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arXiv 2020年

作者： Tang, Zhiqi Cunha, Rita Hamel, Tarek Silvestre, Carlos Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Portugal I3S-CNRS Université Côte d'Azur Nice-Sophia Antipolis France I3S-CNRS Institut Universitaire de France Nice-Sophia Antipolis France Department of Electrical and Computer Engineering The Faculty of Science and Technology The University of Macau China

This paper addresses the problem of bearing-only formation control in d (d ≥ 2)-dimensional space by exploring persistence of excitation (PE) of the desired bearing reference. By defining a desired formation that is bearing PE, distributed bearing-only control laws are proposed, which guarantee exponential stabilization of the desired formation only up to a translation vector. The key outcome of this approach relies in exploiting the bearing PE to significantly relax the conditions imposed on the graph topology to ensure exponential stabilization, when compared to the bearing rigidity conditions, and to remove the scale ambiguity introduced by bearing vectors. Simulation results are provided to illustrate the performance of the proposed control method. Copyright © 2020, The Authors. All rights reserved.

关键词： Stabilization

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Port-Hamiltonian Approach to Neural Network Training

Port-Hamiltonian Approach to Neural Network Training

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IEEE Annual Conference on Decision and Control

作者： Stefano Massaroli Michael Poli Federico Califano Angela Faragasso Jinkyoo Park Atsushi Yamashita Hajime Asama Department of Precision Engineering The University of Tokyo Tokyo Japan Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea Faculty of Electrical Engineering Mathematics & Computer Science (EWI) Robotics and Mechatronics (RAM) University of Twente Enschede The Netherlands

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

Neural networks are discrete entities: subdivided into discrete layers and parametrized by weights which are iteratively optimized via difference equations. Recent work proposes networks with layer outputs which are no longer quantized but are solutions of an ordinary differential equation (ODE);however, these networks are still optimized via discrete methods (e.g. gradient descent). In this paper, we explore a different direction: namely, we propose a novel framework for learning in which the parameters themselves are solutions of ODEs. By viewing the optimization process as the evolution of a port-Hamiltonian system, we can ensure convergence to a minimum of the objective function. Numerical experiments have been performed to show the validity and effectiveness of the proposed methods.

关键词： Training Biological neural networks Convergence Mathematical model Optimization Linear programming

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Cooperative Intelligence for Autonomous Driving

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ZTE Communications 2019年第2期17卷 44-50页

作者： CHENG Xiang DUAN Dongliang YANG Liuqing ZHENG Nanning State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Department of Electrical and Computer Engineering University of WyomingLaramieWY 82071USA Department of Electrical and Computer Engineering Colorado State UniversityFort CollinsCO 80523USA Institute of Artificial Intelligence and Robotics Xi’an Jiaotong UniversityXi’anShaanxi 710049China

Autonomous driving is an emerging technology attracting interests from various sectors in recent *** of existing work treats autonomous vehicles as isolated individuals and has focused on developing separate intelligent *** this paper,we attempt to exploit the connectivity among vehicles and propose a systematic framework to develop autonomous driving *** first introduce a general hierarchical information fusion framework for cooperative sensing to obtain global situational awareness for *** this,a cooperative intelligence framework is proposed for autonomous driving *** general framework can guide the development of data collection,sharing and processing strategies to realize different intelligent functions in autonomous driving.

关键词： autonomous driving cooperative intelligence information fusion vehicular communications and networking

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Sidewall Imaging of Microstructures with a Tilted Quartz Tuning Fork (QTF) Force Sensor 3

Sidewall Imaging of Microstructures with a Tilted Quartz Tun...

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3rd International Conference on Manipulation, Automation and robotics at Small Scales, MARSS 2018

作者： Hussain, Danish Yongbing, Wen Xie, Hui College of Electrical Mechanical Engineering National University of Sciences and Technology Department of Mechatronics Engineering Islamabad Pakistan Harbin Institute of Technology State Key Laboratory of Robotics and Systems 2 Yikuang Harbin150080 China

ISBN: (纸本)9781538648414

Sidewall imaging of micro and nano structures is essential for critical dimensional metrology in the semiconductor industry. Atomic force microscope is an important sidewall imaging instrument due to its three dimensional imaging capability, high accuracy and ultra high resolution. We propose an AFM method for sidewall imaging of high step sidewalls. A tuning fork force sensor with a tungsten tip is tilted at a suitable angle (θ) to access the sidewall. Sidewalls of a micro electromechanical systems (MEME) structure fabricated by deep reactive ion etching (DIRE) process is scanned and sidewall roughness is measured. © 2018 IEEE.

关键词： Atomic force microscopy

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Primal-dual optimization methods for large-scale and distributed data analytics

arXiv

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arXiv 2019年

作者： Jakovetić, Dušan Bajović, Dragana Xavier, João Moura, José M.F. Faculty of Sciences University of Novi Sad Novi Sad Serbia Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia Instituto Superior Tećnico Universidade de Lisboa Institute for Systems and Robotics Laboratory for Robotics and Engineering Systems Lisbon Portugal Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States

The augmented Lagrangian method (ALM) is a classical optimization tool that solves a given "difficult" (constrained) problem via finding solutions of a sequence of "easier" (often unconstrained) sub-problems with respect to the original (primal) variable, wherein constraints satisfaction is controlled via the so-called dual variables. ALM is highly flexible with respect to how primal sub-problems can be solved, giving rise to a plethora of different primal-dual methods. The powerful ALM mechanism has recently proved to be very successful in various large scale and distributed applications. In addition, several significant advances have appeared, primarily on precise complexity results with respect to computational and communication costs in the presence of inexact updates and design and analysis of novel optimal methods for distributed consensus optimization. We provide a tutorial-style introduction to ALM and its analysis via control-theoretic tools, survey recent results, and provide novel insights in the context of two emerging applications: federated learning and distributed energy trading. Copyright © 2019, The Authors. All rights reserved.

