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Chinese automation Congress (CAC)

作者： Jiazhuo Li 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 and the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Polish Academy of Sciences Systems Research Institute Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan Tokyo Institute of Technology Tokyo Japan

With the rapid advances in computer vision, human action recognition has gradually received attention, but the current methods still exhibit some problems in indoor environments. The human skeleton, as the framework of human motion, contains high-quality actional feature information, and the skeleton-based action recognition method effectively avoid the interference of interior background noise and has advantages in indoor action recognition. The outstanding effect of graph convolutional networks on graph structure data processing has led to its rapid development and wide application in skeleton-based action recognition. Second-order skeletal information also contains a large number of actional features but is not effectively utilized. The artificial predefined topology of the human skeleton map has limitations, and cannot reflect the interaction between limbs. To solve the above problems, this article designs an adaptive weighted multi-stream graph convolutional network (AM-GCN) based on skeletal information, using an attention mechanism to enhance the network's ability to extract actional features, and an adaptive layer to make the construction graph more flexible, incorporating second-order skeletal features through a dual-stream architecture. In this article, the NTU-RGB+D dataset has been used for the experiments, the results show that the method in this article has good results.

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DSNRNet: A Network of LiDAR Data Denoising by Differential Stability in Underground Mine

DSNRNet: A Network of LiDAR Data Denoising by Differential S...

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International Conference on Intelligent Transportation

作者： Dejiang Meng Bin Tian Ziyu Pan Junhui Wang Long Chen The Waytous Inc. Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Data and Computer Science Sun Yat-sen University China

Some sprinklers in underground mines spray water mist to lessen the amount of dust in the air. But water mist causes LiDAR to provide inaccurate point measurements, which are referred to as water mist noise that undermines the effectiveness of LiDAR-based localization and object recognition. Therefore, in order to reduce water mist noise, we have developed a new noise segmentation network that can operate on a CPU in real time-differential stability noise removal network (DSNRNet). This network consists of two sub-networks. The first sub-network is aimed at extracting differential stability features, so we named it the differential stability feature extraction network (sub-network 1). The second sub-network: a fully connected neural network (sub-network 2), is used to segment noise. To evaluate the DSNRNet's performance, we built a LiDAR semantic segmentation dataset of underground mines and run the DSNRNet in an Intel i7-11800H CPU. The experimental results demonstrate that this method is able to strike a better balance between speed (26.3 milliseconds) and accuracy (97.8 % ) compared with the other two most possible methods-DSOR (24.2 milliseconds, 3.2 % ) and WeatherNet (436.8ms, 98.5 % ).

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A Maximum Power Point Tracking control Method for Wind Turbines With Uncertainties Based on Fully Actuated System Theory

A Maximum Power Point Tracking Control Method for Wind Turbi...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Yuhan Xu Zelong Wu Le Wei Ying Zhang Fang Fang School of Control and Computer Engineering North China Electric Power University Beijing P. R. China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen P. R. China

In this paper, the maximum power point tracking (MPPT) control problem for wind turbine systems with multi-uncertainties is considered. A tracking controller of optimal tip speed ratio is proposed based on fully actuated system (FAS) approach. First, the dynamics of wind turbines in the region below rated wind speed is represented in FAS model. Then, with the analysis of multi-uncertainties in operation of wind turbines, an extended state observer (ESO) is designed to estimate the uncertainties. A controller with uncertainty-compensation ability is proposed based on FAS theory to obtain a closed-loop tracking system of rotor speed with fully actuated characteristics. Theorems are given to show the advantages on stability analysis, parameters selection under constraints and engineering regulation of FAS approach in MPPT control of wind turbines. Finally, an example of 5MW wind turbine of NREL is given for simulation. The results indicate the effectiveness of the proposed approach for MPPT control.

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Challenges for Monocular 6D Object Pose Estimation in Robotics

arXiv

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arXiv 2023年

作者： Thalhammer, Stefan Bauer, Dominik Hönig, Peter Weibel, Jean-Baptiste García-Rodríguez, José Vincze, Markus The Industrial Engineering Departent UAS Technikum Vienna Austria The Automation and Control Institute TU Wien Austria The Columbia Artificial Intelligence and Robotics Lab Columbia University United States The Department of Computer Technology University of Alicante Spain

Object pose estimation is a core perception task that enables, for example, object manipulation and scene understanding. The widely available, inexpensive and high-resolution RGB sensors and CNNs that allow for fast inference make monocular approaches especially well suited for robotics applications. We observe that previous surveys on establish the state of the art for varying modalities, single- and multi-view settings, and datasets and metrics that consider a multitude of applications. We argue, however, that those works' broad scope hinders the identification of open challenges that are specific to monocular approaches and the derivation of promising future challenges for their application in robotics. By providing a unified view on recent publications from both robotics and computer vision, we find that occlusion handling, pose representations, and formalizing and improving category-level pose estimation are still fundamental challenges that are highly relevant for robotics. Moreover, to further improve robotic performance, large object sets, novel objects, refractive materials, and uncertainty estimates are central, largely unsolved open challenges. In order to address them, ontological reasoning, deformability handling, scene-level reasoning, realistic datasets, and the ecological footprint of algorithms need to be improved. © 2023, CC BY.

关键词： Robots

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention 26

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Bao, Chengao Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Tokyo Institute of Technology Yokohama226-8502 Japan Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798331521950

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method. © 2025 IEEE.

关键词： Modal analysis

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Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

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Chinese automation Congress (CAC)

作者： Liu Hu Luefeng Chen Jie Hu 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 and the 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 Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan The Tokyo Institute of Technology Tokyo Japan

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method.

