中国科学院机构知识库网格系统: 检索

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	Uncovering the key mechanisms of how deep decarbonization benefits air pollution alleviation in China 期刊论文 OAI收割 ENVIRONMENTAL RESEARCH LETTERS, 2022, 卷号: 17, 期号: 11, 页码: 15 作者: Liu, Xiaorui; Guo, Chaoyi; Ma, Xiaotian; Wu, Kai; Wang, Peng \| 收藏 \| 浏览/下载：6/0 \| 提交时间：2024/12/03 1.5 degrees C target 2 degrees C target air pollutant IMED vertical bar CGE model decomposition analysis regression model Greater Bay Area in China
	Siamese Regression Tracking With Reinforced Template Updating 期刊论文 OAI收割 IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 卷号: 30, 页码: 628-640 作者: Zhao, Fei; Zhang, Ting; Song, Yibing; Tang, Ming; Wang, Xiaobo \| 收藏 \| 浏览/下载：32/0 \| 提交时间：2021/03/02 Target tracking Training Reinforcement learning Visualization Task analysis Benchmark testing Head Siamese regression tracking actor-critic network reinforcement learning
	Fire Detection Method Based on Depthwise Separable Convolution and YOLOv3 期刊论文 OAI收割 International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 2, 页码: 300-310 作者: Yue-Yan Qin \| 收藏 \| 浏览/下载：26/0 \| 提交时间：2021/04/22 Fire detection depthwise separable convolution fire classification You Only Look Once version 3 (YOLOv3) target regression
	基于生物网络的关联发现关键技术研究学位论文 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, 2012 作者: 刘西收藏 \| 浏览/下载：73/0 \| 提交时间：2015/09/02 本体生物网络贝叶斯回归网络拓扑致病基因药物靶标网络药理学关联发现 Ontology Bio-network Bayesian Regression Network Topology Disease Gene Drug Target Network Pharmacology Association Discovery
	Tracking error estimate for theodolite based on general regression neural network (EI CONFERENCE) 会议论文 OAI收割 3rd international Conference on Manufacturing Science and Engineering, ICMSE 2012, March 27, 2012 - March 29, 2012, Xiamen, China 作者: Li M. 收藏 \| 浏览/下载：20/0 \| 提交时间：2013/03/25 To efficiently evaluate the tracking index of theodolite as well as avoiding influence resulted from higher harmonics when used opto dynamic target and use equivalent sine to evaluate the tracking index in this paper we resorted to General Regression Neural Network to do the new research on theodolite tracking accuracy evaluation method. Experimental results indicate that the method realized the equivalent sine to evaluate the tracking index of theodolite and avoided the higher harmonics influence with opto dynamic target. The research in this paper has important value to the engineering practice. (2012) Trans Tech Publications.
	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