机构

• 自动化研究所 [47]
• 地理科学与资源研究... [34]
• 沈阳自动化研究所 [17]
• 数学与系统科学研究院 [9]
• 深圳先进技术研究院 [8]
• 心理研究所 [8]
• 更多

采集方式

• OAI收割 [196]
• iSwitch采集 [8]

内容类型

• 期刊论文 [154]
• 会议论文 [31]
• 学位论文 [15]
• SCI/SSCI论文 [4]

发表日期

• 2024 [3]
• 2023 [8]
• 2022 [22]
• 2021 [23]
• 2020 [16]
• 2019 [29]
• 更多

学科主题

• Computer S... [4]
• Robotics [4]
• Automation... [3]
• Engineerin... [3]
• Agricultur... [1]
• Chemistry [1]
• 更多

筛选

浏览/检索结果: 共204条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择提交时间升序提交时间降序发表日期升序发表日期降序题名升序题名降序作者升序作者降序 Identifying stress scores from gait biometrics captured using a camera: A cross-sectional study 期刊论文  OAI收割 GAIT & POSTURE, 2024, 卷号: 109, 页码: 15-21 作者:  Wang, Jingying;  Wen, Yeye;  Zhou, Junhong;  Zhao, Nan;  Zhu, Tingshao   |  收藏  |  浏览/下载：6/0  |  提交时间：2024/04/01 Gait  Perceived stress  Video analysis  Machine learning  Regression   A progressive framework combining unsupervised and optimized supervised learning for debris flow susceptibility assessment 期刊论文  OAI收割 CATENA, 2024, 卷号: 234, 页码: 16 作者:  Liu, Yongqiang;  Chen, Jianping;  Sun, Xiaohui;  Li, Yongchao;  Zhang, Yiwei   |  收藏  |  浏览/下载：14/0  |  提交时间：2023/12/27 Debris flow susceptibility  Watershed unit  Unsupervised learning  Supervised learning  Multi-source data   A feasibility study on applying meta-heuristic optimization and Gaussian process regression for predicting the performance of pantograph-catenary system 期刊论文  OAI收割 ACTA MECHANICA SINICA, 2024, 卷号: 40, 期号: 1, 页码: 12 作者:  Zhang MH(张莫晗);  Yin B(银波);  Sun ZX(孙振旭);  Bai, Ye;  Yang GW(杨国伟)   |  收藏  |  浏览/下载：4/0  |  提交时间：2024/02/26 Pantograph-catenary system  Gaussian process regression  Surrogate model  Physical-based model   Improved Quantum-Classical Treatment of N2-N2 Inelastic Collisions: Effect of the Potentials and Complete Rate Coefficient Data Sets 期刊论文  OAI收割 JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2023, 卷号: 19, 期号: 23, 页码: 8557-8571 作者:  Hong, Qizhen;  Storchi, Loriano;  Sun, Quanhua;  Bartolomei, Massimiliano   |  收藏  |  浏览/下载：4/0  |  提交时间：2024/01/22 Physics-Guided Deep Learning Method for Tool Condition Monitoring in Smart Machining System 期刊论文  OAI收割 IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2023 作者:  Li, Shenshen;  Lin, Xin;  Shi, Hu;  Shi, Yungao;  Zhu, Kunpeng   |  收藏  |  浏览/下载：5/0  |  提交时间：2023/11/17 Deep learning  physics-guided data model  tool condition monitoring   Rotated Object Detection with Circular Gaussian Distribution 期刊论文  OAI收割 ELECTRONICS, 2023, 卷号: 12, 期号: 15, 页码: 12 作者:  Xu, Hang;  Liu, Xinyuan;  Ma, Yike;  Zhu, Zunjie;  Wang, Shuai   |  收藏  |  浏览/下载：8/0  |  提交时间：2023/12/04 rotated object detection  circular Gaussian  CenterNet  CGD   Discovery of PDEs driven by data with sharp gradient or discontinuity 期刊论文  OAI收割 COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2023, 卷号: 140, 页码: 33-43 作者:  Wang, Kang;  Zhang L(张磊);  Tang, Shaoqiang   |  收藏  |  浏览/下载：11/0  |  提交时间：2023/06/15 System identification  Partial differential equations  Gaussian process  Discontinuous data  Machine learning   Oceanic Primary Production Estimation Based On Machine Learning 期刊论文  OAI收割 JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2023, 卷号: 128, 期号: 5, 页码: e2022JC018980 作者:  Ping, Bo;  Meng, Yunshan;  Xue, Cunjin;  Su, Fenzhen   |  收藏  |  浏览/下载：12/0  |  提交时间：2023/07/14 oceanic primary production (OPP)  support vector regression (SVR)  Gaussian regression  random forest regression  vertically generalized productivity model (VGPM)  Eppley-VGPM   Reconstruction based on adaptive group least angle regression for fluorescence molecular tomography 期刊论文  OAI收割 BIOMEDICAL OPTICS EXPRESS, 2023, 卷号: 14, 期号: 5, 页码: 2225-2239 作者:  An, Yu;  Wang, Hanfan;  Li, Jiaqian;  Li, Guanghui;  Ma, Xiaopeng   |  收藏  |  浏览/下载：6/0  |  提交时间：2023/11/17 An improved spatial case-based reasoning considering multiple spatial drivers of geographic events and its application in landslide susceptibility mapping 期刊论文  OAI收割 CATENA, 2023, 卷号: 223 作者:  Zhao, Zheng;  Chen, Jianhua;  Yao, Jiaming;  Xu, Kaihang;  Liao, Yangyang   |  收藏  |  浏览/下载：17/0  |  提交时间：2023/03/03 Case -based reasoning  Spatial drivers  Spatial reuse  Landslide susceptibility mapping  Machine learning