PMID- 31013918
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20200225
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 19
IP  - 8
DP  - 2019 Apr 13
TI  - Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based 
      Multi-Sensor Data Fusion Approaches.
LID - 10.3390/s19081778 [doi]
LID - 1778
AB  - The bulk tobacco flue-curing process is followed by a bulk tobacco curing 
      schedule, which is typically pre-set at the beginning and might be adjusted by 
      the curer to accommodate the need for tobacco leaves during curing. In this 
      study, the controlled parameters of a bulk tobacco curing schedule were 
      presented, which is significant for the systematic modelling of an intelligent 
      tobacco flue-curing process. To fully imitate the curer's control of the bulk 
      tobacco curing schedule, three types of sensors were applied, namely, a gas 
      sensor, image sensor, and moisture sensor. Feature extraction methods were given 
      forward to extract the odor, image, and moisture features of the tobacco leaves 
      individually. Three multi-sensor data fusion schemes were applied, where a least 
      squares support vector machines (LS-SVM) regression model and adaptive 
      neuro-fuzzy inference system (ANFIS) decision model were used. Four experiments 
      were conducted from July to September 2014, with a total of 603 measurement 
      points, ensuring the results' robustness and validness. The results demonstrate 
      that a hybrid fusion scheme achieves a superior prediction performance with the 
      coefficients of determination of the controlled parameters, reaching 0.9991, 
      0.9589, and 0.9479, respectively. The high prediction accuracy made the proposed 
      hybrid fusion scheme a feasible, reliable, and effective method to intelligently 
      control over the tobacco curing schedule.
FAU - Wu, Juan
AU  - Wu J
AUID- ORCID: 0000-0002-7241-2771
AD  - School of Microelectronics and Communication Engineering, Chongqing University, 
      Chongqing 400044, China. juanwucq@126.com.
AD  - Chongqing College of Electronic Engineering, Chongqing 401331, China. 
      juanwucq@126.com.
FAU - Yang, Simon X
AU  - Yang SX
AUID- ORCID: 0000-0002-6888-7993
AD  - School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada. 
      syang@uoguelph.ca.
LA  - eng
GR  - KJ1503002/Chongqing Municipal Education Commission of China/
PT  - Journal Article
DEP - 20190413
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
PMC - PMC6514745
OTO - NOTNLM
OT  - adaptive neuro-fuzzy inference system
OT  - bulk tobacco curing schedule
OT  - electronic nose
OT  - least squares support vector machines
OT  - multi-sensor data fusion
COIS- The authors declare no conflict of interest.
EDAT- 2019/04/25 06:00
MHDA- 2019/04/25 06:01
PMCR- 2019/04/01
CRDT- 2019/04/25 06:00
PHST- 2019/03/07 00:00 [received]
PHST- 2019/04/05 00:00 [revised]
PHST- 2019/04/08 00:00 [accepted]
PHST- 2019/04/25 06:00 [entrez]
PHST- 2019/04/25 06:00 [pubmed]
PHST- 2019/04/25 06:01 [medline]
PHST- 2019/04/01 00:00 [pmc-release]
AID - s19081778 [pii]
AID - sensors-19-01778 [pii]
AID - 10.3390/s19081778 [doi]
PST - epublish
SO  - Sensors (Basel). 2019 Apr 13;19(8):1778. doi: 10.3390/s19081778.