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IEEE Transactions on Intelligent Vehicles 2024年第7期9卷 5130-5133页

作者： Bingyi Xia Peijia Xie Jiankun Wang Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China

This letter reports the insights gained during a Distributed/Decentralized Hybrid Workshop on Foundation/Infrastructure Intelligence (FII), where we discussed the evolving role of Foundation Models in the field of intelligent vehicles. These models, pre-trained on multimodal data, have emerged as pivotal in the landscape of intelligent vehicles by leveraging their capabilities for high-level reasoning. Ongoing research focuses on these models to further improve scene perception and decision-making, aiming to develop adaptive systems for robot navigation and autonomous driving. However, for smart mobility across the Cyber-Physical-Social space, foundation intelligence should learn human-level knowledge to perform sophisticated interactions and collaborations based on human feedback. Agent-based Foundation Models, as the new training paradigm, can generate cross-domain actions consistent with perception information, paving the way to realize interactive and collaborative agents. This letter discusses the challenges of enhancing and leveraging the scene understanding and spatial reasoning capabilities of the pre-trained foundation model for smart mobility. It also offers insights into the embodied employment of foundation and infrastructure intelligence in enhancing multimodal interactions between robots, environments, and humans.

关键词： Collaboration Adaptation models Transportation Data models Cognition Autonomous vehicles Training

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Enhancing Precision Agriculture Through Human-in-the-Loop Planning and Control

Enhancing Precision Agriculture Through Human-in-the-Loop Pl...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

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RESTORATION OF HDR IMAGES FOR SVE-BASED HDRI VIA A NOVEL DCNN

RESTORATION OF HDR IMAGES FOR SVE-BASED HDRI VIA A NOVEL DCN...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Xu, Yilun Liu, Ziyang Wu, Xingming Chen, Weihai Li, Zhengguo School of Automation Science and Electrical Engineering Beihang University China SRO Department Institute for Infocomm Research Singapore

ISBN: (纸本)9781665438643

Ghosting artifacts are believed to be the Achilles' heel for high dynamic range (HDR) imaging (HDRI) via differently exposed images sequentially captured by a digital device. Spatially varying exposure (SVE)-based HDRI is an efficient solution to prevent the ghosting artifacts from appearing in a HDR image. However, it is challenging to restore a high-quality HDR image with the full resolution from a single raw Bayer image for the SVE-based HDRI. In this paper, a novel deep convolution neural network (DCNN) is proposed to address such a challenging problem. The proposed DCNN includes two distinctive components, a spatially varying convolution and an exposedness-aware compensation branch. The evaluations indicate that the quality of our results significantly surpasses several related algorithms. Related materials will be provided at https://***/yilun-xu/SVEHDRI/. © 2021 IEEE

关键词： Convolution

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Hybrid Three-Layer Sub-Harmonic Synchronous Machine using Consequent Pole Permanent Magnets

Hybrid Three-Layer Sub-Harmonic Synchronous Machine using Co...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： S M Sajjad Hossain Rafin Qasim Ali Fei Zhao Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami FL USA Department of Electrical Engineering Sukkur IBA University Sukkur Pakistan School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

A novel three-layer sub-harmonic synchronous machine is proposed, equipped with consequent permanent magnets and windings for superior torque generation and better overall performance. The consequent pole PM rotor design reduces the PM quantity by half versus conventional configurations without compromising performance. Finite element analysis of an 8-pole, 48-slot machine verifies a 6% increase in torque generation and 4 % lower torque ripple compared to a fully PM -assisted design. The proposed machine also outperforms alternatives on metrics, including torque density and efficiency. These favorable characteristics demonstrate the promise of the topology as a cost-effective, high-performing electric propulsion candidate. This work contributes to an innovative machine advancing progress toward affordable PM -assisted synchronous motors.

关键词： Measurement Torque Windings Rotors Magnetic devices Production Propulsion

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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios engineering

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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Towards Designing a Medication Assistive Intelligent System with Automated Insulin-dose Prediction for Elderlies

Towards Designing a Medication Assistive Intelligent System ...

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International Conference on electrical, Computer and Communication engineering (ECCE)

作者： Abrar Ahmed Minhazul Islam Mahi M. Akhtaruzzaman Jamal Uddin Tanvin Fahim Shahryer Sachitra Halder Muhammad Towfiqur Rahman Md Shofiqul Islam Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh Department of Computer Science & Engineering University Asia Pacific (UAP) Dhaka Bangladesh Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Geelong Victoria

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Medication management poses significant challenges for many patients, particularly the elderlies, who often struggle with keeping track of their medication schedules and taking the correct dosages. To address this issue, this study aims to design an AI-integrated Medication Management and Assistive Unit (AMMAU) for elderlies with some important features like, schedule reminder, medicine recognition and count, early indication of shortage, sorted slots, and so on. Automated insulin-dose prediction and alert system makes the system unique and more demanding at these current scenarios. For this study, a suitable machine learning model is designed, analyzed, verified, and embedded in the proposed system so that the elderly diabetic patients can get alert, further compare with the current insulin-doses. Though, the proposed system is currently focusing on only the Basal insulin doses prediction, the system will definitely reduce the risks associated with wrong management of medication by degrading the chances of missed doses or taking wrong pills for elderlies at home.

