Author name cluster

Min Tang

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10 papers

2 author rows

JBHI Journal 2025 Journal Article

An Optimization Strategy Allowing a Tactile Glove With Minimal Tactile Sensors for Soft Object Identification

Min Tang
Xiaoyu Liu
Xiaofeng Qiao
Yuanjie Zhu
Linyuan Fan
Songjun Du
Duo Chen
Jinghui Wang

Humans can easily perceive the shapes and textures of grasped objects due to high-density mechanoreceptor networks in the hand. However, replicating this capability in wearable devices with limited sensors remains challenging. Here, we designed a tactile glove equipped with easily accessible sensors, enabling accurate identification of soft objects during grasping. We propose an optimization strategy to eliminate redundant sensors and determine the minimal sensor configuration, which was then integrated into the tactile glove. The results indicate that the minimal sensor configuration (n = 7) attached to the hand achieved accurate identification comparable to that obtained using a larger number of sensors (n = 22) distributed across the hand before elimination. Furthermore, we found that various machine learning classifiers achieved recognition accuracies of up to 90% for soft objects when using the tactile glove. Correlation analyses were conducted to characterize individual contribution and mutual cooperativity of regional tactile forces on the hand during grasping, aiding in the interpretation of sensor selection or elimination in the optimization strategy. Adequate validation and analysis demonstrate that our strategy allows an easy–to–apply solution for identifying soft objects via a tactile glove with a minimal number of sensors, offering valuable insights for guiding the design of tactile sensor layouts in artificial limbs and robotic teleoperation systems.

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EAAI Journal 2025 Journal Article

Applications of machine vision technology for conveyor belt deviation detection: A review and roadmap

Jiaming Han
Ting Fang
Wensheng Liu
Chenxiao Zhang
Molin Zhu
Jibin Xu
Jie Ji
Xianhua He

Conveyor belt deviation is a frequent challenge in product transportation filed, and failure to promptly detect and rectify this anomaly not only significantly reduces transport efficiency but also poses a risk of serious safety accidents, leading to enormous economic losses. Traditional contact-based deviation detection technologies, with their inherent limitations of high costs and complicated maintenance, have struggled to meet the practical demands of long-distance conveyor belt inspection. In this context, non-contact machine vision technology has emerged as a prominent solution in the field of conveyor belt deviation detection, thanks to its notable advantages of a simple hardware structure and round-the-clock operational capability. Recently, with the rapid development of artificial intelligence theories, this research field has accumulated a series of effective solutions that have been proven through machine vision practical applications. This paper delves into the technical principles of the existing solutions, systematically summarizes them and objectively evaluates their strengths and weaknesses in practical applications. Based on this foundation, this paper also provides an insight on the future development trends of intelligent monitoring for conveyor belt deviation, aiming to offer valuable reference and guidance to technicians in related fields.

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IJCAI Conference 2025 Conference Paper

Flow Matching Based Sequential Recommender Model

Feng Liu
Lixin Zou
Xiangyu Zhao
Min Tang
Liming Dong
Dan Luo
Xiangyang Luo
Chenliang Li

Generative models, particularly diffusion model, have emerged as powerful tools for sequential recommendation. However, accurately modeling user preferences remains challenging due to the noise perturbations inherent in the forward and reverse processes of diffusion-based methods. Towards this end, this study introduces FMRec, a Flow Matching based model that employs a straight flow trajectory and a modified loss tailored for the recommendation task. Additionally, from the diffusion-model perspective, we integrate a reconstruction loss to improve robustness against noise perturbations, thereby retaining user preferences during the forward process. In the reverse process, we employ a deterministic reverse sampler, specifically an ODE-based updating function, to eliminate unnecessary randomness, thereby ensuring that the generated recommendations closely align with user needs. Extensive evaluations on four benchmark datasets reveal that FMRec achieves an average improvement of 6. 53% over state-of-the-art methods. The replication code is available at https: //github. com/FengLiu-1/FMRec.

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EAAI Journal 2025 Journal Article

Multimodal data fusion-based intelligent fault diagnosis for ship rotating machinery: Status quo and perspectives

Yaqiong Lv
Jian Hao
Min Tang
Jun Wu

Ships are indispensable to global transportation and play a critical role in fostering economic growth and cultural exchange. Central to ship operations is the rotating machinery, which, due to the complex and demanding conditions at sea, is susceptible to faults posing a serious threat to the safety of ship navigation. Therefore, timely and effective fault detection and diagnosis are essential for minimizing operational interruptions and maintaining the safety of ship navigation. Fault diagnosis methods based on multimodal data fusion (MDF) strategies can fully utilize the monitoring capabilities of different modal signals for different types of faults, significantly improving diagnostic accuracy, and have attracted widespread attention from researchers in recent years, yielding promising results. Nevertheless, there is currently a notable lack of a comprehensive review of multimodal data fusion approaches. This paper addresses this gap by conducting an exhaustive review of existing literature on multimodal data fusion technology, focusing on fusion methods. It discusses their practical applications in diagnosing faults in ship rotating machinery, analyzes current challenges, and outlines future research directions. This comprehensive synthesis aims to serve as a key reference for researchers in the field, guiding future developments in fault diagnosis technologies.

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NeurIPS Conference 2024 Conference Paper

AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Making

Yizhe Huang
Xingbo Wang
Hao Liu
Fanqi Kong
Aoyang Qin
Min Tang
Song-Chun Zhu
Mingjie Bi

Traditional interactive environments limit agents' intelligence growth with fixed tasks. Recently, single-agent environments address this by generating new tasks based on agent actions, enhancing task diversity. We consider the decision-making problem in multi-agent settings, where tasks are further influenced by social connections, affecting rewards and information access. However, existing multi-agent environments lack a combination of adaptive physical surroundings and social connections, hindering the learning of intelligent behaviors. To address this, we introduce AdaSociety, a customizable multi-agent environment featuring expanding state and action spaces, alongside explicit and alterable social structures. As agents progress, the environment adaptively generates new tasks with social structures for agents to undertake. In AdaSociety, we develop three mini-games showcasing distinct social structures and tasks. Initial results demonstrate that specific social structures can promote both individual and collective benefits, though current reinforcement learning and LLM-based algorithms show limited effectiveness in leveraging social structures to enhance performance. Overall, AdaSociety serves as a valuable research platform for exploring intelligence in diverse physical and social settings. The code is available at https: //github. com/bigai-ai/AdaSociety.

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YNIMG Journal 2022 Journal Article

The role of low-frequency oscillations in three-dimensional perception with depth cues in virtual reality

Zhili Tang
Xiaoyu Liu
Hongqiang Huo
Min Tang
Tao Liu
Zhixin Wu
Xiaofeng Qiao
Duo Chen

Currently, vision-related neuroscience studies are undergoing a trend from simplified image stimuli toward more naturalistic stimuli. Virtual reality (VR), as an emerging technology for visual immersion, provides more depth cues for three-dimensional (3D) presentation than two-dimensional (2D) image. It is still unclear whether the depth cues used to create 3D visual perception modulate specific cortical activation. Here, we constructed two visual stimuli presented by stereoscopic vision in VR and graphical projection with 2D image, respectively, and used electroencephalography to examine neural oscillations and their functional connectivity during 3D perception. We find that neural oscillations are specific to delta and theta bands in stereoscopic vision and the functional connectivity in the two bands increase in cortical areas related to visual pathways. These findings indicate that low-frequency oscillations play an important role in 3D perception with depth cues.

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AAAI Conference 2021 Conference Paper

Toward Realistic Virtual Try-on Through Landmark Guided Shape Matching

Guoqiang Liu
Dan Song
Ruofeng Tong
Min Tang

Image-based virtual try-on aims to synthesize the customer image with an in-shop clothes image to acquire seamless and natural try-on results, which have attracted increasing attentions. The main procedures of image-based virtual try-on usually consist of clothes image generation and try-on image synthesis, whereas prior arts cannot guarantee satisfying clothes results when facing large geometric changes and complex clothes patterns, which further deteriorates the afterwards tryon results. To address this issue, we propose a novel virtual try-on network based on landmark-guided shape matching (LM-VTON). Specifically, the clothes image generation progressively learns the warped clothes and refined clothes in an end-to-end manner, where we introduce a landmarkbased constraint in Thin-Plate Spline (TPS) warping to inject finer deformation constraints around the clothes. The try-on process synthesizes the warped clothes with personal characteristics via a semantic indicator. Qualitative and quantitative experiments on two public datasets validate the superiority of the proposed method, especially for challenging cases such as large geometric changes and complex clothes patterns. Code will be available at https: //github. com/lgqfhwy/LM-VTON.

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AIIM Journal 2020 Journal Article

Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques

Fen Miao
Bo Wen
Zhejing Hu
Giancarlo Fortino
Xi-Ping Wang
Zeng-Ding Liu
Min Tang
Ye Li

Continuous blood pressure (BP) measurement is crucial for reliable and timely hypertension detection. State-of-the-art continuous BP measurement methods based on pulse transit time or multiple parameters require simultaneous electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Compared with PPG signals, ECG signals are easy to collect using wearable devices. This study examined a novel continuous BP estimation approach using one-channel ECG signals for unobtrusive BP monitoring. A BP model is developed based on the fusion of a residual network and long short-term memory to obtain the spatial-temporal information of ECG signals. The public multiparameter intelligent monitoring waveform database, which contains ECG, PPG, and invasive BP data of patients in intensive care units, is used to develop and verify the model. Experimental results demonstrated that the proposed approach exhibited an estimation error of 0. 07 ± 7. 77 mmHg for mean arterial pressure (MAP) and 0. 01 ± 6. 29 for diastolic BP (DBP), which comply with the Association for the Advancement of Medical Instrumentation standard. According to the British Hypertension Society standards, the results achieved grade A for MAP and DBP estimation and grade B for systolic BP (SBP) estimation. Furthermore, we verified the model with an independent dataset for arrhythmia patients. The experimental results exhibited an estimation error of −0. 22 ± 5. 82 mmHg, −0. 57 ± 4. 39 mmHg, and −0. 75 ± 5. 62 mmHg for SBP, MAP, and DBP measurements, respectively. These results indicate the feasibility of estimating BP by using a one-channel ECG signal, thus enabling continuous BP measurement for ubiquitous health care applications.

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AAAI Conference 2019 Conference Paper

Multi-Matching Network for Multiple Choice Reading Comprehension

Min Tang
Jiaran Cai
Hankz Hankui Zhuo

Multiple-choice machine reading comprehension is an important and challenging task where the machine is required to select the correct answer from a set of candidate answers given passage and question. Existing approaches either match extracted evidence with candidate answers shallowly or model passage, question and candidate answers with a single paradigm of matching. In this paper, we propose Multi-Matching Network (MMN) which models the semantic relationship among passage, question and candidate answers from multiple different paradigms of matching. In our MMN model, each paradigm is inspired by how human think and designed under a unified compose-match framework. To demonstrate the effectiveness of our model, we evaluate MMN on a large-scale multiple choice machine reading comprehension dataset (i. e. RACE). Empirical results show that our proposed model achieves a significant improvement compared to strong baselines and obtains state-of-the-art results.

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LORI Conference 2011 Conference Paper

Bayesianism, Elimination Induction and Logical Reliability

Renjie Yang
Min Tang

Abstract The logic of scientific justification is a central problem in the philosophy of science. Bayesianism is usually taken as the leading theory in this area. After a brief review of Bayesian account of scientific justification and learning theorists’ objection against Bayesianism, this paper proposes an argument defending Bayesianism. It is shown that Bayesian conditionalization has the necessary equipment to capture the idea of elimination induction, which functions as an indispensible component in a satisfactory account of scientific justification.

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