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Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding

IEEE Transactions on Automation science and engineering 2025年 22卷 15200-15214页

作者： Song, Yaoxian Sun, Penglei Jin, Piaopiao Ren, Yi Zheng, Yu Li, Zhixu Chu, Xiaowen Zhang, Yue Li, Tiefeng Gu, Jason Zhejiang University Center for X-Mechanics School of Aeronautics and Astronautics Zhejiang Hangzhou China Information Hub Data Science and Analytics Thrust Guangdong China Huawei Technologies Advanced Manufacturing Lab Guangdong Shenzhen China UBTECH Robotics Inc Research Institute Guangdong Shenzhen China Renmin University of China School of Information School of Smart Governance Beijing China Westlake University Institute of Advanced Technology Westlake Institute for Advanced Study School of Engineering Zhejiang Hangzhou China Dalhousie University Department of Electrical and Computer Engineering HalifaxB3H 4R2 Canada

Robotic grasping is a fundamental ability for a robot to interact with the environment. Current methods focus on how to obtain a stable and reliable grasping pose in object level, while little work has been studied on part (shape)-wise grasping which is related to fine-grained grasping and robotic affordance. Parts can be seen as atomic elements to compose an object, which contains rich semantic knowledge and a strong correlation with affordance. However, lacking a large part-wise 3D robotic dataset limits the development of part representation learning and downstream applications. In this paper, we propose a new large Language-guided SHape grAsPing datasEt (named LangSHAPE) to promote 3D part-level affordance and grasping ability learning. From the perspective of robotic cognition, we design a two-stage fine-grained robotic grasping framework (named LangPartGPD), including a novel 3D part language grounding model and a partaware grasp pose detection model, in which explicit language input from human or large language models (LLMs) could guide a robot to generate part-level 6-DoF grasping pose with textual explanation. Our method combines the advantages of humanrobot collaboration and LLMs’ planning ability using explicit language as a symbolic intermediate. To evaluate the effectiveness of our proposed method, we perform 3D part grounding and fine-grained grasp detection experiments on both simulation and physical robot settings, following language instructions across different degrees of textual complexity. Results show our method achieves competitive performance in 3D geometry fine-grained grounding, object affordance inference, and 3D part-aware grasping tasks. © 2004-2012 IEEE.

关键词： Visual languages

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KANsformer for Scalable Beamforming

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IEEE Transactions on Vehicular Technology 2025年

作者： Xie, Xinke Lu, Yang Chi, Chong-Yung Chen, Wei Ai, Bo Niyato, Dusit Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China National Tsing Hua University Institute of Communications Engineering Department of Electrical Engineering Hsinchu30013 Taiwan Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

This paper proposes an unsupervised deep-learning (DL) approach by integrating Transformer and Kolmogorov-Arnold networks (KAN) termed KANsformer to realize scalable beamforming for mobile communication systems. Specifically, we consider a classic multi-input single-output energy efficiency maximization problem subject to the total power budget. The proposed KANsformer first extracts hidden features via a multi-head self-attention mechanism and then reads out the desired beamforming design via KAN. Numerical results are provided to evaluate the KANsformer in terms of generalization performance, transfer learning and ablation experiment. Overall, the KANsformer outperforms the existing benchmark DL approaches, and is adaptable to the variation in the number of mobile users with real-time and near-optimal inference. © 1967-2012 IEEE.

关键词： Budget control

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Multiset FMRI data Analysis with Subject Group Information using Structured Dictionary Learning

Multiset FMRI Data Analysis with Subject Group Information u...

