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Journal of Infrastructure Intelligence and Resilience 2023年第2期2卷

作者： Jin, Shida Yang, Jian Sun, Shuaishuai Deng, Lei Chen, Zexin Gong, Liping Du, Haiping Li, Weihua School of Mechanical Materials Mechatronic and Biomedical Engineering University of Wollongong WollongongNSW2522 Australia School of Electrical Engineering and Automation Anhui University Hefei China CAS Key Laboratory of Mechanical Behavior and Design of Materials Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Anhui Province Hefei230026 China School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia

The attention given to magnetorheological elastomers (MREs) has been on the rise over the last few decades. MREs feature a remarkable field-controllable modulus or mechanical characteristics that are influenced by an external magnetic field. Compared to its family member magnetorheological fluids (MRF), MREs offer advantages in terms of overcoming sealing and sedimentation issues. This makes them highly promising for the development of smart base isolation systems of buildings and other infrastructures. This review paper attempts to highlight the impactful progress of MRE base isolation in civil engineering over the past decades. It begins with a brief introduction of MREs including its fundamental principles, operation modes, and fabrication process. Then, the recent investigations of MREs and MRE base isolators are reviewed. Finally, discussions are made on the challenges and potential topics for further investigations. © 2023 The Author(s)

关键词： Magnetorheological fluids

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No One Left Behind: Real-World Federated Class-Incremental Learning

arXiv

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arXiv 2023年

作者： Dong, Jiahua Li, Hongliu Cong, Yang Sun, Gan Zhang, Yulun Van Gool, Luc The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China The University of Chinese Academy of Sciences Beijing100049 China The Department of Civil and Environmental Engineering Hong Kong Polytechnic University Hong Kong The College of Automation Science and Engineering South China University of Technology Guangzhou510640 China The Computer Vision Lab ETH Zürich Zürich8092 Switzerland

Federated learning (FL) is a hot collaborative training framework via aggregating model parameters of decentralized local clients. However, most FL methods unreasonably assume data categories of FL framework are known and fixed in advance. Moreover, some new local clients that collect novel categories unseen by other clients may be introduced to FL training irregularly. These issues render global model to undergo catastrophic forgetting on old categories, when local clients receive new categories consecutively under limited memory of storing old categories. To tackle the above issues, we propose a novel Local-Global Anti-forgetting (LGA) model. It ensures no local clients are left behind as they learn new classes continually, by addressing local and global catastrophic forgetting. Specifically, considering tackling class imbalance of local client to surmount local forgetting, we develop a category-balanced gradient-adaptive compensation loss and a category gradient-induced semantic distillation loss. They can balance heterogeneous forgetting speeds of hard-to-forget and easy-to-forget old categories, while ensure consistent class-relations within different tasks. Moreover, a proxy server is designed to tackle global forgetting caused by Non-IID class imbalance between different clients. It augments perturbed prototype images of new categories collected from local clients via self-supervised prototype augmentation, thus improving robustness to choose the best old global model for local-side semantic distillation loss. Experiments on representative datasets verify superior performance of our model against comparison methods. The code is available at https://***/JiahuaDong/LGA. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Measurement feedback control of nonlinear systems:a small-gain approach

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Journal of Control and Decision 2020年第1期7卷 64-89页

作者： Tengfei Liu Zhong-Ping Jiang State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110004People’s Republic of China Department of Electrical and Computer Engineering New York UniversityBrooklynNYUSA

This paper presents a new tool for feedback control design of nonlinear systems in the presence of non-smooth measurement *** introduce a small-gain design approach to robust control of nonlinear uncertain systems with disturbed *** a design ingredient,a modified gain assignment technique for measurement feedback control of nonlinear uncertain systems is *** a recursive control design approach,the closed-loop system is transformed into a network of input-to-state stable(ISS)systems and the influences of the measurement errors are represented by ISS *** feedback control objective is achieved by applying the cyclic-small-gain theorem to the closed-loop ***,event-triggered control of nonlinear systems is studied in a unified framework of measurement feedback control.

