Transactions of the Institute of Systems, Control and Information Engineers
New Arrival Alert : OFF

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

Log in / Sign up
ONLINE ISSN: 2185-811X
PRINT ISSN: 1342-5668

Transactions of the Institute of Systems, Control and Information Engineers Vol. 12 (1999), No. 5

  • Evolutionary Segmentation of Texture Image Using Genetic Algorithms and Two-Dimensional Wavelet Transform

    pp. 263-276

    Bookmark

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

    Log in / Sign Up

    DOI:10.5687/iscie.12.263

    In this paper we consider the segmentation problem of a texture image composed of different kinds of texture fields not as a pattern classification problem but a combinatorial optimization problem. We apply a probabilistic and effective successive search procedure of genetic algorithms to the clustering of small regions in a feature space. Moreover, we propose a new feature extraction scheme using two-dimensional wavelet transform, which can extract the hierarchical characteristics of the texture feature, and perform the accurate feature extraction upon the unstationary texture fields.
  • Active Vibration Control of a Building Structure against Earthquakes

    pp. 277-282

    Bookmark

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

    Log in / Sign Up

    DOI:10.5687/iscie.12.277

    This paper is concerned with controller design and its experimental evaluation for active vibration control of a building structure in case of earthquake. Taking account of the mode-decomposition for the building structure model, we design an H controller for active vibration control, where we adopt the linear matrix inequality approach because the standard assumption of Hcontrol is not satisfied in this case. Then we evaluate its effectiveness for a five-story building structure via experiments.
  • An Iterative Learning Control Algorithm within Prescribed Input-Output Subspace

    pp. 283-289

    Bookmark

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

    Log in / Sign Up

    DOI:10.5687/iscie.12.283

    In this paper, we consider an iterative learning control method (for short, ILC method). With the iteration of experiments, the ILC method yields the desired input for tracking the target trajectory. Most of former ILC methods use the time derivative of the error signal or the passivity of systems. Contrary to these former methods, this paper proposes an alternative ILC algorithm which does not use such things. This algorithm has the following property; the input space is restricted in the prescribed subspace, and the iterative learning law uses the modified error signal, which is projected on this input subspace. The effectiveness of the proposal method is demonstrated by a numerical example and an experiment.
  • A Design of a Strongly Stable Self-Tuning Controller Using Coprime Factorization Approach

    pp. 290-296

    Bookmark

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

    Log in / Sign Up

    DOI:10.5687/iscie.12.290

    This paper proposes a new self-tuning controller having a new design parameter. In selecting the design parameter, the controller gives a strongly stable self-tuning controller, that is, not only the closed-loop system is stable, but also the controller itself is stable.The controller consists of a generalized minimum variance controller and a parameter identification law. The proposed controller has an extended minimum variance controller with a newly introduced design parameter. The parameter is introduced by applying the coprime factorization approach and Youla parametrization of stabilizing compensators to the design of minimum variance controller.
  • A Genetic Algorithm Approach to Optimization Problems with Uncertainties

    pp. 297-303

    Bookmark

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

    Log in / Sign Up

    DOI:10.5687/iscie.12.297

    In this paper, we propose a method of optimum seeking in an uncertain environment by extending the conventional genetic algorithms (GA). The key point of our approach is to evaluate an individual not directly by an objective value of a corresponding solution currently observed, but by accumulating values which have been observed at preceding generations. Finally, we confirm the effectiveness of our extended GA through some computaitional experiments using simple function optimization problems.
  • A Discrete-Time SAC System with No Output Tracking Error

    pp. 304-312

    Bookmark

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

    Log in / Sign Up

    DOI:10.5687/iscie.12.304

    A feasible discrete-time SAC (Simple Adaptive Control) algorithm extended from continuous-time system was proposed for a single-input single-output (SISO) system and removed an offset between a plant output and a reference model output, by inserting parallel feedforward compensators to both of the plant and the reference model. Here, we contrive the design parameters instead of adding the parallel feedforward compensator to the model to remove the above-mentioned offset, and propose a discrete-time SAC algorithm which is feasible and applicable to a multi-input multi-output (MIMO) system. We prove the stability of the system by using asymptotic output tracker theory instead of CGT (Command Generator Tracker) theory, which removes the conventional restriction between the plant and the model.
  • Implementation of the Alternating Direction Method of Multipliers for Quadratic Transportation Problems on a Vector Parallel Computer

