The objective of this paper is to provide an effective approach to infrared spectrum recognition. Traditionally, recognizing infrared spectra is a quantitative analysis problem. However, only using quantitative analysis has met two difficulties in practice: (1) quantitative analysis generally very complex, and in some cases it may even become intractable; and (2) when spectral data are inaccurate, it is hard to give concrete solutions. Our approach performs qualitative reasoning before complex quantitative analysis starts so that the above difficulties can be efficiently overcome. We present a novel model for qualitatively decomposing and analyzing infrared spectra. A list of candidates can be obtained based on the solutions of the model, then quantitative analysis will only be applied to the limited candidates. We also present a novel model for handling inaccuracy of spectral data. The model can capture qualitative features of infrared spectra, and can consider qualitative correlations among spectral data as evidence when spectral data are inaccurate. We have tested the approach against about 300 real infrared spectra. This paper also introduces the implementation of the approach.

This paper describes the integration of a qualitative method and a quantitative method by Bottleneck Diagnosis/Improvement Expert Systems for Synchronized queueing network (BDES-S and BIES-S). On the basis of qualitative reasoning, BDES-S can carry out parameter tuning in order to diagnose and improve bottlenecks of synchronized queueing networks. BDES-S can produce several alternative qualitative improvement plans for one bottleneck server. BIES-S can produce quantitative improvement equations for each qualitative improvement plan. Our method using BDES-S and BIES-S can integrate both quantitative and qualitative methods for parameter tuning on complicated queueing synchronized networks.