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25th International Conference on Human-computer Interaction, HCII 2023

作者： Yang, Chien-Chun Yeh, Su-Ling Chien, Sung-En Huang, Tsung-Ren Chang, Yu-Ling Goh, Joshua O. S. Chen, Yi-Chuan Fu, Li-Chen Department of Psychology College of Science National Taiwan University Taipei Taiwan Department of Medicine Mackay Medical College New Taipei City Taiwan Center for Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan Neurobiology and Cognitive Science Center National Taiwan University Taipei Taiwan Graduate Institute of Brain and Mind Sciences College of Medicine National Taiwan University Taipei Taiwan Department of Neurology College of Medicine National Taiwan University Hospital National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan MOST Joint Research Center for AI Technology and All Vista Healthcare Taipei Taiwan

ISBN: (纸本)9783031359910

Interpersonal interactions are often expected to be polite and follow communicative norms to avoid potential social conflicts by employing strategies such as face work. Although face work has been well-documented in human-human interactions, it is unclear whether people would be polite to the robots and give them higher ratings in face-to-face interactions than in paper questionnaires. In addition, according to the Socioemotional Selectivity Theory, older adults are more likely to be polite to reduce potential interpersonal tensions and maintain emotional harmony, which causes them to respond kindly to others. The current study investigated how face-to-face human-robot interactions influence people’s subjective evaluations of the robot and, more critically, whether older and younger adults perform differently. We recruited older and younger adults and assessed their attitudes toward the robot through face-to-face interaction and a paper questionnaire. The results revealed that the older adults rated the robot more positively than the younger adults. Critically, the older adults rated the robot’s personal association and perceived ease of use higher when responding to the robot directly than when answering the paper questionnaire;however, the younger adults did not show this difference. The findings emphasize the importance of face-to-face interaction and social norms in human-robot interactions, especially for older adults. Our findings have implications for human-computer interaction researchers and practitioners who are interested in developing social robots that are sensitive to social norms for different age groups. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Human robot interaction

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Distributed time-varying formation control with uncertainties based on an event-triggered mechanism

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Science China(Information Sciences) 2021年第3期64卷 167-176页

作者： Xiaoduo LI Yumeng BAI Xiwang DONG Qingdong LI Zhang REN Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical EngineeringBeihang University Department of Electrical and Computer Engineering University of Wisconsin-Madison Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates the distributed time-varying formation(TVF) problems for general linear multi-agent systems(MASs) subject to matched bounded uncertainties based on an adaptive event-triggered mechanism. A TVF protocol was designed with an event-triggered mechanism by introducing adaptive weights into the formation control protocol and triggering condition, and the large chattering phenomenon was avoided by the σ-modification adaptive law. According to the Lyapunov stability theory, proof has been established that the MASs in the presence of uncertainties can realize the expected formation that satisfies the given feasible condition. Finally, an example is provided to verify the effectiveness of the proposed algorithm.

关键词： formation control multi-agent systems event-triggered adaptive control

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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Visual Exploration-Enhanced Quadruped Robot with Active-Passive Composite Telescope Mechanism

Visual Exploration-Enhanced Quadruped Robot with Active-Pass...

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Intelligent robotics and Automatic Control (IRAC), International Conference on

作者： Xuchen Wang Shengzhi Wang Yiying Liang Jiajun An Puchen Zhu Weibing Li Jianshu Zhou Xin Ma Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China Division of Advanced Manufacturing Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Agriculture and Biological Engineering Purdue University West Lafayette USA School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Mechanical Engineering University of California Berkeley USA Multi-Scale Medical Robotics Center Hong Kong China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

Navigating hazardous environments and confined spaces during disaster search and rescue operations poses significant challenges to both human rescuers and conventional robotic systems. Recognizing the need for enhanced capabilities, we introduce a Visual Exploration-Enhanced Quadruped Robot (VEQR), a novel platform specifically designed to improve adaptability and exploration in such demanding scenarios. The VEQR combines a quadruped robot with an innovative active-passive composite telescopic mechanism—a compact, 12.8 mm diameter device equipped with a camera—that enables maneuvering through narrow and curved spaces like slits and debris-filled areas. The bending and insertion/retraction capabilities of this mechanism improve the dexterity of the robot in confined spaces and extend its reach into deeper, hard-to-access zones. Experimental results demonstrate the VEQR’s proficiency in overcoming obstacles, inspecting confined spaces, and providing clear visual feedback. Notably, the VEQR successfully traversed narrow and winding passages as small as 50 mm diameter and inspected targets positioned up to 0.6 mm deep within confined spaces, confirming its effectiveness as a robust robot for search and rescue operations in hazardous environments.

关键词： Visualization Navigation Disasters Windings Telescopes Bending Inspection Robustness Quadrupedal robots

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Short - Term Prediction of Coke Pushing Current Peak Based on Improved ARIMA Model

Short - Term Prediction of Coke Pushing Current Peak Based o...