关键词： Nonlinear systems measurement error nonlinear small-gain theorem input-to-state stability

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Adaptive Block Elevation Mapping for Large-scale Scene

Adaptive Block Elevation Mapping for Large-scale Scene

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IEEE International Conference on robotics and Biomimetics

作者： Yang Zhou Shiqiang Zhu Huang Huang Yuehua Li Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Beijing Institute of Control Engineering Beijing China Beijing Research Institute of Zhejiang Laboratory Beijing China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing unit mounted on the robot is limited, mapping algorithm has to make compromise by sacrificing speed and precision. It will be more challenging when the dense mapping scene is very large because the memory consumption will be greatly increased and the map is difficult to be extended if beyonding the initial map. To suppress the negative impact from the increased map scale, we proposed a novel block mapping approach to generate the dense map in large scale of scene. In this work, the elevation map is selected as the base dense map. The entire elevation map is segmented into numerous block maps of which size is much smaller than that of the entire map. The present moment of lidar and vision measurements are used to generate the local elevation map. The local elevation map is used to update block maps which are adaptively generated along the motion trajectory. A memory-disk interaction mechanism, which the block maps will be loaded to memory or saved to local disk when needed, is introduced. Our block mapping approach is tested on the KITTI datasets, and the results demonstrate that the mapping approach can stably operate in a large scale of scene with a much smaller consumption of memory.

关键词： Geometry Laser radar Navigation Motion segmentation Memory management Robot sensing systems Trajectory

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Leveraging convergence behavior to balance conflicting tasks in multi-task learning

arXiv

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arXiv 2022年

作者： Nakamura, Angelica Tiemi Mizuno Grassi, Valdir Wolf, Denis Fernando Mobile Robotics Laboratory Institute of Mathematics and Computer Sciences University of São Paulo São Carlos Brazil Department of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo São Carlos Brazil

Multi-Task Learning is a learning paradigm that uses correlated tasks to improve performance generalization. A common way to learn multiple tasks is through the hard parameter sharing approach, in which a single architecture is used to share the same subset of parameters, creating an inductive bias between them during the training process. Due to its simplicity, potential to improve generalization, and reduce computational cost, it has gained the attention of the scientific and industrial communities. However, tasks often conflict with each other, which makes it challenging to define how the gradients of multiple tasks should be combined to allow simultaneous learning. To address this problem, we use the idea of multi-objective optimization to propose a method that takes into account temporal behaviour of the gradients to create a dynamic bias that adjust the importance of each task during the backpropagation. The result of this method is to give more attention to the tasks that are diverging or that are not being benefited during the last iterations, allowing to ensure that the simultaneous learning is heading to the performance maximization of all tasks. As a result, we empirically show that the proposed method outperforms the state-of-art approaches on learning conflicting tasks. Unlike the adopted baselines, our method ensures that all tasks reach good generalization performances. © 2022, CC BY.

关键词： Multiobjective optimization

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EI-Drive: A Platform for Cooperative Perception with Realistic Communication Models

arXiv

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arXiv 2024年

作者： Zhou, Hanchu Xie, Edward Shao, Wei Gao, Dechen Dong, Michelle Zhang, Junshan Davis AI Robotics and Edge Laboratory University of California DavisCA95616 United States Electrical and Computer Engineering Department of Johns Hopkins University BaltimoreMD21218 United States data61 CSIRO ClaytonVIC Australia Monta Vista High School CupertinoCA95014 United States

The growing interest in autonomous driving calls for realistic simulation platforms capable of accurately simulating cooperative perception process in realistic traffic scenarios. Existing studies for cooperative perception often have not accounted for transmission latency and errors in real-world environments. To address this gap, we introduce EI-Drive, an edge-AI based autonomous driving simulation platform that integrates advanced cooperative perception with more realistic communication models. Built on the CARLA framework, EI-Drive features new modules for cooperative perception while taking into account transmission latency and errors, providing a more realistic platform for evaluating cooperative perception algorithms. In particular, the platform enables vehicles to fuse data from multiple sources, improving situational awareness and safety in complex environments. With its modular design, EI-Drive allows for detailed exploration of sensing, perception, planning, and control in various cooperative driving scenarios. Experiments using EI-Drive demonstrate significant improvements in vehicle safety and performance, particularly in scenarios with complex traffic flow and network conditions. All code and documents are accessible on our GitHub page: https://***/***/. © 2024, CC BY-NC-SA.