    pp. 313-315

    Bookmark

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

    Log in / Sign Up

    DOI:10.5687/iscie.12.313

  • Optimization with Constraint Using Chaos Steepest Descent Method

    pp. 316-318

    Bookmark

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

    Log in / Sign Up

    DOI:10.5687/iscie.12.316

Article Access Ranking

16 May. (Last 30 Days)

  1. Influence of Thermomechanical Treatment on Delayed Fracture Property of Mo-Bearing Medium-Carbon Steel ISIJ International Vol.62(2022), No.2
  2. Interaction Coefficients of Mo, B, Ni, Ti and Nb with Sn in Molten Fe–18mass%Cr Alloy ISIJ International Vol.62(2022), No.3
  3. Fundamentals of Silico-Ferrite of Calcium and Aluminium (SFCA) and SFCA-I Iron Ore Sinter Bonding Phase Formation: Effects of MgO Source on Phase Formation during Heating ISIJ International Vol.62(2022), No.4
  4. Dissolution Kinetics of Synthetic FeCr2O4 in CaO–MgO–Al2O3–SiO2 Slag ISIJ International Vol.62(2022), No.4
  5. Surface Quality Evaluation of Heavy and Medium Plate Using an Analytic Hierarchy Process Based on Defects Online Detection ISIJ International Advance Publication
  6. Influence of Stabilizing Elements on Ductile-Brittle Transition Temperature (DBTT) of 18Cr Ferritic Stainless Steels ISIJ International Vol.62(2022), No.4
  7. Exploration of the Relationship between the Electromagnetic Field and the Hydrodynamic Phenomenon in a Channel Type Induction Heating Tundish Using a Validated Model ISIJ International Vol.62(2022), No.4
  8. Assessment of Blast Furnace Operational Constraints in the Presence of Hydrogen Injection ISIJ International Advance Publication
  9. Phenomenological Understanding about Melting Temperature of Multi-Component Oxides Tetsu-to-Hagané Vol.108(2022), No.4
  10. Numerical Simulation of Charging Biochar Composite Briquette to Blast Furnace ISIJ International Vol.62(2022), No.4

Search Phrase Ranking

16 May. (Last 30 Days)

  1. blast furnace
  2. blast furnace permeability
  3. blast furnace productivity
  4. jet impingement
  5. jet impingement + cooling + runout table
  6. refractory
  7. steel
  8. viscosity of slag fluorine
  9. 1. m. nainar, a. veawab: ind. eng. chem. res., 48(2009), 9299. https://doi.org/10.1021/ie801802a 2. c. h. yu, c. h. huang, c. s. tan: aerosol air qual. res., 12(2012), 745. https://doi.org/10.4209/aaqr.2012.05.0132 3. g. léonard, c. crosset, d. toye, g. heyen: comput. chem. eng., 83(2015), 121. https://doi.org/10.1016/j.compchemeng.2015.05.003 4. e. e. ünveren, b. ö. monkul, ş. sarıoğlan, n. karademir, e. alper: petroleum, 3(2017), 37. https://doi.org/10.1016/j.petlm.2016.11.001 5. m. b. yue, b. sun, y. cao, y. wang, j. wang: chem. eur. j., 14(2008), 3442. https://doi.org/10.1002/chem.200701467 6. w. choi, j. park, c. kim, m. choi: chem. eng. j., 408(2021), 127289. https://doi.org/10.1016/j.cej.2020.127289 7. c. chen, s. t. yang, w. s. ahn, r. ryoo: chem. commun., 24(2009), 3627. https://doi.org/10.1039/b905589d 8. p. zhao, g. zhang, y. xu, y. k. lv, z. yang, h. cheng: energy and fuels, 33(2019), 3357. https://doi.org/10.1021/acs.energyfuels.8b04278 9. k. dong, w. liu, r. zhu: high temp. mater. process, 34(2015), 539. https://doi.org/10.1515/htmp-2014-0076 10. s. wang, s. xu, s. gao, p. xiao, m. jiang, h. zhao, b. huang, l. liu, h. niu, j. wang, d. guo: sci. rep., 11(2021), 1. https://doi.org/10.1038/s41598-021-90532-9 11. q. t. vu, h. yamada, k. yogo: ind. eng. chem. res., 60(2021), 4942. https://doi.org/10.1021/acs.iecr.0c05694 12. m. wang, l. yao, j. wang, z. zhang, w. qiao, d. long, l. ling: appl. energy, 168(2016), 282. https://doi.org/10.1016/j.apenergy.2016.01.085 13. a. heydari-gorji, a. sayari: ind. eng. chem. res., 51(2012), 6887. https://doi.org/10.1021/ie3003446 14. s. a. didas, r. zhu, n. a. brunelli, d. s. sholl, c. w. jones: j. phys. chem. c., 118(2014), 12302. https://doi.org/10.1021/jp5025137 15. q. t. vu, h. yamada, k. yogo: ind. eng. chem. res., 57(2018), 2638. https://doi.org/10.1021/acs.iecr.7b04722 16. q. t. vu, h. yamada, k. yogo: energy & fuels, 33(2019), 3370. https://doi.org/10.1021/acs.energyfuels.8b04307 17. x. zhang, x. zheng, s. zhang, b. zhao, w. wu: ind. eng. chem. res., 51(2012), 15163. https://doi.org/10.1021/ie300180u 18. h. yamada, f. a. chowdhury, j. fujiki, k. yogo: acs sustain. chem. eng., 7(2019), 9574. https://doi.org/10.1021/acssuschemeng.9b01064 19. x. wang, q. guo, t. kong: chem. eng. j., 273(2015), 472. https://doi.org/10.1016/j.cej.2015.03.098 20. f. s. taheri, a. ghaemi, a. maleki: energy and fuels, 33(2019),11465. https://doi.org/10.1021/acs.energyfuels.9b02636 21. a. sayari, y. belmabkhout: j. am. chem. soc., 132(2010), 6312. https://doi.org/10.1021/ja1013773
  10. 10.1016/j.apenergy.2016.01.085