关键词： Constrained optimization

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A Framework for the Sustainable Control and Optimization of Resources in Agriculture

A Framework for the Sustainable Control and Optimization of ...

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IEEE Annual Conference on Decision and Control

作者： Fernando Lobo Pereira Ismael Pena Geraldo Nunes Silva SYSTEC Faculty of Electrical Engineering Porto University and with the Institute for Systems and Robotics Porto Portugal Department of Applied Mathematics São Paulo State University (UNESP) Brasil Department of Applied Mathematics Sao Paulo State University (UNESP) Brasil

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

This article concerns the issue of optimizing strategies of resources management in Agriculture. The increasing human population, and the anthropogenic environmental impacts resulting from the current agricultural food production practice raise tremendous challenges as the latter constitutes a huge threat to the overarching goal of eliminating hunger from the Earth's face. The contribution of this article consists in showing the stability and convergence of a bi-level optimization based control architecture to de-conflict the short-term profit-seeking goals of each agricultural production unit with the long-term goal of attaining the collective environmental equilibrium required to preserve the necessary food production efficiency level. Given the complexity of any realistic scenario, we consider a very simple context clarifying the core mathematical issues of a bi-level control architecture previously proposed by the authors that, under reasonable assumptions, generates control strategies ensuring the asymptotic convergence to an environmentally healthy equilibrium while ensuring the economic (and, thus, social) short term sustainability.

关键词： Production Optimal control Agriculture Economics Optimization Sustainable development Decision making

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A rapid fault reconstruction strategy using a bank of sliding mode observers

arXiv

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arXiv 2019年

作者： Shakarami, Mehran Esfandiari, Kasra Suratgar, Amir Aboulfazl Talebi, Heidar Ali Engineering and Technology Institute Groningen University of Groningen AG Groningen 9747 Netherlands Center for Systems Science Yale University New HavenCT United States Center of Excellence on Control and Robotics Department of Electrical Engineering Tehran Polytechnic Tehran Iran

This paper deals with the design of a model-based rapid fault detection and isolation strategy using sliding mode observers. To address this problem, a new scheme is proposed by adaptively combining the information provided by a bank of observers. In this regard, a new structure for sliding mode observers is considered. Then, the well-known recursive least square algorithm is utilized to merge individual state estimations suitably such that the system fault is detected faster. The required condition for enhancing perfect state estimation is derived, and the stability of the overall system is proven via Lyapunov's direct method. The supremacy of proposed scheme is fully discussed through mathematical analyses as well as simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： State estimation

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CNN-LIDAR pedestrian classification: combining range and reflectance data

CNN-LIDAR pedestrian classification: combining range and ref...

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IEEE International Conference on Vehicular Electronics and Safety (ICVES)

作者： Gledson Melotti Alireza Asvadi Cristiano Premebida Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal

The use of multiple sensors in perception systems is becoming a consensus in the automotive and robotics industries. Camera is the most popular technology, however, radar and LIDAR are increasingly being adopted more often in protection and safety systems for object/obstacle detection. In this paper, we particularly explore the LIDAR sensor as an inter-modality technology which provides two types of data, range (distance) and reflectance (intensity return), and study the influence of high-resolution distance/depth (DM) and reflectance maps (RM) on pedestrian classification using a deep Convolutional Neural Network (CNN). Pedestrian protection is critical for advanced driver assistance system (ADAS) and autonomous driving, and it has regained particular attention recently for known reasons. In this work, CNN-LIDAR based pedestrian classification is studied in three distinct cases: (i) having a single modality as input in the CNN, (ii) by combining distance and reflectance measurements at the CNN input-level (early fusion), and (iii) combining outputs scores from two single-modal CNNs (late fusion). Distance and intensity (reflectance) raw data from LIDAR are transformed to high-resolution (dense) maps which allow a direct implementation on CNNs both as single or multi-channel inputs (early fusion approach). In terms of late-fusion, the outputs from individual CNNs are combined by means of non-learning rules, such as: minimum, maximum, average, product. Pedestrian classification is evaluated on a `binary classification' dataset created from the KITTI Vision Benchmark Suite, and results are shown for the three cases.

关键词： Laser radar Feature extraction Cameras Sensors Three-dimensional displays Safety Automotive engineering

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Robot Force Control on a Beating Heart (22, pg 1736, 2017)

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2017年第6期22卷 2802-2802页

作者： Cortesao, Rui Dominici, Michel Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal Institute of Systems and Robotics University of Coimbra Coimbra Portugal

Cardiac interventions have to deal with several difficulties, where heart motion is a major one. This paper addresses the heart motion compensation problem under contact, relying on robot force control techniques. A double active observer (AOB) architecture is proposed to tackle precise force control in the presence of heart motion. One AOB controls the desired interaction force, and the other one is responsible for compensating heart motion autonomously. Three-degrees of freedom (DOF) force tracking on top of a beating heart is presented, showing that with the proposed control architecture, it is possible to have force controlled interventions with root mean square errors below 0.56 N (based on raw data). The 4-DOF whole arm manipulator (WAM) from Barrett Technologies is the medical arm, while another 3-DOF robot carrying an ex vivo heart generates heart motion based on recorded biosignals.

关键词： Heart Force Force control Robot sensing systems Computer architecture Motion compensation

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