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Joining Edge-Enabled Metaverse Services with Network Externality: A Stackelberg Game Approach

Joining Edge-Enabled Metaverse Services with Network Externa...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Jiang, Yuna Kang, Jiawen Niyato, Dusit Ge, Xiaohu Xiong, Zehui Xu, Minrui Deng, Ruilong Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China Guangdong University of Technology School of Automation Guangzhou China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China Shenzhen Huazhong University of Science and Technology Research Institute China

ISBN: (纸本)9798350333336

The metaverse is defined as a three-dimensional virtual-real fusion network focused on social connection. Edge computing can empower the metaverse by providing computing resources to realize real-time motion tracking, visual or auditory rendering and virtual scene dynamic change at the edge of networks. Users enjoy the immersive experience provided by the Metaverse Service Provider (MSP), and interact through digital avatars in the virtual world by requesting metaverse services. In this paper, we formulate the edge computing resources pricing, metaverse service rendering bitrate pricing and request problem in the metaverse. Especially, we model the interactions among edge servers, the MSP and Metaverse Service Users (MSUs) as a hierarchical three-stage Stackelberg game. The problem of joining edge-enabled metaverse services is also studied with the consideration of network externality, including the network effect and congestion effect. Based on the backward induction method, we derive the optimal computing resources pricing of edge servers, rendering bitrate pricing of the MSP, and rendering bitrate requests of MSUs. We also prove the existence and uniqueness of the Stackelberg game equilibrium. Simulation results show that the network effect can bring more positive utilities to entities when the social tie coefficient among MSUs is higher. Besides, all entities in the edge-enabled metaverse are affected negatively by the congestion effect. © 2023 IEEE.

关键词： Edge computing

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Volume-based Transition Zone Assessment of Hybrid Unmanned Aerial-Underwater Vehicles

Volume-based Transition Zone Assessment of Hybrid Unmanned A...

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IEEE Latin American Robotics Symposium, LARS

作者： Vivian Misaki Aoki Paulo Jefferson Dias De Oliveira Evald Pedro Miranda Pinheiro César Bastos Da Silva Dayana Dos Santos Cardoso Mauro André Barbosa Cunha Paulo Lilles Jorge Drews Intelligent Robotics and Automation Group Federal University of Rio Grande Rio Grande Brazil Department of Computer Engineering and Industrial Automation State University of Campinas Campinas Brazil Automation and Control Research Group Sul-rio-grandense Federal Institute Pelotas Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

Quadrotors have been used in diverse areas, such as agriculture, civil construction, fire monitoring, and so on. However, these vehicles cannot perform missions that require navigation in aquatic environments, such as inspection of underwater structures and water sample collection, due to their hardware limitations. Therefore, hybrid unmanned aerial-underwater vehicles emerge as a feasible solution to accomplish these missions. Although quadrotors have well-accepted mathematical models, hybrid vehicles have a small quantity of works dealing with this topic. Moreover, we identified a gap in transition zone representation. Therefore, this paper presents a method for transition zone modeling based on vehicle geometry. Simulation results support the better representation of the proposed transition model in comparison to a sigmoid model.

关键词： Underwater structures Navigation Simulation Force Inspection Mathematical models Hardware

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DenseSplat: Densifying Gaussian Splatting SLAM with Neural Radiance Prior

arXiv

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arXiv 2025年

作者： Li, Mingrui Liu, Shuhong Deng, Tianchen Wang, Hongyu the Department of Computer Science Dalian University of Technology Dalian China the Department of Mechano-informatics Information Science and Technology The University of Tokyo Tokyo Japan Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education China

Gaussian SLAM systems excel in real-time rendering and fine-grained reconstruction compared to NeRF-based systems. However, their reliance on extensive keyframes is impractical for deployment in real-world robotic systems, which typically operate under sparse-view conditions that can result in substantial holes in the map. To address these challenges, we introduce DenseSplat, the first SLAM system that effectively combines the advantages of NeRF and 3DGS. DenseSplat utilizes sparse keyframes and NeRF priors for initializing primitives that densely populate maps and seamlessly fill gaps. It also implements geometry-aware primitive sampling and pruning strategies to manage granularity and enhance rendering efficiency. Moreover, DenseSplat integrates loop closure and bundle adjustment, significantly enhancing frame-to-frame tracking accuracy. Extensive experiments on multiple large-scale datasets demonstrate that DenseSplat achieves superior performance in tracking and mapping compared to current state-of-the-art methods. © 2025, CC BY.

关键词： Real time systems

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Robust Iterative Learning control Design for a Class of Uncertain Batch Processes

Robust Iterative Learning Control Design for a Class of Unce...

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European control Conference (ECC)

作者： Robert Maniarski Wojciech Paszke Hongfeng Tao Eric Rogers Institute of Automation Electronic and Electrical Engineering University of Zielona Góra Zielona Góra Poland Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi China School of Electronics and Computer Science University of Southampton Southampton UK

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper investigates the problem of designing robust iterative learning control laws for discrete-time batch processes with norm-bounded parameter uncertainties. A law of proportional-differential type is designed to achieve robust convergence of the tracking error in the batch-to-batch direction. It is shown that the design problem can be written as a two-dimensional system. Then, the recently developed non-conservative conditions for (structural) stability analysis for a linear Roesser model are used. The conditions for the existence and computation of the required control law matrices are linear matrix inequality-based. Finally, comparative simulation results show the effectiveness of the new design.

关键词： Uncertain systems Analytical models Simulation Computational modeling Batch production systems Europe Linear matrix inequalities

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