检索结果-内蒙古大学图书馆

Convex Parameterization and Optimization for Robust Tracking of a Magnetically Levitated Planar Positioning System

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Ma, Jun Cheng, Zilong Zhu, Haiyue Li, Xiaocong Tomizuka, Masayoshi Lee, Tong Heng Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Singapore Institute of Manufacturing Technology Singapore138634 Singapore John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Mechanical Engineering University of California BerkeleyCA94720 United States

Magnetic levitation positioning technology has attracted considerable research efforts and dedicated attention due to its extremely attractive features. The technology offers high-precision, contactless, dust/lubricant-free, multi-axis, and large-stroke positioning. In this work, we focus on the accurate and smooth tracking problem of a multi-axis magnetically levitated (maglev) planar positioning system for a specific S-curve reference trajectory. The floating characteristics and the multi-axis coupling make accurate identification of the system dynamics difficult, which lead to a challenge to design a high performance control system. Here, the tracking task is achieved by a 2-Degree of Freedom (DoF) controller consisting of a feedforward controller and a robust stabilizing feedback controller with a prescribed sparsity pattern. The approach proposed in this paper utilizes the basis of an H∞ controller formulation and a suitably established convex inner approximation. Particularly, a subset of robust stabilizable controllers with prescribed structural constraints is characterized in the parameter space, and so thus the re-formulated convex optimization problem can be easily solved by several powerful numerical algorithms and solvers. With this approach, the robust stability of the overall system is ensured with a satisfactory system performance despite the presence of parametric uncertainties. Furthermore, experimental results clearly demonstrate the effectiveness of the proposed approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Convex optimization

Shifts in Brain Dynamics and Drivers of Consciousness State Transitions

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Bodenheimer, Joseph Bogdan, Paul Pequito, Sérgio Ashourvan, Arian Department of Psychology The University of Kansas 1415 Jayhawk Blvd. LawrenceKS66045 United States Ming Hsieh Department of Electrical and Computer Engineering University of Southern California 3740 McClintock Ave Los AngelesCA90089 United States Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Lisbon66045 Portugal

Understanding the neural mechanisms underlying the transitions between different states of consciousness is a fundamental challenge in neuroscience. Thus, we investigate the underlying drivers of changes during the resting-state dynamics of the human brain, as captured by functional magnetic resonance imaging (fMRI) across varying levels of consciousness (awake, light sedation, deep sedation, and recovery). We deploy a model-based approach relying on linear time-invariant (LTI) dynamical systems under unknown inputs (UI). Our findings reveal distinct changes in the spectral profile of brain dynamics – particularly regarding the stability and frequency of the system’s oscillatory modes during transitions between consciousness states. These models further enable us to identify external drivers influencing large-scale brain activity during naturalistic auditory stimulation. Our findings suggest that these identified inputs delineate how stimulus-induced co-activity propagation differs across consciousness states. Notably, our approach showcases the effectiveness of LTI models under UI in capturing large-scale brain dynamic changes and drivers in complex paradigms, such as naturalistic stimulation, which are not conducive to conventional general linear model analysis. Importantly, our findings shed light on how brain-wide dynamics and drivers evolve as the brain transitions towards conscious states, holding promise for developing more accurate biomarkers of consciousness recovery in disorders of consciousness. © 2024, CC BY-NC-ND.

关键词： Brain

Forecasting intracranial hypertension using multi-scale waveform metrics

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Hüser, Matthias Kündig, Adrian Karlen, Walter De Luca, Valeria Jaggi, Martin Biomedical Informatics Group Department of Computer Science ETH Zürich Universitätstrasse 6 Zürich8092 Switzerland Mobile Health Systems Lab Institute of Robotics and Intelligent Systems Department of Health Sciences and Technology ETH Zürich Zürich8008 Switzerland Department of Information Technology and Electrical Engineering ETH Zürich Zürich8092 Switzerland Machine Learning & Optimization Lab EPFL Lausanne1015 Switzerland

Objective: Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively, leading to late detection and lost time for intervention planning. A pro-active approach that predicts critical events several hours ahead of time could assist in directing attention to patients at risk. Approach: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours. It jointly uses cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state of the patient. One-minute base windows were compressed by computing signal metrics, and then stored in a multi-scale history, from which physiological features were derived. Main results: Our model predicted events up to 8 hours in advance with alarm recall rates of 90% at a precision of 30.3% in the MIMIC-III waveform database, improving upon two baselines from the literature. We found that features derived from high-frequency waveforms substantially improved the prediction performance over simple statistical summaries of low-frequency time serie1s, and each of the three feature classes contributed to the performance gain. The inclusion of long-term history up to 8 hours was especially important. Significance: Our results highlight the importance of information contained in high-frequency waveforms in the neurological intensive care unit. They could motivate future studies on pre-hypertensive patterns and the design of new alarm algorithms for critical events in the injured brain. Copyright © 2019, The Authors. All rights reserved.

