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Qiang Ye

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13 papers

2 author rows

AAAI Conference 2026 Conference Paper

RefiDiff: Progressive Refinement Diffusion for Efficient Missing Data Imputation

Md Atik Ahamed
Qiang Ye
Qiang Cheng

Missing values in high-dimensional, mixed-type datasets pose significant challenges for data imputation, particularly under Missing Not At Random (MNAR) mechanisms. Existing methods struggle to integrate local and global data characteristics, limiting performance in MNAR and high-dimensional settings. We propose an innovative framework, RefiDiff, combining local machine learning predictions with a novel Mamba-based denoising network efficiently capturing long-range dependencies among features and samples with low computational complexity. RefiDiff bridges the predictive and generative paradigms of imputation, leveraging pre-refinement for initial warm-up imputations and post-refinement to polish results, enhancing stability and accuracy. By encoding mixed-type data into unified tokens, RefiDiff enables robust imputation without architectural or hyperparameter tuning. RefiDiff outperforms state-of-the-art (SOTA) methods across missing-value settings, demonstrating strong performance in MNAR settings and superior out-of-sample generalization. Extensive evaluations on nine real-world datasets demonstrate its robustness, scalability, and effectiveness in handling complex missingness patterns.

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IROS Conference 2025 Conference Paper

Dynamic Action Localization and Recognition for Intelligent Perception of Surgical Robots

Yaqin Peng
Gui-Bin Bian
Zhen Li 0049
Ruichen Ma
Qiang Ye

Robot-assisted surgery has significantly advanced surgical precision, yet the development of autonomous surgical robots remains hindered by their limited understanding of complex surgical actions. Current systems lack the ability to effectively perceive and interpret intricate surgical relationships, which restricts their capability to assist surgeons in dynamic surgical environments. To overcome these challenges, a novel self-supervised learning method for surgical action recognition has been proposed, aimed at enhancing the understanding of surgical actions. The method has introduced a dynamic masking with attention-based action localization module to focus the model on critical spatial regions where actions occur, enabling surgical view guidance for intelligent surgical robot while extracting key features. Moreover, a graph-enhanced adaptive feature selection module is employed to assign relevance to features and capture the temporal relationships between adjacent frames. Long Short-Term Memory has been utilized to model long-term dependencies across video sequences, while multi-view contrastive learning facilitates the extraction of discriminative features from both masked and unmasked sequences. Experimental results demonstrate a 3. 4% improvement in Average Precision and an Area Under Receiver Operating Characteristic Curve of 92. 9% on Neuro67 dataset for surgical action recognition. The method enables dynamic adjustments to the surgical view, achieving surgical visual navigation. These advancements contribute to the development of intelligent and autonomous surgical robots capable of assisting surgeons in complex and dynamic surgical settings.

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IROS Conference 2025 Conference Paper

High-Precision Tracking of Time-Varying Trajectories for Microsurgical Robots in Constrained Environments

Yu-Peng Zhai
Gui-Bin Bian
Zhen Li 0049
Qiang Ye
Tian-Qi Deng
Ming-Yang Zhang
Pan Fu
Wen-Hao He

This research addresses the challenge of achieving high-precision tracking of time-varying trajectories under nonlinear disturbances and motion constraints in microsurgical robots. A hybrid control framework integrating fuzzy adaptive sliding mode control with radial basis function neural networks is proposed. This framework dynamically adjusts the sliding mode gain to suppress high-frequency jitter and compensate for unmodeled disturbances such as joint friction and tissue contact forces. Experiments conducted on a self-developed microscopic ophthalmic robot platform demonstrated that the trajectory tracking error was reduced to 1. 1 μm, representing improvements of 85. 9%, 76. 1%, and 66. 7% compared to PID control, sliding mode control and non-singular fast terminal sliding mode control respectively. The tracking delay was 19 milliseconds. In experiments on living pigs with central retinal artery occlusion, the system successfully performed intravascular injection, with a maximum error of 3. 97 μm. This solution, through optimization via fuzzy logic and neural networks, achieves micron-level precision and robustness, effectively solving high-frequency control noise and low-frequency environmental disturbances, ensuring both the accuracy and safety of the microsurgical robot.

