Advances in Technology and Management: Proceedings of the by Lianfang Zhu, Ligang Dong, Chuanhuang Li (auth.), Haenakon

By Lianfang Zhu, Ligang Dong, Chuanhuang Li (auth.), Haenakon Kim (eds.)

This publication Advances in know-how and administration comprises 116 complete size papers provided on the foreign convention on expertise and administration, hung on June 12-13, 2012, Jeju-Island, Korea. The aim of ICTAM 2012 is to assemble researchers operating in lots of diverse components of expertise and administration to foster overseas collaborations and alternate of latest ideas.

This quantity may be divided into sections at the foundation of the category of manuscripts thought of. the 1st part offers with know-how. the second one part of this quantity includes management.

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A Correlation and Regression Analysis of the Intentional Standard of Tourism Consumption——A case study. Journal of Beijing International Studies University, 31–35 (May 2008) 2. : Fuzzy mathematics and its application. Huazhong University of Science and Technology Press (2006) 3. : Fuzzy Cluster Analysis of Market Demand for Automobile Products. Journal of Shanghai University of Engineering Science, 362–365 (April 2008) 4. : Fuzzy Cluster Analysis with Excel. cn Abstract. Predicting software development effort accurately is crucial for the timely delivery of quality-assured products within a reasonable timeframe.

A) (c) filtered signal based on traditional filtering algorithm of spatial correlation (d) corresponding frequency spectrum of Fig. (c) The Filtering Algorithm Based on Spatial Correlation for Echo of Ultrasonic 2 200 0 100 -2 0 1000 2000 3000 4000 5000 0 1 200 0 100 -1 0 1000 2000 3000 4000 5000 0 1 200 0 100 -1 0 1000 2000 3000 4000 5000 0 0 50 100 0 50 100 0 50 100 27 Fig. 5. Filtering features when SNR=-10db (a) measured echo signal (b) corresponding frequency spectrum of Fig. (a) (c) filtered signal based on traditional filtering algorithm of spatial correlation (d) corresponding frequency spectrum of Fig.

For an attribute k , if its output value changes most based on the change of sample attribute value, then it is clear that attribute k is important. Otherwise it explains that attribute k is less significant for prediction. The method forms the following formula: C ( k ) = ∑ x(i, k ) − x( j, k ) × sign y (i) − y ( j ) i≠ j (1) where C (k ) is the input output correlation value of attribute k . x(i, k ) and x( j , k ) are values of attribute k in sample i and sample j . y (i ) and y ( j ) are expected values in sample i and sample j .

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