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- research-articleMay 2024
3D-SeqMOS: A Novel Sequential 3D Moving Object Segmentation in Autonomous Driving
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 25, Issue 8Pages 8782–8795https://doi.org/10.1109/TITS.2024.3391286For the SLAM system in robotics and autonomous driving, the accuracy of front-end odometry and back-end loop-closure detection determine the whole intelligent system performance. But the LiDAR-SLAM could be disturbed by current scene moving objects, ...
- research-articleApril 2024
Dual Homogeneity Hypergraph Motifs with Cross-view Contrastive Learning for Multiple Social Recommendations
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 6Article No.: 158, Pages 1–24https://doi.org/10.1145/3653976Social relations are often used as auxiliary information to address data sparsity and cold-start issues in social recommendations. In the real world, social relations among users are complex and diverse. Widely used graph neural networks (GNNs) can only ...
- research-articleJanuary 2023
X-ray CT image denoising with MINF: A modularized iterative network framework for data from multiple dose levels
Computers in Biology and Medicine (CBIM), Volume 152, Issue Chttps://doi.org/10.1016/j.compbiomed.2022.106419AbstractIn clinical applications, multi-dose scan protocols will cause the noise levels of computed tomography (CT) images to fluctuate widely. The popular low-dose CT (LDCT) denoising network outputs denoised images through an end-to-end mapping between ...
Highlights- A novel network framework for low-dose CT step-by-step denoising.
- MCNN module can extract richer feature information.
- research-articleAugust 2022
CCN-CL: A content-noise complementary network with contrastive learning for low-dose computed tomography denoising
Computers in Biology and Medicine (CBIM), Volume 147, Issue Chttps://doi.org/10.1016/j.compbiomed.2022.105759AbstractIn recent years, low-dose computed tomography (LDCT) has played an increasingly important role in the diagnosis CT to reduce the potential adverse effects of x-ray radiation on patients while maintaining the same diagnostic image quality. Current ...
Highlights- A novel network for low-dose CT (LDCT) denoising.
- The network is combined with contrastive learning and a contrastive regularization loss term is proposed to constrain it.
- The content-noise complementary learning strategy is ...
- research-articleJuly 2022
Genetic-GNN: Evolutionary architecture search for Graph Neural Networks
AbstractNeural architecture search (NAS) has seen significant attention throughout the computational intelligence research community and has pushed forward the state-of-the-art of many neural models to address grid-like data such as texts and images. ...
- short-paperJuly 2022
DH-HGCN: Dual Homogeneity Hypergraph Convolutional Network for Multiple Social Recommendations
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 2190–2194https://doi.org/10.1145/3477495.3531828Social relations are often used as auxiliary information to improve recommendations. In the real-world, social relations among users are complex and diverse. However, most existing recommendation methods assume only single social relation (i.e., exploit ...
- ArticleNovember 2021
ALGNN: Auto-Designed Lightweight Graph Neural Network
PRICAI 2021: Trends in Artificial IntelligencePages 500–512https://doi.org/10.1007/978-3-030-89188-6_37AbstractGraph neural networks (GNNs) are widely used on graph-structured data, and its research has made substantial progress in recent years. However, given the various number of choices and combinations of components such as aggregator and activation ...
- research-articleMarch 2021
Learning‐based control for discrete‐time constrained nonzero‐sum games
CAAI Transactions on Intelligence Technology (CIT2), Volume 6, Issue 2Pages 203–213https://doi.org/10.1049/cit2.12015AbstractA generalized policy‐iteration‐based solution to a class of discrete‐time multi‐player non‐zero‐sum games concerning the control constraints was proposed. Based on initial admissible control policies, the iterative value function of each player ...
- research-articleMarch 2021
Mashup tag completion with attention-based topic model
Service Oriented Computing and Applications (SPSOCA), Volume 15, Issue 1Pages 43–54https://doi.org/10.1007/s11761-020-00302-0AbstractThe past few years have witnessed a substantial increase in functional rich API services and their compositions (e.g., Mashup services) on the Internet, which as a result proposes new requirement of organization and management methods for better ...
- research-articleJanuary 2021
Multi-class imbalanced graph convolutional network learning
IJCAI'20: Proceedings of the Twenty-Ninth International Joint Conference on Artificial IntelligenceArticle No.: 398, Pages 2879–2885Networked data often demonstrate the Pareto principle (i.e., 80/20 rule) with skewed class distributions, where most vertices belong to a few majority classes and minority classes only contain a handful of instances. When presented with imbalanced class ...