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IROS Conference 2025 Conference Paper

Spatiotemporal Motion Prediction of Intraocular Microsurgical Robot in Non-Visible Regions

Ya-Wen Deng
Zhen Li 0049
Qiang Ye
Yu-Peng Zhai
Weihong Yu
Zhangguo Yu
Gui-Bin Bian

In intraocular microsurgery with minute operational scales, instruments pass through non-visible regions of the anterior segment, where robot-assisted surgery, which heavily relies on visual perception, fails to determine the instrument’s attitude relative to the eyeball. This compromises surgical flexibility, increases risks, and hinders autonomous surgery development. Therefore, a framework for predicting instrument trajectories in non-visible regions during robot-assisted microsurgery has been proposed to mitigate the risks of retinal and lens injuries caused by blind operations and enhance surgical procedures’ intelligence and autonomy. First, a lightweight reconstruction of the anterior segment environment is performed under controlled knowledge guidance to construct a global map. Second, the tip position of the surgical instrument is detected through multi-sensor fusion, enabling the perception of instrument-environment interactions under visual constraints. Based on this, a long short-term spatiotemporal aggregation algorithm for instrument trajectory prediction is proposed, which enhances surgical safety by providing high-precision predictions of the instrument tip’s motion trajectory. Experiments show that the framework achieved a 0. 0435 mm average prediction error in non-visible regions, corresponding to 0. 03% of the region in a single dimension and 7. 25% of the surgical instrument’s diameter. This significantly enhances the precision of robot-assisted surgery under visual constraints and provides robust technical support for safe, intelligent, and autonomous intraocular robotic surgery.

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ICRA Conference 2024 Conference Paper

A Hybrid Admittance Control Algorithm for Automatic Robotic Cranium-Milling

Chen Qian 0006
Zhen Li 0049
Qiang Ye
Pei-Cong Ge
Jizong Zhao
Gui-Bin Bian

Prior robot-assisted cranium-milling studies only considered controlling the force in the skull’s vertical direction and neglected the milling cutter’s feed force. Additionally, achieving stable force control in multiple directions is challenging for robots due to the uneven skull surface. Here a hybrid admittance control algorithm incorporating a model-free adaptive nonlinear force control and fuzzy control algorithms is proposed to accomplish effective automatic cranial-milling tasks. First, a pure data-driven model-free adaptive control method based on partial form dynamic linearization is used to control the feed force. Second, fuzzy control minimizes the total error of both the vertical and feed force by adaptively adjusting the milling cutter’s velocity and position. 42 ex vivo animal skull-milling experiments conducted by the automatic robotic cranium-milling system indicate that when using the proposed control algorithm, the force error percentage can be maintained below 5. 0% within 3 s and the maximal root mean square error percentages for vertical and feed force are 1. 85% and 1. 94%, respectively. Moreover, no instances of dura mater damage are observed and the robotic system exhibits a high level of autonomy as it performs the skull milling task with minimal human involvement throughout the entire experiment. The results suggest the potential for advancing the intelligence level of neurosurgery in the future.

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IROS Conference 2024 Conference Paper

Design and Modeling of a Thin-walled Multi-segment Continuum Robotic Bronchoscope

Gui-Bin Bian
Ming-Yang Zhang
Qiang Ye
Han Ren
Yu-Peng Zhai
Ruichen Ma
Zhen Li 0049

Cable-driven continuum robots in bronchoscopic procedures hold immense potential to revolutionize the diagnosis and treatment of lung cancer. However, robotic bronchoscopes in current studies are typically large in size and inflexible. Therefore, this article introduces a novel cable-driven continuum robot bronchoscopy system that achieves modular design between the actuation and operation ends. A continuum structure with a dual-segment notched flexible skeleton, featuring a wall thickness of 0. 45 mm, has been designed to perform bending movements exceeding 190°. This enhances flexibility and increases the spatial capacity of the working channels. A kinematic model was developed, integrating the actuation force and the mechanical characteristics of the driving cables for error compensation, estimating the correlation between the displacement of the driving cables and the position of the continuum robot’s end-effector. The verification showed that the root mean square error (RMSE) of the end-effector position is 2. 57 mm, which accounts for 4. 8% of the continuum’s length. A prototype of the robotic bronchoscopy system was created, and its performance and potential applications in bronchoscopic intervention surgeries were validated through vivo pig intervention experiments.

