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Transactions of the Institute of Systems, Control and Information Engineers Vol. 22 (2009), No. 10

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

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  6. Vol. 32 (2019)

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  11. Vol. 27 (2014)

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  16. Vol. 22 (2009)

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  25. Vol. 13 (2000)

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  26. Vol. 12 (1999)

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  27. Vol. 11 (1998)

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  28. Vol. 10 (1997)

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  29. Vol. 9 (1996)

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  30. Vol. 8 (1995)

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  31. Vol. 7 (1994)

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  32. Vol. 6 (1993)

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  33. Vol. 5 (1992)

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  34. Vol. 4 (1991)

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  35. Vol. 3 (1990)

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  36. Vol. 2 (1989)

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  37. Vol. 1 (1988)

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Keyword Ranking

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Transactions of the Institute of Systems, Control and Information Engineers Vol. 22 (2009), No. 10

Intra-generational Redistribution under Public Pension Planning Based on Generation-based Funding Scheme

Daisuke Banjo, Hiroyuki Tamura, Tadahiko Murata

pp. 343-349

DOI:

10.5687/iscie.22.343

Abstract

In this paper, we propose a method of determining the pension in the generation-based funding scheme. In this proposal, we include two types of pensions in the scheme. One is the payment-amount related pension and the other is the payment-frequency related pension. We set the ratio of the total amount of payment-amount related pension to the total amount of both pensions, and simulate income gaps and the relationship between contributions and benefits for each individual when the proposed method is applied.

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Intra-generational Redistribution under Public Pension Planning Based on Generation-based Funding Scheme

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Ris

Simple Adaptive Control Synthesis Enhanced by Scaling and Its Application to Positioning Control of Antagonist-Type Pneumatic Actuation Mechanism

Yudai Fudaba, Yoshio Ebihara, Tomomichi Hagiwara, Yasunao Okazaki, Mayumi Komatsu

pp. 350-356

DOI:

10.5687/iscie.22.350

Abstract

In this paper, we aim at achieving accurate positioning control of a pneumatic actuation mechanism of antagonist-type by means of the simple adaptive control (SAC) method. Since this plant does not satisfy the ASPR-ness condition, we introduce a parallel feedforward compensator (PFC) so that the SAC strategy can be applied. However, the introduction of PFC leads to performance deterioration and in particular causes a steady state error. To suppress the steady-state error while keeping the fundamental structure of the SAC system, we introduce a “constant scaling” to the SAC system. We also consider a “dynamic scaling” so that the transient response of the controlled system can be improved. Through experiments, we show that the SAC strategy with these scalings is indeed effective in achieving accurate positioning control.

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Simple Adaptive Control Synthesis Enhanced by Scaling and Its Application to Positioning Control of Antagonist-Type Pneumatic Actuation Mechanism

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Ris

Decentralized Model Predictive Control via Dual Decomposition

Yuji Wakasa, Mizue Arakawa, Kanya Tanaka, Takuya Akashi

pp. 357-363

DOI:

10.5687/iscie.22.357

Abstract

This paper proposes a decentralized model predictive control method based on a dual decomposition technique. A model predictive control problem for a system with multiple subsystems is formulated as a convex optimization problem. In particular, we deal with the case where the control outputs of the subsystems have coupling constraints represented by linear equalities. A dual decomposition technique is applied to this problem in order to derive the dual problem with decoupled equality constraints. A projected subgradient method is used to solve the dual problem, which leads to a decentralized algorithm. In the algorithm, a small-scale problem is solved at each subsystem, and information exchange is performed in each group consisting of some subsystems. Also, it is shown that the computational complexity in the decentralized algorithm is reduced if the dynamics of the subsystems are all the same.

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Decentralized Model Predictive Control via Dual Decomposition

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Collaborative Filtering Based on Sequential Extraction of User-Item Clusters

Katsuhiro Honda, Akira Notsu, Hidetomo Ichihashi

pp. 364-370

DOI:

10.5687/iscie.22.364

Abstract

Collaborative filtering is a computational realization of “word-of-mouth” in network community, in which the items prefered by “neighbors” are recommended. This paper proposes a new item-selection model for extracting user-item clusters from rectangular relation matrices, in which mutual relations between users and items are denoted in an alternative process of “liking or not”. A technique for sequential co-cluster extraction from rectangular relational data is given by combining the structural balancing-based user-item clustering method with sequential fuzzy cluster extraction appraoch. Then, the tecunique is applied to the collaborative filtering problem, in which some items may be shared by several user clusters.

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Collaborative Filtering Based on Sequential Extraction of User-Item Clusters

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