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IEEE International Symposium on Biomedical Imaging

作者： Shuai Xu Rui Jin Seung-Jun Kim Vince D. Calhoun Tülay Adali Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology and Emory University GA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

A dictionary learning (DL)-based method for analyzing multiple functional magnetic resonance imaging (fMRI) data sets is developed. The algorithm can incorporate subject group information to extract neural activation maps indicative of the group differences and the maps shared across groups. Furthermore, multiple data sets are jointly analyzed to obtain the maps common across data sets and those unique to individual data sets. For this, a novel structured supervised DL problem is formulated. Numerical tests on synthetic and real fMRI data sets verify the effectiveness of the proposed method

关键词： data analysis Accuracy Image color analysis Estimation Machine learning Functional magnetic resonance imaging data mining Biomedical imaging

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Hybrid PCA-Based Machine Learning Models for Predictive Analytics in Urban Health Monitoring Systems

Hybrid PCA-Based Machine Learning Models for Predictive Anal...

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Smart and Sustainable Developments in electrical engineering (SSDEE), IEEE International Conference on

作者： Shreyas Rajendra Hole Girish S Bhavekar Arvind Kumar Prajapati Vinothkumar Kolluru Suraj Rajesh Karpe Jayavrinda Vrindavanam Computer Science & Engineering (AI&ML) Dayananda Sagar University Bangalore India Department of DIC G H Raisoni College of Engineering Nagpur MH India Electrical Engineering Department Motilal Nehru National Institute Technology Allahabad Prayagraj U.P. India Department of data science Stevens Institute of technology Hoboken NJ U.S.A. Electrical Engineering CSMSS Chh Shahu College of Engineering MH India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

In this paper, a hybrid PCA-based approach is proposed for predicting the respiratory imbalance in an urban health monitoring system. To do this, three models namely Random Forest, XGBoost, and Multi-Layer Perceptron (MLP) were integrated into one system to improve the probability of prediction while maintaining the trade-off with the dimension reduction - a common variable in big data tasks (which, this effort is focused on). Several variables regarding the environment and health of the patients were used to form a dataset that was later transformed with the help of PCA meaning the results would get better because models with better dimensionality tend to perform better. The Hybrid PCA + MLP model produced the best accuracy which 99% and AUC - ROC which 0.90, there were also 99% produced by XGBoost but AUC - ROC was 0.83. Random forest produced “slightly” less accurate predictions of 95% with an AUC - ROC of 0.72 however; this proved the best method in terms of the computational burden since the training time recorded was about 0.55 seconds, which was lower than that of XGBoost 0.45 seconds and MLP 0.94 seconds. These findings show that hybrid PCA based models do not only improve the accuracy of predictions made but also reduce the amount of computational resources needed, which is ideal for tasks that occur in real time whereby smart urban health monitoring is a part of a bigger CPS system.

关键词： Training Analytical models Accuracy Computational modeling Predictive models Real-time systems Computational efficiency Monitoring Random forests Principal component analysis

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Candidate Evaluation with Multimodal data-Driven for Recruitment 27th

Candidate Evaluation with Multimodal Data-Driven for Recru...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wu, Xing Liu, Kehong Wang, Jianjia Yao, Junfeng Deng, Bin Lv, Rongqi Song, Jun School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of AI and Advanced Computing XJTLU Entrepreneur College Xi’an Jiaotong-Liverpool University Suzhou215412 China CSSC Seago System Technology Co. Ltd. Shanghai200010 China Science and Technology on Near-Surface Detection Laboratory Jangsu Wuxi214035 China Department of Geography Hong Kong Baptist University 999077 Hong Kong

ISBN: (纸本)9783031781858

In the field of intelligent recruitment, automated resume matching and non-contact interviews have significantly improved the efficiency of companies in finding suitable candidates. This corresponds to the techniques of person-job matching and AI interviews. However, current person-job matching methods lack substantial data support, while AI interview methods struggle to integrate deep information from multimodal data and provide comprehensive evaluations of candidates’ responses. To address these challenges, we propose a multimodal data-driven person-job evaluation model, comprising two key stages: a person-job matching method based on graph attention and a multimodal AI interview method. Using a dual-perspective graph neural network approach, we accomplish the screening of candidates and positions. In the second stage, we conduct a comprehensive evaluation of candidates’ interview performance based on text, audio, and image modalities, providing a more objective, consistent, and efficient interview assessment method. Experimental results demonstrate that our person-job matching method surpasses current popular techniques and effectively transfers features to the next stage. In our multimodal AI interview method, we achieve accurate scoring of candidate responses, assessment of intonation stress levels, and inference of their Big Five personality traits, comprehensively evaluating candidates from multiple perspectives. This confirms the superiority and efficiency of our approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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Longitudinal Patterns of Functional Network Changes Over Four-Year Period Associated with Sex in the Developing Brain

Longitudinal Patterns of Functional Network Changes Over Fou...