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ICRA Conference 2024 Conference Paper

Procedure Recognition by Knowledge-Driven Segmentation in Robotic-Assisted Vitreoretinal Surgery

Zhen Li 0049
Ya-Wen Deng
Qiang Ye
Weihong Yu
Haoxiang Qi
Yaliang Liu
Zhangguo Yu
Gui-Bin Bian

Internal limiting membrane (ILM) peeling is a vital vitreoretinal surgery procedure. However, due to the thickness of just 1-2 micrometers and the intricacies associated with its varying density and adhesion, the difficulty of manipulation exceeds the physiological limits of human perception and operation. Surgical robot is characterized by high precision and stability. However, navigating intricate intraocular environments and handling minuscule high-precision areas remain enormous challenges. These include issues of uneven lighting, field-of-view loss, and motion blur. This paper proposed a perception method named ‘Multimodal Surgical Process Recognition based on Domain Knowledge and Segmentation (MSPR-DKS), ’ designed to address these challenges and provide input for the precise control of robots. Moreover, a comprehensive dataset focused on ILM peeling during macular hole surgeries was established. Experimental results underscore the efficacy of this approach, with segmentation accuracies exceeding 99. 27% for instruments and macular holes and an average accuracy of 98. 97% in recognizing surgical processes. This study paves the way for leveraging domain knowledge and image segmentation to improve robot-assisted manipulation of soft tissues in ophthalmology.

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JMLR Journal 2022 Journal Article

Batch Normalization Preconditioning for Neural Network Training

Susanna Lange
Kyle Helfrich
Qiang Ye

Batch normalization (BN) is a popular and ubiquitous method in deep learning that has been shown to decrease training time and improve generalization performance of neural networks. Despite its success, BN is not theoretically well understood. It is not suitable for use with very small mini-batch sizes or online learning. In this paper, we propose a new method called Batch Normalization Preconditioning (BNP). Instead of applying normalization explicitly through a batch normalization layer as is done in BN, BNP applies normalization by conditioning the parameter gradients directly during training. This is designed to improve the Hessian matrix of the loss function and hence convergence during training. One benefit is that BNP is not constrained on the mini-batch size and works in the online learning setting. Furthermore, its connection to BN provides theoretical insights on how BN improves training and how BN is applied to special architectures such as convolutional neural networks. For a theoretical foundation, we also present a novel Hessian condition number based convergence theory for a locally convex but not strong-convex loss, which is applicable to networks with a scale-invariant property. [abs] [ pdf ][ bib ] &copy JMLR 2022. ( edit, beta )

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AAAI Conference 2020 Conference Paper

Eigenvalue Normalized Recurrent Neural Networks for Short Term Memory

Kyle Helfrich
Qiang Ye

Several variants of recurrent neural networks (RNNs) with orthogonal or unitary recurrent matrices have recently been developed to mitigate the vanishing/exploding gradient problem and to model long-term dependencies of sequences. However, with the eigenvalues of the recurrent matrix on the unit circle, the recurrent state retains all input information which may unnecessarily consume model capacity. In this paper, we address this issue by proposing an architecture that expands upon an orthogonal/unitary RNN with a state that is generated by a recurrent matrix with eigenvalues in the unit disc. Any input to this state dissipates in time and is replaced with new inputs, simulating short-term memory. A gradient descent algorithm is derived for learning such a recurrent matrix. The resulting method, called the Eigenvalue Normalized RNN (ENRNN), is shown to be highly competitive in several experiments.

