Portrait de Hanqing Zhao

Hanqing Zhao

Membre affilié
Professeur adjoint, Université Laval, Département de génie électrique et de génie informatique
Sujets de recherche
Apprentissage multitâche
Apprentissage par renforcement
Intelligence en essaim
Robotique
Systèmes multi-agents
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Biographie

Hanqing Zhao est professeur adjoint au Département de génie électrique et de génie informatique de l’Université Laval. Il est membre du Laboratoire de Vision et Systèmes Numériques (LVSN).

Hanqing a commencé son parcours académique à l’École Centrale de Pékin (Université Beihang). Il a obtenu un diplôme d’ingénieur civil en informatique à l’École Polytechnique de Bruxelles (Université libre de Bruxelles), sous la supervision de Marco Dorigo ; il a ensuite reçu son doctorat en informatique (robotique) à l’Université McGill, sous la direction de Gregory Dudek et Xue (Steve) Liu. Par la suite, il a été chercheur postdoctoral au MIST Lab de l’École Polytechnique de Montréal, encadré par Giovanni Beltrame.

Ses recherches portent sur le développement de robots capables d’accomplir des tâches complexes tout en demeurant résilients face aux pannes et aux perturbations extérieures. Il exploite l’apprentissage automatique, le contrôle adaptatif et des techniques avancées de consensus, telles que l’apprentissage par renforcement, l’apprentissage supervisé et les technologies Blockchain, pour concevoir des systèmes robotiques robustes, en particulier multi-robots.

