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Lei Su

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6 papers

1 author row

EAAI Journal 2026 Journal Article

Sliding mode control for Markov jump power systems: Asynchronous learning-based method

Xiulin Wang
Lei Su
Feng Li

This paper studies the problem of asynchronous learning-based sliding mode control (LSMC) of a single-machine infinite bus (SMIB) power system. Due to the influence of instantaneous faults and stochastic switching, the power system may experience dynamic changes in the structural parameters. To describe this phenomenon, the power system is expressed as a Markov jump power system (MJPS) model. In addition, a common sliding surface is designed to overcome the problem that the sliding surface may be unreachable due to mode switching. By constructing the Lyapunov function, sufficient conditions to ensure the stochastic stability of the system are derived, and a suitable sliding matrix is solved. In order to facilitate the design of learning parameters, an LSMC law related to the hidden Markov observation mode is designed. Compared with the mode-independent LSMC, the proposed method further accelerates the convergence speed and has better control performance. Finally, the superiority of the proposed method is verified by a simulation example.

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AAAI Conference 2025 Conference Paper

Can We Get Rid of Handcrafted Feature Extractors? SparseViT: Nonsemantics-Centered, Parameter-Efficient Image Manipulation Localization Through Spare-Coding Transformer

Lei Su
Xiaochen Ma
Xuekang Zhu
Chaoqun Niu
Zeyu Lei
Ji-Zhe Zhou

Non-semantic features or semantic-agnostic features, which are irrelevant to image context but sensitive to image manipulations, are recognized as evidential to Image Manipulation Localization (IML). Since manual labels are impossible, existing works rely on handcrafted methods to extract non-semantic features. Handcrafted non-semantic features jeopardize IML model's generalization ability in unseen or complex scenarios. Therefore, for IML, the elephant in the room is: How to adaptively extract non-semantic features? Non-semantic features are context-irrelevant and manipulation-sensitive. That is, within an image, they are consistent across patches unless manipulation occurs. Then, spare and discrete interactions among image patches are sufficient for extracting non-semantic features. However, image semantics vary drastically on different patches, requiring dense and continuous interactions among image patches for learning semantic representations. Hence, in this paper, we propose a Sparse Vision Transformer (SparseViT), which reformulates the dense, global self-attention in ViT into a sparse, discrete manner. Such sparse self-attention breaks image semantics and forces SparseViT to adaptively extract non-semantic features for images. Besides, compared with existing IML models, the sparse self-attention mechanism largely reduced the model size (max 80% in FLOPs), achieving stunning parameter efficiency and computation reduction. Extensive experiments demonstrate that, without any handcrafted feature extractors, SparseViT is superior in both generalization and efficiency across benchmark datasets.

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AAAI Conference 2025 Conference Paper

Mesoscopic Insights: Orchestrating Multi-Scale & Hybrid Architecture for Image Manipulation Localization

Xuekang Zhu
Xiaochen Ma
Lei Su
Zhuohang Jiang
Bo Du
Xiwen Wang
Zeyu Lei
Wentao Feng

The mesoscopic level serves as a bridge between the macroscopic and microscopic worlds, addressing gaps overlooked by both. Image manipulation localization (IML), a crucial technique to pursue truth from fake images, has long relied on low-level (microscopic-level) traces. However, in practice, most tampering aims to deceive the audience by altering image semantics. As a result, manipulation commonly occurs at the object level (macroscopic level), which is equally important as microscopic traces. Therefore, integrating these two levels into the mesoscopic level presents a new perspective for IML research. Inspired by this, our paper explores how to simultaneously construct mesoscopic representations of micro and macro information for IML and introduces the Mesorch architecture to orchestrate both. Specifically, this architecture i) combines Transformers and CNNs in parallel, with Transformers extracting macro information and CNNs capturing micro details, and ii) explores across different scales, assessing micro and macro information seamlessly. Additionally, based on the Mesorch architecture, the paper introduces two baseline models aimed at solving IML tasks through mesoscopic representation. Extensive experiments across four datasets have demonstrated that our models surpass the current state-of-the-art in terms of performance, computational complexity, and robustness.

