Author name cluster

Qi Wei

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9 papers

2 author rows

IROS Conference 2025 Conference Paper

A Natural Human-Robot Interaction System for Teleoperation Based on Noncontact Haptic Feedback

Letian Wei
Pengwen Xiong
Qi Wei
Aiguo Song
MengChu Zhou

In order to provide natural and immersive interactive experience for teleoperation in the context of human-robot collaboration and interaction, this work introduces a natural human-robot interaction system for teleoperation based on ultrasonic haptic feedback. Specifically, our system can accurately capture an operator's hand movements and replicate these actions on the remote robot with low latency and high fidelity. It utilizes an ultrasonic phased array to achieve non-contact haptic feedback. We propose a dynamic ultrasonic array acoustic field customization method based on interactive feature information image. This method can dynamically adjust the acoustic field according to the operator's hand characteristics, focus on multiple target points in real time, and project them onto an operator's fingertips, thereby providing force-controllable non-contact haptic feedback to the operator. The operator is integrated into the feedback loop of our system, controlling the system through multimodal feedback to form a high-quality human-in-the-loop closed control system. The system's performance is validated in two classic robotic tasks: block pick-and-place and nut-tightening. The experimental results show that the system exhibits excellent accuracy and dexterity, and can efficiently complete tasks with high accuracy while providing great interactive experience for operators.

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JBHI Journal 2025 Journal Article

Cross-Interaction of Chinese Characters Structures and Boundary Features for Improving Clinical Named Entity Recognition

Ye Wang
Qi Wei
Hong Yu
Guoyin Wang
Chunmeng Shi
Dajiang Lei

In the natural language processing task of clinical named entity recognition (CNER), accurately identifying the boundaries and categories of medical entities is crucial. However, traditional methods struggle to recognize a large number of clinical terms and symbols that have never been encountered before, ultimately limiting the performance of CNER. Besides, there exist some easy-to-confuse Chinese clinical entities that are semantically similar but belong to quite different categories, such as “ 肺结节 ” (pulmonary nodules, a symptom entity) and “ 肺结核 ” (pulmonary tuberculosis, a disease entity), which can lead to entity misidentification. To address these problems, we propose a novel NER model called Cross-Interaction of Chinese characters structures and Boundary Features (CCS). The proposed model leverages Chinese character structural features and boundary information to comprehensively and accurately identify confusing entities. We further design a Cross-Attention mechanism to capture dependency relationships between different entities and radicals of characters, enhancing the model's semantic understanding of specialized terms and symbols, as well as improving its ability to recognize boundaries. Our experimental results show that our proposed model outperforms other state-of-the-art models on various public medical datasets, achieving significant improvements on the CCKS2020, CMeEE, CMI, and IMCS datasets, respectively.

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JBHI Journal 2025 Journal Article

GL-Fusion: A Multi-Omics Integration Method Based on Graph-Level Structure Fusion and Locus-Level Feature Fusion for Cancer Subtype Classification

Kaiwen Tan
Qi Wei
Min Luo
Honghao Zhu
Chun Jiang
Zhenqiu Shu
Jianqiu Kong

The rapid development of multi-omics data has provided new opportunities for cancer subtype classification. Due to the ability to model gene associations by constructing graph among genes, multi-omics cancer classification based on graphs has attracted considerable attention from researchers. However, due to the high-dimensional nature of multi-omics data, existing graph construction methods based on cosine similarity may lead to noise and low-quality edges. Moreover, considering the complexity of associations within and across omics, existing methods either focus solely on multi-omics fusion at the graph structure level or only consider multi-omics fusion at the representation level, making it challenging to comprehensively model this complex relationship. To this end, we propose a novel multi-omics integration method that combines graph-level structure fusion and locus-level feature fusion to enhance the performance of cancer subtype classification (GL-Fusion). In the graph-level structure fusion module, we integrate multi-omics gene-gene graphs using similarity network fusion method and optimize the graph structure with structural entropy and the protein-protein interaction network. In the locus-level feature fusion module, we employ a locus-level graph convolutional network to integrate multi-omics features, learn gene-level fused representations, and predict cancer subtypes. Empirical validation across four publicly accessible datasets (BRCA, HNSC, LGG, THCA) indicated that our method achieved superior performance compared to 12 representative multi-omics cancer subtyping methods. Ablation experiments, experiments with different omics combinations, and evaluation experiments on modeling associations within and across omics were used to further validate the effectiveness of the method. Our source code is available at https://github.com/QiWei0424/GL-Fusion.

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AAAI Conference 2025 Conference Paper

Influence-Based Fair Selection for Sample-Discriminative Backdoor Attack

Qi Wei
Shuo He
Jiahan Zhang
Lei Feng
Bo An

Backdoor attacks have posed a serious threat in machine learning models, wherein adversaries can poison training samples with maliciously crafted triggers to compromise the victim model. Advanced backdoor attack methods have focused on selectively poisoning more vulnerable training samples, achieving a higher attack success rate (ASR). However, we found that when the manipulation strength of the trigger is constrained to a very small value for imperceptible attacks, they suffer from extremely uneven class-wise ASR due to the unequal selection of instances per class. To solve this issue, we propose a novel backdoor attack method based on Influence-based Fair Selection (IFS), including two objectives: 1) selecting samples that significantly contribute to ASR and 2) ensuring class balance during the selection process. Specifically, we adapt Influence Functions, a classic technique in robust statistics, to evaluate the influence of trigger-embedded training samples on ASR. In this case, training samples contributing to reducing the backdoored test risk could possess higher influence scores. Further, a group-based pruning strategy is designed to avoid calculating the influence on ASR for all training samples, thereby significantly reducing the computational cost. Then, based on the influence score, we design an adaptive thresholding scheme to dynamically select samples with higher influence while maintaining class balance. Extensive experiments on four datasets verify the effectiveness of IFS compared with advanced methods.