- research-articleApril 2020
Topic-aware Web Service Representation Learning
ACM Transactions on the Web (TWEB), Volume 14, Issue 2Article No.: 9, Pages 1–23https://doi.org/10.1145/3386041The advent of Service-Oriented Architecture (SOA) has brought a fundamental shift in the way in which distributed applications are implemented. An overwhelming number of Web-based services (e.g., APIs and Mashups) have leveraged this shift and furthered ...
- research-articleJanuary 2020
Topical network embedding
Data Mining and Knowledge Discovery (DMKD), Volume 34, Issue 1Pages 75–100https://doi.org/10.1007/s10618-019-00659-7AbstractNetworked data involve complex information from multifaceted channels, including topology structures, node content, and/or node labels etc., where structure and content are often correlated but are not always consistent. A typical scenario is the ...
- research-articleJuly 2019
Cyber and physical interactions to combat failure propagation in smart grid: Characterization, analysis and evaluation
Computer Networks: The International Journal of Computer and Telecommunications Networking (CNTW), Volume 158, Issue CPages 184–192https://doi.org/10.1016/j.comnet.2019.05.006AbstractThe smart grid is envisioned to use a cyber-physical network paradigm to prevent failures from propagating along large-scale infrastructures, which is a primary cause for massive blackouts. Despite this promising vision, how effective ...
- research-articleApril 2018
How Can Cyber-Physical Interdependence Affect the Mitigation of Cascading Power Failure?
IEEE INFOCOM 2018 - IEEE Conference on Computer CommunicationsPages 2501–2509https://doi.org/10.1109/INFOCOM.2018.8486373Utilizing advanced communication technologies to facilitate power system monitoring and control, the smart grid is envisioned to be more robust and resilient against cascading failures. Although the integration of communication network does benefit the ...
- research-articleJanuary 2017
Q-Learning-Based Vulnerability Analysis of Smart Grid Against Sequential Topology Attacks
IEEE Transactions on Information Forensics and Security (TIFS), Volume 12, Issue 1Pages 200–210https://doi.org/10.1109/TIFS.2016.2607701Recent studies on sequential attack schemes revealed new smart grid vulnerability that can be exploited by attacks on the network topology. Traditional power systems contingency analysis needs to be expanded to handle the complex risk of cyber-physical ...
- research-articleDecember 2015
Intelligent load frequency controller using GrADP for island smart grid with electric vehicles and renewable resources
Neurocomputing (NEUROC), Volume 170, Issue CPages 406–416https://doi.org/10.1016/j.neucom.2015.04.092Increasing deployment of intermittent power generation from renewable resources in the smart grid, such as photovoltaic (PV) or wind farm, will cause large system frequency fluctuation when the load-frequency control (LFC) capacity is not enough to ...
- research-articleMay 2015
Joint Substation-Transmission Line Vulnerability Assessment Against the Smart Grid
IEEE Transactions on Information Forensics and Security (TIFS), Volume 10, Issue 5Pages 1010–1024https://doi.org/10.1109/TIFS.2015.2394240Power grids are often run near the operational limits because of increasing electricity demand, where even small disturbances could possibly trigger major blackouts. The attacks are the potential threats to trigger large-scale cascading failures in the ...
- research-articleDecember 2014
Resilience Analysis of Power Grids Under the Sequential Attack
IEEE Transactions on Information Forensics and Security (TIFS), Volume 9, Issue 12Pages 2340–2354https://doi.org/10.1109/TIFS.2014.2363786The modern society increasingly relies on electrical service, which also brings risks of catastrophic consequences, e.g., large-scale blackouts. In the current literature, researchers reveal the vulnerability of power grids under the assumption that ...
- research-articleFebruary 2014
Reactive power control of grid-connected wind farm based on adaptive dynamic programming
Neurocomputing (NEUROC), Volume 125, Issue CPages 125–133https://doi.org/10.1016/j.neucom.2012.07.046AbstractOptimal control of large-scale wind farm has become a critical issue for the development of renewable energy systems and their integration into the power grid to provide reliable, secure, and efficient electricity. Among many enabling ...