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Corrigendum to "Current challenges of hydrothermal treated wastewater (HTWW) for environmental applications and their perspectives: A review" [Environ. Res. 212 Part D (2022) 113532]

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Environmental research 2024年第PT 3期252卷 119005页

作者： Muhammad Usman Zhijian Shi Nalok Dutta Muhammad Awais Ashraf Bushra Ishfaq Mohamed Gamal El-Din Department of Civil and Environmental Engineering University of Alberta Edmonton AB T6G 2W2 Canada Bioproducts Sciences and Engineering Laboratory (BSEL) Washington State University Tri-Cities Richland WA 99354 United States Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3) Department of Environmental Science and Engineering Fudan University Shanghai 200433 China Shanghai Technical Service Platform for Pollution Control and Resource Utilization of Organic Wastes Shanghai 200438 China. Electronic address: musman4@ualberta.ca. Shanghai Technical Service Platform for Pollution Control and Resource Utilization of Organic Wastes Shanghai 200438 China. Bioproducts Sciences and Engineering Laboratory (BSEL) Washington State University Tri-Cities Richland WA 99354 United States. State Key Laboratory of Multiphase Complex Systems CAS Key Laboratory of Green Process and Engineering Institute of Process Engineering Chinese Academy of Sciences Beijing 100190 China. Food Technology Section Post-harvest Research Center Ayub Agricultural Research Institute Faisalabad Pakistan. Department of Civil and Environmental Engineering University of Alberta Edmonton AB T6G 2W2 Canada. Electronic address: mgamalel-din@ualberta.ca.

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Learning references with Gaussian processes in model predictive control applied to robot assisted surgery

arXiv

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arXiv 2019年

作者： Matschek, Janine Gonschorek, Tim Hanses, Magnus Elkmann, Norbert Ortmeier, Frank Findeisen, Rolf Laboratory for Systems Theory and Automatic Control Otto-von-Guericke Univ. Magdeburg Germany Software Engineering Otto-von-Guericke Univ. Magdeburg Germany Fraunhofer-Institute for Factory Operation and Automation Magdeburg Germany

One of the key benefits of model predictive control is the capability of controlling a system proactively in the sense of taking the future system evolution into account. However, often external disturbances or references are not a priori known, which renders the predictive controllers shortsighted or uninformed. Adaptive prediction models can be used to overcome this issue and provide predictions of these signals to the controller. In this work we propose to learn references via Gaussian processes for model predictive controllers. To illustrate the approach, we consider robot assisted surgery, where a robotic manipulator needs to follow a learned reference position based on optical tracking measurements. Copyright © 2019, The Authors. All rights reserved.

关键词： Model predictive control

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Application of improved PSO-BP neural network in fault detection of liquid-propellant rocket engine 15

Application of improved PSO-BP neural network in fault detec...

引用

15th IEEE International Conference on Mechatronics and Automation, ICMA 2018

作者： Li, Ningning Xue, Wei Zhao, Songbo Gao, Qiang Li, Dahua Xu, Liang Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin300384 China Beijing Aerospace Propulsion Institute Beijing100076 China

ISBN: (纸本)9781538660720

Because liquid-propellant rocket engine (LRE) is working in complex environment, the reliability of some traditional fault detection methods is low due to the characteristics of limited fault samples, small samples, high coupling and nonlinear variation of the main engine. In order to improve the shortcomings of fault detection in traditional method, this paper presents a fault detection algorithm (PSO-BP) based on improved particle swarm optimization (PSO) algorithm and BP neural network. The structure of BP neural network is established, the collected samples are input into PSO algorithm to optimize, and the optimal BP parameters are found, and the fault detection model of the main engine is established. Finally, the established model is used to simulate the fault. The simulation results show that the proposed algorithm can improves the accuracy of fault detection in the health management of LRE and provides a guarantee for the market demand of commercial launch. © 2018 IEEE.

