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arXiv 2019年

作者： Chen, Shuyang Wen, John T. Department of Mechanical Engineering Rensselaer Polytechnic Institute 110 8th St TroyNY12180 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

Fast and precise robot motion is needed in certain applications such as electronic manufacturing, additive manufacturing and assembly. Most industrial robot motion controllers allow externally commanded motion profile, but the trajectory tracking performance is affected by the robot dynamics and joint servo controllers which users have no direct access and little information. The performance is further compromised by time delays in transmitting the external command as a setpoint to the inner control loop. This paper presents an approach of combining neural networks and iterative learning control to improve the trajectory tracking performance for a multi-axis articulated industrial robot. For a given desired trajectory, the external command is iteratively refined using a high fidelity dynamical simulator to compensate for the robot inner loop dynamics. These desired trajectories and the corresponding refined input trajectories are then used to train multi-layer neural networks to emulate the dynamical inverse of the nonlinear inner loop dynamics. We show that with a sufficiently rich training set, the trained neural networks can generalize well to trajectories beyond the training set. In applying the trained neural networks to the physical robot, the tracking performance still improves but not as much as in the simulator. We show that transfer learning can effectively bridge the gap between simulation and the physical robot. In the end, we test the trained neural networks on other robot models in simulation and demonstrate the possibility of a general purpose network. Development and evaluation of this methodology is based on the ABB IRB6640-180 industrial robot and ABB RobotStudio software packages. Copyright © 2019, The Authors. All rights reserved.

关键词： Industrial robots

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Searching for GEMS: Two Super-Jupiters around M-dwarfs – Signatures of Instability or Accretion?

arXiv

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arXiv 2024年

作者： Hotnisky, Andrew Kanodia, Shubham Libby-Roberts, Jessica Mahadevan, Suvrath Cañas, Caleb I. Gupta, Arvind F. Han, Te Kobulnicky, Henry A. Larsen, Alexander Robertson, Paul Rodruck, Michael Stefansson, Gudmundur Cochran, William D. Delamer, Megan Diddams, Scott A. Fernandes, Rachel B. Halverson, Samuel Hebb, Leslie Lin, Andrea S.J. Monson, Andrew Ninan, Joe P. Roy, Arpita Schwab, Christian Department of Astronomy & Astrophysics 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Center for Exoplanets and Habitable Worlds 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Earth and Planets Laboratory Carnegie Science 5241 Broad Branch Road NW WashingtonDC20015 United States Astrobiology Research Center 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States U.S. National Science Foundation National Optical-Infrared Astronomy Research Laboratory 950 N. Cherry Ave. TucsonAZ85719 United States Department of Physics & Astronomy The University of California Irvine IrvineCA92697 United States Department of Physics & Astronomy University of Wyoming LaramieWY82070 United States Department of Physics & Astronomy University of California Irvine IrvineCA92697 United States Department of Physics Engineering and Astrophysics Randolph-Macon College AshlandVA23005 United States Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands McDonald Observatory Department of Astronomy The University of Texas Austin United States Center for Planetary Systems Habitability The University of Texas Austin United States Electrical Computer & Energy Engineering University of Colorado 425 UCB BoulderCO80309 United States Department of Physics University of Colorado 2000 Colorado Avenue BoulderCO80309 United States Jet Propulsion Laboratory 4800 Oak Grove Drive PasadenaCA91109 United States Department of Physics Hobart and William Smith Colleges 300 Pulteney Street GenevaNY14456 United States Steward Observatory The University of Arizona 933 N. Cherry Avenue TucsonAZ85721 United States Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Homi Bhabha Road

We present the discovery of TOI-6303b and TOI-6330b, two massive transiting super-Jupiters orbiting a M0 and a M2 star respectively, as part of the Searching for GEMS survey. These were detected by TESS and then confirmed via ground-based photometry and radial velocity observations with the Habitable-zone Planet Finder (HPF). TOI-6303b has a mass of 7.84 ± 0.31 MJ, a radius of 1.03 ± 0.06 RJ, and an orbital period of 9.485 days. TOI-6330b has a mass of 10.00 ± 0.31 MJ, a radius of 0.97 ± 0.03 RJ, and an orbital period of 6.850 days. We put these planets in context of super-Jupiters around M-dwarfs discovered from radial-velocity surveys, as well as recent discoveries from astrometry. These planets have masses that can be attributed to two dominant planet formation mechanisms — gravitational instability and core-accretion. Their masses necessitate massive protoplanetary disks that should either be gravitationally unstable, i.e. forming through gravitational instability, or be amongst some of the most massive protoplanetary disks to form objects through core-accretion. We also discuss the eccentricity distribution of these objects, as a potential indicator of their formation and evolutionary mechanisms. © 2024, CC BY.

关键词： Orbits

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Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution

Dual Reconstruction with Densely Connected Residual Network ...

