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The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1313-1316页

作者： Tianyu Shen Jinlin Sun Shihan Kong Yutong Wang Juanjuan Li Xuan Li Fei-Yue Wang College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Peng Cheng Laboratory Shenzhen 518000China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real *** this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

关键词： Intelligence DAO TAO

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Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

arXiv

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arXiv 2025年

作者： Imran, Muhammad Krebs, Jonathan R. Sivaraman, Vishal Balaji Zhang, Teng Kumar, Amarjeet Ueland, Walker R. Fassler, Michael J. Huang, Jinlong Sun, Xiao Wang, Lisheng Shi, Pengcheng Rokuss, Maximilian Baumgartner, Michael Kirchhof, Yannick Maier-Hein, Klaus H. Isensee, Fabian Liu, Shuolin Han, Bing Nguyen, Bong Thanh Shin, Dong-Jin Ji-Woo, Park Choi, Mathew Uhm, Kwang-Hyun Ko, Sung-Jea Lee, Chanwoong Chun, Jaehee Kim, Jin Sung Zhang, Minghui Zhang, Hanxiao You, Xin Gu, Yun Pan, Zhaohong Liu, Xuan Liang, Xiaokun Tiefenthaler, Markus Almar-Munoz, Enrique Schwab, Matthias Kotyushev, Mikhail Epifanov, Rostislav Wodzinski, Marek Müller, Henning Qayyum, Abdul Mazher, Moona Niederer, Steven A. Wang, Zhiwei Yang, Kaixiang Ren, Jintao Korreman, Stine Sofia Gao, Yuchong Zeng, Hongye Zheng, Haoyu Zheng, Rui Yue, Jinghua Zhou, Fugen Liu, Bo Cosman, Alexander Liang, Muxuan Zhao, Chang Upchurch, Gilbert R. Ma, Jun Zhou, Yuyin Cooper, Michol A. Shao, Wei Department of Medicine University of Florida GainesvilleFL32611 United States Department of Surgery University of Florida GainesvilleFL32611 United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Information Science and Engineering University of Florida GainesvilleFL32611 United States Shanghai Jiao Tong University Shanghai China Electronic & Information Engineering School Harbin Institute of Technology [Shenzhen Shenzhen China Division of Medical Image Computing German Cancer Research Center [DKFZ Heidelberg Germany CO. LTD Beijing China MedAI Seoul Korea Republic of Korea University Seoul Korea Republic of Department of Radiation Oncology Yonsei Cancer Center Heavy Ion Therapy Research Institute Yonsei University College of Medicine Seoul Korea Republic of Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Medical University of Innsbruck Innsbruck Austria Novosibirsk State University Novosibirsk Russia AGH University of Krakow Krakow Poland Sierre Switzerland University of Geneva Geneva Switzerland The Sense Innovation and Research Center Sion Switzerland National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan China Aarhus University Department of Clinical Medicine Nordre Palle Juul-Jensens Blvd. 11 Aarhus8200 Denmark Shanghaitech University Pudong Shanghai China Image Processing Center Beihang University Beijing China Department of Computer Science and Engineering University of California Santa CruzCA95064 United States Department of Biostatistics University of Florida GainesvilleFL32611 United States University Health Network ON Canada Agronomy Department University of F

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://***. © 2025, CC BY-NC-SA.

关键词： Computerized tomography

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Knowledge presentation by the MNSM-based controller for swimming motion of a snake-like robot

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High Technology Letters 2018年第1期24卷 103-112页

作者：卢振利 Xie Yafei Xu Huigang Borovac Branislav Li Bin School of Electrical Engineering and Automation Changshu Institute of Technology Changshu 215500 P. R. China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110014 P. R. China School of Information and Electrical Engineering China University of Mining and Technology Xuzhou 221116 P. R. China Faculty of Technical Sciences University of Novi Sad Novi Sad 21000 Serbia

A MNSM( mirror neuron system mechanism)-based controller is developed to present the swimming rhythm of a snake-like robot in Cartesian space. From engineering viewpoint,the proposed controller is composed of a neuron for rhythm angle and two neurons for motion knowledge in XY plane. The given knowledge is a rhythm curve for swimming motion of a snake-like robot. Experimental results show that the proposed controller can present the knowledge of swimming rhythm,which represents the corresponding control law to drive the snake-like robot to swim with different speeds and turning motion. This work provides a novel method to present the knowledge for swimming motion of snake-like robots.

关键词： mirror neuron system mechanism (MNSM) swimming motion snake-like robot knowledge presentation

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Engine Speed control Using Gain Scheduling Method

Engine Speed Control Using Gain Scheduling Method

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International Conference on Cybernetics and Informatics

作者： Jakub Osusky Vojtech Vesely Institute of Automotive Mechatronics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information Technology Slovak University of Technology

ISBN: (纸本)9781509018352

this paper presents a gain scheduled controller design for SISO systems applied on laboratory engine. Presented frequency domain method is based on the M-delta structure of closed loop systems. The small gain theory is exploited to obtain the stability condition. The gain scheduling control is applied on laboratory engine with variable load and compared with classic robust control.