关键词： Fault detection

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A consensus protocol for functional connectivity analysis in the rat brain (vol 26, pg 673, 2023)

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NATURE NEUROSCIENCE 2023年第6期26卷 1127-1128页

作者： Grandjean, Joanes Desrosiers-Gregoire, Gabriel Anckaerts, Cynthia Angeles-Valdez, Diego Ayad, Fadi Barriere, David A. Blockx, Ines Bortel, Aleksandra Broadwater, Margaret Cardoso, Beatriz M. Celestine, Marina Chavez-Negrete, Jorge E. Choi, Sangcheon Christiaen, Emma Clavijo, Perrin Colon-Perez, Luis Cramer, Samuel Daniele, Tolomeo Dempsey, Elaine Diao, Yujian Doelemeyer, Arno Dopfel, David Dvorakova, Lenka Falfan-Melgoza, Claudia Fernandes, Francisca F. Fowler, Caitlin F. Fuentes-Ibanez, Antonio Garin, Clement M. Gelderman, Eveline Golden, Carla E. M. Guo, Chao C. G. Henckens, Marloes J. A. G. Hennessy, Lauren A. Herman, Peter Hofwijks, Nita Horien, Corey Ionescu, Tudor M. Jones, Jolyon Kaesser, Johannes Kim, Eugene Lambers, Henriette Lazari, Alberto Lee, Sung-Ho Lillywhite, Amanda Liu, Yikang Liu, Yanyan Y. Lopez-Castro, Alejandra Lopez-Gil, Xavier Ma, Zilu MacNicol, Eilidh Madularu, Dan Mandino, Francesca Marciano, Sabina McAuslan, Matthew J. McCunn, Patrick McIntosh, Alison Meng, Xianzong Meyer-Baese, Lisa Missault, Stephan Moro, Federico Naessens, Daphne M. P. Nava-Gomez, Laura J. Nonaka, Hiroi Ortiz, Juan J. Paasonen, Jaakko Peeters, Lore M. Pereira, Mickael Perez, Pablo D. Pompilus, Marjory Prior, Malcolm Rakhmatullin, Rustam Reimann, Henning M. Reinwald, Jonathan Del Rio, Rodrigo Triana Rivera-Olvera, Alejandro Ruiz-Perez, Daniel Russo, Gabriele Rutten, Tobias J. Ryoke, Rie Sack, Markus Salvan, Piergiorgio Sanganahalli, Basavaraju G. Schroeter, Aileen Seewoo, Bhedita J. Selingue, Erwan Seuwen, Aline Shi, Bowen Sirmpilatze, Nikoloz Smith, Joanna A. B. Smith, Corrie Sobczak, Filip Stenroos, Petteri J. Straathof, Milou Strobelt, Sandra Sumiyoshi, Akira Takahashi, Kengo Torres-Garcia, Maria E. Tudela, Raul van den Berg, Monica van der Marel, Kajo van Hout, Aran T. B. Vertullo, Roberta Vidal, Benjamin Vrooman, Roel M. Wang, Victora X. Wank, Isabel Watson, David J. G. Yin, Ting Zhang, Yongzhi Zurbruegg, Stefan Achard, Sophie Alcauter, Sarael Auer, Dorothee P. Barbier, Emmanuel L. Baudewig, Juergen Beckmann, Chri Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada Integrated Program in Neuroscience McGill University Montreal QC Canada Bio-imaging Lab University of Antwerp Antwerp Belgium µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico Biological and Biomedical Engineering McGill University Montreal QC Canada McConnell Brain Imaging Centre McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France Department of Neurology and Neurosurgery McGill University Montreal QC Canada Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l’Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tuebingen Germany Graduate Training Centre of Neuroscience International Max

