Author name cluster

Pengfei Yang

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6 papers

2 author rows

JBHI Journal 2026 Journal Article

DiffSpkSync: A Muscle Synergy-Guided Spiking Diffusion Model for EMG Signal Generation to Improve Gesture Recognition Performance

Kejia Su
Bo Wan
Jiayang Huang
Zhi-Qiang Zhang
Junhao Zhang
Pengfei Yang
Quan Wang

High-density surface electromyography (HD-sEMG) based hand gesture recognition (HGR) has shown great promise for intuitive human-machine interaction. However, the performance of HGR model is often hindered by a scarcity of available training data, especially in the fields of gesture recognition, rehabilitation, and medicine. To address these issues, we propose DiffSpkSync, a novel generative framework that integrates (1) muscle synergy-guided diffusion modeling for physiologically plausible signal reconstruction, (2) spiking neuron-based sparsification to reduce energy cost, and (3) a time-series mixup strategy to preserve local dynamics during augmentation. Experiments on a public Hyser dataset and a self-collected XDHDEMG dataset demonstrate that training gesture classifiers with data augmented by DiffSpkSync consistently improves classification accuracy in both intrasession and intersession scenarios. Comparative results further demonstrate superior performance over representative generative baselines, including VAE, DCGAN, DANN-CRC, and PatchEMG. Furthermore, real-time validation demonstrates that the proposed method achieves an average of 130. 22 ms end-to-end latency and an average of 95. 87% accuracy predictions, supporting their applicability in real-world applications.

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AAAI Conference 2026 Conference Paper

Lethe: Layer- and Time-Adaptive KV Cache Pruning for Reasoning-Intensive LLM Serving

Hui Zeng
Daming Zhao
Pengfei Yang
WenXuan Hou
Tianyang Zheng
Hui Li
Weiye Ji
Jidong Zhai

Generative reasoning with large language models (LLMs) often involves long decoding sequences, leading to substantial memory and latency overheads from accumulating key-value (KV) caches. While existing KV compression methods primarily focus on reducing prefill memory from long input sequences, they fall short in addressing the dynamic and layer-sensitive nature of long-form generation, which is central to reasoning tasks. We propose Lethe, a dynamic KV cache management framework that introduces adaptivity along both the spatial and temporal dimensions of decoding. Along the spatial dimension, Lethe performs layerwise sparsity-aware allocation, assigning token pruning budgets to each transformer layer based on estimated attention redundancy. Along the temporal dimension, Lethe conducts multi-round token pruning during generation, driven by a Recency-Aware Selective Retention (RASR) mechanism. RASR extends traditional recency-based heuristics by also considering token relevance derived from evolving attention patterns, enabling informed decisions about which tokens to retain or evict. Empirical results demonstrate that Lethe achieves a favorable balance between efficiency and generation quality across diverse models and tasks, increases throughput by up to 2.56×.

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AAAI Conference 2025 Conference Paper

Training Verification-Friendly Neural Networks via Neuron Behavior Consistency

Zongxin Liu
Zhe Zhao
Fu Song
Jun Sun
Pengfei Yang
Xiaowei Huang
Lijun Zhang

Formal verification provides critical security assurances for neural networks, yet its practical application suffers from the long verification time. This work introduces a novel method for training verification-friendly neural networks, which are robust, easy to verify, and relatively accurate. Our method integrates neuron behavior consistency into the training process, making neuron activation states remain consistent across different inputs within a local neighborhood. This reduces the number of unstable neurons and tightens the bounds of neurons thereby enhancing the network's verifiability. We evaluated our method using the MNIST, Fashion-MNIST, and CIFAR-10 datasets with various network architectures. The experimental results demonstrate that networks trained using our method are verification-friendly across different radii and architectures, whereas other tools fail to maintain verifiability as the radius increases. Additionally, we show that our method can be combined with existing approaches to further improve the verifiability of networks.

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ICRA Conference 2024 Conference Paper

A Novel SEA-based Haptic Interface for Robot-Assisted Vascular Interventional Surgery

Yonggan Yan
Shuxiang Guo
Chuqiao Lyu
Jian Guo
Jian Wang
Pengfei Yang
Yongwei Zhang
Yongxin Zhang

Robot-assisted vascular interventional surgery can isolate interventionists and X-ray radiation, and improve surgical accuracy. However, the leader side outside the operating room still has problems such as incomplete collection of operating information and unrealistic tactile feedback. The main objective of this paper is to design a haptic interface that can simultaneously capture the force-position information of the interventionists and generate force to assist the interventionists in performing surgeries on the leader side. It can capture the interventionists’ delivery displacement, twisting angle, clamping force, and provide real-time force feedback. A leader-follower bidirectional force feedback control strategy was proposed. Based on this strategy, on the one hand, the interventionist perceives the multi-modal information fed back from the follower side, makes judgments, and actively adjusts the surgical operation. On the other hand, the interventionist controls the grasping state of the instruments remotely to control the safety operating force threshold. Finally, the experimental setup was built and a series of evaluation experiments were performed. The experimental results verified the feasibility of the designed haptic interface. It can generate dynamic and accurate force feedback and realize leader-follower grasping force control.

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JBHI Journal 2022 Journal Article

Toward Robust, Adaptiveand Reliable Upper-Limb Motion Estimation Using Machine Learning and Deep Learning–A Survey in Myoelectric Control

Tianzhe Bao
Sheng Quan Xie
Pengfei Yang
Ping Zhou
Zhi-Qiang Zhang

To develop multi-functionalhuman-machine interfaces that can help disabled people reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) techniques have been widely implemented to decode human movement intentions from surface electromyography (sEMG) signals. However, due to the high complexity of upper-limb movements and the inherent non-stable characteristics of sEMG, the usability of ML/DL based control schemes is still greatly limited in practical scenarios. To this end, tremendous efforts have been made to improve model robustness, adaptation, and reliability. In this article, we provide a systematic review on recent achievements, mainly from three categories: multi-modal sensing fusion to gain additional information of the user, transfer learning (TL) methods to eliminate domain shift impacts on estimation models, and post-processing approaches to obtain more reliable outcomes. Special attention is given to fusion strategies, deep TL frameworks, and confidence estimation. Research challenges and emerging opportunities, with respect to hardware development, public resources, and decoding strategies, are also analysed to provide perspectives for future developments

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TMLR Journal 2022 Journal Article

Weight Expansion: A New Perspective on Dropout and Generalization

Gaojie Jin
Xinping Yi
Pengfei Yang
Lijun Zhang
Sven Schewe
Xiaowei Huang

While dropout is known to be a successful regularization technique, insights into the mechanisms that lead to this success are still lacking. We introduce the concept of weight expansion, an increase in the signed volume of a parallelotope spanned by the column or row vectors of the weight covariance matrix, and show that weight expansion is an effective means of increasing the generalization in a PAC-Bayesian setting. We provide a theoretical argument that dropout leads to weight expansion and extensive empirical support for the correlation between dropout and weight expansion. To support our hypothesis that weight expansion can be regarded as an indicator of the enhanced generalization capability endowed by dropout, and not just as a mere by-product, we have studied other methods that achieve weight expansion (resp.\ contraction), and found that they generally lead to an increased (resp.\ decreased) generalization ability. This suggests that dropout is an attractive regularizer, because it is a computationally cheap method for obtaining weight expansion. This insight justifies the role of dropout as a regularizer, while paving the way for identifying regularizers that promise improved generalization through weight expansion.

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