关键词： Autonomous vehicles

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Reinforcement Learning with Large Language Model for Hybrid Disassembly Lines in Remanufacturing Contexts

Reinforcement Learning with Large Language Model for Hybrid ...

引用

IEEE International Conference on automation Science and engineering (CASE)

作者： Peng Ji Xi Wang Guo Jiacun Wang Weitian Wang Shu Jin Qin Ying Tang Qi Kang College of Information and Control Engineering Liaoning Petrochemical University Fushun China Dept of Comp. Sci. and Soft. Eng. Monmouth University W. Long Branch NJ USA Collaborative Robotics and Smart Systems Laboratory (CRoSS Lab) Montclair State University Montclair NJ USA College of Economics and Management Shangqiu Normal University Shangqiu China Electrical and Computer Engineering Rowan University Glassboro New Jersey USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Large language models (LLM), ChatGPT is making substantial impact across various fields. This study for the first time presents a novel approach to the hybrid disassembly line balancing problem using LLM and reinforcement learning algorithms in remanufacturing contexts. The problem is divided into two sub-stages. LLM is innovatively used to capture a disassembly sequence well in the first stage, while reinforcement learning is utilized to address the problem in the second stage. Upon comparing the performance with and without LLM, the proposed approach significantly reduces the trial-and-error space and achieves faster convergence to achieve the desired solution. Future work of this study is also discussed.

关键词： computer aided software engineering automation Large language models Reinforcement learning Chatbots Convergence

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Finite-time Consensus for Third-Order Multi-Agent Systems with Directed Communication Networks

Finite-time Consensus for Third-Order Multi-Agent Systems wi...

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

作者： Xirui Zhang Zhi-Liang Zhao Tengfei Liu Zhong-Ping Jiang School of Mathematics and Statistics Shaanxi Normal University Xi’an P. R. China School of Electrical and Control Engineering North University of China Taiyuan P. R. China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang P. R. China Department of Electrical and Computer Engineering New York University Brooklyn NY USA

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This paper focuses on third-order multi-agent systems with directed communication networks. It is for the first time that the finite-time consensus problem is solved for third-order multi-agent systems under digraphs containing spanning trees. Two explicit-coefficient finite-time protocols, namely, edge-based and node-based, are proposed. First, we prove that asymptotic consensus is achievable with the reduced linear protocol. Then, we show that finite-time consensus is achievable with our explicit-coefficient finite-time protocols through homogeneous theory. Finally, numerical simulations are carried out to demonstrate the effectiveness of the proposed protocols.

关键词： Protocols Network topology Numerical simulation Topology Communication networks Closed loop systems Multi-agent systems

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Modelling with NGSI-LD: the VALLPASS project case study

Modelling with NGSI-LD: the VALLPASS project case study

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Tiago Ribeiro João Paulo Coelho Luísa Jorge Joaquim Sardão José Gonçalves Higor Rosse CeDRI - Research Center for Digitization and Intelligent Robotics SusTEC - Associate Laboratory for Sustainability and Technology in Mountains Regions Bragança Portugal Department of Electrical and Computer Engineering INESC Coimbra University of Coimbra Polo 2 Coimbra Portugal Valled Lda Bragança Portugal Intelligent Technologies Collaborative Laboratory Mountains of Research Bragança Portugal

The smart cities paradigm covers multiple domains which span from citizens’ accessibility and mobility to general infrastructures and services. Hence, smart cities can be seen as an excellent showcase of heterogeneity, namely at the data level. For this reason, they are a perfect candidate for linked data and semantic web concept applications. This powerful combination leads to interoperability at the data level which is one of the ultimate goals of the Internet of Things (IoT). In this reference frame, NGSI-LD is an open framework for context information processing consisting of both a semantic information model and a RESTful Application Programming Interface (API). This paper proposes a methodology for creating semantic data models in the context of IoT, namely to represent and describe data associated with digital twins. The methodology is presented in a practical way, through the process of creating an NGSI-LD semantic data model for the VALLPASS project, inserted in the traffic domain, which is one of the most popular in smart cities.

关键词：

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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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