中国科学院机构知识库网格系统: 检索

A convex programming solution based debiased estimator for quantile with missing response and high-dimensional covariables 期刊论文 OAI收割
COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2022, 卷号: 168, 页码: 14
作者: Su, Miaomiao; Wang, Qihua

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收藏 | 浏览/下载：37/0 | 提交时间：2022/04/02

High dimensions Missing at random Marginal response quantile Optimal weights Selection probability function

Scale-up procedure of parameter estimation in selection and breakage functions for impact pin milling 期刊论文 OAI收割
ADVANCED POWDER TECHNOLOGY, 2020, 卷号: 31, 期号: 8, 页码: 3507-3520
作者: Li, Zhipeng; Wang, Li Ge; Chen, Weizhong; Chen, Xizhong; Liu, Chuanqi

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收藏 | 浏览/下载：39/0 | 提交时间：2021/05/25

Scale-up procedure Breakage parameter estimation Population balance model Selection function Breakage function

Consistent habitat preference underpins the geographically divergent autumn migration of individual Mongolian common shelducks 期刊论文 OAI收割
CURRENT ZOOLOGY, 2020, 卷号: 66, 期号: 4, 页码: 355-362
作者: Meng, Fanjuan; Wang, Xin; Batbayar, Nyambayar; Natsagdorj, Tseveenmyadag; Davaasuren, Batmunkh

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收藏 | 浏览/下载：23/0 | 提交时间：2021/08/31

common shelducks habitat selection individual variation resource selection function

Support Vector Machines Based Methodology for Credit Risk Analysis 专著章节/文集论文 OAI收割
出自: Handbook of Financial Econometrics, Mathematics, Statistics, and Technology, Singapore, Singapore:World Scientific, World Scientific, 2020
作者: Jianping Li; Mingxi Liu; Cheng-Few Lee; Dengsheng Wu

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收藏 | 浏览/下载：11/0 | 提交时间：2021/01/26

Support Vector Machines Feature Extraction Kernel Function Selection Hyper-Parameter Optimization Credit Risk Classification Support Vector Machines Feature Extraction Kernel Function Selection Hyper-Parameter Optimization Credit Risk Classification

Evolutionary divergence of the PISTILLATA-like proteins in Hedyosmum orientale (Chloranthaceae) after gene duplication 期刊论文 OAI收割
JOURNAL OF SYSTEMATICS AND EVOLUTION, 2013, 卷号: 51, 期号: 6, 页码: 681-692
作者: Liu, Shu-Jun; Du, Xiao-Qiu; Wu, Feng; Lin, Xue-Lei; Xu, Qi-Jing

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收藏 | 浏览/下载：8/0 | 提交时间：2023/04/25

B-function coding sequence divergence gene duplication PISTILLATA-like genes relaxed selection

Classification of hyperspectral image based on SVM optimized by a new particle swarm optimization (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: Gao X.; Yu P.; Yu P.

收藏 | 浏览/下载：22/0 | 提交时间：2013/03/25

Support Vector Machine (SVM) is used to classify hyperspectral remote sensing image in this paper. Radial Basis Function (RBF) which is most widely used is chosen as the kernel function of SVM. Selection of kernel function parameter is a pivotal factor which influences the performance of SVM. For this reason Particle Swarm Optimization (PSO) is provided to get a better result. In order to improve the optimization efficiency of kernel function parameter firstly larger steps of grid search method is used to find the appropriate rang of parameter. Since the PSO tends to be trapped into local optimal solutions a weight and mutation particle swam optimization algorithm was proposed in which the weight dynamically changes with a liner rule and the global best particle mutates per iteration to optimize the parameters of RBF-SVM. At last a 220-bands hyperspectral remote sensing image of AVIRIS is taken as an experiment which demonstrates that the method this paper proposed is an effective way to search the SVM parameters and is available in improving the performance of SVM classifiers. 2012 IEEE.

An auto-focus algorithm of fast search based on combining rough and fine adjustment (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: Zhang S.; Zhang Y.

收藏 | 浏览/下载：26/0 | 提交时间：2013/03/25

A coarse and fine combined fast search and auto-focusing algorithm was suggested in this paper. This method can automatically search and find the focal plane by evaluating the image definition. The Krisch operator based edge energy function was used as the big-step coarse focusing and then the wavelet transform based image definition evaluation function which is sensitivity to the variation in image definition was used to realize the small-step fine focusing in a narrow range. The un-uniform sampling function of the focusing area selection used in this method greatly reduces the workload and the required time for the data processing. The experimental results indicate that this algorithm can satisfy the requirement of the optical measure equipment for the image focusing. (2012) Trans Tech Publications.

Design of thermostat system based on Proteus simulation software (EI CONFERENCE) 会议论文 OAI收割
2011 International Conference on Electronic and Mechanical Engineering and Information Technology, EMEIT 2011, August 12, 2011 - August 14, 2011, Harbin, China
Han Z.; Song K.

收藏 | 浏览/下载：39/0 | 提交时间：2013/03/25

In order to solve the problem of precise temperature control the thermoelectric cooler (TEC) principle widely used is analyzed for the design of the whole control process and selection of control parameters and then accurate simulation model of the TEC is established in Proteus simulation software. Moreover combined with the traditional circuit simulation model the temperature control loop is designed and the response characteristics of the system are tested using an input signal similar to the unit-step function to achieve the precise temperature control. Simulation results show that the proposed control circuits can precisely convert error signal to output voltage sent to TEC model and TEC model behaves approximately like a two-pole system. The first pole starts at 20mHz and a second pole at 1Hz. 2011 IEEE.

Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Wu Z.-G.; Wang M.-J.; Han G.-L.

收藏 | 浏览/下载：77/0 | 提交时间：2013/03/25

Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.

Astronomical image restoration through atmosphere turbulence by lucky imaging (EI CONFERENCE) 会议论文 OAI收割
3rd International Conference on Digital Image Processing, ICDIP 2011, April 15, 2011 - April 17, 2011, Chengdu, China
作者: Zhao J.; Wang J.; Zhang S.

收藏 | 浏览/下载：27/0 | 提交时间：2013/03/25

In this paper we develop a lucky imaging system to restore astronomical images through atmosphere turbulence. Our system takes very short exposures on the order of the atmospheric coherence time. The rapidly changing turbulence leads to a very variable point spread function (PSF) and the variability of the PSF leads to some frames having better quality than the rest. Only the best frames are selected aligned and co-added to give a final image with much improved angular resolution. Our system mainly consists of five parts: preprocessing frame selection image registration image reconstruction and image enhancement. Our lucky imaging system has been successfully applied to restore the astronomical images taken by a 1.23m telescope. We have got clear images of moon surface and Jupiter and our system can be demonstrated to greatly improve the imaging resolution through atmospheric turbulence. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).