中国科学院机构知识库网格系统: 检索

Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China 期刊论文 OAI收割
REMOTE SENSING, 2023, 卷号: 15, 期号: 6, 页码: 1513
作者: Dai, Xiaoliang; Zhu, Yunqiang; Sun, Kai; Zou, Qiang; Zhao, Shen

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收藏 | 浏览/下载：11/0 | 提交时间：2023/05/06

landslide susceptibility geographical random forest spatial heterogeneity local feature importance spatial cross validation

Performance prediction of disc and doughnut extraction columns using bayes optimization algorithm-based machine learning models 期刊论文 OAI收割
CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION, 2023, 卷号: 183, 页码: 11
作者: Su, Zhenning; Wang, Yong; Tan, Boren; Cheng, Quanzhong; Duan, Xiaofei

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收藏 | 浏览/下载：2/0 | 提交时间：2023/05/19

Pulsed disk and doughnut column Machine learning Modeling Feature importance

Rice Yield Prediction and Model Interpretation Based on Satellite and Climatic Indicators Using a Transformer Method 期刊论文 OAI收割
REMOTE SENSING, 2022, 卷号: 14, 期号: 19, 页码: 21
作者: Liu, Yuanyuan; Wang, Shaoqiang; Chen, Jinghua; Chen, Bin; Wang, Xiaobo

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收藏 | 浏览/下载：46/0 | 提交时间：2022/11/09

crop yield prediction remote sensing deep learning feature importance attention

Rice Yield Prediction and Model Interpretation Based on Satellite and Climatic Indicators Using a Transformer Method 期刊论文 OAI收割
REMOTE SENSING, 2022, 卷号: 14, 期号: 19, 页码: 21
作者: Liu, Yuanyuan; Wang, Shaoqiang; Chen, Jinghua; Chen, Bin; Wang, Xiaobo

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收藏 | 浏览/下载：38/0 | 提交时间：2022/11/09

crop yield prediction remote sensing deep learning feature importance attention

Rice Yield Prediction and Model Interpretation Based on Satellite and Climatic Indicators Using a Transformer Method 期刊论文 OAI收割
REMOTE SENSING, 2022, 卷号: 14, 期号: 19, 页码: 21
作者: Liu, Yuanyuan; Wang, Shaoqiang; Chen, Jinghua; Chen, Bin; Wang, Xiaobo

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收藏 | 浏览/下载：40/0 | 提交时间：2022/11/14

crop yield prediction remote sensing deep learning feature importance attention

Application of Multi-Source Data for Mapping Plantation Based on Random Forest Algorithm in North China 期刊论文 OAI收割
REMOTE SENSING, 2022, 卷号: 14, 期号: 19, 页码: 4946-1-19
作者: Wu, Fan; Ren, Yufen; Wang, Xiaoke

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收藏 | 浏览/下载：18/0 | 提交时间：2023/02/02

plantation forest classification random forest feature importance multi-source data

Profiles, spatial distributions and inventory of brominated dioxin and furan emissions from secondary nonferrous smelting industries in China 期刊论文 OAI收割
JOURNAL OF HAZARDOUS MATERIALS, 2021, 卷号: 55, 期号: 19, 页码: 12741-12754
作者: Yang, Yuanping; Zheng, Minghui; Yang, Lili; Jin, Rong; Li, Cui

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收藏 | 浏览/下载：16/0 | 提交时间：2022/01/04

applicability domain artificial intelligence best practices feature importance machine learning modeling model applications model interpretation predictive modeling

Disruption prediction and model analysis using LightGBM on J-TEXT and HL-2A 期刊论文 OAI收割
Plasma Physics and Controlled Fusion, 2021, 卷号: 63
作者: Zhong,Y; Zheng,W; Chen,Z Y; Xia,F; Yu,L M

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收藏 | 浏览/下载：27/0 | 提交时间：2021/06/21

disruption prediction machine learning LightGBM feature importance

Road Safety Performance Function Analysis With Visual Feature Importance of Deep Neural Nets 期刊论文 OAI收割
IEEE/CAA Journal of Automatica Sinica, 2020, 卷号: 7, 期号: 3, 页码: 735-744
作者: Guangyuan Pan; Liping Fu; Qili Chen; Ming Yu; Matthew Muresan

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收藏 | 浏览/下载：24/0 | 提交时间：2021/03/11

Deep learning deep neural network (DNN) feature importance road safety performance function

An underlying clock in the extreme flip-flop state transitions of the black hole transient Swift J1658.2-4242 期刊论文 OAI收割
Astronomy and Astrophysics, 2020, 卷号: 641, 页码: A101
作者: HXMT

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收藏 | 浏览/下载：58/0 | 提交时间：2022/02/08

accretion accretion disks black hole physics X-rays: binaries time Astrophysics - High Energy Astrophysical Phenomena Abstract:
Aims: Flip-flops are top-hat-like X-ray flux variations, which have been observed in some transient accreting black hole binary systems, and feature simultaneous changes in the spectral hardness and the power density spectrum (PDS). They occur at a crucial time in the evolution of these systems, when the accretion disc emission starts to dominate over coronal emission. Flip-flops remain a poorly understood phenomenon, so we aim to thoroughly investigate them in a system featuring several such transitions.
Methods: Within the multitude of observations of Swift J1658.2-4242 during its outburst in early 2018, we detected 15 flip-flops, enabling a detailed analysis of their individual properties and the differences between them. We present observations by XMM-Newton, NuSTAR, Astrosat, Swift, Insight-HXMT, INTEGRAL, and ATCA. We analysed their light curves, searched for periodicities, computed their PDSs, and fitted their X-ray spectra, to investigate the source behaviour during flip-flop transitions and how the interval featuring flip-flops differs from the rest of the outburst.
Results: The flip-flops of Swift J1658.2-4242 are of an extreme variety, exhibiting flux differences of up to 77% within 100 s, which is much larger than what has been seen previously. We observed radical changes in the PDS simultaneous with the sharp flux variations, featuring transitions between the quasi-periodic oscillation types C and A, which have never been observed before. Changes in the PDS are delayed, but more rapid than changes in the light curve. Flip-flops occur in two intervals within the outburst, separated by about two weeks in which these phenomena were not seen. Transitions between the two flip-flop states occurred at random integer multiples of a fundamental period of 2.761 ks in the first interval and 2.61 ks in the second. Spectral analysis reveals the high and low flux flip-flop states to be very similar, but distinct from intervals lacking flip-flops. A change of the inner temperature of the accretion disc is responsible for most of the flux difference in the flip-flops. We also highlight the importance of correcting for the influence of the dust scattering halo on the X-ray spectra.