No abstract

No abstract

Electronic nose (e-nose) vapor identification is an efficient approach to monitor air contaminants in space stations and shuttles in order to ensure the health and safety of astronauts. Data preprocessing (measurement denoising and feature extraction) and pattern classification are important components of an e-nose system. In this paper, a wavelet-based denoising method is applied to filter the noisy sensor measurements. Transient-state features are then extracted from the denoised sensor measurements, and are used to train multiple classifiers such as multi-layer perceptions (MLP), support vector machines (SVM), k nearest neighbor (KNN), and Parzen classifier. The Dempster-Shafer (DS) technique is used at the end to fuse the results of the multiple classifiers to get the final classification. Experimental analysis based on real vapor data shows that the wavelet denoising method can remove both random noise and outliers successfully, and the classification rate can be improved by using classifier fusion.

One of the major drawbacks to neural networks is the inability of the user to understand what is happening inside the network. Quadrant-Distance graphs, or QD graphs for short, allow the user to graphidly display a network's weight vector at any point in tmining, for networks of any size. This allows the user to quickly and easily identi& similarities or diferences between solution sets. QD graphs may also be used for a variety of other analysis functions, such as comparing initial weights to final weights, and observing the path of the network as it finds a solution.

No abstract