Author name cluster

Heqing Li

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

EAAI Journal 2026 Journal Article

A fine-grained information fusion and inference method for out-of-distribution detection in fault diagnosis

Guoliang Wu
Xinde Li
Fir Dunkin
Chuanfei Hu
Heqing Li
Zhentong Zhang
Kaixuan Wu
Erfeng Liu

While intelligent fault diagnosis has achieved remarkable success on known faults, its reliance on in-distribution (ID) assumptions often leads to overconfident misclassification of out-of-distribution (OOD) samples. Existing OOD detection methods typically exploit only single-granularity class information and thus fail to capture fine-grained subclass distinctions, limiting detection accuracy. To address this limitation, we propose FIFI (Fine-grained Information Fusion and Inference), which integrates fine-grained subclass evidence to improve the separability between ID and OOD samples. FIFI consists of three phases: (1) Bayesian Gaussian Mixture Model (BGMM)-based adaptive subclass modeling, (2) fuzzy fusion from subclass- to class-level memberships, and (3) membership aggregation for ID membership score estimation in OOD detection. Experiments show that FIFI consistently outperforms strong baselines in terms of FPR95, demonstrating a practical and reliable solution for fault diagnosis under complex industrial conditions.

Details DOI

TCS Journal 2026 Journal Article

k-Submodular and approximately non-k-submodular maximization under p-system and ℓ knapsack constraints

Hanlu Ye
Heqing Li
Min Li
Yang Zhou
Qian Liu

This paper addresses the problem of k-submodular and approximately non-k-submodular maximization under p-system and ℓ knapsack constraints. For monotone k-submodular functions, we first propose a greedy algorithm, achieving a 1 ( 1 + ϵ ′ ) ( 1 + p + 2 ℓ ) -approximation and a 1 ( 1 + ϵ ′ ) ( 2 + p + 2 ℓ ) -approximation for non-monotone case, with the O ( n 2 ( 1 + k ) log ( 2 n ) log ( 1 + ϵ ′ ) ) time complexity, where ϵ′ is a very small positive number. We further introduce an improved algorithm that enhances the approximation ratio to 1 ( 1 + ϵ ′ + ϵ ′ 2 ) ( 1 + p + 7 4 ℓ ) and 1 ( 1 + ϵ ′ + ϵ ′ 2 ) ( 2 + p + 7 4 ℓ ), respectively, while reducing the time complexity to O ( n k log n ϵ ′ log ( 2 n ) ). For monotone k-submodular functions with curvature c, we obtain an approximation result of 1 ( 1 + ϵ ′ ) ( p + c + ϵ ′ + 7 4 ℓ ). Additionally, we provide an approximation guarantee of min { 1, 1 α ( 1 + ϵ ′ ) } 1 + 1 + ϵ α 2 ( 1 − ϵ ) [ ( 1 + ϵ ′ ) ( p + α ϵ ′ ) + 7 4 ℓ ] for ϵ-approximately α-weakly diminishing returns functions.

Details DOI

IROS Conference 2023 Conference Paper

Energy Constrained Multi-Agent Reinforcement Learning for Coverage Path Planning

Chenyang Zhao 0009
Juan Liu
Suk-Un Yoon
Xinde Li
Heqing Li
Zhentong Zhang

For multi-agent area coverage path planning problem, existing researches regard it as a combination of Traveling Salesman Problem (TSP) and Coverage Path Planning (CPP). However, these approaches have disadvantages of poor observation ability in online phase and high computational cost in offline phase, making it difficult to be applied to energy-constrained Unmanned Aerial Vehicles (UAVs) and adjust strategy dynamically. In this paper, we decompose the task into two sub-problems: multi-agent path planning and sub-region CPP. We model the multi-agent path planning problem as a Collective Markov Decision Process (C-MDP), and design an Energy Constrained Multi-Agent Reinforcement Learning (ECMARL) algorithm based on the centralized training and distributed execution concept. Taking into account energy constraint of UAVs, the UAV propulsion power model is established to measure the energy consumption of UAVs, and load balancing strategy is applied to dynamically allocate target areas for each UAV. If the UAV is under energy-depleted situation, ECMARL can adjust the mission strategy in real time according to environmental information and energy storage conditions of other UAVs. When UAVs reach each sub-region of interest, Back-an-Forth Paths (BFPs) are adopted to solve CPP problem, which can ensure full coverage, optimality and complexity of the sub-problem. Comprehensive theoretical analysis and experiments demonstrate that ECMARL is superior to the traditional offline TSP-CPP strategy in terms of solution quality and computational time, and can effectively deal with the energy-constrained UAVs.

Details