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For injection molding process, a typical multistage batch process, the final product qualities are usually available at the end of the batch, which make it difficult for on-line quality control. A sub-stage moving window generalized regression neural network (GRNN) is proposed for dedicating to reveal the nonlinearly and dynamic relationship between process variables and final qualities at different stages. Firstly, using an clustering arithmetic, PCA P-loading matrices of time-slice matrices is clustered and the batch process is divided into several operation stages, the most relevant stage to the quality variable is defined, and then applying moving windows to un-fold stage data according to time, and sub-stage GRNN models are developed for every windows for on-line quality prediction. For comparison purposes a sub-MPLS quality model of every moving window was establish. The results prove the effectiveness of the proposed quality prediction method is supervior to sub- MPLS quality prediction method. [ABSTRACT FROM AUTHOR]

This paper presents an efficient iris recognition method based on wavelet multi-scale decompositions. A two-dimensional iris image should be transformed into a set of one-dimensional signals initially and then the wavelet coefficients matrix is generated by one-dimensional quadratic spline wavelet multi-scale decompositions. From the basic principles of probability theory, the elements at the same position in different wavelet coefficients matrices can be considered as a high correlated sequence. By applying a predetermined threshold, these wavelet coefficients matrices are finally transformed into a binary vector to represent iris features. The Hamming distance classifier is adopted to perform pattern matching between two feature vectors. Using an available iris database, final experiments show promising results for iris recognition with our proposed approach. [ABSTRACT FROM AUTHOR]