Hongbing Huang

dblp:07/7777 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

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

Systems, architecture and hardware · 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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 75% Electronic design automation · 25%
Computer networks
1 paper
Internet of things and sensor networks · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Embedded and real-time systems › model-based design
code generation
0.712023
AIoTML: A Unified Modeling Language for AIoT-Based Cyber-Physical Systems · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2023
Embedded and real-time systems
cyber-physical system platforms
0.712023
AIoTML: A Unified Modeling Language for AIoT-Based Cyber-Physical Systems · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2023
Electronic design automation
design space exploration
0.712023
AIoTML: A Unified Modeling Language for AIoT-Based Cyber-Physical Systems · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2023
Embedded and real-time systems
model-based design
0.712023
AIoTML: A Unified Modeling Language for AIoT-Based Cyber-Physical Systems · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2023
Internet of things and sensor networks
AIoT
0.212023
AIoTML: A Unified Modeling Language for AIoT-Based Cyber-Physical Systems · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2023

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

domain-specific language · 1.3digital twin · 1.3
YearPublicationVenuePosition
2023 AIoTML: A Unified Modeling Language for AIoT-Based Cyber-Physical Systems
abstract
Due to deeply intertwined physical and hardware/software components together with an increasing number of interconnected heterogeneous devices powered by artificial intelligence (AI) techniques, the design complexity of cyber–physical systems (CPSs) becomes skyrocketing. Model-driven engineering (MDE) methods have been proven to be effective in increasing the productivity of CPS design. However, there is still a lack of MDE approaches that enable design space exploration as well as the code generation for the design of Artificial Intelligence of Things (AIoT)-based CPSs. To mitigate the situation, this article presents a unified modeling language named AIoTML for AIoT-based CPSs, which enables the construction of AI-based components across different modeling levels for the purposes of intelligent sensing and control. By extending the constructs of state-of-the-art domain-specific language (DSL) ThingML, AIoTML can seamlessly unify the modeling of both autonomous executions of AIoT devices and their surrounding physical environment, which facilitates both platform-independent simulation and control optimization for platform-specific CPSs. The compiler developed for AIoTML provides a family of code generators to support the construction of digital twins on various heterogeneous target AIoT platforms. Comprehensive evaluations on two complex real-world designs demonstrate the effectiveness of our AIoTML approach in the fast development of AIoT-based CPSs with high control quality.
Ming Hu 0003, E. Cao, Hongbing Huang, Min Zhang 0002, Xiaohong Chen 0007, Mingsong Chen 0001
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.3