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. 8 (1995), No. 1

  • Design of IIR Hilbert Transformers Based on Weighted Optimization for Magnitude and Phase Responses

    pp. 1-7

    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.8.1

    In this paper, a new design method of IIR Hilbert transformers is proposed by using a nonlinear optimization algorithm. The initial conditions for nonlinear optimization are given by controlling the tap values of IIR Hilbert transformer in the time domain. A parameter optimization technique is used to obtain an IIR Hilbert transformer whose output characteristics in the time domain are close to those of the reference FIR Hilbert transformer. Then, the tap values are optimized by the Fletcher-Powell method in the frequency domain. In the proposed method, the performance criterion for optimization consists of two functions. One represents the error of magnitude response and the other represents that of phase responses. The error ratio of magnitude and phase responses is controlled by different weights to each function. Finally, some examples of IIR Hilbert transformers using this method are shown.
  • A Bayesian Method for the Dynamic Regression Analysis

    pp. 8-16

    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.8.8

    A Bayesian time varying coefficient regression model with smoothness priors is introduced for inferring the dynamic relationship between two time series. Smoothness prior in the form of a Gaussian stochastic difference equation is imposed on the regression coefficient. The estimates of hyperparameters and the order of the difference equation are determined by maximizing marginal likelihood of the hyperparameters and using the minimum ABIC procedure. The estimate of the time varying regression coefficient is obtained by maximizing a posterior density of the coefficient. A numerical example and two simulation studies on the accuracy of the procedure are given. The model is applied to the analyses of the dynamic dependences of steel consumption on GNP for four countries.
  • Estimation Algorithms of the Baseline Length by Utilizing GPS

    pp. 17-24

    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.8.17

    In this paper we present two estimation algorithms of static relative positioning by GPS, where the vector between two antenna stations is determined. There are two important types of GPS observable quantities ; pseudo ranges and carrier phases. The first algorithm here is to utilize the double difference of the carrier phases. And the second algorithm for relative positioning is shown based upon the difference of the estimates for point positioning by using the pseudo ranges, specially, applying Dilution of Precision (DOP). Finally, the experimental results for estimation of the baseline length are shown by using real receiver data obtained at two static points.
  • Three-Dimensional Motion Compensation of a Cube

    pp. 25-33

    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.8.25

    This study offers a three-dimensional motion compensation system that is effective when the moving body is a polyhedron. Features of this system are the introduction of a borderline extraction method which extracts only the contour of a pattern-bearing solid, as well as the extraction of the information on surface disappearance and appearance which uses frame-to-frame changes in the vertex position. Detected three-dimensional motion information has also been used to achieve accurate synthesis of a moving-body image.
  • On a New Axiom of Dominance and Constructing a Measurable Value Function under Uncertainty

    pp. 34-42

    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.8.34

    In this paper, we revise a previous axiom of dominance for constructing a measurable value function under uncertainty based on Dempster-Shafer theory of probability. The previous axiom of dominance has dealt with only the best and the worst results in the set element. In this paper we propose a new axiom of dominance after defining the value of the set element taking into account the average of the value of all the results included in the set element. We could construct a measurable value function under uncertainty for an ordinary, pessimistic or optimistic person, based on this new axiom of dominance. An example of evaluating the alternatives to regulate CO2emission for avoiding global warming is included.

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