关键词： Morphology

Robotic Control Optimization Through Kernel Selection in Safe Bayesian Optimization

学校读者我要写书评

暂无评论

Robotic Control Optimization Through Kernel Selection in Saf...

IEEE International Conference on robotics and Biomimetics

作者： Lihao Zheng Hongxuan Wang Xiaocong Li Jun Ma Prahlad Vadakkepat School of Data Science The Chinese University of Hong Kong Research Intern at the Singapore Institute of Manufacturing Technology A*STAR Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research (A*STAR) Singapore Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Control system optimization has long been a fundamental challenge in robotics. While recent advancements have led to the development of control algorithms that leverage learning-based approaches, such as SafeOpt, to optimize single feedback controllers, scaling these methods to high-dimensional complex systems with multiple controllers remains an open problem. In this paper, we propose a novel learning-based control optimization method, which enhances the additive Gaussian process-based Safe Bayesian Optimization algorithm to efficiently tackle high-dimensional problems through kernel selection. We use PID controller optimization in drones as a representative example and test the method on Safe Control Gym, a benchmark designed for evaluating safe control techniques. We show that the proposed method provides a more efficient and optimal solution for high-dimensional control optimization problems, demonstrating significant improvements over existing techniques.

关键词： Additives Trajectory tracking Optimization methods Control systems Bayes methods Kernel Robots Tuning Standards Quadrotors

EFaR 2023: Efficient Face Recognition Competition

学校读者我要写书评

暂无评论

EFaR 2023: Efficient Face Recognition Competition

IEEE International Joint Conference on Biometrics (IJCB)

作者： Jan Niklas Kolf Fadi Boutros Jurek Elliesen Markus Theuerkauf Naser Damer Mohamad Alansari Oussama Abdul Hay Sara Alansari Sajid Javed Naoufel Werghi Klemen Grm Vitomir Štruc Fernando Alonso-Fernandez Kevin Hernandez Diaz Josef Bigun Anjith George Christophe Ecabert Hatef Otroshi Shahreza Ketan Kotwal Sébastien Marcel Iurii Medvedev Bo Jin Diogo Nunes Ahmad Hassanpour Pankaj Khatiwada Aafan Ahmad Toor Bian Yang Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Université de Lausanne (UNIL) Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal Department of Information Security and Communication Technology eHealth and Welfare Security Group Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.

关键词：

Teaching Autonomous systems Hands-On: Leveraging Modular Small-Scale Hardware in the robotics Classroom

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Betz, Johannes Zheng, Hongrui Zang, Zirui Sauerbeck, Florian Zheng, Y. Rosa Biswas, Joydeep Walas, Krzysztof Dimitrov, Velin Behl, Madhur Krovi, Venkat Mangharam, Rahul The Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States The Institute of Automotive Technology Technical University of Munich Germany The College of Engineering and Applied Science Lehigh University BethlehemPA18015 United States The Department of Computer Science University of Texas Austin AustinTX United States The Institute of Robotics and Machine Intelligence Poznan University of Technology Poland The Department of Computer Science University of Virginia CharlottesvilleVA United States College of Engineering Computing and Applied Sciences Clemson University ClemsonSC29634 United States The Toyota Research Institute CambridgeMA02139 United States

Although robotics courses are well established in higher education, the courses often focus on theory and sometimes lack the systematic coverage of the techniques involved in developing, deploying, and applying software to real hardware. Additionally, most hardware platforms for robotics teaching are low-level toys aimed at younger students at middle-school levels. To address this gap, an autonomous vehicle hardware platform, called F1TENTH, is developed for teaching autonomous systems hands-on. This article describes the teaching modules and software stack for teaching at various educational levels with the theme of "racing" and competitions that replace exams. The F1TENTH vehicles offer a modular hardware platform and its related software for teaching the fundamentals of autonomous driving algorithms. From basic reactive methods to advanced planning algorithms, the teaching modules enhance students’ computational thinking through autonomous driving with the F1TENTH vehicle. The F1TENTH car fills the gap between research platforms and low-end toy cars and offers hands-on experience in learning the topics in autonomous systems. Four universities have adopted the teaching modules for their semester-long undergraduate and graduate courses for multiple years. Student feedback is used to analyze the effectiveness of the F1TENTH platform. More than 80% of the students strongly agree that the hardware platform and modules greatly motivate their learning, and more than 70% of the students strongly agree that the hardware enhanced their understanding of the subjects. The survey results show that more than 80% of the students strongly agree that the competitions motivate them for the course. © 2022, CC BY.

