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Wei Yan

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8 papers

2 author rows

YNICL Journal 2026 Journal Article

Distinct neurologic state in patients with traumatic brain injury and hemorrhagic stroke during the stage of acute disorders of consciousness and the correlation with the neurological prognosis: A multi-modal PET/rs-fMRI study

Danjing Yu
Kemeng Gao
Xiefeng Wang
Lin Zhao
Yi Sun
Zhiyan Shen
Yu Wang
Ying Wang

PURPOSE: The exact mechanisms underlying the distinct neurological outcomes between Traumatic Brain Injury (TBI) and Hemorrhagic Stroke (HS) remain unclear. Our objective is to assess distinct features of neurologic state between comatose patients with TBI and HS during the stage of acute disorder of consciousness (aDoC) and to identify the correlation of neurologic features with prognosis. METHODS: Data were analyzed from TBI and HS patients examined by positron emission tomography (PET) and resting-state functional magnetic resonance imaging (rs-fMRI) simultaneously. Primary clinical outcomes consisted of the state of consciousness and neurological prognosis. The regional neural activity was assessed by the amplitude of fractional low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) on rs-fMRI scans. The standardized uptake value (SUV) on PET scans quantified neural metabolism. Functional connectivity (FC) and graph theoretic approach (GTA) were employed to compare the FC patterns between TBI and HS. Correlations of PET/rs-fMRI indicators with the prognosis of HS and TBI were identified. RESULTS: Muti-modal PET/rs-fMRI analysis showed more active local neurological state in TBI patients than HS patients, specifically in the right precentral gyrus (PreCG.R), right postcentral gyrus (PoCG.R), right superior temporal gyrus (STG.R) and right middle temporal gyrus (MTG.R). TBI patients demonstrated significantly higher clustering coefficient and nodal efficiency of the sensorimotor network (SMN) along with lower connectivity and network efficiency in the default network (DMN) compared to HS patients. PET/rs-fMRI indicators significantly correlated with the neurological prognosis of TBI and HS. CONCLUSIONS: This study elucidated the underlying mechanisms contributing to the distinct neurologic prognosis between comatose TBI and HS patients, and may contribute to the development of early targeted intervention strategies for specific diseases.

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AAAI Conference 2026 Conference Paper

WaveFormer: Frequency-Time Decoupled Vision Modeling with Wave Equation

Zishan Shu
Juntong Wu
Wei Yan
Xudong Liu
Hongyu Zhang
Chang Liu
Youdong Mao
Jie Chen

Vision modeling has advanced rapidly with Transformers, whose attention mechanisms capture visual dependencies but lack a principled account of how semantic information propagates spatially. We revisit this problem from a wave-based perspective: feature maps are treated as spatial signals whose evolution over an internal propagation time (aligned with network depth) is governed by an underdamped wave equation. In this formulation, spatial frequency—from low-frequency global layout to high-frequency edges and textures—is modeled explicitly, and its interaction with propagation time is controlled rather than implicitly fixed. We derive a closed-form, frequency–time decoupled solution and implement it as the Wave Propagation Operator (WPO), a lightweight module that models global interactions in O(NlogN) time—far lower than attention. Building on WPO, we propose a family of WaveFormer models as drop-in replacements for standard ViTs and CNNs, achieving competitive accuracy across image classification, object detection, and semantic segmentation, while delivering up to 1.6× higher throughput and 30% fewer FLOPs than attention-based alternatives. Furthermore, our results demonstrate that wave propagation introduces a complementary modeling bias to heat-based methods, effectively capturing both global coherence and high-frequency details essential for rich visual semantics.

