arXiv subjects

Chaoqun Ma

Publications and source records attributed to Chaoqun Ma.

4 recordsLinked to original sources

arXiv2026

Deep learning reveals a stronger fossil fuel influence than biomass burning in shaping remote tropospheric ozone

Tropospheric ozone (O3) is a key greenhouse gas and atmospheric oxidant, yet its sources in the remote troposphere remain strongly debated. Observation-based tracer analyses suggest that O3 attributed to biomass burning is much greater than that from fossil fuel sources (by a factor of ~2-10), contradicting state-of-the-art global models. Here we show that this discrepancy primarily arises from the strong sensitivity of tracer methods to differences in tracer lifetimes, especially after extended transport to the remote regions. To resolve this discrepancy, we develop a deep learning (DL) framework that synthesizes global observations and chemical transport model simulations. The DL approach accurately infers source contributions and reveals that fossil fuel emissions contribute over three times more O3 to the remote troposphere than biomass burning. Our findings underscore that phasing out fossil fuels remains the most powerful lever for mitigating remote tropospheric ozone.

physics.ao-ph↗

arXiv2025

Predictive reinforcement learning based adaptive PID controller

Purpose: This study aims to address the challenges of controlling unstable and nonlinear systems by proposing an adaptive PID controller based on predictive reinforcement learning (PRL-PID), where the PRL-PID combines the advantages of both data-driven and model-driven approaches. Design/methodology/approach: A predictive reinforcement learning framework is introduced, incorporating action smooth strategy to suppress overshoot and oscillations, and a hierarchical reward function to support training. Findings: Experimental results show that the PRL-PID controller achieves superior stability and tracking accuracy in nonlinear, unstable, and strongly coupled systems, consistently outperforming existing RL-tuned PID methods while maintaining excellent robustness and adaptability across diverse operating conditions. Originality/Value: By adopting predictive learning, the proposed PRL-PID integrates system model priors into data-driven control, enhancing both the control framework's training efficiency and the controller's stability. As a result, PRL-PID provides a balanced blend of model-based and data-driven approaches, delivering robust, high-performance control.

eess.SY↗

arXiv2022

A survey on the development status and application prospects of knowledge graph in smart grids

With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge-driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision-making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.

cs.AI↗

arXiv2020

Matching preclusion and strong matching preclusion of the bubble-sort star graphs

Since a plurality of processors in a distributed computer system working in parallel, to ensure the fault tolerance and stability of the network is an important issue in distributed systems. As the topology of the distributed network can be modeled as a graph, the (strong) matching preclusion in graph theory can be used as a robustness measure for missing edges in parallel and distributed networks, which is defined as the minimum number of (vertices and) edges whose deletion results in the remaining network that has neither a perfect matching nor an almost-perfect matching. The bubble-sort star graph is one of the validly discussed interconnection networks related to the distributed systems. In this paper, we show that the strong matching preclusion number of an $n$-dimensional bubble-sort star graph $BS_n$ is $2$ for $n\geq3$ and each optimal strong matching preclusion set of $BS_n$ is a set of two vertices from the same bipartition set. Moreover, we show that the matching preclusion number of $BS_n$ is $2n-3$ for $n\geq3$ and that every optimal matching preclusion set of $BS_n$ is trivial.

math.CO↗