July 16, 2017

Download Advanced Intelligent Systems by Yong Soo Kim, Young J. Ryoo, Moon-soo Jang, Young-Chul Bae PDF

By Yong Soo Kim, Young J. Ryoo, Moon-soo Jang, Young-Chul Bae

Intelligent platforms were initiated with the try to imitate the human mind. humans desire to allow machines practice clever works. Many concepts of clever platforms are in keeping with synthetic intelligence. in response to altering and novel standards, the complicated clever structures hide a large spectrum: substantial info processing, clever keep watch over, complicated robotics, synthetic intelligence and desktop studying. This ebook makes a speciality of coordinating clever structures with hugely built-in and foundationally useful elements. The e-book contains 19 contributions that includes social network-based recommender structures, program of fuzzy enforcement, power visualization, ultrasonic muscular thickness size, local research and predictive modeling, research of 3D polygon facts, blood strain estimation method, fuzzy human version, fuzzy ultrasonic imaging technique, ultrasonic cellular clever expertise, pseudo-normal photo synthesis, subspace classifier, cellular item monitoring, standing-up movement suggestions method, reputation constitution, multi-CAM and multi-viewer, powerful Gaussian Kernel, multi human move trajectory extraction and style coordination. This version is released in unique, peer reviewed contributions protecting from preliminary layout to ultimate prototypes and authorization.

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Gas consumption is estimated based on shape characteristics of numerals. The system uses number of endpoints and holes in numerical character, and it calculates a direction histogram and sum of absolute difference (SAD). The system recognizes the numeral by fuzzy inference from the acquired shape characteristic. When the system failed to recognize gas consumption by some accidents, the consumption is interpolated from time-serious data. In the result, our method estimated 32 and 29 numerals in 33 pieces for front and slant measurement respectively.

Consequently, the formal concept of attribute reduction with fuzzy rough sets[13,14] and a generalized interval type-2 fuzzy rough set[15] have been proposed. In [16], the authors incorporate Gaussian kernel with fuzzy rough sets and proposed a Gaussian kernel approximation based fuzzy rough set model. Consequently, the authors introduce parameterized attribute reduction with the derived model of fuzzy rough sets [17]. Ma [18] introduced weights into the variable precision rough set model to represent the importance of each sample, and discussed the influence of weights on attribute reduction.

2 Related Works The outlier problem of feature selection is rarely discussed in the most previous works. We will incorporate similarity margin concept and Gaussian kernel fuzzy rough sets to deal with the SDS problem and it is also an optimizing problem. The above theories or concepts are briefly stated as follows. 1 Similarity-Margin Based Feature Selection Algorithm[24] Hedjazi [24] proposes a feature selection method for symbolic interval data based on similarity margin. In this method, classes are parameterized by an interval prototype based on an appropriate learning process.

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