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JBHI Journal 2025 Journal Article

EDG-Net: Encryption and Decryption based Gan-attention Network for CT images in the Internet of Medical Things and Telemedicine

Kai Chen
Yuchen Li
Shipeng Xie
Zhan Wu
Yikun Zhang
Jean-Louis Coatrieux
Wei Yan
Yang Chen

CT images provide medical practitioners with a scientific and intuitive rationale for the diagnosis of clinical diseases. The Internet of Medical Things (IoMT) and telemedicine facilitate the preservation, transmission, and application of medical data and drive the sharing of medical data, especially medical images. Encryption and decryption of CT images distributed in the IoMT and telemedicine are becoming critical because they contain a large amount of private patient–ensitive information and are vulnerable to third-party attacks, resulting in information exposure and privacy leakage. In this paper, we propose an Encryption and Decryption based Gan-attention network (EDG-Net) for CT images in the IoMT and telemedicine. EDG-Net consists of a generator, two discriminators, a domain transfer of attention, and adaptive normalization. In addition, a double encryption and decryption strategy is introduced by EDG-Net to effectively improve the security of the ciphertext image and the fidelity of the decrypted plaintext image. Specifically, during the encryption or decryption phase, the generator transforms the CT images mutually in the plaintext and ciphertext domains. Two discriminators to identify and modify the differences between these two domain transformations, especially improve the accuracy of the reconstruction during decryption. The parameters of the trained encryption and decryption network are considered as the secret keys of encryption and decryption. Qualitative and quantitative analysis of public and private datasets demonstrates the superior performance of EDG-Net regarding encryption security and robustness as well as decryption accuracy.

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ICRA Conference 2024 Conference Paper

A Meter-scale Ornithopter Capable of Jumping Take-off

Wei Yan
Genliang Chen
Zhuang Zhang
Hao Wang 0015

Flapping wing air vehicles(FWAV) or ornithopters are bio-inspired aerial robots that mimic the flying principles of insects and birds. Autonomous take-off is an important capability for FWAV to enhance its performance and extend its working time, which is equipped by almost every kind of bird. As a common method of take-off for birds, jumping take-off has a great ability to adapt to different terrain and high energy efficiency compared with running and rotor-based take-off. Despite recent research, there is no FWAV capable of jumping take-off to this day. In this paper, we present a process to realize the jumping take-off of a meter-scale FWAV from flat ground. To lower the mechanical complexity, we eliminate the design of traditional robotic legs. Instead, we realize steady standing through a tripod-like structure that consists of two wings and a jumping mechanism. Two flapping wings are directly driven by two independent servos. Three carbon fiber springs are employed to build a lightweight jumping module with high elastic energy. We build the dynamic model to analyze the aerodynamic effect during the jumping phase and realize a stable transition to flapping flight. This work lays the foundation for outdoor flight without human assistance.

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YNICL Journal 2024 Journal Article

Right superior frontal gyrus: A potential neuroimaging biomarker for predicting short-term efficacy in schizophrenia

Yongfeng Yang
Xueyan Jin
Yongjiang Xue
Xue Li
Yi Chen
Ning Kang
Wei Yan
Peng Li

Antipsychotic drug treatment for schizophrenia (SZ) can alter brain structure and function, but it is unclear if specific regional changes are associated with treatment outcome. Therefore, we examined the effects of antipsychotic drug treatment on regional grey matter (GM) density, white matter (WM) density, and functional connectivity (FC) as well as associations between regional changes and treatment efficacy. SZ patients (n = 163) and health controls (HCs) (n = 131) were examined by structural magnetic resonance imaging (sMRI) at baseline, and a subset of SZ patients (n = 77) were re-examined after 8 weeks of second-generation antipsychotic treatment to assess changes in regional GM and WM density. In addition, 88 SZ patients and 81 HCs were examined by resting-state functional MRI (rs-fMRI) at baseline and the patients were re-examined post-treatment to examine FC changes. The Positive and Negative Syndrome Scale (PANSS) and MATRICS Consensus Cognitive Battery (MCCB) were applied to measure psychiatric symptoms and cognitive impairments in SZ. SZ patients were then stratified into response and non-response groups according to PANSS score change (≥50 % decrease or <50 % decrease, respectively). The GM density of the right cingulate gyrus, WM density of the right superior frontal gyrus (SFG) plus 5 other WM tracts were reduced in the response group compared to the non-response group. The FC values between the right anterior cingulate and paracingulate gyrus and left thalamus were reduced in the entire SZ group (n = 88) after treatment, while FC between the right inferior temporal gyrus (ITG) and right medial superior frontal gyrus (SFGmed) was increased in the response group. There were no significant changes in regional FC among the non-response group after treatment and no correlations with symptom or cognition test scores. These findings suggest that the right SFG is a critical target of antipsychotic drugs and that WM density and FC alterations within this region could be used as potential indicators in predicting the treatment outcome of antipsychotics of SZ.