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IEEE International Symposium on Biomedical Imaging

作者： Rekha Saha Debbrata K. Saha Zening Fu Tulay Adali Vince D. Calhoun Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta GA Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Maryland USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Functional network connectivity (FNC) is a valuable measure for assessing the temporal interdependence and intrinsic functional relationships among brain networks. While longitudinal research on intrinsic functional connectivity has been useful for characterizing age-related changes across whole-brain networks, there has been limited focus on capturing multivariate patterns of FNC changes during brain development. In this study, we employed a previously proposed method that utilizes FNC matrices to estimate multiple overlapping brain functional change patterns (FCPs). This method was applied to the extensive Adolescent Brain and Cognitive Development (ABCD) dataset. Our findings reveal several well-structured FCPs indicating significant changes over a four-year period, including brain functional connectivity between visual network (VSN) and sensorimotor network (SMN) domains. This FNC expression pattern strengthens with age. Additionally, we observed a distinct pattern of changes between male and female individuals for two components including males show stronger increasing coupling between the default mode network (DMN) and VSN compared to females.

关键词： Couplings Visualization Functional magnetic resonance imaging Biomedical measurement Biomedical imaging

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Predictive Analytics for Ear Health Management Using IoT and Ensemble Learning

Predictive Analytics for Ear Health Management Using IoT and...

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2025 International Conference on Intelligent Control, computing and Communications, IC3 2025

作者： Saravanan, S.K. Parvathy, Palaniraj Rajidurai Meenakshi, R. Prabu, K. Ganeshbabu, T.R. Meenakshi, B. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Big Data and Network Security Tamil Nadu Chennai India Mphasis Corporation ChandlerAZ United States Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Redhills Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331527495

Proactive health monitoring solutions are becoming more important due to a growing population of older people. To better manage the ear health of the elderly, this research investigates ensemble learning methods within the domain of predictive analytics using the Internet of Things (IoT). It provides an ensemble learning approach that uses IoT capabilities to predict potential ear health problems in this population. It evaluates the efficacy of many ensembles learning approaches, including bagging, boosting, and stacking, using real-world sensor data acquired from IoT devices. Our results show that ensemble learning methods considerably improve prediction accuracy compared to standalone models. Ensemble learning overcomes the limitations of individual algorithms and maximizes their combined prediction potential by efficiently combining varied models. Our findings have important implications for the management of healthcare for the elderly, providing new opportunities to enhance early diagnosis and intervention techniques for ear health concerns via the use of predictive analytics provided by the IoT. It highlights the potential of ensemble learning approaches to improve predictive analytics in the senior healthcare sector, especially with IoT-driven solutions. © 2025 IEEE.

关键词： mHealth

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Cloud-Based AI Robotics for Precision Pruning of Fruit Trees to Improve Sustainable Farming

Cloud-Based AI Robotics for Precision Pruning of Fruit Trees...

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International Symposium on Advanced computing and Communication (ISACC)

作者： R.C. Ilambirai Barkathulla. A G. Mahalakshmi J. Jagan Babu Prabu K C. Srinivasan Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.D Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Gojan School of Business and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The crucial agricultural practice of precision pruning fruit trees greatly impacts fruit production yield and quality. Traditional pruning techniques take a lot of time and money because they need skilled workers and are labor-intensive. It suggests a novel cloud-based AI robotics system intended to precisely prune fruit trees to address these issues. This system uses cutting-edge robotics fitted with Convolutional Neural Networks (CNNs) to carry out precise and effective tasks related to pruning. Cloud computing integration makes it possible to process and make real-time decisions, improving the system's flexibility in responding to changing tree conditions and pruning needs. The system's primary parts are robotic arms for accurate cuts, various sensors for analyzing tree structures, and CNN models for maximizing pruning techniques. Early field trials show noticeable gains in operational efficiency and pruning accuracy compared to conventional techniques. This study demonstrates how cloud-integrated AI robotics can transform agricultural practices and improve fruit farming's sustainability and productivity.

关键词： Training Cloud computing Accuracy Crops Robot sensing systems Real-time systems Sensor systems Convolutional neural networks Robots Farming

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Joint Power and Spectrum Orchestration for D2D Semantic Communication Underlying Energy-Efficient Cellular Networks

arXiv

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arXiv 2025年

作者： Xia, Le Sun, Yao Sun, Haijian Hu, Rose Qingyang Niyato, Dusit Imran, Muhammad Ali James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom School of Electrical and Computer Engineering University of Georgia AthensGA30602 United States Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Semantic communication (SemCom) has been recently deemed a promising next-generation wireless technique to enable efficient spectrum savings and information exchanges, thus naturally introducing a novel and practical network paradigm where cellular and device-to-device (D2D) SemCom approaches coexist. Nevertheless, the involved wireless resource management becomes complicated and challenging due to the unique semantic performance measurements and energy-consuming semantic coding mechanism. To this end, this paper jointly investigates power control and spectrum reuse problems for energy-efficient D2D SemCom cellular networks. Concretely, we first model the user preference-aware semantic triplet transmission and leverage a novel metric of semantic value to identify the semantic information importance conveyed in SemCom. Then, we define the additional power consumption from semantic encoding in conjunction with basic power amplifier dissipation to derive the overall system energy efficiency (semantics/Joule). Next, we formulate an energy efficiency maximization problem for joint power and spectrum allocation subject to several SemCom-related and practical constraints. Afterward, we propose an optimal resource management solution by employing the fractional-to-subtractive problem transformation and decomposition while developing a three-stage method with theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the adequate performance superiority of our proposed solution compared with different benchmarks. Copyright © 2025, The Authors. All rights reserved.

关键词： Energy utilization

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Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Kou, Wei-Bin Lin, Qingfeng Tang, Ming Ye, Rongguang Wang, Shuai Zhu, Guangxu Wu, Yik-Chung The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518055 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Shenzhen518172 China

Street Scene Semantic Understanding (denoted as TriSU) is a complex task for autonomous driving (AD). However, inference model trained from data in a particular geographical region faces poor generalization when applied in other regions due to inter-city data domain-shift. Hierarchical Federated Learning (HFL) offers a potential solution for improving TriSU model generalization by collaborative privacy-preserving training over distributed datasets from different cities. Unfortunately, it suffers from slow convergence because the data from different cities are with disparate statistical properties. Going beyond existing HFL methods, we propose a Gaussian heterogeneous HFL algorithm (FedGau) to address inter-city data heterogeneity so that convergence can be accelerated. In the proposed FedGau algorithm, both single RGB image and RGB dataset are modelled as Gaussian distributions for aggregation weight design. This approach not only differentiates each RGB image by respective statistical distribution, but also exploits the statistics of dataset from each city in addition to the conventionally considered data volume. With the proposed approach, the convergence is accelerated by 35.5%-40.6% compared to existing state-of-the-art (SOTA) HFL methods. On the other hand, to reduce the involved communication resource, we further introduce a novel performance-aware adaptive resource scheduling (AdapRS) policy. Unlike the traditional static resource scheduling policy that exchanges a fixed number of models between two adjacent aggregations, AdapRS adjusts the number of model aggregation at different levels of HFL so that unnecessary communications are minimized. Extensive experiments demonstrate that AdapRS saves 29.65% communication overhead compared to conventional static resource scheduling policy while maintaining almost the same performance. © 2000-2011 IEEE.

关键词： Gaussian distribution

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