Purpose – The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E‐nose in the detection of wound infection. When an electronic nose (E‐nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy. Design/methodology/approach – An enhanced quantum‐behaved particle swarm optimization based on genetic algorithm, genetic quantum‐behaved particle swarm optimization (G‐QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance‐factor (I‐F) method is used to weight the sensors of E‐nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification. Findings – The classification accuracy of E‐nose is the highest when the weighting coefficients of the I‐F method and classifier’s parameters are optimized by G‐QPSO. All results make it clear that the proposed method is an ideal optimization method of E‐nose in the detection of wound infection. Research limitations/implications – To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E‐nose. Practical implications – In this paper, E‐nose is used to distinguish the class of wound infection; meanwhile, G‐QPSO is used to realize a synchronous optimization of sensor array and classifier of E‐nose. These are all important for E‐nose to realize its clinical application in wound monitoring. Originality/value – The innovative concept improves the performance of E‐nose in wound monitoring and paves the way for the clinical detection of E‐nose.
Sensor Review – Emerald Publishing
Published: Jun 10, 2014
Keywords: Signal processing; Sensors; Surgery
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera