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Modular Adaptive Aerial Manipulation under Unknown Dynamic Coupling Forces

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arXiv 2024年

作者： Yadav, Rishabh Dev Dantu, Swati Pan, Wei Sun, Sihao Roy, Spandan Baldi, Simone Department of Computer Science The Univerity of Manchester United Kingdom Multi-robot systems group Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Department of Cognitive Robotics Delft Univesrity of Technology Netherlands Robotics Research Center International Institute of Information Technology Hyderabad India School of Mathematics Southeast University Nanjing China

Successful aerial manipulation largely depends on how effectively a controller can tackle the coupling dynamic forces between the aerial vehicle and the manipulator. However, this control problem has remained largely unsolved as the existing control approaches either require precise knowledge of the aerial vehicle/manipulator inertial couplings, or neglect the state-dependent uncertainties especially arising during the interaction phase. This work proposes an adaptive control solution to overcome this long standing control challenge without any a priori knowledge of the coupling dynamic terms. Additionally, in contrast to the existing adaptive control solutions, the proposed control framework is modular, that is, it allows independent tuning of the adaptive gains for the vehicle position sub-dynamics, the vehicle attitude sub-dynamics, and the manipulator sub-dynamics. Stability of the closed loop under the proposed scheme is derived analytically, and real-time experiments validate the effectiveness of the proposed scheme over the state-of-the-art approaches. © 2024, CC BY.

关键词： Adaptive control systems

DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning

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arXiv 2025年

作者： Guo, Jiaxin Chen, C.L. Philip Li, Shuzhen Zhang, Tong Guangdong Provincial Key Laboratory of Computational AI Models and Cognitive Intelligence School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Lab Guangzhou China Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Cold-start active learning (CSAL) selects valuable instances from an unlabeled dataset for manual annotation. It provides high-quality data at a low annotation cost for label-scarce text classification. However, existing CSAL methods overlook weak classes and hard representative examples, resulting in biased learning. To address these issues, this paper proposes a novel dual-diversity enhancing and uncertainty-aware (DEUCE) framework for CSAL. Specifically, DEUCE leverages a pre-trained language model (PLM) to efficiently extract textual representations, class predictions, and predictive uncertainty. Then, it constructs a Dual-Neighbor Graph (DNG) to combine information on both textual diversity and class diversity, ensuring a balanced data distribution. It further propagates uncertainty information via density-based clustering to select hard representative instances. DEUCE performs well in selecting class-balanced and hard representative data by dual-diversity and informativeness. Experiments on six NLP datasets demonstrate the superiority and efficiency of DEUCE. © 2025, CC BY.

关键词： Active learning

Temporal Group Attention Network with Affective Complementary Learning for Gait Emotion Recognition

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Temporal Group Attention Network with Affective Complementar...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ziyi Zhu C. L. Philip Chen Haiqi Liu Tong Zhang School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Computational AI Models and Cognitive Intelligence Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Pazhou Lab Guangzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Skeleton-based methods in Gait Emotion Recognition (GER) that abstract gait as a spatial-temporal graph in a non-Euclidean space have achieved remarkable success. However, existing studies neglect capturing crucial spatial-temporal implicit dependencies and make insufficient use of manually crafted affective complementary information. In this paper, we propose a novel Temporal Group Attention Network with Affective Complementary Learning (T2A). Specifically, we propose a Temporal Group Attention (TGA) module, which captures spatial-temporal implicit dependencies and crucial features across space-time. Moreover, we design an affective complementary learning strategy, which first introduces an Affective Denoising (AD) module to reduce the impact of noise in weak emotion-related information and learn augmented affective features. Then, we adopt a decision-level fusion of the denoised affective features with the deep skeleton features to compensate for the discrepancies between different feature spaces. Experimental results on Emotion-Gait and EMOGAIT demonstrate that our proposed method significantly outperforms state-of-the-art methods.

关键词： Emotion recognition Noise reduction Noise Skeleton Bioinformatics

A Deep Penetration Network for Sentence Classification

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SSRN

SSRN 2023年

作者： Chen, Yanping Zhong, Xinyang Liu, Yijun Dong, Bo Zheng, Qinghua The College of Computer Science and Technology The Engineering Research Center of Text Computing and Cognitive Intelligence Lab Guizhou University Guizhou Guiyang550025 China Department of Computer Science and Technology Xi’an Jiaotong University Shanxi Xi’an710049 China

Sentence classification is an important task in natural language processing. In deep architectures, the task suffers from a serious semantic vanishing problem when stacking a large number of networks. To address this problem, the current methods try to enclose a mass of features with different granularities in a deep network. The main problem the methods face is that the silent feature representations are easily weakened and mixed with noisy information, which is not effective in learning contextual features and constructing semantic dependencies in a sentence. In this paper, a deep penetration network (DPN) is designed to improve deep architectures' ability to preserve the favourable semantic features. The DPN enables salient features to penetrate through deeper architectures and to construct long semantic dependencies between them. This approach is evaluated on seven public datasets. Our experiments show that the DPN exhibits a stable performance with deeper architectures. It improves the performance on three types of sentence classification tasks, outperforming the existing state-of-the-art models. © 2023, The Authors. All rights reserved.

关键词： Semantics

FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition

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arXiv 2025年

作者： Liu, Haiqi Chen, C.L. Philip Zhang, Tong Guangdong Provincial Key Laboratory of Computational AI Models and Cognitive Intelligence School of Computer Science and Engineering South China University of Technology Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Cross-subject EEG emotion recognition is challenged by significant inter-subject variability and intricately entangled intra-subject variability. Existing works have primarily addressed these challenges through domain adaptation or generalization strategies. However, they typically require extensive target subject data or demonstrate limited generalization performance to unseen subjects. Recent few-shot learning paradigms attempt to address these limitations but often encounter catastrophic overfitting during subject-specific adaptation with limited samples. This article introduces the few-shot adapter with a cross-view fusion method called FACE for cross-subject EEG emotion recognition, which leverages dynamic multi-view fusion and effective subject-specific adaptation. Specifically, FACE incorporates a cross-view fusion module that dynamically integrates global brain connectivity with localized patterns via subject-specific fusion weights to provide complementary emotional information. Moreover, the few-shot adapter module is proposed to enable rapid adaptation for unseen subjects while reducing overfitting by enhancing adapter structures with meta-learning. Experimental results on three public EEG emotion recognition benchmarks demonstrate FACE’s superior generalization performance over state-of-the-art methods. FACE provides a practical solution for cross-subject scenarios with limited labeled data. Copyright © 2025, The Authors. All rights reserved.

关键词： Emotion Recognition

Deep Natural Image Reconstruction from Human Brain Activity Based on Conditional Progressively Growing Generative Adversarial Networks

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Neuroscience Bulletin 2021年第3期37卷 369-379页

作者： Wei Huang Hongmei Yan Chong Wang Xiaoqing Yang Jiyi Li Zhentao Zuo Jiang Zhang Huafu Chen The MOE Key Lab for Neuroinformation University of Electronic Science and Technology of ChinaChengdu 610054China State Key Laboratory of Brain and Cognitive Science Beijing MR Center for Brain ResearchInstitute of BiophysicsChinese Academy of SciencesBeijing 100101China Department of Medical Information Engineering Sichuan UniversityChengdu 610065China

Brain decoding based on functional magnetic resonance imaging has recently enabled the identification of visual perception and mental ***,due to the limitations of sample size and the lack of an effective reconstruction model,accurate reconstruction of natural images is still a major *** current,rapid development of deep learning models provides the possibility of overcoming these ***,we propose a deep learning-based framework that includes a latent feature extractor,a latent feature decoder,and a natural image generator,to achieve the accurate reconstruction of natural images from brain *** latent feature extractor is used to extract the latent features of natural *** latent feature decoder predicts the latent features of natural images based on the response signals from the higher visual *** natural image generatoris applied to generate reconstructed images from the predicted latent features of natural images and the response signals from the visual *** and qualitative evaluations were conducted with test *** results showed that the reconstructed image achieved comparable,accurate reproduction of the presented image in both highlevel semantic category information and low-level pixel *** framework we propose shows promise for decoding the brain activity.

关键词： Brain decoding fMRI Deep learning

Reler: Relearning Controversial Regions to Accurately Segment Nasopharyngeal Carcinoma

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Reler: Relearning Controversial Regions to Accurately Segmen...

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Tao, Guihua Li, Haojiang Lu, Dandan Ling, Ziqin Liu, Lizhi Cai, Hongmin South China University of Technology School of Computer Science and Engineering Guangzhou China Sun Yat-sen University Cancer Center Sun Yat-sen University State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangzhou China Pazhou Lab Brain and Affective Cognitive Research Center Guangzhou China

ISBN: (纸本)9781665468190

Accurate nasopharyngeal carcinoma (NPC) segmentation is significant in preventing local recurrence and improving patients' survival rates. However, existing deep learning-based methods often yield unsatisfactory segmentation results, especially in fine-grained detail. Because NPC is a tiny and infiltrative tumor with a huge background, traditional deep neural networks tend to be dominated by salient information, thus missing the fine-grained details of NPC. To achieve accurate NPC segmentation, a relearning controversial regions method (Reler) is proposed. It consists of three modules, including the controversial features generator (CFG), controversial features finding module (CFF), and controversial regions arbitration module (CRA). First, CFG constructs global and local feature extractors to generate two types of different features. Then, CFF finds the controversial features and corresponding regions by comparing the global and local features' estimates of the segmentation results of the same input regions. Next, the CRA focuses on controversial features, relearns new features, and produces new segmentation results through a proposed Transformer-based self-attention network. Finally, the uncontroversial segmentation results from CFF and CRA are combined as the final segmentation results. Extensive experiments are conducted on a large NPC dataset containing 6342 images from 596 patients. The experimental results show that the proposed method Reler is effective and superior to the nine state-of-the-art methods. © 2022 IEEE.

关键词： Deep neural networks

Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognition

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arXiv 2023年

作者： Liu, Haiqi Chen, C.L. Philip Gong, Xinrong Zhang, Tong The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Brain and Affective Cognitive Research Center Pazhou Lab. Guangzhou510335 China The Engineering Research Center The Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou510006 China

Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision. Existing literature addresses this challenge by employing local-based representation approaches, which may not sufficiently facilitate meaningful object-specific semantic understanding, leading to a reliance on apparent background correlations. Moreover, they primarily rely on high-dimensional local descriptors to construct complex embedding space, potentially limiting the generalization. To address the above challenges, this article proposes a novel model, Robust Saliency-aware Distillation (RSaD), for few-shot fine-grained visual recognition. RSaD introduces additional saliency-aware supervision via saliency detection to guide the model toward focusing on the intrinsic discriminative regions. Specifically, RSaD utilizes the saliency detection model to emphasize the critical regions of each sub-category, providing additional object-specific information for fine-grained prediction. RSaD transfers such information with two symmetric branches in a mutual learning paradigm. Furthermore, RSaD exploits inter-regional relationships to enhance the informativeness of the representation and subsequently summarize the highlighted details into contextual embeddings to facilitate the effective transfer, enabling quick generalization to novel sub-categories. The proposed approach is empirically evaluated on three widely used benchmarks, demonstrating its superior performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

An Untethered Soft robotic Gripper with Adjustable Grasping Modes and Force Feedback

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An Untethered Soft Robotic Gripper with Adjustable Grasping ...

2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Yang, Yang Zhu, Honghui Liu, Jia Li, Yingtian Zhou, Jianshu Ren, Tao Ren, Yi School of Automation Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong Hong Kong School of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu610059 China Advanced Manufacturing Lab Huawei Technologies Shenzhen China

ISBN: (纸本)9781665481090

In this paper, we propose a three-finger soft robotic gripper, which adopts an untethered and under-actuated design with simple structure and easy operation. The rotation of two servomotors is used to control the positions of the fingers, which provides the fingers with adjustable grasping modes and improves the adaptation of the gripper to objects with different shapes. A pressure sensor is integrated at the end of the finger to provide force feedback, which is used to detect the contact force when grasping the object and enables the gripper to provide the proper grasping force. The experimental section characterizes the bending of the soft robotic finger, and the gripper is integrated at the end of a collaborative robotic arm to demonstrate its adjustable grasping posture and force feedback function. The soft robotic gripper is expected to be used in a variety of scenarios, such as service robot, food industry or garbage sorting. © 2022 IEEE.

关键词： Grippers