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Haozhen Liu

dblp:228/5927 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Internet of things and sensor networks · 77% Edge and fog computing · 23%
Computer graphics and multimedia
1 paper
Virtual and augmented reality · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Internet of things and sensor networks
industrial iot		 0.312018
IoT for the Power Industry: Recent Advances and Future Directions with Pavatar · SenSys 2018
Edge and fog computing
industrial edge computing		 0.112018
IoT for the Power Industry: Recent Advances and Future Directions with Pavatar · SenSys 2018

Methods — techniques the papers use, named apart from their topics

machine learning · 0.7cross-technology communication · 0.7battery-free sensing · 0.7
YearPublicationVenuePosition
2019 Poster: Enhanced Chatting Based on Multimodal Emotion Estimation
Luyao Chong, Junchen Guo, Haozhen Liu, Meng Jin 0002, Yuan He 0004
 EWSN4
2019 A Multi-Sensing Collaborative Diagnosis System for the Reliability of Industrial IoT
Haozhen Liu, Weiguo Wang, Qilong Zhao, Meng Jin 0002, Ziqiang Zhou, Zhoubin Liu
 EWSN1
2018 IoT for the Power Industry: Recent Advances and Future Directions with Pavatar
abstract
The development of Internet-of-Things (IoT) technologies in recent years brings us unprecedented opportunities for innovations in the power industry. This demo abstract introduces our research and practice with Pavatar - IoT for the power industry. Pavatar includes a series of system deployments in the core sections of Global Energy Internet (GEI), for the purposes of automatic surveillance and remote diagnosis of ultra-high-voltage converter stations (UHVCSs). Pavatar incorporates technologies like lower-power or battery-free sensing, cross-technology communication, edge computing, machine learning, and enhances the user experience with 3D virtual reality. The deployed system significantly reduces the manpower cost and enhances the operational efficiency of the UHVCS.
Yuan He 0004, Junchen Guo, Haozhen Liu, Qilong Zhao, Xiaolong Zheng 0002, Meng Jin 0002, Chunya Liu, Yao Luo, Songzhen Yang, Chengkun Jiang, Xiuzhen Guo
 SenSys4