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Bingbing Li

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6 papers

2 author rows

EAAI Journal 2026 Journal Article

A whole-life fatigue crack growth rate prediction method based on active learning and physics-informed loss

Qixuan Zhang
Wei Zhang
Rui Huang
Xinghui Chen
Bingbing Li
Fang Wang
Yiming Zheng
Changyu Zhou

Whole-life fatigue crack growth presents a critical challenge in structural integrity assessment, particularly under complex loading conditions. To address the limitations of standard physics-informed neural networks (PINNs) in capturing the temporal dynamics of fatigue crack growth, this study proposes an active learning-based physics-informed recurrent neural network (AC-PI-RNN). Specifically, a recurrent neural network (RNN) is integrated with a fully connected network, where dynamic features (stress intensity factor range) and static features (stress ratio, load amplitude, and pre-strain) are fused at the RNN input layer to provide comprehensive loading information. To optimize sample selection under data-limited conditions, a query-by-committee active learning strategy is employed. Furthermore, a modified Jones physical model is embedded into the network's loss function to enforce adherence to the underlying physics of fatigue crack growth. Comprehensive evaluations validate the efficacy of the proposed framework, demonstrating enhanced predictive fidelity, robust generalization, and improved physical consistency. A comparative analysis reveals that the AC-PI-RNN significantly outperforms traditional RNN and PINN models, showing a distinct advantage in capturing the complete trajectory of whole-life crack propagation with high precision. The proposed framework provides an effective and interpretable approach for whole-life fatigue crack growth rate prediction under complex loading conditions.

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EAAI Journal 2025 Journal Article

A knowledge-guided reinforcement learning method for lateral path tracking

Bo Hu
Sunan Zhang
Yuxiang Feng
Bingbing Li
Hao Sun
Mingyang Chen
Weichao Zhuang
Yi Zhang

Lateral Control algorithms in autonomous vehicles often necessitates an online fine-tuning procedure in the real world. While reinforcement learning (RL) enables vehicles to learn and improve the lateral control performance through repeated trial and error interactions with a dynamic environment, applying RL directly to safety-critical applications in real physical world is challenging because ensuring safety during the learning process remains difficult. To enable safe learning, a promising direction is to make use of previously gathered offline data, which is frequently accessible in engineering applications. In this context, this paper presents a set of knowledge-guided RL algorithms that can not only fully leverage the prior collected offline data without the need of a physics-based simulator, but also allow further online policy improvement in a smooth, safe and efficient manner. To evaluate the effectiveness of the proposed algorithms on a real controller, a hardware-in-the-loop and a miniature vehicle platform are built. Compared with the vanilla RL, behavior cloning and the existing controller, the proposed algorithms realize a closed-loop solution for lateral control problems from offline training to online fine-tuning, making it attractive for future similar RL-based controller to build upon.

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JBHI Journal 2025 Journal Article

Semi-Supervised Gland Segmentation via Feature-Enhanced Contrastive Learning and Dual-Consistency Strategy

Jiejiang Yu
Bingbing Li
Xipeng Pan
Zhenwei Shi
Huadeng Wang
Rushi Lan
Xiaonan Luo

In the field of gland segmentation in histopathology, deep-learning methods have made significant progress. However, most existing methods not only require a large amount of high-quality annotated data but also tend to confuse the internal of the gland with the background. To address this challenge, we propose a new semi-supervised method named DCCL-Seg for gland segmentation, which follows the teacher-student framework. Our approach can be divided into follows steps. First, we design a contrastive learning module to improve the ability of the student model's feature extractor to distinguish between gland and background features. Then, we introduce a Signed Distance Field (SDF) prediction task and employ dual-consistency strategy (across tasks and models) to better reinforce the learning of gland internal. Next, we proposed a pseudo label filtering and reweighting mechanism, which filters and reweights the pseudo labels generated by the teacher model based on confidence. However, even after reweighting, the pseudo labels may still be influenced by unreliable pixels. Finally, we further designed an assistant predictor to learn the reweighted pseudo labels, which do not interfere with the student model's predictor and ensure the reliability of the student model's predictions. Experimental results on the publicly available GlaS and CRAG datasets demonstrate that our method outperforms other semi-supervised medical image segmentation methods.

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IJCAI Conference 2023 Conference Paper

Towards Lossless Head Pruning through Automatic Peer Distillation for Language Models

Bingbing Li
Zigeng Wang
Shaoyi Huang
Mikhail Bragin
Ji Li
Caiwen Ding

Pruning has been extensively studied in Transformer-based language models to improve efficiency. Typically, we zero (prune) unimportant model weights and train a derived compact model to improve final accuracy. For pruned weights, we treat them as useless and discard them. This usually leads to significant model accuracy degradation. In this paper, we focus on attention head pruning as head attention is a key component of the transformer-based language models and provides interpretable knowledge meaning. We reveal the relationship between pruned attention heads and retained heads and provide a solution to recycle the discarded knowledge from the pruned heads, named peer distillation. We also develop an automatic framework to locate the to-be-pruned attention heads in each layer, freeing the time-consuming human labor in tuning hyperparameters. Experimental results on the General Language Understanding Evaluation (GLUE) benchmark are provided using BERT model. By recycling discarded knowledge from pruned heads, the proposed method maintains model performance across all nine tasks while reducing heads by over 58% on average and outperforms state-of-the-art techniques (e. g. , Random, HISP, L0 Norm, SMP).

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IJCAI Conference 2021 Conference Paper

Enabling Retrain-free Deep Neural Network Pruning Using Surrogate Lagrangian Relaxation

Deniz Gurevin
Mikhail Bragin
Caiwen Ding
Shanglin Zhou
Lynn Pepin
Bingbing Li
Fei Miao

Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline, i. e. , training, pruning and retraining (fine-tuning) significantly increases the overall training trails. In this paper, we develop a systematic weight-pruning optimization approach based on Surrogate Lagrangian relaxation (SLR), which is tailored to overcome difficulties caused by the discrete nature of the weight-pruning problem while ensuring fast convergence. We further accelerate the convergence of the SLR by using quadratic penalties. Model parameters obtained by SLR during the training phase are much closer to their optimal values as compared to those obtained by other state-of-the-art methods. We evaluate the proposed method on image classification tasks using CIFAR-10 and ImageNet, as well as object detection tasks using COCO 2014 and Ultra-Fast-Lane-Detection using TuSimple lane detection dataset. Experimental results demonstrate that our SLR-based weight-pruning optimization approach achieves higher compression rate than state-of-the-arts under the same accuracy requirement. It also achieves a high model accuracy even at the hard-pruning stage without retraining (reduces the traditional three-stage pruning to two-stage). Given a limited budget of retraining epochs, our approach quickly recovers the model accuracy.

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ICRA Conference 2014 Conference Paper

Interactive robots as social partner for communication care

Lili Liu
Bingbing Li
I-Ming Chen 0001
Tze Jui Goh
Min Sung

Recent research suggests children with autism show certain positive social behaviors while interacting with robots without the presence of peer pressure. This paper explores possible use of interactive robots for interaction with children with autism. Logical artificial intelligence, reasoning about beliefs, desires and intentions (BDI model) serve as a basis to construct a set of scenarios. The present invention also describes a novel real-world motivated learning method. It uses a supervised reinforcement learning approach combined with goal creating. Autonomous agent learns problems in the real world through interaction with the patient. Methods and systems for management of brain and body functions and sensory perception, observing/analyzing, interactive behavior are presented. In one instance, the virtual agent is integrated with a computer-aided system for diagnosis, monitoring, and therapy.

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