Author name cluster

Fei Han

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7 papers

2 author rows

EAAI Journal 2025 Journal Article

Classification of non-standard mechanical parts with graph convolutional networks

Zirui Li
Xiaomeng Tong
Peng Shi
Maolin Cai
Fei Han

The structure and functionality of non-standard mechanical parts (NSMPs) are intricate with a gradual increase in proportion in the complex customized products. The classification for standard mechanical parts usually struggles to effectively encompass the diversity and complexity of NSMPs. The existing standard classification system seems to be insufficient and lacks flexibility. The research classifies NSMPs into four categories according to processing methods, aiming to efficiently allocate batches of NSMPs to the workshops with suitable processing capabilities. First, the attributed graph was constructed from boundary representations, in which dimensionless geometric parameters with geometric invariance serve as associated features. Secondly, we trained the improved graph convolutional network, named NSMP-Net, which uses nonlinear projections for representing implicit relationships among surfaces and curves. Specific attention mechanisms are used to readout node hidden features, reducing redundant features' influence on graph-level embeddings. Finally, the experimental results demonstrate that NSMP-Net achieved 91. 3 % accuracy in classification tasks, outperforming other models with higher consistency after spatial and dimensional transformation, demonstrating great potential in classification.

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ICRA Conference 2025 Conference Paper

Learn to Swim: Data-Driven LSTM Hydrodynamic Model for Quadruped Robot Gait Optimization

Fei Han
Pengming Guo
Hao Chen
Weikun Li
Jingbo Ren
Naijun Liu
Ning Yang
Dixia Fan

This paper presents a Long Short-Term Memory network-based Fluid Experiment Data-Driven model (FEDLSTM) for predicting unsteady, nonlinear hydrodynamic forces on the underwater quadruped robot we constructed. Trained on experimental data from leg force and body drag tests conducted in both a recirculating water tank and a towing tank, FED-LSTM outperforms traditional Empirical Formulas (EF) commonly used for flow prediction over flat surfaces. The model demonstrates superior accuracy and adaptability in capturing complex fluid dynamics, particularly in straightline and turning-gait optimizations via the NSGA-II algorithm. FED-LSTM reduces deflection errors during straight-line swimming and improves turn times without increasing the turning radius. Hardware experiments further validate the model's precision and stability over EF. This approach provides a robust framework for enhancing the swimming performance of legged robots, laying the groundwork for future advances in underwater robotic locomotion.

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JBHI Journal 2025 Journal Article

MORPSO_ECD+ELM: A Unified Framework for Gene Selection and Cancer Classification

Sumet Mehta
Fei Han
Qinghua Ling
Muhammad Sohail
Arfan Nagra

Gene selection and cancer classification are inherently multi-objective tasks that require balancing competing objectives, such as maximizing classification accuracy while minimizing irrelevant or redundant genes. Existing methods often optimize a single objective or treat gene selection and classification independently, limiting their overall effectiveness. This study proposes a unified framework, MORPSO_ECD+ELM, which formulates gene selection and classification as a multimodal multi-objective optimization problem (MMOP) to optimize both objectives simultaneously. The framework introduces two key innovations: (1) an enhanced crowding distance (ECD) metric to improve diversity preservation and (2) an advanced multi-objective particle swarm optimization variant (MORPSO_ECD) that incorporates ECD and ring topography to effectively explore the MMOP solution space. Integrated with the Extreme Learning Machine (ELM), this framework achieves robust and efficient cancer classification. Extensive experimental validations demonstrate that the proposed approach achieves high classification accuracy while identifying biologically meaningful gene subsets, providing a powerful solution to bridge the gap between gene selection and cancer classification.