关键词： Students

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

学校读者我要写书评

暂无评论

Reinforcement Learning with Large Language Model for Hybrid ...

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

HRI Pioneers Workshop 2013 13

学校读者我要写书评

暂无评论

HRI Pioneers Workshop 2013

ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Solace Shen Henny Admoni Caroline Harriott Yunkyung Kim Matt Marge Marynel Vázquez Astrid Rosenthal-von der Pütten Matt Beane Yasuhiko Hato Daniel Lazewatsky Robin Read Steve Vozar Department of Psychology University of Washington Seattle USA Department of Social Psychology University of Duisburg-Essen Duisburg Germany Department of Computer Science Yale University New Haven USA Sloan School of Management Massachusetts Institute of Technology Cambridge USA Department of Electrical Engineering & Computer Science Vanderbilt University Nashville USA Graduate School of Science & Technology Keio University Yokohama Japan Department of Industrial Design Korea Advanced Institute of Science and Technology Daejeon South Korea Industrial & Manufacturing Engineering Oregon State University Corvallis USA Language Technologies Institute Carnegie Mellon University Pittsburgh USA Centre for Robotics & Neural Systems University of Plymouth Plymouth UK Robotics Institute Carnegie Mellon University Pittsburgh USA Department of Mechanical Engineering University of Michigan Ann Arbor USA

ISBN: (纸本)9781467330558

The 2013 Human-Robot Interaction (HRI) Pioneers Workshop, which will be held in conjunction with the 8 th ACM/IEEE International Conference on Human-Robot Interaction, is the premiere venue for student research in the field. This highly selective workshop seeks to foster creativity, communication, and collaboration between the world's top student researchers in the field of human-robot interaction. Pioneers Workshop participants will have the opportunity to learn about the current state of HRI, to present their research, and to network with one another and with select senior researchers in a setting that is less formal and more interactive then the main conference. The theme for this year is Re-envisioning HRI: Dealing With the Elephants in Our Room. The workshop aims to bring forward difficult to discuss issues to help the field achieve more balanced perspectives and a clearer picture of what sort of future we are building.

关键词： Educational institutions Conferences Human-robot interaction Psychology computer science Service robots

Smart decentralised energy management

学校读者我要写书评

暂无评论

Smart decentralised energy management

CIRED 2020 Berlin Workshop (CIRED 2020)

作者： Stefan Werner Thomas Walter Christian Wiezorek Christian Backe Miguel Bande Firvida Thomas Vögele Peter Conradi Samrat Bose Enrique Kremers Easy Smart Grid GmbH (ESG) Karlsruhe Germany Technical University of Berlin (TUB) Department of Electrical Engineering and Computer Sciences Berlin Germany German Research Center for Artificial Intelligence (DFKI) Robotics Innovation Center Bremen Germany ALL4IP Technologies Darmstadt Germany European Institute for Energy Research (EIFER) Department of Complex Systems Research Karlsruhe Germany

The German–Finnish research project FUture Smart Energy shows, how flexible devices, consuming or producing electricity in electric grids, can be self-organised in a fully decentralised way, using autonomous algorithms integrated with the devices' controllers. By shifting operation time, existing flexible devices are hereby utilised as ‘virtual batteries’, providing high storage capacity and power. To gain sufficient flexibility, a large number of devices like combined heat and power generators, heat pumps (HP), heaters, coolers, charging stations, pumps, household appliances and industrial plants, has to be coordinated. This results in a high system complexity for which the evaluated method provides an easy, resilient, cyber-secure and cost-effective solution. This novel technology uses a new market approach for electric energy systems. A real-time price signal is generated directly out of grid state variables, like frequency, voltage, power or current, and broadcast to the flexible devices. Without a need for central control, the flexible devices react like a natural swarm to the price signal. The system is easily and highly scalable, as adding and removing flexibilities does not imply adapting a central control system. The system can be operated parallel or in addition to existing energy markets.

关键词： Internet of Things smart power grids energy management systems decentralised control power distribution control decision making pricing