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

关键词： Databases Neural circuits

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Sustainable research software hand-over

arXiv

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arXiv 2019年

作者： Fehr, Jörg Himpe, Christian Rave, Stephan Saak, Jens Institute of Engineering and Computational Mechanics University of Stuttgart Pfaffenwaldring 9 Stuttgart70569 Germany Computational Methods in Systems and Control Theory Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstr. 1 Magdeburg39106 Germany Institute for Computational and Applied Mathematics University of Münster Einsteinstrasse 62 Münster48149 Germany

Scientific software projects evolve rapidly in their initial development phase, yet at the end of a funding period, the completion of a research project, thesis, or publication, further engagement in the project may slow down or cease completely. To retain the invested effort for the sciences, this software needs to be preserved or handed over to a succeeding developer or team, such as the next generation of (PhD) students. Comparable guides provide top-down recommendations for project leads. This paper intends to be a bottom-up approach for sustainable hand-over processes from a developer's perspective. An important characteristic in this regard is the project's size, by which this guideline is structured. Furthermore, checklists are provided, which can serve as a practical guide for implementing the proposed *** Codes 68N30, 97N80, 97P99 Copyright © 2019, The Authors. All rights reserved.

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Distributed optimization for real-time railway traffic management

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IFAC-PapersOnLine 2018年第9期51卷 106-111页

作者： Luan, Xiaojie Schutter, Bart De van den Boom, Ton Corman, Francesco Lodewijks, Gabriel Section Transport Engineering and Logistics Delft University of Technology DelftCD2628 Netherlands Delft Center for Systems and Control Delft University of Technology DelftCD2628 Netherlands Institute for Transport Planning and Systems ETH Zürich Stefano-Franscini-Platz 5 Zürich8093 Switzerland School of Aviation Faculty of Science University of New South Wales Sydney Australia

We introduce a distributed optimization method for improving the computational efficiency of real-time traffic management approaches for large-scale railway networks. We first decompose the whole network into a pre-defined number of regions by using an integer linear optimization approach. For each resulting region, a mixed-integer linear programming approach is used to address the traffic management problem, with micro details of the network and incorporated with the train control problem. For handling the interactions among regions, an alternating direction method of multipliers (ADMM) algorithm based solution approach is developed to solve the subproblem of each region through coordination with the other regions in an iterative manner. A priority rule based solution approach is proposed to generate feasible suboptimal solutions, in case of lack of convergence. Numerical experiments are conducted based on the Dutch railway network to show the performance of the proposed solution approaches, in terms of effectiveness and efficiency. We also show the trade-off between solution quality and computational efficiency. © 2018

关键词： Integer programming Clustering algorithms Computational efficiency Decomposition Distributed computer systems Economic and social effects Efficiency Iterative methods Railroad traffic control Railroad transportation Railroads Alternating direction method of multiplier (ADMM) clustering Distributed optimization Mixed integer linear programming (MILP) Railway traffic management

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Mars helicopter technology demonstrator

Mars helicopter technology demonstrator

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AIAA Atmospheric Flight Mechanics Conference, 2018

作者： Bob Balaram, J. Canham, Timothy Duncan, Courtney Golombek, Matt Grip, Håvard Fjær Johnson, Wayne Maki, Justin Quon, Amelia Stern, Ryan Zhu, David Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States NASA Ames Research Center Moffet Field CA94035 United States Robotics Technologist JPL Mobility and Robotic Systems Section JPL Flight Software and Avionics Systems Section JPL Flight Communications Systems Section JPL Planetary Science Section Robotics Technologist JPL Guidance and Control Section NASA Ames Aeromechanics Office JPL Instrument Software and Science Data Systems Section JPL Spacecraft Mechanical Engineering Section JPL Flight Electronics Section Robotics Electrical Engineer JPL Mobility and Robotic Systems Section

We describe a helicopter that is being developed as a technology demonstrator of Mars aerial mobility. The key design features of the helicopter, associated test infrastructure, and results from a full-scale prototype... 详细信息

ISBN: (数字)9781624105258

ISBN: (纸本)9781624105258

关键词： Antennas

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Power Consumption Model of a Circulating Wet Runner Pump