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NeurIPS Conference 2024 Conference Paper

Performative Control for Linear Dynamical Systems

Songfu Cai
Fei Han
Xuanyu Cao

We introduce the framework of performative control, where the policy chosen by the controller affects the underlying dynamics of the control system. This results in a sequence of policy-dependent system state data with policy-dependent temporal correlations. Following the recent literature on performative prediction \cite{perdomo2020performative}, we introduce the concept of a performatively stable control (PSC) solution. We first propose a sufficient condition for the performative control problem to admit a unique PSC solution with a problem-specific structure of distributional sensitivity propagation and aggregation. We further analyze the impacts of system stability on the existence of the PSC solution. Specifically, for {almost surely strongly stable} policy-dependent dynamics, the PSC solution exists if the sum of the distributional sensitivities is small enough. However, for almost surely unstable policy-dependent dynamics, the existence of the PSC solution will necessitate a temporally backward decaying of the distributional sensitivities. We finally provide a repeated stochastic gradient descent scheme that converges to the PSC solution and analyze its non-asymptotic convergence rate. Numerical results validate our theoretical analysis.

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AAAI Conference 2019 Conference Paper

Visual Place Recognition via Robust ℓ2-Norm Distance Based Holism and Landmark Integration

Kai Liu
Hua Wang
Fei Han
Hao Zhang

Visual place recognition is essential for large-scale simultaneous localization and mapping (SLAM). Long-term robot operations across different time of the days, months, and seasons introduce new challenges from significant environment appearance variations. In this paper, we propose a novel method to learn a location representation that can integrate the semantic landmarks of a place with its holistic representation. To promote the robustness of our new model against the drastic appearance variations due to long-term visual changes, we formulate our objective to use non-squared ℓ2-norm distances, which leads to a difficult optimization problem that minimizes the ratio of the ℓ2,1-norms of matrices. To solve our objective, we derive a new efficient iterative algorithm, whose convergence is rigorously guaranteed by theory. In addition, because our solution is strictly orthogonal, the learned location representations can have better place recognition capabilities. We evaluate the proposed method using two large-scale benchmark data sets, the CMU-VL and Nordland data sets. Experimental results have validated the effectiveness of our new method in long-term visual place recognition applications.

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AAAI Conference 2018 Conference Paper

Learning Integrated Holism-Landmark Representations for Long-Term Loop Closure Detection

Fei Han
Hua Wang
Hao Zhang

Loop closure detection is a critical component of large-scale simultaneous localization and mapping (SLAM) in loopy environments. This capability is challenging to achieve in longterm SLAM, when the environment appearance exhibits signiﬁcant long-term variations across various time of the day, months, and even seasons. In this paper, we introduce a novel formulation to learn an integrated long-term representation based upon both holistic and landmark information, which integrates two previous insights under a uniﬁed framework: (1) holistic representations outperform keypoint-based representations, and (2) landmarks as an intermediate representation provide informative cues to detect challenging locations. Our new approach learns the representation by projecting input visual data into a low-dimensional space, which preserves both the global consistency (to minimize representation error) and the local consistency (to preserve landmarks’ pairwise relationship) of the input data. To solve the formulated optimization problem, a new algorithm is developed with theoretically guaranteed convergence. Extensive experiments have been conducted using two large-scale public benchmark data sets, in which the promising performances have demonstrated the effectiveness of the proposed approach.

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IJCAI Conference 2016 Conference Paper

Enforcing Template Representability and Temporal Consistency for Adaptive Sparse Tracking

Xue Yang
Fei Han
Hua Wang
Hao Zhang

Sparse representation has been widely studied in visual tracking, which has shown promising tracking performance. Despite a lot of progress, the visual tracking problem is still a challenging task due to appearance variations over time. In this paper, we propose a novel sparse tracking algorithm that well addresses temporal appearance changes, by enforcing template representability and temporal consistency (TRAC). By modeling temporal consistency, our algorithm addresses the issue of drifting away from a tracking target. By exploring the templates' long-term-short-term representability, the proposed method adaptively updates the dictionary using the most descriptive templates, which significantly improves the robustness to target appearance changes. We compare our TRAC algorithm against the state-of-the-art approaches on 12 challenging benchmark image sequences. Both qualitative and quantitative results demonstrate that our algorithm significantly outperforms previous state-of-the-art trackers.

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