An adaptive spatiotemporal modeling method for curing thermal process

Kang-Kang Xu; Han-Xiong Li

doi:10.1109/smc.2017.8123137

An adaptive spatiotemporal modeling method for curing thermal process

Kang-Kang Xu
, Han-Xiong Li

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

Abstract

The temperature distribution in the curing oven is a typical distributed parameter system (DPS). Modeling of this kind of system is very difficult as only few sensors are available inside. Besides, thermal behaviors of the oven are time-varying in the directions of space and time. In this paper, an adaptive spatiotemporal modeling method is designed for the curing thermal process. Time-varying spatial basis functions are first obtained under adaptive time/space separation. An online sequential extreme learning machine (OS-ELM) is further developed for online modeling of the time-varying dynamics in time direction. Finally, the temperature distribution of the oven can be estimated by the adaptive spatiotemporal model. Simulation results demonstrate the superior of the proposed modeling method.

Original language	English
Title of host publication	2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Publisher	IEEE
Pages	3296-3301
ISBN (Electronic)	9781538616451
ISBN (Print)	9781538616444, 9781538616468
DOIs	https://doi.org/10.1109/smc.2017.8123137
Publication status	Published - Oct 2017
Event	2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2017) - Banff Centre, Banff, Canada Duration: 5 Oct 2017 → 8 Oct 2017

Conference

Conference	2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2017)
Place	Canada
City	Banff
Period	5/10/17 → 8/10/17

Research Keywords

curing oven
distributed parameter system
online sequential extreme learning machine
DISTRIBUTED-PARAMETER PROCESSES
PROPER ORTHOGONAL DECOMPOSITION
KARHUNEN-LOEVE DECOMPOSITION
ALGORITHM
SYSTEMS

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10.1109/smc.2017.8123137

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@inproceedings{6b229d6ffb4c4b469131afbd54b7467c,

title = "An adaptive spatiotemporal modeling method for curing thermal process",

abstract = "The temperature distribution in the curing oven is a typical distributed parameter system (DPS). Modeling of this kind of system is very difficult as only few sensors are available inside. Besides, thermal behaviors of the oven are time-varying in the directions of space and time. In this paper, an adaptive spatiotemporal modeling method is designed for the curing thermal process. Time-varying spatial basis functions are first obtained under adaptive time/space separation. An online sequential extreme learning machine (OS-ELM) is further developed for online modeling of the time-varying dynamics in time direction. Finally, the temperature distribution of the oven can be estimated by the adaptive spatiotemporal model. Simulation results demonstrate the superior of the proposed modeling method.",

keywords = "curing oven, distributed parameter system, online sequential extreme learning machine, DISTRIBUTED-PARAMETER PROCESSES, PROPER ORTHOGONAL DECOMPOSITION, KARHUNEN-LOEVE DECOMPOSITION, ALGORITHM, SYSTEMS",

author = "Kang-Kang Xu and Han-Xiong Li",

year = "2017",

month = oct,

doi = "10.1109/smc.2017.8123137",

language = "English",

isbn = "9781538616444",

pages = "3296--3301",

booktitle = "2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)",

publisher = "IEEE",

address = "United States",

note = "2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2017) ; Conference date: 05-10-2017 Through 08-10-2017",

}

TY - GEN

T1 - An adaptive spatiotemporal modeling method for curing thermal process

AU - Xu, Kang-Kang

AU - Li, Han-Xiong

PY - 2017/10

Y1 - 2017/10

N2 - The temperature distribution in the curing oven is a typical distributed parameter system (DPS). Modeling of this kind of system is very difficult as only few sensors are available inside. Besides, thermal behaviors of the oven are time-varying in the directions of space and time. In this paper, an adaptive spatiotemporal modeling method is designed for the curing thermal process. Time-varying spatial basis functions are first obtained under adaptive time/space separation. An online sequential extreme learning machine (OS-ELM) is further developed for online modeling of the time-varying dynamics in time direction. Finally, the temperature distribution of the oven can be estimated by the adaptive spatiotemporal model. Simulation results demonstrate the superior of the proposed modeling method.

AB - The temperature distribution in the curing oven is a typical distributed parameter system (DPS). Modeling of this kind of system is very difficult as only few sensors are available inside. Besides, thermal behaviors of the oven are time-varying in the directions of space and time. In this paper, an adaptive spatiotemporal modeling method is designed for the curing thermal process. Time-varying spatial basis functions are first obtained under adaptive time/space separation. An online sequential extreme learning machine (OS-ELM) is further developed for online modeling of the time-varying dynamics in time direction. Finally, the temperature distribution of the oven can be estimated by the adaptive spatiotemporal model. Simulation results demonstrate the superior of the proposed modeling method.

KW - curing oven

KW - distributed parameter system

KW - online sequential extreme learning machine

KW - DISTRIBUTED-PARAMETER PROCESSES

KW - PROPER ORTHOGONAL DECOMPOSITION

KW - KARHUNEN-LOEVE DECOMPOSITION

KW - ALGORITHM

KW - SYSTEMS

UR - https://www.scopus.com/pages/publications/85044281730

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85044281730&origin=recordpage

U2 - 10.1109/smc.2017.8123137

DO - 10.1109/smc.2017.8123137

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781538616444

SN - 9781538616468

SP - 3296

EP - 3301

BT - 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

PB - IEEE

T2 - 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2017)

Y2 - 5 October 2017 through 8 October 2017

ER -

An adaptive spatiotemporal modeling method for curing thermal process

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Research Keywords

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