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浏览/检索结果: 共11条，第1-10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 Using Automated Machine Learning for Spatial Prediction-The Heshan Soil Subgroups Case Study 期刊论文  OAI收割 LAND, 2024, 卷号: 13, 期号: 4, 页码: 12 作者:  Liang, Peng;  Qin, Cheng-Zhi;  Zhu, A-Xing   |  收藏  |  浏览/下载：11/0  |  提交时间：2024/05/27 automated method selection  digital soil mapping  soil subgroups  classification methods   AI4AD: Artificial intelligence analysis for Alzheimer's disease classification based on a multisite DTI database 期刊论文  OAI收割 Brain Disorders, 2021, 卷号: 1, 期号: 1, 页码: 10005 作者:  Qu, Yida;  Wang, Pan;  Liu, Bing;  Song, Chengyuan;  Wang, Dawei   |  收藏  |  浏览/下载：36/0  |  提交时间：2022/06/16 Alzheimer's disease (AD)  Diffusion tensor imaging (DTI)  Multisite  Automated fiber quantification (AFQ)  Classification   Acoustic mapping and classification of benthic habitat using unsupervised learning in artificial reef water 期刊论文  OAI收割 ESTUARINE COASTAL AND SHELF SCIENCE, 2017, 卷号: 185, 页码: 11-21 作者:  Li, Dong;  Tang, Cheng;  Xia, Chunlei;  Zhang, Hua;  Tang, C 收藏  |  浏览/下载：38/0  |  提交时间：2017/04/06 Artificial reef  Acoustic mapping  Automated classification  Multibeam echosounder   Bayesian model for semi-automated zooplankton classification with predictive confidence and rapid category aggregation 期刊论文  OAI收割 MARINE ECOLOGY PROGRESS SERIES, 2011, 卷号: 441, 页码: 185-196 作者:  Ye, Lin;  Chang, Chun-Yi;  Hsieh, Chih-hao 收藏  |  浏览/下载：25/0  |  提交时间：2017/02/27 Automated classification  Naive Bayesian classifier  Predictive confidence  Rapid category aggregation  Zooplankton community  ZooScan   View Independent Object Classification Based on Automated Ground Plane Rectification for Traffic Scene Surveillance 会议论文  OAI收割 France, 17th October 2008 作者:  Zhaoxiang Zhang;  Min Li;  Kaiqi Huang;  Tieniu Tan   |  收藏  |  浏览/下载：14/0  |  提交时间：2016/12/30 Traffic Scene Surveillance  Object Feature  Automated Ground Plane Rectification  View Independent Object Classification  Online Learning Framework   恒星光谱的自动识别与分类方法研究 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, 2006 作者:  刘中田 收藏  |  浏览/下载：91/0  |  提交时间：2015/09/02 天体光谱  自动分类  红移测量  小波变换  离散傅里叶变换  卷积型小波包变换  多尺度特征匹配  Celestial object spectra  automated classification  redshifts measurement  wavelet transform  discrete fourier transform  convolution type of wavelet packet transform  multi-scaling feature matching   天体光谱自动处理及其软件系统实现 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:  杨金福 收藏  |  浏览/下载：56/0  |  提交时间：2015/09/02 天体光谱  自动分类  覆盖算法  SVM  PCA  NMF  系统设计  系统实现  软件测试  Celestial object spectrum  automated classification  covering algorithm  support vector machine  principal component analysis  non-negative matrix factorization  system design  system implementation  software testing   几个学习算法及其在星系光谱分类中的应用 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:  李乡儒 收藏  |  浏览/下载：50/0  |  提交时间：2015/09/02 活动星系核  类星体  光谱自动分类  预处理  特征提取  对数波长  流量标准化  均值漂移  相关向量机  相融性度量  active galactic nuclei(AGN)  quasar(quasi-stellar object ,QSO)  spectra automated classification  preprocessing  feature extraction  log-wavelength  flux standization  mean shift  relevance vector machine (RVM)  coherence measure   星系光谱的自动识别与分类技术研究 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, 2006 作者:  赵梅芳 收藏  |  浏览/下载：65/0  |  提交时间：2015/09/02 天体光谱  自动分类  径向基神经网络  动态衰减调节  Adaboost  特征融合  Celestial object spectra  automated classification  radial basis function neural network  dynamic decay adjustment  Adaboost  feature fusion   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：25/0  |  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.