Fengchun Tian

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Bilateral similarity function is designed for analyzing the similarities of biological sequences such as DNA, RNA secondary structure or protein in this paper. The defined function can perform comprehensive comparison between sequences remarkably well, both in terms of the Hamming distance of two compared sequences and the corresponding location difference.(More)
—In this paper, a new method for classifying electronic nose data in rats wound infection detection based on support vector machine (SVM) and wavelet analysis was developed. Signals of the sensors were decomposed using wavelet analysis for feature extraction and a PSO-SVM classifier was developed for pattern recognition. The sensor array was optimized and(More)
As wireless sensor networks (WSNs) increasingly attract more attention, new ideas for specific applications are continually being developed, many of which involve the energy consumption of nodes. However, not much has been done to optimize the quality of services (QoS) of WSNs. Many applications like target tracking require some QoS guarantees. Besides,(More)
Ensemble method is a learning paradigm that has been shown to improve the performance of classical learning methods which are based on single model. However, for an ensemble method to be effective, it is essential that the base models are sufficiently accurate and error-independent (i.e. diverse) in their predictions. Moreover, ensemble integration is one(More)
In this paper, the relationship between typical circuit structures of gas sensor circuits and their output noise is analyzed. By using averaged segmenting periodical graph and improved histogram estimation methods, we estimated their noise power spectra and optimal probability distribution functions (pdf). The results were confirmed through experiment(More)
In this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC) has been proposed to analyze signals of an electronic nose (E-nose) used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optimization (QPSO) algorithm is implemented in conjunction with(More)
—In this paper a new method based on the support vector machine (SVM) combined with particle swarm optimization (PSO) is proposed to analyze signals of wound infection detection based on electronic nose (enose). Owing to the strong impact of sensor array optimization and SVM parameters selection on the classification accuracy of SVM, PSO is used to realize(More)