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	Reducing Redundancy of Musculoskeletal Robot With Convex Hull Vertexes Selection 期刊论文 OAI收割 IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2020, 卷号: 12, 期号: 3, 页码: 601-617 作者: Zhong, Shanlin; Chen, Jiahao; Niu, Xingyu; Fu, Hang; Qiao, Hong \| 收藏 \| 浏览/下载：26/0 \| 提交时间：2021/01/07 Muscles Robots Mathematical model Hardware Redundancy Numerical models Convex hull vertexes human-like robot motion experiment musculoskeletal system reduce redundancy
	On hyperspectral remotely sensed image classification based on MNF and AdaBoosting (EI CONFERENCE) 会议论文 OAI收割 2012 3rd IEEE/IET International Conference on Audio, Language and Image Processing, ICALIP 2012, July 16, 2012 - July 18, 2012, Shanghai, China 作者: Yu P.; Yu P.; Gao X. 收藏 \| 浏览/下载：23/0 \| 提交时间：2013/03/25 As an effective statistical learning tool AdaBoosting has been widely used in the field of pattern recognition. In this paper a new method is proposed to improve the classification performance of hyperspectral images by combining the minimum noise fraction (MNF) and AdaBoosting. Because the hyperspectral imagery has many bands which have strong correlation and high redundancy the hyperspectral data are pre-processed by the minimum noise fraction to reduce the data's dimensionality whilst to remove noise bands simultaneously. Then we use an AdaBoost algorithm to conduct the classification of hyperspectral remotely sensed image. Experimental results show that the classification accuracy is improved and the time of calculation is reduced as well. 2012 IEEE.
	An improved hyperspectral classification algorithm based on back-propagation neural networks (EI CONFERENCE) 会议论文 OAI收割 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012, Nanjing, China 作者: Yu P.; Yu P. 收藏 \| 浏览/下载：34/0 \| 提交时间：2013/03/25 In this paper a new method is proposed to improve the classification performance of hyperspectral images by combining the principal component analysis (PCA) genetic algorithm (GA) and artificial neural networks (ANNs). First some characteristics of the hyperspectral remotely sensed data such as high correlation high redundancy etc. are investigated. Based on the above analysis we propose to use the principal component analysis to capture the main information existing in the hyperspectral images and reduce its dimensionality consequently. Next we use neural networks to classify the reduced hyperspectral data. Since the back-propagation neural network we used is easy to suffer from the local minimum problem we adopt a genetic algorithm to optimize the BP network's weights and the threshold. Experimental results show that the classification accuracy is improved and the time of calculation is reduced as well. 2012 IEEE.
	An improved two-dimensional entropy method for star trail tracing in deep sky (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Wang Y.-J.; Yao Z.-J. 收藏 \| 浏览/下载：32/0 \| 提交时间：2013/03/25 The trace of star trail is an important component of deep sky detection. The stars are low contrast targets and their self-rotation will make their brightness change in cycle. Above all the trail trace is vulnerable to the block and disturbance of other stars. Traditional one-dimensional maximum entropy thresholding algorithm is vulnerable to the noise and the calculation of two-dimensional entropy methods is too large and takes too much time. This paper proposes an improved two-dimensional entropy threshold algorithm. We use recursion iteration method to eliminate the redundancy calculation and reduce the size of two-dimensional histogram based on the deep sky stars characteristic such as low contrast fuzziness and the centralized histogram. We also combine our algorithm with the space trail trace model to forecast the star trace. Experiments results show when the star are blocked or they turn dark the method still can well extrapolate the star trace. Our method improves the capability of trailing the ebb and small star and increases the precision of tracing. It is also robust to the noise so there is a good application foreground for the method.