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Self-propelled detachment upon coalescence of surface bubbles

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arXiv 2021年

作者： Lv, Pengyu Peñas, Pablo Le The, Hai Eijkel, Jan van den Berg, Albert Zhang, Xuehua Lohse, Detlef State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science BIC-ESAT College of Engineering Peking University Beijing100871 China Physics of Fluids group Faculty of Science and Technology Max Planck - University of Twente Center for Complex Fluid Dynamics MESA+ Institute J. M. Burgers Centre for Fluid Dynamics University of Twente P.O. Box 217 Enschede7500 AE Netherlands BIOS Lab-on-a-Chip group Faculty of Electrical Engineering Max Planck - University of Twente Center for Complex Fluid Dynamics Mathematics and Computer Science MESA+ Institute University of Twente P.O. Box 217 Enschede7500 AE Netherlands Department of Chemical & Materials Engineering University of Alberta EdmontonABT6G1H9 Canada Max Planck Institute for Dynamics and Self-Organization Göttingen37077 Germany

The removal of microbubbles from substrates is crucial for the efficiency of many catalytic and electrochemical gas evolution reactions in liquids. The current work investigates the coalescence and detachment of bubbles generated from catalytic decomposition of hydrogen peroxide. Self-propelled detachment, induced by the coalescence of two bubbles, is observed at sizes much smaller than those determined by buoyancy. Upon coalescence, the released surface energy is partly dissipated by the bubble oscillations, working against viscous drag. The remaining energy is converted to the kinetic energy of the out-of-plane jumping motion of the merged bubble. The critical ratio of the parent bubble sizes for the jumping to occur is theoretically derived from an energy balance argument and found to be in agreement with the experimental results. The present results provide both physical insight for the bubble interactions and practical strategies for applications in chemical engineering and renewable energy technologies like electrolysis. Copyright © 2021, The Authors. All rights reserved.

关键词： Coalescence

Designing and Training of A Dual CNN for Image Denoising

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arXiv 2020年

作者： Zuo, Wangmeng Zhang, David Lin, Chia-Wen Xu, Yong Tian, Chunwei Du, Bo Bio-Computing Research Center Harbin Institute of Technology Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition ShenzhenGuangdong518055 China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science and Technology Harbin Institute of Technology 150001 HarbinHeilongjiang China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science Wuhan University 430072 WuhanHubei China Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan 518172 ShenzhenGuangdong China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Deep convolutional neural networks (CNNs) for image denoising have recently attracted increasing research interest. However, plain networks cannot recover fine details for a complex task, such as real noisy images. In this paper, we propose a Dual denoising Network (DudeNet) to recover a clean image. Specifically, DudeNet consists of four modules: a feature extraction block, an enhancement block, a compression block, and a reconstruction block. The feature extraction block with a sparse mechanism extracts global and local features via two sub-networks. The enhancement block gathers and fuses the global and local features to provide complementary information for the latter network. The compression block refines the extracted information and compresses the network. Finally, the reconstruction block is utilized to reconstruct a denoised image. The DudeNet has the following advantages: (1) The dual networks with a parse mechanism can extract complementary features to enhance the generalized ability of denoiser. (2) Fusing global and local features can extract salient features to recover fine details for complex noisy images. (3) A Small-size filter is used to reduce the complexity of denoiser. Extensive experiments demonstrate the superiority of DudeNet over existing current state-of-the-art denoising methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Recovery

Signal space cognitive cooperation

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arXiv 2019年

作者： Erdogan, Eylem Afana, Ali Umit Sokun, Hamza Ikki, Salama Durak-Ata, Lutfiye Yanikomeroglu, Halim Department of Electrical and Electronics Engineering Istanbul Medeniyet University Uskudar Istanbul Turkey Department of Electrical Engineering Lakehead University Thunder BayON Canada Informatics Institute Istanbul Technical University Maslak Istanbul Turkey Systems and Computer Engineering Carleton University OttawaON Canada

—In this work, a new transmission scheme, signal space cognitive cooperation, is introduced by applying the idea of signal space diversity in an underlay spectrum sharing decode- and-forward multi-relay cooperative network. In the proposed structure, the secondary source signal is rotated by a certain angle and then the source and the secondary best relay transmit the in-phase and the quadrature components of the rotated signal. As a consequence, two source signals, rather than one, are transmitted in two-time slots which improves data rates considerably, compared to the conventional cognitive cooperative schemes. In this work, proactive relaying mode is used in which the best relay is selected based on the max-min selection criterion before executing the transmission. Considering both statistical and the instantaneous channel state information of the feedback channel between the primary receiver and the secondary network, two power allocation methods are adopted at the source. For both methods, closed-form expressions of error probability are derived. Moreover, asymptotic analysis is performed and diversity gain is obtained to provide further insights about the system performance. Finally, analytical expressions are verified by Monte-Carlo simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： Channel state information

Industrial robot trajectory tracking using multi-layer neural networks trained by iterative learning control

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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

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

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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

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

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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

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

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Dual Reconstruction with Densely Connected Residual Network ...

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

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

Robot learning via human adversarial games

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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