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Industrial Cyber-Physical Systems (ICPS)

作者： Haiyang Wei Luefeng Chen Jie Hu Yi Ren Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan

Coke pushing current is an indicator to evaluate the difficulty of coke pushing operation. The higher coke pushing current is, the greater coke pushing resistance is, and the more difficult coke is to push out. Accurate prediction of current peak during future coke pushing operation can provide more time for production personnel to adjust production status and avoid difficult coke pushing in carbonization chamber. In this paper, a combination prediction model based on VMD (Variational Mode Decomposition) and improved ARIMA (Autoregressive Integrated Moving Average) models is proposed. Firstly, VMD algorithm is used to decompose time series of coke pushing current peak, de-noising data, and extracting main information of time series. Then, ARIMA model is used to predict mean change of linear elasticity and GARCH (Generalized Autore-gressive Conditional Heteroskedasticity) model is introduced to predict ARIMA model residual and improve heteroscedasticity of nonlinear part of time series, and then ARIMA-GARCH model is established. Finally, predicted value is obtained by the sum of each component prediction. The experimental results show that the proposed prediction model has a high prediction accuracy in the short-term prediction of coke pushing current peak. The scheme is applied to actual coking production to guide production of coke.

关键词： Resistance Ovens Time series analysis Noise reduction Production Predictive models Prediction algorithms

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Corrections to “Intelligent Traffic Data Transmission and Sharing Based on Optimal Gradient Adaptive Optimization Algorithm”

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IEEE Transactions on Intelligent Transportation Systems 2024年第3期25卷 3202-3204页

作者： Xing Li Haotian Zhang Yajing Shen Lina Hao Wanfeng Shang School of Electrical Engineering and Intelligentization Dongguan University of Technology Dongguan China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China College of Computer Science and Technology Jilin University Changchun China Department of Biomedical Engineering City University of Hong Kong Hong Kong China School of Mechanical and Engineering Northeastern University Shenyang China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

In [1] , inaccuracies in several critical equations along with their accompanying descriptions appear in the article. Furthermore, some references are missing, and certain analyses of experiments are flawed.

关键词： Adaptive systems Data communication Optimization Intelligent transportation systems

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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Chinese automation Congress (CAC)

作者： Sheng Du Xian Ma Xiang Li Min Wu Weihua Cao Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer/Istanbul Turkiye

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes.

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Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

Partially Occluded Face Expression Recognition with CBAM-Bas...

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Chinese automation Congress (CAC)

作者： Yanxing Bai Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene.

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A Multiple Ant Colony System for the Electric Vehicle Routing Problem with Time Windows

A Multiple Ant Colony System for the Electric Vehicle Routin...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Michalis Mavrovouniotis Georgios Ellinas Changhe Li Marios Polycarpou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781665487696

Ant colony optimization (ACO) has been found to be useful on several vehicle routing problem variations. In this work, ACO is applied to the electric vehicle routing problem with time windows (E-VRPTW). The E-VRPTW has a hierarchical multiple objective function, which is to minimize the number of electric vehicles and the total distance traveled. A multiple ACO is applied to E-VRPTW in which two colonies cooperate to minimize the objectives in parallel. A local search is embedded in ACO to improve the quality of the output. The experimental results on a set of benchmark instances show that the multiple ACO is competitive with existing methods.

关键词： Ant colony optimization Vehicle routing Benchmark testing Electric vehicles Routing Search problems Linear programming

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5G Light-Emitting Biomedical Robots for Hospital Disinfection

5G Light-Emitting Biomedical Robots for Hospital Disinfectio...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Ugochukwu O. Matthew Renata L. Rosa Nwamaka U. Okafor Jazuli S. Kazaure Ogobuchi Daniel Okey Matthew Abiola Oladipupo Lateef Olawale Fatai Victor Nosakhare Oriakhi Demostenes Z. Rodriguez Computer Science Dept Federal University of Lavras Minas Gerais Brazil School of Elect Electronics Engineering University College Dublin Dublin Ireland Electrical Engineering Dept Hussaini Adamu Federal Polytechnic Kazaure Nigeria Center for Engineering Federal University of ABC Sao Paulo Brazil Data Science Department University of Salford Manchester England UK Robotics and Automation University of Salford Manchester England UK

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Hospital-acquired infections (HAIs) pose a significant challenge to healthcare systems worldwide, exacerbated by the COVID-19 pandemic. Current disinfection methods often fall short in ensuring comprehensive sterilization of high-contact areas, leading to persistent risks. This study addresses this gap by leveraging 5G biomedical internet of things (IoT) robots equipped with ultra-violet light emitting diodes (UV-LEDs) for effective disinfection. Our approach focuses on destroying the genetic material of pathogens, thereby preventing their spread within medical facilities. By integrating autonomous IoT 5G disinfection robots, we aim to enhance hospital sanitation and continuous infectious disease surveillance. A thorough review of nine biomedical studies underscores the efficacy of UV-based disinfection methods. We demonstrate that advancements in 5G infrastructure will significantly boost IoT and 5G medical robotic innovations, facilitating their application in both hospital settings and industrial healthcare automation. Thus, this paper presents a robust framework for utilizing 5G and IoT technologies in medical robotics to improve healthcare safety and efficacy.

关键词： COVID-19 5G mobile communication Hospitals Pandemics Service robots Robot kinematics Surveillance Light emitting diodes Safety Standards

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