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EAAI Journal 2025 Journal Article

Semi-supervised dual-constraint centroid contrastive prototypical network for flip chip defect detection under limited labeled data

Yunxia Lou
Lei Su
Jiefei Gu
Xinwei Zhao
Ke Li
Michael Pecht

Flip chips are widely used in electronic systems for defense, aerospace, and other applications where packaging reliability is critical. However, flip chip defect samples present a variety of defect types and few samples with labels in actual industrial applications. The paucity of labeled defect samples indicates that the existing data volume cannot be matched with deep learning detection models. Therefore, flip chip intelligent defect detection faces the problems of poor model adaptability and weak generalization performance. As a solution to these problems, a semi-supervised dual-constraint centroid contrastive prototypical network (SSDCPN) for flip chip defect detection under limited labeled data is proposed in this paper. First, a prototype-based supervised contrastive learning strategy is developed to construct the contrastive prototypical network, which increases the inter-class sparsity and intra-class compactness of features to acquire more discriminative features. Then, to address the susceptibility of the support set prototypes to outliers, dual constraints are imposed on the support set prototypes to calibrate and refine the prototypes. Finally, a pseudo-labeled sample selection mechanism based on epistemic uncertainty and entropy is proposed to obtain rich semi-supervised information to guide the model training. The mechanism can select high-confidence pseudo-labeled samples that can complement the training samples to further strengthen the generalization performance of the model. Defect detection experiments on flip chip vibration signals indicate that the present method is superior to other methods in the case of limited labeled samples.

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EAAI Journal 2025 Journal Article

Unscented Kalman filter neural network with double-layer decomposition algorithm applied to the prediction of current efficiency in aluminum electrolysis processes

Xiaoyan Fang
Xihong Fei
Zhenyi Xu
Lei Su
Jing Wang

Predictive modeling in predictive optimization control technology can effectively reduce energy consumption and improve production efficiency in the electrolytic aluminum process (EAP). Among the various modeling approaches, artificial neural networks (ANN) have been widely adopted in the EAP due to their strong capability to capture nonlinear relationships inherent in complex industrial systems. However, conventional ANN heavily rely on historical data to achieve optimal models, which limits their accuracy and generalization performance under strong disturbances and time-varying conditions. To address these problems, this article proposes a novel dynamic prediction model of unscented Kalman filter neural network with double-layer decomposition (UKFNN-DD), building upon the foundation of Kalman filter neural network (KFNN). First, singular value decomposition (SVD) is adopted to compute the square root of covariance matrices, enhancing the numerical robustness of the prediction algorithm and overcoming the shortcomings of traditional Cholesky decomposition. Furthermore, a dual-layer KFNN strategy is introduced to overcome the absence of one-step-ahead prediction in conventional state-space formulations. By applying a two-stage correction to the state variables using measurement data, the proposed method improves the adaptability of the model to external environmental variations. Finally, the prediction error of the state variables is optimized using a gradient descent algorithm, which improves the stability and reliability of the model’s prediction performance. Experimental results demonstrate that the proposed method significantly outperforms baseline methods, achieving a 4. 38-fold reduction in mean absolute error (MAE) and a 77. 29-fold reduction in the sum of squared errors (SSE), thereby verifying its superiority in dynamic prediction for aluminum electrolysis.

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NeurIPS Conference 2024 Conference Paper

IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization

Xiaochen Ma
Xuekang Zhu
Lei Su
Bo Du
Zhuohang Jiang
Bingkui Tong
Zeyu Lei
Xinyu Yang

A comprehensive benchmark is yet to be established in the Image Manipulation Detection & Localization (IMDL) field. The absence of such a benchmark leads to insufficient and misleading model evaluations, severely undermining the development of this field. However, the scarcity of open-sourced baseline models and inconsistent training and evaluation protocols make conducting rigorous experiments and faithful comparisons among IMDL models challenging. To address these challenges, we introduce IMDL-BenCo, the first comprehensive IMDL benchmark and modular codebase. IMDL-BenCo: i) decomposes the IMDL framework into standardized, reusable components and revises the model construction pipeline, improving coding efficiency and customization flexibility; ii) fully implements or incorporates training code for state-of-the-art models to establish a comprehensive IMDL benchmark; and iii) conducts deep analysis based on the established benchmark and codebase, offering new insights into IMDL model architecture, dataset characteristics, and evaluation standards. Specifically, IMDL-BenCo includes common processing algorithms, 8 state-of-the-art IMDL models (1 of which are reproduced from scratch), 2 sets of standard training and evaluation protocols, 15 GPU-accelerated evaluation metrics, and 3 kinds of robustness evaluation. This benchmark and codebase represent a significant leap forward in calibrating the current progress in the IMDL field and inspiring future breakthroughs. Code is available at: https: //github. com/scu-zjz/IMDLBenCo

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