机构

• 力学研究所 [1]
• 长春光学精密机械与物... [1]
• 心理研究所 [1]

采集方式

• OAI收割 [3]

内容类型

• 会议论文 [2]
• 期刊论文 [1]

发表日期

• 2018 [1]
• 2016 [1]
• 2008 [1]

学科主题

• 感知觉心理学 [1]

筛选

浏览/检索结果: 共3条，第1-3条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 An efficient single-loop strategy for reliability-based multidisciplinary design optimization under non-probabilistic set theory 期刊论文  OAI收割 AEROSPACE SCIENCE AND TECHNOLOGY, 2018, 卷号: 73, 页码: 148-163 作者:  Wang XJ;  Wang RX(王睿星);  Wang L;  Chen XJ;  Geng XY   |  收藏  |  浏览/下载：41/0  |  提交时间：2018/10/30 Non-probabilistic reliability based  multidisciplinary design optimization  Single loop strategy  Uncertainty multidisciplinary analysis  Non-probabilistic reliability analysis  Translating distance calculation   视觉空间概率分布的启发式表征：k-means聚类方式 会议论文  OAI收割 2016年第一届北京视觉科学会议, 北京, 2016-07 作者:  Sun Jingwei;  Li Jian;  Zhang Hang 收藏  |  浏览/下载：23/0  |  提交时间：2017/01/09 distribution representation  k-means clustering  probabilistic calculation  decision under risk   A MLP-PNN neural network for CCD image super-resolution in wavelet packet domain (EI CONFERENCE) 会议论文  OAI收割 2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, October 12, 2008 - October 14, 2008, Dalian, China Zhao X.; Fu D.; Zhai L. 收藏  |  浏览/下载：68/0  |  提交时间：2013/03/25 Image super-resolution methods process an input image sequence of a scene to obtain a still image with increased resolution. Classical approaches to this problem involve complex iterative minimization procedures  typically with high computational costs. In this paper is proposed a novel algorithm for super-resolution that enables a substantial decrease in computer load. First  decompose and reconstruct the image by wavelet packet. Before constructing the image  use neural network in place of other rebuilding method to reconstruct the coefficients in the wavelet packet domain. Second  probabilistic neural network architecture is used to perform a scattered-point interpolation of the image sequence data in the wavelet packet domain. The network kernel function is optimally determined for this problem by a MLP-PNN (Multi Layer Perceptron - Probabilistic Neural Network) trained on synthetic data. Network parameters dependent on the sequence noise level. This super-sampled image is spatially Altered to correct finite pixel size effects  to yield the final high-resolution estimate. This method can decrease the calculation cost and get perfect PSNR. Results are presented  showing the quality of the proposed method. 2008 IEEE.