Optimization of Sensor Array in Electronic Nose: A Rough Set-Based Approach

Bag, Anil Kumar; Tudu, Bipan; Roy, Jayanta; Bhattacharyya, Nabarun; Bandyopadhyay, Rajib

doi:10.1109/jsen.2011.2151186

Cited by 31 publications

(9 citation statements)

References 31 publications

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“…In addition, the experiment result shows that the number of the sensor to detect the gas mixture is the lowest. In the other case, the rough set-based approach to classify the quality of black tea was proposed [29]. The number of optimum sensors reduces from 8 to 4.…”

Section: Related Workmentioning

confidence: 99%

Stability Assessment of Feature Selection Algorithms on Homogeneous Datasets: A Study for Sensor Array Optimization Problem

Wijaya

Afianti

2020

IEEE Access

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A feature selection algorithm (FSA) is used to eliminate redundant and irrelevant features. Obviously, it can reduce dimensionality as well as the complexity of the original problem. Furthermore, the stability of FSA output becomes a major issue in real-world applications. Stability refers to the consistency of its feature preference related to the perturbation of data samples. In sensor array optimization, an FSA is used to find the best sensor combination in a sensor array. Typically, the main objectives of sensor array optimization are reducing data dimensions, electrical consumption, production cost, computational and traffic overhead, etc. Furthermore, the stable outputs of FSA in several observations are necessary to make a firm conclusion of selected sensors. The contribution of this research is to investigate the stability of FSAs in twelve homogeneous datasets in relation to the sensor array optimization problem. In this study, the stability of seventeen filter-based FSAs is compared across twelve homogeneous datasets. These datasets are generated from the electronic nose (e-nose) used to monitor twelve types of beef cuts. In this case, gas sensor array must have good generalization to differentiate all beef types. The experimental results show that a single FSA cannot guarantee stable sensors recommendation in sensor array optimization. Thus, it becomes a caution to researchers and practitioners to use a proper approach when performing sensor array optimization.

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Section: Related Workmentioning

confidence: 99%

Stability Assessment of Feature Selection Algorithms on Homogeneous Datasets: A Study for Sensor Array Optimization Problem

Wijaya

Afianti

2020

IEEE Access

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“…The sensor array may produce an imprecise, incomplete, redundant and inconsistent data set, and thus, the classification accuracy degrades due to these redundant sensors. To obtain high classification accuracy, both the conflicting data and irrelevant features must be removed (Bag et al, 2011(Bag et al, , 2014. For the research of wound infection detection algorithm of E-nose in this paper, optimization algorithm, which has been widely used in function optimization, data mining, pattern recognition and other fields in recent years (Ch et al, 2013;Liu and Yang, 2013;Liu and Sun, 2009;Zou et al, 2012), will play a great role in the aspects of feature selection, classifier parameters and feature subset optimization.…”

Section: Introductionmentioning

confidence: 99%

Hybrid feature matrix construction and feature selection optimization-based multi-objective QPSO for electronic nose in wound infection detection

et al. 2016

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2016),"Hybrid feature matrix construction and feature selection optimization-based multi-objective QPSO for electronic nose in wound infection detection", Sensor Review, Vol. 36 Iss 1 pp. 23 -33 Permanent link to this document: http://dx. Users who downloaded this article also downloaded:(2016),"Time series estimation of gas sensor baseline drift using ARMA and Kalman based models", Sensor Review, Vol. 36 Iss 1 pp. 34-39 http://dx.(2016),"A portable embedded explosion gas detection and identification device based on intelligent electronic nose system", Sensor Review, Vol. 36 Iss 1 pp. 57-63 http://dx.If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to improve the performance of E-nose in the detection of wound infection. Feature extraction and selection methods have a strong impact on the performance of pattern classification of electronic nose (E-nose). A new hybrid feature matrix construction method and multi-objective binary quantum-behaved particle swarm optimization (BQPSO) have been proposed for feature extraction and selection of sensor array. Design/methodology/approach -A hybrid feature matrix constructed by maximum value and wavelet coefficients is proposed to realize feature extraction. Multi-objective BQPSO whose fitness function contains classification accuracy and a number of selected sensors is used for feature selection. Quantum-behaved particle swarm optimization (QPSO) is used for synchronization optimization of selected features and parameter of classifier. Radical basis function (RBF) network is used for classification. Findings -E-nose obtains the highest classification accuracy when the maximum value and db 5 wavelet coefficients are extracted as the hybrid features and only six sensors are selected for classification. All results make it clear that the proposed method is an ideal feature extraction and selection method of E-nose in the detection of wound infection. Originality/value -The innovative concept improves the performance of E-nose in wound monitoring, and is beneficial for realizing the clinical application of E-nose.

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“…In this paper, rough set theory (RST) [13][14][15][16][17] is utilised to handle the inconsistency 18,19 of the raw dataset. RST is used as a mathematical tool to improve the trial-and-errorbased design approach.…”

Section: Introductionmentioning

confidence: 99%

A rough set-based method for aiming angle tuning of luminaires for outdoor sports lighting

Nath

Mazumdar

Chandra

et al. 2015

Lighting Research & Technology

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When designing outdoor sports lighting it is a challenging task to achieve uniform illuminance over the whole field. Design simulations accomplish this objective by adjusting the aiming angles of the luminaires in horizontal and vertical planes. The lighting design software that is typically used for these calculations does not use any optimization technique. In this paper, rough set theory has been used to find an optimal design for a large scale outdoor sports lighting installation. This modified design process takes the aiming angles of determinant luminaires as the input for the optimization of a total of six lighting parameters. The initial results are very encouraging.

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Optimization of Sensor Array in Electronic Nose: A Rough Set-Based Approach

Cited by 31 publications

References 31 publications

Stability Assessment of Feature Selection Algorithms on Homogeneous Datasets: A Study for Sensor Array Optimization Problem

Stability Assessment of Feature Selection Algorithms on Homogeneous Datasets: A Study for Sensor Array Optimization Problem

Hybrid feature matrix construction and feature selection optimization-based multi-objective QPSO for electronic nose in wound infection detection

A rough set-based method for aiming angle tuning of luminaires for outdoor sports lighting

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