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	Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making 期刊论文 OAI收割 FRONTIERS IN MARINE SCIENCE, 2024, 卷号: 11, 页码: 24 作者: Li, Yong; Liu, Xiaotong; Wang, Zhishan; Mei, Qiang; Xie, Wenxin \| 收藏 \| 浏览/下载：3/0 \| 提交时间：2024/12/06 knowledge graph graph embedding intelligent maritime ship classification similar berth recommendation
	Question Classification for Intelligent Question Answering: A Comprehensive Survey 期刊论文 OAI收割 ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2023, 卷号: 12, 期号: 10, 页码: 415 作者: Sun, Hao; Wang, Shu; Zhu, Yunqiang; Yuan, Wen; Zou, Zhiqiang \| 收藏 \| 浏览/下载：20/0 \| 提交时间：2023/12/04 Intelligent Question Answering (IQA) GeoAI Question Classification (QC) IQA_QC framework evaluation metrics literature review
	Vehicle Re-Identification Using Quadruple Directional Deep Learning Features 期刊论文 OAI收割 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2020, 卷号: 21, 期号: 1, 页码: 410-420 作者: Zhu, Jianqing; Zeng, Huanqiang; Huang, Jingchang; Liao, Shengcai; Lei, Zhen \| 收藏 \| 浏览/下载：50/0 \| 提交时间：2020/03/30 Deep learning Feature extraction Convolutional neural networks Databases Measurement Cameras Intelligent transportation systems Computer vision artificial neural networks feature extraction image classification
	Stellar Spectra Classification by Support Vector Machine with Unlabeled Data 期刊论文 OAI收割 SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 卷号: 39, 期号: 3, 页码: 948-952 作者: Zhang Jing; Tu Liang-ping; Wang Jie; Liu Zhong-bao; Lei Yu-fei \| 收藏 \| 浏览/下载：61/0 \| 提交时间：2019/05/23 Stellar spectra Intelligent classification Twin support vector machine Unlabeled data
	Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model 期刊论文 OAI收割 INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING, 2011, 卷号: 7, 期号: 3, 页码: 88-101 作者: Sun, DongHong; Liu, Li; Zhang, Peng; Zhu, Xingquan; Shi, Yong \| 收藏 \| 浏览/下载：8/0 \| 提交时间：2019/12/16 Classification Data Mining Intelligent Knowledge Management Multi-Criteria Optimization Multiple Criteria Linear Programming
	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.