Author name cluster

Yuchen Dai

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.

4 papers

1 author row

EAAI Journal 2026 Journal Article

A look-ahead dispatch method via evolution strategies embedded with domain knowledge

Yuchen Dai
Weiran Jiao
Yi Tang
Minghui Yan
Feng Xue
Jianfeng Zhao

Modern power systems require proactive look-ahead dispatch strategies to address various uncertainties. Traditional decision-making methods based on physical models often suffer from slow processing speeds and struggle to handle multiple uncertain scenarios. Meanwhile, reinforcement learning methods face challenges such as hyperparameter sensitivity and a tendency to converge to local optima. To overcome these limitations, a look-ahead dispatch method via evolution strategies embedded with domain knowledge is proposed. First, a knowledge-embedded Markov decision process model of look-ahead dispatch is developed. This model encodes critical physical knowledge into the action space without computational burden. Second, a decision-making approach based on evolution strategies and physical models is introduced. This method enhances parallel exploration efficiency and reduce communication burden by leveraging synchronous random seeds and mirror perturbation techniques. Then, physical models are used to fine-tune agents in new scenarios with limited data. Finally, case studies based on the IEEE 118 system show that the proposed method significantly improves decision-making efficiency without sacrificing accuracy. Compared to deep reinforcement learning, the evolution strategies algorithm offers superior training efficiency and performance, effectively addressing the high-dimensional uncertainties and complexities of modern power systems. This establishes the proposed method as an effective solution for complex decision-making tasks in power system operations.

Details DOI

AAAI Conference 2026 Conference Paper

FIA-Edit: Frequency-Interactive Attention for Efficient and High-Fidelity Inversion-Free Text-Guided Image Editing

Kaixiang Yang
Boyang Shen
Xin Li
Yuchen Dai
Yuxuan Luo
Yueran Ma
Wei Fang
Qiang Li

Text-guided image editing has advanced rapidly with the rise of diffusion models. While flow-based inversion-free methods offer high efficiency by avoiding latent inversion, they often fail to effectively integrate source information, leading to poor background preservation, spatial inconsistencies, and over-editing due to the lack of effective integration of source information. In this paper, we present FIA-Edit, a novel inversion-free framework that achieves high-fidelity and semantically precise edits through a Frequency-Interactive Attention. Specifically, we design two key components: (1) a Frequency Representation Interaction (FRI) module that enhances cross-domain alignment by exchanging frequency components between source and target features within self-attention, and (2) a Feature Injection (FIJ) module that explicitly incorporates source-side queries, keys, values, and text embeddings into the target branch's cross-attention to preserve structure and semantics. Comprehensive and extensive experiments demonstrate that FIA-Edit supports high-fidelity editing at low computational cost (~6s per 512 * 512 image on an RTX 4090) and consistently outperforms existing methods across diverse tasks in visual quality, background fidelity, and controllability. Furthermore, we are the first to extend text-guided image editing to clinical applications. By synthesizing anatomically coherent hemorrhage variations in surgical images, FIA-Edit opens new opportunities for medical data augmentation and delivers significant gains in downstream bleeding classification.

PDF Details DOI

EAAI Journal 2025 Journal Article

Deep reinforcement learning explanation-assisted integer variable reduction method for security-constrained unit commitment

Yuchen Dai
Wei Xu
Minghui Yan
Feng Xue
Jianfeng Zhao

The large-scale security-constrained unit commitment (SCUC) is pivotal for ensuring the secure and economical operation of modern power systems. Formulated as a mixed-integer nonlinear programming problem, mathematical model-based methods struggle to balance computation efficiency and solution accuracy. While artificial intelligence methods offer promising potential, they face several obstacles, including limited interpretability and generalizability constraints. In light of these challenges, this paper proposes an interpretation method for deep reinforcement learning models that is used to reduce integer variables for large-scale SCUC problem. This method employs a Gaussian Mixture Model to cluster the decision outcomes of the agents and utilizes an improved decision tree to interpret the clustering results. We analyze the physical implications behind the phenomenon of unit output distributions exhibiting multiple independent Gaussian distributions. Then, these interpretations are applied to identify active integer variables, thereby simplifying the complexity of the SCUC problem and enhancing solution efficiency. Furthermore, an improved Markov decision process model with domain knowledge pertinent of power systems is constructed to enhance the interpretability and reliability of the agents. A distinctive feature of this model is the incorporation of a bidirectional mapping of unsafe and safe actions. The case studies on the SG-126 system demonstrate that the proposed method achieves a significant increase in solution speed without loss of accuracy. The identified active integer variables are proven to be accurate and effective, contributing to improve computation efficiency of unit commitment. The proposed method also provides a novel explainable artificial intelligence-assisted method for complex decision-making problems in other fields.

Details DOI

EAAI Journal 2021 Journal Article

Disturbance-observer based prescribed-performance fuzzy sliding mode control for PMSM in electric vehicles

Yuchen Dai
Shuangfei Ni
Dezhi Xu
Liyan Zhang
Xing-Gang Yan

This paper investigates the problem of accurate speed tracking control of electric vehicles powered by permanent magnet synchronous motor (PMSM) under external load torque disturbance. Firstly, the dynamic model of PMSM is given, and the controller is designed based on backstepping control. Secondly, a second-order sliding mode differentiator is used to approximate the derivative of virtual control law and solve the problem of explosion of complexity. Considering the load disturbance of PMSM in the actual operation due to road roughness, a novel disturbance observer is proposed to the estimate the load disturbance. In addition, in order to achieve prescribed tracking error performance, the prescribed-performance control is proposed to guarantee the tracking error within a given prescribed boundary. Finally, the finite-time stability of the system is proved, the simulation and real-time implementation results verify the effectiveness of the designed controller.

Details DOI