关键词： Gain scheduled control Robust control Small gain theorem Frequency domain

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Robust Decentralized controller Design in Frequency Domain

Robust Decentralized Controller Design in Frequency Domain

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International Conference on Cybernetics and Informatics

ISBN: (纸本)9781509018352

The paper presents control system design based on robust decentralized control. controller design will be done in the frequency domain for nominal model with time delay. Robust condition ensuring stability also by delay change, based on M-delta structure is included in controller design. In controller design for multivariable systems equivalent subsystem method is used. For subsystems of equivalent model, frequency method ensuring desired phase margin and crossover frequency is applied. Design procedure is illustrated on example and compare conservativism by using of different characteristic functions.

关键词： Robust control Network control systems M-delta structure Phase margin

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Contrast enhancement of optical videoscope images

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Iran Journal of Computer Science 2023年第3期6卷 261-275页

作者： Ammar, Reda Aboshosha, Sahar El-Hag, Noha A. Saifeldeen, Reda El-Shafai, Walid El-Banby, Ghada M. Ashiba, Huda I. Khalaf, Ashraf A. M. Elazm, Atef Abou El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. El-Safrawey, Amir Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Comm. Faculty of Engineering Minia University Minya Egypt Department of Management of Information Systems Higher Institute of Commercial Science El-Mahala El-Kobra Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Communications Engineering Bilbis Higher Institute of Engineering Sharqia Bilbis Egypt Security Engineering Laboratory Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

The videoscope (VS) images have poor quality and low contrast. Hence, in this paper, three proposed frameworks to improve the quality of VS images are presented. The first framework depends on contrast-limited adaptive histogram equalization (CLAHE) with adaptive gamma correction. The second framework depends on homomorphic processing. The third framework is based on the homomorphic enhancement with histogram equalization. The first one is superior from the entropy perspective, while the third one is superior from the contrast perspective. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Fuzzy enhancement Histogram equalization Homomorphic enhancement Spectral entropy

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Analysis of fast prototyping of microcontroller-based ML software for acoustic signal classification 26

Analysis of fast prototyping of microcontroller-based ML sof...

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26th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Marciniak, Filip Marciniak, Wojciech Marciniak, Tomasz Poznan University of Technology Faculty of Computing and Telecommunications Poznań60-965 Poland Poznan University of Technology DECYBEL Science Club Poznań60-965 Poland Poznan University of Technology Faculty of Automatic Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Division of Electronic Systems and Signal Processing Poznań60-965 Poland

ISBN: (纸本)9798350304985

The paper analyzes the preparation of software for acoustic signal classification with machine learning techniques for microcontrollers. The design process was tested for three types of devices: Nordic Thingy:53, *** and Arduino Nano 33 BLE Sense Lite. The classifier training process was carried out using the Edge Impulse platform. Experimental studies were carried out for the process of classifying sound signals generated by the vacuum cleaner motor. The results of the training and the model test were presented for different configurations. © 2023 Division of Signal Processing and Electronic Systems, Poznan University of Technology (DSPES PUT).

关键词： Microcontrollers

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Fault Detection and Isolation for a 3-DOF Helicopter with Sliding Mode Strategies 15

Fault Detection and Isolation for a 3-DOF Helicopter with Sl...

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15th International Workshop on Variable Structure Systems, VSS 2018

作者： Perez, U. Capello, E. Punta, E. Perea, J. Fridman, L. Department of Control and Robotics Division of Electrical Engineering Engineering Faculty National Autonomous University of Mexico C.P. 04510 Mexico Politecnico di Torino Department of Mechanical and Aerospace Engineering Torino10129 Italy National Research Council of Italy CNR-IEIIT Institute of Electronics Computer and Telecommunication Engineering Torino Italy

ISBN: (纸本)9781538664391

This paper analyzes the problem of detection and isolation of faults in the actuators of a 3-Degree-of-Freedom (3-DOF) helicopter by a residual-based approach. A third-order sliding mode differentiator is designed for the evaluation of the residuals. Moreover, two sliding mode controllers are suitably designed to stabilize the angular positions and velocities: (i) a super twisting control system and (ii) a continuous twisting control system. The performance of the designed controllers and the effectiveness of the residual-based method are illustrated by simulations. © 2018 IEEE.

关键词： controllers

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology 33

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Bogacki, P. Dlugosz, M. Talaska, T. Dlugosz, R. Aptiv Services Poland Kraków30-399 Poland Agh University of Science and Technology Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications Kraków30-059 Poland Poznan University of Technology Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350347760

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock. © 2023 IEEE.

关键词： Pixels

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Toward lightweight acoustic fault detection and identification of UAV rotors

Toward lightweight acoustic fault detection and identificati...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Marek Kołodziejczak Radosław Puchalski Adam Bondyra Sasa Sladic Wojciech Giernacki Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Poznań Poland Faculty of Engineering University of Rijeka Rijeka Croatia

Data-driven Fault Detection and Isolation (FDI) systems receive a lot of attention from researchers. Several recent applications utilize acoustic signals recorded on-board of the Unmanned Aerial Vehicle (UAV) to assess the condition of propulsion system and diagnose rotor blade impairments. In this work, we propose two major improvements to the previously developed FDI scheme. They are aimed at reducing the computational load of the deep LSTM-based (Long ShortTerm Memory) fault classifier. First, the PCA-based (Principal Component Analysis) feature space reduction allows reducing the size of neural networks and thus decreasing the number of mathematical operations. Secondly, a modified algorithm introduces an ensemble of multiple weak classifiers with a decision-fusion strategy that provides the final status of the system. The developed schemes were evaluated in comparison to the original algorithm, using an extensive dataset of real-flight acoustic data. The results show that the proposed improvements significantly reduce the computation time within the assumed performance constraints.

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