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Library of Congress Cataloging-in-Publication Data Tolimieri, Richard, 1940-Algorithms for discrete Fourier transform and convolution / Richard Tolimieri, Myoung An, Chao Lu. p. cm. -(Signal processing and digital filtering) Includes bibliographical references (p. ) and index. ISBN 978-1-4419-3115-3 (alk. paper) I. Fourier transformations-Data processing. 2. Convolutions (Mathematics)-Data processing. 3. Digital filters (Mathematics) I.

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The recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selecting threshold for each braking intensity level manually, an unsupervised Gaussian Mixture Model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on Artificial Neural Networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios.

As a kind of excellent diesel-blending component, polyoxymethylene dimethyl ethers (PODEn) have received widespread attention. Herein, Al-SBA-15 molecular sieves with different Si/Al ratios and pore sizes were synthesized and used to investigate the catalytic performance for the synthesis of polyoxymethylene dimethyl ethers from methylal and trioxane. X-ray diffraction, N 2 adsorption−desorption, scanning electron microscopy, transmission electron microscopy, X-ray fluorescence, and 27 Al NMR were used to characterize the structures of obtained catalysts. Ammonia temperature-programmed desorption and pyridine adsorption were carried out to investigate the acid properties of the catalysts. Through comparison of the catalysts with different Al contents, it was found that the relatively weak acid was more suitable for the synthesis of PODEn than the relatively strong acid in the catalytic system of Al-SBA-15. On the Al-SBA-15(2)-150 catalyst, which has only a weak acid of 0.163 mmol/g, the highest TOX conversion rate and highest PODEn yield and selectivity were achieved, showing the best catalytic performance. It appears that the PODEn synthesis can be catalyzed by not only a Bronsted acid but also a Lewis acid. The catalysts with strong acid and/or with a large number of acids will cause the generation of significant amount of methyl formate byproduct. Through comparison of the catalysts with different pore sizes, it was found that a relative larger pore size of the catalyst was beneficial for the PODEn synthesis to a certain extent under the catalysts with strong acid and/or large acid amount, but on the catalyst that had only weak acid and a relatively lesser amount, the change of pore size had almost no effect on the yield and selectivity of PODEn products.