Author name cluster

Ping Xie

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

2 author rows

EAAI Journal 2026 Journal Article

Semantic-driven seasonal data classification: An artificial intelligence-enabled cost-effective storage system

Zhu Yuan
Xueqiang Lv
Yunchao Gong
Ping Xie
Xiao Qin
Xindong You

Modern storage systems face significant challenges in balancing energy costs and performance, especially for text-rich workloads (e. g. , e-commerce logs, social media archives). Traditional frequency-based classification methods fail to exploit semantic patterns in textual metadata, leading to suboptimal resource allocation. In this paper, we propose a novel deep learning-method based on Bidirectional Encoder Representations from Transformers-Recurrent Convolutional Neural Networks (BERT-RCNN) to extract seasonal features from data for classification, enabling cost-effective storage management. Our approach addresses the limitations of traditional frequency-based classification by incorporating semantic features from data text. Additionally, we explore the long-period seasonal features embedded within the text, offering a new approach for multi-feature classification. To evaluate the practical impact of our method, we developed a cost module within CloudSimDisk to simulate storage system operating costs. By constructing models for energy consumption and operating costs and generating real-world workloads from Baidu Index data, we implement and test our solution. Experimental results show that the BERT-RCNN model outperforms traditional K-means and other deep learning methods, reducing energy consumption by 3. 90%–5. 69%, saving operational costs by 3. 62%–6. 13%, and shortening response time by 15. 12%–26. 09%.

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JBHI Journal 2025 Journal Article

Optimized Drug-Drug Interaction Extraction With BioGPT and Focal Loss-Based Attention

Zhu Yuan
Shuailiang Zhang
Huiyun Zhang
Ping Xie
Yaxun Jia

Drug-drug interactions (DDIs) are a significant focus in biomedical research and clinical practice due to their potential to compromise treatment outcomes or cause adverse effects. While deep learning approaches have advanced DDI extraction, challenges such as severe class imbalance and the complexity of biomedical relationships persist. This study introduces BioFocal-DDI, a framework combining BioGPT for data augmentation, BioBERT and BiLSTM for contextual and sequential feature extraction, and Relational Graph Convolutional Networks (ReGCN) for relational modeling. To address class imbalance, a Focal Loss-based Attention mechanism is employed to enhance learning on underrepresented and challenging instances. Evaluated on the DDI Extraction 2013 dataset, BioFocal-DDI achieves a precision of 86. 75%, recall of 86. 53%, and an F1 Score of 86. 64%. These results suggest that the proposed method is effective in improving DDI extraction.

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YNICL Journal 2018 Journal Article

Abnormal functional corticomuscular coupling after stroke

Xiaoling Chen
Ping Xie
Yuanyuan Zhang
Yuling Chen
Shengcui Cheng
Litai Zhang

Motor dysfunction is a major consequence after stroke and it is generally believed that the loss of motor ability is caused by the impairments in neural network that controls movement. To explore the abnormal mechanisms how the brain controls shoulder abduction and elbow flexion in "flexion synergy" following stroke, we used the functional corticomuscular coupling (FCMC) between the brain and the muscles as a tool to identify the temporal evolution of corticomuscular interaction between the synkinetic and separate phases. 59-channel electroencephalogram (EEG) over brain scalp and 2-channel electromyogram (EMG) from biceps brachii (BB)/deltoid (DT) were recorded in sixteen stroke patients with motor dysfunction and eight healthy controls during a task of uplifting the arm (stage 1) and maintaining up to the chest (stage 2). As a result, compared to healthy controls, stroke patients had abnormally reduced coherence in EEG-BB combination and increased coherence in EEG-DT combination. Compared to synkinetic stroke patients, separate ones exhibited higher coupling at gamma-band during stage 1 and higher at beta-band during stage 2 in EEG-BB combination, but lower at beta-band during stage 2 in EEG-DT combination. Therefore, we infer that the disorders of efferent control and afferent proprioception in sensorimotor system for stroke patients effect on the oscillation at beta and gamma bands. Patients need integrate more information for shoulder abduction to compensate for the functional loss of elbow flexion in the recovery process, so that partial cortical cortex controlling on the elbow flexion may work on the shoulder abduction during "flexion synergy". Such researches could provide new perspective on the temporal evolution of corticomuscular interaction after stroke and add to our understanding of possible pathomechanisms how the brain abnormally controls shoulder abduction and elbow flexion in "flexion synergy".

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ICRA Conference 2014 Conference Paper

A synchronous and multi-domain feature extraction method of EEG and sEMG in power-assist rehabilitation robot

Yan Song
Yihao Du
Xiaoguang Wu
Xiaoling Chen
Ping Xie

To propose a synchronous and multi-domain feature extraction method of electroencephalogram (EEG) and surface electromyogram (sEMG) signals is of great significance to power-assist rehabilitation robot control with humancomputer interface (HCI). In this paper, nonnegative Tucker decomposition which is one model of nonnegative tensor factorization (NTF) is used to fuse two kinds of bioelectricity signals (EEG and sEMG) and extract multi-domain features of EEG and sEMG signals for classification which contain time, frequency, and space domains. In the first step the EEG and sEMG data are transformed into multidimensional information using continuous wavelet transform and the 4-D EEG-sEMG tensor is established. Then the tensor is decomposed into four components (spatial components, spectral components, temporal components and category components) and the core tensor is the feature extracted. The feature after being eliminated and compressed are fed into KNN, LDA and SVM classifiers for pattern recognition, and a comparison is done in single EEG analysis, single sEMG analysis and both EEG and sEMG analysis. An experiment about 10 healthy participants' upper limb movements was carried out to verify the validity of this algorithm. The result implied that NTF is a meaningful and valuable synchronous and multi-domain feature extraction method which may be promising in power-assist rehabilitation robot control.

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