Power Consumption Model of a Circulating Wet Runner Pump

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control Technology and Applications (CCTA),

作者： Benedikt Meier Martin Oettmeier Jens O. Fiedler Torsten Bertram Wilo SE Dortmund Germany Institute of Control Theory and Systems Engineering TU Dortmund University Dortmund Germany

In this paper the power consumption model of a circulating wet runner pump is presented. Such a model provides several advantages since it can be used for example to detect issues during operation of the pump. For instance, dry running which might damage the bearings of the pump can be detected based on deviations between the measured power and the power predicted by the model. Furthermore, based on the power model the volume flow which passes the pump can be determined without a volume flow sensor and thus save costs. As the overall power consumption of the pump is influenced by different components such as electrical and friction losses, models of the individual components which have an influence on the power consumption are presented. Then, the components are combined to one overall power consumption model and the model parameters are identified by measurements. Finally, the model is validated by comparison of the dynamic behaviour of the model with the actual measured power consumption.

关键词： Power demand Mathematical model Friction Permanent magnet motors Magnetic hysteresis Hydraulic systems Iron

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Local Refinement of l-complete Approximations for Supervisory control of Hybrid systems

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IFAC-PapersOnLine 2018年第7期51卷 472-479页

作者： Yang, Jung–Min Moor, Thomas Raisch, Jörg School of Electronics Engineering Kyungpook National University 80 Daehakro Bukgu Daegu41566 Korea Republic of Lehrstuhl für Regelungstechnik at Friedrich-Alexander Universität Erlangen-Nürnberg ErlangenD-91058 Germany Fachgebiet Regelungssysteme Technische Universitaet Berlin Berlin10587 Germany Systems and Control Theory Group Max Planck Institute for Dynamics of Complex Technical Systems MagdeburgD-39106 Germany

l-complete approximation is a discrete abstraction method for a specific class of hybrid control problems involving purely discrete specifications. It allows for global refinement if the subsequent synthesis of supervisory control should fail for the currently selected abstraction level. In this paper, we present a methodology of local refinement for l-complete approximations. If the strongest l-complete approximation of a given hybrid system does not guarantee the existence of a suitable supervisor for a given specification, the proposed scheme refines the abstract model only in a local set of states that violate a controllability condition. Compared to the standard unfocused and global refinement procedure, this may significantly reduce the computational burden both in the abstraction step and the subsequent controller synthesis step. © 2018

关键词： Hybrid systems Abstracting controllability Discrete event simulation Discrete time control systems Specifications Abstract modeling Computational burden controllability condition controller synthesis Discrete abstraction l complete approximations Local refinement Supervisory control

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Online learning with stability guarantees: A memory-based real-time model predictive controller∗

arXiv

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arXiv 2018年

作者： Schwenkel, Lukas Gharbi, Meriem Trimpe, Sebastian Ebenbauer, Christian Institute for Systems Theory and Automatic Control University of Stuttgart Germany Institute for Data Science in Mechanical Engineering RWTH Aachen University Germany Intelligent Control Systems Group Max Planck Institute for Intelligent Systems Stuttgart Germany

We propose and analyze a real-time model predictive control (MPC) scheme that utilizes stored data to improve its performance by learning the value function online with stability guarantees. For linear and nonlinear systems, a learning method is presented that makes use of basic analytic properties of the cost function and is proven to learn the MPC control law and the value function on the limit set of the closed-loop state trajectory. The main idea is to generate a smart warm start based on historical data that improves future data points and thus future warm starts. We show that these warm starts are asymptotically exact and converge to the solution of the MPC optimization problem. Thereby, the suboptimality of the applied control input resulting from the real-time requirements vanishes over time. Numerical show that existing real-time MPC schemes can be improved by storing optimizatising the proposed ng scheme. Copyright © 2018, The Authors. All rights reserved.

关键词： Model predictive control

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