Suchergebnisse - "graph convolutional autoencoder (GCAE)"

Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps.

Autoren: Yan, Xiongfeng Ai, Tinghua Yang, Min et al.

Quelle: International Journal of Geographical Information Science. Mar2021, Vol. 35 Issue 3, p490-512. 23p.

Schlagwörter: *Maps, Cognition, Learning strategies, Vector data

Seismic damage identification by graph convolutional autoencoder using adjacency matrix based on structural modes.

Autoren: Kim, Minkyu Song, Junho

Quelle: Earthquake Engineering & Structural Dynamics; Feb2024, Vol. 53 Issue 2, p815-837, 23p

Schlagwörter: GROUND motion, SEISMIC response, SPACE frame structures, STRUCTURAL frames, SYSTEM failures, STEEL framing

A Novel Unsupervised Structural Damage Detection Method Based on TCN-GAT Autoencoder.

Autoren: Ni, Yanchun Jin, Qiyuan Hu, Rui

Quelle: Sensors (14248220); Nov2025, Vol. 25 Issue 21, p6724, 27p

Schlagwörter: AUTOENCODERS, STRUCTURAL health monitoring, ONLINE monitoring systems, VIBRATION (Mechanics), SPATIOTEMPORAL processes, GRAPH neural networks, MACHINE learning, DEEP learning

Learning geometric invariant features for classification of vector polygons with graph message-passing neural network.

Autoren: Huang, Zexian Khoshelham, Kourosh Tomko, Martin

Quelle: GeoInformatica; Oct2025, Vol. 29 Issue 4, p951-974, 24p

Schlagwörter: SHAPE analysis (Computational geometry), GRAPH neural networks, TOPOLOGICAL property, REPRESENTATIONS of graphs, GENERALIZATION

Computational approaches for predicting drug-disease associations: a comprehensive review.

Autoren: Huang, Zhaoyang Xiao, Zhichao Ao, Chunyan et al.

Quelle: Frontiers of Computer Science; May2025, Vol. 19 Issue 5, p1-15, 15p

A novel intelligent multicross domain fault diagnosis of servo motor-bearing system based on Domain Generalized Graph Convolution Autoencoder.

Autoren: Zhao, Xiaoli Hu, Yuanhao Liu, Jiahui et al.

Quelle: Structural Health Monitoring; May2025, Vol. 24 Issue 3, p1485-1499, 15p

Schlagwörter: AUTOENCODERS, FAULT diagnosis, DEEP learning, REPRESENTATIONS of graphs, ROLLER bearings

Overlapping Community Detection in Vehicular Social Networks Based on Graph Attention Autoencoder.

Autoren: Gu, Xiang Huang, Qiwei Yang, Jie

Quelle: Sensors (14248220); Apr2025, Vol. 25 Issue 8, p2601, 21p

Schlagwörter: AUTOENCODERS, SOCIAL networks, RESOURCE allocation, ACCURACY of information, TOPOLOGY

Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder.

Autoren: He, Chaobo Zheng, Yulong Cheng, Junwei et al.

Quelle: Information Sciences. Aug2022, Vol. 608, p1464-1479. 16p.

Schlagwörter: *ARTIFICIAL neural networks

Deep Learning for Information Systems Research.

Autoren: Samtani, Sagar Zhu, Hongyi Padmanabhan, Balaji et al.

Quelle: Journal of Management Information Systems; 2023, Vol. 40 Issue 1, p271-301, 31p, 3 Diagrams, 7 Charts

Schlagwörter: INFORMATION storage & retrieval systems, DEEP learning, ARTIFICIAL intelligence, INSTRUCTIONAL systems, KNOWLEDGE base, BIG data

Classifying the Shapes of Buildings by Combining Distance Field Enhancement and a Convolution Neural Network.

Autoren: Zou, Xinyan Yang, Min Li, Siyu et al.

Quelle: ISPRS International Journal of Geo-Information; Nov2024, Vol. 13 Issue 11, p411, 17p

Schlagwörter: CONVOLUTIONAL neural networks, DEEP learning, CLASSIFICATION, GENERALIZATION, PIXELS

Seismic damage identification by graph convolutional autoencoder using adjacency matrix based on structural modes

Autoren: Minkyu Kim Junho Song

Quelle: Earthquake Engineering & Structural Dynamics. 53:815-837

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0201 civil engineering

A comprehensive survey on community detection methods and applications in complex information networks.

Autoren: Diboune, Abdelhani Slimani, Hachem Nacer, Hassina et al.

Quelle: Social Network Analysis & Mining; 4/18/2024, Vol. 14 Issue 1, p1-47, 47p

Measuring the similarity between shapes of buildings using graph edit distance.

Autoren: Lu, Xiechun Li, Houpu Xu, Yongyang et al.

Quelle: International Journal of Digital Earth; Jan2024, Vol. 17 Issue 1, p1-29, 29p

Learning from vector data: enhancing vector-based shape encoding and shape classification for map generalization purposes.

Autoren: Knura, Martin

Quelle: Cartography & Geographic Information Science; Jan2024, Vol. 51 Issue 1, p146-167, 22p

Schlagwörter: VECTOR data, RECURRENT neural networks, CONVOLUTIONAL neural networks, GENERALIZATION, DEEP learning

A deep learning approach for polyline and building simplification based on graph autoencoder with flexible constraints.

Autoren: Yan, Xiongfeng Yang, Min

Quelle: Cartography & Geographic Information Science; Jan2024, Vol. 51 Issue 1, p79-96, 18p

Schlagwörter: DEEP learning, FOURIER transforms, CARTOGRAPHY, TRIANGULATION

Thermal infrared image semantic segmentation for night-time driving scenes based on deep learning.

Autoren: Maheswari, B. Reeja, S. R.

Quelle: Multimedia Tools & Applications; Dec2023, Vol. 82 Issue 29, p44885-44910, 26p

DeepThink IoT: The Strength of Deep Learning in Internet of Things.

Autoren: Thakur, Divyansh Saini, Jaspal Kaur Srinivasan, Srikant

Quelle: Artificial Intelligence Review; Dec2023, Vol. 56 Issue 12, p14663-14730, 68p

Recognition of building shape in maps using deep graph filter neural network.

Autoren: Xu, Junkui Zhang, Hao Liu, Chun et al.

Quelle: Geocarto International; 2023, Vol. 38 Issue 1, p1-30, 30p

Schlagwörter: SIGNAL filtering, SIGNAL theory, FEATURE extraction, SIGNAL processing

Few-Shot Building Footprint Shape Classification with Relation Network.

Autoren: Hu, Yaohui Liu, Chun Li, Zheng et al.

Quelle: ISPRS International Journal of Geo-Information; May2022, Vol. 11 Issue 5, p311-311, 16p

Schlagwörter: DIGITAL maps, DEEP learning, URBAN planning, CLASSIFICATION, SPACE

A review of location encoding for GeoAI: methods and applications.

Autoren: Mai, Gengchen Janowicz, Krzysztof Hu, Yingjie et al.

Quelle: International Journal of Geographical Information Science; Apr2022, Vol. 36 Issue 4, p639-673, 35p

Schlagwörter: ENCODING, ARTIFICIAL intelligence, DEEP learning, MACHINE learning