中国科学院机构知识库网格系统: 检索

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收藏 | 浏览/下载：44/0 | 提交时间：2022/01/20

Fault detection Kernel Monitoring Heuristic algorithms Nonlinear dynamical systems Entropy Principal component analysis Dynamic feature quality-related fault detection KECA DKECR

A Deep Nonnegative Matrix Factorization Approach via Autoencoder for Nonlinear Fault Detection 期刊论文 OAI收割
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2020, 卷号: 16, 期号: 8, 页码: 5042-5052
作者: Ren, Zelin; Zhang, Wensheng; Zhang, Zhizhong

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收藏 | 浏览/下载：39/0 | 提交时间：2020/07/06

Kernel Fault detection Feature extraction Matrix decomposition Neural networks Informatics Data-driven fault detection deep autoencoder nonlinear industrial process nonnegative matrix factorization (NMF)

A Clustering-Based Nonlinear Ensemble Approach for Exchange Rates Forecasting 期刊论文 OAI收割
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2020, 卷号: 50, 期号: 6, 页码: 2284-2292
作者: Sun, Shaolong; Wang, Shouyang; Wei, Yunjie; Zhang, Guowei

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收藏 | 浏览/下载：36/0 | 提交时间：2020/06/30

Forecasting Exchange rates Predictive models Self-organizing feature maps Clustering algorithms exchange rates forecasting kernel-based extreme learning machine (KELM) nonlinear ensemble

Convolutional neural network with nonlinear competitive units 期刊论文 OAI收割
SIGNAL PROCESSING-IMAGE COMMUNICATION, 2018, 卷号: 60, 页码: 193-198
作者: Chen, Zhang-Ling; Wang, Jun; Li, Wen-Juan; Li, Nan; Wu, Hua-Ming

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收藏 | 浏览/下载：24/0 | 提交时间：2019/12/16

Nonlinear competitive unit Feature fusion Activation function Face verification Visual classification

Feature space locality constraint for kernel based nonlinear discriminant analysis 期刊论文 OAI收割
PATTERN RECOGNITION, 2012, 卷号: 45, 期号: 7, 页码: 2733-2742
作者: Lei, Zhen; Mang, Zhiwei; Li, Stan Z.

收藏 | 浏览/下载：21/0 | 提交时间：2015/09/18

Locality constraint Feature space Nonlinear discriminant analysis Face recognition

The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China
作者: Sun H.; Han H.-X.; Sun H.

收藏 | 浏览/下载：59/0 | 提交时间：2013/03/25

Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring precision and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure but in order to capture the change of the state space it need a certain amount of particles to ensure samples is enough and this number will increase in accompany with dimension and increase exponentially this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining" we expand the classic Mean Shift tracking framework.Based on the previous perspective we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value.Last because of the gray and fusion target motion information this approach also inhibit interference from the background ultimately improve the stability and the real-time of the target track. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Nonlinear feature of the abrupt transitions between multiple equilibria states of an ecosystem model 期刊论文 iSwitch采集
Advances in atmospheric sciences, 2009, 卷号: 26, 期号: 2, 页码: 293-304
作者: Sun Guodong; Mu Mu

收藏 | 浏览/下载：36/0 | 提交时间：2019/05/10

Nonlinear feature Abrupt transition Grassland and desert ecosystem Shading effect

Space optics remote sensor focusing components mechanics characteristic analysis based on FEM (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2009: Material and Device Technology for Sensors, June 17, 2009 - June 19, 2009, Beijing, China
作者: Wang B.; Ren J.-Y.; Wang B.

收藏 | 浏览/下载：157/0 | 提交时间：2013/03/25

Space optical remote sensor is very important in many fields of science and military significance. It is widely applied. Space optical remote sensor design and manufacturing require precision and stability. Focusing mechanics is an important component of remote sensors. Focusing mechanics can guarantee the stability of the entire mechanics focusing accuracy therefore the stability of research focusing mechanics is very important. In order to guarantee the space optics remote sensor focusing mechanics the stability takes steps the space optics remote sensor focusing organization mechanics characteristic analysis from the classics contact theory. This article uses international general non-linear finite element analysis software ABAQUS to carry on mechanics characteristic analysis to the space optics remote sensor focusing mechanics. First acts according to the focusing mechanics unique feature carries on the finite element to the structural model the grid division the material attribute disposition and the boundary condition indeed grades. Then establishment contact non-linear finite element model and to focusing organization finite element model infliction unit action. Like this contacts the result and the equivalent static analysis result which the nonlinear analysis obtains carries on the contrast and the analysis. This article last count result modality is 90HZ satisfies the space optics remote sensor structure overall modality to request to be bigger than 50HZ.This article when carries on the dynamic analysis extracts the structure kinetic energy and the acceleration curve. In the dynamic analysis obtained transient response analysis modality 70.5Hz this also is bigger than 50HZ. The dynamic analysis indicated the structure dynamic stability has the distinct enhancement has provided certain foundation for the space optics remote sensor following development work reduced the overall system development cycle. 2009 SPIE.

Intelligent MRTD testing for thermal imaging system using ANN (EI CONFERENCE) 会议论文 OAI收割
ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China
Sun J.; Ma D.

收藏 | 浏览/下载：27/0 | 提交时间：2013/03/25

The Minimum Resolvable Temperature Difference (MRTD) is the most widely accepted figure for describing the performance of a thermal imaging system. Many models have been proposed to predict it. The MRTD testing is a psychophysical task for which biases are unavoidable. It requires laboratory conditions such as normal air condition and a constant temperature. It also needs expensive measuring equipments and takes a considerable period of time. Especially when measuring imagers of the same type the test is time consuming. So an automated and intelligent measurement method should be discussed. This paper adopts the concept of automated MRTD testing using boundary contour system and fuzzy ARTMAP but uses different methods. It describes an Automated MRTD Testing procedure basing on Back-Propagation Network. Firstly we use frame grabber to capture the 4-bar target image data. Then according to image gray scale we segment the image to get 4-bar place and extract feature vector representing the image characteristic and human detection ability. These feature sets along with known target visibility are used to train the ANN (Artificial Neural Networks). Actually it is a nonlinear classification (of input dimensions) of the image series using ANN. Our task is to justify if image is resolvable or uncertainty. Then the trained ANN will emulate observer performance in determining MRTD. This method can reduce the uncertainties between observers and long time dependent factors by standardization. This paper will introduce the feature extraction algorithm demonstrate the feasibility of the whole process and give the accuracy of MRTD measurement.

Dim target detection based on nonlinear multifeature fusion by Karhunen-Loeve transform 期刊论文 OAI收割
OPTICAL ENGINEERING, 2004, 卷号: 43, 期号: 12, 页码: 2954-2958
作者: Peng, ZM; Zhang, QH; Wang, JR; Zhang, QP

收藏 | 浏览/下载：21/0 | 提交时间：2015/09/21

nonlinear features Karhunen-Loeve transform feature fusion dim target detection