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TCS Journal 2020 Journal Article

Group sweep coverage with guaranteed approximation ratio

Chuang Liu
Hongwei Du
Qiang Ye
Wen Xu

Wireless Sensor Networks (WSNs) are often deployed to monitor a region of interest. With sweep coverage, mobile sensor nodes are scheduled to move along a planned route (i. e. sweep route) in order to collect the data from a series of Point of Interests (POIs) sequentially. In this paper, we generalize the sweep coverage problem by proposing a new coverage paradigm, group sweep coverage. With group sweep coverage, the POIs are divided into several groups. A group is said to be covered when one of the POIs in the group is covered. The goal in group sweep coverage is to construct a sweep route that mobile sensor nodes should follow in order to cover all groups during each predefined period. In our research, we devised two algorithms for group sweep coverage: AGSC and DSRM. AGSC is a centralized scheme whose approximation ratio is 5Δ. Namely, the length of the sweep route generated by AGSC is at most 5Δ times that of the optimal sweep route. DSRM is a distributed scheme for large-scale networks with dynamic POIs. Compared with AGSC, DSRM leads to the same approximation ratio and better scalability. Our experimental results indicate that both AGSC and DSRM outperform the state-of-the-art schemes in terms of average and maximal sweep route length.

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IS Journal 2020 Journal Article

Stock Selection Model Based on Machine Learning with Wisdom of Experts and Crowds

Xianjiao Wu
Qiang Ye
Hong Hong
Yijun Li

Both stock recommendations from sell-side analysts and online user generated content from crowds have great significance in the stock market. We examine and compare different effects of analyst attitude and crowd sentiment on stock prices in this article with data from CSMAR. By estimating a multivariate linear regression model, we find that although the wisdom of both experts and crowds has impact on stock prices, the latter's impact on stock prices prevails. We also adopt LightGBM, a novel machine learning model, to predict stock trends based on empirical results. Portfolio returns of different models also suggest that crowd wisdom is more valuable for creating investment strategy than expert wisdom. And it is necessary to take the wisdom of both experts and crowds into consideration when making investment decision.

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AAAI Conference 2019 Conference Paper

Complex Unitary Recurrent Neural Networks Using Scaled Cayley Transform

Kehelwala D. G. Maduranga
Kyle E. Helfrich
Qiang Ye

Recurrent neural networks (RNNs) have been successfully used on a wide range of sequential data problems. A well known difficulty in using RNNs is the vanishing or exploding gradient problem. Recently, there have been several different RNN architectures that try to mitigate this issue by maintaining an orthogonal or unitary recurrent weight matrix. One such architecture is the scaled Cayley orthogonal recurrent neural network (scoRNN) which parameterizes the orthogonal recurrent weight matrix through a scaled Cayley transform. This parametrization contains a diagonal scaling matrix consisting of positive or negative one entries that can not be optimized by gradient descent. Thus the scaling matrix is fixed before training and a hyperparameter is introduced to tune the matrix for each particular task. In this paper, we develop a unitary RNN architecture based on a complex scaled Cayley transform. Unlike the real orthogonal case, the transformation uses a diagonal scaling matrix consisting of entries on the complex unit circle which can be optimized using gradient descent and no longer requires the tuning of a hyperparameter. We also provide an analysis of a potential issue of the modReLU activiation function which is used in our work and several other unitary RNNs. In the experiments conducted, the scaled Cayley unitary recurrent neural network (scuRNN) achieves comparable or better results than scoRNN and other unitary RNNs without fixing the scaling matrix.

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TCS Journal 2012 Journal Article

Polynomial-time approximation scheme for minimum connected dominating set under routing cost constraint in wireless sensor networks

Hongwei Du
Qiang Ye
Jiaofei Zhong
Yuexuan Wang
Wonjun Lee
Haesun Park

To reduce routing cost in wireless sensor networks, we study a problem of minimizing the size of connected dominating set D under constraint that for any two nodes u and v, m D ( u, v ) ≤ α ⋅ m ( u, v ) where α is a constant, m D ( u, v ) is the number of intermediate nodes on a shortest path connecting u and v through D and m ( u, v ) is the number of intermediate nodes in a shortest path between u and v in a given unit disk graph. We show that for α ≥ 5, this problem has a polynomial-time approximation scheme, that is, for any ε > 0, there is a polynomial-time ( 1 + ε ) -approximation.

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