineers Vol. 21 (2008), No. 6

Machine-Learning-Based Transformation of Japanese Passive Sentences into Active by Separating Training Data into Each Input Particle

Masaki MURATA, Toshiyuki KANAMARU, Tamotsu SHIRADO, Hitoshi ISAHARA

pp. 165-175

DOI:

10.5687/iscie.21.165

Abstract

We developed a new method of transforming Japanese case particles when transforming Japanese passive sentences into active sentences. This method separates training data into each input particle and uses machine learning for each particle. We also used numerous rich features for learning. Murata et al. conducted a previous study on transforming Japanese passive sentences into active sentences [2]. They used machine learning but did not separate training data for any input particles and did not have many rich features for learning. They achieved an accuracy rate of 89.77%. We added many rich features to those used in Murata et al.'s study and obtained an accuracy rate of 92.00%. In addition, we used our method of separating training data into each input particle and using machine learning for each particle, and obtained an accuracy rate of 94.30%. We confirmed the significance of these improvements through a statistical test. We also conducted experiments utilizing traditional methods using verb dictionaries and manually prepared heuristic rules and confirmed that our method achieved much higher accuracy rates than traditional methods.

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Machine-Learning-Based Transformation of Japanese Passive Sentences into Active by Separating Training Data into Each Input Particle

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Ris

Description and Reasoning about Hybrid System Based on Nonstandard Model

Katsunori NAKAMURA, Akira FUSAOKA

pp. 176-184

DOI:

10.5687/iscie.21.176

Abstract

In this paper, we introduce a nonstandard analysis into a logical modeling of continuous dynamics and present a new framework called hyper-finite hybrid automaton (HHA). HHA is a nonstandard interpretation of hybrid automata in the domain of *R. We also enlarge the linear temporal logic LTL to *LTL to describe the system specification. In this framework, we examine the validation of the system consistency of the hybrid system based on model checking, especially the existence and reachability of Zeno point.

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Description and Reasoning about Hybrid System Based on Nonstandard Model

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Ris

WOODS : Write Once based Data Management Structure for P2P Environment

Yutaka YASUDA

pp. 185-194

DOI:

10.5687/iscie.21.185

Abstract

Along with the trend towards low cost, high performance computer systems and the widespread availability of the Internet, Peer to Peer (P2P) technology is receiving increased attention. One aspect of P2P, decentralized storage utilizing optimistic concurrency control, suffers from a number of problems such as the detection of collisions and the resolution of conflicts. Solutions to date have been application-specific. This paper proposes a new data management structure dubbed WOODS (Write Once Oriented Data management Structure) which does not require conflict resolution for applications such as email, where only read and append access in required and where the order of concurrent updates is not crucial. The paper applies WOODS to a mailing list system, demonstrating that inter-application synchronization is not required for concurrent updates and that it can be used even when the network is partitioned and later reintegrated.

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WOODS : Write Once based Data Management Structure for P2P Environment

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A Design of Delayed Feedback via Sampled-Data H∞ Control

Hisaya FUJIOKA

pp. 195-200

DOI:

10.5687/iscie.21.195

Abstract

A design problem of DFC (delayed feedback control) satisfying H∞ performance specification is considered for sampled-data systems. It is shown that stabilization of UPOs (unstable periodic orbits) can be cast into an H∞ control synthesis with an unstable weight. The nonstandard synthesis problem is then reduced to a standard one under mild condition, which also concludes that the DFC structure is necessary for the stabilization of UPOs. A synthesis problem is formulated by putting extra sampled-data H∞ specifications to the problem related to the stabilization of UPOs, and the properties including the order of the resultant controller are discussed. The validity of the proposed method is confirmed by numerical examples.

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A Design of Delayed Feedback via Sampled-Data H∞ Control

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