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TCS Journal 2025 Journal Article

On-line exploration of an unbounded region with one obstacle

Qi Wei
Xuehou Tan
Haonan Wu
Xiaolin Yao
Yonggong Ren

This paper considers the problem of exploring an unbounded region with an unknown polygonal obstacle using a mobile robot. The robot has to see all points of the region along a path, where the obstacle blocks the view and motion of the robot. The goal of this work is to find the shortest exploration path. The performance of the strategy is measured by competitive ratio, that is, the ratio between the length of the on-line path and the length of the optimal off-line path. We consider the obstacle in two scenarios: convex polygon and concave polygon. For the first scenario, we propose a 19. 71-competitive strategy which significantly improves upon the previously known 50. 207-competitive strategy and prove a lower bound of 9 which improves upon the previously known lower bound of 3. For the second scenario, we propose a hybrid strategy and prove that the lower and upper bounds are 9. 06 and 22. 71 respectively.

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ICRA Conference 2025 Conference Paper

Task-Specific Embodied Tactile Sensing for Dexterous Hand

Qi Wei
Pengwen Xiong
Aiguo Song
Qiang Li

In order to obtain a good tactile sensing, traditional dexterous hands always enable all the sensing units installed on them all the time, even if just a few sensor units are actually used, which make the tactile sensing system resource-wasting and energy consuming. In order to reduce their complexities by placing the tactile sensing units only at critical locations, this work proposes an embodied tactile dexterous hand (ET-Hand) and a novel multimodal sensor placement framework that learns multiple tasks to generate optimal placement proposal. Furthermore, our ET-Hand can dynamically adjust the perceived tactile sensor positions, types and numbers during robotic manipulation, providing novel tools and methods for investigating the tactile channels and placement scale required for robot exploration. In the object recognition and slip detection tasks, the results show that our proposed method performs close to or even better than traditional sensing way with large-scale placement.

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TCS Journal 2022 Journal Article

Improved exploration of unknown polygons

Xuehou Tan
Qi Wei

We present an on-line strategy for a mobile robot to explore an unknown simple polygon P, so as to output a so-called watchman route such that every interior point of P is visible from at least one point along the route. The length of robot's route is guaranteed to be at most 7 times that of the shortest watchman route that could be computed off-line. This significantly improves upon the previously known 26. 5-competitive strategy and confirms a conjecture due to Hoffmann et al. (2001) [10]. Our result is mainly obtained by implementing on-line a known off-line algorithm that approximates the shortest watchman route to a factor of 2.

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NeurIPS Conference 2017 Conference Paper

An inner-loop free solution to inverse problems using deep neural networks

Kai Fan
Qi Wei
Lawrence Carin
Katherine Heller

We propose a new method that uses deep learning techniques to accelerate the popular alternating direction method of multipliers (ADMM) solution for inverse problems. The ADMM updates consist of a proximity operator, a least squares regression that includes a big matrix inversion, and an explicit solution for updating the dual variables. Typically, inner loops are required to solve the first two sub-minimization problems due to the intractability of the prior and the matrix inversion. To avoid such drawbacks or limitations, we propose an inner-loop free update rule with two pre-trained deep convolutional architectures. More specifically, we learn a conditional denoising auto-encoder which imposes an implicit data-dependent prior/regularization on ground-truth in the first sub-minimization problem. This design follows an empirical Bayesian strategy, leading to so-called amortized inference. For matrix inversion in the second sub-problem, we learn a convolutional neural network to approximate the matrix inversion, i. e. , the inverse mapping is learned by feeding the input through the learned forward network. Note that training this neural network does not require ground-truth or measurements, i. e. , data-independent. Extensive experiments on both synthetic data and real datasets demonstrate the efficiency and accuracy of the proposed method compared with the conventional ADMM solution using inner loops for solving inverse problems.

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TCS Journal 2014 Journal Article

A FPTAS for a two-stage hybrid flow shop problem and optimal algorithms for identical jobs

Qi Wei
Erfang Shan
Liying Kang

In this paper, a two-machine two-stage flow shop problem with flexible tasks is considered. Each job has two tasks: the first task can be processed on either machine, called flexible task, while the second task must be processed on the second machine and canʼt be processed unless the first task is completed. The problem is to determine the assignment of the flexible tasks to the machines for each job, with the objective of minimizing the makespan. We present a fully polynomial time approximation scheme (FPTAS) for the problem. Moreover, we consider the problems with identical jobs and buffer capacity, and present some optimal algorithms for them. For the problems with identical jobs, we find an interesting result: If the buffer is not less than 2, more buffer capacity cannot bring better result.

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