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EAAI Journal 2023 Journal Article

Intelligent predictive maintenance of hydraulic systems based on virtual knowledge graph

Wei Yan
Yu Shi
Zengyan Ji
Yuan Sui
Zhenzhen Tian
Wanjing Wang
Qiushi Cao

In the manufacturing industry, a hydraulic system harnesses liquid fluid power to create powerful machines. Under the trend of Industry 4. 0, the predictive maintenance of hydraulic systems is transforming to more intelligent and automated approaches that leverage the strong power of artificial intelligence and data science technologies. However, due to the knowledge-intensive and heterogeneous nature of the manufacturing domain, the data and information required for predictive maintenance are normally collected from ubiquitous sensing networks. This leads to the gap between massive heterogeneous data/information resources in hydraulic system components and the limited cognitive ability of system users. Moreover, how to capture and structure useful domain knowledge (in a machine-readable way) for solving domain-specific tasks remains an open challenge for the predictive maintenance of hydraulic systems. To address these challenges, in this paper we propose a virtual knowledge graph-based approach for the digital modeling and intelligent predictive analytics of hydraulic systems. We evaluate the functionalities and effectiveness of the proposed approach on a predictive maintenance task under real-world industrial contexts. Results show that our proposed approach is capable and feasible to be implemented for digital modeling, data access, data integration, and predictive analytics.

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IJCAI Conference 2021 Conference Paper

AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System

Pengyu Zhao
Kecheng Xiao
Yuanxing Zhang
Kaigui Bian
Wei Yan

Recently, deep learning models have been widely explored in recommender systems. Though having achieved remarkable success, the design of task-aware recommendation models usually requires manual feature engineering and architecture engineering from domain experts. To relieve those efforts, we explore the potential of neural architecture search (NAS) and introduce AMEIR for Automatic behavior Modeling, interaction Exploration and multi-layer perceptron (MLP) Investigation in the Recommender system. Specifically, AMEIR divides the complete recommendation models into three stages of behavior modeling, interaction exploration, MLP aggregation, and introduces a novel search space containing three tailored subspaces that cover most of the existing methods and thus allow for searching better models. To find the ideal architecture efficiently and effectively, AMEIR realizes the one-shot random search in recommendation progressively on the three stages and assembles the search results as the final outcome. The experiment over various scenarios reveals that AMEIR outperforms competitive baselines of elaborate manual design and leading algorithmic complex NAS methods with lower model complexity and comparable time cost, indicating efficacy, efficiency, and robustness of the proposed method.

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EAAI Journal 2015 Journal Article

Finger-vein pattern restoration with Direction-Variance-Boundary Constraint Search

Tong Liu
Jianbin Xie
Wei Yan
Peiqin Li
Huanzhang Lu

Finger-vein verification is an emerging biometrics technology. Its first task is extracting finger-vein patterns. Although existing algorithms can extract most finger-vein patterns robustly, some branch of these patterns always breaks, which leads to adverse effects for features extraction and matching. In this paper, a Direction-Variance-Boundary Constraint Search (DVBCS) model is presented to restore the broken finger-vein patterns. At the beginning, endpoints of broken finger-vein branches are located. Then, a direction constraint for searching candidate point set is demonstrated. Following the second stage, an optimal target point is selected from the candidate point set according to a minimum within-cluster variance criterion. Eventually, the boundary constraint and variance constraint are introduced as the termination conditions. Experimental results illustrate that, while maintaining low segmentation error, the proposed method can restore above 10% lost target points. Moreover, the equal error rate of finger-vein recognition is reduced from 0. 57% to 0. 29% when using the proposed method to restore finger-vein patterns.

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