Read e-book online Autonomous Systems Self-Organization, Management, and PDF

By Bernd Mahr, Huanye Sheng

ISBN-10: 1402088884

ISBN-13: 9781402088889

The 2008 TUB-SJTU joint workshop on “Autonomous platforms – Self-Organization, administration, and keep an eye on” was once hung on October 6, 2008 at Shanghai Jiao Tong collage, Shanghai, China. The workshop, subsidized by means of Shanghai Jiao Tong college and Technical collage of Berlin introduced jointly scientists and researchers from either universities to give and talk about the newest development on self sufficient structures and its functions in assorted components. self reliant structures are designed to combine machines, computing, sensing, and software program to create clever structures in a position to interacting with the complexities of the true international. self reliant structures symbolize the actual embodiment of computing device intelligence. issues of curiosity contain, yet should not restricted to thought and modeling for self sufficient platforms; association of self reliant structures; studying and conception; advanced platforms; multi-agent platforms; robotics and regulate; functions of self sustaining platforms.

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Read Online or Download Autonomous Systems Self-Organization, Management, and Control: Proceedings of the 8th International Workshop held at Shanghai Jiao Tong University, Shanghai, China, October 6-7, 2008 PDF

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Extra resources for Autonomous Systems Self-Organization, Management, and Control: Proceedings of the 8th International Workshop held at Shanghai Jiao Tong University, Shanghai, China, October 6-7, 2008

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In previous works, audio or visual stimulus is used and the subject is required to give some responses [8]. Reaction time performance is the measurement for vigilance. In this mode, supervised learning methods such as artificial neural network (ANN) can be used to classify different states of vigilance. But stimulus may introduce some noise. So in [10], the author proposed a semi-supervised learning algorithm which can quickly label huge amount of data. Here we propose another kind of semi-supervised learning method based on probabilistic principle component analysis (PPCA) to classify EEG data with little label information.

3), (8), (14), and (15), we can derive the trajectory of COG in the sagittal plane for any known surface. 4 Experiments This section shows two selected experiments of the biped robot SHR-1. Both are performed with the pattern generation proposed above. 1 Prototype Biped Robot The biped robot shown in Fig. 1a is developed since 2006. 85 m and 32 kg, and there are 6 DOFs for each leg (3 DOFs for the hip, 1 DOF for the knee, and 2 DOFs for the ankle). The hip joint has a cantilever type structure as HRP [2].

Furthermore, after some sessions, subjects learned to play the BCI game better and better with two adaptation cycles of man and machine. Then, we also tested the performance with a shorter trial length and obtained fast online BCI. The best performance with fast response time attained by a subject was about 75%, which 36 Q. Zhao et al. demonstrates that our BCI system has the ability of providing much fast BCI at a response time of only 1 s. 5 Conclusion We have proposed a spatial-frequency filter optimization algorithm for the single trial EEG classification problem, which utilized the method of CWT in order to extend the CSP algorithm to the time-frequency space.

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Autonomous Systems Self-Organization, Management, and Control: Proceedings of the 8th International Workshop held at Shanghai Jiao Tong University, Shanghai, China, October 6-7, 2008 by Bernd Mahr, Huanye Sheng


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