关键词： Training Schedules Analytical models Prototypes Machine learning Predictive models Internet of Things Older adults Insulin Physical design

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Robot Path Planning via Neural-Network-Driven Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第3期3卷 451-460页

作者： Wang, Jiankun Liu, Jianbang Chen, Weinan Chi, Wenzheng Meng, Max Q.-H. Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen518055 China The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Hong Kong Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Suzhou215021 China Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen518172 China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong999077 Hong Kong

This article presents a novel heuristic method named neural-network-driven prediction (NEED) for robot path planning problems. Different from classical heuristic methods, NEED is not designed for some specific environments, which can be applied to various environments. NEED has an encoder-decoder structure, which consists of three modules: convolutional neural network backbone, spatial pooling module, and decoder module. By learning from a number of successful path planning cases, NEED can learn to analyze the environment structure and predict the promising search region for the new path planning problem. This predicted region serves as a heuristic to guide the search direction of path planning algorithms. A series of simulation experiments are conducted to demonstrate that NEED significantly improves the algorithm performance on solving new path planning problems. Meanwhile, NEED can also be applied to highly dynamic environments since it can output the prediction results at a speed of over 100 Hz on a laptop. © 2020 IEEE.

关键词： Intelligent robots

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Stabilizing a Class of Periodical Time-Delay Milling Systems by Adaptive Active Control Method

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IEEE Transactions on Industrial Informatics 2025年第6期21卷 4457-4465页

作者： Wu, Yue Zhang, Hai-Tao Xu, Bowen Ren, Gui-Ping Shi, Yang Chen, Guanrong Beijing Forestry University State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation School of Technology Beijing100083 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation the State Key Laboratory of Digital Manufacturing Equipment and Technology Institute of Artificial Intelligence MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems Wuhan430074 China Huazhong University of Science and Technology Guangdong HUST Industrial Technology Research Institute Dongguan523808 China Guangdong Provincial Key Laboratory of Manufacturing Equipment Digitization Dongguan523808 China Northwestern Polytechnical University School of Artificial Intelligence Optics and Electronics Xi'an710072 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 2Y2 Canada City University of Hong Kong Department of Electrical Engineering 999077 Hong Kong

Periodical time-delay scenario is often encountered in industrial manufacturing processes. However, the presence of time delays and periodical coefficients brings challenges to controller design and system analysis, which thereby hinders the performance improvement of such systems. In this work, the dynamics of milling systems are transformed into a time-invariant finite-dimensional uncertain model described by Fourier series and Padé approximation. An adaptive active control law is accordingly designed to stabilize such complex dynamics. With the assistance of LaSalle–Yoshizawa theorem, conditions are derived to guarantee sufficiently large stability regions of the corresponding closed-loop system. A numerical case study is conducted on a standard two degrees of freedom milling perturbation system to substantiate the superiority of the proposed adaptive active control technique in terms of enlarged stable operational regions. © 2005-2012 IEEE.

关键词： Milling (machining)

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KGNet: Knowledge-Guided Networks for Category-Level 6D Object Pose and Size Estimation

KGNet: Knowledge-Guided Networks for Category-Level 6D Objec...

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IEEE International Conference on robotics and automation (ICRA)

作者： Qiwei Meng Jason Gu Shiqiang Zhu Jianfeng Liao Tianlei Jin Fangtai Guo Wen Wang Wei Song Zhejiang Lab Research Center for Intelligent Robotics Research Institute of Interdis-ciplinary Innovation Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

Despite the giant leap made in object 6D pose estimation and robotic grasping under structured scenarios, most approaches depend heavily on the exact CAD models of target objects beforehand, thereby limiting their wide applications. To address this, we propose a novel knowledge-guided network - KGNet to estimate the pose and size of category-level unseen objects. This network includes three primary innovations: knowledge-guided categorical model generation, pointwise deformation probability matrix and synergetic RGBD feature fusion, with the former two leveraging categorical object knowledge for unseen object reconstruction and the latter one facilitating pose-sensitive feature extraction. Exten-sive experiments on CAMERA25 and REAL275 verify their effectiveness, and KGNet achieves the SOTA performance on these two acknowledged benchmarks. Additionally, a real-world robotic grasping experiment is conducted, and its results further qualitatively prove the practicability and robustness of KGNet.

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Coordinated Operation of Electrified Transportation Networks Considering Road Capacity Drop

Coordinated Operation of Electrified Transportation Networks...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Xin Cui Yunhe Hou Liang Liang Department of Electrical and Electronic Engineering The University of Hong Kong and HKU Shenzhen Institute of Research and Innovation Hong Kong Shenzhen China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

This paper proposes an optimal transportation-power flow (OTPF) model to manage electrified transportation networks (ETNs) with dynamic wireless charging (DWC) facilities. The transportation network (TN) model, considering the road capacity drop, is adopted for traffic flow assignment. The Distflow model is used to calculate the power flow in the power distribution network (PDN). Energy storage systems (ESSs) and flexible loads (FLs) are integrated into the coordinated operation of ETNs to enhance the economy and security of the system. To address fluctuations and variations in renewable energy generation, model predictive control (MPC) is adopted. The proposed MPC-based OTPF model is formulated as a nonlinear and non-convex problem. To obtain the global optimal solution, it is further transformed into a mixed-integer second-order cone programming (MISOCP) problem by using second-order cone (SOC) relaxation, piecewise linearization, and the Big-M method. Case studies verify the significant influence of road capacity drop on the operation of ETNs.

关键词： Renewable energy sources Fluctuations Roads Transportation Europe Programming Dynamic scheduling Smart grids Security Load modeling

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