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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Chih-Chung Hsu Chia-Hsiang Lin Department of Management Information Systems National Pingtung University of Science and Technology Pingtung Taiwan Department of Electrical Engineering Institute of Computer and Communication Engineering National Cheng Kung University Tainan City 701 Taiwan

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Deep learning-based single image super-resolution enables very fast and high-visual-quality reconstruction. Recently, an enhanced super-resolution based on generative adversarial network (ESRGAN) has achieved excellent performance in terms of both qualitative and quantitative quality of the reconstructed high-resolution image. In this paper, we propose to add one more shortcut between two dense-blocks, as well as add shortcut between two convolution layers inside a dense-block. With this simple strategy of adding more shortcuts in the proposed network, it enables a faster learning process as the gradient information can be back-propagated more easily. Based on the improved ESRGAN, the dual reconstruction is proposed to learn different aspects of the super-resolved image for judiciously enhancing the quality of the reconstructed image. In practice, the super-resolution model is pre-trained solely based on pixel distance, followed by fine-tuning the parameters in the model based on adversarial loss and perceptual loss. Finally, we fuse two different models by weighted-summing their parameters to obtain the final super-resolution model. Experimental results demonstrated that the proposed method achieves excellent performance in the real-world image super-resolution challenge. We have also verified that the proposed dual reconstruction does further improve the quality of the reconstructed image in terms of both PSNR and SSIM.

关键词： Image reconstruction Spatial resolution Convolution Feature extraction Interpolation Machine learning

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Modeling and Operation of the Power-to-Gas System for Renewables Integration: A Review

引用

CSEE Journal of Power and Energy systems 2018年第2期4卷 168-178页

作者： Xuetao Xing Jin Lin Yonghua Song You Zhou Shujun Mu Qiang Hu State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Electrical and Computer Engineering University of MacaoMacaoChina Department of Electrical Engineering Tsinghua UniversityBeijing 100084China National Institute of Clean and Low Carbon Energy Beijing 102211China Tsinghua-Sichuan Energy Internet Research Institute Chengdu 610213China

Power-to-Gas(P2G)plays an important role in enhancing large-scale renewable energy integration in power *** an emerging inter-disciplinary subject,P2G technology requires knowledge in electrochemistry,electrical engineering,thermodynamic engineering,chemical engineering and system *** at P2G modeling and operational problems concerning the research field of power systems and the energy internet,this paper briefly reviews the main technologies and application potentials of the P2G system,and makes systematic summaries of major progresses related to P2G’s integration into the power grid in a bottom-top manner,including the modeling of high/room-temperature electrolysis cells,steady-state/dynamic optimization control of the P2G system,P2G’s integrated model and operational strategies at the grid *** the final part of this paper,suggestions are put forward on future research directions of P2G systems from the aspects of modeling and operational optimization.

关键词： Electrolysis cell multi-energy network modeling and operation power-to-gas

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A Chance Constrained Optimal Reserve Scheduling Approach for Economic Dispatch Considering Wind Penetration

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IEEE/CAA Journal of Automatica Sinica 2017年第2期4卷 186-194页

作者： Yufei Tang Chao Luo Jun Yang Haibo He IEEE Department of Computer & Electrical Engineering and Computer Science and the Institute for Sensing and Embedded Network Systems EngineeringFlorida Atlantic University School of Electrical Engineering Wuhan University Department of Electrical Computer and Biomedical EngineeringUniversity of Rhode Island

The volatile wind power generation brings a full spectrum of problems to power system operation and management, ranging from transient system frequency fluctuation to steady state supply and demand balancing issue. In this paper, a novel wind integrated power system day-ahead economic dispatch model, with the consideration of generation and reserve cost is modelled and investigated. The proposed problem is first formulated as a chance constrained stochastic nonlinear programming U+0028 CCSNLP U+0029, and then transformed into a deterministic nonlinear programming U+0028 NLP U+0029. To tackle this NLP problem, a three-stage framework consists of particle swarm optimization U+0028 PSO U+0029, sequential quadratic programming U+0028 SQP U+0029 and Monte Carlo simulation U+0028 MCS U+0029 is proposed. The PSO is employed to heuristically search the line power flow limits, which are used by the SQP as constraints to solve the NLP problem. Then the solution from SQP is verified on benchmark system by using MCS. Finally, the verified results are feedback to the PSO as fitness value to update the particles. Simulation study on IEEE 30-bus system with wind power penetration is carried out, and the results demonstrate that the proposed dispatch model could be effectively solved by the proposed three-stage approach. © 2017 Chinese Association of Automation.

关键词： Constrained optimization Economics Electric load flow Electric power generation Intelligent systems Monte Carlo methods Nonlinear programming Optimization Particle swarm optimization (PSO) Problem solving Quadratic programming Scheduling Stochastic systems Wind power

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Robot learning via human adversarial games

arXiv

引用

arXiv 2019年

作者： Duan, Jiali Wang, Qian Pinto, Lerrel Jay Kuo, C.-C. Nikolaidis, Stefanos Department of Electrical and Computer Engineering University of Southern California Los Angeles90089 United States Department of Computer Science University of Southern California Los Angeles90089 United States Robotics Institute Carnegie Mellon University Pittsburgh15213 United States

Much work in robotics has focused on "human-in-the-loop" learning techniques that improve the efficiency of the learning process. However, these algorithms have made the strong assumption of a cooperating human supervisor that assists the robot. In reality, human observers tend to also act in an adversarial manner towards deployed robotic systems. We show that this can in fact improve the robustness of the learned models by proposing a physical framework that leverages perturbations applied by a human adversary, guiding the robot towards more robust models. In a manipulation task, we show that grasping success improves significantly when the robot trains with a human adversary as compared to training in a self-supervised manner. Copyright © 2019, The Authors. All rights reserved.