Publications

Establishment of a tissue culture system with adventitious bud regeneration for the new raspberry germplasm 'autumn–winter yellow raspberry'
Zihan Zhu
Hanqing Zhao
Jinyu Liu
Ye Guo
Chenxing Zhang
Yingyue Li
2026-06-09
Plant (publié)
doi.org
Self-assembled chamber-like cardiac organoids for modeling cardiac chamber formation and cardiotoxicity assessment
Xinle Zou
Fanwen Wang
Huilin Zheng
Xianzhuang Liu
Tianci Kong
Rui Jiang
Yingying Guo
Yu Liang
Hanqing Zhao
Bo Wang
Duanqing Pei
2026-06-01
Nature Communications (publié)
doi.org
Key Issues and Future Directions in the Construction and Control of Geocentric Orbit Constellations for Gravitational Wave Detection
Yue LIU
Borui YAO
Hanqing Zhao
Meng LU
Yanchao HE
Ming LI
Lihua ZHANG
Jianying WANG
Mingying HUO
2026-05-31
China basic science (publié)
doi.org
Body Keypoint Detection Algorithm Based on Channel Attention Mechanism
Shaojun Yu
Wenhao Huo
Yuping Lu
Hanqing Zhao
Yilin Wang
Lili Wang
Rizwan Anjum Muhammad
With the implementation of national strategies aimed at building a leading sporting nation and promoting nationwide fitness, physical fitnes… (voir plus)s assessment has gained increasing attention as a crucial metric for evaluating students' physical condition and motor abilities. Concurrently, advancements in computer vision have enabled body keypoint detection technology to gradually replace traditional manual measurement methods, demonstrating significant potential for application in automated assessment systems. Accurate recognition of keypoints serves as the fundamental support for intelligent physical fitness testing and smart sports. However, existing keypoint detection algorithms often suffer from drifting of extremity keypoints, such as those of the hands and feet keypoints, in physical fitness test scenarios, thereby compromising the accuracy of the assessment. To address this challenge, this paper proposes Channel Attention BlazePose(CA-BlazePose), a body keypoint detection algorithm based on a channel attention mechanism, specifically designed for count-based physical fitness test scenarios, namely sit-ups and pull-ups. To tackle the issue of keypoint drift in motion detection, CA-BlazePose aims to enhance keypoint detection accuracy. It employs a two-stage network architecture consisting of heatmap training and regression fine-tuning, incorporating a channel attention module. This module strengthens the feature extraction process for extremity keypoints such as hands and feet, thereby improving recognition accuracy during detection.Experimental results demonstrate that, compared to mainstream keypoint detection algorithms such as OpenPose and BlazePose, the proposed CA-BlazePose algorithm achieves improvements in the PCK on two representative motion datasets, Common Objects in Context(COCO) and Leeds Sports Pose Extended(LSPET). Specifically, it shows an approximate increase of 7% for hand and foot keypoints and 8% for overall keypoints. Furthermore, in real-time detection tests for sit-ups and pull-ups captured from various viewing angles, CA-BlazePose demonstrates superior performance in handling frames with missing or drifting keypoints compared to existing algorithms, exhibiting more stable recognition performance under identical detection conditions.
2026-05-24
Journal of Intelligent Computing and Networking (publié)
doi.org
TCMIIES: A Browser-Based LLM-Powered Intelligent Information Extraction System for Academic Literature
Hanqing Zhao
The exponential growth of academic publications has created an urgent need for automated tools capable of extracting structured knowledge fr… (voir plus)om unstructured scientific texts. While large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and information extraction, existing solutions often require specialized infrastructure, programming expertise, or fine-tuned domain-specific models that create barriers for researchers in specialized fields. This paper presents TCMIIES, a browser-based, zero-installation platform that leverages commercial LLM APIs to perform structured information extraction from academic literature. The system employs a novel schema-guided prompting framework with automatic system prompt generation, enabling researchers to define custom extraction schemas through an intuitive graphical interface without any programming. TCMIIES features a pure front-end architecture that ensures data privacy by processing all information locally in the browser, supports five major LLM providers, implements concurrent batch processing with automatic retry mechanisms, and provides intelligent field mapping for Chinese academic databases including CNKI and Wanfang. We demonstrate the system's effectiveness through comprehensive evaluation across multiple extraction scenarios in Traditional Chinese Medicine research, achieving structured output compliance rates exceeding 94\% and information extraction accuracy comparable to domain-expert annotation. The system represents a practical, accessible solution that bridges the gap between advanced LLM capabilities and domain-specific academic information extraction needs, particularly for researchers in specialized fields who require flexible, privacy-preserving, and cost-effective extraction tools.
2026-05-07
arXiv (prépublication)
doi.org
arxiv.org
Beyond the total NIHSS score: association between impaired level of consciousness and early neurological deterioration in mild large vessel occlusion stroke
Qiangze Ji
Liangliang Sun
Zenghui Liu
Hanqing Zhao
Jing Yu
Ying Zhang
Kaiyue Duan
Lili Guo
Qiuyi Zhang
2026-05-06
Neuroradiology (publié)
doi.org
Physics-informed cross-coupled information flow modeling for spatiotemporal dynamical systems
Hangyi Yu
Hanqing Zhao
Yu Zhang
Lianlei Lin
Zongwei Zhang
Sheng Gao
Junkai Wang
2026-04-30
Information Sciences (publié)
doi.org
Unraveling microplastic retention distribution in porous media: A unified framework coupling flow conditions and particle properties
Haiyu Yuan
Guangqiu Jin
Qihao Jiang
Shuo Wang
Hao Lin
Zhongtian Zhang
Xiangfei Qi
Hanqing Zhao
2026-04-06
Environmental Pollution (publié)
doi.org
CoB Doped Ni-MOF/NF Composite Catalyst for Efficient Hydrogen Evolution Reaction in Alkaline Media
Haibo Liu
Hongming Zhang
Jiasheng Wang
Bo Li
Hanqing Zhao
Yicong Zhu
Yuchen Zhang
Junteng Lv
Zhiwu Qiao
Jinxiang Yang
The advancement of efficient and stable non-precious metal electrocatalysts is crucial for promoting the development of alkaline water elect… (voir plus)rolysis, a key clean energy technology for hydrogen production. This study presents a rational design of a self-supported CoB@Ni-MOF/NF catalyst for scalable hydrogen production, constructed by building a hierarchical Ni-MOF/NF conductive scaffold, incorporating amorphous CoB active phases, and establishing a synergistic Ni-Co-B interface. The optimized electrode exhibits exceptional hydrogen evolution reaction performance in alkaline media, achieving an ultralow overpotential of 33.2 mV at 10 mA cm-2-performance that rivals some noble-metal-doped systems—along with stable operation exceeding 28 hours. Comprehensive characterization confirms that the superior activity originates from abundant accessible active sites and optimized reaction energetics enabled by the composite architecture, offering a generalizable design strategy that integrates MOFs, conductive substrates, and transition metal borides for advanced energy conversion materials.
2026-03-16
Journal of the Electrochemical Society (publié)
doi.org
Comparison between DNA- and RNA-based nucleic acid amplification tests for detecting Mycoplasma pneumoniae in pediatric specimens
Boyi Jiang
Hanqing Zhao
Chao Yan
Mingxuan Wang
Zhen Wang
Yanling Feng
Shijie Wang
Jing Yuan
Yuehua Ke
2026-02-27
European Journal of Clinical Microbiology and Infectious Diseases (publié)
doi.org
A Blockchain Framework for Equitable and Secure Task Allocation in Robot Swarms
Hanqing Zhao
Alexandre Pacheco
Giovanni Beltrame
Xue Liu
Marco Dorigo
Gregory Dudek
Recent studies demonstrate the potential of blockchain to enable robots in a swarm to achieve secure consensus about the environment, partic… (voir plus)ularly when robots are homogeneous and perform identical tasks. Typically, robots receive rewards for their contributions to consensus achievement, but no studies have yet targeted heterogeneous swarms, in which the robots have distinct physical capabilities suited to different tasks. We present a novel framework that leverages domain knowledge to decompose the swarm mission into a hierarchy of tasks within smart contracts. This allows the robots to reach a consensus about both the environment and the action plan, allocating tasks among robots with diverse capabilities to improve their performance while maintaining security against faults and malicious behaviors. We refer to this concept as equitable and secure task allocation. Validated in Simultaneous Localization and Mapping missions, our approach not only achieves equitable task allocation among robots with varying capabilities, improving mapping accuracy and efficiency, but also shows resilience against malicious attacks.
2024-12-31
IEEE Robotics and Automation Letters (inconnu)
doi.org
A Generic Framework for Byzantine-Tolerant Consensus Achievement in Robot Swarms
Hanqing Zhao
Alexandre Pacheco
Volker Strobel
Andreagiovanni Reina
Xue Liu
Gregory Dudek
Marco Dorigo
Recent studies show that some security features that blockchains grant to decentralized networks on the internet can be ported to swarm robo… (voir plus)tics. Although the integration of blockchain technology and swarm robotics shows great promise, thus far, research has been limited to proof-of-concept scenarios where the blockchain-based mechanisms are tailored to a particular swarm task and operating environment. In this study, we propose a generic framework based on a blockchain smart contract that enables robot swarms to achieve secure consensus in an arbitrary observation space. This means that our framework can be customized to fit different swarm robotics missions, while providing methods to identify and neutralize Byzantine robots, that is, robots which exhibit detrimental behaviours stemming from faults or malicious tampering.
2023-09-30
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)
doi.org