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	A novel self-adapting filter based navigation algorithm for autonomous underwater vehicles 期刊论文 OAI收割 Ocean Engineering, 2019, 卷号: 187, 页码: 1-12 作者: Xu CH(徐春晖); Shao G(邵刚); Wang Y(王轶群); Liu J(刘健); Qu DK(曲道奎) \| 收藏 \| 浏览/下载：70/0 \| 提交时间：2019/08/04 Autonomous underwater vehicle Ultra short baseline Condition-adaptive Confidence measure operator Integrated navigation system
	Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion 期刊论文 OAI收割 IEEE SIGNAL PROCESSING LETTERS, 2016, 卷号: 23, 期号: 6, 页码: 5 作者: Cong, Runmin; Lei, Jianjun; Zhang, Changqing; Huang, Qingming; Cao, Xiaochun \| 收藏 \| 浏览/下载：25/0 \| 提交时间：2019/12/12 Color and depth-based compactness depth confidence measure multiple cues saliency detection
	Character confidence based on N-best list for keyword spotting in online Chinese handwritten documents 期刊论文 OAI收割 PATTERN RECOGNITION, 2014, 卷号: 47, 期号: 5, 页码: 1880-1890 作者: Zhang, Heng; Wang, Da-Han; Liu, Cheng-Lin 收藏 \| 浏览/下载：60/0 \| 提交时间：2015/08/12 Online Chinese handwritten documents Keyword spotting Posterior probability N-best list Confidence measure Confusion network
	多语言语种识别技术的研究学位论文 OAI收割工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2007 作者: 姜洪臣收藏 \| 浏览/下载：151/0 \| 提交时间：2015/09/02 多语言语种识别音频分类支持向量机高斯混合模型-全局背景模型识别置信度 multilingual language identification audio classification SVM GMM-UBM recognition confidence measure
	语音识别中的置信度研究与应用学位论文 OAI收割工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2006 作者: 梁家恩收藏 \| 浏览/下载：108/0 \| 提交时间：2015/09/02 语音识别关键词检测置信度在线垃圾模型 MCE训练 ASR KWS Confidence Measure Online Garbage MCE Training
	Study on color model conversion for camera with neural network based on the combination between second general revolving combination design and genetic algorithm (EI CONFERENCE) 会议论文 OAI收割 ICO20: Illumination, Radiation, and Color Technologies, August 21, 2005 - August 26, 2005, Changchun, China 作者: Li Z.; Zhou F.; Wang C.; Li Z. 收藏 \| 浏览/下载：36/0 \| 提交时间：2013/03/25 Munsell color system is selected to establish the mutual conversion between RGB and Lab* color model for camera. The color luminance meter and CCD camera synchronously measure the same color card XYZ value is gotten from the color luminance meter the training error is 0.000748566 it can show that the method combining second general revolving combination design with genetic algorithm can optimize the hidden-layer structure of neural network. Using the data of testing set to test this network and calculating the color difference between forecast value and true value the color picture captured from CCD camera is expressed for RGB value as the input of neural network and the Lab* value converted from XYZ value is regarded as the real color value of target card which the difference is not obvious comparing with forecast result the maximum is 5.6357 NBS namely the output of neural network. The neural network of two hidden-layers is considered the minimum is 0.5311 NBS so the second general revolving combination design is introduced into optimizing the structure of neural network and the average of color difference is 3.1744 NBS. which can carry optimization through unifying project design data processing and the precision of regression equation. Their mathematics model of encoding space is gained and the significance inspection shows the confidence degree of regression equation is 99%. The mathematics model is optimized by genetic algorithm optimization solution is gotten and function value of the goal is 0.0007168. The neural network of the optimization solution is trained