关键词： robotics

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Predictive Identification of Electromagnetic Micro-mirrors*

Predictive Identification of Electromagnetic Micro-mirrors*

引用

IEEE Conference on Cybernetics and Intelligent systems

作者： Qingmei Cao Yonghong Tan Ruili Dong Qingyuan Tan Institute of Robotics and Automation Nankai University Tianjin China College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai China College of Information Science and Technology Donghua University Shanghai China Department of Electrical and Computer Engineering University of Windsor Windsor Canada

ISBN: (数字)9781728134581

ISBN: (纸本)9781728134598

This paper presents an identification approach for electromagnetic micro-mirrors using data-driven model with hysteresis. Usually, linear models are applied to modeling of electromagnetic micro-mirrors. However, from the experimental results, it can find that hysteresis phenomenon exists in the electromagnetic micro-mirror. Thus, it is not proper for using the linear model for modeling of electromagnetic micro-mirror. In this paper, a data-driven model with hysteresis is proposed for modeling the performance of electromagnetic micro-mirrors. In this model, a linear dynamic submodel is employed to model the deflection mechanism, whilst a dynamic hysteresis submodel is used to describe the hysteresis phenomenon in the electromagnetic actuating mechanism of the micro-mirror. One of the challenges of identification for electromagnetic micro-mirror is the electromagnetic torque cannot be measured directly. For handling this problem, a so-called predictive identification algorithm is applied to parameter estimation of the model. The effect of random noise on the identification is also considered. Then, the paper also presents the experimental results of modeling an electromagnetic micro-mirror.

关键词： Conferences Cybernetics Intelligent systems Robots Automation Mechatronics Random access memory

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Cover Image, Volume 6, Issue 10

引用

Health Science Reports 2023年第10期6卷

作者： Mohammad Faranoush Pooya Faranoush Iraj Heydari Mohammad Reza Foroughi-Gilvaee Azita Azarkeivan Ali Parsai Kia Negin Sadighnia Ali Elahinia Afsoon Zandi Mohammad Reza Rezvany Nahid Hashemi-Madani Amir Ziaee Reza Nekouian Farzaneh Rohani Pediatric Growth and Development Research Center Institute of Endocrinology Iran University of Medical Sciences Tehran Iran Cardio-Oncology Research Center Rajaie Cardiovascular Medical & Research Center Iran University of Medical Sciences Tehran Iran Nano Bio Electronic Devices Lab Cancer Electronics Research Group School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Blood Transfusion Research Center High Institute for Research and Education in Transfusion Medicine Tehran Iran Robotics Research Laboratory School of Mechanical Engineering Iran University of Science and Technology Tehran Iran Department of Otolaryngology Head & Neck Surgery Taleghani Hospital Shahid Beheshti University of Medical Sciences Tehran Iran

The cover image is based on the Research ArticleComplications in patients with transfusion dependent thalassemia: A descriptive cross‐sectional studyby Mohammad Faranoush et al.,https://***/10.1002/hsr2.1624

The cover image is based on the Research Article Complications in patients with transfusion dependent thalassemia: A descriptive cross‐sectional study by Mohammad Faranoush et al., https://***/10.1002/hsr2.1624

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

arXiv

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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Digital-Based Soft-Switching Modulation for High-Frequency Three-Phase Inverters with Reactive Power Transfer Capability 10

Digital-Based Soft-Switching Modulation for High-Frequency T...

引用

10th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2018

作者： Huang, Zhengrong Li, Qiang Lee, Fred C. Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Center for Power Electronics Systems BlacksburgVA United States

ISBN: (纸本)9781479973118

In this paper, a novel critical-conduction-mode (CRM)-based soft-switching modulation technique for three-phase inverters is extended from unity to non-unity power factor operating conditions. Zero-voltage-switch (ZVS) soft switching is still achievable with this modulation at non-unity power factor operating conditions, which is especially beneficial for silicon-carbide (SiC)-based systems at high switching frequency operation in order to achieve both high power density and high efficiency. Differences in operation modes between unity and non-unity power factor operating conditions are analyzed and a generalized modulation concept is summarized which covers both unity and non-unity power factor operating conditions. All the control functions are implemented digitally with one low-cost microcontroller (MCU). This modulation concept is verified in simulations and also experimentally implemented on a 25-kW SiC-based three-phase inverter prototype with 127 W/in3 power density. © 2018 IEEE.

关键词： Reactive power

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