Transactions of the Institute of Systems, Control and Information Engineers
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ONLINE ISSN: 2185-811X
PRINT ISSN: 1342-5668

Transactions of the Institute of Systems, Control and Information Engineers Vol. 30 (2017), No. 9

  • Fundamental Study on Acoustic Distance Measurement Using Cross-spectrum between 2ch Observed Signals of Linear Chirp Sound

    pp. 339-346

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    DOI:10.5687/iscie.30.339

    The distance to a target is fundamental information in many engineering applications. Recently,an acoustic distance measurement (ADM) method has been proposed based on the interference between transmitted and reflected waves, but it requires two applications of the Fourier transform. The ADM method in which a linear chirp whose frequency changes linearly with lapse of time is adopted as a transmitted sound has been also proposed. However, due to the influence of the measuring system from the loudspeaker to the microphone, the ADM would often estimate the spurious short distance different from true distance. This paper describes a fundamental study on the ADM method by applying the cross-spectral method to observed signals of the adjacent two-channel (2ch) microphones, adopting a linear chirp as transmitted wave and removing the influence of the measuring system. More concretely, since the component of the interference of the transmitted wave and reflected wave is included in the observed signal and the interference is a function of the distance from the microphone to the target, the distance can be estimated by utilizing the interference. We confirmed the validity of the chirp-based ADM method by performing a computer simulation and by applying it to an actual sound field.
  • Autonomous Distributed Scheduling by Using Multi-agent Reinforcement Learning for Minimizing Sum of Earliness and Tardiness

    pp. 347-355

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    DOI:10.5687/iscie.30.347

    Multi-agent reinforcement learning have been applied to scheduling method based on utility values for autonomous distributed manufacturing systems in order to improve the objective functions of individual job agents and resource agents, in the previous researches. New distributed scheduling method based on utility values is proposed to improve the sum of earliness and tardiness of all the job agents by applying the multi-agent reinforcement learning to the resource agents, in this research. The resource agents learn the suitable criteria to determine the utility values of the candidate job agents for next machining operation based on the status of manufacturing systems. Some case studies have been carried out to verify the effectiveness of the proposed method.
  • Model Predictive Control Evaluating Tower Base Bending Moment in a Floating Offshore Wind Turbine

    pp. 356-365

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    DOI:10.5687/iscie.30.356

    In this paper, vibration control of the bending moment of a tower base is considered to extend fatigue life of a floating offshore wind turbine (FOWT). A model of the moment is derived and a model predictive controller is designed to accomplish the goal. The controller is designed on the basis of a linear dynamic model of the FOWT. The controller uses the collective blade pitch angle as its control input. A linear dynamic model of the FOWT is constructed from the steady state responses of a precise simulator called FAST (Fatigue, Aerodynamics, Structures, and Turbulence). The control performances are then evaluated through numerical simulations on FAST. The simulations are executed under typical conditions of wind and wave. Simulation results show higher reduction rates of the moment by the model predictive controller compared to those of other control methods.
  • Spot Pricing of Electricity Market with Network Structure Composed of Consumers, Suppliers, and Transmission Companies

    pp. 366-372

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    DOI:10.5687/iscie.30.366

    In this paper, we propose a spot pricing method of an electrical network composed of consumers,suppliers, and transmission companies. The electricity pricing is formulated as a maximization problem of the sum of consumers surplus and profits of suppliers and transmission companies. If there exists a solution to the maximization problem, it is the unique solution for a system of differential algebraic equations with boundary conditions. A numerical simulation demonstrates that an electricity market consisting of two areas is designed to reduce the areal difference of electricity prices.
  • Design of an Input-multiple Dual-rate Sampled-data Control System Using Null Space

    pp. 373-377

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    DOI:10.5687/iscie.30.373

    This paper discusses a design method for an input multiple single-input single-output dual-rate control system, where the sampling interval of the plant output is an integer multiple of the hold interval of the control input. A dual-rate control law that stabilizes a closed-loop system is extended using null-space. In the extended control system, a redundant input that is designed independently of the closed-loop system, is obtained. Because the redundant input is designed so that the control input is constant in the steady-state, the intersample ripples are eliminated without changing the reference response from the reference input to the plant output. Finally, the effectiveness of the proposed method is demonstrated through numerical examples.

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  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