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Huachao Cui

dblp:343/1956 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2024
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

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.

Theoretical computer science
1 paper
Mathematical optimization · 100%
Artificial intelligence
1 paper
Reinforcement learning · 100%

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

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning
reinforcement learning for combinatorial optimization
0.812024
ReinforceNS: Reinforcement Learning-based Multi-start Neighborhood Search for Solving the Traveling Thief Problem · IJCAI 2024
Mathematical optimization
combinatorial optimization
0.812024
ReinforceNS: Reinforcement Learning-based Multi-start Neighborhood Search for Solving the Traveling Thief Problem · IJCAI 2024
Mathematical optimization › metaheuristic optimization
neighborhood search
0.212024
ReinforceNS: Reinforcement Learning-based Multi-start Neighborhood Search for Solving the Traveling Thief Problem · IJCAI 2024

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

reinforcement learning · 1.5neighborhood search · 1.5
YearPublicationVenuePosition
2024 ReinforceNS: Reinforcement Learning-based Multi-start Neighborhood Search for Solving the Traveling Thief Problem
Huachao Cui, Yuesong Wang 0001
IJCAI2
2023 Parallel Emergency Management of Incidents by Integrating OODA and PREA Loops: The C2 Mechanism and Modes
abstract
Given the differences of command and control (C2) activities between the field command center and the emergency operations center (EOC), this article combined the edge C2 theory with the parallel C2 theory, and proposed a parallel incident C2 mode based on the observe–orient–decide–act (OODA) loop and planning–readiness–execution–assessment (PREA) loop. The aim is to build up a PREA loop-based parallel incident C2 mode and its related operating mechanism of edge empowerment and energy release in parallel incident C2 mode. The parallel incident C2 mode based on the PREA loop and OODA loop supports the co-existence and connection of the two roles of the incident C2 agent at the emergency scene. The two roles are the executive role of emergency response and operation and the command and organization role of the edge emergency system. This article initiates a deep integration of two different C2 process mechanisms in the emergency response and operation process, taking into account the local emergency scene and the global emergency system. Taken together, a key issue has been well addressed regarding the contradiction that the traditional emergency response cannot be reconciled in terms of rapidity and thoroughness.
Fenghua Zhu, Huachao Cui, Jirong Qin
IEEE Trans. Syst. Man Cybern. Syst.4