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Transactions of the Institute of Systems, Control and Information Engineers Vol. 5 (1992), No. 12

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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  1. Vol. 37 (2024)

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  2. Vol. 36 (2023)

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  3. Vol. 35 (2022)

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  4. Vol. 34 (2021)

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  5. Vol. 33 (2020)

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  6. Vol. 32 (2019)

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  7. Vol. 31 (2018)

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  8. Vol. 30 (2017)

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  9. Vol. 29 (2016)

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  10. Vol. 28 (2015)

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  11. Vol. 27 (2014)

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  12. Vol. 26 (2013)

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  13. Vol. 25 (2012)

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  14. Vol. 24 (2011)

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  15. Vol. 23 (2010)

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  16. Vol. 22 (2009)

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  17. Vol. 21 (2008)

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  18. Vol. 20 (2007)

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  19. Vol. 19 (2006)

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  20. Vol. 18 (2005)

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  21. Vol. 17 (2004)

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  22. Vol. 16 (2003)

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  23. Vol. 15 (2002)

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  24. Vol. 14 (2001)

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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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    3. No.5
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Transactions of the Institute of Systems, Control and Information Engineers Vol. 5 (1992), No. 12

A Generalization of Bayes Theorem in Theory of Evidence

Yutaka MAEDA, Hidetomo ICHIHASHI

pp. 481-490

DOI:

10.5687/iscie.5.481

Abstract

A generalized Bayes theorem in the frame of Shafer's theory of evidence is proposed. Smets proposed a procedure for obtaining a posterior basic probability assignment from prior and conditional basic probability assignments using Dempster rule of combination. We show that the uncertainty in the sense of non-specificity of a probability distribution changes by the Smets' procedure. Hence, it contradict with Bayes theorem. We formulate a new combination rule and construct a procedure for obtaining a posterior basic probability assignment without changing the uncertainty. We verify that the proposed rule is reduced to Bayes theorem when each of basic probability assignment is a regular probability assignment. It is also Jeffrey's rule when observation is obtained by a probability distribution and prior distributions are probability distributions.

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A Generalization of Bayes Theorem in Theory of Evidence

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Recognition of Burden Profile at the Top of Blast Furnace Using Neural Network Model

Yoshihisa OTSUKA, Masami KONISHI, Korehito KADOGUCHI

pp. 491-498

DOI:

10.5687/iscie.5.491

Abstract

Recently, neural network models have been applied to solve various problems and multi layer neural network with back propagation algorithm is shown to be useful for pattern recognition. In this paper, a recognition system for burden profile at the top of blast furnace using the neural network is described. Here, two problems of the neural network are studied. One is how to escape from local minima in learning process. For this purpose, a new method which updates the teaching data from simple to complicated one is proposed, and the effectiveness of the method is shown. Another problem is the selection of teaching data in recognition of scaler value, such as length. For this problem, two teaching data, which we call digital and analog type teaching data, are compared, and the digital type teaching data is shown to be better for the recognition of blast furnace data.

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Recognition of Burden Profile at the Top of Blast Furnace Using Neural Network Model

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A Mathematical Model for Growth of Sargassum Horneri and Its Environment

Nobuo SANNOMIYA, Ermin BAO, Hiroshi NAKAMINE, Wataru SAKAMOTO, Kotaro OGINO

pp. 499-506

DOI:

10.5687/iscie.5.499

Abstract

Seaweed plays an important role in aquatic ecosystem. It supplies nutrient and provides the place of life for many kinds of living organism in the sea. In order to control the marine resources reasonably, a study on the growth dynamics of seaweed is necessary.
First, effects of solar radiation and water temperature on photosynthetic activity are considered. Secondly, a seasonal effect is introduced as a physiological phenomenon. Finally, a mathematical model of growth dynamics of Sargassum horneri is constructed from the idea of operating these effects multiplicatively. The model, parameters are determined on the basis of the observation data which were measured for Sargassum horneri at Notojima Aquarium, Ishikawa Prefecture. By a computer simulation, the weight of Sargassum horneri is estimated for any change of the environmental conditions.

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A Mathematical Model for Growth of Sargassum Horneri and Its Environment

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Robustness of Double-Adaptive Observers

Yong Li, Yoh YONEZAWA, Yukio NISHIMURA

pp. 507-512

DOI:

10.5687/iscie.5.507

Abstract

The usual process identification methods for parameter adjustment, along with dead zone, are effective in providing a robust adaptive observer of an unknown plant under uncertain input disturbance and output measurement noise. But it is difficult to decide the size of the dead zone due to the lack of knowledge concerning the disturbance. Furthermore, an error may remain in the dead zone despite the existence or nonexistence of the disturbance and the noise. In this paper, we propose a double-adaptive observer and discuss its robustness. Unlike previous work, this scheme does not introduce parameters that depend on the characteristics of the unknown disturbance. So, when the disturbance or the noise does not exist, the parameters converge over time. The stability of the system is discussed and computer simulation results are presented.

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Robustness of Double-Adaptive Observers

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Share from Local Shading Expansion

Toshio ITO

pp. 513-520

DOI:

10.5687/iscie.5.513

Abstract

This paper is concerned with an extended shape from shading and a photometric stereo using local shading analysis. In general the direction of the steepest change in the image plane corresponds to the steepest gradient of the reflectance map. The local shading expansion method is proposed using this characteristic. The shape from shading can be applied locally.by use of this method to extract surface orientation of objects with uniform Lambertian reflectance properties. The number of light sources of the photometric stereo can be reduced to two light sources by use of this local shading expansion method. Surface orientation of objects with non-uniform Lambertian or uniform specular reflectance properties can be obtained using this photometric stereo.
This paper presents the local shading expansion method and shows reconstructed shapes of objects using extracted surface orientation by this method.

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A Solution of Parallel Path Selection Problem by Using Genetic Algorithm

Kyoichi TATEMURA, Nobuo SANNOMIYA

pp. 524-526

DOI:

10.5687/iscie.5.524
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A Solution of Parallel Path Selection Problem by Using Genetic Algorithm

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