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A novel mooring system anomaly detection framework for SEMI based on improved residual network with attention mechanism and feature fusion
期刊论文
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2024, 卷号: 245, 页码: 21
作者:
Mao, Yixuan
;
Li, Xiaorong
;
Duan, Menglan
;
Feng, Yongcun
;
Wang, Jinjia
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2024/04/29
Mooring line anomaly detection
Residual network
Feature fusion
Semi-submersible platform
Population parameters and heterogeneity in survival rates of Indo-Pacific humpback dolphins in a heavily urbanized coastal region of southeast China: implications for conservation
期刊论文
FRONTIERS IN MARINE SCIENCE, 2024, 卷号: 11, 页码: 18
作者:
Lin, Wenzhi
;
Karczmarski, Leszek
;
Chan, Stephen C. Y.
;
Zheng, Ruiqiang
;
Ho, Yuen-Wa
收藏
  |  
浏览/下载:1/0
  |  
提交时间:2024/05/07
Sousa chinensis
inshore cetaceans
Lingding Bay/Pearl River Estuary
mark-recapture population modeling
population size estimate
apparent survival rates
spatial heterogeneity
declining abundance
WeldNet: A deep learning based method for weld seam type identification and initial
期刊论文
EXPERT SYSTEMS WITH APPLICATIONS, 2024, 卷号: 238, 页码: 15
作者:
Ma, Yunkai
;
Fan, Junfeng
;
Zhou, Zhen
;
Zhao, Sihan
;
Jing, Fengshui
收藏
  |  
浏览/下载:3/0
  |  
提交时间:2023/12/21
Weld seam type identification
Initial point guidance
Deep learning
Robot welding
Vision sensors
From dynamics to sustainability: Modeling the shrimp
(Penaeus
chinensis)
fishery in Shandong, China
期刊论文
ECOLOGICAL MODELLING, 2024, 卷号: 489, 页码: 8
作者:
Liu, Xin
;
Geng, Ruiying
;
Lv, Xin
;
Hu, Xiaoke
;
Do, Thang Nam
收藏
  |  
浏览/下载:1/0
  |  
提交时间:2024/05/07
Shrimp fishery
System dynamic model
Sustainable management
Fishing policy
Stock enhancement program
Fishing ban duration
Linking soil erosion and sediment yield with landscape heterogeneity: Exploration using the lower Jinsha River Basin, China
期刊论文
JOURNAL OF HYDROLOGY, 2024, 卷号: 631, 页码: 14
作者:
Zhang, Haozhe
;
Lv, Jiaorong
;
Bao, Yuhai
;
Tang, Qiang
;
Collins, Adrian L.
收藏
  |  
浏览/下载:0/0
  |  
提交时间:2024/04/19
Sediment system
Landscape heterogeneity
WaTEM/SEDEM
Lower Jinsha River
A small landslide induced a large disaster prior to the heavy rainy season in Jinkouhe, Sichuan, China: characteristics, mechanism, and lessons
期刊论文
LANDSLIDES, 2024, 页码: 13
作者:
Hou, Runing
;
Li, Zhi
;
Ye, Weihao
;
Peng, Taixin
;
Tian, Shufeng
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2024/04/01
Landslide
Hollow colluvial deposits
Hysteresis
Dadu River
Bedrock seepage
Identifying the key factors influencing spatial and temporal variations of regional coastal fishing activities
期刊论文
OCEAN & COASTAL MANAGEMENT, 2024, 卷号: 248, 页码: 11
作者:
Chen, Renli
;
Wang, Yueqi
;
Wu, Xiaoqing
;
Gao, Zhiqiang
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2024/02/23
Coastal fishing activity
Marine environment
Marine policy
Influence factors
Automatic Identification System (AIS)
Knowledge enhanced ensemble method for remaining useful life prediction under variable working conditions
期刊论文
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2024, 卷号: 242, 页码: 13
作者:
Li, Yuan
;
Li, Jingwei
;
Wang, Huanjie
;
Liu, Chengbao
;
Tan, Jie
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2024/02/22
Remaining useful life
Ensemble learning
Attention mechanism
Convolutional neural network
Transfer learning
Monitoring Off-Shore Fishing in the Northern Indian Ocean Based on Satellite Automatic Identification System and Remote Sensing Data
期刊论文
SENSORS, 2024, 卷号: 24, 期号: 3, 页码: 19
作者:
Li, Jie
;
Xing, Qianguo
;
Li, Xuerong
;
Arif, Maham
;
Li, Jinghu
收藏
  |  
浏览/下载:1/0
  |  
提交时间:2024/05/07
automatic identification system (AIS)
fishing vessels
spatial-temporal distribution
northern Indian Ocean
sea surface temperature (SST)
chlorophyll a concentration (Chl-a)
New Paradigm for Economic and Financial Research With Generative AI: Impact and Perspective
期刊论文
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2024, 页码: 11
作者:
Zheng, Xiaolong
;
Li, Jingyu
;
Lu, Mengyao
;
Wang, Fei-Yue
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2024/02/22
Economics
Biological system modeling
Data models
Task analysis
Behavioral sciences
Big Data
Analytical models
finance
generative artificial intelligence (GAI)
reinforcement learning from human feedback (RLHF)
research paradigm
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