Components, packaging and manufacturing technology : selected, peer reviewed paper from 2010 International Conference on Components, Packaging and Manufacturing Technology (ICCPMT 2010) Sanya, China, December 9-10, 2010

• Corporate Author: International Conference on Components, Packaging and Manufacturing Technology Sanya Shi, China)
• Other Authors: Wu, Yanwen. (Editor)
• Format: eBook
• Language: English
• Published: Switzerland : Trans Tech Publications, 2011.
• Series: Key engineering materials ; v. 460-461.
• Subjects: Electronic apparatus and appliances > Congresses.; Electronic packaging > Congresses.; Microelectronic packaging > Congresses.; Manufacturing processes > Congresses.; elektronické knihy; electronic books
• ISBN: 9781613446713; 1613446713; 9783038134800; 3038134805; 9780878492138; 0878492135
• Physical Description: 1 online resource : illustrations
• ISSN: 1662-9795
• Access: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty

The objective of this special collection is to provide a showcase for researchers, educators, engineers and government officials, involved in the general areas of Components, Packaging and Manufacturing Technology, by which to highlight the latest research results and to exchange views on the future direction of research in these fields. The topics covered include: Advanced Measurement, Test and Information Technology, Components, Packaging and Manufacturing Technology. This work thus constitutes a handy guide to current thinking in the field. Review from Book News Inc.: Nearly 150 selected and peer-reviewed papers discuss first advanced measurement, test, and information technology; then components, packaging, and manufacturing technology. The topics include exploring the structure of the crisis management team, the mutual information model of adaptive waveform design, counter-measures of electronic business development for Chinese travel companies, an improved attack-resistant collaborative filtering algorithm, analyzing the characteristics of textile materials based on pre-judgement mechanisms, applying chaos particle swarm optimized neural networks for evaluating credit risk, next-day load forecasting using local temperature-sensitive information, and applying data mining technology in mechanical fault diagnosis.